Effects of Land Use, Topography and Socio-Economic Factors on River Water Quality in a Mountainous Watershed with Intensive Agricultural Production in East China

Abstract

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.

Impacts of land use and population density on seasonal surface water quality using a modified geographically weighted regression

“…The positive and negative relationships between AG and TN, TP and NH 4 + -N in GWR analysis (Figs.4, 5 and 8) showed divergence from our OLS results (Table 2) and other studies using traditional global statistical methods (e.g., Seeboonruang, 2012;Wang et al, 2014;Wan et al, 2014;Chen and Lu, 2014). In these studies, AG had a strong positive effect on a b water pollution indicators because agricultural activities, such as fertilizer and pesticide application and livestock farming, are often the main non-point pollution source.…”

“…As an important regulator of pollutants in overland flow and interflow, land use/land cover has become a critical research topic for elucidating the relationship between surface water quality and non-point source pollutants. Several statistical methods are widely used in these studies, such as correlation analysis, cluster analysis, principal component analysis, linear regression models, linear mixed effects models and exponential models Wan et al, 2014;Chen and Lu, 2014;Seeboonruang, 2012;Ahearn et al, 2005;Madrinan et al, 2012). Previous results often show that land-use types closely related to human activities, such as agriculture and urban, were positively correlated with river pollution indicators (e.g., nitrogen, phosphorus, ammonia), while woodlands and grasslands that were less affected by human activities had negative correlations.…”

Impacts of Land Use on Surface Water Quality in a Subtropical River Basin: A Case Study of the Dongjiang River Basin, Southeastern China

“…(2) Are there seasonal differences in the impacts of land use on water quality? (3) What are the implications of our findings for managing and mitigating pollutant loadings?…”

Understanding Snake Bite Cases Pattern Related to Volcano-Seismic Activity: An Evidence in Bondowoso, Indonesia

“…The record of SBC showed that all of the cases occurred near the watersheds (figure 3). Watershed is an important component for the rice field, and the water quality is strongly affected the socio-economic condition of society living near the area [22]. Therefore, most of the agricultural and the settlement area are located near the watershed.…”