In this study, we present a method for identifying sources of water pollution and their relative contributions in pollution disasters. The method uses a combination of principal component analysis and factor analysis. We carried out a case study in three rural villages close to Beijing after torrential rain on July 21, 2012. Nine water samples were analyzed for eight parameters, namely turbidity, total hardness, total dissolved solids, sulfates, chlorides, nitrates, total bacterial count, and total coliform groups. All of the samples showed different degrees of pollution, and most were unsuitable for drinking water as concentrations of various parameters exceeded recommended thresholds. Principal component analysis and factor analysis showed that two factors, the degree of mineralization and agricultural runoff, and flood entrainment, explained 82.50% of the total variance. The case study demonstrates that this method is useful for evaluating and interpreting large, complex water-quality data sets.

The potential contaminations of 16 trace elements (Cr, Mn, Ni, Cu, Zn, As, Cd, Sb, Ba, Pb, Co, Be, V, Ti, Tl, Al) in drinking water collected in two remote areas in China were analyzed. The average levels of the trace elements were lower than the allowable concentrations set by national agencies, except for several elements (As, Sb, Mn, and Be) in individual samples. A health risk assessment model was conducted and carcinogenic and non-carcinogenic risks were evaluated separately. The results indicated that the total carcinogenic risks were higher than the maximum allowed risk level set by most organizations (1 × 10(-6)). Residents in both study areas were at risk of carcinogenic effects from exposure to Cr, which accounted for 80-90 % of the total carcinogenic risks. The non-carcinogenic risks (Cu, Zn, Ni) were lower than the maximum allowance levels. Among the four population groups, infants incurred the highest health risks and required special attention. Correlation analysis revealed significant positive associations among most trace elements, indicating the likelihood of a common source. The results of probabilistic health risk assessment of Cr based on Monte-Carlo simulation revealed that the uncertainty of system parameters does not affect the decision making of pollution prevention and control. Sensitivity analysis revealed that ingestion rate of water and concentration of Cr showed relatively high sensitivity to the health risks.

Self-supplied wells, an important water resource in remote and scattered regions, are commonly deteriorated by environmental pollution and human activity. In this study, 156 self-supplied well-water samples were collected from remote and scattered areas of Inner Mongolia (NMG), Heilongjiang (HLJ), and the suburbs of Beijing (BJ) in Northern China. Twenty-four heavy metals were identified by using the inductively coupled plasma-mass spectrometry (ICP-MS) and inductively coupled plasma-optical emission spectrometry (ICP-OES), and the associated human health risks were assessed by using standards of the US Environmental Protection Agency (US EPA). The concentrations of four heavy metals (As, Fe, Mn, and Tl) in HLJ, one heavy metal (Tl) in BJ, and ten heavy metals (Al, As, B, Cr, Fe, Mn, Mo, Se, Tl, and Zn) in NMG exceeded the limits set by China or the World Health Organization (WHO). The total carcinogenic risk (TCR) and total non-carcinogenic risk (THQ) exceeding set limits mainly occurred in NMG, compared to HLJ and BJ. Moreover, As accounted for 97.87% and 60.06% of the TCR in HLJ and BJ, respectively, while Cr accounted for 70.83% of the TCR in NMG. The TCR caused by Cd in all three areas had a negligible hazard (<10−4). As accounted for 51.11%, 32.96%, and 40.88% of the THQ in HLJ, BJ, and NMG, respectively. According to the results of the principal component analysis, heavy metals in well water from HLJ and NMG mainly originated from mixed natural processes and anthropogenic sources, whereas, in BJ, most heavy metals probably originated from natural sources. In the future, long-term monitoring of heavy metals in water from self-supplied wells should be conducted for an extensive range of well-water sites, and well water with high As contamination should be monitored more and fully assessed before being used as a drinking-water source.

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