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Due to the lack of monitoring systems and water purification facilities, residents in western China may face the risk of drinking water pollution. Therefore, 673 samples were collected from Lhasa's agricultural and pastoral areas to reveal the status quo of drinking water. We used inductively coupled plasma-mass spectrometry to determine trace elements concentrations for water quality appraisal, source apportionment, and health risk assessment. The results indicate that concentrations of V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Cd, Ba, and Pb are below the guidelines, while As concentrations in a few samples exceed the standard. All samples were classified into "excellent water" for drinking purpose based on Entropy-weighted water quality index. Thereafter by principal component analysis, three potential sources of trace elements were extracted, including natural, anthropogenic, and mining activities. It is worth noting that geotherm and mining exploitation does not threaten drinking water safety. Finally, health risks were assessed using Monte Carlo technique. We found that the 95th percentiles of hazard index are 1.80, 0.80, and 0.79 for children, teenagers, and adults, indicating a non-carcinogenic risk for children, but no risks for the latter two age groups. In contrast, the probabilities of unacceptable cautionary risk are 7.15, 2.95 and 0.69% through exposure to Cr, Ni, As, and Cd for adults, children, and teenagers. Sensitivity analyses reveal As concentration and ingestion rate are most influential factors to health risk. Hence, local governments should pay more attention to monitoring and removal of As in the drinking water. [ABSTRACT FROM AUTHOR]

A multivariate statistical approach integrating the absolute principal components score (APCS) and multivariate linear regression (APCS-MLR), along with structural equation modeling (SEM), was used to model the influence of water chemistry variables on chlorophyll a (Chl a) in Lake Qilu, a severely polluted lake in southwestern China. Water quality was surveyed monthly from 2000 to 2005. APCS-MLR was used to identify key water chemistry variables, mine data for SEM, and predict Chl a. Seven principal components (PCs) were determined as eigenvalues >1, which explained 68.67% of the original variance. Four PCs were selected to predict Chl a using APCS-MLR. The results showed a good fit between the observed data and modeled values, with R 2 =0.80. For SEM, Chl a and eight variables were used: NH4-N (ammonia-nitrogen), total phosphorus (TP), Secchi disc depth (SD), cyanide (CN), arsenic (As), cadmium (Cd), fluoride (F), and temperature (T). A conceptual model was established to describe the relationships among the water chemistry variables and Chl a. Four latent variables were also introduced: physical factors, nutrients, toxic substances, and phytoplankton. In general, the SEM demonstrated good agreement between the sample covariance matrix of observed variables and the model-implied covariance matrix. Among the water chemistry factors, T and TP had the greatest positive influence on Chl a, whereas SD had the largest negative influence. These results will help researchers and decision-makers to better understand the influence of water chemistry on phytoplankton and to manage eutrophication adaptively in Lake Qilu. [Copyright &y& Elsevier]

Urban air quality is subject to the increasing pressure of urbanization, and, consequently, the potential impact of air quality changes must be addressed. A Bayesian hierarchical model was developed in this paper for urban air quality predication. Literature data on three pollutants and four external driving factors in Xiamen City, China, were studied. The air quality model structure and prior distributions of model parameters were determined by multivariate statistical methods, including correlation analysis, classification and regression trees (CART), hierarchical cluster analysis (CA), and discriminant analysis (DA). A multiple linear regression (MLR) equation was proposed to measure the relationship between pollutant concentrations and driving variables; and Bayesian hierarchical model was introduced for parameters estimation and uncertainty analysis. Model fit between the observed data and the modeled values was demonstrated, with mean and median values and two credible levels (2.5% and 97.5%). The average relative errors between the observed data and the mean values of SO2, NO x , and dust fall were 6.81%, 6.79%, and 3.52%, respectively. [Copyright &y& Elsevier]