Insect pest control is one of the major issues facing the agriculture sector because of the need for new agrochemicals and biocontrol agents that are environmentally friendly, economically affordable, and safe for human health. Spodoptera frugiperda (fall armyworm) is one of the insect pests that causes huge damage to various crops around the globe due to its generalist nature. In the present study, three selected Embelia ribes Burm F (Myrsinaceae) constituents, which include embelin, 5-O-methylembelin, and vilangin; one semi-synthetic compound (potassium embelate); three synthetic compounds, namely coenzyme Q10, dopaquinone, and idebenone; and two reference compounds (azadirachtin and amitraz) were assessed on the docking behavior of S. frugiperda (beta glycosidase and caspase-1. The docking studies showed that coenzyme Q10 exhibited the highest binding energies (-130.61 and -434.56 kcal.mol-1) for the target enzymes S. frugiperda (beta glycosidase and caspase-1, respectively). Thus, the present investigation provides new knowledge in understanding Embelia ribes Burm F (Myrsinaceae) constituents as possible inhibitors against S. frugiperda (beta glycosidase and caspase-1) enzyme activities. Furthermore, the present work can help to develop new insecticides and pesticides against S. frugiperda and other related insect pests.

Free trade causes damage to the environment substantially as it puts pressure on natural resources in its venture to expand economic activities. On the other hand, there is also an argument that free trade has the potential to improve environmental quality by contributing to growth. Such growth enhancement may help individuals, organizations, or governments raise funds and spend more on environmental protection. This paper highlights the links between trade and the environment. Besides, it also shows how international trade and environmental protection are both essential to the well-being of humanity and, conversely, how they are mutually supportive. This paper also scrutinizes how the WTO plays a role in balancing trade and the environment, as many WTO agreements have environmental exceptions.

Arsenic, aluminum, iron, lead, chromium, copper, zinc, manganese, and cadmium are some of the heavy metal pollutants in the air that cause severe impacts on the biotic and abiotic environment. This study intended to find the accumulation capacity of the heavy metals on the leaves of tree species such as Terminalia catappa, Syzygium cumini, Saraca asoca, Pongamia glabra, and Ficus religiosa and predict their accuracy by comparing different machine learning (ML) models. The samples were collected at six different locations (likely Vellagate, Cancer Institute, CSI hospital area, Moongilmandapam, Collectorate, and Pallavarmedu) and distributed in a manner within Kanchipuram town, Tamil Nadu, in February and March of 2018 and 2019, respectively. Six ML methods were selected, such as KStar (K*), Lazy IKB, Logistic Regression Algorithm (LR), LogitBoost Classifier (LB), Meta Randomizable Filtered Classifier (MRFC), and Random Tree (RT), for prediction and to compare the efficiency of their predictions. Out of six models, Logistic functions perform well in terms of TP rate when compared to other classifiers (93.21%-99.81% TPR– 0.93–0.99) and Logitboost attained a low TP rate that ranged from 0.76 to 0.82. This study indicates the feasibility of different ML methods in the prediction of species capabilities toward the accumulation of heavy metals.

The purpose of the paper is to estimate the environmental efficiency of the Vietnamese textile and garment industry and evaluate the impact of the factors on environmental efficiency. The study uses firm-level panel data from the Vietnam annual enterprise survey data for the 2012–2018 period in the Vietnam textile and garment industry to evaluate the environmental efficiency by using the Super-SBM DEA model with undesirable output and applies the Tobit regression model to measure the impact of the factors on the environmental efficiency. This study evaluates environmental efficiency and assesses the impact of some core factors, including the origin of imported machinery and equipment, the origin of imported materials, the management of industrial zones, and the presence of FDI firms, on environmental efficiency at the firm level. The results indicate that the average score for environmental efficiency is 0.233. Some factors, such as income per employee, machined goods imported from developed countries, industrial zones, firm improvement processes, and the presence of FDI, have a positive impact on a firm’s environmental efficiency, whereas materials made in Vietnam have a negative impact.

In the current research work, the authors proposed a list of tactics to eliminate Cr (VI) with the help of pig bone biochar. The Cr (VI) was adsorbed in batches onto pig bone biochar to scrutinize the adsorption data. The studies determine the impact of adsorption dose, pH, and concentration. From the results, it was inferred that the optimum pH level was 7 for the removal of metal. The study calculated the adsorption isotherms in terms of affinity and adsorption capacity by leveraging Temkin, Langmuir, and Freundlich equations. According to the reports, the Langmuir model is suitable for the adsorption data, followed by Temkin and Freundlich equations. In this model, rapid adsorption kinetic rates were observed, whereas the equilibrium state was achieved after two hours. There seems to be a perfect collation between the kinetic adsorption data and the pseudo-second-order equation. The researchers determined both Lagergren and Ho’sconstants. When biochar was characterized with SEM (Scanning Electron Microscope), EDX (Energy Dispersive X-ray spectrometer), and FTIR (Fourier Transform Infrared spectrometer), it was revealed that the Cr (VI) ions interacted with the isolated aggregates formed on the biosorbent surface. From the results, it can be understood that the pig bone biochar can be effectively used to eliminate chromium ions from an aqueous solution.

Households, restaurants, canteens, and hotel wastes constitute kitchen waste. Every day our growing cities generate more and more waste, which is overloading our municipal systems. The main aim of the present work was to prepare a microbial consortium that can effectively and rapidly bring about the degradation of kitchen wastes that can be used in agricultural soils. More than 100 different bacterial isolates were obtained from various kitchen waste dumping areas. The bacterial isolates were studied to produce enzymes like amylase, gelatinase, lipase, and protease on respective media plates. The best 20 isolates were subjected to enzyme quantification. The isolates showing maximum production for all four enzymes were selected for consortia preparation. The consortia of isolates were prepared by permutation combinations. Amongst all consortia prepared consortium No. 7 showed maximum enzymatic potential. The bacterial isolates in the best consortium (No. 7) were further characterized and identified as KW104 Serratia marcescens, KW37 Micrococcus luteus, KW128 Brevindimonas mediterranea, KW91 Bacillus tequilensis, and KW97 Exiguobacterium mexicanum. This consortium showed rapid degradation of waste as compared to others in 15 days duration of time showing good potential for compost formation when applied to plant growth.

Globally, vehicular pollution is one of the greatest concerns in urban areas. Several studies on air pollution have been conducted using deterministic, statistical, and soft computing methods. However, there has been little research on how soft-computing methods like Artificial Neural Networks (ANN) can help us comprehend vehicular pollution’s non-linear and highly complex dispersion. This study uses an ANN-based vehicular pollution model to investigate the effect of vehicular traffic on PM2.5 concentrations in built-up and open terrain-surrounding environments. Five distinct pollution models were developed for two locations in Delhi, considering PM2.5 pollutants, meteorological variables, traffic flow, and traffic composition into account. The results concluded that under open terrain conditions, the significance of the traffic variable in its association with PM2.5 is almost half the significance observed under built-up conditions. Also, in terms of PM2.5 reductions, the maximum reduction observed at Location-1 (built-up environment), and Location-2 (open terrain environment) is 1.85 and 2.44 times the percent reduction in traffic during peak hours, respectively. The study’s findings have significant ramifications for the current practices of ignoring the contribution of traffic and the built environment to pollution and adopting measures like an odd-even rule and high fuel and parking prices to combat pollution.

In recent years, there has been a significant increase in the needs of the overgrowing population. Naturally, industrial belts increased worldwide to satisfy the variety and quantity of needs. While producing the products, a huge quantity of waste is generated and added continuously to the environment, causing hazardous effects on the flora and fauna. Spent wash is one of the most important types of industrial waste since it is the liquid waste that is left over after making alcohol and it pollutes the environment. Despite effluent quality standards, untreated or partially treated sewage frequently enters water courses or soils. It is always overloaded with organic and inorganic substances. The stabilization of the effluent by microorganisms is reflected in the form of COD reduction. The present study attempted to isolate the PHB-producing organisms and use them to stabilize the spent wash. The locally isolated, characterized, and identified 11 PHB-producing microorganisms from the Karad region of Maharashtra were used to stabilize spent wash. They include Candida orthopsilosis, Bacillus subtilis, Bacillus cereus, Bacillus megaterium, Klebsiella grimontii, Citrobacter fruendii, and members of Staphylococcus and Rhodococcus. All potential organisms were reported to stabilize the spent wash. The degree of stabilization was measured in terms of the percent reduction of COD and BOD. The % reduction was reported at 95.31% and 81.39% of COD and BOD, respectively, by Klebsiella grimontii, followed by 92.18% and 80.46% reductions of COD and BOD, respectively, by Staphylococcus. These organisms are promising in the treatment of spent wash.

The present study selects cities such as Zhengzhou, Kaifeng, Anyang, Hebi, Xinxiang, Jiaozuo, and Puyang along the Yellow River Basin in Henan Province. The data of six pollutants, such as PM2.5, PM10, SO2, CO, NO2, and O3, in various cities from 2019 to 2021, and the monthly primary pollutant data of seven cities in the past five years were collected through various channels. The air quality of the above seven cities was analyzed with the spatial-temporal distribution of pollutants as the research objective and geographic information system as the research tool. The results show that affected by the distribution of key pollution sources and meteorological conditions in the urban area, the PM2.5 concentration generally shows a zonal feature of decreasing from northwest to Southeast. The high-value area is located in the north and west of the integrated area of the seven cities, and the low-value area is located in the Southeast of the seven cities.

In the present study, a total of 17 morphologically different gut-associated bacteria were isolated from four species of estuarine polychaetes: polychaetes Capitella capitata, Scalibregma inflatum, Dendronereis aesturiana, and Namalycastis abiuma. The isolated strains were evaluated for their probiotic activities, such as digestive enzymes including protease, amylase, and lipase, and antimicrobial activities by the agar well diffusion method against fish pathogens. Based on their better enzymatic and antibacterial activities, two bacterial strains, CMST Poly1 and CMST Poly2, were selected for further probiotic studies. Based on the biochemical and morphological characterization, both probiotic strains were characterized as Gram-positive, rod-shaped, non-motile, non-spore-forming, homofermentative, absence of catalyzing enzymes and notable proteolytic activity, and susceptibility to various antibiotics. Further, these two strains were confirmed by 16S rRNA gene sequence analysis as Bacillus subtilis CMST Poly1 and Priestia megaterium CMST Poly2. Our results revealed that strains Bacillus subtilis CMST Poly1 and Priestia megaterium CMST Poly2 can potentially be used as probiotic strains in aquaculture applications.