Cytochrome P450 enzymes (CYPs) play an essential role in the bio-transformation of polychlorinated biphenyls (PCBs). The present work implemented quantum mechanic/molecular mechanic methods (QM/MM) and density functional theory (DFT) to study the metabolic activation of 2,2′,3,3′,6,6′-hexachlorobiphenyl (PCB136) catalyzed by CYP2B6. Electrophilic additions at the Cα and Cβ positions generate different active intermediates. The electrophilic addition energy barrier of Cβ is 10.9 kcal/mol higher than that of Cα, and Cα is the preferred site for the electrophilic addition reaction. Based on the previous experimental studies, this work investigated the mechanism of converting active intermediates into OH-PCB136, which has high toxicity in a non-enzymatic environment. Structural analysis via the electrostatic and noncovalent interactions indicates that Phe108, Ile114, Phe115, Phe206, Phe297, Ala298, Leu363, Val367, TIP32475 and TIP32667 play crucial roles in substrate recognition and metabolism. The analysis suggests that the halogen-π interactions are important factors for the metabolism of CYP2B6 to halogenated environmental pollutants. This work improved the understanding of the metabolism and activation process of chiral PCBs, and can be used as a guide to improve the microbial degradation efficiency of PCB136.Soil salinization resulting from shallow saline groundwater is a major global environmental issue causing land degradation, especially in semi-arid regions such as Australia. The adverse impact of shallow saline groundwater on soil salinization varies in space and time due to the variation in groundwater levels and salt concentration. Understanding the spatio-temporal variation is therefore vital to develop an effective salinity management strategy. In New South Wales, Australia, a hydrogeological landscape unit approach is generally applied, based on spatial information and expert operators, classifying the landscape in relation to landscape and climate. In this paper, a data science approach (random forest model) is introduced, based on historical groundwater quality and quantity data providing predictions in a 4-dimensional space. As a case study, we demonstrate the spatio-temporal factors impacting standing water levels (SWL) and associated salinity and predict the spatial and temporal variability in the Muttama catchment (1059 km2), in NSW, south eastern Australia. The random forest model explains 77% of the variance in the groundwater salinity (electrical conductivity) and 65% of the SWL. Spatial factors were the most significant variables determining the space-time variation in groundwater salinity and the occurrence of groundwater at the surface. Drilled piezometer depth and elevation are dominant factors controlling SWL, while salinity is mainly determined by underlying geology. The methodology in this study predicts salinity and SWL in the landscape at fine scales, through time, improving options for salinity management.Natural zeolite clinoptilolite was used as the primary ammonium removal method from the permeate of an anaerobic membrane bioreactor (AnMBR) treating high-strength blackwater generated from a community toilet facility. This zeolite-based nutrient capture system (NCS) was a sub-component of a non-sewered sanitation system (NSSS) called the NEWgenerator and was field tested for 1.5 years at an informal settlement in South Africa. The NCS was operated for three consecutive loading cycles, each lasting 291, 110, and 52 days, respectively. Both blackwater (from toilets) and blackwater with yellow water (from toilets and urinals) were treated during the field trial. Over the three cycles, the NCS was able to remove 80 ± 28%, 64 ± 23%, and 94 ± 11%, respectively, of the influent ammonium. The addition of yellow water caused the rapid exhaustion of zeolite and the observed decrease of ammonium removal during Cycle 2. After Cycles 1 and 2, onsite regeneration was performed to recover the sorption capacity of the spent zeolite. The regenerant was comprised of NaCl under alkaline conditions and was operated as a recycle-batch to reduce the generation of regenerant waste. Modifications to the second regeneration process, including an increase in regenerant contact time from 15 to 30 h, improved the zeolite regeneration efficiency from 76 ± 0.7% to 96 ± 1.0%. The mass of recoverable ammonium in the regenerant was 2.63 kg NH4-N and 3.15 kg NH4-N after Regeneration 1 and 2, respectively. However, the mass of ammonium in the regenerant accounted for only 52.8% and 54.4% of the estimated NH4-N originally sorbed onto the zeolite beds after Cycles 1 and 2, respectively. The use of zeolite clinoptilolite is a feasible method for ammonium removal by NSSS that observe variable nitrogen loading rates, but further research is still needed to recover the nitrogen from the regenerant waste.We made the first and successful attempt to detect SARS-CoV-2 genetic material in the vicinity wastewaters of an isolation centre i.e. Shaheed Bhulu Stadium, situated at Noakhali, Southeastern Bangladesh. PD-1 inhibitor Owing to the fact that isolation centre, in general, always contained a constant number of 200 COVID-19 patients, the prime objective of the study was to check if several drains carrying RNA of coronavirus are actually getting diluted or accumulated along with the sewage network. Our finding suggested that while the temporal variation of the genetic load decreased in small drains over the span of 50 days, the main sewer exhibited accumulation of SARS-CoV-2 RNA. Other interesting finding displays that probably distance of sampling location in meters is not likely to have a significant impact on the detected gene concentration, although the quantity of the RNA extracted in the downstream of the drain was higher. These findings are of immense value from the perspective of wastewater surveillance of COVID-19, as they largely imply that we do not need to monitor every wastewater system, and probably major drains monitoring may illustrate the city health. Perhaps, we are reporting the accumulation of SARS-CoV-2 genetic material along with the sewer network i.e. from primary to tertiary drains. The study sought further data collection in this line to simulate conditions prevailed in most of the developing countries and to shed further light on decay/accumulation processes of the genetic load of the SARS-COV-2.