Impact of land use and seasonality on faecal coliform abundance and physicochemical water quality in Batang Layar river, Sarawak, Malaysian Borneo

Impact of land use and seasonality on faecal coliform abundance and physicochemical water quality in Batang Layar river, Sarawak, Malaysian Borneo | 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 Impact of land use and seasonality on faecal coliform abundance and physicochemical water quality in Batang Layar river, Sarawak, Malaysian Borneo Sabella Justin, Samuel Lihan, Jacqleen Mik, Jongkar Grinang, Kenneth Kueh Woon Hou, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6966017/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Batang Layar, a biodiverse tropical river in Sarawak, is a vital water source for the local communities. However, it is threatened by contamination from agricultural runoff and untreated sewage, posing public health risks. This study examines seasonal variations, land use impacts on faecal coliform concentrations, and physicochemical water quality. Water sampling at five (5) sites during wet (December 2023) and dry (June 2024) seasons used a YSI ProDSS Multiparameter Water Quality Meter for in-situ analysis. Faecal Coliform Count (FCC) and Total Coliform Count (TCC) were conducted alongside DOE-WQI (Department of Environment-Water Quality Index) that incorporates pH, dissolved oxygen (DO), suspended solids (SS), biochemical oxygen demand (BOD), chemical oxygen demand (COD), and ammoniacal nitrogen (NH 3 -N). Results showed higher concentrations of FCC in the wet season (228.94 cfu/100 mL- 992.87 cfu/100 mL), exceeding DOE recreational standards (< 400 cfu/100 mL), particularly near schools and settlements, likely attributed to sediment resuspension and non-point sewage runoff during high flow. WQI analysis showed sites LS1, LS2, and LS3 are consistently Class I throughout both wet and dry seasons, while LS4 and LS5 shifted from Class II in the wet season to Class I in the dry season, exhibiting a seasonal water quality fluctuation. Principal component analysis (PCA) and correlation matrices show a significant relationship between water quality parameters across land use and seasonal variations. This study highlights the importance of integrated water resource management (IWRM), which considers seasonal dynamics and land-use impacts to protect aquatic ecosystems and ensure sustainable water quality in Batang Layar river. water quality seasonal variations land use impact public health sustainability Figures Figure 1 Figure 2 Figure 3 1. Introduction The term Batang is commonly used to denote a large river in the local context [ 1 ]. The Batang Layar watershed, located in the Betong region of Sarawak, has a diverse landscape of forest, agricultural areas, and human settlements. The Dayak Iban communities living within this watershed have strong ties to the land, relying on subsistence agriculture (pepper farming and rubber), fishing, and traditional crafts for their livelihoods [ 2 ]. However, the river faces increasing pressure from agricultural activities and expanding human settlements, leading to potential contaminants like faecal coliforms and other pollutants. Understanding the impact of land use and seasonal variations on water quality is crucial for effective water resource management and safeguarding public health in the region. In line with the Sustainable Development Goal (SDG) No. 6, the Malaysian government has adopted a myriad of policies to safeguard the nation’s water resources and ensure water sustainability [ 3 ]. Assessing water quality requires understanding microbial indicators, which are essential for protecting ecosystem health, conducting accurate risk assessments, and remediating impaired water bodies. In Malaysia, water quality monitoring includes the assessment of total coliforms, faecal coliforms, Escherichia coli , faecal streptococci, and enterococci as indicators of potential pathogens to identify public health risks[ 4 – 6 ]. Faecal coliforms, particularly Escherichia coli , are widely recognised due to their prevalence in human and animal intestines [ 7 – 9 ]. Studies have identified key sources of pathogenic contamination in surface waters, including urban stormwater runoff influenced by seasonal variations, agricultural runoff, wild animal waste, effluent from wastewater treatment plants, and leakage from failing septic systems [ 10 – 14 ]. All the water quality data, including in-situ and ex-situ , were used to classify the river according to the Water Quality Index (WQI) Malaysia. WQI is a commonly used tool for assessing water quality, integrating physical, chemical, and biological parameters. With extensive social and economic growth, human factors, climate, and hydrology may lead to the accumulation of pollutants in the surface water, gradually altering water quality over time [ 15 ]. Hence, maintaining acceptable water quality remains challenging in water resources management [ 16 ]. For a more efficient evaluation of water quality, it is essential to collect samples and analyse various parameters at specific locations. The Malaysian WQI includes six variables- Dissolved Oxygen (DO), Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Ammoniacal Nitrogen (NH 3 -N), suspended solids (SS) and pH- to classify water quality [ 17 ]. This study focuses on streams within the Batang Layar watershed, selected based on their drainage pattern and proximity to animal feeding operations, agricultural land, residential areas, and forest land. By employing R statistics, this study investigates the correlation among water quality parameters across the Batang Layar watershed, assessing the influence of land use and seasonal variations (wet or dry seasons) on faecal coliform abundance and physicochemical factors. Notably, this study contributes methodologically by integrating spatial, seasonal, and multivariate analyses to form a unique framework for assessing tropical mixed-land-use watersheds. The findings will support more targeted and effective water management strategies, contributing to SDG 6 and addressing a critical gap in Malaysia’s pollution monitoring efforts. 2. Materials and Methods 2.1 Study Area Water quality assessments were conducted at five (5) sites along Batang Layar and its tributaries, namely Paoh (LS1), Nanga Tiga (LS2), Linsum (LS3), Nanga Spak (LS4), and Jelau (LS5). Sampling was conducted during the wet (December 2023) and dry (June 2024) seasons. Fifteen (15) sampling points were chosen based on accessibility and anthropogenic activities (Fig. 1 ). The coordinates and description of each site are listed in Table 1 , while land area and land use percentages are provided in Table 2 . Table 1 Study area coordinates and description Site Name Point GPS Description LS1 Paoh A N 01.57791° E 111.76753° Upstream; far from built-up areas as a reference site and surrounded by dense forest B N 01.57859° E 111.76764° C N 01.57906° E 111.76776° LS2 Nanga Tiga A N 01.60737° E 111.70507° Midstream; inactive * tagang system, school area and village settlements B N 01.60663° E 111.70447° C N 01.60721° E 111.70407° LS3 Linsum A N 01.59943° E 111.67016° Midstream; village settlements and agricultural farm B N 01.59916° E 111.66927° C N 01.59833° E 111.66789° LS4 Nanga Spak A N 01.52844° E 111.61006° Midstream; school area B N 01.52801° E 111.60981° C N 01.52801° E 111.60950° LS5 Jelau A N 01.49652° E 111.58996° Downstream; village settlements, and agricultural farm B N 01.49688° E 111.58953° C N 01.49667° E 111.58882° *tagang system- a method used to conserve aquatic life. Table 2 Land area and land use percentage Site Name Total Land Area (Ha) Land use Land use (%) LS1 Paoh 20.23 Dense Forest 98.50 0.31 Cleared Land 1.50 LS2 Nanga Tiga 14.05 Dense Forest 59.60 9.53 Urban and Associated Areas 40.40 LS3 Linsum 17.41 Dense Forest 75.00 3.44 Cleared Land 14.80 2.36 Urban and Associated Areas 10.20 LS4 Nanga Spak 14.05 Dense Forest 59.60 9.53 Urban and Associated Areas 40.40 LS5 Jelau 13.41 Dense Forest 71.40 3.56 Urban and Associated Areas 19.00 1.81 River/Canal/Waterway/Drain 9.60 2.2 Sampling and Analysis Surface water samples were collected at depths of 20 to 40 cm using sterile bottles. In-situ parameters, including temperature, DO, specific conductance (SPC), conductivity, total dissolved solids (TDS), pH, and turbidity, were measured using a YSI ProDSS Multiparameter Water Quality Meter. Ex-situ parameters included BOD, COD, total suspended solids (TSS) and NH 3 -N, following the standard method [ 18 ]. Faecal coliform counts (FCC) and total coliform counts (TCC) were determined using the membrane filtration method and incubation on HiChrome agar at 37◦C [ 19 ]. The coliform density of each specimen (colony forming unit, CFU/100 mL) was calculated using the formula [ 20 ], as stated in Eq. ( 1 ): The geometric mean of coliform density (CFU/100 mL) was calculated to determine the average coliform count across samples. The geometric mean was calculated using the Nth root method [ 20 ], as shown in Eq. (2): Geometric Mean = \(\:\sqrt[n]{{x}_{1}{x}_{2}{x}_{3}\dots\:..{x}_{n}}\) …………………………………………….. (2) Where \(\:{x}_{1}{x}_{2}{x}_{3}\dots\:..{x}_{n}\) represent the coliform densities observed from the “ \(\:n\) ” number of specimens, respectively. 2.3 Water Quality Index (WQI) WQI was calculated using DOE's WQI equation, which integrates six parameters: DO, BOD, COD, NH 3 -N, SS and pH. Water quality was then classified according to DOE standards [ 17 ]. The overall WQI of water was calculated using the formula as stated in Eq. (3): [WQI = (0.22×SIDO) + (0.19×SIBOD) + (0.16×SICOD) + (0.15×SIAN) + (0.16×SISS) + (0.12×SIpH) (3)] 2.4 Statistical Analysis Principal Component Analysis (PCA) was used to explore the relationship between water quality parameters, land use, and seasonal variations. The Spearman correlation coefficient was used to measure the strength and direction of association between two water quality parameters. All statistical analyses were performed using R version 4.4.1 software. 3. Results and Discussion 3.1 Seasonal and Land Use Influence on Water Quality The WQI classification shows that LS1, LS2, and LS3 consistently exhibited Class I water quality throughout both seasons. At the same time, downstream sites (LS4 and LS5) shifted from Class II in the wet season to Class I in the dry season (Table 3 ). Although the WQI classifies the sites as Class I or II, indicating good water quality, the elevated coliform counts highlight a discrepancy (Table 4 ). The study revealed significant seasonal variations in faecal coliform concentrations, with higher levels observed during the wet season (228.94 to 992.87 cfu/100 mL). Malaysian WQI classification does not include coliform data as it is based on parameters that focus on BOD, DO, COD, NH 3 -N, SS and pH, leaving microbiological contamination unassessed. Faecal coliform, particularly E. coli , indicates faecal contamination and potential health risks from waterborne pathogens, even when WQI suggests good water quality. These high coliform counts could be attributed to localised pollution (e.g., sewage, agricultural runoff or animal waste), intermittent events like heavy rainfall, and persistent contamination from past events or upstream sources [ 21 – 24 ]. These concentrations exceeded DOE standards for recreational waters (400 counts/100 mL), posing potential health risks, particularly near schools and settlement areas, likely due to anthropogenic activities related to land use nearby. While water may be suitable for certain uses, it is unsafe for direct consumption without proper treatment to reduce microbial contamination and minimise health risks [ 25 ]. Table 3 Water Quality Index (WQI) classification of each site (DOE, 2020) Parameter Paoh (LS1) Nanga Tiga (LS2) Linsum (LS3) Nanga Spak (LS4) Jelau (LS5) Wet Dry Wet Dry Wet Dry Wet Dry Wet Dry Dissolved oxygen (%) 93.67 90.87 94.80 95.13 96.73 95.92 94.80 93.84 94.13 93.10 Biochemical oxygen demand (mg/L) 1.34 0.88 1.43 1.18 1.48 1.13 1.29 1.02 1.55 0.89 Chemical oxygen demand (mg/L) 1.33 1.33 4.67 3.33 10.33 6.33 10.33 7.67 12.67 9.33 Ammoniacal nitrogen (NH 3 -N) 0.02 0.01 0.04 0.03 0.16 0.14 0.08 0.06 0.18 0.13 Total suspended solid (mg/L) 6.67 2.00 9.56 3.33 8.56 6.33 33.09 8.33 33.34 10.00 pH 7.21 7.22 6.95 7.40 7.02 7.61 7.07 7.53 7.07 7.07 Water Quality Index 97.12 97.49 95.80 96.85 92.81 94.08 92.14 95.00 89.90 93.81 Class I I I I I I II I II I Table 4 Faecal coliform count (FCC) and Total coliform count (TCC) of each site Parameter Paoh (LS1) Nanga Tiga (LS2) Linsum (LS3) Nanga Spak (LS4) Jelau (LS5) Wet Dry Wet Dry Wet Dry Wet Dry Wet Dry Faecal Coliform count** (CFU/100 mL) 228.94 210.86 992.87 506.58 526.86 660.00 433.12 343.77 683.10 345.52 Total coliform count** (CFU/100 mL) 2615.27 3533.02 4505.92 4211.34 4255.37 3903.96 3933.40 3476.03 7242.64 4145.27 **: Geometric mean Land-use-wise, downstream sites like LS4 and LS5 are primarily surrounded by dense forest and some built-up areas like towns and settlements. These built-up areas are also mostly concentrated in the lower reaches of the Batang Layar watershed. As a result, the anthropogenic activities in these areas may likely contribute to the seasonal variations in water quality. During the wet season, runoffs from the anthropogenic activities by these built-up areas, including from agricultural activities, domestic or untreated sewage discharge, or even stormwater drainage, may introduce pollutants and contaminants to the water system, thus creating an influx to the water quality during wet season but improved to Class I during dry season, as runoff contributions are reduced. Of note is the location of LS5, which lies near the raw water intake point for the gazetted Betong/Debak/Spaoh Water Catchment Area (WCA), with a catchment size of approximately 33,898 hectares (ha). The WCA was gazetted in 2000 via Swk. L.N. 46/2000. Within the 8-km radius from the raw water intake point, there is strict control on land releases and activities to ensure that they do not affect the quality of raw water at the intake point. On the other hand, upstream sites like LS1, LS2, and LS3 are located in less disturbed environments and sparse populations where land used in these areas is mostly dominated by extensive coverage of dense forests. As these areas are relatively undisturbed with high forest coverage, along with their lower population density, they help to maintain stable water quality across seasons. Additionally, the presence of the tagang system in some of the sampling sites like Melabu Bair (near Paoh) and Nanga Tiga (though inactive), could have a positive impact on the water quality as the tagang system requires pristine water quality for the aquatic conservation. All of these, in turn, allow the WQI of these three sites to remain consistent throughout seasonal variations. Overall, land use plays a significant role in shaping seasonal water quality variations. As shown in the findings, the concentrations of anthropogenic activities downstream of Batang Layar watershed result in greater seasonal variations, while upstream areas, which are relatively undisturbed with expansive forest cover, exhibit consistent water quality throughout. 3.2 Multivariate Statistical Approaches 3.2.1 Principal Component Analysis (PCA) The PCA biplot revealed the relationship between water quality parameter patterns across land uses and seasons (Fig. 3 ). The first two principal components (PCs) are the most essential factors, which signify > 74.14% (PC1: 48.39%, PC2: 25.75%) of the variance in the river quality of Batang Layar. From the biplot, it can be observed that farm areas in LS5 exhibited higher levels of TSS, turbidity, ammoniacal nitrogen and COD during wet seasons, suggesting the possibility of runoff from these areas that contribute to the elevated microbial contaminations. Besides, high BOD levels significantly affect the levels of FCC and TCC. This finding aligns with previous studies where high BOD contributes to high organic matter activities by microbial organisms in the water, causing oxygen depletion and elevation of microbial contaminants due to anthropogenic activities or wastewater effluent [ 26 – 28 ]. In contrast, high BOD levels in LS4 have an inverse relationship with temperature, implying that lower temperatures might contribute to high BOD activity, especially in the wet season. Meanwhile, dense forest (DF) land uses in LS1 showed lower levels of these parameters, indicating less anthropogenic impact. The relative stability of upstream water quality can be seen from the clustering of LS1 and S2 in both seasons due to the high density of forest land with limited exposure to human stressors and, eventually, low anthropogenic influence. Furthermore, the clustering of wet season samples separately from dry season samples indicates the influence of rainfall-driven runoff on water quality, which concurred with previous studies that demonstrate seasonal variations significantly impact water quality, where the wet season often results in greater deterioration compared to the dry season [ 29 ], [ 30 ]. Based on the overall PCA displayed, it can be determined that seasonality has a significant impact on the microbial and physicochemical water quality, with the wet season posing higher risks of faecal contamination. These findings concurred with previous studies on similar impact and seasonal variations [ 28 ]. These findings also highlight the importance of land use impact and seasonal dynamics on water quality, emphasising the need for targeted pollution control, especially in downstream regions, while conserving the upstream quality. 3.2.2 Correlation Matrix Figure 4 shows the Spearman correlation coefficient (ρ) value between two variables, ranging from − 1 to 1, where 1 indicates a strong positive correlation, -1 indicates a strong negative correlation, and 0 means no correlation. Based on the correlation matrix, no significant correlation observed between TCC and temperature (ρ = 0.10), COD and specific conductance (ρ = -0.14), COD and conductivity (ρ = -0.17), COD and TDS (ρ = -0.13), DO and TSS (ρ = 0.04), specific conductance with ammoniacal nitrogen (ρ = 0.04), conductivity with ammoniacal nitrogen (ρ = 0.03), TDS with ammoniacal nitrogen (ρ = 0.09), and pH with ammoniacal nitrogen (ρ = -0.10), suggesting minimal influence. Previously, through PCA, COD showed a long eigenvalue gradient in the LS5 area, likely due to human-induced stresses from agricultural farms. Furthermore, COD levels were higher in the wet season than the dry season, potentially due to heavy rainfall and water intrusion from farm areas, which cause agricultural effluents (e.g., pesticides, fertilisers, etc) into the river. These effluents were absorbed in the soil and transported into rivers, which influences the oxygen level required to break down the organic and inorganic matter in water. This is supported by the Spearman correlation that COD is significantly correlated with ammoniacal nitrogen (ρ = 0.90), indicating farm effluents from nearby areas as a significant contributor to elevated levels of COD in the area [ 31 ]. Moderate positive correlations between FCC/TCC with BOD (ρ = 0.53–0.56), FCC/TCC with COD (ρ = 0.46–0.55), and FCC/TCC with ammoniacal nitrogen (ρ = 0.61 − 0.55) indicating the influence of organic matter, point and non-point pollution sources distributed along Batang Layar, which these correlations align with the previous study [ 32 ]. A strong positive correlation between FCC and TCC (ρ = 0.82) indicates that a high level of faecal contamination leads to elevated total coliform levels, likely due to the same source of contamination. TSS and turbidity exhibited a strong positive correlation (ρ = 0.92), indicating that higher suspended solids increase water cloudiness. The moderate correlation between BOD/COD with TSS (ρ = 0.81–0.82) suggests organic pollution linked with suspended solids. Notably, a moderate negative correlation between BOD and pH (ρ = -0.72), indicates that low pH levels may increase the oxidation of organic matter in rivers from agriculture farms and sewage discharge, resulting in depletion of dissolved oxygen, consistent with previous studies [ 33 ]. 4. Conclusion Overall, these findings highlight the interconnected nature of water quality parameters, with strong and moderate correlations underscoring the relationships between microbial contamination, organic pollution, and physical water properties. The study emphasises that the WQI and coliform count provide different but complementary information about water quality. The WQI assesses general water quality based on physicochemical parameters, while coliform count indicates the presence of faecal contamination and potential health risks. The discrepancy between the WQI and coliform counts highlights the importance of integrating both datasets for comprehensive water quality assessment. Recommendations include microbial source tracking by using molecular techniques to identify contamination sources, increasing the frequency of coliform monitoring to capture intermittent pollution events and conducting sanitary surveys to assess potential contamination sources in the watershed. This study highlights the significant impact of land use and seasonal variations on water quality in the Batang Layar River, with elevated faecal coliform concentrations during the wet season posing a public health risk, particularly near schools and settlements. Integrated water resource management strategies are essential to mitigate these impacts and ensure the long-term sustainability of this vital water resource. Declarations Conflict of Interest Statement The authors declare no conflict of interest. Acknowledgements This study was funded by Universiti Malaysia Sarawak under the High Impact Research Grant no. UNI/I01/VC-HIRG/85485/P03-02/2022, with additional support from the Institute of Biodiversity and Environmental Conservation (IBEC), and the Faculty of Resource Science and Technology (FRST) for data procurement and field facilities. This research is the result of a multidisciplinary collaboration involving the Batang Layar communities and UNIMAS researchers. Additionally, the research team also gratefully acknowledges Jabatan Bekalan Air Luar Bandar (JBALB) for their valuable assistance and contributions in providing crucial information on the water catchment areas within the study area for land use mapping. Data Availability Statement: All data generated or analysed during this study are included in this published article [and its supplementary information files]. Funding: This study was funded by Universiti Malaysia Sarawak under the High Impact Research Grant no. UNI/I01/VC-HIRG/85485/P03-02/2022. 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Chinedu I, Njoku JD, Nwaogu LA, Ebe TE. Effects of meteorological events on the levels and interactions of chemical indices of a polluted freshwater system. Int J Environ Sci . 2012;3(1):1-12 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 21 Jul, 2025 Reviews received at journal 16 Jul, 2025 Reviews received at journal 07 Jul, 2025 Reviewers agreed at journal 07 Jul, 2025 Reviewers agreed at journal 03 Jul, 2025 Reviewers invited by journal 02 Jul, 2025 Editor assigned by journal 01 Jul, 2025 Editor invited by journal 30 Jun, 2025 Submission checks completed at journal 29 Jun, 2025 First submitted to journal 29 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-6966017","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":480413404,"identity":"6e287fe0-e59b-4d2d-9872-96f1ba175cb3","order_by":0,"name":"Sabella Justin","email":"","orcid":"","institution":"Universiti Malaysia Sarawak","correspondingAuthor":false,"prefix":"","firstName":"Sabella","middleName":"","lastName":"Justin","suffix":""},{"id":480413406,"identity":"0319ac54-5507-41d8-a988-bdbb1a08e0e4","order_by":1,"name":"Samuel Lihan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwElEQVRIiWNgGAWjYBACPghlIcfAwNhAnBY2CCVhTLqWRCLVg7TwH366madCIn3D7ebWDT/+MEQbHCCkRSLN7DbPGYncDXcOtt3sbWPI3UBYC4PZbd42oJYbiW03eBuI0cJ//Ntt3n8S6QZALTf//CFGC0MO0JYGiQSQlts8bEQ5LKfs5pxjEoYzQVpkgS6cSUgLP//xbTfe1NjI891If3bzzR+b3D5CWtCBBIMCqVoYGOQbSNYyCkbBKBgFwxwAACI+RjdJxSrhAAAAAElFTkSuQmCC","orcid":"","institution":"Universiti Malaysia Sarawak","correspondingAuthor":true,"prefix":"","firstName":"Samuel","middleName":"","lastName":"Lihan","suffix":""},{"id":480413409,"identity":"a63be99b-19d0-4225-8216-95c086e831ec","order_by":2,"name":"Jacqleen Mik","email":"","orcid":"","institution":"Universiti Malaysia Sarawak","correspondingAuthor":false,"prefix":"","firstName":"Jacqleen","middleName":"","lastName":"Mik","suffix":""},{"id":480413411,"identity":"ddcadb60-f0d7-44a9-ac41-0b489859d5be","order_by":3,"name":"Jongkar Grinang","email":"","orcid":"","institution":"Universiti Malaysia Sarawak","correspondingAuthor":false,"prefix":"","firstName":"Jongkar","middleName":"","lastName":"Grinang","suffix":""},{"id":480413415,"identity":"24d9e3ff-90c2-44eb-8aa9-06dc4061a325","order_by":4,"name":"Kenneth Kueh Woon Hou","email":"","orcid":"","institution":"Universiti Malaysia Sarawak","correspondingAuthor":false,"prefix":"","firstName":"Kenneth","middleName":"Kueh Woon","lastName":"Hou","suffix":""},{"id":480413416,"identity":"ae1a39e3-629d-45a3-a115-fb2c609bbeef","order_by":5,"name":"Lamuel Lazlus","email":"","orcid":"","institution":"Universiti Malaysia Sarawak","correspondingAuthor":false,"prefix":"","firstName":"Lamuel","middleName":"","lastName":"Lazlus","suffix":""},{"id":480413417,"identity":"bb350183-906d-4a48-a5a1-8f06cd389327","order_by":6,"name":"Mazzaellynn Brasenia Umang Thomas","email":"","orcid":"","institution":"Universiti Malaysia Sarawak","correspondingAuthor":false,"prefix":"","firstName":"Mazzaellynn","middleName":"Brasenia Umang","lastName":"Thomas","suffix":""},{"id":480413418,"identity":"2b58cef9-a999-4cea-a1fa-5860a1b3a3df","order_by":7,"name":"Francis Senen Alau","email":"","orcid":"","institution":"Universiti Malaysia Sarawak","correspondingAuthor":false,"prefix":"","firstName":"Francis","middleName":"Senen","lastName":"Alau","suffix":""},{"id":480413419,"identity":"9d2fe820-6b1e-412f-9e84-7bc70ccf384b","order_by":8,"name":"Thracesy Munah Assan","email":"","orcid":"","institution":"Universiti Malaysia Sarawak","correspondingAuthor":false,"prefix":"","firstName":"Thracesy","middleName":"Munah","lastName":"Assan","suffix":""}],"badges":[],"createdAt":"2025-06-24 13:08:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6966017/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6966017/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86012161,"identity":"22ba17ee-748f-4e20-9dba-aa265272b1e0","added_by":"auto","created_at":"2025-07-04 09:59:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":969457,"visible":true,"origin":"","legend":"\u003cp\u003eMap of the sampling sites (as shown in the smaller boxes and amplified in the bigger boxes) and land uses in the study area\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6966017/v1/e629ce8bc2d08f0177f3acf7.png"},{"id":86011098,"identity":"6fea4a58-3b00-466a-9bec-bac0a40fe3c4","added_by":"auto","created_at":"2025-07-04 09:51:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":152304,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig.3\u003c/strong\u003e PCA biplot of water quality across sites for both wet and dry seasons across different land use\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6966017/v1/8a6b9cd5eb4defb99c83fcad.png"},{"id":86012162,"identity":"caf58560-550c-42b8-9b89-02e9a21e6a15","added_by":"auto","created_at":"2025-07-04 09:59:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":268080,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig.4\u003c/strong\u003e Spearman correlation matrix between all parameters studied\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6966017/v1/5e72dc9883a605e46ff1ed43.png"},{"id":86012580,"identity":"7d00dccc-20e4-44dc-bcb5-8c5ccc6128a2","added_by":"auto","created_at":"2025-07-04 10:07:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2206342,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6966017/v1/3665c47c-1fd2-4ef5-9454-64c4f7ae12b9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impact of land use and seasonality on faecal coliform abundance and physicochemical water quality in Batang Layar river, Sarawak, Malaysian Borneo","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe term \u003cem\u003eBatang\u003c/em\u003e is commonly used to denote a large river in the local context [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The Batang Layar watershed, located in the Betong region of Sarawak, has a diverse landscape of forest, agricultural areas, and human settlements. The Dayak Iban communities living within this watershed have strong ties to the land, relying on subsistence agriculture (pepper farming and rubber), fishing, and traditional crafts for their livelihoods [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. However, the river faces increasing pressure from agricultural activities and expanding human settlements, leading to potential contaminants like faecal coliforms and other pollutants. Understanding the impact of land use and seasonal variations on water quality is crucial for effective water resource management and safeguarding public health in the region. In line with the Sustainable Development Goal (SDG) No. 6, the Malaysian government has adopted a myriad of policies to safeguard the nation\u0026rsquo;s water resources and ensure water sustainability [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAssessing water quality requires understanding microbial indicators, which are essential for protecting ecosystem health, conducting accurate risk assessments, and remediating impaired water bodies. In Malaysia, water quality monitoring includes the assessment of total coliforms, faecal coliforms, \u003cem\u003eEscherichia coli\u003c/em\u003e, faecal streptococci, and enterococci as indicators of potential pathogens to identify public health risks[\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Faecal coliforms, particularly \u003cem\u003eEscherichia coli\u003c/em\u003e, are widely recognised due to their prevalence in human and animal intestines [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Studies have identified key sources of pathogenic contamination in surface waters, including urban stormwater runoff influenced by seasonal variations, agricultural runoff, wild animal waste, effluent from wastewater treatment plants, and leakage from failing septic systems [\u003cspan additionalcitationids=\"CR11 CR12 CR13\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAll the water quality data, including \u003cem\u003ein-situ\u003c/em\u003e and \u003cem\u003eex-situ\u003c/em\u003e, were used to classify the river according to the Water Quality Index (WQI) Malaysia. WQI is a commonly used tool for assessing water quality, integrating physical, chemical, and biological parameters. With extensive social and economic growth, human factors, climate, and hydrology may lead to the accumulation of pollutants in the surface water, gradually altering water quality over time [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Hence, maintaining acceptable water quality remains challenging in water resources management [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. For a more efficient evaluation of water quality, it is essential to collect samples and analyse various parameters at specific locations. The Malaysian WQI includes six variables- Dissolved Oxygen (DO), Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Ammoniacal Nitrogen (NH\u003csub\u003e3\u003c/sub\u003e-N), suspended solids (SS) and pH- to classify water quality [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. This study focuses on streams within the Batang Layar watershed, selected based on their drainage pattern and proximity to animal feeding operations, agricultural land, residential areas, and forest land.\u003c/p\u003e \u003cp\u003eBy employing R statistics, this study investigates the correlation among water quality parameters across the Batang Layar watershed, assessing the influence of land use and seasonal variations (wet or dry seasons) on faecal coliform abundance and physicochemical factors. Notably, this study contributes methodologically by integrating spatial, seasonal, and multivariate analyses to form a unique framework for assessing tropical mixed-land-use watersheds. The findings will support more targeted and effective water management strategies, contributing to SDG 6 and addressing a critical gap in Malaysia\u0026rsquo;s pollution monitoring efforts.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Area\u003c/h2\u003e \u003cp\u003eWater quality assessments were conducted at five (5) sites along Batang Layar and its tributaries, namely Paoh (LS1), Nanga Tiga (LS2), Linsum (LS3), Nanga Spak (LS4), and Jelau (LS5). Sampling was conducted during the wet (December 2023) and dry (June 2024) seasons. Fifteen (15) sampling points were chosen based on accessibility and anthropogenic activities (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The coordinates and description of each site are listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, while land area and land use percentages are provided in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStudy area coordinates and description\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSite\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eName\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePoint\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGPS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eLS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePaoh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN 01.57791\u0026deg; E 111.76753\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eUpstream;\u003c/p\u003e \u003cp\u003efar from built-up areas as a reference site and surrounded by dense forest\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN 01.57859\u0026deg; E 111.76764\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN 01.57906\u0026deg; E 111.76776\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eLS2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eNanga Tiga\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN 01.60737\u0026deg; E 111.70507\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMidstream;\u003c/p\u003e \u003cp\u003einactive *\u003cem\u003etagang\u003c/em\u003e system, school area and village settlements\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN 01.60663\u0026deg; E 111.70447\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN 01.60721\u0026deg; E 111.70407\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eLS3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eLinsum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN 01.59943\u0026deg; E 111.67016\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMidstream;\u003c/p\u003e \u003cp\u003evillage settlements and agricultural farm\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN 01.59916\u0026deg; E 111.66927\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN 01.59833\u0026deg; E 111.66789\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eLS4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eNanga Spak\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN 01.52844\u0026deg; E 111.61006\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMidstream;\u003c/p\u003e \u003cp\u003eschool area\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN 01.52801\u0026deg; E 111.60981\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN 01.52801\u0026deg; E 111.60950\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eLS5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eJelau\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN 01.49652\u0026deg; E 111.58996\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eDownstream;\u003c/p\u003e \u003cp\u003evillage settlements, and agricultural farm\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN 01.49688\u0026deg; E 111.58953\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN 01.49667\u0026deg; E 111.58882\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e*tagang system-\u003c/em\u003e a method used to conserve aquatic life.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLand area and land use percentage\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSite\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eName\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal Land Area (Ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLand use\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLand use (%)\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\u003eLS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePaoh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDense Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e98.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCleared Land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLS2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNanga Tiga\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDense Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e59.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUrban and Associated Areas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eLS3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eLinsum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDense Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e75.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCleared Land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUrban and Associated Areas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLS4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNanga Spak\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDense Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e59.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUrban and Associated Areas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eLS5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eJelau\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDense Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e71.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUrban and Associated Areas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRiver/Canal/Waterway/Drain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Sampling and Analysis\u003c/h2\u003e \u003cp\u003eSurface water samples were collected at depths of 20 to 40 cm using sterile bottles. \u003cem\u003eIn-situ\u003c/em\u003e parameters, including temperature, DO, specific conductance (SPC), conductivity, total dissolved solids (TDS), pH, and turbidity, were measured using a YSI ProDSS Multiparameter Water Quality Meter. \u003cem\u003eEx-situ\u003c/em\u003e parameters included BOD, COD, total suspended solids (TSS) and NH\u003csub\u003e3\u003c/sub\u003e-N, following the standard method [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Faecal coliform counts (FCC) and total coliform counts (TCC) were determined using the membrane filtration method and incubation on HiChrome agar at 37◦C [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The coliform density of each specimen (colony forming unit, CFU/100 mL) was calculated using the formula [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], as stated in Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e):\u003c/p\u003e \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"623\" height=\"44\"\u003e\u003c/p\u003e\u003cp\u003eThe geometric mean of coliform density (CFU/100 mL) was calculated to determine the average coliform count across samples. The geometric mean was calculated using the Nth root method [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], as shown in Eq.\u0026nbsp;(2):\u003c/p\u003e \u003cp\u003eGeometric Mean = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sqrt[n]{{x}_{1}{x}_{2}{x}_{3}\\dots\\:..{x}_{n}}\\)\u003c/span\u003e\u003c/span\u003e \u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.. (2)\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{x}_{1}{x}_{2}{x}_{3}\\dots\\:..{x}_{n}\\)\u003c/span\u003e\u003c/span\u003e represent the coliform densities observed from the \u0026ldquo;\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:n\\)\u003c/span\u003e\u003c/span\u003e\u0026rdquo; number of specimens, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Water Quality Index (WQI)\u003c/h2\u003e \u003cp\u003eWQI was calculated using DOE's WQI equation, which integrates six parameters: DO, BOD, COD, NH\u003csub\u003e3\u003c/sub\u003e-N, SS and pH. Water quality was then classified according to DOE standards [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The overall WQI of water was calculated using the formula as stated in Eq.\u0026nbsp;(3):\u003c/p\u003e \u003cp\u003e[WQI = (0.22\u0026times;SIDO) + (0.19\u0026times;SIBOD) + (0.16\u0026times;SICOD) + (0.15\u0026times;SIAN) + (0.16\u0026times;SISS) + (0.12\u0026times;SIpH) (3)]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Statistical Analysis\u003c/h2\u003e \u003cp\u003ePrincipal Component Analysis (PCA) was used to explore the relationship between water quality parameters, land use, and seasonal variations. The Spearman correlation coefficient was used to measure the strength and direction of association between two water quality parameters. All statistical analyses were performed using R version 4.4.1 software.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and Discussion","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Seasonal and Land Use Influence on Water Quality\u003c/h2\u003e \u003cp\u003eThe WQI classification shows that LS1, LS2, and LS3 consistently exhibited Class I water quality throughout both seasons. At the same time, downstream sites (LS4 and LS5) shifted from Class II in the wet season to Class I in the dry season (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Although the WQI classifies the sites as Class I or II, indicating good water quality, the elevated coliform counts highlight a discrepancy (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The study revealed significant seasonal variations in faecal coliform concentrations, with higher levels observed during the wet season (228.94 to 992.87 cfu/100 mL). Malaysian WQI classification does not include coliform data as it is based on parameters that focus on BOD, DO, COD, NH\u003csub\u003e3\u003c/sub\u003e-N, SS and pH, leaving microbiological contamination unassessed. Faecal coliform, particularly \u003cem\u003eE. coli\u003c/em\u003e, indicates faecal contamination and potential health risks from waterborne pathogens, even when WQI suggests good water quality. These high coliform counts could be attributed to localised pollution (e.g., sewage, agricultural runoff or animal waste), intermittent events like heavy rainfall, and persistent contamination from past events or upstream sources [\u003cspan additionalcitationids=\"CR22 CR23\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. These concentrations exceeded DOE standards for recreational waters (400 counts/100 mL), posing potential health risks, particularly near schools and settlement areas, likely due to anthropogenic activities related to land use nearby. While water may be suitable for certain uses, it is unsafe for direct consumption without proper treatment to reduce microbial contamination and minimise health risks [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eWater Quality Index (WQI) classification of each site (DOE, 2020)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePaoh\u003c/p\u003e \u003cp\u003e(LS1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eNanga Tiga (LS2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eLinsum (LS3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eNanga Spak (LS4)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003eJelau\u003c/p\u003e \u003cp\u003e(LS5)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWet\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWet\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWet\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eWet\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eDry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eWet\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003eDry\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDissolved oxygen (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e94.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e96.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e94.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e93.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e94.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e93.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiochemical oxygen demand (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemical oxygen demand (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e12.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e9.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmmoniacal nitrogen (NH\u003csub\u003e3\u003c/sub\u003e-N)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal suspended solid (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e33.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e33.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e10.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e7.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWater Quality Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e96.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e92.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e94.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e92.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e95.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e89.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e93.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFaecal coliform count (FCC) and Total coliform count (TCC) of each site\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePaoh\u003c/p\u003e \u003cp\u003e(LS1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eNanga Tiga (LS2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eLinsum (LS3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eNanga Spak (LS4)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eJelau\u003c/p\u003e \u003cp\u003e(LS5)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWet\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWet\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWet\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eWet\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eDry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eWet\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eDry\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFaecal Coliform count** (CFU/100 mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e228.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e210.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e992.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e506.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e526.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e660.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e433.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e343.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e683.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e345.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal coliform count**\u003c/p\u003e \u003cp\u003e(CFU/100 mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2615.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3533.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4505.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4211.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4255.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3903.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3933.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3476.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7242.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4145.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003e**: Geometric mean\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eLand-use-wise, downstream sites like LS4 and LS5 are primarily surrounded by dense forest and some built-up areas like towns and settlements. These built-up areas are also mostly concentrated in the lower reaches of the Batang Layar watershed. As a result, the anthropogenic activities in these areas may likely contribute to the seasonal variations in water quality. During the wet season, runoffs from the anthropogenic activities by these built-up areas, including from agricultural activities, domestic or untreated sewage discharge, or even stormwater drainage, may introduce pollutants and contaminants to the water system, thus creating an influx to the water quality during wet season but improved to Class I during dry season, as runoff contributions are reduced.\u003c/p\u003e \u003cp\u003eOf note is the location of LS5, which lies near the raw water intake point for the gazetted Betong/Debak/Spaoh Water Catchment Area (WCA), with a catchment size of approximately 33,898 hectares (ha). The WCA was gazetted in 2000 via Swk. L.N. 46/2000. Within the 8-km radius from the raw water intake point, there is strict control on land releases and activities to ensure that they do not affect the quality of raw water at the intake point.\u003c/p\u003e \u003cp\u003eOn the other hand, upstream sites like LS1, LS2, and LS3 are located in less disturbed environments and sparse populations where land used in these areas is mostly dominated by extensive coverage of dense forests. As these areas are relatively undisturbed with high forest coverage, along with their lower population density, they help to maintain stable water quality across seasons. Additionally, the presence of the \u003cem\u003etagang\u003c/em\u003e system in some of the sampling sites like Melabu Bair (near Paoh) and Nanga Tiga (though inactive), could have a positive impact on the water quality as the \u003cem\u003etagang\u003c/em\u003e system requires pristine water quality for the aquatic conservation. All of these, in turn, allow the WQI of these three sites to remain consistent throughout seasonal variations.\u003c/p\u003e \u003cp\u003eOverall, land use plays a significant role in shaping seasonal water quality variations. As shown in the findings, the concentrations of anthropogenic activities downstream of Batang Layar watershed result in greater seasonal variations, while upstream areas, which are relatively undisturbed with expansive forest cover, exhibit consistent water quality throughout.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Multivariate Statistical Approaches\u003c/h2\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Principal Component Analysis (PCA)\u003c/h2\u003e \u003cp\u003eThe PCA biplot revealed the relationship between water quality parameter patterns across land uses and seasons (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The first two principal components (PCs) are the most essential factors, which signify\u0026thinsp;\u0026gt;\u0026thinsp;74.14% (PC1: 48.39%, PC2: 25.75%) of the variance in the river quality of Batang Layar. From the biplot, it can be observed that farm areas in LS5 exhibited higher levels of TSS, turbidity, ammoniacal nitrogen and COD during wet seasons, suggesting the possibility of runoff from these areas that contribute to the elevated microbial contaminations. Besides, high BOD levels significantly affect the levels of FCC and TCC. This finding aligns with previous studies where high BOD contributes to high organic matter activities by microbial organisms in the water, causing oxygen depletion and elevation of microbial contaminants due to anthropogenic activities or wastewater effluent [\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In contrast, high BOD levels in LS4 have an inverse relationship with temperature, implying that lower temperatures might contribute to high BOD activity, especially in the wet season. Meanwhile, dense forest (DF) land uses in LS1 showed lower levels of these parameters, indicating less anthropogenic impact. The relative stability of upstream water quality can be seen from the clustering of LS1 and S2 in both seasons due to the high density of forest land with limited exposure to human stressors and, eventually, low anthropogenic influence. Furthermore, the clustering of wet season samples separately from dry season samples indicates the influence of rainfall-driven runoff on water quality, which concurred with previous studies that demonstrate seasonal variations significantly impact water quality, where the wet season often results in greater deterioration compared to the dry season [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Based on the overall PCA displayed, it can be determined that seasonality has a significant impact on the microbial and physicochemical water quality, with the wet season posing higher risks of faecal contamination. These findings concurred with previous studies on similar impact and seasonal variations [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. These findings also highlight the importance of land use impact and seasonal dynamics on water quality, emphasising the need for targeted pollution control, especially in downstream regions, while conserving the upstream quality.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 Correlation Matrix\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the Spearman correlation coefficient (ρ) value between two variables, ranging from \u0026minus;\u0026thinsp;1 to 1, where 1 indicates a strong positive correlation, -1 indicates a strong negative correlation, and 0 means no correlation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBased on the correlation matrix, no significant correlation observed between TCC and temperature (ρ\u0026thinsp;=\u0026thinsp;0.10), COD and specific conductance (ρ = -0.14), COD and conductivity (ρ = -0.17), COD and TDS (ρ = -0.13), DO and TSS (ρ\u0026thinsp;=\u0026thinsp;0.04), specific conductance with ammoniacal nitrogen (ρ\u0026thinsp;=\u0026thinsp;0.04), conductivity with ammoniacal nitrogen (ρ\u0026thinsp;=\u0026thinsp;0.03), TDS with ammoniacal nitrogen (ρ\u0026thinsp;=\u0026thinsp;0.09), and pH with ammoniacal nitrogen (ρ = -0.10), suggesting minimal influence. Previously, through PCA, COD showed a long eigenvalue gradient in the LS5 area, likely due to human-induced stresses from agricultural farms. Furthermore, COD levels were higher in the wet season than the dry season, potentially due to heavy rainfall and water intrusion from farm areas, which cause agricultural effluents (e.g., pesticides, fertilisers, etc) into the river. These effluents were absorbed in the soil and transported into rivers, which influences the oxygen level required to break down the organic and inorganic matter in water. This is supported by the Spearman correlation that COD is significantly correlated with ammoniacal nitrogen (ρ\u0026thinsp;=\u0026thinsp;0.90), indicating farm effluents from nearby areas as a significant contributor to elevated levels of COD in the area [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Moderate positive correlations between FCC/TCC with BOD (ρ\u0026thinsp;=\u0026thinsp;0.53\u0026ndash;0.56), FCC/TCC with COD (ρ\u0026thinsp;=\u0026thinsp;0.46\u0026ndash;0.55), and FCC/TCC with ammoniacal nitrogen (ρ\u0026thinsp;=\u0026thinsp;0.61\u0026thinsp;\u0026minus;\u0026thinsp;0.55) indicating the influence of organic matter, point and non-point pollution sources distributed along Batang Layar, which these correlations align with the previous study [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. A strong positive correlation between FCC and TCC (ρ\u0026thinsp;=\u0026thinsp;0.82) indicates that a high level of faecal contamination leads to elevated total coliform levels, likely due to the same source of contamination. TSS and turbidity exhibited a strong positive correlation (ρ\u0026thinsp;=\u0026thinsp;0.92), indicating that higher suspended solids increase water cloudiness. The moderate correlation between BOD/COD with TSS (ρ\u0026thinsp;=\u0026thinsp;0.81\u0026ndash;0.82) suggests organic pollution linked with suspended solids. Notably, a moderate negative correlation between BOD and pH (ρ = -0.72), indicates that low pH levels may increase the oxidation of organic matter in rivers from agriculture farms and sewage discharge, resulting in depletion of dissolved oxygen, consistent with previous studies [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eOverall, these findings highlight the interconnected nature of water quality parameters, with strong and moderate correlations underscoring the relationships between microbial contamination, organic pollution, and physical water properties. The study emphasises that the WQI and coliform count provide different but complementary information about water quality. The WQI assesses general water quality based on physicochemical parameters, while coliform count indicates the presence of faecal contamination and potential health risks. The discrepancy between the WQI and coliform counts highlights the importance of integrating both datasets for comprehensive water quality assessment. Recommendations include microbial source tracking by using molecular techniques to identify contamination sources, increasing the frequency of coliform monitoring to capture intermittent pollution events and conducting sanitary surveys to assess potential contamination sources in the watershed. This study highlights the significant impact of land use and seasonal variations on water quality in the Batang Layar River, with elevated faecal coliform concentrations during the wet season posing a public health risk, particularly near schools and settlements. Integrated water resource management strategies are essential to mitigate these impacts and ensure the long-term sustainability of this vital water resource.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of Interest Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by Universiti Malaysia Sarawak under the High Impact Research Grant no. UNI/I01/VC-HIRG/85485/P03-02/2022, with additional support from the Institute of Biodiversity and Environmental Conservation (IBEC), and the Faculty of Resource Science and Technology (FRST) for data procurement and field facilities. This research is the result of a multidisciplinary collaboration involving the Batang Layar communities and UNIMAS researchers. Additionally, the research team also gratefully acknowledges \u003cem\u003eJabatan Bekalan Air\u003c/em\u003e \u003cem\u003eLuar Bandar\u003c/em\u003e (JBALB) for their valuable assistance and contributions in providing crucial information on the water catchment areas within the study area for land use mapping.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u0026nbsp;\u003c/strong\u003eAll data generated or analysed during this study are included in this published article [and its supplementary information files].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This study was funded by Universiti Malaysia Sarawak under the High Impact Research Grant no. UNI/I01/VC-HIRG/85485/P03-02/2022.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics, Consent to Participate, and Consent to Publish declarations\u003c/strong\u003e: not applicable.\u003cbr\u003e\u003cstrong\u003eClinical trial number:\u003c/strong\u003e not applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLing TY, Soo CL, Phan TP, Nyanti L, Sim SF, Grinang J. Assessment of water quality of Batang Rajang at Pelagus area, Sarawak, Malaysia. \u003cem\u003eSains Malays\u003c/em\u003e. 2017;46(3):401\u0026ndash;411. doi:10.17576/jsm-2017-4603-07.\u003c/li\u003e\n\u003cli\u003eShin C. Iban as a koine language in Sarawak. \u003cem\u003eWacana\u003c/em\u003e. 2021;22(1):102. doi:10.17510/wacana.v22i1.985.\u003c/li\u003e\n\u003cli\u003eMinistry of Economy Malaysia. Sustainable Development Goals [Internet]. [cited 2024]. 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Effects of meteorological events on the levels and interactions of chemical indices of a polluted freshwater system. \u003cem\u003eInt J Environ Sci\u003c/em\u003e. 2012;3(1):1-12\u003c/li\u003e\n\u003c/ol\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":"discover-applied-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Applied Sciences](https://link.springer.com/journal/42452)","snPcode":"42452","submissionUrl":"https://submission.springernature.com/new-submission/42452/3","title":"Discover Applied Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"water quality, seasonal variations, land use impact, public health, sustainability","lastPublishedDoi":"10.21203/rs.3.rs-6966017/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6966017/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBatang Layar, a biodiverse tropical river in Sarawak, is a vital water source for the local communities. However, it is threatened by contamination from agricultural runoff and untreated sewage, posing public health risks. This study examines seasonal variations, land use impacts on faecal coliform concentrations, and physicochemical water quality. Water sampling at five (5) sites during wet (December 2023) and dry (June 2024) seasons used a YSI ProDSS Multiparameter Water Quality Meter for \u003cem\u003ein-situ\u003c/em\u003e analysis. Faecal Coliform Count (FCC) and Total Coliform Count (TCC) were conducted alongside DOE-WQI (Department of Environment-Water Quality Index) that incorporates pH, dissolved oxygen (DO), suspended solids (SS), biochemical oxygen demand (BOD), chemical oxygen demand (COD), and ammoniacal nitrogen (NH\u003csub\u003e3\u003c/sub\u003e-N). Results showed higher concentrations of FCC in the wet season (228.94 cfu/100 mL- 992.87 cfu/100 mL), exceeding DOE recreational standards (\u0026lt;\u0026thinsp;400 cfu/100 mL), particularly near schools and settlements, likely attributed to sediment resuspension and non-point sewage runoff during high flow. WQI analysis showed sites LS1, LS2, and LS3 are consistently Class I throughout both wet and dry seasons, while LS4 and LS5 shifted from Class II in the wet season to Class I in the dry season, exhibiting a seasonal water quality fluctuation. Principal component analysis (PCA) and correlation matrices show a significant relationship between water quality parameters across land use and seasonal variations. This study highlights the importance of integrated water resource management (IWRM), which considers seasonal dynamics and land-use impacts to protect aquatic ecosystems and ensure sustainable water quality in Batang Layar river.\u003c/p\u003e","manuscriptTitle":"Impact of land use and seasonality on faecal coliform abundance and physicochemical water quality in Batang Layar river, Sarawak, Malaysian Borneo","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-04 09:50:57","doi":"10.21203/rs.3.rs-6966017/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-21T09:41:36+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-16T06:17:17+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-07T09:25:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"28022057056802963912830685704002267779","date":"2025-07-07T04:04:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"169747152481606604235631177794991348390","date":"2025-07-03T23:48:43+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-02T13:18:06+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-01T09:17:55+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-30T06:42:42+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-29T17:13:22+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Applied Sciences","date":"2025-06-29T17:10:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-applied-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Applied Sciences](https://link.springer.com/journal/42452)","snPcode":"42452","submissionUrl":"https://submission.springernature.com/new-submission/42452/3","title":"Discover Applied Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ca1bf410-922a-4544-8063-7e58a17f0426","owner":[],"postedDate":"July 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-11-08T15:53:36+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-04 09:50:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6966017","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6966017","identity":"rs-6966017","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}