Drinking water sources, as important carriers of drinking water resources, are closely related to human health(Huang et al., 2024; Huang et al., 2023). The World Health Organization has confirmed that 80% of human diseases are closely related to drinking water pollution(Maoyong and Guibin, 2020), which causes over 5 million deaths annually(Cabral, 2010). Surface drinking water sources are highly susceptible to human activities due to their poor fluidity and long static residence time(Wu et al., 2023), exhibiting poor stability and obvious fragility(Huang, Guo, Xie, Bian, Wang, Qi and Liu, 2023). Although the safety guarantee of drinking water sources has been highly valued by various countries, with the acceleration of urbanization and industrialization, a large number of pollutants are discharged into environmental water bodies(Huang et al., 2018), leading to problems such as eutrophication and heavy metal pollution in many regions where drinking water sources are located worldwide(Chowdhury et al., 2016; Wang et al., 2019).
Eutrophic elements such as nitrogen and phosphorus in water bodies can have serious impacts on aquatic ecology. High concentrations of nitrogen exacerbate aquatic biological diseases(Camargo and Alonso, 2006), which in turn pose a risk to human health through the food chain. Compared to other pollutants, the exposure level of heavy metals in drinking water directly affects human health, and can easily lead to neurological, cardiovascular, visceral, and bone diseases(Planchart et al., 2018; Zhao et al., 2020a), and in severe cases, even death. Due to different physiological characteristics and individual differences, different age groups such as adults and children face varying degrees of threat to heavy metal exposure(Qu et al., 2012). Therefore, the Monte Carlo uncertainty analysis model is introduced(Liu et al., 2023a) to evaluate the different risks of heavy metals to adults and children. This model generates random numbers for iterative calculation based on the health risk calculation formula, and expresses them in the form of probability distribution to improve the accuracy of evaluation(Liu et al., 2023b).
The sources of heavy metals in water are complex, both natural and anthropogenic sources contribute to the accumulation of water trace elements through both direct and indirect processes. Natural sources determine the background value of local heavy metals. Intense human activities generally bring additional heavy metal enrichment and environmental pollution(Islam et al., 2018; Zhang and Rickaby, 2020). Accurate heavy metal source analysis is crucial for preventing and controlling possible heavy metal pollution in water.
Multivariate statistical analysis, including correlation analysis, principal componentanalysis, and and Positive matrix factorization (PMF), has been broadly applied in research on the correlations between pollution severity and pollutant sources(Li et al., 2022a; Zhang et al., 2019). These methods do not require explicit information on the propagation processes and source composition of the emission factors, the data can be collected easily(Fei et al., 2020). Previous studies have indicated that the main factors influencing heterogeneity of heavy metals vary in different regions(Marrugo-Negrete et al., 2017). The relationship between the spatial distribution of heavy metals and environmental factors is generally ignored.
In contrast, the statistical method of Geodetector does not require linearity and is not affected by co-linear effects from multiple variables(Jinfeng and Chengdong, 2017). This model assumes that, once the independent variable affects the dependent variable, the spatial distribution changes in response, creating a correlation, and allow the model to quantitatively determine how each factor affects the spatial heterogeneity of trace elements in water. Its influencing factors can autonomously choose between natural factors (such as DEM, Precipitation, etc.) and human factors (such as Population Density, GDP, etc.), and can quantify the impact of the interaction between natural and human factors on heavy metals. The method measures the contribution of each factor in a more intuitive way and more rapidly(Qiao et al., 2019). To date, Geodetector has been widely applied to individual factors and their interactions that affect environmental issues such as soils(Tao et al., 2020), ecological vulnerability(Dai et al., 2021), and heavy metals research(Huang et al., 2021). Few studies have applied geographic detectors to the source apportionment of heavy metals in water. The reason for this is that individual rivers and lakes are not well suited for this model due to their narrow widths, small spatial spans and low spatial heterogeneity. However, the drinking water source areas in this study are distributed over a wide range of the study area, and there is significant spatial heterogeneity between the locations of each water source area, making it suitable for Geodetector.
According to studies, the contribution from anthropogenic sources has come to exceed that of natural sources to become the major contributor to heavy metal accumulation in water, especially in developing countries with high population densities and abundant industrial activities(Chowdhury, Mazumder, Al-Attas and Husain, 2016; Li et al., 2022b). Studies on drinking water source areas also shows that population density is higher(Yang et al., 2016), and pollution is more severe in areas with dense factories(Bai et al., 2016). However, the results of this study using Geodetector show that the dominant factors for certain metals are natural factors such as Precipitation and DEM. The influence of natural factors cannot be ignored or even become the dominant factor in the contamination of certain heavy metals. The detection results of interaction show that the interaction between natural factors and human factors significantly improved the explanatory power of heavy metals. However, such factors are more severely affected by the emissions of heavy metals from anthropogenic activities into the environment. Drinking water sources are better protected than ordinary surface water, and human activities can directly affect certain heavy metals in drinking water sources, as well as improve the interpretation of heavy metals through natural activities such as precipitation and elevation. Therefore, more than the investigation of a single source of pollution, the study of the effects of interactions between factors is also essential.
The Huaihe River basin is one of the most densely populated areas in China(Zhongqing et al., 2023), and the drinking water sources in the region support 1/6 of the country's population(Zihang et al., 2023). 98.2% of the study area belongs to the upper Huaihe River Basin(Yong, 2022), which is responsible for transporting nearly 10 billion cubic meters of water to the middle and lower reaches of the Huaihe River every year(Zihang, Juanjuan and Yu, 2023). Compared to the middle and lower reaches of the Huai River, the level of urbanization in the upper basin has increased almost exponentially since 2008(Kai-fang et al., 2022; Qin et al., 2019). Based on the above discussion, the objectives of this study are to (1) analyze the water quality, eutrophication status, and spatial distribution of drinking water sources in the upper reaches of the Huaihe River Basin, (2) evaluate the carcinogenic and non-carcinogenic risks of five heavy metals for adults and children based on the Monte Carlo model, and (3) quantify the influence of each factor on heavy metals and reveal the main sources of heavy metals. The results of this study indicated that the geodetector can be a helpful method for us to understand the factors affecting the distribution patterns of heavy metals in water, and help provide a universal result for the pollution sources of drinking water sources worldwide, and provide useful information for the prevention and control strategies of water pollution.