Reconstructing the release history of a contaminant source with different precision via the ensemble smoother with multiple data assimilation.

Identifying a contaminant time-varying release history is an ill-posed problem but crucial for groundwater contamination issues. A precise inversed release history offers a promising estimation of contaminant movement and is of great importance for environmental monitoring and further management. In this paper, a recent emerging data assimilation method, the ensemble smoother with multiple data assimilation (ES-MDA) is employed to handle this conundrum. The study starts with some synthetic cases in which several factors are analyzed, such as the observation data frequency, covariance inflation schemes, iteration numbers used in the ES-MDA for the purpose of identifying a time-varying contaminant injection event with different precision. The results show that the ES-MDA performs well in recovering the release history when the injection is discretized into 50 or 100-time steps but encounters fluctuation problems in the cases with 300-time steps. Further comparison reveals that the observation data frequency is a very influential factor, while the number of iterations or the kind of covariance inflation used has a lesser effect. Nevertheless, this is a first test in a non-synthetic environment, in which the ES-MDA has proven its ability to recover the release history in two close-to-reality sandbox experiments. The outcome shows that the ES-MDA with Rafiee's inflation scheme has the ability to capture the main pattern of the release history. But in order to move one more step to field cases, a more detailed description of uncertainties or elaborated parameterization of the time functions is paramount.

Heavy Metals in the Mainstream Water of the Yangtze River Downstream: Distribution, Sources and Health Risk Assessment.

Since the mainstream of the Yangtze River lower reach is an important drinking water source for residents alongside it, it is essential to investigate the concentration, distribution characteristics and health risks of heavy metals in the water. In this study, a total of 110 water samples were collected on both the left and right banks from the upstream to the downstream. Principal component analysis (PCA) was used to determine the sources of heavy metals. Their non-carcinogenic and carcinogenic risks were studied with health risk assessment models, and uncertainties were determined through Monte Carlo simulation. Results showed that concentrations of all heavy metals were significantly lower than the relevant authoritative standards in the studied area. From the upstream to the downstream, Ni, Cu and Cr had similar concentration distribution rules and mainly originated from human industrial activities. Pb, Cd and Zn had a fluctuating but increasing trend, which was mainly due to the primary geochemistry, traffic pollution and agricultural activities. The maximum As concentration appeared in the upstream mainly because of the carbonatite weathering or mine tail water discharge. Concentrations of Zn, As, Cd and Pb on the left bank were higher than those on the right bank, while concentrations of Cu, Ni and Cr on the right bank were higher than those on the left bank. The non-carcinogenic risk index (HI) was less than 1 (except of L11), and HI on the left bank was higher than that on the right bank. The carcinogenic risk (CR) was generally larger than 1.0 × 10-4, CR on the right bank overall was higher than that on the left bank, and the health risk of kids was greater than that of adults. Furthermore, Monte Carlo simulation results and the actual calculated values were basically the same.

Determining landscape-level drivers of variability for over fifty soil chemical elements.

Syntheses of large datasets have allowed increased clarity of distribution patterns and variation in soil major and trace elements. However, the drivers of variation in topsoil elements across biogeographical scales are not well understood. Our aim was to (1) identify how landscape-scale climate, geographical features, and edaphic factors influence soil elements, and (2) determine key environmental thresholds for shifts in soil element concentration. We analyzed patterns of variation in topsoil elements using 9830 samples collected across 39,000km2 in subtropical land in southeast China. Canonical correlations and multiple linear regressions were used to model variations of each element across mean annual temperature (MAT), mean annual precipitation (MAP), land use, spatial topography, and soil pH. Element concentrations show significant latitudinal and longitudinal trends, and are significantly influenced by climate, land use, spatial topography, and soil pH. Longitude, pH, MAT, and MAP were the environmental factors most tightly correlated with element concentrations. Climate and soil pH drove positive or negative alterations in soil elements, with threshold indicators of MAP=1000mm/1500mm, MAT=17.8°C/18.0°C, and pH=5.8/5.0, respectively. Our results indicate topsoil elements have structural and functional thresholds of climate and soil pH in relatively wet and acidic environments. Our findings can facilitate holistic soil element concentration predictions and help elucidate the specific influences of climate and soil pH, enabling development of more complete biogeochemical models.