Variations In River Water Quality Research Articles

MAPPING THE SPATIAL VARIATION OF RIVERS WATER QUALITY USING MULTIVARIATE ANALYSIS. A CASE STUDY OF GREATER JAKARTA, INDONESIA

Urban activities and runoff deteriorated the river water. Aim: This study aims to determine the primary contamination and mapping river's water quality variation in Greater Jakarta. Methodology and Results: Multivariate analysis was employed by Factor Analysis and Cluster Analysis (FA and CA) using the 12 parameters of the water quality dataset from 76 sampling stations in 19 rivers in the Greater Jakarta Region, Indonesia, between 2014 and 2015. The FA result showed that almost 66.6% of the total variance in water quality data was associated with four significant variators of which 36.12% identified the main parameters BOD, COD, TP, ammonia, nitrate, and detergent. The cluster analysis grouped the sampling locations into 3 (three) clusters to indicate the spatial classifications of river water. Cluster 1 indicates pollutants from the residential area, Cluster 2 from residential and commercial areas, while Cluster 3 is majorly pollutants from industrial areas. Conclusion, significance, and impact study: The results showed the conductivity and TDS concentrations in cluster 3 are higher than the others since most of the sampling points are located near an industrial area or downstream close to the estuary. The mapping gave a better understanding of the river water quality characteristic spatially and could assist local governments in prioritizing river pollution management.

River water quality variation and grade characteristic values analysis method based on “hydrological frequency-water quality” fitting curves

River water quality variation and grade characteristic values analysis method based on “hydrological frequency-water quality” fitting curves

Investigation of long‐term river water quality variations using different urbanization indices and assessment of common scientific perspectives of urbanization on water quality

AbstractThis study investigated the water quality variation spanning 30 years (1986–2017) in 16 catchments of Hong Kong against different urbanization indices, namely, built area fraction; population; and product of population and built area fraction. Pearson correlations of three different periods of time (1988–1990, 1998–2000, and 2015–2017) indicated that water quality trends were dependent on the urbanization index. Total solids, nitrite‐nitrogen, total phosphorus, electrical conductivity, dissolved oxygen, and flow rate had significant deteriorative trends (Pearson r > 0.5 and p < 0.05) with population and product of built area and population. Results also interpreted that built area fraction and product of built area and population were the worst and best indices that represented urbanization and/or its impacts, respectively. Mann‐Kendall test for the entire 30 year period showed that water quality had improved with time with respect to certain water quality parameters (e.g., dissolved oxygen, ammoniacal nitrogen and total suspended solids). The results portrayed that although the urbanization of catchments had increased with time, the river water quality with respect to many parameters showed signs of improvement and the legislative measures implemented seemed to be effective in controlling pollution.

The quantification of the influencing factors for spatial and temporal variations in surface water quality in recent ten years of the Huaihe River Basin, China.

Temporal and spatial variations in river water quality and the factors influencing such variations are important basis and prerequisites for identifying pollution sources and improving river water environment. Monthly data for 22 indicators at 485 surface water quality (SWQ) monitoring sites (46,560 groups) in the Huaihe River Basin (HRB) from 2011 to 2018 were analyzed. This paper assessed temporal and spatial changes in SWQ in the HRB and identified the main factors influencing the changes and each factor's contribution to the changes. The five-day biological oxygen demand, permanganate index, fluoride, ammonium nitrogen, and total phosphorus were the main pollutants. Spatial cluster analysis indicated that the HRB could be divided by SWQ into areas I-IV from light to heavy pollution. Areas I and IV were nitrogen and phosphorus nutrients pollution, and areas II and III were heavy metals and organic pollution. Area IV (poor SWQ) locates mainly north of the Huaihe mainstream with annual average rainfall ≤ 640mm. SWQ in the HRB has been improving for two decades, with an inflection point in 2015 between 2011 and 2018, and rainfall change is an important factor for the inflection point. The urbanization rate, industrial water consumption, and rainfall were the key factors influencing SWQ changes in the watershed with significant hydrological zonation, with urbanization rate and rainfall increased, industrial water consumption decreased, the SWQ was gradually improved. The key factors contributing to SWQ changes in the future will be the sewage treatment rate and rainfall changes caused by natural variations.

Analysis of spatio-temporal variations of river water quality and construction of a novel cost-effective assessment model: a case study in Hong Kong.

Assessment of river water quality has been attracting a great deal of attention because of its important implications for the living environment of human beings and aquatic organisms. River water quality is commonly assessed using dozens of different water quality parameters. However, different parameters may contain redundant information, which could lead to the waste of monitoring efforts. Thus, this study constructed a novel cost-effective assessment model of river water quality using the 1-year monitoring data collected from 23 sampling stations in the water control zone of Tolo Harbour and Channel in Hong Kong. First, the spatio-temporal variations of water quality parameters and the overall status of river water quality were analyzed based on all 19 parameters using Kruskal-Wallis test, hierarchical cluster analysis, and the water quality index (WQI). The results indicated that most water quality parameters and overall water quality status varied significantly over space, but did not exhibit obvious seasonal differences; and 99.27% of water samples were identified to be in good or excellent status of overall WQI. Then, using principal component analysis (PCA)/factor analysis (FA) and Pearson's correlation analysis, eight parameters, including 5-day biochemical oxygen demand (BOD5), chemical oxygen demand (COD), ammonia-nitrogen (NH3-N), nitrate-nitrogen (NO3-N), chlorophyll-a (Chl-a), fluoride (F-), total suspended solids (TSS), and arsenic (As), were verified to be responsible for the greatest contributions to water quality, the assessment of overall water quality status. These eight crucial parameters were further employed to establish six cost-effective water quality assessment models. Using the overall WQI as the benchmark, the results of linear regression analysis demonstrated that the cost-effective model constructed based on BOD5, COD, NH3-N, NO3-N, F-, TSS, and As were the optimal water quality assessment model, which can achieve the most reliable results with reduced parameters.

Spatial and temporal variations of river water quality using multivariate statistical techniques

Spatial and temporal variations of river water quality using multivariate statistical techniques

Water quality variability affected by landscape patterns and the associated temporal observation scales in the rapidly urbanizing watershed

Landscape patterns are a major factor affecting river water quality variations. However, the related temporal scaling effects of water quality variations and associated responses to landscape have not been fully explored in rapidly urbanizing watersheds. In this study, we explored the event and seasonal water quality variations by multi-section monitoring, and annual water quality variations using model simulation. Results showed that spatial heterogeneity of river water quality varied from event to annual scales. Rainfall showed greater impacts on event water quality variations (changed by 155.20%–183.70% after the rainfall) than seasonal variations (changed by 35.32%–92.25% from dry to wet season). Nutrients varied more significantly than other pollutants, such as chemical oxygen demand and suspended solids. Compared to annual scale, landscape pattern showed a more significant correlation with event and seasonal variations of water quality. Besides, grassland and forest might change from sink to source landscapes as observation scales changed from season and annum to event. Fragmentation (diversity) metrics showed negative (positive) relationships with event and seasonal water quality variations. However, the impacts of these landscape metrics would become heterogeneous for annual water quality variations. These results highlighted the consideration of multi-temporal studies and provided useful suggestions for urban water quality protection.

Evaluation of spatio-temporal variations in water quality and suitability of an ecologically critical urban river employing water quality index and multivariate statistical approaches: A study on Shitalakhya river, Bangladesh

The aim of this study was to investigate the spatio-seasonal variations in water quality and suitability of the Shitalakhya river, an economically important and ecologically critical urban river in Bangladesh, along with associated influencing factors and possible sources of water pollution. Therefore, surface water samples were collected monthly from five sampling sites, and fourteen water quality parameters were evaluated. The results showed that some studied water quality parameters, e.g., temperature, TDS, TA, TH, NO2 –, and NO3 –, exceeded the maximum allowable limit, whereas statistically significant (p < .05) variations were observed among pre-monsoon (February–May), monsoon (June–September), and post-monsoon (October–January) seasons. The values of water quality index (WQI) exhibited that the water quality was found to be very poor to unsuitable for drinking, fisheries, or aquatic environment. The principal component analysis (PCA) extracted two PCs explaining 91.092% of the total variance, which suggested that the variations in water quality are attributed mainly to point and nonpoint sources of contamination including municipal and industrial wastewater discharge, and agricultural runoff of inorganic fertilizers. The cluster analysis (CA) also showed relative spatial and seasonal variations in river water quality, indicating the influence of hydrological changes and pollution sources. The study revealed that the water of the Shitalakhya river is highly polluted and potentially hazardous for human uses, and thus more attention should be given to safeguard such an important urban river.

Spatial and temporal variations in river water quality of the Middle Ganga Basin using unsupervised machine learning techniques.

In this study, cluster analysis (CA), principal component analysis (PCA) and correlation were applied to access the river water quality status and to understand spatiotemporal patterns in the Ganga River Basin, Uttara Pradesh. The study was carried out using data collected over 12years (2005-2017) regarding 20 water quality parameters (WQPs) covering spatially from upstream to downstream Ankinghat to Chopan, respectively (20 stations under CWC Middle Ganga Basin). The temporal variations of river water quality were established using the Spearman non-parametric correlation coefficient test (Spearman R). The highest Spearman R (-0.866) was observed for temperature with the season and a very significant p value of (0.0000). The parameters EC, pH, TDS, T, Ca, Cl, HCO3, Mg, NO2 + NO3, SiO2 and DO had a significant correlation with the season (p < 0. 05). K-means clustering algorithm grouped the stations into four different clusters in dry and wet seasons. Based on these clusters, box and whisker plots were generated to study individual clusters in different seasons. The spatial patterns of river WQ on both seasons were examined. PCA was applied to screen out the most significant water quality parameters due to spatial and seasonal variations out of a large data set. It is a data reduction process and a more conventional way of speeding up any machine learning algorithms. A reduced number of three principal components (PCs) were drawn for 20 WQPs with an explained total variance of 75.84% and 80.57% is observed in the dry and wet season, respectively. The parameters DO, EC_ Gen, P-Tot, SO4 are the most dominating parameters with PC score more than 0.8 in the dry season; similarly, TDS, K, COD, Cl, Na, SiO2 in the wet season. The different components of water quality monitoring, such as spatiotemporal patterns, scrutinize the most relevant water quality parameters and monitoring stations are well addressed in this study and could be used for the better management of the Ganga River Basin.

Assessment of spatial and temporal variations in water quality by the application of multivariate statistical methods in the Kali River, Uttar Pradesh, India.

The Kali River is a significant source of surface water as well as the main tributary of River Hindon that flows through major cities of western Uttar Pradesh, India. It flows throughout the urban and industrial regions; hence, it carries various amounts of pollutant. Therefore, a study was conducted to examine spatial-temporal variations in river water quality by determining physicochemical variables and heavy metal concentrations at seventeen sampling stations (S1-S17) throughout the river stretch. Various physicochemical variables, namely pH, EC, TDS, turbidity, BOD, COD, TH, TA, Ca, Mg, Na, K, HCO3-, Cl-, SO42-, NO3-, and PO43- were higher in summer than in winter. The order of mean metal concentrations was Fe > Pb > Mn > Ni > Zn > Cu > Cr > Cd. The relationships among measured physicochemical variables and pollution index were examined. Furthermore, multivariate statistical methods were used to assess spatial-temporal variation in water quality to identify current pollution sources and validate results. Water quality index and comprehensive pollution index indicated that the Kali River was less polluted from S1 to S8. However, downstream sampling sites were polluted. Pollution starts from S9 and drastically increases at and beyond S13 because of effluents from industries and sugar mills in Muzaffarnagar. The study suggests cleaning the downstream region of river to restore human health and flora and fauna in the river ecosystem.

Analysis of spatiotemporal variation in river water quality using clustering techniques: a case study in the Yeongsan River, Republic of Korea.

Herein, cluster analysis was applied to evaluate the spatiotemporal variations in water quality variables of a river. The analysis was performed using the data obtained from 15 monitoring stations during 2007-2018 in the Yeongsan River, Republic of Korea. The spatiotemporal analysis successfully clustered the annual water quality variables temporally into years of poor water quality (2007-2012) and good water quality (2013-2018), and spatially into stations observing bad water quality (midstream) and good water quality (upstream and downstream). For the spatial cluster analysis results before and after a large river engineering project, the water quality was grouped into four clusters according to regional effects and water pollutant sources. The clustering analysis results clearly reflected changes in the water quality along the river due to the project. Overall, this study demonstrates that cluster analysis can be effectively used for evaluating spatiotemporal variations in river water quality.

Assessment of spatiotemporal variations in river water quality for sustainable environmental and recreational management in the highly urbanized Danshui River basin.

Rivers are an important urban resource, and water quality influences the use of river water. Thus, analyzing spatiotemporal variations in river water quality is crucial for sustainable use and management of water resources in a highly urbanized region. This study employed river pollution index (RPI) data obtained in 2013 to assess spatiotemporal variations in river water quality for sustainable environmental and recreational management in the highly urbanized Danshui River basin. First, ordinary kriging was adopted to analyze monthly RPI distributions. Subsequently, different percentiles of monthly estimated RPI distributions were probabilistically determined at a river segment. Finally, three measurement methods of local uncertainty, namely-conditional variance, local entropy, and interquartile range-were used to characterize spatiotemporal variations in river water quality in the Danshui River basin. Assessment results revealed that more highly polluted river water quality resulted in higher seasonal variations. Moreover, high and very high seasonal variations were mainly concentrated in urban river segments, whereas low and very low seasonal variations were primarily located in upstream river segments. Thus, to achieve sustainable development goals, artificial wetlands should be established at downstream and midstream urban riverbanks and urban recreational activities should be developed in upstream riverbank parks in the Danshui River basin before the comprehensive improvement of river water quality.

Assessment and interpretation of river water quality in Little Akaki River using multivariate statistical techniques

Indiscriminant waste disposal is limiting the usability of the Little Akaki River in Addis Ababa, Ethiopia. Besides, there are inadequate comprehensive studies on the river principally due to insufficient research fund. Therefore, in this study, water quality investigation that is both regular and economical is sought. In October and November 2015, twenty-seven locations were sampled from the river and tributaries. Multivariate statistical tools were engaged to investigate data from measurements and laboratory analysis. Consequently, cluster analysis divided sampled sites into three according to level of their pollution. This indicates that water quality variation was caused because of the difference in land-use conditions. In addition, for the spatial analysis of the three pollution groups, backward stepwise approach of discriminant analysis was identified to provide data reduction (87.5%) to two parameters resulting in 85.2% correct assignment. The principal component analysis/factor analysis identified ten parameters accounting for 81.9% of total variation. However, data reduction was not significant. The factors that were latent and identified from the principal components’ varimax rotation suggest that variation in water quality was caused mainly by domestic sewage. The outcomes show that the methods can be applied to evaluate the river water quality variation using three monitoring sites and ten parameters: total nitrogen, total suspended solids, total ammonia, chemical oxygen demand, nitrite, total phosphorus, phosphate, nitrate, biological oxygen demand and electrical conductivity. This, in consequence, requires lesser cost and effort and hence paves way for more affordable, regular water quality evaluation of Little Akaki River.

A data-mining framework for exploring the multi-relation between fish species and water quality through self-organizing map.

The steep slopes of rivers can easily lead to large variations in river water quality during typhoon seasons in Taiwan, which may poses significant impacts on riverine eco-hydrological environments. This study aims to investigate the relationship between fish communities and water quality by using artificial neural networks (ANNs) for comprehending the upstream eco-hydrological system in northern Taiwan. We collected a total of 276 heterogeneous datasets with 8 water quality parameters and 25 fish species from 10 sampling sites. The self-organizing feature map (SOM) was used to cluster, analyze and visualize the heterogeneous datasets. Furthermore, the structuring index (SI) was adopted to determine the relative importance of each input variable of the SOM and identify the indicator factors. The clustering results showed that the SOM could suitably reflect the spatial characteristics of fishery sampling sites. Besides, the patterns of water quality parameters and fish species could be distinguishably (visually) classified into three eco-water quality groups: 1) typical upstream freshwater fishes that depended the most on dissolved oxygen (DO); 2) typical middle-lower reach riverine freshwater fishes that depended the most on total phosphorus (TP) and ammonia nitrogen; and 3) low lands or pond (reservoirs) freshwater fishes that depended the most on water temperature, suspended solids and chemical oxygen demand. According to the results of the SI, the representative indicators of water quality parameters and fish species consisted of DO, TP and Onychostoma barbatulum. This grouping result suggested that the methodology can be used as a guiding reference to comprehensively relate ecology to water quality. Our methods offer a cost-effective alternative to more traditional methods for identifying key water quality factors relating to fish species. In addition, visualizing the constructed topological maps of the SOM could produce detailed inter-relation between water quality and the fish species of stream habitat units.

Unravel biophysical factors on river water quality response in Chilean Central-Southern watersheds.

Identifying the key anthropogenic (land uses) and natural (topography and climate) biophysical drivers affecting river water quality is essential for efficient management of water resources. We tested the hypothesis that water quality can be predicted by different biophysical factors. Multivariate statistics based on a geographical information system (GIS) were used to explore the influence of factors (i.e., precipitation, topography, and land uses) on water quality (i.e., nitrate (NO 3 (-)), phosphate (PO 4 (3-)), silicate (Si(OH)4), dissolved oxygen (DO), suspended solids (TSS), biological oxygen demand (DO), temperature (T), conductivity (EC), and pH) for two consecutive years in the Itata and Biobío river watersheds, Central Chile (36° 00' and 38° 30'). The results showed that (NO 3 (-)), (PO 4 (3-)), Si(OH)4, TSS, EC, and DO were higher during rainy season (austral fall, winter, and spring), whereas BOD and temperature were higher during dry season. The spatial variation of these parameters in both watersheds was related to land use, topography (e.g., soil moisture, soil hydrological group, and erodability), and precipitation. Soil hydrological group and soil moisture were the strongest explanatory predictors for PO 4 (3-) , Si(OH)4 and EC in the river, followed by land use such as agriculture for NO 3 (-) and DO and silviculture for TSS and Si(OH)4. High-resolution water leaching and runoff maps allowed us to identify agriculture areas with major probability of water leaching and higher probability of runoff in silviculture areas. Moreover, redundancy analysis (RDA) revealed that land uses (agriculture and silviculture) explained in 60 % the river water quality variation. Our finding highlights the vulnerability of Chilean river waters to different biophysical drivers, rather than climate conditions alone, which is amplified by human-induced degradation.

Spatio-Temporal Patterns and Source Identification of Water Pollution in Lake Taihu (China)

Various multivariate methods were used to analyze datasets of river water quality for 11 variables measured at 20 different sites surrounding Lake Taihu from 2006 to 2010 (13,200 observations), to determine temporal and spatial variations in river water quality and to identify potential pollution sources. Hierarchical cluster analysis (CA) grouped the 12 months into two periods (May to November, December to the next April) and the 20 sampling sites into two groups (A and B) based on similarities in river water quality characteristics. Discriminant analysis (DA) was important in data reduction because it used only three variables (water temperature, dissolved oxygen (DO) and five-day biochemical oxygen demand (BOD5)) to correctly assign about 94% of the cases and five variables (petroleum, volatile phenol, dissolved oxygen, ammonium nitrogen and total phosphorus) to correctly assign &gt;88.6% of the cases. In addition, principal component analysis (PCA) identified four potential pollution sources for Clusters A and B: industrial source (chemical-related, petroleum-related or N-related), domestic source, combination of point and non-point sources and natural source. The Cluster A area received more industrial and domestic pollution-related agricultural runoff, whereas Cluster B was mainly influenced by the combination of point and non-point sources. The results imply that comprehensive analysis by using multiple methods could be more effective for facilitating effective management for the Lake Taihu Watershed in the future.

Differentiating the Spatiotemporal Distribution of Natural and Anthropogenic Processes on River Water–Quality Variation Using a Self-Organizing Map With Factor Analysis

To elucidate the historical improvement and advanced measure of river water quality in the Taipei metropolitan area, this study applied the self-organizing map (SOM) technique with factor analysis (FA) to differentiate the spatiotemporal distribution of natural and anthropogenic processes on river water-quality variation spanning two decades. The SOM clustered river water quality into five groups: very low pollution, low pollution, moderate pollution, high pollution, and very high pollution. FA was then used to extract four latent factors that dominated water quality from 1991 to 2011 including three anthropogenic process factors (organic, industrial, and copper pollution) and one natural process factor [suspended solids (SS) pollution]. The SOM revealed that the water quality improved substantially over time. However, the downstream river water quality was still classified as high pollution because of an increase in anthropogenic activity. FA showed the spatiotemporal pattern of each factor score decreasing over time, but the organic pollution factor downstream of the Tamsui River, as well as the SS factor scores in the upstream major tributary (the Dahan Stream), remained within the high pollution level. Therefore, we suggest that public sewage-treatment plants should be upgraded from their current secondary biological processing to advanced treatment processing. The conservation of water and soil must also be reinforced to decrease the SS loading of the Dahan Stream from natural erosion processes in the future.

Combined multivariate statistical techniques, Water Pollution Index (WPI) and Daniel Trend Test methods to evaluate temporal and spatial variations and trends of water quality at Shanchong River in the Northwest Basin of Lake Fuxian, China.

Understanding spatial and temporal variations in river water quality and quantitatively evaluating the trend of changes are important in order to study and efficiently manage water resources. In this study, an analysis of Water Pollution Index (WPI), Daniel Trend Test, Cluster Analysis and Discriminant Analysis are applied as an integrated approach to quantitatively explore the spatial and temporal variations and the latent sources of water pollution in the Shanchong River basin, Northwest Basin of Lake Fuxian, China. We group all field surveys into 2 clusters (dry season and rainy season). Moreover, 14 sampling sites have been grouped into 3 clusters for the rainy season (highly polluted, moderately polluted and less polluted sites) and 2 clusters for the dry season (highly polluted and less polluted sites) based on their similarities and the level of pollution during the two seasons. The results show that the main trend of pollution was aggravated during the transition from the dry to the rainy season. The Water Pollution Index of Total Nitrogen is the highest of all pollution parameters, whereas the Chemical Oxygen Demand (Chromium) is the lowest. Our results also show that the main sources of pollution are farming activities alongside the Shanchong River, soil erosion and fish culture at Shanchong River reservoir area and domestic sewage from scattered rural residential area. Our results suggest that strategies to prevent water pollutionat the Shanchong River basin need to focus on non-point pollution control by employing appropriate fertilizer formulas in farming, and take the measures of soil and water conservation at Shanchong reservoir area, and purifying sewage from scattered villages.

Evaluation of river water quality variations using multivariate statistical techniques: Sava River (Croatia): a case study.

For the evaluation of seasonal and spatial variations and the interpretation of a large and complex water quality dataset obtained during a 7-year monitoring program of the Sava River in Croatia, different multivariate statistical techniques were applied in this study. Basic statistical properties and correlations of 18 water quality parameters (variables) measured at 18 sampling sites (a total of 56,952 values) were examined. Correlations between air temperature and some water quality parameters were found in agreement with the previous studies of relationship between climatic and hydrological parameters. Principal component analysis (PCA) was used to explore the most important factors determining the spatiotemporal dynamics of the Sava River. PCA has determined a reduced number of seven principal components that explain over 75 % of the data set variance. The results revealed that parameters related to temperature and organic pollutants (CODMn and TSS) were the most important parameters contributing to water quality variation. PCA analysis of seasonal subsets confirmed this result and showed that the importance of parameters is changing from season to season. PCA of the four seasonal data subsets yielded six PCs with eigenvalues greater than one explaining 73.6 % (spring), 71.4 % (summer), 70.3 % (autumn), and 71.3 % (winter) of the total variance. To check the influence of the outliers in the data set whose distribution strongly deviates from the normal one, in addition to standard principal component analysis algorithm, two robust estimates of covariance matrix were calculated and subjected to PCA. PCA in both cases yielded seven principal components explaining 75 % of the total variance, and the results do not differ significantly from the results obtained by the standard PCA algorithm. With the implementation of robust PCA algorithm, it is demonstrated that the usage of standard algorithm is justified for data sets with small numbers of missing data, nondetects, and outliers (less than 4 %). The clustering procedure highlighted four different groups in which the sampling sites have similar characteristics and pollution levels. The first and the second group correspond to relatively low and moderately polluted sites while stations which are located in the middle of the river belong to the third and fourth group and correspond to highly and moderately polluted sites.

Effects of land use, topography and socio-economic factors on river water quality in a mountainous watershed with intensive agricultural production in East china.

Understanding the primary effects of anthropogenic activities and natural factors on river water quality is important in the study and efficient management of water resources. In this study, analysis of Variance (ANOVA), Principal component analysis (PCA), Pearson correlations, Multiple regression analysis (MRA) and Redundancy analysis (RDA) were applied as an integrated approach in a GIS environment to explore the temporal and spatial variations in river water quality and to estimate the influence of watershed land use, topography and socio-economic factors on river water quality based on 3 years of water quality monitoring data for the Cao-E River system. The statistical analysis revealed that TN, pH and temperature were generally higher in the rainy season, whereas BOD5, DO and turbidity were higher in the dry season. Spatial variations in river water quality were related to numerous anthropogenic and natural factors. Urban land use was found to be the most important explanatory variable for BOD5, CODMn, TN, DN, NH4 +-N, NO3 −-N, DO, pH and TP. The animal husbandry output per capita was an important predictor of TP and turbidity, and the gross domestic product per capita largely determined spatial variations in EC. The remaining unexplained variance was related to other factors, such as topography. Our results suggested that pollution control of animal waste discharge in rural settlements, agricultural runoff in cropland, industrial production pollution and domestic pollution in urban and industrial areas were important within the Cao-E River basin. Moreover, the percentage of the total overall river water quality variance explained by an individual variable and/or all environmental variables (according to RDA) can assist in quantitatively identifying the primary factors that control pollution at the watershed scale.