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One of the biggest challenges in characterizing clandestine groundwater pollution sources is correctly estimating the number of such sources and their physical locations. Indiscreet underground disposal of toxic waste leading to groundwater pollution is widespread, and it is extremely difficult to detect the number of such sources and their locations. In this study, a real-life scenario of groundwater pollution in suburban New South Wales, Australia, is solved using a simulated annealing (SA)–based linked simulation optimization (LSO) technique with Benzene, Toluene, Ethylbenzene and Xylene (BTEX). The developed LSO considers the number of pollutant sources and their locations as explicit unknowns. This overcomes a crucial limitation in the earlier methods that limited the search to a few known potential source locations. The results show the applicability of the developed methodology to real-life groundwater pollution problems without making limiting assumptions about the number of sources or potential source locations being known, which has not been addressed to date. The developed methodology can potentially improve the process of remediation of polluted groundwater aquifers by simultaneously identifying the number of pollution source locations and release flux histories. The results of the study can be used to assist remediation engineers in developing an appropriate reclamation approach for a polluted aquifer, where knowledge of groundwater pollutant source characteristics is vital. The suggested method will aid in holding polluters accountable, contributing to predictions about the fate and movement of pollution, and developing a strategy for monitoring compliance. When implementing the results from the modeling technique to real-life scenarios of groundwater pollution, the equifinality of the solution cannot be entirely ruled out. The accuracy of the results is governed by assumptions and simplifications made in the model to simulate the aquifer response. Even the model calibration is dependent on the availability of data, which may be noisy and erroneous, resulting in nonunique solutions. [ABSTRACT FROM AUTHOR]

Groundwater pollution is the biggest threat to sustainability of groundwater resources and even more difficult to detect in case of clandestine sources. At the time when pollution is first detected in randomly located sparse wells, very little is known about the pollution sources. Finding the precise locations of clandestine sources of pollution and their release flux history is the biggest challenge and often termed as a problem belonging to the class of environmental forensics. In this study, two linked simulation optimization–based novel techniques are developed to estimate locations and release flux history from clandestine point sources of groundwater pollution. Simulation model is clubbed with optimization solver to determine the locations and release flux histories of groundwater pollution sources by minimizing the residual error between observed and simulated concentration values. Simulated annealing (SA) and particle swarm optimization (PSO) are used as optimization algorithms. A detailed comparative analysis of these two meta-heuristic optimization algorithms in minimizing the residual error is presented in this study. The performance evaluation of both the algorithms in identifying the sources locations and release flux history is carried out for two synthetic cases and a real-life scenario of groundwater pollution in an aquifer in New South Wales, Australia, which has not been attempted in the past. The results of source location identification and release flux history show the selective applicability of each algorithm in solving real-life scenarios of groundwater pollution. [ABSTRACT FROM AUTHOR]

The World Health Organization Global Action Plan recommends integrated surveillance programs as crucial strategies for monitoring antibiotic resistance. Although several national surveillance programs are in place for clinical and veterinary settings, no such schemes exist for monitoring antibiotic-resistant bacteria in the environment. In this transnational study, we developed, validated, and tested a low-cost surveillance and easy to implement approach to evaluate antibiotic resistance in wastewater treatment plants (WWTPs) by targeting cefotaxime-resistant (CTX-R) coliforms as indicators. The rationale for this approach was: i) coliform quantification methods are internationally accepted as indicators of fecal contamination in recreational waters and are therefore routinely applied in analytical labs; ii) CTX-R coliforms are clinically relevant, associated with extended-spectrum β-lactamases (ESBLs), and are rare in pristine environments. We analyzed 57 WWTPs in 22 countries across Europe, Asia, Africa, Australia, and North America. CTX-R coliforms were ubiquitous in raw sewage and their relative abundance varied significantly (<0.1% to 38.3%), being positively correlated (p < 0.001) with regional atmospheric temperatures. Although most WWTPs removed large proportions of CTX-R coliforms, loads over 10 3 colony-forming units per mL were occasionally observed in final effluents. We demonstrate that CTX-R coliform monitoring is a feasible and affordable approach to assess wastewater antibiotic resistance status., (Copyright © 2020 The Authors. Published by Elsevier Ltd.. All rights reserved.)