Source identification of a contaminant has always been challenging for accurately modeling groundwater transport. Source identification problems are classified into several parts, such as identifying the location of contamination, the strength of contamination, the time the contaminant is introduced into the groundwater, and the duration of its activity. Identifying the sources considering all the parts as variables increases the computational complexity. Reducing the number of variables in source identification problems is necessary for a swift solution through optimization approaches. The most challenging variable in source identification modeling is the location of contamination, as it is a discrete variable for almost all the numerical solutions of groundwater models. In this research study, we have created a methodology to narrow the location of contamination from a random distribution throughout the aquifer to a reasonable number of probable locations. Although methods to identify the location of contamination were devised earlier, we have attempted an approach of combining a particle tracking approach with Bayesian method of updating the probabilities as a novel approach, where the observation data is limited. We have considered the aquifer parameters and observation well data and devised a method with a Lagrangian approach to particle movement to identify the potential source locations. We have refined the source locations to a narrower probability distribution using the Bayesian method of updating the probability through new information of refined grid space. We have tested the models to identify the potential sources with different hypothetical problems and identified the sources in advective dominant transport with an average probability of 0.53, diffusion dominant transport with an average probability of 0.62, heterogenous soils with an average probability of 0.99, anisotropic aquifer with an average probability of 0.91, and aquifer with irregular boundary with an average probability of 0.96 to identify the location nearest to the actual contaminant source. The results are satisfactory in identifying the number of potential sources with an accuracy of 88 % (15 identified out of 17 sources with a probability greater than 0.4) and their locations in the aquifer with a probability of 0.223 for exact location identification. The probability of finding a source nearest to the actual location is 0.745 at an average distance of 11.6 m from the actual source location.

Ocular surface diseases are common in the plateau city, Kunming China, the continued daily exposure to heavy metals in dust may be an important inducement. In this study, the 150 road dust samples from five functional areas in Kunming were collected. The concentrations, distribution, possible sources, and bioaccessibility of heavy metals were analyzed. The adverse effects of dust extracts on human corneal epithelial cells and the underlying mechanisms were also assessed. The concentrations (mgkg(-1)) of As (19.1), Cd (2.67), Cr (90.5), Cu (123), Pb (78.4), and Zn (389) in road dust were higher than the soil background, with commercial and residential areas showing the highest pollution. Their bioaccessibility in artificial tears was As (6.59 %) > Cu (5.11 %) > Ni (1.47 %) > Cr (1.17 %) > Mn (0.84 %) > Cd (0.76 %) > Zn (0.50 %) > Pb (0.31 %). The two main sources of heavy metals included tire and mechanical abrasion (24.5 %) and traffic exhaust (21.6 %). All dust extracts induced cytotoxicity, evidenced by stronger inhibition of cell viability, higher production of ROS, and altered mRNA expression of antioxidant enzymes and cell cycle-related genes, with commercial- areas-2 (CA2)-dust extract showing the greatest oxidative damage and cell cycle arrest. Our data may provide new evidence that dust exposure in high geological background cities could trigger human cornea damage.

Trace elements (TEs) in water are crucial parameters for assessing water quality. However, detailed studies are limited on TEs in the hydrological system of the Tibetan plateau (TP). Here, we sampled snow, river water, and groundwater in Yulong Snow Mountain (Mt. Yulong) region, southeast TP, in 2016 and analyzed the concentrations of nine TEs (namely Al, Mn, Fe, Cr, Ni, Cu, Zn, As, and Pb). In snow, the average concentrations of Fe, Zn, and Al were >10 mu g/L, whereas other elements, including Cr, Ni, Cu, As, Hg, and Pb, exhibited average concentrations <1 mu g/L. The concentrations of Al, Mn, Fe, Zn, and As were higher in rivers than in snow. According to enrichment factors (EFs), Zn concentration in snow was highly influenced by anthropogenic activities, whereas Mn, Fe, Cr, and As were uninfluenced. River and lake/reservoir water near human settlements were affected by anthropogenic activities. However, groundwater around Mt. Yulong is not contaminated yet. The increasing EFs in Mt. Yulong snowpit are consistent with those of southern TP snowpits, suggesting that the area has been affected by anthropogenic activities both from local emissions and long-distance transport of pollutants from South Asia. A conceptual model was proposed to show TEs in the water cycle. Although water quality is good overall in Mt. Yulong region, threats to the water environment still exit due to increasing anthropogenic activities and climate warming. The accelerated ablation of cryosphere due to climate warming could be a source of TEs in rivers and groundwater, which should be paid attention to in the future. (C) 2020 Elsevier B.V. All rights reserved.