One of the fundamental sustainable development goals has been recognized as having access to clean water for drinking purposes. In the Anthropocene era, rapid urbanization put further stress on water resources, and associated groundwater contamination expanded into a significant global environmental issue. Natural arsenic and related water pollution have already caused a burden issue on groundwater vulnerability and corresponding health hazard in and around the Ganges delta. A field based hydrogeochemical analysis has been carried out in the elevated arsenic prone areas of moribund Ganges delta, West Bengal, a part of western Ganga- Brahmaputra delta (GBD). New data driven heuristic algorithms are rarely used in groundwater vulnerability studies, specifically not yet used in the elevated arsenic prone areas of Ganges delta, India. Therefore, in the current study, emphasis has been given on integration of heuristic algorithms and random forest (RF) i.e., “RF-particle swarm optimization (PSO)”, “RF-grey wolf optimizer (GWO)” and “RF-grasshopper optimization algorithm (GOA)”, to identify groundwater vulnerable zones on the basis of field based hydrogeochemical parameters. In addition, correspondence health hazard of this area was assessed through human health hazard index. The spatial distribution of groundwater vulnerability revealed that middle-eastern and north-western part of the study area covered by very high and high, whereas central, western and south-western part are covered by very low and low vulnerability zones in outcomes of all the applied models. The evaluation result indicates that RF-GOA (AUC = 0.911) model performed the best considering testing dataset, and thereafter RF-GWO, RF-PSO and RF with AUC value is 0.901, 0.892 and 0.812 respectively. Findings also revealed the groundwater in this study region is quite unfavorable for drinking and irrigation purposes. The suggested models demonstrate their usefulness in foretelling sustainable groundwater resource management in various deltaic regions of the world through taking appropriate measures by policy-makers.

Impacts of toxic substances from oil production in the Alberta oil sands (AOS), such as polycyclic aromatic hydrocarbons (PAHs), have been widely debated. Studies have been largely restricted to exposures from surface mining in aquatic species. We measured PAHs in Woodland caribou (Rangifer tarandus caribou), moose (Alces americanus), and Grey wolf (Canis lupus) across three areas that varied in magnitude of in situ oil production. Our results suggest a distinction of PAH level and source profile (petro/pyrogenic) between study areas and species. Caribou samples indicated pyrogenic sourced PAHs in the study area previously devastated by forest fire. Moose and wolf samples from the high oil production area demonstrated PAH ratios indicative of a petrogenic source and increased PAHs, respectively. These findings emphasize the importance of broadening monitoring and research programs in the AOS.

Climate change intensifies, so does the need to reduce carbon emissions to achieve the goal of being “carbon neutral” for China. This paper focuses on carbon emission prediction and constructs a comprehensive model integrating least absolute shrinkage and selection operator (LASSO), principal component analysis (PCA), support vector regression (SVR), and differential evolution-gray wolf optimization (DE-GWO). Firstly, LASSO is used for feature selection, and important information is extracted from various influencing factors to find out what have a great impact on carbon emission. Principal component analysis is used to extract the features of the remaining variables to avoid missing information caused by feature selection. Secondly, DE-GWO algorithm is used to optimize the parameters of SVR to improve the prediction accuracy. The scenario analysis and prediction algorithm are combined to predict China’s carbon emissions. The results show that (1) coal and oil consumption, plate glass, pig iron, and crude steel production are important factors affecting carbon emissions; (2) the use of PCA to comprehensively consider the impact of remaining factors on carbon emissions has a positive influence on carbon emissions prediction; and (3) DE-GWO optimized SVR has higher prediction accuracy than other algorithms.

In recent years, proton exchange membrane fuel cells (PEMFCs) have been known to be a viable method for meeting the electrical energy needs, thereby enhancing the overall reliability of renewable energy systems. PEMFCs demonstrate various promising attributes like pollution-free, totally sustainable, non-self-discharging. These need hydrogen as fuel, and air for their operation, while the final product is pure water only. Thus, under varying operating conditions, the appropriate modeling and parameter optimization of PEMFCs have gained considerable importance in recent times. The evolutionary optimization approaches had been utilized in recent past for estimating PEMFCs parameters as exact modeling of the same does not exist in the literature. For the evaluation of PEMFCs performance criteria, a newly proposed algorithm is developed in this manuscript i.e. black widow optimization (BWO). Firstly, the performance of this proposed algorithm is checked by complex benchmark results. After that, this proposed algorithm is applied to extract the parameters of PEMFCs models under different operating temperatures. The parameter optimization results are obtained using BWO and are further compared with those obtained with five other algorithms, i.e., particle swarm optimization (PSO), multi-verse optimizer (MVO), sine cosine algorithm (SCA), whale optimization algorithm (WOA), and grey wolf optimization (GWO). The complete error analysis is carried out for the two data sheets of the PEMFCs to establish the superiority of BWO. It has been observed that the developed proposed algorithm gives better results when compared to those obtained with rest of the algorithms considered in this work. After calculating the error, non-parametric test is performed which suggests that the BWO is better than the rest of the compared algorithms.

Persistent organic pollutants (POPs) constitute a wide range of chemicals. Their release into the environment has raised great concern due to their potentially harmful impact in humans and wildlife species. The aim of this current study was to detect selected POPs in hair samples of wild terrestrial mammals from Primorsky Krai, Russia, so as to assess potential environmental exposure. The tested wild species were leopard cat, musk deer, wolf, amur hedgehog, and raccoon dog. The targeted organochlorines were hexachlorobenzene (HCB) and DDTs (opDDE, ppDDE, and opDDD), polychlorinated biphenyl (PCB) congeners (28, 52, 101, 118, 138, 153, and 180), and polycyclic aromatic hydrocarbons (PAHs) (acenaphylene (ACEN), fluorene (FLU), anthracene (ANTH) phenathrene (PHEN), and pyrene (PYR)). The detection of POPs was conducted in hair samples by a one-step hair extraction method, by using a headspace solid-phase microextraction technique (HS-SPME) and analyzed then by GC-MS. The majority of the wild animal hair samples were found positive in all tested pollutants. More specifically, the percentage of positive hair samples for HCB was 93.3% and for DDTs, PCBs, and PAHs, 20.0 to 100.0%, 6.7 to 100.0%, and 75.0 to 100.0%, respectively. DDT, PCB, and PAH detection ranged from 1.26 to 52.06 pg mg⁻¹, 0.73 to 31.34 pg mg⁻¹, and 2.59 to 35.00 pg mg⁻¹, respectively. The highest mean concentration levels of all tested pollutants were found for musk deer (PCBs 12.41 pg mg⁻¹, DDTs 21.87 pg mg⁻¹, PAHs 22.12 pg mg⁻¹) compared to the other wild species. To the best of our knowledge, this is the first study that provides results regarding contamination in different terrestrial mammals by POP exposure. The use of hair as a matrix is proven to be an effective tool for nondestructive biological monitoring of POP contamination in terrestrial ecosystems.

A study was carried out to determine the incidents of illegal poisoning of griffon vulture (Gyps fulvus), Egyptian vulture (Neophron percnopterus), black vulture (Aegypius monachus), and bearded vulture (Gypaetus barbatus) in the southern Balkan Peninsula between 1982 and 2017. A total of 38 poisoning cases affecting 224 vultures were analyzed to identify their causes and the primary target species for poisoning. Nine different compounds were used in these incidents and the most frequently applied were strychnine, carbamate, and organophosphoros compounds. The poison used to kill gray wolf had the most significant collateral damage to the vulture populations in comparison to the other investigated reasons. It was the primary cause of 60% of all registered vulture poisoning events in the southern Balkan Peninsula during the last 36 years. Establishing permanent feeding sites for vultures in areas with wolves appears to be an effective way to minimize the risk of poisoning. There is a pressing need for the development of an appropriate conservation practice taking into consideration relationships among the main and casual target species for poisoning as an essential element in conjunction with the human activities.

Top-predators can be important components of resilient ecosystems, but they are still controlled in many places to mitigate a variety of economic, environmental and/or social impacts. Lethal control is often achieved through the broad-scale application of poisoned baits. Understanding the direct and indirect effects of such lethal control on subsequent movements and behaviour of survivors is an important pre-requisite for interpreting the efficacy and ecological outcomes of top-predator control. In this study, we use GPS tracking collars to investigate the fine-scale and short-term movements of dingoes (Canis lupus dingo and other wild dogs) in response to a routine poison-baiting program as an example of how a common, social top-predator can respond (behaviourally) to moderate levels of population reduction. We found no consistent control-induced differences in home range size or location, daily distance travelled, speed of travel, temporal activity patterns or road/trail usage for the seven surviving dingoes we monitored immediately before and after a typical lethal control event. These data suggest that the spatial behaviour of surviving dingoes was not altered in ways likely to affect their detectability, and if control-induced changes in dingoes' ecological function did occur, these may not be related to altered spatial behaviour or movement patterns.