Even moderate arsenic exposure may lead to health problems, and thus quantifying inorganic arsenic (iAs) exposure from food for different population groups in China is essential. By analyzing the data from the China National Nutrition and Health Survey (CNNHS) and collecting reported values of iAs in major food groups, we developed a framework of calculating average iAs daily intake for different regions of China. Based on this framework, cancer risks from iAs in food was deterministically and probabilistically quantified. The article presents estimates for health risk due to the ingestion of food products contaminated with arsenic. Both per individual and for total population estimates were obtained. For the total population, daily iAs intake is around 42 μg day(-1), and rice is the largest contributor of total iAs intake accounting for about 60%. Incremental lifetime cancer risk from food iAs intake is 106 per 100,000 for adult individuals and the median population cancer risk is 177 per 100,000 varying between regions. Population in the Southern region has a higher cancer risk than that in the Northern region and the total population. Sensitive analysis indicated that cancer slope factor, ingestion rates of rice, aquatic products and iAs concentration in rice were the most relevant variables in the model, as indicated by their higher contribution to variance of the incremental lifetime cancer risk. We conclude that rice may be the largest contributor of iAs through food route for the Chinese people. The population from the South has greater cancer risk than that from the North and the whole population.

Eutrophication is a major environmental concern in lake systems, impacting the ecological risks of contaminants and drinking water safety. It has long been believed that eutrophication and thus algal blooms would reduce methylmercury (MeHg) levels in water, as well as MeHg bioaccumulation and trophic transfer (e.g., by growth dilution). In this study, however, we demonstrated that algae settlement and decomposition after algal blooms increased MeHg levels in sediments (54-514% higher), as evidenced by the results from sediments in 10 major lakes in China. These could in turn raise concerns about enhanced trophic transfer of MeHg and deterioration of water quality after algal blooms, especially considering that 9 out of the 10 examined lakes also serve as drinking water sources. The enhanced microbial MeHg production in sediments could be explained by the algal organic matter (AOM)-enhanced abundances of microbial methylators as well as the input of algae-inhabited microbes into sediments, but not Hg speciation in sediments: (1) Several AOM components (e.g., aromatic proteins and soluble microbial by product-like material with generally low molecular weights), rather than the bulk AOM, played key roles in enhancing the abundances of microbial methylators. The copies of Archaea-hgcA methylation genes were 51-397% higher in algae-added sediments; thus, MeHg production was also higher. (2) Input of algal biomass-inhabited microbial methylators contributed to 2-21% of total Archaea-hgcA in the 10 lake sediments with added algal biomass. (3) However, AOM-induced changes in Hg speciation, with implications on Hg availability to microbial methylators, played a minor role in enhancing microbial Hg methylation in sediments as seen in X-ray absorption near edge structure (XANES) data. Our results suggest the need to better understand the biogeochemistry and risks of contaminants in eutrophic lakes, especially during the period of algae settlement and decomposition following algal blooms.

Three optimization models are proposed to select the best subset of stations from a large groundwater monitoring network: (1) one that maximizes spatial accuracy; (2) one that minimizes temporal redundancy; and (3) a model that both maximizes spatial accuracy and minimizes temporal redundancy. The proposed optimization models are solved with simulated annealing, along with an algorithm parametrization using statistical entropy. A synthetic case-study with 32 stations is used to compare results of the proposed models when a subset of 17 stations are to be chosen. The first model tends to distribute the stations evenly in space; the second model clusters stations in areas of higher temporal variability; and results of the third model provide a compromise between the first two, i.e., spatial distributions that are less regular in space, but also less clustered. The inclusion of both temporal and spatial information in the optimization model, as embodied in the third model, contributes to selection of the mos...