Spatial Variability of Phosphate Groundwater Based on Land Use - Land Cover and Groundwater Quality on Increasing Rural to Urban Areas

Spatial Variability of Phosphate Groundwater Based on Land Use - Land Cover and Groundwater Quality on Increasing Rural to Urban Areas | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Spatial Variability of Phosphate Groundwater Based on Land Use - Land Cover and Groundwater Quality on Increasing Rural to Urban Areas Arif Gunawan, Dasapta Erwin Irawan, Achmad Darul, Rusmawan Suwarman This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6972589/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 Dec, 2025 Read the published version in Environmental Monitoring and Assessment → Version 1 posted 9 You are reading this latest preprint version Abstract Phosphate pollution in groundwater represents a significant global environmental challenge, particularly in regions experiencing rapid urban expansion and intensified agricultural activities. This study addresses this issue by investigating the spatial variation of phosphate concentrations in groundwater within the Surabaya-Lamongan Groundwater Basin (SLGB) in East Java, Indonesia. The research aimed to assess the spatial distribution of phosphate in unconfined aquifers across various land use-land cover (LULC) categories, identify potential sources of contamination, and determine whether phosphate concentrations exceed the 0.2 mg/L threshold for potable water. A total of fifty-eight groundwater samples were systematically collected using a grid-based sampling method and analyzed with a UV-VIS spectrophotometer employing the phosphomolybdenum blue method. LULC classification was conducted using Landsat 7 ETM+ (2000) and Landsat 8 OLI TIRS (2021) imagery. The findings indicated that phosphate levels were significantly higher in shallow wells compared to deep wells (p < 0.05). Residential areas exhibited the highest average phosphate concentration (1.107 mg/L), followed by agricultural land (0.337 mg/L), both surpassing the drinking water standard. Industrial areas demonstrated the lowest concentration (0.168 mg/L). Significant spatial differences were observed among administrative regions (p < 0.05), with Gresik recording the highest average concentration (1.201 mg/L). A one-sample t-test confirmed that the overall average phosphate concentration (0.627 mg/L) significantly exceeded the permissible limit (p < 0.001). These results suggest that anthropogenic activities, particularly in residential and agricultural zones, are major contributors to phosphate contamination. Immediate groundwater protection measures are essential to prevent further degradation of water quality in the SLGB. Groundwater Phosphate Contaminant Land Use Anthropogenic Rural Peri-Urban Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Background The present El Nino tropical storms have contributed to the creation of a global drought phenomenon, which has further increased demand for water resources, particularly groundwater (Chung et al., 2023 ). The sustainability of groundwater is threatened by this phenomenon, which causes droughts and compels people to use groundwater resources more intensively. The possibility for a decrease in groundwater quality is inevitable given the immense demand for it (Levy et al., 2021 ). Climate change is anticipated to affect groundwater and agriculture through both direct and indirect mechanisms. In addition to the impacts of regional climate variations, global societal transformations and changes in local socio-economic conditions, partly driven by the anticipation of climate change, such as modifications in land use or water demand, alter the natural and societal boundaries of the system, thereby influencing its behavior (Barthel et al., 2012 ). These systemic changes are expected to have significant implications for groundwater resources, as global changes affect both the quantity and quality of these resources. Consequently, this impacts the conditions necessary to ensure the provision of safe drinking water, with the status of groundwater resources often being indicated by fluctuations in groundwater levels or concentrations (Barthel & Reyes, 2016 ). Groundwater presently supplies more than half of the global demand for potable water (Bi et al., 2022 ). However, the contamination of this vital resource has become a prevalent consequence of rapid urban expansion (Y. Huang et al., 2018 ; M. Zhang et al., 2020 ). A primary contributor to this contamination is the transport of reactive pollutants through dynamic preferential flow, a complex process shaped by soil heterogeneity, flow dynamics, and the characteristics of contaminants. This issue is particularly pronounced in urban-rural and agricultural regions, where variations in soil structure create preferential pathways that accelerate water movement and result in uneven contaminant distribution (Tang, Bartholomeus, et al., 2024). High concentrations of groundwater phosphate in regions impacted by human activity are one element of groundwater quality that might worry people today. Urbanization is occurring along with those (G. Huang et al., 2020 ; R. Zhang et al., 2019 ). Concerns have been raised about the expansion of agricultural operations in rural regions, which is occurring concurrently with urbanization development. Agricultural operations have significantly boosted the usage of fertilizer and phytosanitary products for more than a century. These, however, frequently contribute to groundwater pollution, particularly the more soluble ones like phosphates and nitrate (Borggaard et al., 2004 ; Serio et al., 2018 ). Phosphate's occurrence and fate in surface waters have been extensively studied (Carpenter et al., 1998 ), while its fate in aquifers has gotten less attention. According to certain research, the release of groundwater enriched in phosphates into surface waters is frequently the reason for the decline in surface water quality (Lewandowski et al., 2015 ; Slomp & Van Cappellen, 2004 ). Thus, a deeper comprehension of what happens to groundwater's phosphate concentrations is needed. Furthermore, the utilization of groundwater, particularly within shallow aquifer systems in certain developing nations, remains substantial. As observed in urban activities, including agriculture and residential development, these factors substantially influence the distribution of phosphate within the shallow aquifers of the Nanfei River Basin in China (Qian et al., 2011 ). This observation is relevant to the issue of phosphate pollution in unconfined aquifers in developing countries. As agricultural activities intensify in rural areas, there is a general increase in the use of fertilizers and pesticides over time. This trend results in a heightened likelihood of persistent pollutants accumulating in the vadose zone, particularly in irrigated regions during dry years when leaching is minimal. While this accumulation poses a direct threat to crop and soil health, the broader environmental impacts under wastewater irrigation are predominantly influenced by local hydrogeological and hydroclimatic conditions. Importantly, the characteristics of the saturated (phreatic) zone have been identified as more critical in determining the extent of contaminant movement and associated risks than the properties of the vadose zone (Tang et al., 2023 ; Tang, Van der Zee, et al., 2024 ). Furthermore, the concentration of industrial centers with varying capacities inside urban and peri-urban areas would lead to elevated phosphate levels as a consequence of these activities. It is believed that the phosphate level in the Surabaya-Lamongan Groundwater Basin (SLGB) has risen above the permitted threshold of 0.2 mg/L for drinking water purposes (Indonesian Minister of Health Regulation Number 2, 2023). The largest economic region on Java Island is the North Java Region, home of the Surabaya-Lamongan Groundwater Basin. It has experienced rapid urbanization and has been designated as the nation's rice center (Central Bureau of Statistics of East Java Province, 2020; Regional Development Planning Board of East Java, 2019). The SLGB serves primarily as a drinking water source. Thus, it constitutes the region of interest according to the research theme. Drawing from the aforementioned, the current investigation sought to: (i) ascertain the spatial distributions of groundwater phosphate in specifically dominated by unconfined aquifers and regions with varying land cover types within the SLGB; (ii) pinpoint the origins of groundwater phosphate; and (iii) investigate the conjecture that the phosphate concentration in the SLGB surpasses the threshold limit of 0.2 mg/L for potable water. The results of this investigation help understand how phosphorus enters studied areas' aquifers. Geography and Hydrogeology Regional Research covering an area of 219,355 hectares was conducted in the SLGB, East Java-Indonesia, which covers the administrative areas of Surabaya City, Gresik Regency, Lamongan Regency, Bojonegoro Regency and Tuban Regency. The basin is dominated by agricultural centers (rice commodities) on the north coast of eastern Java, according to the Regional Spatial Plan (East Java Province Spatial Planning, 2011; Central Bureau of Statistics of East Java Province, 2020). Significant economic growth, particularly in the domestic-municipal industrial (DMI) sector, which includes agricultural areas, has the potential to negatively impact groundwater basin conditions due to contamination (Česonienė et al., 2023 ; Quddoos et al., 2024 ). Furthermore, the research area's spatial design emphasizes the sector of rice production. As a result, fertilizers are being used extensively. Farmers utilize a variety of fertilizers, both chemical and non-chemical. Upon closer inspection, the SLGB agricultural rice field profile covers 113,526.15 hectares in total. This region makes up roughly 52% of the 219,355.81 hectares SLGB. This suggests that the groundwater basin area is dominated by rice-growing activities. The SLGB encompasses a total area of 219,355.81 hectares, distributed across four regencies and one city: Bojonegoro Regency, Lamongan Regency, Tuban Regency, Gresik Regency, and Surabaya City. The largest portion of this area is situated in Lamongan Regency, covering 76,242.36 hectares (34.76%), followed by Bojonegoro Regency with 61,661.41 hectares (28.11%), Gresik Regency with 41,134.02 hectares (18.75%), Tuban Regency with 34,494.82 hectares (15.73%), and the smallest area is in Surabaya City, comprising 5,823.2 hectares (2.65%). Within the entire expanse of the SLGB, 51.8% is characterized by land cover in the form of paddy fields. The most extensive agricultural area is located in Bojonegoro Regency, followed by Lamongan Regency, while the smallest agricultural area is found in Surabaya City, due to its relatively smaller inclusion within the SLGB compared to the other four districts, as stipulated in Fig. 1 . The SLGB is geologically located in the East Java sedimentation basin that has experienced intensive sedimentation processes since the Eocene extensional phase (Lunt, 2019 ; Simo et al., 2011 ). The Bengawan Solo River is the main river through the basin, which eventually flows (discharges) to the Madura Strait and the Java Sea. Regional hydrogeologic conditions in SLGB can be comprehensively observed through the aquifer productivity map. In a regional context, the hydrogeologic characteristics of this area show significant variations in terms of productivity and aquifer types. In general, the SLGB is dominated by aquifer zones with inter-grain flow systems that have moderate levels of productivity and a very extensive distribution pattern across the region. In the northern part of the region, there are different characteristics with the presence of aquifer zones that have flow systems through fracture media, showing quite good productivity, although with a relatively limited spatial distribution. Within the boundary of this SLGB, there are also several aquifer zones with diverse characteristics, including zones with fractured flow systems with high levels of productivity, as well as some areas characterized by low productivity aquifer zones and zones with very limited or scarce groundwater availability, as can be observed in Fig. 2 . In addition, pumping tests of an unconfined aquifer employing the Theis method were conducted at 25 well locations in the field, yielding average values for transmissivity (T), storativity (S), and hydraulic conductivity (K) of 37.6 m²/d, 4.27, and 12.94 m/d, respectively. These findings are depicted in a map detailing the distribution of wells involved in the pumping test, as illustrated in Fig. 3 a and Table 1 . Documentation about the pumping test in the field is presented in Fig. 3 b. Table 1 Hydrostratigraphy Units. Lithology Hydro-stratigraphy Units Hydraulic Conductivity (m/s) Well Codes Clayey sandstone Aquifer 1.3 x10 − 4 BJN 10, BJN 11, BJN 12, & BJN 14 Sandy Claystone Aquitard 1.4 x 10 − 4 LM 4, LM 6, LM 8, LM 10, LM 11, LM 13, LM 14, LM 16, LM 17, LM 18, GS 1, GS 2, & GS 5 Tuff Aquitard 1.8 x 10 − 4 TU 1, TU 2, & TU 4 Sandy Tuff Aquitard 1.3 x 10 − 4 BJN 3, BJN 4, BJN 6, BJN 8, & BJN 16 Land Use and Land Cover Classification In the domain of supervised classification image processing, Landsat imagery was utilized to create single-band combinations (red and near-infrared) for the Land Use–Land Cover (LULC) classification spectrum. Additionally, a 1:25,000 scale administrative boundary map of Indonesia was provided by the Geospatial Information Board in 2020. To investigate temporal changes in LULC within the groundwater basin downstream of the Bengawan Solo River, two satellite images were analyzed: Landsat 7 ETM from 2000 and Landsat 8 OLI TIRS from 2021. The 2000 image, rendered in Red-Green-Blue with band combination 543 (RGB 543), represents an initial landscape dominated by vegetation and open land, whereas the 2021 image, displayed with Red-Green-Blue with band 654 (RGB 654), indicates notable increases in water bodies, agricultural fields, and built-up areas, particularly around Gresik, Lamongan, and Surabaya. By integrating these Landsat datasets as illustrated in studies, the spatial analysis gains contextual depth that strengthens data-driven decision-making for sustainable groundwater basin management. (Ajayi et al., 2022 ; Mandal et al., 2016 ; Sekrecka & Kedzierski, 2018 ; Speiser & Largier, 2024 ; Srinivasa Rao & Jugran, 2003 ; Tran Vu Van Hoa et al., 2024). Consequently, this comprehensive approach not only clarifies the spatial dynamics of LULC changes but also enhances the understanding of their implications for groundwater quality and groundwater vulnerability in the study area. By integrating multi-temporal satellite data and administrative boundary data from the Geospatial Information Board, this study enables the precise mapping of urban settlements, agricultural areas, water bodies, and open land. The entire land cover classification process was conducted using supervised classification methods, as illustrated in the flowchart (Fig. 4 ). The process of supervised classification began with the acquisition and pre-processing of Landsat 7 ETM + images from 2000 and Landsat 8 OLI TIRS images from 2021 via Google Earth Engine (GEE). This process involved cloud masking and the selection of appropriate band combinations to enhance the visualization of land features. Subsequent procedures were conducted in QGIS utilizing the Semi-Automatic Classification Plugin (SCP) developed (Congedo, 2021 ). Key land cover classes, such as water, agriculture, settlements, and mining, were represented by selected training samples. The Maximum Likelihood Classification (MLC) algorithm was employed to generate initial land cover maps for both years. The accuracy of the classification was validated against reference data, and only those results with accuracy levels exceeding 70% were considered for further spatial analysis. The final classified outputs were converted into vector format, integrated with additional contextual information, and compiled into comprehensive, map-ready products (Fig. 5 ). To make the most of the landcover classification outcomes, the developed map was utilized as a basis for pinpointing various spatial traits associated with contamination potential. The LULC map enhanced classification by highlighting spatial features tied to contamination risks in the SLGB. By merging land cover data with socio-environmental information, this research distinguished rural, urban, and peri-urban areas for selecting representative sampling sites. This approach is in line with multi-criteria typology, which employs GIS-based methods to outline peri-urban and rural zones. The upstream basin area, including Bojonegoro, largely maintains peri-urban characteristics despite urban influences. In the central basin, Tuban and Lamongan retain pre-urbanization patterns with minimal urban encroachment. The downstream regions of Gresik and Surabaya exhibit significant urbanization and peri-urban growth, heightening groundwater contamination risks. This spatial typology, consistent with research conducted in Ghana (Hagan et al., 2022 ), India (Balakrishnan et al., 2023 ), Bangladesh (Hossain et al., 2024 ), aids in data-driven site selection and water quality evaluation across the SLGB. Non-point Source Contamination Few studies have been done on nonpoint sources, like agriculture, despite site-specific studies having been done on local or "point" sources of phosphate in groundwater, like wastewater releases and residential underground septic tank systems(Domagalski & Johnson, 2011 ; Robertson et al., 2019 ; Spiteri et al., 2007 ; Stollenwerk, 1996 ) Agriculture contributes 38% of the anthropogenic phosphate burdens to freshwater ecosystems worldwide (Mekonnen & Hoekstra, 2018 ). In order to assess the relative effects of various phosphate sources, ranging from wastewater to agriculture, we measure phosphate concentrations in relation to land cover or use types (Warrack et al., 2022 ). These sources are scattered and non-specific. Because there are so many contributing causes, these kinds of contaminations are often scattered in the field, and challenging to identify a single source. Non-point sources can originate from a variety of possible sources of contamination. The formation of fertilizers, pesticides that can be carried by rainwater, and puddle surfaces that seep into the aquifer's subsoil are a few examples of these effects of rice growing operations (Abdelwaheb et al., 2019 ). Additional sources of waste include domestic home garbage, septic tank waste, and trash from industrial operations that is transported by rainwater into the subsurface soil layer and the aquifer. Table 2 reveals that non-point sources can be given for any number of reasons. The rural land use classification in Bojonegoro, Tuban, and Lamongan regencies is characterized by the presence of anthropogenic activities, such as rice farming, villages, and the transition of upstream-mid groundwater basin. In light of this composition, the three previously described activities have the potential to act as non-point sources of pollution. Bojonegoro, Tuban, Lamongan, and Gresik are exemplary cities of the peri-urban class, which is an extension of the preceding rural change's transition. Numerous human activities that define peri-urban areas, such as the massive expansion of densely populated residential areas with rising demand over time, dominate these cities (Choir et al., 2024 ; Savitri et al., 2023 ). This aims to make up for the growth in a number of industrial zones, including manufacturing, petroleum, and the infrastructure that supports them (Central Bureau of Statistics of East Java Province, 2020; Bappeda of East Java Province, 2019). Table 2 Non-Point Sources and Withdrawals No Reach-zone Non-Point Source and withdrawals Landuse Classification 1 Surabaya Industry Urban Settlements Downstream of the Groundwater basin 2 Gresik Industry Urban Settlements Downstream of the Groundwater basin 3 Lamongan Agriculture Area (Paddy field) Rural Settlements The central region of the groundwater basin Industry Peri-Urban Settlements City of Regency 4 Bojonegoro Agriculture Area (Paddy field) Rural Settlements Upstream of the groundwater basin Industry Peri-Urban Settlements City of Regency 5 Tuban Agriculture Area (Paddy field) Rural Settlements The central region of the groundwater basin Industry Peri-Urban Settlements City of Regency Consequently, the aforementioned activities raise a strong suspicion that they may contribute as a non-point source of pollution. Surabaya City falls into the metropolitan category of urban classes (Central Bureau Statistics of Surabaya, 2018). In this groundwater basin, the western portion of Surabaya is primarily characterized by a number of notable human activities, including large, densely inhabited townships that frequently exhibit irregularities. In order to offset the growth in a number of industrial zones, including those for manufacturing, finance, processing goods, and supporting services, this is being done (Central Bureau of Statistics of East Java Province, 2020; Regional Development Planning Board of East Java Province, 2019). These circumstances also have a role in non-point pollution. Sampling and Analytical Methods To evaluate the phosphate concentration, we described a straightforward random sampling campaign and used a grid system measuring 8 km by 8 km in 58 sampling data points from groundwater wells located throughout the basin (Fig. 6 a). The chosen grid system pattern aims to distribute uniformly throughout the research site and enable the collection of representative samples. For the sampling process of phosphate compound levels in groundwater in the field, the grab samples test was carried out using plastic buckets in several shallow wells while for deep wells the bailer samples test was used. Moreover, based on the field sampling, we are taking grab samples of water from the groundwater at each test location using a plastic bucket to obtain grab samples from the shallow wells. We utilized a bailer sampler (Indonesian Standard No. 06. 6989.31, 2005) for the deep wells and collected the samples in 1,000 mL pre-sealed polypropylene bottles. Meanwhile, related to the testing of phosphate compound levels in the laboratory, we conduct testing at one of the external testing institutions that already have legality, namely the Surabaya Health Laboratory Center (BBLK), Indonesia. For additional information, independent field testing was also conducted for pH and TDS parameters. The process was carried out by measuring each groundwater sample from all wells in the study area with a HACH HQ40d multimeter to determine TDS levels, and HACH Sension 2 to measure pH values. A phosphomolybdenum blue spectrophotometer UV-VIS (i.e. Shimadzu UV 1601 PC) in ascorbic acid was used to analyze orthophosphate and total phosphorus in groundwater test samples (Fig. 6 b). The wavelength of the instrument was 880 nm, and the analysis ranges from 0.05 mg PO 4 /L to 1.5 mg PO 4 /L for orthophosphate and 0.15 mg P/L to 1.3 mg P/L for total phosphorus (Badan Standardisasi Nasional, 2005). All that is required for a 20 mL sample volume is minimal preparation. Add four level spoons of reagent PO 4 - and twenty drops of reagent PO 4 - (with the included micro spoon). The sample's color intensity is measured in the photometer after a five-minute reaction period, and the photometer's display provides a direct reading of the phosphate content. This method's basic idea is based on UV-Vis spectroscopy, which can significantly improve routine groundwater monitoring by offering data with a greater temporal and geographical resolution than that of wet chemical analyzers. Phosphorus species typically do not absorb light in the UV-Vis spectrum (Zhu & Ma, 2020 ). Recent research, however, indicates that the UV-Vis spectra can still be used to forecast the quantities of phosphorus fractions (Birgand et al., 2016 ; Vaughan et al., 2018 ). A one-quart zip-top box was filled with some of the water samples that were free of big rocks and plant debris, and the sample site, date, and time of collection were labeled. Within 48 hours of being collected, the samples were examined in the analytical laboratory using ice cubes in a box (Indonesian Standard No.6989.57, 2008; United States Environmental Protection Agency, 2002) The independent sample t-test approach was employed in the mean difference test analysis conducted in this investigation. It examines the variations in phosphate concentrations between deep and shallow wells. The analysis of variance (one-way ANOVA) approach is the second analysis. Its purpose is to determine how much phosphate varies by district and city within the SLGB. Furthermore, the study employed the one-way ANOVA method to examine the variations in phosphate levels across three distinct land cover types: agricultural, residential, and industrial. The idea that the phosphate level in the SLGB area is over the 0.2 mg/L threshold limit necessary for drinking water was tested using another analysis. The threshold limit of phosphate as seen Table 3 below. To determine the difference between the sample mean and the phosphate threshold value, the one-sample t-test method is employed for analysis. Table 3 Threshold value of each element according to Indonesian Minister of Health Regulations No.2/2023 (in mg/L; NTU) No Coordinate UTM Zone 49S Water Table Depth of Well LULC Phosphate and Physical Parameters TDS NTU T (°C) pH PO 4 3 - 300 3 - 6.5–8 0.2 X Y Z Value of each element sampled in the field TDS NTU T (°C) pH PO 4 3 - 1 652938 9230218 19 0 5.00 Settlement 204 0.43 28.6 7.4 0.124 2 667370 9224078 5 0 10.00 Settlement 509 0.65 28.8 7.2 2.961 3 651661 9229439 13 0 5.00 Settlement 334 0.61 28.4 7.1 0.572 4 661312 9228625 18 21 90.00 Settlement 230 0.44 28.1 7.1 0.166 5 615810 9221619 32 25 97.00 Settlement 244 0.62 28.6 7.3 0.127 6 666960 9226658 12 22 80.00 Settlement 241 0.89 25 7 0.168 7 656880 9229825 19 13 76.00 Rice fields 240 0.38 25 7.3 0.065 8 620893 9224568 52 26 114.00 Settlement 214 0.43 25 7.2 0.079 9 667267 9223574 11 0.5 0.00 Industry 1997 0.95 25 8.01 0.502 10 635766 9214300 7 1.08 0.00 Settlement 1280 4.05 25 8.21 4.059 11 635370 9214214 9 11 60.00 Industry 1671 0.23 24.7 7.95 0.024 12 619604 9221713 20 2.14 40.00 Industry 1067 69.2 25 7.92 0.024 13 607504 9216469 19 25 100.00 Rice fields 3390 1.08 25 8.24 0.024 14 678962 9198563 14 0.58 1.00 Rice fields 2180 2.3 25 7.99 0.024 15 678455 9199793 6 0 5.00 Industry 10.65 0.65 25 8.32 0.196 16 590592 9210034 23 4.95 0.00 Rice fields 761 3.49 25 7.7 0.024 17 674477 9205870 5 0.82 0.00 Industry 1246 0.89 25 8.2 0.092 18 642845 9228574 7 1.02 7.00 Rice fields 1900 0.63 28.7 7.32 0.292 19 630329 9213894 8 0.2 3.60 Rice fields 975 0.73 28.8 7.49 1.228 20 651021 9213747 6 5.35 8.00 Settlement 1228.9 0.53 28.9 7.84 0.248 21 664415 9220583 4 0.62 3.82 Settlement 1652 6.96 28.9 7.32 2.885 22 657186 9209680 11 0.97 8.00 Settlement 1096.53 0.72 28.6 7.52 0.372 23 635852 9228332 8 8.42 14.00 Settlement 1384.18 0.93 28.8 7.83 0.429 24 636079 9222287 10 1.02 5.45 Rice fields 683.74 0.53 28.4 7.57 0.126 25 642253 9225409 4 0.52 2.60 Rice fields 420 0.94 28.1 7.89 0.298 26 649110 9225380 3 0.65 5.02 Rice fields 774.52 0.43 28.6 7.81 0.183 27 637296 9207891 21 0.66 5.00 Rice fields 532 0.66 28.3 7.65 0.228 28 643084 9208662 18 0.73 9.01 Rice fields 801.62 0.61 28.5 7.92 0.087 29 642330 9212418 13 0.68 7.00 Rice fields 457.41 0.44 28.9 7.63 0.042 30 630334 9208039 15 0.40 3.40 Settlement 667.83 0.62 28.7 7.82 0.191 31 657552 9212858 3 5.13 10.00 Settlement 401.88 0.47 28.4 7.66 0.329 32 673785 9222401 4 3.05 10.00 Settlement 1284.08 0.73 28.2 7.78 1.084 33 675558 9212072 3 1.27 3.50 Settlement 1056.08 0.65 28.2 7.92 2.403 34 682812 9204984 15 6.5 10.00 Settlement 4228.74 0.93 28.6 7.64 1.094 35 667944 9209795 2 2.51 5.00 Settlement 474 0.54 28.3 7.96 3.19 36 666586 9208831 2 6.3 10.00 Settlement 856 0.76 28.5 7.83 3.19 37 627455 9223113 12 8 12.00 Settlement 4000 0.6 28.4 7.43 0.1 38 626626 9226522 23 3.6 70.00 Rice fields 662.9 0.63 28.6 7.32 0.075 39 608298 9224908 309 1.7 7.80 Settlement 854 0.82 28.5 7 0.159 40 624035 9228697 59 0.1 7.10 Rice fields 741.3 0.54 28.6 7.45 0.124 41 680625 9199123 4 0.78 5.70 Settlement 920 0.74 28.5 7.58 1.401 42 574674 9207957 35 3 8.00 Rice fields 530 0.45 28.8 7.48 0.113 43 572449 9210688 29 3 12.00 Rice fields 844 13.7 28.5 7.59 0.1 44 581411 9211881 25 3.3 16.60 Rice fields 733 0.52 28.6 7.72 0.056 45 581800 9206386 41 0.5 8.00 Rice fields 684 0.38 28.8 7.68 0.071 46 587516 9206631 29 6.1 13.00 Rice fields 426 1.06 28.5 7.97 0.113 47 596370 9206296 21 10 80.00 Rice fields 621 0.42 28.6 7.63 0.086 48 597935 9201191 35 0.5 3.30 Rice fields 563.08 0.49 28.4 7.52 0.428 49 605723 9200891 25 0.4 4.70 Rice fields 998 0.64 28.4 7.5 0.01 50 611146 9201837 20 0.1 10.00 Rice fields 650.74 0.91 28.7 7.66 1.752 51 614711 9206335 16 16 42.00 Rice fields 3132 0.45 28.1 7.34 0.1 52 604390 9205222 21 1 8.00 Rice fields 1206 0.68 28.2 7.29 0.1 53 619997 9205267 18 0.1 4.00 Rice fields 3642.75 0.84 28.7 7.51 0.096 54 617774 9211522 16 5 20.00 Rice fields 1872.9 0.42 28.4 7.62 0.154 55 611811 9210705 19 2.5 12.00 Rice fields 754 0.28 28.2 7.63 1.963 56 594532 9211537 19 4.2 45.00 Rice fields 1067 0.54 28.2 7.75 0.732 57 603256 9212477 43 0 126.00 Settlement 291 0.74 28.7 7.59 0.124 58 636113 9214258 7 3.66 20.00 Rice fields 225 0.45 25 7.52 1.401 The findings of grab samples on shallow and deep wells (n = 58) were then given values on the t-test and ANOVA in order to do statistical computations. Confidence intervals for subsets of the population produced by using the students' t-distribution were the main focus of the t-test study to determine contamination levels and compare water quality between two groups, as seen in Eq. 1 below (Singh & Kumar, 2011 ). $$\:t=\frac{{X}_{1}-{X}_{2}}{\sqrt{{S}^{2}(\frac{1}{{n}_{1}}+\frac{1}{{n}_{2}})}}$$ $$\:{S}^{2}=\frac{{(n}_{1}-1){S}_{1}^{2}+({n}_{2}-1){S}_{2}^{2}}{{n}_{1}+{n}_{2}-2}$$ 1 \(\:{X}_{1}\) and \(\:{X}_{2}\) are the average scores of group 1 and group 2, while \(\:{S}_{1}^{2}\) and \(\:{S}_{2}^{2}\) represent their variance scores. \(\:{n}_{1}\) and \(\:{n}_{2}\) indicate sample sizes, and \(\:{S}^{2}\) is the pooled variance, combining both groups' variances based on sample size. To determine if the mean of the deep well and shallow well was statistically different when collected by phosphate content, it was related to two sample groups. ANOVA (analysis of variance) tests by conducted in one method were utilized to ascertain if the mean land cover was rural, peri-urban, or urban. One-way ANOVA used to compare variances among multiple groups. Subsequently, ANOVA serves as a tool for effectively evaluating the potential of groundwater resources, refer to Eq. 2 below. \(\:F=\frac{MST}{MSE}\) \(\:MST=\frac{\sum\:_{i=1}^{k}\left(\frac{{T}_{i}^{2}}{{n}_{i}}\right)-\frac{{G}^{2}}{n}}{k-1}\) \(\:MSE=\frac{\sum\:_{i=1}^{k}{\sum\:}_{j=1}^{{n}_{i}}{Y}_{ij}^{2}-\:\sum\:_{i=1}^{k}\left(\frac{{T}_{i}^{2}}{{n}_{i}}\right)}{n-k}\) (2) Where \(\:F\) is the variance ratio for the overall test, \(\:MST\) is the mean square due to treatments/groups (between groups), \(\:MSE\) is the mean square due to error (within groups, residual mean square), \(\:{Y}_{ij}\) is an observation, \(\:{T}_{i}\) is a group total, \(\:G\) is the grand total of all observations, \(\:{n}_{i}\) is the number in group \(\:i\) and \(\:n\) is the total number of observations. Additionally, the latter test will be linked to the determination of the variations in phosphate load between areas within the SLGB as well as certain human activities including farming, industry, and habitations. The statistical data analysis was performed utilizing version 1.8 of the Jamovi program. The jamovi tool has been utilized in a variety of environmental studies beyond groundwater research, contributing to broader investigations of environmental quality and contamination (Berwanger et al., 2023 ; Meza-Ramirez et al., 2021 ; Schrank et al., 2022 ; William & Katambara, 2025 ). Results One-sample t-test analysis aims to determine the amount of phosphate compound levels in the research sample based on the average, which is categorized as above or below the threshold. Ho = 0.2 mg/L means that the null hypothesis states that the phosphate compound level based on the average is equal to the threshold limit (not higher), while Ha > 0.2 mg/L means that the alternative hypothesis states that the phosphate compound level based on the average is greater than the 0.2 mg/L threshold limit. Table 4 below is the result of a one-sample t-test analysis on phosphate compounds. Table 4 The one sample t-test of phosphate compared to their threshold Phosphate Statistic df Hₐ p student’s t 3.34 57.0 µ > 0.2 < 0.001 Descriptives Phosphate N Mean Median SD SE 58 0.627 0.163 0.974 0.128 N: sum of sample; Mean: mean of phosphate concentration from all samples, p: significant value One-sample t-test analysis shows that the levels of phosphate compounds in the study area fall into the category above the safe threshold when viewed from the average. The p-value shows < 0.001, which means that the average value of phosphate compound levels is significantly above the normal threshold based on the Ministry of Health regulation of 0.2mg/L. The mean value of phosphate levels is 0.627 mg/L, which has a considerable difference with the tolerable phosphate threshold of 0.2 mg/L. Based on these results, it is an initial finding that phosphate compounds are one of the substances that have a high potential to become groundwater pollutants in the study area. Although in the research location, the majority of land cover is in the form of agricultural rice fields and is in an area categorized as rural, chemical compounds that have the potential to become sources of pollutants, especially phosphate, are quite high. Unlike the general assumption that rural areas are relatively free from sources of environmental pollution, this is not the case. The Independent Sample t-test analysis in this section was conducted to determine the average difference in the 2 groups based on well depth. The sample groups based on well depth were divided into shallow wells and deep wells. Hₐ µ1 ≠ µ2 compounds in shallow wells and in deep wells. Table 5 below shows the results of the independent sample t-test analysis based on well depth categorized as shallow wells and deep wells. The results of the independent sample t-test analysis showed that there was a significant mean difference in the shallow well and deep well groups (p < 0.05), where the mean in the shallow well group was higher than the mean in the deep well group. This means that phosphate levels are affected by well depth, where the deeper the well, the smaller the phosphate levels in groundwater. Groundwater phosphate in shallow wells reached levels above the threshold reaching 0.769 mg/L while the phosphate threshold limit is 0.2 mg/L. The box plot complements the results of the independent sample t-test analysis based on well depth. The box plot illustrates the data variance of each sample group. This is expected to provide a clearer picture to complement the results of the analysis aimed at proving the hypothesis. One-way anova analysis was conducted to determine the mean differences in more than 2 sample groups. The one-way anova analysis in this section is conducted to see the mean differences in sample groups based on districts, which consist of 5 districts, namely Gresik district, Tuban district, Lamongan district, Bojonegoro district and Surabaya city. The null hypothesis states that there is no difference in the level of phosphate compounds in each district, and the alternative hypothesis states that there is a difference in the level of phosphate compounds in each district. Table 6 is the result of one-way anova analysis by district. There was no discernible change in the results for p = 0.435 (p > 0.05). Descriptive analysis, however, revealed a variation in the mean for Gresik District, which was ranked top and had a phosphate concentration of 1.201 mg/L. Surabaya City and Lamongan Regency had respective mean phosphate contents of 0.540 and 0.523 mg/L and second place, respectively. Subsequently, Tuban Regency recorded a mean phosphate level of 0.489 mg/L, whereas Bojonegoro Regency had the lowest content value, measuring 0.369 mg/L. The distribution pattern of groundwater phosphate levels for each district and city in the study area is depicted in a box plot. Other than Tuban, the sample distribution pattern for phosphate levels is generally homogeneous. The more varied box-plots indicate that the phosphate levels in Lamongan, Bojonegoro, Surabaya, and Gresik, respectively, have a more heterogeneous sample distribution pattern. As a result, box plot homogeneous and heterogeneous characteristic data will both show notable distribution patterns. Moreover, these are typical circumstances for converting dispersive data into countable parameters. The one-way ANOVA analysis in this section aims to determine the differences in phosphate levels in each classification based on population density, namely rural, transitional (peri-urban) and urban. Based box plot in the table. 7 shows the variance of phosphate levels for three characteristics (rural, peri-urban, and urban). The analysis was conducted based on the hypothesis that there is a difference in the level of phosphate compounds in each regional category. Urban areas are expected to have the highest phosphate levels compared to transitional and rural areas. Table 7 presents the results of a one-way ANOVA analysis based on population density classification. According to the research hypothesis, the study site's phosphate level is already higher than the threshold value. This is dependent on a number of things, including human activities like industry, settlements (home trash), and agriculture (use of pesticides and fertilizers). The null hypothesis (Ho: µ = 0.2) was not proven by hypothesis testing, but the working hypothesis (Ha: µ > 0.2) was, as shown in Table 4 . This demonstrates that the mean phosphate levels in the samples were greater than the regulatory threshold of 0.2 mg/L, as demonstrated by significant (p < 0.001) evidence. Another one-way ANOVA analysis was aimed at looking at differences in the level of phosphate compounds in classifications based on LULC. In this study, the classification based on LULC is divided into groups of residential areas, agricultural areas and industrial areas. The null hypothesis states that there is no difference in the level of phosphate compounds in the three regional classifications, while the alternative hypothesis states that there is a difference in the level of phosphate levels in each area. The results of one-way ANOVA of phosphate based on LULC are presented in Table 8 . Discussion In this research region, phosphate (PO 4 3 -) contamination of groundwater does not appear to threaten human health. Nevertheless, investigating the spatial variables and potential sources that control groundwater phosphate distributions remains imperative to test the theory that the phosphate content of the SLGB is higher than the drinking water threshold. Thus, monitoring its concentration in groundwater is essential to implement mitigation strategies aimed at preventing further contamination of the area. Source of phosphate contamination for unconfined and confined groundwater Phosphate loads from specific LULC surfaces have been shown to leave their source imprints in groundwater (Warrack et al., 2022 ). Thus, to analyze the phosphate pollution in groundwater systems, details of LULC are essential. Because phosphate enters the subsurface system through several channels that heavily depend on the type of LULC, the source factor of phosphate contaminations in groundwater was determined by evaluating its content in conjunction with an examination of LULC. The increased phosphate levels are indicative of anthropogenic influences, notably those associated with agricultural practices, domestic waste management, and industrial activities, which collectively contribute to the escalation of phosphate concentrations (Bi et al., 2022 ). The utilization of shallow groundwater in addition to surface river water as the primary source of water supply is directly tied to all of these activities. This provides compelling evidence that past human activity is what caused the process of phosphate pollution of groundwater to begin in shallow groundwater. Furthermore, the lack of quality control in the anthropogenic activity process will negatively impact both the environment and human health (Maulida et al., 2023 ). This indicates that surface environmental conditions are starting to be affected by the process of groundwater phosphate contamination. Because of human activity near agricultural, industrial, residential, and other comparable regions, anthropogenic groundwater contamination will spread in the direction of groundwater flow, contaminating shallow groundwater (Chang et al., 2008 ). The average (mean) phosphate level in the shallow well type has a value of 0.783 mg/L, which is over the threshold value (0.2 mg/L), it supports the assertion in the previous results section. Table 9 below reveals that the results of the correlation test between groundwater phosphate levels and well depth show a significant negative relationship. Accordingly, the deeper the groundwater sampled, the lower the phosphate content in the water; the high phosphate content in groundwater is inversely related to the well's depth. On the other hand, a higher phosphate level was found in groundwater samples taken from shallower wells. This is another proof that human activity is causing phosphate pollution in groundwater. On the surface of the earth, anthropogenic activities take place. Agricultural practices that employ phosphate as a component of insecticides and fertilizers are among them. Typical anthropogenic activities include household activities that generate waste, both liquid and solid. Examples of these include settlements. Phosphate from production operations is another way that industrial activities contribute to the process of groundwater contamination. The manufacturing sector, petrochemicals, common use minerals, and other industrial processes are some of the major sources of phosphate pollution in groundwater and the environment. Table 9 Phosphate Correlation Matrix for Shallow and Deep Wells Correlation Matrix Phosphate Depth of Well Phosphate Pearson’s r - -0.265 p-value - 0.044 Depth of Well Pearson’s r -0.265 - p-value 0.044 - Pearson’s r: correlation coefficient by Pearson formula, p-value: significant value In addition to the results of the correlation test between phosphate levels and well depth calculated by the product moment correlation method (Table 9 ). Despite the low correlation category, the relationship between phosphate levels and well depth demonstrates a substantial negative association. On the other hand, Table 9 suggests that there is a strong correlation and fluctuation in the phosphate data at shallow wells, defined as those with a depth of less than 50 meters. An overall low correlation is the outcome of low or less diversified variance in the phosphate data in deep wells. Phosphate levels at depths of more than 50 meters are not so different or diverse (homogeneous variants), so if additional inferences are made from the data, it is suspected that the potential for pollution in shallow wells is relatively high while deep wells are observed to have less severe levels of related contaminants. Phosphate Contaminant Characteristics by Administrative Location in Groundwater Basins It is possible to assess the degree of variation in phosphate concentration values for every administrative area in the groundwater basin. The intriguing finding is that all groundwater basin administrative areas have phosphate levels over the safety level (threshold value). Placing first with an average phosphate level of 1.2 mg/L was the Gresik district. We may understand this because of the region's features, which are characterized by rapidly expanding residential and industrial regions. Gresik is part of the integrated economic development area, which is a crucial national growth area that supports the Surabaya metropolitan area. Accordingly, Gresik serves as a buffer zone/peri-urban area for Surabaya, to create some separate but integrated zones for industry, habitation, and other services that Surabaya is unable to provide. With an average phosphate content of 0.54 mg/L, Surabaya City's western region ranks second. One of the major commercial and urban hubs in Java Island's east is Surabaya City. The presence of levels above this cutoff indicates that anthropogenic activities have an impact on the area, which raises average phosphate levels. When we look at the location of Surabaya's western region, we get data from the analysis of LULC based on Fig. 7 , which shows that the area is already home to a large number of densely populated industrial and residential districts. Western Surabaya, particularly the neighbourhoods that border Gresik, is not only a residential region but also an industrial area and a freight transport firm (Central Bureau of Statistics of East Java Province, 2020). Similar traits can be found in other districts, such as Lamongan, Tuban, and Bojonegoro, where agriculture, rice being the primary crop, dominates human activity. The obtained phosphate content averages are 0.52, 0.49, and 0.37, in that order. The spatial analysis of phosphate concentrations (mg/L) in relation to LULC within the SLGB reveals a significant association between elevated phosphate levels and anthropogenic land uses. Areas characterized by residential development and open fields or mining activities exhibit the highest phosphate concentrations (> 1 mg/L), suggesting substantial nutrient input, likely originating from domestic wastewater and surface runoff. Conversely, agricultural and moorland regions generally present moderate phosphate levels (0.2–0.5 mg/L), which may indicate controlled or diluted inputs due to irrigation practices or vegetative buffering. These findings highlight the considerable influence of land use on nutrient pollution, advocating for integrated land–water management strategies to mitigate eutrophication in the Bengawan Solo River system. The map presented in Fig. 8 illustrates the distribution of phosphate contaminant concentration levels, with the contour zoning pattern established through the application of the kriging method (Krige, 1951 ). In this context, the contour zoning corresponds with the pattern of data distribution. Land Use-Land Cover Types Linked with Phosphorus Concentrations in Groundwater The land use-land cover's description class has a significant value (p < 0.05) according to the findings of the one-way ANOVA (Welch's) study. Note that most of the land (n = 30) in the three land cover classifications chosen to represent the research locus is agricultural land. In addition, the second area (n = 23) is made up of residential areas, and the last region (n = 5) is an industrial zone. The existence of hubs dedicated to the cultivation of specific rice types characterizes and dominates these agricultural zones. A national center for rice production serves as the study site (East Java Province Spatial Planning, 2011; Regional Development Planning Board, 2019). Table 8 further demonstrates this state. As a result of the aforementioned, fertilizers and insecticides are used more frequently. The average phosphate concentration on residential land was determined to be 1.107 mg/L. Although increased concentrations have been linked to urban contexts such as residential neighborhoods, groundwater contributions of phosphorus have traditionally been considered modest (Fitzgerald et al., 2015 ). Phosphorus in groundwater used for irrigation in fields may also be stored on soil particles and subsequently transported to streams by sediment during periods of strong flow (Welch et al., 2010 ) There are several reasons why residential areas have higher phosphate levels. Among them is the inadequate and poor state of residential areas' septic tank systems, which allows phosphate compounds that are present in wastewater to flow out. Septic systems ought to be able to efficiently treat wastewater provided they are installed, maintained, and operated appropriately. But there's growing evidence that septic systems in places with geological settings like sandy or clayey soils and high groundwater levels are transferring phosphorus (Mechtensimer & Toor, 2017 ) Additionally, it was discovered that several significant developments could have an impact on industrial zones as a result of these operations. Solid waste materials, wastewater, and industrial waste are a few of them. Industrial waste is waste that is emitted during the manufacturing process and includes any useless items produced throughout industrial operations, such as factories and mills. Acid rain is an example of sulfur dioxide and nitrogen oxide emissions from chimneys and exhaust pipes(Burri et al., 2019 ) Manufacturing sectors produce liquid, solid, and gaseous wastes that can have a negative influence on the environment and people (Masi et al., 2018 ). Industrial pollution can eventually find its way into the rivers and oceans by contaminating the surrounding water sources, the air, or the land. The most important determining factors for the quality of water resources and the risk of solid waste contamination from solid waste materials may have been the existence, coverage, kind, and upkeep of infrastructure, such as landfills (Han et al., 2014 ). Particularly for large volumes of manufacturing solid waste, landfills (including tailings facilities) continue to be the most popular and economical way to dispose of solid waste worldwide (Ferronato & Torretta, 2019 ). Even though trash disposal landfill leachate is known to be a source of contaminants in groundwater, not all landfills have solid landfill lining installed. Furthermore, there are a lot of landfills from the industrial revolution that have little or no lining. Currently, areas lacking access to effective preservation or disposal methods may have to rely on shallow subsurface disposal for their solid waste. Moreover, solid trash includes a wide range of items, including cardboard, paper, plastic, scrap metal, wood, packaging, automotive parts, food waste, and any other solid waste that is no longer able to be used for its intended purpose (Abdel-Shafy & Mansour, 2018 ). Wastewater is the term used for industrial liquid waste. Organic substances (proteins, lipids, and carbohydrates) and dissolved inorganic pollutants are the sources of this type of contamination in industrial fluids. Large amounts of water are needed in the majority of production businesses, and these can come into contact with hazardous substances. Drainage systems, drains, septic reservoirs, and sewer networks are among the particular infrastructures for liquid waste (Marszelewski & Piasecki, 2020 ) Since effluent is frequently better tracked for environmental protection, centralized wastewater collection systems combined with a well-run treatment plant that uses cutting-edge treatment technology and frequent maintenance are the suggested answers. Another type of infrastructure is extensive fuel storage facilities and pipeline networks used by industry. These elements have the potential to leak or accidentally spill, resulting in non-aqueous liquid pollution (Burri et al., 2019 ; Jackson et al., 2013 ). To better illustrate the spatial relationship between groundwater flow dynamics and the distribution pattern of phosphate compound contaminants, this phenomenon can be modeled using the finite difference method for groundwater simulation. A conceptual hydrostratigraphic model, developed through either a stochastic or deterministic approach, serves as a robust framework for analyzing contaminant transport mechanisms, facilitating a more comprehensive understanding of their spatial distribution and movement (Darul et al., 2025 ). Knowing the hydrostratigraphic condition of the groundwater basin, the next step is to plot the groundwater level of the study area and the distribution pattern of phosphate compounds associated with regional geological conditions, which will be processed using Modflow software. Hypothesis Testing for Phosphate Contamination To support the hypothesis that the phosphate concentration at the research location was generally higher than the threshold, a one-sample t-test was conducted. All samples in this investigation had an average phosphate level (from 58 samples) 0.621 mg/L, and the p-value was less than 0.05 (p < 0.001). This indicates that the research site's phosphate levels are higher than the Ministry of Health's recommended threshold of 0.2 mg/L for drinking water. These results offer information that merits consideration, particularly with regard to the use of groundwater at the study site which needs to be closely watched, as phosphate content that has surpassed the threshold is thought to pose a risk to human health as well as the ecosystem as a whole. Chemical pollution-related public health issues can grow into major complex issues if they are not recognized or addressed as soon as possible. The discovery that the average phosphate content at the research site exceeds the threshold limit not only offers early warning information about groundwater pollution, but also offers empirical proof that groundwater conservation efforts against the effects of human activity must be launched right away as an emergency response. It is important to keep in mind that this research's findings and analysis are still preliminary. This is corroborated by the scant attention to detail that has been paid to the process of groundwater pollution in rural (i.e., non-urban) areas. It is therefore envisaged that this research will serve as one of the main sources for its comprehensive initiation. Conclusion The findings of the study reveal that phosphate concentrations in groundwater remain consistent across different regional categories, including rural, urban, and peri-urban areas, as well as within the five administrative regions analysed. However, phosphate levels in all districts exceeded the permissible limit. The research identifies land cover as a significant determinant of phosphate concentrations. Regions characterized by residential and agricultural activities exhibited phosphate levels above acceptable thresholds, whereas industrial areas maintained concentrations below the limit. These results suggest a direct correlation between land use patterns and groundwater quality. Furthermore, the depth of groundwater wells was identified as a critical factor influencing phosphate contamination. The study highlights the disparity in phosphate concentrations between shallow and deep wells. Phosphate levels in shallow wells consistently surpassed the recommended safety thresholds, rendering the water unsuitable for human consumption. In contrast, deep wells-maintained phosphate concentrations within safe limits, reinforcing the notion that surface-level anthropogenic activities, such as agricultural runoff, improper waste disposal, and urban discharge, play a significant role in groundwater contamination. This pattern suggests that phosphate pollution is predominantly concentrated in shallower water sources, underscoring the necessity for targeted interventions to mitigate the associated risks. Furthermore, hypothesis testing underscored the severity of phosphate pollution at the investigated site. The statistical analysis, particularly the mean difference test, indicated a significant deviation from the established drinking water threshold of 0.2 mg/L (p < 0.05), underscoring the urgent need to address this environmental concern. Given the magnitude of the contamination, the study highlights the imperative for effective environmental management and mitigation strategies to prevent phosphate infiltration into groundwater systems. Protecting public health, especially for communities reliant on shallow wells for drinking water, necessitates immediate policy interventions and sustainable water resource management practices. Limitations This research has several limitations, particularly regarding the comparison of the Landsat 8 OLI TIRS image from 2021 with the Landsat 7 ETM image from 2000. The results indicate a significant increase in the area dedicated to rice farming (agriculture). Moreover, the observed relationship with groundwater quality and phosphate concentration levels is applicable only at the time of observation. In the analysis of sample classification based on population density, almost all samples (N = 50) were categorized as rural areas, while the rest were classified as rural to urban transition (N = 6) and urban (N = 2). This shows that the representation of each area classification is uneven. It could be argued that this study is actually located in an area that is categorized as rural. However, there was no intention in this study to specialize in data collection in rural areas only. The sample was taken randomly using the grid sampling method, so it does not look at the distribution of points in areas categorized as rural, transitional or urban. Future research needs to consider the representativeness of samples based on population density because groundwater pollution is identical to human activities which may have diversity when viewed from population density in each region. This study is subject to several limitations, including the omission of dispersion parameters, the lack of groundwater flow advection modelling, the dependence on statistical values for zoning rather than physical processes, and the failure to account for temporal dynamics. Declarations Author contribution All authors contributed equally to the conceptualization, methodology, investigation, data curation, formal analysis, writing (original draft preparation, review and editing), and visualization of this manuscript. Funding The authors did not receive support from any organization for the submitted work. Data availability The authors confirm that the data supporting the findings of this study are available in this manuscript and its supplementary material. Raw data supporting this study’s findings are available from The Surabaya-Lamongan Groundwater Basin's Chemical-Physical, Statistics, and LULC Properties - Mendeley Data All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript. Competing interest The authors declare no competing interests Acknowledgments We are grateful to Bengawan Solo River Basin Management (BBWS), the Ministry of Public Works, and Badan Geologi for their support in data collection, as well as Bandung Institute of Technology (ITB) and Muhammadiyah University of East Kalimantan (UMKT) for their contributions to data analysis. We would like to especially acknowledge Adi Guna Prasetyo and Novandri Kusuma W and their colleagues for their invaluable assistance during field surveys. Lastly, we extend our gratitude to the dissertation examiners and paper reviewers for their valuable insights, which significantly improved the quality of this study. References Abdel-Shafy, H. I., & Mansour, M. S. M. (2018). Solid waste issue: Sources, composition, disposal, recycling, and valorization. In Egyptian Journal of Petroleum (Vol. 27, Issue 4, pp. 1275–1290). Egyptian Petroleum Research Institute. https://doi.org/10.1016/j.ejpe.2018.07.003 Abdelwaheb, M., Jebali, K., Dhaouadi, H., & Dridi-Dhaouadi, S. (2019). Adsorption of nitrate, phosphate, nickel and lead on soils: Risk of groundwater contamination. Ecotoxicology and Environmental Safety , 179 , 182–187. https://doi.org/10.1016/j.ecoenv.2019.04.040 Ajayi, O. G., Nwadialor, I. J., Odumosu, J. O., Adetunji, O. O., & Abdulwasiu, I. O. (2022). Assessment and delineation of groundwater potential zones using integrated geospatial techniques and analytic hierarchy process. Applied Water Science , 12 (12). https://doi.org/10.1007/s13201-022-01802-4 Balakrishnan, S., Udayashankar, H. N., Wankhede, S. F., Shetty, N., Sharma, A. V., & Balakrishna, K. (2023). Investigating the impact of long-term agricultural practices on selected peri-urban aquifers in tropical Southwest India. Urban Climate , 47 . https://doi.org/10.1016/j.uclim.2022.101356 Barthel, R., Reichenau, T. G., Krimly, T., Dabbert, S., Schneider, K., & Mauser, W. (2012). Integrated modeling of global change impacts on agriculture and groundwater resources. Water Resources Management , 26 (7), 1929–1951. https://doi.org/10.1007/s11269-012-0001-9 Barthel, R., & Reyes, F. A. P. (2016). Estimating the change in groundwater quality resulting from changes to land use and groundwater recharge. In Regional Assessment of Global Change Impacts (pp. 601–607). Springer International Publishing. https://doi.org/10.1007/978-3-319-16751-0_69 Berwanger, G. G., Reginato, P. A. R., & Vieiro, A. P. (2023). Hydrogeological assessment of coastal aquifers in areas of infiltration basins in southern Brazil . https://doi.org/10.21203/rs.3.rs-3366944/v1 Bi, P., Huang, G., Liu, C., & Li, L. (2022). Geochemical factors controlling natural background levels of phosphate in various groundwater units in a large-scale urbanized area. Journal of Hydrology , 608 . https://doi.org/10.1016/j.jhydrol.2022.127594 Birgand, F., Aveni-Deforge, K., Smith, B., Maxwell, B., Horstman, M., Gerling, A. B., & Carey, C. C. (2016). First report of a novel multiplexer pumping system coupled to a water quality probe to collect high temporal frequency in situ water chemistry measurements at multiple sites. Limnology and Oceanography: Methods , 14 (12), 767–783. https://doi.org/10.1002/lom3.10122 Borggaard, O. K., Szilas, C., Gimsing, A. L., & Rasmussen, L. H. (2004). Estimation of soil phosphate adsorption capacity by means of a pedotransfer function. Geoderma , 118 (1–2), 55–61. https://doi.org/10.1016/S0016-7061(03)00183-6 Burri, N. M., Weatherl, R., Moeck, C., & Schirmer, M. (2019). A review of threats to groundwater quality in the anthropocene. In Science of the Total Environment (Vol. 684, pp. 136–154). Elsevier B.V. https://doi.org/10.1016/j.scitotenv.2019.05.236 Carpenter, S. R., Caraco, N. F., Correll, D. L., Howarth, R. W., Sharpley, A. N., & Smith, V. H. (1998). Nonpoint Pollution Of Surface Waters With Phosphorus And Nitrogen. In Ecological Applications (Vol. 8, Issue 3). Česonienė, L., Šileikienė, D., Čiteikė, L., Mozgeris, G., & Takayoshi, K. (2023). The Impact of Organic and Intensive Agricultural Activity on Groundwater and Surface Water Quality. Water (Switzerland) , 15 (6). https://doi.org/10.3390/w15061240 Chang, C. L., Kuan, W. H., Lui, P. S., & Hu, C. Y. (2008). Relationship between landscape characteristics and surface water quality. Environmental Monitoring and Assessment , 147 (1–3), 57–64. https://doi.org/10.1007/s10661-007-0097-1 Choir, M., Prayitno, G., Wahid Hasyim, A., Amire Dzvimbo, M., Mawonde, A., & Ken, S. (2024). Changes in Land Use of Protected Rice Land to Support Food Independence using Remote Sensing Technology. Civil and Environmental Science , 7 (1), 32–42. https://doi.org/10.21776/ub.civense.2024.007.01.5 Chung, S. Y., Kim, G. B., & Senapathi, V. (2023). Drought and Groundwater Development. In Water (Switzerland) (Vol. 15, Issue 10). MDPI. https://doi.org/10.3390/w15101908 Congedo, L. (2021). Semi-Automatic Classification Plugin: A Python tool for the download and processing of remote sensing images in QGIS. Journal of Open Source Software , 6 (64), 3172. https://doi.org/10.21105/joss.03172 Darul, A., Irawan, D. E., Sumintadireja, P., & Ramadhan, G. (2025). Model description of combined numerical and stochastic groundwater flow in Bandung-Soreang Groundwater Basin, West Java, Indonesia. MethodsX , 14 . https://doi.org/10.1016/j.mex.2024.103112 Domagalski, J. L., & Johnson, H. M. (2011). Subsurface transport of orthophosphate in five agricultural watersheds, USA. Journal of Hydrology , 409 (1–2), 157–171. https://doi.org/10.1016/j.jhydrol.2011.08.014 Ferronato, N., & Torretta, V. (2019). Waste mismanagement in developing countries: A review of global issues. In International Journal of Environmental Research and Public Health (Vol. 16, Issue 6). MDPI AG. https://doi.org/10.3390/ijerph16061060 Fitzgerald, A., Roy, J. W., & Smith, J. E. (2015). Calculating discharge of phosphorus and nitrogen with groundwater base flow to a small urban stream reach. Journal of Hydrology , 528 , 138–151. https://doi.org/10.1016/j.jhydrol.2015.06.038 Hagan, G. B., Minkah, R., Yiran, G. A. B., & Dankyi, E. (2022). Assessing groundwater quality in peri-urban Accra, Ghana: Implications for drinking and irrigation purposes. Groundwater for Sustainable Development , 17 . https://doi.org/10.1016/j.gsd.2022.100761 Han, D., Tong, X., Currell, M. J., Cao, G., Jin, M., & Tong, C. (2014). Evaluation of the impact of an uncontrolled landfill on surrounding groundwater quality, Zhoukou, China. Journal of Geochemical Exploration , 136 , 24–39. https://doi.org/10.1016/j.gexplo.2013.09.008 Hossain, M. J., Rahman, M. Z., Kamal, A. S. M. M., Chowdhury, M. A., Hossain, M. S., Rahman, M. M., Zahid, A., & Islam, A. R. M. T. (2024). Quantitative and qualitative assessment of groundwater resources for drinking water supply in the peri-urban area of Dhaka, Bangladesh. Groundwater for Sustainable Development , 25 . https://doi.org/10.1016/j.gsd.2024.101146 Huang, G., Liu, C., Zhang, Y., & Chen, Z. (2020). Groundwater is important for the geochemical cycling of phosphorus in rapidly urbanized areas: a case study in the Pearl River Delta. Environmental Pollution , 260 . https://doi.org/10.1016/j.envpol.2020.114079 Huang, Y., Hickman, J. E., & Wu, S. (2018). Impacts of enhanced fertilizer applications on tropospheric ozone and crop damage over sub-Saharan Africa. Atmospheric Environment , 180 , 117–125. https://doi.org/10.1016/j.atmosenv.2018.02.040 Jackson, R. E., Gorody, A. W., Mayer, B., Roy, J. W., Ryan, M. C., & Van Stempvoort, D. R. (2013). Groundwater protection and unconventional gas extraction: The critical need for field-based hydrogeological research. Groundwater , 51 (4), 488–510. https://doi.org/10.1111/gwat.12074 Krige. (1951). Chemical Journal of The Metallurgical & Society of South Mining Africa . Levy, Z. F., Jurgens, B. C., Burow, K. R., Voss, S. A., Faulkner, K. E., Arroyo-Lopez, J. A., & Fram, M. S. (2021). Critical Aquifer Overdraft Accelerates Degradation of Groundwater Quality in California’s Central Valley During Drought. Geophysical Research Letters , 48 (17). https://doi.org/10.1029/2021GL094398 Lewandowski, J., Meinikmann, K., Nützmann, G., & Rosenberry, D. O. (2015). Groundwater - the disregarded component in lake water and nutrient budgets. Part 2: Effects of groundwater on nutrients. Hydrological Processes , 29 (13), 2922–2955. https://doi.org/10.1002/hyp.10384 Lunt, P. (2019). The origin of the East Java Sea basins deduced from sequence stratigraphy. Marine and Petroleum Geology , 105 , 17–31. https://doi.org/10.1016/j.marpetgeo.2019.03.038 Mandal, U., Sahoo, S., Munusamy, S. B., Dhar, A., Panda, S. N., Kar, A., & Mishra, P. K. (2016). Delineation of Groundwater Potential Zones of Coastal Groundwater Basin Using Multi-Criteria Decision Making Technique. Water Resources Management , 30 (12), 4293–4310. https://doi.org/10.1007/s11269-016-1421-8 Marszelewski, W., & Piasecki, A. (2020). Changes in Water and Sewage Management after Communism: example of the Oder River Basin (Central Europe). Scientific Reports , 10 (1). https://doi.org/10.1038/s41598-020-62957-1 Masi, F., Rizzo, A., & Regelsberger, M. (2018). The role of constructed wetlands in a new circular economy, resource oriented, and ecosystem services paradigm. Journal of Environmental Management , 216 , 275–284. https://doi.org/10.1016/j.jenvman.2017.11.086 Maulida, D. A., Anna, A. N., & Cholil, M. (2023). The Effect of Anthropogenic Activity on the Distribution of Phosphate and Nitrate Content in Shallow Groundwater in Kartasura Subdistrict (pp. 233–246). https://doi.org/10.2991/978-2-38476-066-4_15 Mechtensimer, S., & Toor, G. S. (2017). Septic systems contribution to phosphorus in shallow groundwater: Field-scale studies using conventional drainfield designs. PLoS ONE , 12 (1). https://doi.org/10.1371/journal.pone.0170304 Mekonnen, M. M., & Hoekstra, A. Y. (2018). Global Anthropogenic Phosphorus Loads to Freshwater and Associated Grey Water Footprints and Water Pollution Levels: A High-Resolution Global Study. Water Resources Research , 54 (1), 345–358. https://doi.org/10.1002/2017WR020448 Meza-Ramirez, V., Espinoza-Ortiz, X., Ramirez-Verdugo, P., Hernandez-Lazcano, P., & Rojas Hermosilla, P. (2021). Pb-Contaminated Soil from Quintero-Ventanas, Chile: Remediation Using Sarcocornia neei. Scientific World Journal , 2021 . https://doi.org/10.1155/2021/2974786 Qian, J., Wang, L., Zhan, H., & Chen, Z. (2011). Urban land-use effects on groundwater phosphate distribution in a shallow aquifer, Nanfei River basin, China. Hydrogeology Journal , 19 (7), 1431–1442. https://doi.org/10.1007/s10040-011-0770-x Quddoos, A., Muhmood, K., Naz, I., Aslam, R. W., & Usman, S. Y. (2024). Geospatial insights into groundwater contamination from urban and industrial effluents in Faisalabad. Discover Water , 4 (1). https://doi.org/10.1007/s43832-024-00110-z Robertson, W. D., Van Stempvoort, D. R., & Schiff, S. L. (2019). Review of phosphorus attenuation in groundwater plumes from 24 septic systems. Science of the Total Environment , 692 , 640–652. https://doi.org/10.1016/j.scitotenv.2019.07.198 Savitri, A., Pravitasari, A. E., & Rosandi, V. B. (2023). Dynamics of land cover change, regional development, and its local dependence driving factors in Bojonegoro Regency. IOP Conference Series: Earth and Environmental Science , 1263 (1). https://doi.org/10.1088/1755-1315/1263/1/012014 Schrank, I., Löder, M. G. J., Imhof, H. K., Moses, S. R., Heß, M., Schwaiger, J., & Laforsch, C. (2022). Riverine microplastic contamination in southwest Germany: A large-scale survey. Frontiers in Earth Science , 10 . https://doi.org/10.3389/feart.2022.794250 Sekrecka, A., & Kedzierski, M. (2018). Integration of satellite data with high resolution ratio: Improvement of spectral quality with preserving spatial details. Sensors (Switzerland) , 18 (12). https://doi.org/10.3390/s18124418 Serio, F., Miglietta, P. P., Lamastra, L., Ficocelli, S., Intini, F., De Leo, F., & De Donno, A. (2018). Groundwater nitrate contamination and agricultural land use: A grey water footprint perspective in Southern Apulia Region (Italy). Science of the Total Environment , 645 , 1425–1431. https://doi.org/10.1016/j.scitotenv.2018.07.241 Simo, T., Weidmer, M., Van Simaeys, S., Sekti, R., Van Gorsel, H., Strohmenger, C., & Derewetzky, A. (2011). Sequence Stratigraphic Correlation and Sedimentological Implications, East Java Basin: Comparisons and Lessons Learned from Outcrop and Subsurface Studies . Singh, Y., & Kumar, M. (2011). Application of Statistical Methods to Analyze Groundwater Quality. In Journal of Earth Sciences and Geotechnical Engineering (Vol. 1, Issue 1). online) International Scientific Press. Slomp, C. P., & Van Cappellen, P. (2004). Nutrient inputs to the coastal ocean through submarine groundwater discharge: Controls and potential impact. Journal of Hydrology , 295 (1–4), 64–86. https://doi.org/10.1016/j.jhydrol.2004.02.018 Speiser, W. H., & Largier, J. L. (2024). High-Resolution Nearshore Sea Surface Temperature from Calibrated Landsat Brightness Data. Remote Sensing , 16 (23). https://doi.org/10.3390/rs16234477 Spiteri, C., Slomp, C. P., Regnier, P., Meile, C., & Van Cappellen, P. (2007). Modelling the geochemical fate and transport of wastewater-derived phosphorus in contrasting groundwater systems. Journal of Contaminant Hydrology , 92 (1–2), 87–108. https://doi.org/10.1016/j.jconhyd.2007.01.002 Srinivasa Rao, Y., & Jugran, D. K. (2003). Delineation of groundwater potential zones and zones of groundwater quality suitable for domestic purposes using remote sensing and GIS. Hydrological Sciences Journal , 48 (5), 821–833. https://doi.org/10.1623/hysj.48.5.821.51452 Stollenwerk, K. G. (1996). Simulation of phosphate transport in sewage-contaminated groundwater, Cape Cod, Massachusetts. In Applied Geochemistry (Vol. 11). Tang, D. W. S., Bartholomeus, R. P., & Ritsema, C. J. (2024). Wastewater irrigation beneath the water table: analytical model of crop contamination risks. Agricultural Water Management , 298 . https://doi.org/10.1016/j.agwat.2024.108848 Tang, D. W. S., Van der Zee, S. E. A. T. M., & Bartholomeus, R. P. (2024). Phreatic zone wastewater irrigation: Sensitivity analysis of contaminant fate. Journal of Hydrology , 645 . https://doi.org/10.1016/j.jhydrol.2024.132263 Tang, D. W. S., Van der Zee, S. E. A. T. M., Narain-Ford, D. M., van den Eertwegh, G. A. P. H., & Bartholomeus, R. P. (2023). Managed phreatic zone recharge for irrigation and wastewater treatment. Journal of Hydrology , 626 . https://doi.org/10.1016/j.jhydrol.2023.130208 Tran Vu Van Hoa, Thien Chi Nguyen, Tung Thanh Truong, Tuan Anh Nguyen, Hoang Bao Lam, & Son Thai Dang. (2024). Enhancing Flood Impact Analysis through the Integration of Landsat and MODIS Imagery. International Journal of Scientific Research in Science, Engineering and Technology , 11 (2), 381–391. https://doi.org/10.32628/ijsrset2411257 Vaughan, M. C. H., Bowden, W. B., Shanley, J. B., Vermilyea, A., Wemple, B., & Schroth, A. W. (2018). Using in situ UV-Visible spectrophotometer sensors to quantify riverine phosphorus partitioning and concentration at a high frequency. Limnology and Oceanography: Methods , 16 (12), 840–855. https://doi.org/10.1002/lom3.10287 Warrack, J., Kang, M., & Von Sperber, C. (2022). Groundwater phosphorus concentrations: Global trends and links with agricultural and oil and gas activities. Environmental Research Letters , 17 (1). https://doi.org/10.1088/1748-9326/ac31ef Welch, H. L., Geological, U. S., & Kingsbury, J. A. (2010). Mississippi Water Resources Conference . William, M., & Katambara, Z. (2025). Assessment of Spatial Water Quality Variations in Shallow Wells Using Principal Component Analysis in Half London Ward, Tanzania. Journal of Water Resource and Protection , 17 (02), 108–143. https://doi.org/10.4236/jwarp.2025.172007 Zhang, M., Huang, G., Liu, C., Zhang, Y., Chen, Z., & Wang, J. (2020). Distributions and origins of nitrate, nitrite, and ammonium in various aquifers in an urbanized coastal area, south China. Journal of Hydrology , 582 . https://doi.org/10.1016/j.jhydrol.2019.124528 Zhang, R., Yin, A., & Gao, C. (2019). Sediment phosphorus fraction and release potential in the major inflow rivers of Chaohu Lake, Eastern China. Environmental Earth Sciences , 78 (4). https://doi.org/10.1007/s12665-019-8086-6 Zhu, X., & Ma, J. (2020). Recent advances in the determination of phosphate in environmental water samples: Insights from practical perspectives. In TrAC - Trends in Analytical Chemistry (Vol. 127). Elsevier B.V. https://doi.org/10.1016/j.trac.2020.115908 Tables Tables 5 to 8 are available in the Supplementary Files section Additional Declarations No competing interests reported. Supplementary Files Tables.docx Cite Share Download PDF Status: Published Journal Publication published 06 Dec, 2025 Read the published version in Environmental Monitoring and Assessment → Version 1 posted Editorial decision: Revision requested 03 Aug, 2025 Reviews received at journal 01 Aug, 2025 Reviews received at journal 26 Jul, 2025 Reviewers agreed at journal 15 Jul, 2025 Reviewers agreed at journal 14 Jul, 2025 Reviewers invited by journal 14 Jul, 2025 Editor assigned by journal 27 Jun, 2025 Submission checks completed at journal 27 Jun, 2025 First submitted to journal 25 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6972589","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":485756168,"identity":"b60c1c4c-451a-434d-9cd0-407795c67e68","order_by":0,"name":"Arif Gunawan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyElEQVRIiWNgGAWjYFAC5gMHPlQwMEhAeBKMDYS1sCU+nHEGqJaNeC08xsa8bXAtDIS18PevMZPmnXc4WnJ+A+OHHwwWsgS1SNx4ViY5d9vh3NlsDMySPQwSxoQdduPwNom3QC3zgA6TBhqRSFCL/I0DZhK8c8BamH8TpcXgfIuxIW8D2GFsxNlieAMUyMfSc2e2JbZZ9hgQ4Re584eBUVljnTvj8OHDN35U1BEOMQaJBBgLFCMGBNUDAf8BYlSNglEwCkbBiAYAwE4/j2rxU1QAAAAASUVORK5CYII=","orcid":"","institution":"Bandung Institute of Technology (ITB)","correspondingAuthor":true,"prefix":"","firstName":"Arif","middleName":"","lastName":"Gunawan","suffix":""},{"id":485756169,"identity":"54e5aaa2-86dd-42e6-a4cf-2c5764cb26b1","order_by":1,"name":"Dasapta Erwin Irawan","email":"","orcid":"","institution":"Bandung Institute of Technology (ITB)","correspondingAuthor":false,"prefix":"","firstName":"Dasapta","middleName":"Erwin","lastName":"Irawan","suffix":""},{"id":485756170,"identity":"61246a8f-5601-4840-a94a-a07c93579efb","order_by":2,"name":"Achmad Darul","email":"","orcid":"","institution":"Sumatera Institute of Technology (ITERA)","correspondingAuthor":false,"prefix":"","firstName":"Achmad","middleName":"","lastName":"Darul","suffix":""},{"id":485756171,"identity":"4648f665-4d8b-4605-abd6-dc4dd1e43524","order_by":3,"name":"Rusmawan Suwarman","email":"","orcid":"","institution":"Bandung Institute of Technology (ITB)","correspondingAuthor":false,"prefix":"","firstName":"Rusmawan","middleName":"","lastName":"Suwarman","suffix":""}],"badges":[],"createdAt":"2025-06-25 08:53:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6972589/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6972589/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10661-025-14879-6","type":"published","date":"2025-12-06T15:57:02+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":86961788,"identity":"bee160a9-dbb7-4023-89e2-e9e5f8419677","added_by":"auto","created_at":"2025-07-17 16:20:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":224354,"visible":true,"origin":"","legend":"\u003cp\u003eThe Surabaya-Lamongan Groundwater Basin's (SLGB) agricultural rice field profile\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6972589/v1/b5aeaf058f22bdffd13adaf4.png"},{"id":86961793,"identity":"fa512d25-746a-45bd-9ee8-8edfb033316f","added_by":"auto","created_at":"2025-07-17 16:20:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1286324,"visible":true,"origin":"","legend":"\u003cp\u003eProductivity aquifer map of Surabaya-Lamongan Groundwater Basin, East Java\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6972589/v1/163e747fa98984e1aacdb664.png"},{"id":86962374,"identity":"e865c063-96c8-47f0-9422-bcf60142c36c","added_by":"auto","created_at":"2025-07-17 16:28:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":826171,"visible":true,"origin":"","legend":"\u003cp\u003e(a). Distribution of well-pumping test (n=25), and (b) documentation of pumping test activity\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6972589/v1/d9ac08545d542ad9b4716380.png"},{"id":86961794,"identity":"7da28c02-9606-4319-b2ec-0c2ab1eea6a5","added_by":"auto","created_at":"2025-07-17 16:20:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":209743,"visible":true,"origin":"","legend":"\u003cp\u003eThe flow diagram of LULC analysis including supervised classification\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6972589/v1/4dfd6390f72c79f49b8a127a.png"},{"id":86962505,"identity":"26c1b723-2796-4b43-993b-58945ffeb48d","added_by":"auto","created_at":"2025-07-17 16:36:08","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":974695,"visible":true,"origin":"","legend":"\u003cp\u003e(a). Processing satellite images based on Landsat 7 ETM+ (2000), and (b). ORI TRIS (2021) into each land use land cover analysis for year 2000 (c) and year 2021 (d)\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6972589/v1/2acd73034a929a29488a316c.png"},{"id":86962376,"identity":"7e1ad21d-cebb-49c7-9960-ad4898a51439","added_by":"auto","created_at":"2025-07-17 16:28:08","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":708197,"visible":true,"origin":"","legend":"\u003cp\u003e(a). Distribution of wells (n=58) in research area, and (b). Shimadzu UV 1600 for phosphate analyzing\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6972589/v1/e7e827567d211f2e6cf4c15b.png"},{"id":86961800,"identity":"faf6e603-1ddc-4791-8e67-4b2b5bed0b21","added_by":"auto","created_at":"2025-07-17 16:20:09","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2392658,"visible":true,"origin":"","legend":"\u003cp\u003e(a). Distribution of shallow and deep wells based on LULC, and (b). Phosphate concentrations based on LULC\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6972589/v1/f088c504d1ea41011b60af0c.png"},{"id":86963074,"identity":"def28093-1218-4e9a-b797-26d3d25948cb","added_by":"auto","created_at":"2025-07-17 16:44:09","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1266334,"visible":true,"origin":"","legend":"\u003cp\u003eLeveling of phosphate concentrations distribution\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-6972589/v1/cec772736409a51339824320.png"},{"id":97723761,"identity":"b5dc547e-ff95-42aa-88fc-121595a49488","added_by":"auto","created_at":"2025-12-08 16:04:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9512630,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6972589/v1/5b2df8d2-d4ae-4575-a9df-628a469e2c9a.pdf"},{"id":86962504,"identity":"87926c40-dadc-4f50-9b9e-10c5f084e7f1","added_by":"auto","created_at":"2025-07-17 16:36:08","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":139646,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-6972589/v1/1a0b25c3a6f397c32d35dbad.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Spatial Variability of Phosphate Groundwater Based on Land Use - Land Cover and Groundwater Quality on Increasing Rural to Urban Areas","fulltext":[{"header":"Background","content":"\u003cp\u003eThe present El Nino tropical storms have contributed to the creation of a global drought phenomenon, which has further increased demand for water resources, particularly groundwater (Chung et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). The sustainability of groundwater is threatened by this phenomenon, which causes droughts and compels people to use groundwater resources more intensively. The possibility for a decrease in groundwater quality is inevitable given the immense demand for it (Levy et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Climate change is anticipated to affect groundwater and agriculture through both direct and indirect mechanisms. In addition to the impacts of regional climate variations, global societal transformations and changes in local socio-economic conditions, partly driven by the anticipation of climate change, such as modifications in land use or water demand, alter the natural and societal boundaries of the system, thereby influencing its behavior (Barthel et al., \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e). These systemic changes are expected to have significant implications for groundwater resources, as global changes affect both the quantity and quality of these resources. Consequently, this impacts the conditions necessary to ensure the provision of safe drinking water, with the status of groundwater resources often being indicated by fluctuations in groundwater levels or concentrations (Barthel \u0026amp; Reyes, \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eGroundwater presently supplies more than half of the global demand for potable water (Bi et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, the contamination of this vital resource has become a prevalent consequence of rapid urban expansion (Y. Huang et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; M. Zhang et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). A primary contributor to this contamination is the transport of reactive pollutants through dynamic preferential flow, a complex process shaped by soil heterogeneity, flow dynamics, and the characteristics of contaminants. This issue is particularly pronounced in urban-rural and agricultural regions, where variations in soil structure create preferential pathways that accelerate water movement and result in uneven contaminant distribution (Tang, Bartholomeus, et al., 2024). High concentrations of groundwater phosphate in regions impacted by human activity are one element of groundwater quality that might worry people today. Urbanization is occurring along with those (G. Huang et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; R. Zhang et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eConcerns have been raised about the expansion of agricultural operations in rural regions, which is occurring concurrently with urbanization development. Agricultural operations have significantly boosted the usage of fertilizer and phytosanitary products for more than a century. These, however, frequently contribute to groundwater pollution, particularly the more soluble ones like phosphates and nitrate (Borggaard et al., \u003cspan class=\"CitationRef\"\u003e2004\u003c/span\u003e; Serio et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003ePhosphate\u0026apos;s occurrence and fate in surface waters have been extensively studied (Carpenter et al., \u003cspan class=\"CitationRef\"\u003e1998\u003c/span\u003e), while its fate in aquifers has gotten less attention. According to certain research, the release of groundwater enriched in phosphates into surface waters is frequently the reason for the decline in surface water quality (Lewandowski et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e; Slomp \u0026amp; Van Cappellen, \u003cspan class=\"CitationRef\"\u003e2004\u003c/span\u003e). Thus, a deeper comprehension of what happens to groundwater\u0026apos;s phosphate concentrations is needed. Furthermore, the utilization of groundwater, particularly within shallow aquifer systems in certain developing nations, remains substantial. As observed in urban activities, including agriculture and residential development, these factors substantially influence the distribution of phosphate within the shallow aquifers of the Nanfei River Basin in China (Qian et al., \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e). This observation is relevant to the issue of phosphate pollution in unconfined aquifers in developing countries.\u003c/p\u003e\n\u003cp\u003eAs agricultural activities intensify in rural areas, there is a general increase in the use of fertilizers and pesticides over time. This trend results in a heightened likelihood of persistent pollutants accumulating in the vadose zone, particularly in irrigated regions during dry years when leaching is minimal. While this accumulation poses a direct threat to crop and soil health, the broader environmental impacts under wastewater irrigation are predominantly influenced by local hydrogeological and hydroclimatic conditions. Importantly, the characteristics of the saturated (phreatic) zone have been identified as more critical in determining the extent of contaminant movement and associated risks than the properties of the vadose zone (Tang et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Tang, Van der Zee, et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). Furthermore, the concentration of industrial centers with varying capacities inside urban and peri-urban areas would lead to elevated phosphate levels as a consequence of these activities. It is believed that the phosphate level in the Surabaya-Lamongan Groundwater Basin (SLGB) has risen above the permitted threshold of 0.2 mg/L for drinking water purposes (Indonesian Minister of Health Regulation Number 2, 2023). The largest economic region on Java Island is the North Java Region, home of the Surabaya-Lamongan Groundwater Basin. It has experienced rapid urbanization and has been designated as the nation\u0026apos;s rice center (Central Bureau of Statistics of East Java Province, 2020; Regional Development Planning Board of East Java, 2019). The SLGB serves primarily as a drinking water source. Thus, it constitutes the region of interest according to the research theme.\u003c/p\u003e\n\u003cp\u003eDrawing from the aforementioned, the current investigation sought to: (i) ascertain the spatial distributions of groundwater phosphate in specifically dominated by unconfined aquifers and regions with varying land cover types within the SLGB; (ii) pinpoint the origins of groundwater phosphate; and (iii) investigate the conjecture that the phosphate concentration in the SLGB surpasses the threshold limit of 0.2 mg/L for potable water. The results of this investigation help understand how phosphorus enters studied areas\u0026apos; aquifers.\u003c/p\u003e\n\u003ch3\u003eGeography and Hydrogeology Regional\u003c/h3\u003e\n\u003cp\u003eResearch covering an area of 219,355 hectares was conducted in the SLGB, East Java-Indonesia, which covers the administrative areas of Surabaya City, Gresik Regency, Lamongan Regency, Bojonegoro Regency and Tuban Regency. The basin is dominated by agricultural centers (rice commodities) on the north coast of eastern Java, according to the Regional Spatial Plan (East Java Province Spatial Planning, 2011; Central Bureau of Statistics of East Java Province, 2020). Significant economic growth, particularly in the domestic-municipal industrial (DMI) sector, which includes agricultural areas, has the potential to negatively impact groundwater basin conditions due to contamination (Česonienė et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Quddoos et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). Furthermore, the research area\u0026apos;s spatial design emphasizes the sector of rice production. As a result, fertilizers are being used extensively. Farmers utilize a variety of fertilizers, both chemical and non-chemical.\u003c/p\u003e\n\u003cp\u003eUpon closer inspection, the SLGB agricultural rice field profile covers 113,526.15 hectares in total. This region makes up roughly 52% of the 219,355.81 hectares SLGB. This suggests that the groundwater basin area is dominated by rice-growing activities. The SLGB encompasses a total area of 219,355.81 hectares, distributed across four regencies and one city: Bojonegoro Regency, Lamongan Regency, Tuban Regency, Gresik Regency, and Surabaya City. The largest portion of this area is situated in Lamongan Regency, covering 76,242.36 hectares (34.76%), followed by Bojonegoro Regency with 61,661.41 hectares (28.11%), Gresik Regency with 41,134.02 hectares (18.75%), Tuban Regency with 34,494.82 hectares (15.73%), and the smallest area is in Surabaya City, comprising 5,823.2 hectares (2.65%). Within the entire expanse of the SLGB, 51.8% is characterized by land cover in the form of paddy fields. The most extensive agricultural area is located in Bojonegoro Regency, followed by Lamongan Regency, while the smallest agricultural area is found in Surabaya City, due to its relatively smaller inclusion within the SLGB compared to the other four districts, as stipulated in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003eThe SLGB is geologically located in the East Java sedimentation basin that has experienced intensive sedimentation processes since the Eocene extensional phase (Lunt, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Simo et al., \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e). The Bengawan Solo River is the main river through the basin, which eventually flows (discharges) to the Madura Strait and the Java Sea. Regional hydrogeologic conditions in SLGB can be comprehensively observed through the aquifer productivity map. In a regional context, the hydrogeologic characteristics of this area show significant variations in terms of productivity and aquifer types. In general, the SLGB is dominated by aquifer zones with inter-grain flow systems that have moderate levels of productivity and a very extensive distribution pattern across the region. In the northern part of the region, there are different characteristics with the presence of aquifer zones that have flow systems through fracture media, showing quite good productivity, although with a relatively limited spatial distribution. Within the boundary of this SLGB, there are also several aquifer zones with diverse characteristics, including zones with fractured flow systems with high levels of productivity, as well as some areas characterized by low productivity aquifer zones and zones with very limited or scarce groundwater availability, as can be observed in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003eIn addition, pumping tests of an unconfined aquifer employing the Theis method were conducted at 25 well locations in the field, yielding average values for transmissivity (T), storativity (S), and hydraulic conductivity (K) of 37.6 m\u0026sup2;/d, 4.27, and 12.94 m/d, respectively. These findings are depicted in a map detailing the distribution of wells involved in the pumping test, as illustrated in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea and Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Documentation about the pumping test in the field is presented in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eb.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eHydrostratigraphy Units.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLithology\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHydro-stratigraphy Units\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHydraulic Conductivity (m/s)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWell Codes\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClayey sandstone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAquifer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.3 x10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBJN 10, BJN 11, BJN 12, \u0026amp; BJN 14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSandy Claystone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAquitard\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.4 x 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLM 4, LM 6, LM 8, LM 10, LM 11, LM 13, LM 14, LM 16, LM 17, LM 18, GS 1, GS 2, \u0026amp; GS 5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTuff\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAquitard\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.8 x 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTU 1, TU 2, \u0026amp; TU 4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSandy Tuff\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAquitard\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.3 x 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBJN 3, BJN 4, BJN 6, BJN 8, \u0026amp; BJN 16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eLand Use and Land Cover Classification\u003c/h2\u003e\n \u003cp\u003eIn the domain of supervised classification image processing, Landsat imagery was utilized to create single-band combinations (red and near-infrared) for the Land Use\u0026ndash;Land Cover (LULC) classification spectrum. Additionally, a 1:25,000 scale administrative boundary map of Indonesia was provided by the Geospatial Information Board in 2020.\u003c/p\u003e\n \u003cp\u003eTo investigate temporal changes in LULC within the groundwater basin downstream of the Bengawan Solo River, two satellite images were analyzed: Landsat 7 ETM from 2000 and Landsat 8 OLI TIRS from 2021. The 2000 image, rendered in Red-Green-Blue with band combination 543 (RGB 543), represents an initial landscape dominated by vegetation and open land, whereas the 2021 image, displayed with Red-Green-Blue with band 654 (RGB 654), indicates notable increases in water bodies, agricultural fields, and built-up areas, particularly around Gresik, Lamongan, and Surabaya. By integrating these Landsat datasets as illustrated in studies, the spatial analysis gains contextual depth that strengthens data-driven decision-making for sustainable groundwater basin management. (Ajayi et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Mandal et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e; Sekrecka \u0026amp; Kedzierski, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Speiser \u0026amp; Largier, \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Srinivasa Rao \u0026amp; Jugran, \u003cspan class=\"CitationRef\"\u003e2003\u003c/span\u003e; Tran Vu Van Hoa et al., 2024). Consequently, this comprehensive approach not only clarifies the spatial dynamics of LULC changes but also enhances the understanding of their implications for groundwater quality and groundwater vulnerability in the study area.\u003c/p\u003e\n \u003cp\u003eBy integrating multi-temporal satellite data and administrative boundary data from the Geospatial Information Board, this study enables the precise mapping of urban settlements, agricultural areas, water bodies, and open land. The entire land cover classification process was conducted using supervised classification methods, as illustrated in the flowchart (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe process of supervised classification began with the acquisition and pre-processing of Landsat 7 ETM\u0026thinsp;+\u0026thinsp;images from 2000 and Landsat 8 OLI TIRS images from 2021 via Google Earth Engine (GEE). This process involved cloud masking and the selection of appropriate band combinations to enhance the visualization of land features. Subsequent procedures were conducted in QGIS utilizing the Semi-Automatic Classification Plugin (SCP) developed (Congedo, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Key land cover classes, such as water, agriculture, settlements, and mining, were represented by selected training samples. The Maximum Likelihood Classification (MLC) algorithm was employed to generate initial land cover maps for both years. The accuracy of the classification was validated against reference data, and only those results with accuracy levels exceeding 70% were considered for further spatial analysis. The final classified outputs were converted into vector format, integrated with additional contextual information, and compiled into comprehensive, map-ready products (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eTo make the most of the landcover classification outcomes, the developed map was utilized as a basis for pinpointing various spatial traits associated with contamination potential. The LULC map enhanced classification by highlighting spatial features tied to contamination risks in the SLGB. By merging land cover data with socio-environmental information, this research distinguished rural, urban, and peri-urban areas for selecting representative sampling sites. This approach is in line with multi-criteria typology, which employs GIS-based methods to outline peri-urban and rural zones. The upstream basin area, including Bojonegoro, largely maintains peri-urban characteristics despite urban influences. In the central basin, Tuban and Lamongan retain pre-urbanization patterns with minimal urban encroachment. The downstream regions of Gresik and Surabaya exhibit significant urbanization and peri-urban growth, heightening groundwater contamination risks. This spatial typology, consistent with research conducted in Ghana (Hagan et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e), India (Balakrishnan et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e), Bangladesh (Hossain et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), aids in data-driven site selection and water quality evaluation across the SLGB.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eNon-point Source Contamination\u003c/h3\u003e\n\u003cp\u003eFew studies have been done on nonpoint sources, like agriculture, despite site-specific studies having been done on local or \u0026quot;point\u0026quot; sources of phosphate in groundwater, like wastewater releases and residential underground septic tank systems(Domagalski \u0026amp; Johnson, \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e; Robertson et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Spiteri et al., \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e; Stollenwerk, \u003cspan class=\"CitationRef\"\u003e1996\u003c/span\u003e) Agriculture contributes 38% of the anthropogenic phosphate burdens to freshwater ecosystems worldwide (Mekonnen \u0026amp; Hoekstra, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). In order to assess the relative effects of various phosphate sources, ranging from wastewater to agriculture, we measure phosphate concentrations in relation to land cover or use types (Warrack et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThese sources are scattered and non-specific. Because there are so many contributing causes, these kinds of contaminations are often scattered in the field, and challenging to identify a single source. Non-point sources can originate from a variety of possible sources of contamination. The formation of fertilizers, pesticides that can be carried by rainwater, and puddle surfaces that seep into the aquifer\u0026apos;s subsoil are a few examples of these effects of rice growing operations (Abdelwaheb et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). Additional sources of waste include domestic home garbage, septic tank waste, and trash from industrial operations that is transported by rainwater into the subsurface soil layer and the aquifer.\u003c/p\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e reveals that non-point sources can be given for any number of reasons. The rural land use classification in Bojonegoro, Tuban, and Lamongan regencies is characterized by the presence of anthropogenic activities, such as rice farming, villages, and the transition of upstream-mid groundwater basin. In light of this composition, the three previously described activities have the potential to act as non-point sources of pollution. Bojonegoro, Tuban, Lamongan, and Gresik are exemplary cities of the peri-urban class, which is an extension of the preceding rural change\u0026apos;s transition. Numerous human activities that define peri-urban areas, such as the massive expansion of densely populated residential areas with rising demand over time, dominate these cities (Choir et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Savitri et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). This aims to make up for the growth in a number of industrial zones, including manufacturing, petroleum, and the infrastructure that supports them (Central Bureau of Statistics of East Java Province, 2020; Bappeda of East Java Province, 2019).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eNon-Point Sources and Withdrawals\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eReach-zone\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNon-Point Source and withdrawals\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLanduse Classification\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eSurabaya\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndustry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSettlements\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDownstream of the Groundwater basin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eGresik\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndustry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSettlements\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDownstream of the Groundwater basin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003eLamongan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAgriculture Area (Paddy field)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSettlements\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe central region of the groundwater basin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndustry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePeri-Urban\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSettlements\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCity of Regency\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003eBojonegoro\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAgriculture Area (Paddy field)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSettlements\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUpstream of the groundwater basin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndustry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePeri-Urban\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSettlements\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCity of Regency\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003eTuban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAgriculture Area (Paddy field)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSettlements\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe central region of the groundwater basin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndustry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePeri-Urban\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSettlements\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCity of Regency\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eConsequently, the aforementioned activities raise a strong suspicion that they may contribute as a non-point source of pollution. Surabaya City falls into the metropolitan category of urban classes (Central Bureau Statistics of Surabaya, 2018). In this groundwater basin, the western portion of Surabaya is primarily characterized by a number of notable human activities, including large, densely inhabited townships that frequently exhibit irregularities. In order to offset the growth in a number of industrial zones, including those for manufacturing, finance, processing goods, and supporting services, this is being done (Central Bureau of Statistics of East Java Province, 2020; Regional Development Planning Board of East Java Province, 2019). These circumstances also have a role in non-point pollution.\u003c/p\u003e\n\u003ch3\u003eSampling and Analytical Methods\u003c/h3\u003e\n\u003cp\u003eTo evaluate the phosphate concentration, we described a straightforward random sampling campaign and used a grid system measuring 8 km by 8 km in 58 sampling data points from groundwater wells located throughout the basin (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ea). The chosen grid system pattern aims to distribute uniformly throughout the research site and enable the collection of representative samples. For the sampling process of phosphate compound levels in groundwater in the field, the grab samples test was carried out using plastic buckets in several shallow wells while for deep wells the bailer samples test was used. Moreover, based on the field sampling, we are taking grab samples of water from the groundwater at each test location using a plastic bucket to obtain grab samples from the shallow wells. We utilized a bailer sampler (Indonesian Standard No. 06. 6989.31, 2005) for the deep wells and collected the samples in 1,000 mL pre-sealed polypropylene bottles.\u003c/p\u003e\n\u003cp\u003eMeanwhile, related to the testing of phosphate compound levels in the laboratory, we conduct testing at one of the external testing institutions that already have legality, namely the Surabaya Health Laboratory Center (BBLK), Indonesia. For additional information, independent field testing was also conducted for pH and TDS parameters. The process was carried out by measuring each groundwater sample from all wells in the study area with a HACH HQ40d multimeter to determine TDS levels, and HACH Sension 2 to measure pH values.\u003c/p\u003e\n\u003cp\u003eA phosphomolybdenum blue spectrophotometer UV-VIS (i.e. Shimadzu UV 1601 PC) in ascorbic acid was used to analyze orthophosphate and total phosphorus in groundwater test samples (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eb). The wavelength of the instrument was 880 nm, and the analysis ranges from 0.05 mg PO\u003csub\u003e4\u003c/sub\u003e/L to 1.5 mg PO\u003csub\u003e4\u003c/sub\u003e/L for orthophosphate and 0.15 mg P/L to 1.3 mg P/L for total phosphorus (Badan Standardisasi Nasional, 2005). All that is required for a 20 mL sample volume is minimal preparation. Add four level spoons of reagent PO\u003csub\u003e4\u003c/sub\u003e- and twenty drops of reagent PO\u003csub\u003e4\u003c/sub\u003e- (with the included micro spoon). The sample\u0026apos;s color intensity is measured in the photometer after a five-minute reaction period, and the photometer\u0026apos;s display provides a direct reading of the phosphate content. This method\u0026apos;s basic idea is based on UV-Vis spectroscopy, which can significantly improve routine groundwater monitoring by offering data with a greater temporal and geographical resolution than that of wet chemical analyzers.\u003c/p\u003e\n\u003cp\u003ePhosphorus species typically do not absorb light in the UV-Vis spectrum (Zhu \u0026amp; Ma, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Recent research, however, indicates that the UV-Vis spectra can still be used to forecast the quantities of phosphorus fractions (Birgand et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e; Vaughan et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). A one-quart zip-top box was filled with some of the water samples that were free of big rocks and plant debris, and the sample site, date, and time of collection were labeled. Within 48 hours of being collected, the samples were examined in the analytical laboratory using ice cubes in a box (Indonesian Standard No.6989.57, 2008; United States Environmental Protection Agency, 2002)\u003c/p\u003e\n\u003cp\u003eThe independent sample t-test approach was employed in the mean difference test analysis conducted in this investigation. It examines the variations in phosphate concentrations between deep and shallow wells. The analysis of variance (one-way ANOVA) approach is the second analysis. Its purpose is to determine how much phosphate varies by district and city within the SLGB. Furthermore, the study employed the one-way ANOVA method to examine the variations in phosphate levels across three distinct land cover types: agricultural, residential, and industrial. The idea that the phosphate level in the SLGB area is over the 0.2 mg/L threshold limit necessary for drinking water was tested using another analysis. The threshold limit of phosphate as seen Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e below. To determine the difference between the sample mean and the phosphate threshold value, the one-sample t-test method is employed for analysis.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThreshold value of each element according to Indonesian Minister of Health Regulations No.2/2023 (in mg/L; NTU)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\" rowspan=\"3\"\u003e\n \u003cp\u003eCoordinate\u003c/p\u003e\n \u003cp\u003eUTM Zone 49S\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003eWater Table\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003eDepth of\u003c/p\u003e\n \u003cp\u003eWell\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003eLULC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003ePhosphate and Physical Parameters\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTDS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNTU\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eT (\u0026deg;C)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e3\u003c/sup\u003e-\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e300\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e6.5\u0026ndash;8\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eZ\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eValue of each element sampled in the field\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNTU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT (\u0026deg;C)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e3\u003c/sup\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e652938\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9230218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSettlement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.124\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e667370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9224078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSettlement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.961\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e651661\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9229439\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSettlement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.572\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e661312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9228625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e90.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSettlement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.166\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e615810\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9221619\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e97.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSettlement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.127\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e666960\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9226658\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e80.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSettlement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.168\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e656880\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9229825\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e76.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRice fields\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e620893\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9224568\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e114.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSettlement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e667267\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9223574\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndustry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.502\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e635766\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9214300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSettlement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.059\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e635370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9214214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndustry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1671\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e619604\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9221713\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndustry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e607504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9216469\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRice fields\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e678962\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9198563\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRice fields\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e678455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9199793\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndustry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.196\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e590592\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9210034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRice fields\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e761\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e674477\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9205870\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndustry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e642845\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9228574\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRice fields\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.292\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e630329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9213894\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRice fields\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e975\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.228\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e651021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9213747\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSettlement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1228.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.248\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e664415\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9220583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSettlement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1652\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.885\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e657186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9209680\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSettlement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1096.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.372\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e635852\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9228332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSettlement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1384.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.429\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e636079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9222287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRice fields\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e683.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.126\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e642253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9225409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRice fields\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e420\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.298\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e649110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9225380\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRice fields\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e774.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.183\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e637296\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9207891\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRice fields\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e532\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.228\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e643084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9208662\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRice fields\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e801.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e642330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9212418\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRice fields\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e457.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e630334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9208039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSettlement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e667.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.191\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e657552\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9212858\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSettlement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e401.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.329\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e673785\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9222401\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSettlement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1284.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.084\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e675558\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9212072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSettlement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1056.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.403\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e682812\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9204984\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSettlement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4228.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.094\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e667944\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9209795\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSettlement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e666586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9208831\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSettlement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e856\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e627455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9223113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSettlement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e626626\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9226522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e70.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRice fields\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e662.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e608298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9224908\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSettlement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e854\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.159\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e624035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9228697\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRice fields\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e741.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.124\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e680625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9199123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSettlement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e920\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.401\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e574674\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9207957\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRice fields\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e530\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e572449\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9210688\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRice fields\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e581411\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9211881\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRice fields\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e733\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e581800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9206386\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRice fields\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e684\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e587516\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9206631\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRice fields\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e426\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e596370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9206296\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e80.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRice fields\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e621\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e597935\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9201191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRice fields\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e563.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.428\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e605723\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9200891\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRice fields\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e611146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9201837\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRice fields\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e650.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.752\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e614711\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9206335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRice fields\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e604390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9205222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRice fields\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e619997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9205267\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRice fields\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3642.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e617774\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9211522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRice fields\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1872.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.154\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e611811\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9210705\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRice fields\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e754\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.963\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e594532\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9211537\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRice fields\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.732\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e603256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9212477\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e126.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSettlement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.124\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e636113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9214258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRice fields\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.401\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe findings of grab samples on shallow and deep wells (n\u0026thinsp;=\u0026thinsp;58) were then given values on the t-test and ANOVA in order to do statistical computations. Confidence intervals for subsets of the population produced by using the students\u0026apos; t-distribution were the main focus of the t-test study to determine contamination levels and compare water quality between two groups, as seen in Eq. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e below (Singh \u0026amp; Kumar, \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e$$\\:t=\\frac{{X}_{1}-{X}_{2}}{\\sqrt{{S}^{2}(\\frac{1}{{n}_{1}}+\\frac{1}{{n}_{2}})}}$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e$$\\:{S}^{2}=\\frac{{(n}_{1}-1){S}_{1}^{2}+({n}_{2}-1){S}_{2}^{2}}{{n}_{1}+{n}_{2}-2}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{1}\\)\u003c/span\u003e\u003c/span\u003eand \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{2}\\)\u003c/span\u003e\u003c/span\u003e are the average scores of group 1 and group 2, while \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{S}_{1}^{2}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{S}_{2}^{2}\\)\u003c/span\u003e\u003c/span\u003erepresent their variance scores. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{n}_{1}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{n}_{2}\\)\u003c/span\u003e\u003c/span\u003e indicate sample sizes, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{S}^{2}\\)\u003c/span\u003e\u003c/span\u003e is the pooled variance, combining both groups\u0026apos; variances based on sample size. To determine if the mean of the deep well and shallow well was statistically different when collected by phosphate content, it was related to two sample groups. ANOVA (analysis of variance) tests by conducted in one method were utilized to ascertain if the mean land cover was rural, peri-urban, or urban. One-way ANOVA used to compare variances among multiple groups. Subsequently, ANOVA serves as a tool for effectively evaluating the potential of groundwater resources, refer to Eq. 2 below.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u003ctable id=\"Taba\" border=\"1\"\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:F=\\frac{MST}{MSE}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:MST=\\frac{\\sum\\:_{i=1}^{k}\\left(\\frac{{T}_{i}^{2}}{{n}_{i}}\\right)-\\frac{{G}^{2}}{n}}{k-1}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:MSE=\\frac{\\sum\\:_{i=1}^{k}{\\sum\\:}_{j=1}^{{n}_{i}}{Y}_{ij}^{2}-\\:\\sum\\:_{i=1}^{k}\\left(\\frac{{T}_{i}^{2}}{{n}_{i}}\\right)}{n-k}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:F\\)\u003c/span\u003e\u003c/span\u003e is the variance ratio for the overall test, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:MST\\)\u003c/span\u003e\u003c/span\u003e is the mean square due to treatments/groups (between groups), \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:MSE\\)\u003c/span\u003e\u003c/span\u003e is the mean square due to error (within groups, residual mean square), \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Y}_{ij}\\)\u003c/span\u003e\u003c/span\u003e is an observation, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{T}_{i}\\)\u003c/span\u003e\u003c/span\u003e is a group total, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:G\\)\u003c/span\u003e\u003c/span\u003e is the grand total of all observations, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{n}_{i}\\)\u003c/span\u003e\u003c/span\u003e is the number in group \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:n\\)\u003c/span\u003e\u003c/span\u003e is the total number of observations. Additionally, the latter test will be linked to the determination of the variations in phosphate load between areas within the SLGB as well as certain human activities including farming, industry, and habitations. The statistical data analysis was performed utilizing version 1.8 of the Jamovi program. The jamovi tool has been utilized in a variety of environmental studies beyond groundwater research, contributing to broader investigations of environmental quality and contamination (Berwanger et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Meza-Ramirez et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Schrank et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; William \u0026amp; Katambara, \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eOne-sample t-test analysis aims to determine the amount of phosphate compound levels in the research sample based on the average, which is categorized as above or below the threshold. Ho\u0026thinsp;=\u0026thinsp;0.2 mg/L means that the null hypothesis states that the phosphate compound level based on the average is equal to the threshold limit (not higher), while Ha\u0026thinsp;\u0026gt;\u0026thinsp;0.2 mg/L means that the alternative hypothesis states that the phosphate compound level based on the average is greater than the 0.2 mg/L threshold limit. Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e below is the result of a one-sample t-test analysis on phosphate compounds.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe one sample t-test of phosphate compared to their threshold\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ePhosphate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStatistic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHₐ\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003estudent\u0026rsquo;s t\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e3.34\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e57.0\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026micro;\u0026thinsp;\u0026gt;\u0026thinsp;0.2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eDescriptives\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ePhosphate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.627\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.974\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.128\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003eN: sum of sample; Mean: mean of phosphate concentration from all samples, p: significant value\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eOne-sample t-test analysis shows that the levels of phosphate compounds in the study area fall into the category above the safe threshold when viewed from the average. The p-value shows\u0026thinsp;\u0026lt;\u0026thinsp;0.001, which means that the average value of phosphate compound levels is significantly above the normal threshold based on the Ministry of Health regulation of 0.2mg/L. The mean value of phosphate levels is 0.627 mg/L, which has a considerable difference with the tolerable phosphate threshold of 0.2 mg/L. Based on these results, it is an initial finding that phosphate compounds are one of the substances that have a high potential to become groundwater pollutants in the study area. Although in the research location, the majority of land cover is in the form of agricultural rice fields and is in an area categorized as rural, chemical compounds that have the potential to become sources of pollutants, especially phosphate, are quite high. Unlike the general assumption that rural areas are relatively free from sources of environmental pollution, this is not the case.\u003c/p\u003e\n\u003cp\u003eThe Independent Sample t-test analysis in this section was conducted to determine the average difference in the 2 groups based on well depth. The sample groups based on well depth were divided into shallow wells and deep wells. Hₐ \u0026micro;1\u0026thinsp;\u0026ne;\u0026thinsp;\u0026micro;2 compounds in shallow wells and in deep wells. Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e below shows the results of the independent sample t-test analysis based on well depth categorized as shallow wells and deep wells.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003eThe results of the independent sample t-test analysis showed that there was a significant mean difference in the shallow well and deep well groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), where the mean in the shallow well group was higher than the mean in the deep well group. This means that phosphate levels are affected by well depth, where the deeper the well, the smaller the phosphate levels in groundwater. Groundwater phosphate in shallow wells reached levels above the threshold reaching 0.769 mg/L while the phosphate threshold limit is 0.2 mg/L. The box plot complements the results of the independent sample t-test analysis based on well depth. The box plot illustrates the data variance of each sample group. This is expected to provide a clearer picture to complement the results of the analysis aimed at proving the hypothesis.\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eOne-way anova analysis was conducted to determine the mean differences in more than 2 sample groups. The one-way anova analysis in this section is conducted to see the mean differences in sample groups based on districts, which consist of 5 districts, namely Gresik district, Tuban district, Lamongan district, Bojonegoro district and Surabaya city. The null hypothesis states that there is no difference in the level of phosphate compounds in each district, and the alternative hypothesis states that there is a difference in the level of phosphate compounds in each district. Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e is the result of one-way anova analysis by district. There was no discernible change in the results for p\u0026thinsp;=\u0026thinsp;0.435 (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Descriptive analysis, however, revealed a variation in the mean for Gresik District, which was ranked top and had a phosphate concentration of 1.201 mg/L. Surabaya City and Lamongan Regency had respective mean phosphate contents of 0.540 and 0.523 mg/L and second place, respectively. Subsequently, Tuban Regency recorded a mean phosphate level of 0.489 mg/L, whereas Bojonegoro Regency had the lowest content value, measuring 0.369 mg/L.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003eThe distribution pattern of groundwater phosphate levels for each district and city in the study area is depicted in a box plot. Other than Tuban, the sample distribution pattern for phosphate levels is generally homogeneous. The more varied box-plots indicate that the phosphate levels in Lamongan, Bojonegoro, Surabaya, and Gresik, respectively, have a more heterogeneous sample distribution pattern. As a result, box plot homogeneous and heterogeneous characteristic data will both show notable distribution patterns. Moreover, these are typical circumstances for converting dispersive data into countable parameters.\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003eThe one-way ANOVA analysis in this section aims to determine the differences in phosphate levels in each classification based on population density, namely rural, transitional (peri-urban) and urban. Based box plot in the table. 7 shows the variance of phosphate levels for three characteristics (rural, peri-urban, and urban). The analysis was conducted based on the hypothesis that there is a difference in the level of phosphate compounds in each regional category. Urban areas are expected to have the highest phosphate levels compared to transitional and rural areas. Table \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e presents the results of a one-way ANOVA analysis based on population density classification.\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eAccording to the research hypothesis, the study site\u0026apos;s phosphate level is already higher than the threshold value. This is dependent on a number of things, including human activities like industry, settlements (home trash), and agriculture (use of pesticides and fertilizers). The null hypothesis (Ho: \u0026micro;\u0026thinsp;=\u0026thinsp;0.2) was not proven by hypothesis testing, but the working hypothesis (Ha: \u0026micro;\u0026thinsp;\u0026gt;\u0026thinsp;0.2) was, as shown in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. This demonstrates that the mean phosphate levels in the samples were greater than the regulatory threshold of 0.2 mg/L, as demonstrated by significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) evidence.\u003c/p\u003e\n\u003cp\u003eAnother one-way ANOVA analysis was aimed at looking at differences in the level of phosphate compounds in classifications based on LULC. In this study, the classification based on LULC is divided into groups of residential areas, agricultural areas and industrial areas. The null hypothesis states that there is no difference in the level of phosphate compounds in the three regional classifications, while the alternative hypothesis states that there is a difference in the level of phosphate levels in each area. The results of one-way ANOVA of phosphate based on LULC are presented in Table \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this research region, phosphate (PO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e3\u003c/sup\u003e-) contamination of groundwater does not appear to threaten human health. Nevertheless, investigating the spatial variables and potential sources that control groundwater phosphate distributions remains imperative to test the theory that the phosphate content of the SLGB is higher than the drinking water threshold. Thus, monitoring its concentration in groundwater is essential to implement mitigation strategies aimed at preventing further contamination of the area.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eSource of phosphate contamination for unconfined and confined groundwater\u003c/h2\u003e\u003cp\u003ePhosphate loads from specific LULC surfaces have been shown to leave their source imprints in groundwater (Warrack et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Thus, to analyze the phosphate pollution in groundwater systems, details of LULC are essential. Because phosphate enters the subsurface system through several channels that heavily depend on the type of LULC, the source factor of phosphate contaminations in groundwater was determined by evaluating its content in conjunction with an examination of LULC. The increased phosphate levels are indicative of anthropogenic influences, notably those associated with agricultural practices, domestic waste management, and industrial activities, which collectively contribute to the escalation of phosphate concentrations (Bi et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The utilization of shallow groundwater in addition to surface river water as the primary source of water supply is directly tied to all of these activities. This provides compelling evidence that past human activity is what caused the process of phosphate pollution of groundwater to begin in shallow groundwater. Furthermore, the lack of quality control in the anthropogenic activity process will negatively impact both the environment and human health (Maulida et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This indicates that surface environmental conditions are starting to be affected by the process of groundwater phosphate contamination. Because of human activity near agricultural, industrial, residential, and other comparable regions, anthropogenic groundwater contamination will spread in the direction of groundwater flow, contaminating shallow groundwater (Chang et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The average (mean) phosphate level in the shallow well type has a value of 0.783 mg/L, which is over the threshold value (0.2 mg/L), it supports the assertion in the previous results section.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e below reveals that the results of the correlation test between groundwater phosphate levels and well depth show a significant negative relationship. Accordingly, the deeper the groundwater sampled, the lower the phosphate content in the water; the high phosphate content in groundwater is inversely related to the well's depth. On the other hand, a higher phosphate level was found in groundwater samples taken from shallower wells. This is another proof that human activity is causing phosphate pollution in groundwater. On the surface of the earth, anthropogenic activities take place. Agricultural practices that employ phosphate as a component of insecticides and fertilizers are among them. Typical anthropogenic activities include household activities that generate waste, both liquid and solid. Examples of these include settlements. Phosphate from production operations is another way that industrial activities contribute to the process of groundwater contamination. The manufacturing sector, petrochemicals, common use minerals, and other industrial processes are some of the major sources of phosphate pollution in groundwater and the environment.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePhosphate Correlation Matrix for Shallow and Deep Wells\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eCorrelation Matrix\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePhosphate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDepth of Well\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ePhosphate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePearson\u0026rsquo;s r\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.265\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.044\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eDepth of Well\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePearson\u0026rsquo;s r\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.265\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.044\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003ePearson\u0026rsquo;s r: correlation coefficient by Pearson formula, p-value: significant value\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn addition to the results of the correlation test between phosphate levels and well depth calculated by the product moment correlation method (Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). Despite the low correlation category, the relationship between phosphate levels and well depth demonstrates a substantial negative association. On the other hand, Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e suggests that there is a strong correlation and fluctuation in the phosphate data at shallow wells, defined as those with a depth of less than 50 meters. An overall low correlation is the outcome of low or less diversified variance in the phosphate data in deep wells. Phosphate levels at depths of more than 50 meters are not so different or diverse (homogeneous variants), so if additional inferences are made from the data, it is suspected that the potential for pollution in shallow wells is relatively high while deep wells are observed to have less severe levels of related contaminants.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ePhosphate Contaminant Characteristics by Administrative Location in Groundwater Basins\u003c/h3\u003e\n\u003cp\u003eIt is possible to assess the degree of variation in phosphate concentration values for every administrative area in the groundwater basin. The intriguing finding is that all groundwater basin administrative areas have phosphate levels over the safety level (threshold value). Placing first with an average phosphate level of 1.2 mg/L was the Gresik district. We may understand this because of the region's features, which are characterized by rapidly expanding residential and industrial regions. Gresik is part of the integrated economic development area, which is a crucial national growth area that supports the Surabaya metropolitan area. Accordingly, Gresik serves as a buffer zone/peri-urban area for Surabaya, to create some separate but integrated zones for industry, habitation, and other services that Surabaya is unable to provide. With an average phosphate content of 0.54 mg/L, Surabaya City's western region ranks second. One of the major commercial and urban hubs in Java Island's east is Surabaya City. The presence of levels above this cutoff indicates that anthropogenic activities have an impact on the area, which raises average phosphate levels. When we look at the location of Surabaya's western region, we get data from the analysis of LULC based on Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, which shows that the area is already home to a large number of densely populated industrial and residential districts. Western Surabaya, particularly the neighbourhoods that border Gresik, is not only a residential region but also an industrial area and a freight transport firm (Central Bureau of Statistics of East Java Province, 2020). Similar traits can be found in other districts, such as Lamongan, Tuban, and Bojonegoro, where agriculture, rice being the primary crop, dominates human activity. The obtained phosphate content averages are 0.52, 0.49, and 0.37, in that order.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe spatial analysis of phosphate concentrations (mg/L) in relation to LULC within the SLGB reveals a significant association between elevated phosphate levels and anthropogenic land uses. Areas characterized by residential development and open fields or mining activities exhibit the highest phosphate concentrations (\u0026gt;\u0026thinsp;1 mg/L), suggesting substantial nutrient input, likely originating from domestic wastewater and surface runoff. Conversely, agricultural and moorland regions generally present moderate phosphate levels (0.2\u0026ndash;0.5 mg/L), which may indicate controlled or diluted inputs due to irrigation practices or vegetative buffering. These findings highlight the considerable influence of land use on nutrient pollution, advocating for integrated land\u0026ndash;water management strategies to mitigate eutrophication in the Bengawan Solo River system. The map presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e illustrates the distribution of phosphate contaminant concentration levels, with the contour zoning pattern established through the application of the kriging method (Krige, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1951\u003c/span\u003e). In this context, the contour zoning corresponds with the pattern of data distribution.\u003c/p\u003e\n\u003ch3\u003eLand Use-Land Cover Types Linked with Phosphorus Concentrations in Groundwater\u003c/h3\u003e\n\u003cp\u003eThe land use-land cover's description class has a significant value (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) according to the findings of the one-way ANOVA (Welch's) study. Note that most of the land (n\u0026thinsp;=\u0026thinsp;30) in the three land cover classifications chosen to represent the research locus is agricultural land. In addition, the second area (n\u0026thinsp;=\u0026thinsp;23) is made up of residential areas, and the last region (n\u0026thinsp;=\u0026thinsp;5) is an industrial zone. The existence of hubs dedicated to the cultivation of specific rice types characterizes and dominates these agricultural zones. A national center for rice production serves as the study site (East Java Province Spatial Planning, 2011; Regional Development Planning Board, 2019). Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e further demonstrates this state. As a result of the aforementioned, fertilizers and insecticides are used more frequently. The average phosphate concentration on residential land was determined to be 1.107 mg/L.\u003c/p\u003e\u003cp\u003eAlthough increased concentrations have been linked to urban contexts such as residential neighborhoods, groundwater contributions of phosphorus have traditionally been considered modest (Fitzgerald et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Phosphorus in groundwater used for irrigation in fields may also be stored on soil particles and subsequently transported to streams by sediment during periods of strong flow (Welch et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) There are several reasons why residential areas have higher phosphate levels. Among them is the inadequate and poor state of residential areas' septic tank systems, which allows phosphate compounds that are present in wastewater to flow out. Septic systems ought to be able to efficiently treat wastewater provided they are installed, maintained, and operated appropriately. But there's growing evidence that septic systems in places with geological settings like sandy or clayey soils and high groundwater levels are transferring phosphorus (Mechtensimer \u0026amp; Toor, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAdditionally, it was discovered that several significant developments could have an impact on industrial zones as a result of these operations. Solid waste materials, wastewater, and industrial waste are a few of them. Industrial waste is waste that is emitted during the manufacturing process and includes any useless items produced throughout industrial operations, such as factories and mills. Acid rain is an example of sulfur dioxide and nitrogen oxide emissions from chimneys and exhaust pipes(Burri et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) Manufacturing sectors produce liquid, solid, and gaseous wastes that can have a negative influence on the environment and people (Masi et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Industrial pollution can eventually find its way into the rivers and oceans by contaminating the surrounding water sources, the air, or the land. The most important determining factors for the quality of water resources and the risk of solid waste contamination from solid waste materials may have been the existence, coverage, kind, and upkeep of infrastructure, such as landfills (Han et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Particularly for large volumes of manufacturing solid waste, landfills (including tailings facilities) continue to be the most popular and economical way to dispose of solid waste worldwide (Ferronato \u0026amp; Torretta, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Even though trash disposal landfill leachate is known to be a source of contaminants in groundwater, not all landfills have solid landfill lining installed. Furthermore, there are a lot of landfills from the industrial revolution that have little or no lining. Currently, areas lacking access to effective preservation or disposal methods may have to rely on shallow subsurface disposal for their solid waste. Moreover, solid trash includes a wide range of items, including cardboard, paper, plastic, scrap metal, wood, packaging, automotive parts, food waste, and any other solid waste that is no longer able to be used for its intended purpose (Abdel-Shafy \u0026amp; Mansour, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWastewater is the term used for industrial liquid waste. Organic substances (proteins, lipids, and carbohydrates) and dissolved inorganic pollutants are the sources of this type of contamination in industrial fluids. Large amounts of water are needed in the majority of production businesses, and these can come into contact with hazardous substances. Drainage systems, drains, septic reservoirs, and sewer networks are among the particular infrastructures for liquid waste (Marszelewski \u0026amp; Piasecki, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) Since effluent is frequently better tracked for environmental protection, centralized wastewater collection systems combined with a well-run treatment plant that uses cutting-edge treatment technology and frequent maintenance are the suggested answers. Another type of infrastructure is extensive fuel storage facilities and pipeline networks used by industry. These elements have the potential to leak or accidentally spill, resulting in non-aqueous liquid pollution (Burri et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Jackson et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo better illustrate the spatial relationship between groundwater flow dynamics and the distribution pattern of phosphate compound contaminants, this phenomenon can be modeled using the finite difference method for groundwater simulation. A conceptual hydrostratigraphic model, developed through either a stochastic or deterministic approach, serves as a robust framework for analyzing contaminant transport mechanisms, facilitating a more comprehensive understanding of their spatial distribution and movement (Darul et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Knowing the hydrostratigraphic condition of the groundwater basin, the next step is to plot the groundwater level of the study area and the distribution pattern of phosphate compounds associated with regional geological conditions, which will be processed using Modflow software.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eHypothesis Testing for Phosphate Contamination\u003c/h2\u003e\u003cp\u003eTo support the hypothesis that the phosphate concentration at the research location was generally higher than the threshold, a one-sample t-test was conducted. All samples in this investigation had an average phosphate level (from 58 samples) 0.621 mg/L, and the p-value was less than 0.05 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This indicates that the research site's phosphate levels are higher than the Ministry of Health's recommended threshold of 0.2 mg/L for drinking water. These results offer information that merits consideration, particularly with regard to the use of groundwater at the study site which needs to be closely watched, as phosphate content that has surpassed the threshold is thought to pose a risk to human health as well as the ecosystem as a whole. Chemical pollution-related public health issues can grow into major complex issues if they are not recognized or addressed as soon as possible.\u003c/p\u003e\u003cp\u003eThe discovery that the average phosphate content at the research site exceeds the threshold limit not only offers early warning information about groundwater pollution, but also offers empirical proof that groundwater conservation efforts against the effects of human activity must be launched right away as an emergency response. It is important to keep in mind that this research's findings and analysis are still preliminary. This is corroborated by the scant attention to detail that has been paid to the process of groundwater pollution in rural (i.e., non-urban) areas. It is therefore envisaged that this research will serve as one of the main sources for its comprehensive initiation.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe findings of the study reveal that phosphate concentrations in groundwater remain consistent across different regional categories, including rural, urban, and peri-urban areas, as well as within the five administrative regions analysed. However, phosphate levels in all districts exceeded the permissible limit. The research identifies land cover as a significant determinant of phosphate concentrations. Regions characterized by residential and agricultural activities exhibited phosphate levels above acceptable thresholds, whereas industrial areas maintained concentrations below the limit. These results suggest a direct correlation between land use patterns and groundwater quality. Furthermore, the depth of groundwater wells was identified as a critical factor influencing phosphate contamination.\u003c/p\u003e\u003cp\u003eThe study highlights the disparity in phosphate concentrations between shallow and deep wells. Phosphate levels in shallow wells consistently surpassed the recommended safety thresholds, rendering the water unsuitable for human consumption. In contrast, deep wells-maintained phosphate concentrations within safe limits, reinforcing the notion that surface-level anthropogenic activities, such as agricultural runoff, improper waste disposal, and urban discharge, play a significant role in groundwater contamination. This pattern suggests that phosphate pollution is predominantly concentrated in shallower water sources, underscoring the necessity for targeted interventions to mitigate the associated risks.\u003c/p\u003e\u003cp\u003eFurthermore, hypothesis testing underscored the severity of phosphate pollution at the investigated site. The statistical analysis, particularly the mean difference test, indicated a significant deviation from the established drinking water threshold of 0.2 mg/L (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), underscoring the urgent need to address this environmental concern. Given the magnitude of the contamination, the study highlights the imperative for effective environmental management and mitigation strategies to prevent phosphate infiltration into groundwater systems. Protecting public health, especially for communities reliant on shallow wells for drinking water, necessitates immediate policy interventions and sustainable water resource management practices.\u003c/p\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eLimitations\u003c/h2\u003e\u003cp\u003eThis research has several limitations, particularly regarding the comparison of the Landsat 8 OLI TIRS image from 2021 with the Landsat 7 ETM image from 2000. The results indicate a significant increase in the area dedicated to rice farming (agriculture). Moreover, the observed relationship with groundwater quality and phosphate concentration levels is applicable only at the time of observation.\u003c/p\u003e\u003cp\u003eIn the analysis of sample classification based on population density, almost all samples (N\u0026thinsp;=\u0026thinsp;50) were categorized as rural areas, while the rest were classified as rural to urban transition (N\u0026thinsp;=\u0026thinsp;6) and urban (N\u0026thinsp;=\u0026thinsp;2). This shows that the representation of each area classification is uneven. It could be argued that this study is actually located in an area that is categorized as rural. However, there was no intention in this study to specialize in data collection in rural areas only. The sample was taken randomly using the grid sampling method, so it does not look at the distribution of points in areas categorized as rural, transitional or urban. Future research needs to consider the representativeness of samples based on population density because groundwater pollution is identical to human activities which may have diversity when viewed from population density in each region.\u003c/p\u003e\u003cp\u003eThis study is subject to several limitations, including the omission of dispersion parameters, the lack of groundwater flow advection modelling, the dependence on statistical values for zoning rather than physical processes, and the failure to account for temporal dynamics.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed equally to the conceptualization, methodology, investigation, data curation, formal analysis, writing (original draft preparation, review and editing), and visualization of this manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors did not receive support from any organization for the submitted work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors confirm that the data supporting the findings of this study are available in this manuscript and its supplementary material. Raw data supporting this study\u0026rsquo;s findings are available from The Surabaya-Lamongan Groundwater Basin\u0026apos;s Chemical-Physical, Statistics, and LULC Properties - Mendeley Data\u003c/p\u003e\n\u003cp\u003eAll authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to Bengawan Solo River Basin Management (BBWS), the Ministry of Public Works, and Badan Geologi for their support in data collection, as well as Bandung Institute of Technology (ITB) and Muhammadiyah University of East Kalimantan (UMKT) for their contributions to data analysis. We would like to especially acknowledge Adi Guna Prasetyo and Novandri Kusuma W and their colleagues for their invaluable assistance during field surveys. Lastly, we extend our gratitude to the dissertation examiners and paper reviewers for their valuable insights, which significantly improved the quality of this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAbdel-Shafy, H. I., \u0026amp; Mansour, M. S. M. (2018). Solid waste issue: Sources, composition, disposal, recycling, and valorization. In \u003cem\u003eEgyptian Journal of Petroleum\u003c/em\u003e (Vol. 27, Issue 4, pp. 1275\u0026ndash;1290). Egyptian Petroleum Research Institute. https://doi.org/10.1016/j.ejpe.2018.07.003\u003c/li\u003e\n \u003cli\u003eAbdelwaheb, M., Jebali, K., Dhaouadi, H., \u0026amp; Dridi-Dhaouadi, S. (2019). Adsorption of nitrate, phosphate, nickel and lead on soils: Risk of groundwater contamination. \u003cem\u003eEcotoxicology and Environmental Safety\u003c/em\u003e, \u003cem\u003e179\u003c/em\u003e, 182\u0026ndash;187. https://doi.org/10.1016/j.ecoenv.2019.04.040\u003c/li\u003e\n \u003cli\u003eAjayi, O. G., Nwadialor, I. J., Odumosu, J. O., Adetunji, O. O., \u0026amp; Abdulwasiu, I. O. (2022). Assessment and delineation of groundwater potential zones using integrated geospatial techniques and analytic hierarchy process. \u003cem\u003eApplied Water Science\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(12). https://doi.org/10.1007/s13201-022-01802-4\u003c/li\u003e\n \u003cli\u003eBalakrishnan, S., Udayashankar, H. N., Wankhede, S. F., Shetty, N., Sharma, A. V., \u0026amp; Balakrishna, K. (2023). Investigating the impact of long-term agricultural practices on selected peri-urban aquifers in tropical Southwest India. \u003cem\u003eUrban Climate\u003c/em\u003e, \u003cem\u003e47\u003c/em\u003e. https://doi.org/10.1016/j.uclim.2022.101356\u003c/li\u003e\n \u003cli\u003eBarthel, R., Reichenau, T. G., Krimly, T., Dabbert, S., Schneider, K., \u0026amp; Mauser, W. (2012). Integrated modeling of global change impacts on agriculture and groundwater resources. \u003cem\u003eWater Resources Management\u003c/em\u003e, \u003cem\u003e26\u003c/em\u003e(7), 1929\u0026ndash;1951. https://doi.org/10.1007/s11269-012-0001-9\u003c/li\u003e\n \u003cli\u003eBarthel, R., \u0026amp; Reyes, F. A. P. (2016). Estimating the change in groundwater quality resulting from changes to land use and groundwater recharge. In \u003cem\u003eRegional Assessment of Global Change Impacts\u003c/em\u003e (pp. 601\u0026ndash;607). Springer International Publishing. https://doi.org/10.1007/978-3-319-16751-0_69\u003c/li\u003e\n \u003cli\u003eBerwanger, G. G., Reginato, P. A. R., \u0026amp; Vieiro, A. P. (2023). \u003cem\u003eHydrogeological assessment of coastal aquifers in areas of infiltration basins in southern Brazil\u003c/em\u003e. https://doi.org/10.21203/rs.3.rs-3366944/v1\u003c/li\u003e\n \u003cli\u003eBi, P., Huang, G., Liu, C., \u0026amp; Li, L. (2022). Geochemical factors controlling natural background levels of phosphate in various groundwater units in a large-scale urbanized area. \u003cem\u003eJournal of Hydrology\u003c/em\u003e, \u003cem\u003e608\u003c/em\u003e. https://doi.org/10.1016/j.jhydrol.2022.127594\u003c/li\u003e\n \u003cli\u003eBirgand, F., Aveni-Deforge, K., Smith, B., Maxwell, B., Horstman, M., Gerling, A. B., \u0026amp; Carey, C. C. (2016). First report of a novel multiplexer pumping system coupled to a water quality probe to collect high temporal frequency in situ water chemistry measurements at multiple sites. \u003cem\u003eLimnology and Oceanography: Methods\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(12), 767\u0026ndash;783. https://doi.org/10.1002/lom3.10122\u003c/li\u003e\n \u003cli\u003eBorggaard, O. K., Szilas, C., Gimsing, A. L., \u0026amp; Rasmussen, L. H. (2004). Estimation of soil phosphate adsorption capacity by means of a pedotransfer function. \u003cem\u003eGeoderma\u003c/em\u003e, \u003cem\u003e118\u003c/em\u003e(1\u0026ndash;2), 55\u0026ndash;61. https://doi.org/10.1016/S0016-7061(03)00183-6\u003c/li\u003e\n \u003cli\u003eBurri, N. M., Weatherl, R., Moeck, C., \u0026amp; Schirmer, M. (2019). A review of threats to groundwater quality in the anthropocene. In \u003cem\u003eScience of the Total Environment\u003c/em\u003e (Vol. 684, pp. 136\u0026ndash;154). Elsevier B.V. https://doi.org/10.1016/j.scitotenv.2019.05.236\u003c/li\u003e\n \u003cli\u003eCarpenter, S. R., Caraco, N. F., Correll, D. L., Howarth, R. W., Sharpley, A. N., \u0026amp; Smith, V. H. (1998). Nonpoint Pollution Of Surface Waters With Phosphorus And Nitrogen. In \u003cem\u003eEcological Applications\u003c/em\u003e (Vol. 8, Issue 3).\u003c/li\u003e\n \u003cli\u003eČesonienė, L., \u0026Scaron;ileikienė, D., Čiteikė, L., Mozgeris, G., \u0026amp; Takayoshi, K. (2023). The Impact of Organic and Intensive Agricultural Activity on Groundwater and Surface Water Quality. \u003cem\u003eWater (Switzerland)\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(6). https://doi.org/10.3390/w15061240\u003c/li\u003e\n \u003cli\u003eChang, C. L., Kuan, W. H., Lui, P. S., \u0026amp; Hu, C. Y. (2008). Relationship between landscape characteristics and surface water quality. \u003cem\u003eEnvironmental Monitoring and Assessment\u003c/em\u003e, \u003cem\u003e147\u003c/em\u003e(1\u0026ndash;3), 57\u0026ndash;64. https://doi.org/10.1007/s10661-007-0097-1\u003c/li\u003e\n \u003cli\u003eChoir, M., Prayitno, G., Wahid Hasyim, A., Amire Dzvimbo, M., Mawonde, A., \u0026amp; Ken, S. (2024). Changes in Land Use of Protected Rice Land to Support Food Independence using Remote Sensing Technology. \u003cem\u003eCivil and Environmental Science\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e(1), 32\u0026ndash;42. https://doi.org/10.21776/ub.civense.2024.007.01.5\u003c/li\u003e\n \u003cli\u003eChung, S. Y., Kim, G. B., \u0026amp; Senapathi, V. (2023). Drought and Groundwater Development. In \u003cem\u003eWater (Switzerland)\u003c/em\u003e (Vol. 15, Issue 10). MDPI. https://doi.org/10.3390/w15101908\u003c/li\u003e\n \u003cli\u003eCongedo, L. (2021). Semi-Automatic Classification Plugin: A Python tool for the download and processing of remote sensing images in QGIS. \u003cem\u003eJournal of Open Source Software\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(64), 3172. https://doi.org/10.21105/joss.03172\u003c/li\u003e\n \u003cli\u003eDarul, A., Irawan, D. E., Sumintadireja, P., \u0026amp; Ramadhan, G. (2025). Model description of combined numerical and stochastic groundwater flow in Bandung-Soreang Groundwater Basin, West Java, Indonesia. \u003cem\u003eMethodsX\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e. https://doi.org/10.1016/j.mex.2024.103112\u003c/li\u003e\n \u003cli\u003eDomagalski, J. L., \u0026amp; Johnson, H. M. (2011). Subsurface transport of orthophosphate in five agricultural watersheds, USA. \u003cem\u003eJournal of Hydrology\u003c/em\u003e, \u003cem\u003e409\u003c/em\u003e(1\u0026ndash;2), 157\u0026ndash;171. https://doi.org/10.1016/j.jhydrol.2011.08.014\u003c/li\u003e\n \u003cli\u003eFerronato, N., \u0026amp; Torretta, V. (2019). Waste mismanagement in developing countries: A review of global issues. In \u003cem\u003eInternational Journal of Environmental Research and Public Health\u003c/em\u003e (Vol. 16, Issue 6). MDPI AG. https://doi.org/10.3390/ijerph16061060\u003c/li\u003e\n \u003cli\u003eFitzgerald, A., Roy, J. W., \u0026amp; Smith, J. E. (2015). Calculating discharge of phosphorus and nitrogen with groundwater base flow to a small urban stream reach. \u003cem\u003eJournal of Hydrology\u003c/em\u003e, \u003cem\u003e528\u003c/em\u003e, 138\u0026ndash;151. https://doi.org/10.1016/j.jhydrol.2015.06.038\u003c/li\u003e\n \u003cli\u003eHagan, G. B., Minkah, R., Yiran, G. A. B., \u0026amp; Dankyi, E. (2022). Assessing groundwater quality in peri-urban Accra, Ghana: Implications for drinking and irrigation purposes. \u003cem\u003eGroundwater for Sustainable Development\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e. https://doi.org/10.1016/j.gsd.2022.100761\u003c/li\u003e\n \u003cli\u003eHan, D., Tong, X., Currell, M. J., Cao, G., Jin, M., \u0026amp; Tong, C. (2014). Evaluation of the impact of an uncontrolled landfill on surrounding groundwater quality, Zhoukou, China. \u003cem\u003eJournal of Geochemical Exploration\u003c/em\u003e, \u003cem\u003e136\u003c/em\u003e, 24\u0026ndash;39. https://doi.org/10.1016/j.gexplo.2013.09.008\u003c/li\u003e\n \u003cli\u003eHossain, M. J., Rahman, M. Z., Kamal, A. S. M. M., Chowdhury, M. A., Hossain, M. S., Rahman, M. M., Zahid, A., \u0026amp; Islam, A. R. M. T. (2024). Quantitative and qualitative assessment of groundwater resources for drinking water supply in the peri-urban area of Dhaka, Bangladesh. \u003cem\u003eGroundwater for Sustainable Development\u003c/em\u003e, \u003cem\u003e25\u003c/em\u003e. https://doi.org/10.1016/j.gsd.2024.101146\u003c/li\u003e\n \u003cli\u003eHuang, G., Liu, C., Zhang, Y., \u0026amp; Chen, Z. (2020). Groundwater is important for the geochemical cycling of phosphorus in rapidly urbanized areas: a case study in the Pearl River Delta. \u003cem\u003eEnvironmental Pollution\u003c/em\u003e, \u003cem\u003e260\u003c/em\u003e. https://doi.org/10.1016/j.envpol.2020.114079\u003c/li\u003e\n \u003cli\u003eHuang, Y., Hickman, J. E., \u0026amp; Wu, S. (2018). Impacts of enhanced fertilizer applications on tropospheric ozone and crop damage over sub-Saharan Africa. \u003cem\u003eAtmospheric Environment\u003c/em\u003e, \u003cem\u003e180\u003c/em\u003e, 117\u0026ndash;125. https://doi.org/10.1016/j.atmosenv.2018.02.040\u003c/li\u003e\n \u003cli\u003eJackson, R. E., Gorody, A. W., Mayer, B., Roy, J. W., Ryan, M. C., \u0026amp; Van Stempvoort, D. R. (2013). Groundwater protection and unconventional gas extraction: The critical need for field-based hydrogeological research. \u003cem\u003eGroundwater\u003c/em\u003e, \u003cem\u003e51\u003c/em\u003e(4), 488\u0026ndash;510. https://doi.org/10.1111/gwat.12074\u003c/li\u003e\n \u003cli\u003eKrige. (1951). \u003cem\u003eChemical Journal of The Metallurgical \u0026amp; Society of South Mining Africa\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eLevy, Z. F., Jurgens, B. C., Burow, K. R., Voss, S. A., Faulkner, K. E., Arroyo-Lopez, J. A., \u0026amp; Fram, M. S. (2021). Critical Aquifer Overdraft Accelerates Degradation of Groundwater Quality in California\u0026rsquo;s Central Valley During Drought. \u003cem\u003eGeophysical Research Letters\u003c/em\u003e, \u003cem\u003e48\u003c/em\u003e(17). https://doi.org/10.1029/2021GL094398\u003c/li\u003e\n \u003cli\u003eLewandowski, J., Meinikmann, K., N\u0026uuml;tzmann, G., \u0026amp; Rosenberry, D. O. (2015). Groundwater - the disregarded component in lake water and nutrient budgets. Part 2: Effects of groundwater on nutrients. \u003cem\u003eHydrological Processes\u003c/em\u003e, \u003cem\u003e29\u003c/em\u003e(13), 2922\u0026ndash;2955. https://doi.org/10.1002/hyp.10384\u003c/li\u003e\n \u003cli\u003eLunt, P. (2019). The origin of the East Java Sea basins deduced from sequence stratigraphy. \u003cem\u003eMarine and Petroleum Geology\u003c/em\u003e, \u003cem\u003e105\u003c/em\u003e, 17\u0026ndash;31. https://doi.org/10.1016/j.marpetgeo.2019.03.038\u003c/li\u003e\n \u003cli\u003eMandal, U., Sahoo, S., Munusamy, S. B., Dhar, A., Panda, S. N., Kar, A., \u0026amp; Mishra, P. K. (2016). Delineation of Groundwater Potential Zones of Coastal Groundwater Basin Using Multi-Criteria Decision Making Technique. \u003cem\u003eWater Resources Management\u003c/em\u003e, \u003cem\u003e30\u003c/em\u003e(12), 4293\u0026ndash;4310. https://doi.org/10.1007/s11269-016-1421-8\u003c/li\u003e\n \u003cli\u003eMarszelewski, W., \u0026amp; Piasecki, A. (2020). Changes in Water and Sewage Management after Communism: example of the Oder River Basin (Central Europe). \u003cem\u003eScientific Reports\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(1). https://doi.org/10.1038/s41598-020-62957-1\u003c/li\u003e\n \u003cli\u003eMasi, F., Rizzo, A., \u0026amp; Regelsberger, M. (2018). The role of constructed wetlands in a new circular economy, resource oriented, and ecosystem services paradigm. \u003cem\u003eJournal of Environmental Management\u003c/em\u003e, \u003cem\u003e216\u003c/em\u003e, 275\u0026ndash;284. https://doi.org/10.1016/j.jenvman.2017.11.086\u003c/li\u003e\n \u003cli\u003eMaulida, D. A., Anna, A. N., \u0026amp; Cholil, M. (2023). \u003cem\u003eThe Effect of Anthropogenic Activity on the Distribution of Phosphate and Nitrate Content in Shallow Groundwater in Kartasura Subdistrict\u003c/em\u003e (pp. 233\u0026ndash;246). https://doi.org/10.2991/978-2-38476-066-4_15\u003c/li\u003e\n \u003cli\u003eMechtensimer, S., \u0026amp; Toor, G. S. (2017). Septic systems contribution to phosphorus in shallow groundwater: Field-scale studies using conventional drainfield designs. \u003cem\u003ePLoS ONE\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(1). https://doi.org/10.1371/journal.pone.0170304\u003c/li\u003e\n \u003cli\u003eMekonnen, M. M., \u0026amp; Hoekstra, A. Y. (2018). Global Anthropogenic Phosphorus Loads to Freshwater and Associated Grey Water Footprints and Water Pollution Levels: A High-Resolution Global Study. \u003cem\u003eWater Resources Research\u003c/em\u003e, \u003cem\u003e54\u003c/em\u003e(1), 345\u0026ndash;358. https://doi.org/10.1002/2017WR020448\u003c/li\u003e\n \u003cli\u003eMeza-Ramirez, V., Espinoza-Ortiz, X., Ramirez-Verdugo, P., Hernandez-Lazcano, P., \u0026amp; Rojas Hermosilla, P. (2021). Pb-Contaminated Soil from Quintero-Ventanas, Chile: Remediation Using Sarcocornia neei. \u003cem\u003eScientific World Journal\u003c/em\u003e, \u003cem\u003e2021\u003c/em\u003e. https://doi.org/10.1155/2021/2974786\u003c/li\u003e\n \u003cli\u003eQian, J., Wang, L., Zhan, H., \u0026amp; Chen, Z. (2011). Urban land-use effects on groundwater phosphate distribution in a shallow aquifer, Nanfei River basin, China. \u003cem\u003eHydrogeology Journal\u003c/em\u003e, \u003cem\u003e19\u003c/em\u003e(7), 1431\u0026ndash;1442. https://doi.org/10.1007/s10040-011-0770-x\u003c/li\u003e\n \u003cli\u003eQuddoos, A., Muhmood, K., Naz, I., Aslam, R. W., \u0026amp; Usman, S. Y. (2024). Geospatial insights into groundwater contamination from urban and industrial effluents in Faisalabad. \u003cem\u003eDiscover Water\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e(1). https://doi.org/10.1007/s43832-024-00110-z\u003c/li\u003e\n \u003cli\u003eRobertson, W. D., Van Stempvoort, D. R., \u0026amp; Schiff, S. L. (2019). Review of phosphorus attenuation in groundwater plumes from 24 septic systems. \u003cem\u003eScience of the Total Environment\u003c/em\u003e, \u003cem\u003e692\u003c/em\u003e, 640\u0026ndash;652. https://doi.org/10.1016/j.scitotenv.2019.07.198\u003c/li\u003e\n \u003cli\u003eSavitri, A., Pravitasari, A. E., \u0026amp; Rosandi, V. B. (2023). Dynamics of land cover change, regional development, and its local dependence driving factors in Bojonegoro Regency. \u003cem\u003eIOP Conference Series: Earth and Environmental Science\u003c/em\u003e, \u003cem\u003e1263\u003c/em\u003e(1). https://doi.org/10.1088/1755-1315/1263/1/012014\u003c/li\u003e\n \u003cli\u003eSchrank, I., L\u0026ouml;der, M. G. J., Imhof, H. K., Moses, S. R., He\u0026szlig;, M., Schwaiger, J., \u0026amp; Laforsch, C. (2022). Riverine microplastic contamination in southwest Germany: A large-scale survey. \u003cem\u003eFrontiers in Earth Science\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e. https://doi.org/10.3389/feart.2022.794250\u003c/li\u003e\n \u003cli\u003eSekrecka, A., \u0026amp; Kedzierski, M. (2018). Integration of satellite data with high resolution ratio: Improvement of spectral quality with preserving spatial details. \u003cem\u003eSensors (Switzerland)\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e(12). https://doi.org/10.3390/s18124418\u003c/li\u003e\n \u003cli\u003eSerio, F., Miglietta, P. P., Lamastra, L., Ficocelli, S., Intini, F., De Leo, F., \u0026amp; De Donno, A. (2018). Groundwater nitrate contamination and agricultural land use: A grey water footprint perspective in Southern Apulia Region (Italy). \u003cem\u003eScience of the Total Environment\u003c/em\u003e, \u003cem\u003e645\u003c/em\u003e, 1425\u0026ndash;1431. https://doi.org/10.1016/j.scitotenv.2018.07.241\u003c/li\u003e\n \u003cli\u003eSimo, T., Weidmer, M., Van Simaeys, S., Sekti, R., Van Gorsel, H., Strohmenger, C., \u0026amp; Derewetzky, A. (2011). \u003cem\u003eSequence Stratigraphic Correlation and Sedimentological Implications, East Java Basin: Comparisons and Lessons Learned from Outcrop and Subsurface Studies\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eSingh, Y., \u0026amp; Kumar, M. (2011). Application of Statistical Methods to Analyze Groundwater Quality. In \u003cem\u003eJournal of Earth Sciences and Geotechnical Engineering\u003c/em\u003e (Vol. 1, Issue 1). online) International Scientific Press.\u003c/li\u003e\n \u003cli\u003eSlomp, C. P., \u0026amp; Van Cappellen, P. (2004). Nutrient inputs to the coastal ocean through submarine groundwater discharge: Controls and potential impact. \u003cem\u003eJournal of Hydrology\u003c/em\u003e, \u003cem\u003e295\u003c/em\u003e(1\u0026ndash;4), 64\u0026ndash;86. https://doi.org/10.1016/j.jhydrol.2004.02.018\u003c/li\u003e\n \u003cli\u003eSpeiser, W. H., \u0026amp; Largier, J. L. (2024). High-Resolution Nearshore Sea Surface Temperature from Calibrated Landsat Brightness Data. \u003cem\u003eRemote Sensing\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(23). https://doi.org/10.3390/rs16234477\u003c/li\u003e\n \u003cli\u003eSpiteri, C., Slomp, C. P., Regnier, P., Meile, C., \u0026amp; Van Cappellen, P. (2007). Modelling the geochemical fate and transport of wastewater-derived phosphorus in contrasting groundwater systems. \u003cem\u003eJournal of Contaminant Hydrology\u003c/em\u003e, \u003cem\u003e92\u003c/em\u003e(1\u0026ndash;2), 87\u0026ndash;108. https://doi.org/10.1016/j.jconhyd.2007.01.002\u003c/li\u003e\n \u003cli\u003eSrinivasa Rao, Y., \u0026amp; Jugran, D. K. (2003). Delineation of groundwater potential zones and zones of groundwater quality suitable for domestic purposes using remote sensing and GIS. \u003cem\u003eHydrological Sciences Journal\u003c/em\u003e, \u003cem\u003e48\u003c/em\u003e(5), 821\u0026ndash;833. https://doi.org/10.1623/hysj.48.5.821.51452\u003c/li\u003e\n \u003cli\u003eStollenwerk, K. G. (1996). Simulation of phosphate transport in sewage-contaminated groundwater, Cape Cod, Massachusetts. In \u003cem\u003eApplied Geochemistry\u003c/em\u003e (Vol. 11).\u003c/li\u003e\n \u003cli\u003eTang, D. W. S., Bartholomeus, R. P., \u0026amp; Ritsema, C. J. (2024). Wastewater irrigation beneath the water table: analytical model of crop contamination risks. \u003cem\u003eAgricultural Water Management\u003c/em\u003e, \u003cem\u003e298\u003c/em\u003e. https://doi.org/10.1016/j.agwat.2024.108848\u003c/li\u003e\n \u003cli\u003eTang, D. W. S., Van der Zee, S. E. A. T. M., \u0026amp; Bartholomeus, R. P. (2024). Phreatic zone wastewater irrigation: Sensitivity analysis of contaminant fate. \u003cem\u003eJournal of Hydrology\u003c/em\u003e, \u003cem\u003e645\u003c/em\u003e. https://doi.org/10.1016/j.jhydrol.2024.132263\u003c/li\u003e\n \u003cli\u003eTang, D. W. S., Van der Zee, S. E. A. T. M., Narain-Ford, D. M., van den Eertwegh, G. A. P. H., \u0026amp; Bartholomeus, R. P. (2023). Managed phreatic zone recharge for irrigation and wastewater treatment. \u003cem\u003eJournal of Hydrology\u003c/em\u003e, \u003cem\u003e626\u003c/em\u003e. https://doi.org/10.1016/j.jhydrol.2023.130208\u003c/li\u003e\n \u003cli\u003eTran Vu Van Hoa, Thien Chi Nguyen, Tung Thanh Truong, Tuan Anh Nguyen, Hoang Bao Lam, \u0026amp; Son Thai Dang. (2024). Enhancing Flood Impact Analysis through the Integration of Landsat and MODIS Imagery. \u003cem\u003eInternational Journal of Scientific Research in Science, Engineering and Technology\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(2), 381\u0026ndash;391. https://doi.org/10.32628/ijsrset2411257\u003c/li\u003e\n \u003cli\u003eVaughan, M. C. H., Bowden, W. B., Shanley, J. B., Vermilyea, A., Wemple, B., \u0026amp; Schroth, A. W. (2018). Using in situ UV-Visible spectrophotometer sensors to quantify riverine phosphorus partitioning and concentration at a high frequency. \u003cem\u003eLimnology and Oceanography: Methods\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(12), 840\u0026ndash;855. https://doi.org/10.1002/lom3.10287\u003c/li\u003e\n \u003cli\u003eWarrack, J., Kang, M., \u0026amp; Von Sperber, C. (2022). Groundwater phosphorus concentrations: Global trends and links with agricultural and oil and gas activities. \u003cem\u003eEnvironmental Research Letters\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e(1). https://doi.org/10.1088/1748-9326/ac31ef\u003c/li\u003e\n \u003cli\u003eWelch, H. L., Geological, U. S., \u0026amp; Kingsbury, J. A. (2010). \u003cem\u003eMississippi Water Resources Conference\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eWilliam, M., \u0026amp; Katambara, Z. (2025). Assessment of Spatial Water Quality Variations in Shallow Wells Using Principal Component Analysis in Half London Ward, Tanzania. \u003cem\u003eJournal of Water Resource and Protection\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e(02), 108\u0026ndash;143. https://doi.org/10.4236/jwarp.2025.172007\u003c/li\u003e\n \u003cli\u003eZhang, M., Huang, G., Liu, C., Zhang, Y., Chen, Z., \u0026amp; Wang, J. (2020). Distributions and origins of nitrate, nitrite, and ammonium in various aquifers in an urbanized coastal area, south China. \u003cem\u003eJournal of Hydrology\u003c/em\u003e, \u003cem\u003e582\u003c/em\u003e. https://doi.org/10.1016/j.jhydrol.2019.124528\u003c/li\u003e\n \u003cli\u003eZhang, R., Yin, A., \u0026amp; Gao, C. (2019). Sediment phosphorus fraction and release potential in the major inflow rivers of Chaohu Lake, Eastern China. \u003cem\u003eEnvironmental Earth Sciences\u003c/em\u003e, \u003cem\u003e78\u003c/em\u003e(4). https://doi.org/10.1007/s12665-019-8086-6\u003c/li\u003e\n \u003cli\u003eZhu, X., \u0026amp; Ma, J. (2020). Recent advances in the determination of phosphate in environmental water samples: Insights from practical perspectives. In \u003cem\u003eTrAC - Trends in Analytical Chemistry\u003c/em\u003e (Vol. 127). Elsevier B.V. https://doi.org/10.1016/j.trac.2020.115908\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 5 to 8 are available in the Supplementary Files section\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-monitoring-and-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emas","sideBox":"Learn more about [Environmental Monitoring and Assessment](http://link.springer.com/journal/10661)","snPcode":"10661","submissionUrl":"https://submission.nature.com/new-submission/10661/3","title":"Environmental Monitoring and Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Groundwater, Phosphate, Contaminant, Land Use, Anthropogenic, Rural, Peri-Urban","lastPublishedDoi":"10.21203/rs.3.rs-6972589/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6972589/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePhosphate pollution in groundwater represents a significant global environmental challenge, particularly in regions experiencing rapid urban expansion and intensified agricultural activities. This study addresses this issue by investigating the spatial variation of phosphate concentrations in groundwater within the Surabaya-Lamongan Groundwater Basin (SLGB) in East Java, Indonesia. The research aimed to assess the spatial distribution of phosphate in unconfined aquifers across various land use-land cover (LULC) categories, identify potential sources of contamination, and determine whether phosphate concentrations exceed the 0.2 mg/L threshold for potable water. A total of fifty-eight groundwater samples were systematically collected using a grid-based sampling method and analyzed with a UV-VIS spectrophotometer employing the phosphomolybdenum blue method. LULC classification was conducted using Landsat 7 ETM+ (2000) and Landsat 8 OLI TIRS (2021) imagery. The findings indicated that phosphate levels were significantly higher in shallow wells compared to deep wells (p \u0026lt; 0.05). Residential areas exhibited the highest average phosphate concentration (1.107 mg/L), followed by agricultural land (0.337 mg/L), both surpassing the drinking water standard. Industrial areas demonstrated the lowest concentration (0.168 mg/L). Significant spatial differences were observed among administrative regions (p \u0026lt; 0.05), with Gresik recording the highest average concentration (1.201 mg/L). A one-sample t-test confirmed that the overall average phosphate concentration (0.627 mg/L) significantly exceeded the permissible limit (p \u0026lt; 0.001). These results suggest that anthropogenic activities, particularly in residential and agricultural zones, are major contributors to phosphate contamination. Immediate groundwater protection measures are essential to prevent further degradation of water quality in the SLGB.\u003c/p\u003e","manuscriptTitle":"Spatial Variability of Phosphate Groundwater Based on Land Use - Land Cover and Groundwater Quality on Increasing Rural to Urban Areas","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-17 16:20:04","doi":"10.21203/rs.3.rs-6972589/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-03T06:20:09+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-01T19:23:46+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-26T09:14:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"46784060552783414092550400623920420425","date":"2025-07-15T12:11:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"186585970103076466325727614786664926260","date":"2025-07-14T12:48:17+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-14T09:36:56+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-28T00:32:28+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-28T00:30:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Monitoring and Assessment","date":"2025-06-25T08:42:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-monitoring-and-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emas","sideBox":"Learn more about [Environmental Monitoring and Assessment](http://link.springer.com/journal/10661)","snPcode":"10661","submissionUrl":"https://submission.nature.com/new-submission/10661/3","title":"Environmental Monitoring and Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"65893b46-34cb-4891-a585-a78ba6b7c11e","owner":[],"postedDate":"July 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-08T15:59:12+00:00","versionOfRecord":{"articleIdentity":"rs-6972589","link":"https://doi.org/10.1007/s10661-025-14879-6","journal":{"identity":"environmental-monitoring-and-assessment","isVorOnly":false,"title":"Environmental Monitoring and Assessment"},"publishedOn":"2025-12-06 15:57:02","publishedOnDateReadable":"December 6th, 2025"},"versionCreatedAt":"2025-07-17 16:20:04","video":"","vorDoi":"10.1007/s10661-025-14879-6","vorDoiUrl":"https://doi.org/10.1007/s10661-025-14879-6","workflowStages":[]},"version":"v1","identity":"rs-6972589","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6972589","identity":"rs-6972589","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}