Sea-level rise and arsenic-rich soils: A toxic relationship.

In the United States, dangerously high arsenic (As) levels have been found in drinking water wells in more than 25 states, potentially exposing 2.1 million people to drinking water high in As; a known carcinogen. The anticipated sea-level rise (SLR) is expected to alter soil biogeochemical and hydrological conditions, potentially impacting their ability to sequester As. In our study of coastal Wilmington, DE, an area projected to experience a 1 -meter SLR by 2100, we examined the spatial distribution, speciation, and release possibilities of As due to SLR. To understand the complex dynamics at play, we employed a comprehensive approach, including bulk and micro X-ray absorption spectroscopy measurements, hydrological pattern evaluation, and macroscopic stirred-flow experiments. Our results suggest that introducing reducing and saline conditions can increase As release in both river water and seawater inundation scenarios, most likely due to ionic competition and the dissolution of As-bearing Fe/Mn oxides. Regardless of the salinity source, the released As concentrations consistently exceeded the EPA threshold for drinking water. Our results provide valuable insights for developing appropriate remedial and management strategies for this site and numerous others facing similar environmental challenges. ENVIRONMENTAL IMPLICATION: With nearly two hundred million individuals living within coastal flood plains and with two million square kilometers of land and one trillion dollars' worth of assets lying less than 1 m above current sea level, sea-level rise (SLR) is one of the significant socio-economic threats associated with global warming. Arsenic is a prevalent contaminant in coastal areas impacted by industrial activities, many of which are susceptible to being impacted by SLR. This study examines SLR's impact on arsenic fate and speciation in a densely populated coastline in Wilmington, DE, expecting 1 meter of SLR by 2100.

Sea-level-rise-induced flooding drives arsenic release from coastal sediments.

Sea-level rise (SLR) has a vital influence on coastal hydrogeological systems, biogeochemical processes, and the fate of coastal contaminants. However, the effects of SLR-induced perturbations on the mobilization of coastal pollutants are not fully understood. In this study, the impact of SLR-induced flooding on the concentration and speciation of arsenic and selected hazardous chemicals is investigated using exceedingly contaminated sediments (5-6% As) collected from an urban coastal site in Wilmington, DE, USA. The release of contaminants from sediments was monitored before, during, and after flooding with different intensities (bottom shear stresses) through laboratory-based erosion chamber experiments. Significantly increased release of As (up to 150%) and NO3 (up to 50%) from sediments at shear stress levels typically measured in estuaries were found. The release of toxic chemicals from contaminated coastal sediments is thus not restricted to extreme flooding events but can occur throughout the year. The results also suggest that the dissolved concentrations of pollutants continue to be considerably high even after the flooding. SLR-induced flooding can hence increase the release of contaminants not only during erosion events but over longer timescales. The release mechanism proposed here contributes to improving the risk assessment of coastal water pollution as climate change and SLR continue to occur.

Estimating the chemical oxygen demand of petrochemical wastewater treatment plants using linear and nonlinear statistical models - A case study.

In this research, twelve linear and nonlinear regression models were performed and evaluated to formulate the best one for the estimation of chemical oxygen demand level in the effluent of the clarifier unit of a petrochemical wastewater treatment plant. The input variables measured twice a day in the influent of the biological unit over a period of 13 months using standard methods. The piece-wise linear regression with breakpoint method, with a mean squared error value equal to 0.041, mean absolute error of 0.144, and correlation coefficient equal to 0.835 was found to estimate the output chemical oxygen demand parameter more sustainable rather than other linear and nonlinear methods. However, some of the other applied models such as radial basis function neural network and gene expressing programming models achieved good performance considering their correlation coefficient, robustness in presence of outliers, mean squared error and mean absolute error test. Mathematical and intelligent modeling proved useful as an accurate alternative to estimate the amount of chemical oxygen demand rather than spending time and cost for its laboratory tests.