Influence of land use type and rainfall on the water quality of North Canal basin in Beijing, China

Influence of land use type and rainfall on the water quality of North Canal basin in Beijing, China | 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 Influence of land use type and rainfall on the water quality of North Canal basin in Beijing, China Xueyu Zhang, Jingshan Yu, Qianyang Wang, Juan Sun, Boyu lv, Qixin Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3944136/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Influence of land use type and rainfall on the water quality of North Canal basin in Beijing was evaluated.The monthly monitoring data of 63 water quality stations from 2015 to 2021, the land use type data from 1980 to 2020, the rainfall and runoff data of the North Canal basin in Beijing from 1919 to 2021 were analyzed. Results showed that the water quality of the North Canal basin in Beijing was getting better year by year, and showed temporal and spatial variations. In general, water quality in flood season was better than that in non-flood season, and better in upstream than that in downstream.Cluster analysis showed that the 63 water quality stations were divided into three types. Spearman correlation analysis results clarified that the concentration of water quality indexes were significant negatively correlated with forest land, and a positive relationship between cultivated land and water quality indexes in flood season or non-flood season.In addition, the concentration of COD Mn , NH 3 and TP were significantly positively correlated with rainfall in flood season, and the concentration of TN was negatively related to rainfall in non-flood season.This study provided important support for scientific and reasonable promotion of urban water environment management at the basin scale. North Canal basin Water quality Land use Rainfall Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1 Introduction River water quality is closely related to various environmental factors, such as climatic change(Guo et al. 2020 ), hydrological conditions (Yu et al. 2022 , Zhang et al. 2010 ), geomorphology characteristics, and land use change in the drainage basin (Deng 2019 ). Compared with natural rivers, the hydrological processes and pollutant migration paths of urban rivers are strongly disturbed by human activities (Wang et al. 2022 , Yu et al. 2022 ). The change of hydrological process caused by urbanization and utilization of water resources makes it more difficult to study the influencing mechanism of urban river-water quality. Land use as one of the important ways that human activities affect river water quality, can affect the hydrological process of the basin and the surface migration and transformation process of pollutants before entering into river (Guo et al. 2020 ). Due to the greatest influence on the main ionic chemistry and water quality of river water, land use can be a good predictor of river water quality(Hossain et al. 2022 , Kaur et al. 2021 , Li et al. 2023 ). Lei et al. ( 2021 ) found that river water quality was closely related to the composition of land use (arable or pasture land, forest, and urban). Sharma et al. ( 2016 ) clarified that river water quality was a function of land use at Varanasi. Meanwhile, relevant studies have found that industrial land and agricultural land often lead to the decline of water environment quality, while forest land and grassland are negatively correlated with the concentration of pollutants (Bahara &Yamamuro 2008 , Jian et al. 2011 ). In addition, the influence of land use on water environment was diverse at different spatial scales from small scale to river basin (Deng 2019 , Wang et al. 2021 ) . Seasonal variation as one of affecting factors on river water quality has been widely noted as well. There were spatial differences of water quality at different river reaches or hydrologic units with the change of pollution characteristics or climatic characteristics in non-flood season and flood season (Du et al. 2019 , Ma et al. 2021 ,Tao et al. 2022 ). Studies showed that the water quality was better in the flood season than that in the non-flood season across the Shaying River Basin (Tao et al. 2022 ) or in Haikou, China (Chen et al. 2021 ), and the distribution of pollutant varied with spatial differences. Zhai et al.(2018) found that detection of diffuse phosphorous load tended to decrease in flood season, and increased in non-flood season at the upstream of Qiantang River Basin in Southeast China. Du et al. ( 2019 ) clarified that the water quality of the Upper Qinhuai River was better in flood season than that of non-flood season with the pollution characteristics of urban industry and domestic sewage, and the contrary conclusion was found in the Outer Qinhuai River and inner Qinhuai River where showed the typical pollution characteristics of urban sewage and catering. Chen et al.(2015) illustrated that the water quality of water sources in non-flood season was more improved than that in flood season, and the water quality of river type source water was more obvious than that of reservoir type source water. Increased precipitation and runoff during flood season could result in high non-point source pollution loads (Du et al. 2016 ). Zeng et al.(2021)found that the total phosphorus concentration in non-flood season changed little, but increased significantly in flood season in Huaihe River of china. Changs in river water quality involves the superposition effects of climate and land use change, and there is no consensus conclusion. In order to explore the driving mechanism on water quality in the process of urbanization, we took the North Canal basin as an example to carry out the research.This study analyzed the land use change, hydrological and water quality characteristics of the North Canal basin with the help of historical remote sensing data, hydrological and water quality data, and revealed the effects of land use type and rainfall conditions on water quality in the basin, thus providing important support for scientific and reasonable promotion of urban water environment management at the basin scale. 2 Materials and methods 2.1 Study area The North Canal basin in Beijing has a total length of 90 km (main river channel) and a drainage area of 4250 km 2 in Beijing, including 22.1% in mountainous area and 77.9% in plain area, which is the largest river system in plain drainage area of Beijing, shown as Fig. 1 . The annual average temperature is 11 ~ 12℃, and the annual average rainfall is 581.7mm. The North Canal basin is the only one of the five major water systems in Beijing that originates from Beijing and has water in its main stream all year round (Zhang et al. 2021 ), undertake 90% of the drainage tasks in the central urban area of Beijing. The basin accounts for more than 70% of the city's population and more than 80% of the city's GDP, which is the basin with the highest concentration of industry and the highest level of urbanization in Beijing(Jing et al. 2013 ). According to the river network and topographical features, the North Canal basin in Beijing was divided into 38 sub-basins (Fig. 1 d). 2.2 Data collection The DEM map at 90m resolution was collected from the Geospatial Data Cloud of China.Land use maps at a 100m resolution for 1980, 1990, 1995, 2000, 2005, 2010 and 2015 were from the Resource and Environment Data Cloud Platform of China. Land use map at a 30m resolution for 2020 was derived from the Naitonal Geomatics Center of China. Hydrological data were from Beijing Hydrologic Center and Beijing Water Authority. Water quality data were from the Beijing District Ecological Environment Bureau and the China Environmental Monitoring Station. Spatial variation of water quality and land use were expressed by ArcGIS 10.8. 2.3 Statistical analysis Spearman correlation analysis was conducted between water quality and land use types in different water quality stations or sub-basin units. To identify the spatial variation characteristics of water quality, the water quality stations were grouped using cluster analysis. 3 Results and and discussion 3.1 Temporal and spatial variation characteristics of water quality Interannual variation of water quality. The annual water quality change trend of North Canal basin from 2015 to 2021, was investigated by the water quality data of 63 water quality stations (Fig. 2 ). With the implementation of the Action Plan for the Prevention and Control of Water Pollution in China (Meng et al. 2022 , Wu et al. 2020 ), the water quality of the North Canal basin has been improved year by year. Variation characteristics of water quality within the year. To analyze the rainfall characteristics of North Canal basin, the hydrological data of the Tongxian hydrological station during 1919–2020 was investigated, as shown in Fig. 3 . The flood season of North Canal basin was distributed from June to September, and concentrated in July to August, accounting for more than 50% of the annual precipitation. Therefore, June to September was classified as the flood season, while the other months as non-flood season in this study. Monthly monitoring data of 63 water quality stations in the North Canal basin from 2015 to 2021 were investigated to analyze the variation characteristics of water quality within annual year, results as shown in Table 1 and Fig. 4 . According to Table 1 , the mean concentration of NH 3 -N, TP,TN and anionic surfactant in non-flood season was significantly higher than that in flood season. The concentration of NH 3 -N and TP fluctuated greatly in non-flood season,but the fluctuation of TN and anionic surfactant in flood season was slightly higher than that of the non-flood season. Although the mean values of COD Mn , COD and BOD 5 were close in flood season and non-flood season, the coefficient of variation was small in flood season and large in non-flood season.From the monthly average concentration of water quality indexes from 2015 to 2021, the water quality of the North Canal were better in flood season than in non-flood season, i.e. with lower mean concentration in flood season,which was consistent with the previous conclusion (Chen et al. 2021 , Tao et al. 2022 , Zhai &Zhang 2018 ). Zhang Yuhang (2021) found that the water quality in wet season (May to September) was better than that in dry season (January, February, December) and normal season (March, April, October, November) in North Canal basin, which indicated that the runoff in flood season can reduce the concentration of pollutants in the river, and the internal secondary pollution was an important reason for the worst water quality in normal season. Table 1 Statistical results of water quality indexes of North Canal basin from 2015 to 2021. Water quality parameters Period Min Max Mean S.D. Skewness Kurtosis Coefficient of Variation COD(mg/L) Annual 8.00 194.00 30.75 ± 1.75 26.77 2.73 ± 0.16 9.76 ± 0.32 0.87 Flood 6.00 213.00 30.58 ± 1.65 25.27 3.29 ± 0.16 16.40 ± 0.32 0.83 Non-flood 8.00 185.00 30.77 ± 1.90 29.11 2.74 ± 0.16 9.04 ± 0.32 0.95 COD Mn (mg/L) Annual 1.97 28.50 5.99 ± 0.26 3.99 2.59 ± 0.16 9.80 ± 0.31 0.67 Flood 1.75 25.68 5.92 ± 0.21 3.26 2.13 ± 0.16 8.23 ± 0.31 0.55 Non-flood 1.83 35.85 6.03 ± 0.30 4.65 2.93 ± 0.16 12.09 ± 0.31 0.77 BOD(mg/L) Annual 0.50 63.22 7.35 ± 0.58 8.93 2.97 ± 0.16 10.73 ± 0.32 1.22 Flood 0.45 67.18 7.07 ± 0.53 8.08 3.49 ± 0.16 18.00 ± 0.32 1.14 Non-flood 0.50 61.24 7.50 ± 0.64 9.81 2.96 ± 0.16 9.78 ± 0.32 1.31 NH 3 -N(mg/L) Annual 0.07 41.53 3.01 ± 0.37 5.77 3.15 ± 0.16 12.71 ± 0.31 1.92 Flood 0.04 40.15 2.71 ± 0.33 5.16 3.62 ± 0.16 18.10 ± 0.31 1.90 Non-flood 0.05 42.21 3.18 ± 0.40 6.29 3.05 ± 0.16 11.31 ± 0.31 1.98 TP(mg/L) Annual 0.01 3.78 0.37 ± 0.04 0.59 2.54 ± 0.16 7.62 ± 0.32 1.62 Flood 0.01 3.53 0.36 ± 0.04 0.57 2.72 ± 0.16 8.87 ± 0.32 1.58 Non-flood 0.01 3.90 0.38 ± 0.04 0.63 2.45 ± 0.16 6.79 ± 0.32 1.67 TN(mg/L) Annual 1.00 48.00 9.13 ± 0.61 8.27 1.40 ± 0.18 3.09 ± 0.36 0.91 Flood 1.00 44.00 8.1 ± 0.57 7.70 1.51 ± 0.18 3.26 ± 0.36 0.95 Non-flood 1.00 49.00 9.67 ± 0.66 8.88 1.39 ± 0.18 2.93 ± 0.36 0.92 Anionic surfactant (mg/L) Annual 0.02 1.93 0.14 ± 0.02 0.24 5.03 ± 0.18 30.93 ± 0.35 1.68 Flood 0.02 1.90 0.16 ± 0.02 0.25 4.16 ± 0.18 21.71 ± 0.35 1.54 Non-flood 0.02 1.91 0.18 ± 0.02 0.27 3.66 ± 0.18 16.45 ± 0.35 1.52 Spatial distribution characteristics of water quality in flood season and non-flood season. As shown in Fig. 4 , the water quality of the North Canal basin showed temporal and spatial variations. In general, water quality in flood season was better than that in non-flood season, and water quality in upstream was better than that in downstream, which was consistent with other studies (Wang et al. 2021 ). In flood season, there were larger water quantity, higher water temperature, longer sunshine duration, which lead to stronger aquatic biological activity and faster pollutant degradation rate. On the other hand, the spatially heterogeneous of water quality characteristics between water quality stations in flood season and non-flood season was investigated. The mean values of COD, COD Mn , BOD, NH 3 -N, TP, TN and anionic surfactants of each water quality station in flood season and non-flood season during 2015–2021 was taken as the evaluation quantity, and the cluster analysis was carried out by hierarchical clustering method (HCA). As shown in Fig. 5 , the 63 water quality stations were divided into three types with a distance of 10 as the orientation line. HCA grouped the water quality stations on the basis of similarity and dissimilarities in their characteristics.The cluster 1 was ST49, located in downstream of the North Canal basin, with poorer water quality (COD 101.61mg/l, NH 3 -N 23.52mg/l), all the seven water quality indexes show that water quality in flood season was significantly better than that in non-flood season, and the difference of COD concentration between flood season and non-flood season > 50mg/l. The second cluster include ST12,ST27,ST28, and ST35, the water quality of COD, COD Mn , BOD and NH 3 -N in flood season was better than that in non-flood season, while the water quality of TP, TN and anionic surfactant in flood season was better than that in non-flood season or there was little difference. The rest of the other stations were included in the third cluster, each water quality index difference between flood season and non-flood season was not significant or characteristics were not uniform. 3.2 Influence of land use type on the water quality The change of land use in North Canal basin .With the rapid development of society, the demand for land was increasing, the problem of land use was becoming more and more prominent, and the types of regional land use were constantly changing. The analysis results of land use patterns change in the North Canal Basin from 1980 to 2020 were showed in Table 2 . It can be seen that the proportion of urban and rural construction land (Artificial surface) had been increasing, from less than 22% in 1980 to more than 45% in 2020, as shown in Fig. 6 . Meanwhile, the area and proportion of cultivated land had been declining.Compared to 2015, the area and proportion of cultivated land, forest and Artificial surface declined in 2020, while there was a remarkable increase in the proportion of grassland. Table 2 The change of land use in North Canal basin (Beijing) from 1980 to 2020. Year Proportion of Cultivated Land Proportion of Forest Proportion of Grassland Proportion of Water Body Proportion of Artificial Surfaces Proportion of Other 1980 55.13% 21.44% 0.51% 1.85% 21.07% 0.00% 1990 55.14% 21.46% 0.53% 1.84% 21.03% 0.00% 1995 43.22% 22.39% 0.44% 2.55% 31.40% 0.00% 2000 42.31% 22.38% 0.44% 2.57% 32.29% 0.00% 2005 35.99% 22.04% 0.44% 2.24% 39.29% 0.00% 2010 33.09% 18.97% 0.58% 0.89% 46.45% 0.02% 2015 29.74% 18.95% 0.51% 0.84% 49.94% 0.02% 2020 27.05% 17.86% 5.11% 1.04% 48.88% 0.06% Influence of land use type on river water quality. Spearman correlation analysis was conducted between the water quality indexes of different water quality stations and the land use structure (area proportion) of the corresponding region from 2015 to 2020. As shown in Table 3 , all the water quality indexes showed significant correlation with cultivated land and forest land, among which, there was a significant negative correlation with forest land, and a positive correlation with cultivated land in flood season or non-flood season. In addition, the concentration of COD Mn and COD were negatively correlated with water, the concentration of NH 3 was negatively correlated with artificial surfaces, in non-flood season. The concentration of TP was negatively correlated with artificial surfaces, and the concentration of anionic surfactants were negatively correlated with other land in flood season or non-flood season. Except TP, the correlation between other indexes and non-flood season was more significant. Table 3 Spearman rank correlation analysis of water quality and proportion of land use type (2015–2020) Period Cropland Forest Grassland Water Impervious_ surface Others COD Non-flood a .302** − .353** 0.037 − .215* -0.108 -0.065 Flood b .226* − .289** 0.152 -0.081 -0.068 0.032 Annual c .292** − .329** 0.085 -0.163 -0.109 -0.029 COD Mn Non-flood .339** − .307** 0.021 − .209* -0.141 -0.11 Flood .270** − .273** 0.095 -0.096 -0.124 -0.042 Annual .325** − .307** 0.046 -0.168 -0.14 -0.088 BOD Non-flood .354** − .335** 0.112 -0.166 -0.163 0.015 Flood .298** − .272** 0.177 0.007 -0.123 0.032 Annual .321** − .335** 0.123 -0.105 -0.132 0 NH 3 Non-flood .427** − .202* 0.132 -0.124 − .298** 0.056 Flood .347** − .218* 0.076 -0.074 -0.171 -0.044 Annual .408** − .209* 0.135 -0.081 − .262** 0.029 TP Non-flood .409** − .238* 0.108 -0.094 − .221* 0.027 Flood .427** -0.129 0.186 0.014 − .270** 0.102 Annual .417** − .211* 0.141 -0.058 − .244* 0.063 TN Non-flood .287** -0.201 0.062 -0.067 -0.171 -0.147 Flood 0.202 − .213* 0.057 -0.081 -0.078 -0.159 Annual .259* − .223* 0.04 -0.072 -0.131 -0.166 Anionic surfactant Non-flood .248* -0.19 -0.069 -0.071 -0.113 − .277** Flood .204* − .251* − .266** -0.182 -0.052 − .368** Annual .241* − .233* -0.143 -0.139 -0.087 − .345** a,b n=112; c n=110. **.The correlation was significant when the confidence (double measure) was 0.01. *. The correlation was significant when the confidence (double measure) was 0.05. Previous studies observed that the similar relationship between land use pattern and water quality as well. Jian et al.(2011)found that the river water quality was negatively correlated with the relative area proportion of farmland all year round and positively correlated with the proportion of woodland area in non-flood season. Bahara and Yamamuro ( 2008 ) reported that forest land was negatively correlated with all ions chemistry of the Shimousa Upland in Japan, farmland coverage and residential areas were positively correlated with the major ion chemistry.Forest land was rich in vegetation and the process of vegetation root absorption and soil interception can effectively reduce pollutants (Guo et al. 2020 ), meanwhile, human activities in this region often weak. Water quality index was positively correlated with cultivated land due to the not fully utilized fertilizer and insecticides together with runoff along with the erosion of rainfall(Wang et al. 2021 ). On the other hand, a high proportion of agricultural and residential land always lead to more non-point pollutants (Du et al. 2016 ), which was driven by surface runoff and discharged into the river network (Deng 2019 ). 3.3 Influence of rainfall on the water quality Non-point source pollution caused by rainwater has become one of the important reasons for the deterioration of urban water environment. In order to investigate the influence of rainfall on water quality in Beijing, the correlation analysis between monthly mean water quality and rainfall during 2015–2021 and the correlation analysis between daily mean water quality and rainfall in 2021 of ST19 were conducted. Spearman correlation analysis showed that the concentration of COD Mn , NH 3 and TP were significantly positively correlated with rainfall in flood season, and the concentration of TN was significantly negatively related to rainfall in non-flood season (Table 4 ). Spearman correlation analysis between rainfall and runoff in Tongxian hydrological station from 1919 to 2020 was analyzed as well, results showed that rainfall and runoff were positively correlated with a correlation coefficient of 0.4300, indicating a significant correlation when the confidence (double measure) was 0.01 (Fig. 7 ). Table 4 Spearman correlation analysis between water quality and rainfall of ST19 from 2015 to 2021 Period Group COD Mn NH 3 TP TN Rainfall Flood monthly water quality and rainfall in 2015-2021 a 0.016 0.495 * 0.444 * 0.188 daily water quality and rainfall in 2021 b 0.358 ** 0.686 ** 0.500 ** 0.172 Non-flood monthly water quality and rainfall in 2015-2021 c -0.133 -0.074 -0.123 -0.039 daily water quality and rainfall in 2021 d 0.005 0.086 -0.038 -0.200 ** a n = 26; b n = 54; C n = 121; d n=241. **.The correlation was significant when the confidence (double measure) was 0.01. *. The correlation was significant when the confidence (double measure) was 0.05. In flood season, there were larger rainfall, runoff and water quantity.Increased precipitation and runoff during flood season could result in a greater contribution of sewage-derived organic matter (Xuan et al. 2020 ) and non-point source pollution loads (Du et al. 2016 , Ma et al. 2021 , Zeng et al. 2021 ). In a short period of time, with the increase of precipitation and urban runoff, the concentration of COD Mn, NH 3, TP increased (as shown in Table 4 ) in flood seasons, except TN. Previous studies indicated that nitrogen loading was more sensitive to changes in temperature than precipitation (Zhu et al. 2022 ). Particulate-P and NH 4 -N were delivered primarily from overland sources and transported by runoff (Mihiranga et al. 2021 ). On a long time series scale (seasonal), combining other factors such as higher water temperature, longer sunshine duration, stronger aquatic biological activity and faster pollutant degradation rate, the overall water quality in flood season was better than that in non-flood season in the North Canal basin in Beijing (as shown in Table 1 ). 4 Conclusions This study analyzed the land use, hydrological and water quality characteristics of the North Canal basin in Beijing, and revealed the influence of land use type and rainfall conditions on water quality. Results showed that the water quality of the North Canal basin was getting better year by year from 2015 to 2021, and showed temporal and spatial variations. In general, water quality in flood season was better than that in non-flood season, and better in upstream than that in downstream. Additionally, cluster analysis shown that the 63 water quality stations were divided into three types. The influence of land use type and rainfall conditions on water quality were evaluated by Spearman correlation analysis. Results showed that the concentration of water quality indexes were significant negatively correlated with forest land, and a positive relationship between cultivated land and water quality indexes in flood season or non-flood season. In addition, the concentration of COD Mn , NH 3 and TP were significantly positively correlated with rainfall in flood season, and the concentration of TN was negatively related to rainfall in non-flood season.The impact of land use type and rainfall on water quality in flood season and non-flood season should be noticed. This study contributes effective suggestions for implementing the refined management of water environment and the countermeasures of regional water pollution control. Declarations Ethical Approval Not applicable. Consent to Participate Not applicable. Consent to Publish Not applicable. Author Contributions Xueyu Zhang contributed to work design, investigation and writing—original draft,review and editing; Qianyang Wang, Qixin Wang and Boyu Lv contributed to data collection and analysis;Jingshan Yu and Juan Sun contributed to review, and editing. All authors read and approved the submitted version. Funding Not applicable. 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Zhang YY, Xia J, Liang T, Shao QX (2010): Impact of Water Projects on River Flow Regimes and Water Quality in Huai River Basin. Water Resources Management 24, 889-908. Zhu XP, Chang K, Cai WJ, Zhang AR, Yue GT, Zhao XH (2022): Response of runoff and nitrogen loadings to climate and land use changes in the middle Fenhe River basin in Northern China. Journal Of Water And Climate Change. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 30 May, 2024 Reviewers invited by journal 02 Apr, 2024 Editor invited by journal 29 Feb, 2024 Editor assigned by journal 21 Feb, 2024 First submitted to journal 14 Feb, 2024 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-3944136","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":286532446,"identity":"5adafa6b-7e69-41ce-ba26-55010f95bfee","order_by":0,"name":"Xueyu Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFklEQVRIiWNgGAWjYDACCTiL+QADg4EFgwGYw0aUFrYEoBYJkrTwGIC5BLXIz25+9vBr22E5gxs5Xzf8KJBgMJfuMWD4UHaYgX92A1YtjHOOmRvLnDlsbHAjd9vNHqDDLOecMWCcce4wg8SdA1i1MEskmElLVBxO3ADUcoMH5JcbOQbMvG2Hgf5KwKqFTSL9m7SEweH6DTdynt38A9PyF48WHokcM8kPFYcTgCrZbsNtYcSjRUIip0ya4Uy64cwzz8xuyxhI8FjOSCs42HMunUfiBnYt8jPSt0n+bLOW5zue/Ozmmz82cuYSyRsf/CizluOfgV0LOAh4gIQCNHhAbIYDMAYuwPgDZF0DPiWjYBSMglEwogEASPdcSawp7gcAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-3583-6655","institution":"Beijing Municipal Ecological Environment Appraisal and Complaint Center","correspondingAuthor":true,"prefix":"","firstName":"Xueyu","middleName":"","lastName":"Zhang","suffix":""},{"id":286532447,"identity":"ccfb0bd1-2cfc-47a3-874f-11080d4b08b6","order_by":1,"name":"Jingshan Yu","email":"","orcid":"","institution":"Beijing Normal University","correspondingAuthor":false,"prefix":"","firstName":"Jingshan","middleName":"","lastName":"Yu","suffix":""},{"id":286532448,"identity":"1976c6c1-9851-4449-ba4a-51b80d1c3e65","order_by":2,"name":"Qianyang Wang","email":"","orcid":"","institution":"Beijing Normal University","correspondingAuthor":false,"prefix":"","firstName":"Qianyang","middleName":"","lastName":"Wang","suffix":""},{"id":286532449,"identity":"bcae5182-226b-49f6-b961-4caa419f7481","order_by":3,"name":"Juan Sun","email":"","orcid":"","institution":"Beijing Municipal Ecological Environment Appraisal and Complaint Center","correspondingAuthor":false,"prefix":"","firstName":"Juan","middleName":"","lastName":"Sun","suffix":""},{"id":286532450,"identity":"8d606f51-f826-4ba6-b578-2219bf8bb843","order_by":4,"name":"Boyu lv","email":"","orcid":"","institution":"Beijing municipal ecological environment appraisal and complaint center","correspondingAuthor":false,"prefix":"","firstName":"Boyu","middleName":"","lastName":"lv","suffix":""},{"id":286532451,"identity":"ba82bc9f-6274-4b37-b896-1751b73afc8b","order_by":5,"name":"Qixin Wang","email":"","orcid":"","institution":"Beijing Municipal ecological environment appraisal and comlaint center","correspondingAuthor":false,"prefix":"","firstName":"Qixin","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-02-09 20:45:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3944136/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3944136/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54143191,"identity":"e3461f6c-1914-4554-b29a-1e5ec173e9fe","added_by":"auto","created_at":"2024-04-05 08:38:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1248557,"visible":true,"origin":"","legend":"\u003cp\u003eDescription of study region. (a) Location of of the study area in China, (b) Location of the study area in Beijing, (c) Spatial distribution of water quality stations and main rivers, (d) 38 sub-basin units and spatial distribution of rainfall stations.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3944136/v1/b07ddea6314064108f6c00c7.png"},{"id":54143186,"identity":"5ff77793-7ea0-4445-96fa-6c1ff2f4df2c","added_by":"auto","created_at":"2024-04-05 08:38:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":194399,"visible":true,"origin":"","legend":"\u003cp\u003eVariation of water quality in North Canal basin from 2015 to 2021. The solid bar in box marks the median, and the box denotes the 25% and 75% percentiles.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3944136/v1/d137fad3b49949676ade9d56.png"},{"id":54143192,"identity":"0a966884-6bb6-4ba9-85b3-ebed123318b8","added_by":"auto","created_at":"2024-04-05 08:38:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":60261,"visible":true,"origin":"","legend":"\u003cp\u003eMonthly average rainfall from 1919 to 2020 (a), and monthly average runoff from 1931 to 2020 (b) of Tongxian hydrological station in North Canal basin.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-3944136/v1/f8946c02fc5f0f6da40f386c.png"},{"id":54143190,"identity":"fbaf82fd-f167-4631-802e-e42aac025b88","added_by":"auto","created_at":"2024-04-05 08:38:36","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":751780,"visible":true,"origin":"","legend":"\u003cp\u003eThe average concentration of COD,NH\u003csub\u003e3\u003c/sub\u003e-N (b),TP in flood season and non-flood season of each sub-basin unit.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-3944136/v1/35b489f2266a01160edb495d.png"},{"id":54143193,"identity":"63b15de6-2011-4d2b-aa58-3297d1546465","added_by":"auto","created_at":"2024-04-05 08:38:37","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":476873,"visible":true,"origin":"","legend":"\u003cp\u003eDendrogram showing cluster analysis(a) and spatial distribution (b) of the water quality stations.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-3944136/v1/2a9c5031fe66b60c0c5cc7a7.png"},{"id":54143713,"identity":"4dc9c469-29b9-4ef1-a0fb-bf352d005491","added_by":"auto","created_at":"2024-04-05 08:46:36","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":414080,"visible":true,"origin":"","legend":"\u003cp\u003eLand use change of the North Canal basin in Beijing from 1980 to 2020.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-3944136/v1/41a1bac035e609967929a068.png"},{"id":54143188,"identity":"a5b0af61-6952-49c0-ba41-3075f54cd811","added_by":"auto","created_at":"2024-04-05 08:38:36","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":118616,"visible":true,"origin":"","legend":"\u003cp\u003eRainfall and runoff data of Tongxian hydrological station from 1920 to 2020.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-3944136/v1/34c1ca102836b34de8fb4110.png"},{"id":54144638,"identity":"c3d8dd04-6916-44a5-a692-44a38f941166","added_by":"auto","created_at":"2024-04-05 08:54:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2441007,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3944136/v1/7410a499-275a-4357-ac66-570813dcb2cf.pdf"}],"financialInterests":"","formattedTitle":"Influence of land use type and rainfall on the water quality of North Canal basin in Beijing, China","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eRiver water quality is closely related to various environmental factors, such as climatic change(Guo et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), hydrological conditions (Yu et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Zhang et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), geomorphology characteristics, and land use change in the drainage basin (Deng \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Compared with natural rivers, the hydrological processes and pollutant migration paths of urban rivers are strongly disturbed by human activities (Wang et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Yu et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The change of hydrological process caused by urbanization and utilization of water resources makes it more difficult to study the influencing mechanism of urban river-water quality.\u003c/p\u003e \u003cp\u003eLand use as one of the important ways that human activities affect river water quality, can affect the hydrological process of the basin and the surface migration and transformation process of pollutants before entering into river (Guo et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Due to the greatest influence on the main ionic chemistry and water quality of river water, land use can be a good predictor of river water quality(Hossain et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Kaur et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Li et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Lei et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) found that river water quality was closely related to the composition of land use (arable or pasture land, forest, and urban). Sharma et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) clarified that river water quality was a function of land use at Varanasi. Meanwhile, relevant studies have found that industrial land and agricultural land often lead to the decline of water environment quality, while forest land and grassland are negatively correlated with the concentration of pollutants (Bahara \u0026amp;Yamamuro \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2008\u003c/span\u003e, Jian et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). In addition, the influence of land use on water environment was diverse at different spatial scales from small scale to river basin (Deng \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Wang et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) .\u003c/p\u003e \u003cp\u003eSeasonal variation as one of affecting factors on river water quality has been widely noted as well. There were spatial differences of water quality at different river reaches or hydrologic units with the change of pollution characteristics or climatic characteristics in non-flood season and flood season (Du et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Ma et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e,Tao et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Studies showed that the water quality was better in the flood season than that in the non-flood season across the Shaying River Basin (Tao et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) or in Haikou, China (Chen et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and the distribution of pollutant varied with spatial differences. Zhai et al.(2018) found that detection of diffuse phosphorous load tended to decrease in flood season, and increased in non-flood season at the upstream of Qiantang River Basin in Southeast China. Du et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) clarified that the water quality of the Upper Qinhuai River was better in flood season than that of non-flood season with the pollution characteristics of urban industry and domestic sewage, and the contrary conclusion was found in the Outer Qinhuai River and inner Qinhuai River where showed the typical pollution characteristics of urban sewage and catering. Chen et al.(2015) illustrated that the water quality of water sources in non-flood season was more improved than that in flood season, and the water quality of river type source water was more obvious than that of reservoir type source water. Increased precipitation and runoff during flood season could result in high non-point source pollution loads (Du et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Zeng et al.(2021)found that the total phosphorus concentration in non-flood season changed little, but increased significantly in flood season in Huaihe River of china.\u003c/p\u003e \u003cp\u003eChangs in river water quality involves the superposition effects of climate and land use change, and there is no consensus conclusion. In order to explore the driving mechanism on water quality in the process of urbanization, we took the North Canal basin as an example to carry out the research.This study analyzed the land use change, hydrological and water quality characteristics of the North Canal basin with the help of historical remote sensing data, hydrological and water quality data, and revealed the effects of land use type and rainfall conditions on water quality in the basin, thus providing important support for scientific and reasonable promotion of urban water environment management at the basin scale.\u003c/p\u003e"},{"header":"2 Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study area\u003c/h2\u003e \u003cp\u003eThe North Canal basin in Beijing has a total length of 90 km (main river channel) and a drainage area of 4250 km\u003csup\u003e2\u003c/sup\u003e in Beijing, including 22.1% in mountainous area and 77.9% in plain area, which is the largest river system in plain drainage area of Beijing, shown as Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The annual average temperature is 11\u0026thinsp;~\u0026thinsp;12℃, and the annual average rainfall is 581.7mm. The North Canal basin is the only one of the five major water systems in Beijing that originates from Beijing and has water in its main stream all year round (Zhang et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), undertake 90% of the drainage tasks in the central urban area of Beijing. The basin accounts for more than 70% of the city's population and more than 80% of the city's GDP, which is the basin with the highest concentration of industry and the highest level of urbanization in Beijing(Jing et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). According to the river network and topographical features, the North Canal basin in Beijing was divided into 38 sub-basins (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data collection\u003c/h2\u003e \u003cp\u003eThe DEM map at 90m resolution was collected from the Geospatial Data Cloud of China.Land use maps at a 100m resolution for 1980, 1990, 1995, 2000, 2005, 2010 and 2015 were from the Resource and Environment Data Cloud Platform of China. Land use map at a 30m resolution for 2020 was derived from the Naitonal Geomatics Center of China.\u003c/p\u003e \u003cp\u003eHydrological data were from Beijing Hydrologic Center and Beijing Water Authority. Water quality data were from the Beijing District Ecological Environment Bureau and the China Environmental Monitoring Station. Spatial variation of water quality and land use were expressed by ArcGIS 10.8.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Statistical analysis\u003c/h2\u003e \u003cp\u003eSpearman correlation analysis was conducted between water quality and land use types in different water quality stations or sub-basin units. To identify the spatial variation characteristics of water quality, the water quality stations were grouped using cluster analysis.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results and and discussion","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Temporal and spatial variation characteristics of water quality\u003c/h2\u003e \u003cp\u003e \u003cb\u003eInterannual variation of water quality.\u003c/b\u003eThe annual water quality change trend of North Canal basin from 2015 to 2021, was investigated by the water quality data of 63 water quality stations (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). With the implementation of the Action Plan for the Prevention and Control of Water Pollution in China (Meng et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Wu et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), the water quality of the North Canal basin has been improved year by year.\u003c/p\u003e \u003cp\u003e \u003cb\u003eVariation characteristics of water quality within the year.\u003c/b\u003e To analyze the rainfall characteristics of North Canal basin, the hydrological data of the Tongxian hydrological station during 1919\u0026ndash;2020 was investigated, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The flood season of North Canal basin was distributed from June to September, and concentrated in July to August, accounting for more than 50% of the annual precipitation. Therefore, June to September was classified as the flood season, while the other months as non-flood season in this study. Monthly monitoring data of 63 water quality stations in the North Canal basin from 2015 to 2021 were investigated to analyze the variation characteristics of water quality within annual year, results as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. According to Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the mean concentration of NH\u003csub\u003e3\u003c/sub\u003e-N, TP,TN and anionic surfactant in non-flood season was significantly higher than that in flood season. The concentration of NH\u003csub\u003e3\u003c/sub\u003e-N and TP fluctuated greatly in non-flood season,but the fluctuation of TN and anionic surfactant in flood season was slightly higher than that of the non-flood season. Although the mean values of COD\u003csub\u003eMn\u003c/sub\u003e, COD and BOD\u003csub\u003e5\u003c/sub\u003e were close in flood season and non-flood season, the coefficient of variation was small in flood season and large in non-flood season.From the monthly average concentration of water quality indexes from 2015 to 2021, the water quality of the North Canal were better in flood season than in non-flood season, i.e. with lower mean concentration in flood season,which was consistent with the previous conclusion (Chen et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Tao et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Zhai \u0026amp;Zhang \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Zhang Yuhang (2021) found that the water quality in wet season (May to September) was better than that in dry season (January, February, December) and normal season (March, April, October, November) in North Canal basin, which indicated that the runoff in flood season can reduce the concentration of pollutants in the river, and the internal secondary pollution was an important reason for the worst water quality in normal season.\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\u003eStatistical results of water quality indexes of North Canal basin from 2015 to 2021.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWater quality parameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePeriod\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eS.D.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSkewness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eKurtosis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCoefficient of Variation\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\u003eCOD(mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnnual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e194.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e30.75\u0026thinsp;\u0026plusmn;\u0026thinsp;1.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e26.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e2.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e9.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFlood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e213.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e30.58\u0026thinsp;\u0026plusmn;\u0026thinsp;1.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e25.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e3.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e16.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-flood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e185.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e30.77\u0026thinsp;\u0026plusmn;\u0026thinsp;1.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e29.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e2.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e9.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eCOD\u003csub\u003eMn\u003c/sub\u003e(mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnnual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e5.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e2.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e9.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFlood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e5.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e2.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e8.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-flood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e6.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e2.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e12.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eBOD(mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnnual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e63.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e7.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e2.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e10.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFlood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e67.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e7.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e3.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e18.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-flood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e61.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e7.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e2.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e9.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eNH\u003csub\u003e3\u003c/sub\u003e-N(mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnnual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e3.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e3.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e12.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFlood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e2.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e3.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e18.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-flood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e3.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e3.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e11.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eTP(mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnnual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e2.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e7.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFlood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e2.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e8.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-flood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e2.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e6.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eTN(mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnnual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e9.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e1.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e3.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFlood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e8.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e1.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e3.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-flood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e49.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e9.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e1.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e2.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAnionic surfactant\u003c/p\u003e \u003cp\u003e(mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnnual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e5.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e30.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFlood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e4.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e21.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-flood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e3.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e16.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.52\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 \u003c/p\u003e \u003cp\u003e \u003cb\u003eSpatial distribution characteristics of water quality in flood season and non-flood season.\u003c/b\u003e As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the water quality of the North Canal basin showed temporal and spatial variations. In general, water quality in flood season was better than that in non-flood season, and water quality in upstream was better than that in downstream, which was consistent with other studies (Wang et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In flood season, there were larger water quantity, higher water temperature, longer sunshine duration, which lead to stronger aquatic biological activity and faster pollutant degradation rate. On the other hand, the spatially heterogeneous of water quality characteristics between water quality stations in flood season and non-flood season was investigated. The mean values of COD, COD\u003csub\u003eMn\u003c/sub\u003e, BOD, NH\u003csub\u003e3\u003c/sub\u003e-N, TP, TN and anionic surfactants of each water quality station in flood season and non-flood season during 2015\u0026ndash;2021 was taken as the evaluation quantity, and the cluster analysis was carried out by hierarchical clustering method (HCA). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the 63 water quality stations were divided into three types with a distance of 10 as the orientation line. HCA grouped the water quality stations on the basis of similarity and dissimilarities in their characteristics.The cluster 1 was ST49, located in downstream of the North Canal basin, with poorer water quality (COD 101.61mg/l, NH\u003csub\u003e3\u003c/sub\u003e-N 23.52mg/l), all the seven water quality indexes show that water quality in flood season was significantly better than that in non-flood season, and the difference of COD concentration between flood season and non-flood season\u0026thinsp;\u0026gt;\u0026thinsp;50mg/l. The second cluster include ST12,ST27,ST28, and ST35, the water quality of COD, COD\u003csub\u003eMn\u003c/sub\u003e, BOD and NH\u003csub\u003e3\u003c/sub\u003e-N in flood season was better than that in non-flood season, while the water quality of TP, TN and anionic surfactant in flood season was better than that in non-flood season or there was little difference. The rest of the other stations were included in the third cluster, each water quality index difference between flood season and non-flood season was not significant or characteristics were not uniform.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Influence of land use type on the water quality\u003c/h2\u003e \u003cp\u003e \u003cb\u003eThe change of land use in North Canal basin\u003c/b\u003e.With the rapid development of society, the demand for land was increasing, the problem of land use was becoming more and more prominent, and the types of regional land use were constantly changing. The analysis results of land use patterns change in the North Canal Basin from 1980 to 2020 were showed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. It can be seen that the proportion of urban and rural construction land (Artificial surface) had been increasing, from less than 22% in 1980 to more than 45% in 2020, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. Meanwhile, the area and proportion of cultivated land had been declining.Compared to 2015, the area and proportion of cultivated land, forest and Artificial surface declined in 2020, while there was a remarkable increase in the proportion of grassland.\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\u003eThe change of land use in North Canal basin (Beijing) from 1980 to 2020.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProportion of Cultivated Land\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProportion of Forest\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProportion of Grassland\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eProportion of Water Body\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eProportion of Artificial Surfaces\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eProportion of Other\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1980\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e55.13%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.44%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.51%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.85%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e21.07%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e55.14%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.46%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.53%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.84%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e21.03%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43.22%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.39%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.44%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.55%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e31.40%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42.31%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.38%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.44%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.57%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e32.29%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35.99%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.04%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.44%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.24%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e39.29%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33.09%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.97%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.58%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.89%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e46.45%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.02%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29.74%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.95%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.51%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.84%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e49.94%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.02%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27.05%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.86%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.11%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.04%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e48.88%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.06%\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 \u003c/p\u003e \u003cp\u003e \u003cb\u003eInfluence of land use type on river water quality.\u003c/b\u003e Spearman correlation analysis was conducted between the water quality indexes of different water quality stations and the land use structure (area proportion) of the corresponding region from 2015 to 2020. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, all the water quality indexes showed significant correlation with cultivated land and forest land, among which, there was a significant negative correlation with forest land, and a positive correlation with cultivated land in flood season or non-flood season. In addition, the concentration of COD\u003csub\u003eMn\u003c/sub\u003e and COD were negatively correlated with water, the concentration of NH\u003csub\u003e3\u003c/sub\u003e was negatively correlated with artificial surfaces, in non-flood season. The concentration of TP was negatively correlated with artificial surfaces, and the concentration of anionic surfactants were negatively correlated with other land in flood season or non-flood season. Except TP, the correlation between other indexes and non-flood season was more significant.\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\u003eSpearman rank correlation analysis of water quality and proportion of land use type (2015\u0026ndash;2020)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePeriod\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCropland\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eForest\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGrassland\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWater\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eImpervious_\u003c/p\u003e \u003cp\u003esurface\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOthers\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\u003eCOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-flood\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.302**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.353**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.215*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.065\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFlood\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.226*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.289**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnnual\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.292**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.329**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eCOD\u003csub\u003eMn\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-flood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.339**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.307**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.209*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFlood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.270**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.273**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnnual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.325**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.307**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.088\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eBOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-flood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.354**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.335**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFlood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.298**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.272**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnnual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.321**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.335**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eNH\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-flood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.427**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.202*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.298**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFlood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.347**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.218*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.044\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnnual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.408**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.209*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.262**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eTP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-flood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.409**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.238*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.221*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFlood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.427**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.270**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnnual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.417**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.211*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.244*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eTN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-flood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.287**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.147\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFlood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.213*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.159\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnnual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.259*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.223*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.166\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAnionic surfactant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-flood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.248*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.277**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFlood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.204*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.251*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.266**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.368**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnnual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.241*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.233*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.345**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003ea,b\u003c/sup\u003en=112; \u003csup\u003ec\u003c/sup\u003en=110.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e**.The correlation was significant when the confidence (double measure) was 0.01.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e*. The correlation was significant when the confidence (double measure) was 0.05.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ePrevious studies observed that the similar relationship between land use pattern and water quality as well. Jian et al.(2011)found that the river water quality was negatively correlated with the relative area proportion of farmland all year round and positively correlated with the proportion of woodland area in non-flood season. Bahara and Yamamuro (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) reported that forest land was negatively correlated with all ions chemistry of the Shimousa Upland in Japan, farmland coverage and residential areas were positively correlated with the major ion chemistry.Forest land was rich in vegetation and the process of vegetation root absorption and soil interception can effectively reduce pollutants (Guo et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), meanwhile, human activities in this region often weak. Water quality index was positively correlated with cultivated land due to the not fully utilized fertilizer and insecticides together with runoff along with the erosion of rainfall(Wang et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). On the other hand, a high proportion of agricultural and residential land always lead to more non-point pollutants (Du et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), which was driven by surface runoff and discharged into the river network (Deng \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Influence of rainfall on the water quality\u003c/h2\u003e \u003cp\u003eNon-point source pollution caused by rainwater has become one of the important reasons for the deterioration of urban water environment. In order to investigate the influence of rainfall on water quality in Beijing, the correlation analysis between monthly mean water quality and rainfall during 2015\u0026ndash;2021 and the correlation analysis between daily mean water quality and rainfall in 2021 of ST19 were conducted. Spearman correlation analysis showed that the concentration of COD\u003csub\u003eMn\u003c/sub\u003e, NH\u003csub\u003e3\u003c/sub\u003e and TP were significantly positively correlated with rainfall in flood season, and the concentration of TN was significantly negatively related to rainfall in non-flood season (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Spearman correlation analysis between rainfall and runoff in Tongxian hydrological station from 1919 to 2020 was analyzed as well, results showed that rainfall and runoff were positively correlated with a correlation coefficient of 0.4300, indicating a significant correlation when the confidence (double measure) was 0.01 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\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\u003eSpearman correlation analysis between water quality and rainfall of ST19 from 2015 to 2021\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePeriod\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCOD\u003csub\u003eMn\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNH\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTN\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eRainfall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFlood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emonthly water quality\u003c/p\u003e \u003cp\u003eand rainfall in 2015-2021\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.495\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.444\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.188\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003edaily water quality and\u003c/p\u003e \u003cp\u003erainfall in 2021\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.358\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.686\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.500\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNon-flood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emonthly water quality\u003c/p\u003e \u003cp\u003eand rainfall in 2015-2021\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.039\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003edaily water quality and\u003c/p\u003e \u003cp\u003erainfall in 2021\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.200\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003ea\u003c/sup\u003e n\u0026thinsp;=\u0026thinsp;26; \u003csup\u003eb\u003c/sup\u003e n\u0026thinsp;=\u0026thinsp;54; \u003csup\u003eC\u003c/sup\u003e n\u0026thinsp;=\u0026thinsp;121; \u003csup\u003ed\u003c/sup\u003en=241.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e**.The correlation was significant when the confidence (double measure) was 0.01.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e*. The correlation was significant when the confidence (double measure) was 0.05.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn flood season, there were larger rainfall, runoff and water quantity.Increased precipitation and runoff during flood season could result in a greater contribution of sewage-derived organic matter (Xuan et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and non-point source pollution loads (Du et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, Ma et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Zeng et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In a short period of time, with the increase of precipitation and urban runoff, the concentration of COD\u003csub\u003eMn,\u003c/sub\u003e NH\u003csub\u003e3,\u003c/sub\u003e TP increased (as shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) in flood seasons, except TN. Previous studies indicated that nitrogen loading was more sensitive to changes in temperature than precipitation (Zhu et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Particulate-P and NH\u003csub\u003e4\u003c/sub\u003e-N were delivered primarily from overland sources and transported by runoff (Mihiranga et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). On a long time series scale (seasonal), combining other factors such as higher water temperature, longer sunshine duration, stronger aquatic biological activity and faster pollutant degradation rate, the overall water quality in flood season was better than that in non-flood season in the North Canal basin in Beijing (as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Conclusions","content":"\u003cp\u003eThis study analyzed the land use, hydrological and water quality characteristics of the North Canal basin in Beijing, and revealed the influence of land use type and rainfall conditions on water quality. Results showed that the water quality of the North Canal basin was getting better year by year from 2015 to 2021, and showed temporal and spatial variations. In general, water quality in flood season was better than that in non-flood season, and better in upstream than that in downstream. Additionally, cluster analysis shown that the 63 water quality stations were divided into three types. The influence of land use type and rainfall conditions on water quality were evaluated by Spearman correlation analysis. Results showed that the concentration of water quality indexes were significant negatively correlated with forest land, and a positive relationship between cultivated land and water quality indexes in flood season or non-flood season. In addition, the concentration of COD\u003csub\u003eMn\u003c/sub\u003e, NH\u003csub\u003e3\u003c/sub\u003e and TP were significantly positively correlated with rainfall in flood season, and the concentration of TN was negatively related to rainfall in non-flood season.The impact of land use type and rainfall on water quality in flood season and non-flood season should be noticed. This study contributes effective suggestions for implementing the refined management of water environment and the countermeasures of regional water pollution control.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXueyu Zhang contributed to work design, investigation and writing\u0026mdash;original draft,review and editing; Qianyang Wang, Qixin Wang and Boyu Lv contributed to data collection and analysis;Jingshan Yu and Juan Sun contributed to review, and editing. All authors read and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analysed during this study are included in this published article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBahara M, Yamamuro M (2008): Assessing the influence of watershed land use patterns on the major ion chemistry of river waters in the Shimousa Upland, Japan. Chemistry And Ecology 24, 341-355.\u003c/li\u003e\n\u003cli\u003eChen HT, Shi R, Wang WC (2021): Spatial Heterogeneity and Cause Analysis of Water Quality of Coastal City River System in Flood and Non-Flood Season: a Case Study of Haikou, China. Polish Journal Of Environmental Studies 30, 5493-5502.\u003c/li\u003e\n\u003cli\u003eChen ZH, Zhu ZH, Yin L, Wei S, Deng LL (2015): The Changing Water Quality Characteristics from Urban Drinking Water Sources in Guangdong, China. Water Resources Management 29, 987-1002.\u003c/li\u003e\n\u003cli\u003eDeng XJ (2019): Correlations between water quality and the structure and connectivity of the river network in the Southern Jiangsu Plain, Eastern China. Science Of the Total Environment 664, 583-594.\u003c/li\u003e\n\u003cli\u003eDu HH, Wang YM, Liu KL, Cheng L (2019): Exceedance probability of precipitation for the Shuhe to Futuan Water Transfer Project in China. Environmental Earth Sciences 78.\u003c/li\u003e\n\u003cli\u003eDu XZ, Su JJ, Li XY, Zhang WS (2016): Modeling and Evaluating of Non-Point Source Pollution in a Semi-Arid Watershed: Implications for Watershed Management. Clean-Soil Air Water 44, 247-255.\u003c/li\u003e\n\u003cli\u003eGuo YX, Fang GH, Xu YP, Tian X, Xie JK (2020): Identifying how future climate and land use/cover changes impact streamflow in Xinanjiang Basin, East China. Science Of the Total Environment 710.\u003c/li\u003e\n\u003cli\u003eHossain MS, Akter SA, Sarker S (2022): Environmental controls of plankton community dynamics in a sub-tropical river system of Bangladesh. Aquatic Ecology 56, 1271-1286.\u003c/li\u003e\n\u003cli\u003eJian HU, Maosong LIU, Wen Z, Chi XU, Xuejiao Y, Shaowei Z, Lei W (2011): Correlations between water quality and land use pattern in Taihu Lake basin. 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Environmental Science And Pollution Research 27, 35449-35458.\u003c/li\u003e\n\u003cli\u003eXuan YX, Tang CY, Liu GL, Cao YJ (2020): Carbon and nitrogen isotopic records of effects of urbanization and hydrology on particulate and sedimentary organic matter in the highly urbanized Pearl River Delta, China. Journal Of Hydrology 591.\u003c/li\u003e\n\u003cli\u003eYu ZH, Wang Q, Xu YP, Lu M, Lin ZX, Gao B (2022): Dynamic impacts of changes in river structure and connectivity on water quality under urbanization in the Yangtze River Delta plain. Ecological Indicators 135.\u003c/li\u003e\n\u003cli\u003eZeng F, Yang G, Wang P, Jiao H, Zhang T (2021): Temporal and Spatial Variation of Water Quality and Pollution Trend of Huaihe River. Journal of Hydroecology 42, 86-94.\u003c/li\u003e\n\u003cli\u003eZhai XY, Zhang YY (2018): Impact assessment of projected climate change on diffuse phosphorous loss in Xin\u0026apos;anjiang catchment, China. Environmental Science And Pollution Research 25, 4570-4583.\u003c/li\u003e\n\u003cli\u003eZhang Y, Zhang L, Sun Q, Peng Y (2021): Content characteristics of DOM components in overlying water of Beiyun River and its influence on water quality. China Environmental Science 41, 3816-3824.\u003c/li\u003e\n\u003cli\u003eZhang YH 2021: Assessment and prediction of the temporal and spatial evolution trends of water quality in the North Canal Basin. Master Thesis, Beijing University of Chemical Technology.\u003c/li\u003e\n\u003cli\u003eZhang YY, Xia J, Liang T, Shao QX (2010): Impact of Water Projects on River Flow Regimes and Water Quality in Huai River Basin. Water Resources Management 24, 889-908.\u003c/li\u003e\n\u003cli\u003eZhu XP, Chang K, Cai WJ, Zhang AR, Yue GT, Zhao XH (2022): Response of runoff and nitrogen loadings to climate and land use changes in the middle Fenhe River basin in Northern China. Journal Of Water And Climate Change.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"North Canal basin, Water quality, Land use, Rainfall","lastPublishedDoi":"10.21203/rs.3.rs-3944136/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3944136/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eInfluence of land use type and rainfall on the water quality of North Canal basin in Beijing was evaluated.The monthly monitoring data of 63 water quality stations from 2015 to 2021, the land use type data from 1980 to 2020, the rainfall and runoff data of the North Canal basin in Beijing from 1919 to 2021 were analyzed. Results showed that the water quality of the North Canal basin in Beijing was getting better year by year, and showed temporal and spatial variations. In general, water quality in flood season was better than that in non-flood season, and better in upstream than that in downstream.Cluster analysis showed that the 63 water quality stations were divided into three types. Spearman correlation analysis results clarified that the concentration of water quality indexes were significant negatively correlated with forest land, and a positive relationship between cultivated land and water quality indexes in flood season or non-flood season.In addition, the concentration of COD\u003csub\u003eMn\u003c/sub\u003e, NH\u003csub\u003e3\u003c/sub\u003e and TP were significantly positively correlated with rainfall in flood season, and the concentration of TN was negatively related to rainfall in non-flood season.This study provided important support for scientific and reasonable promotion of urban water environment management at the basin scale.\u003c/p\u003e","manuscriptTitle":"Influence of land use type and rainfall on the water quality of North Canal basin in Beijing, China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-05 08:38:30","doi":"10.21203/rs.3.rs-3944136/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2024-05-31T03:54:38+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-04-02T09:26:26+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Environmental Science and Pollution Research","date":"2024-02-29T18:55:28+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-02-22T04:42:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Science and Pollution Research","date":"2024-02-15T04:20:45+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"bc0fe899-2044-43df-956b-31db6f641930","owner":[],"postedDate":"April 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-04-05T08:38:30+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-05 08:38:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3944136","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3944136","identity":"rs-3944136","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}