Date Published: June 5, 2019
Publisher: Public Library of Science
Author(s): Qinghui You, Na Fang, Lingling Liu, Wenjing Yang, Li Zhang, Yeqiao Wang, Rodolfo Nóbrega.
The deterioration of water quality has become a primary environmental concern worldwide. Understanding the status of water quality and identifying the influencing factors are important for water resources management. However, reported analyses have mostly been conducted in small and focused areas. It is still unclear if factors driving spatial variation in water quality would be different in extended spatial scales. In this paper, we analyzed spatial pattern of inland surface water quality in China using a dataset with four water quality parameters (i.e., pH, DO, NH4+-N and CODMn) and the water quality level. We tested the effects of anthropogenic (i.e., land use and socio-economic) and natural (i.e., climatic and topographic) factors on spatial variation in water quality. The study concluded that the overall inland surface water quality in China was at level III (fair). Water quality level was strongly correlated with CODMn and NH4+-N concentration. In contrast to reported studies that suggested land use patterns were the determinants of inland surface water quality, this study revealed that both anthropogenic and natural factors played important roles in explaining spatial variation of inland surface water quality in China. Among the tested explanatory variables, mean elevation within watershed appeared as the best predictor for pH, while annual precipitation and mean air temperature were the most important explanatory variables for CODMn and DO, respectively. NH4+-N concentration and water quality level were most strongly correlated with the percent of forest cover in watershed. Compared to studies at smaller spatial scales, this study found different influencing factors of surface water quality, suggesting that factors may play different roles at different spatial scales of consideration. Therefore management policies and measures in water quality control must be established and implemented accordingly. Since currently adopted parameters for monitoring of inland surface water quality in China are largely influenced by natural variables, additional physicochemical and biological indicators are needed for a robust assessment of human impacts on water quality.
Inland surface water areas include different forms of open water bodies such as rivers and streams, lakes and reservoirs, permanent and seasonal wetlands. The deterioration of surface water quality has become a primary environmental concern worldwide, following the increasing demand of high-quality freshwater . Inland surface water quality is considered to be influenced by a wide range of anthropogenic and natural factors, such as land use, social-economic status, topographic and climate variations [2, 3]. Understanding the status of surface water quality and identifying the key influencing factors are important for establishing policies for sustainable water resource management.
This study showed that variables of land use composition had significant relationships with NH4+-N, CODMn and water quality level, whereas their relationships with pH and DO were relatively weak (Tables 3 and 4). Forest was negatively correlated with NH4+-N, CODMn and water quality level, while farmland and built-up land were positively correlated with them. This observation is consistent with most of reported studies [7, 11] and thus supports H1. In contrast with previous studies indicating either farmland  or built-up land  as the primary land use predictor for surface water quality, this study found that forest had stronger explanatory power than farmland and built-up land, and was the most important predictor in the multivariate models for NH4+-N and water quality level (Table 4). This may be because higher percent of forest means less human-influenced land cover and therefore less potential sources of pollution. Moreover, forest helps maintain high water quality through minimizing soil erosion, thus reducing sediment in water bodies, and through trapping or filtering other water pollutants. In addition, densely growing plants in forest can absorb and concentrate pollutants (e.g., nitrogen and phosphorus) from water, while highly diversified microbial communities in surface litter, debris and organically enriched soil can degrade the pollutants efficiently.
Analysis of field-based weekly published monitoring data showed that the overall inland surface water quality in China was rated as level III (fair). More monitoring sites in watersheds of southern China indicated level II (good), while multiple sites in Songhua River watershed in northeastern China and Huai River watershed between Yellow and Yangtze Rivers in eastern China showed the level IV (poor) and even worse level V (very poor). Water quality level was mainly determined by CODMn and NH4+-N, suggesting that organic matter and nitrogen were the major types of pollutants in China’s inland surface water. The main explanatory variables varied considerably for the tested water quality parameters and water quality level. Among the tested explanatory variables, elevation was the most important explanatory variable for pH, while air temperature and precipitation were the strongest predictors for DO and CODMn, respectively. NH4+-N concentration and water quality level were most strongly correlated with the percent of forest cover in watersheds that was highly inversely proportional to the percent of human-influenced land cover. In general, mountainous forested areas with high precipitation showed better water quality than lowland areas with high percent of human-influenced landscape. Compared to studies carried out at local spatial scales, this study found different influencing factors of surface water quality, suggesting that factors may play different roles at different spatial scales. Therefore management policies and measures in water quality control must be established and implemented accordingly. Since currently adopted parameters for monitoring of inland surface water quality in China are influenced by climatic and topographic variables, additional physicochemical and biological indicators are needed for a robust assessment of human impacts on water quality. For example, total nitrogen and phosphorus, and the content of pathogenic bacteria in surface water are considered to be mainly influenced by agricultural and domestic pollution [7, 20], and can be included as additional indicators for surface water quality.