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.

Supersaturation of total dissolved gas (TDG) is mainly produced by high dam discharge, excess oxygen production by plant photosynthesis, and a sharp increase in water temperature, which may directly lead to fish and aquatic organisms suffering from “gas bubble disease” (GBD) or death. Aeration was one of the methods used to solve the dissipation of supersaturated TDG. In this paper, aeration had an obvious promotion effect on the dissipation of supersaturated TDG. For the calculation and analysis of supersaturated TDG dissipation coefficient, the aeration rate was proportional to TDG dissipation coefficient and had a promoting effect on it, while the aeration depth and aeration aperture were inversely proportional to TDG dissipation coefficient and played an inhibitory effect on it. The supersaturated TDG dissipation coefficient was affected by a factor of KTDG,Q> KTDG,D> KTDG,H. A quantitative relationship between the supersaturated TDG dissipation coefficient and aeration rate, aeration depth, and aeration aperture was obtained, respectively, as well as important expressions with comprehensive effect factors; their margins of error average within 10%. This research method has an important guiding significance for improving the living environment of fish and other aquatic organisms, alleviating the adverse effects of supersaturated TDG.

Heavy metal contamination is one of the significant concerns of environmental pollution. The present study was conducted to find out the correlation between soil and crop/food matrices grown at the exact location for Al, As, Ca, Cd, Cr, Cu, Fe, Hg, K, Mg, Mn, Ni, Na, P, Zn, and Pb elements near the industrial areas of Narol, Changodar, Vatva, Makarpura, Nandesari, and Ankleshwar in Gujarat, India. Soil samples were collected from 64 sampling sites in an industrial area. Twenty of these sites contained crop/food matrices used for the correlation study. The ranges of concentration of Cr (17-74.4 mg.kg-1), Cu (9.6-82.4 mg.kg-1), Ni (10.6-55.9 mg.kg-1), Pb (4.5- 20.7 mg.kg-1), Zn (21.5-112.4 mg.kg-1), and Al (7075-44557.5 mg.kg-1) for Soil and for crop Cr (0.3-0.6 mg.kg-1), Cu (0.3-8.6 mg.kg-1), Zn (1.1-43.5 mg.kg-1), Fe (12.6-69.4 mg.kg-1), and Al (5.8-102.2 mg.kg-1). According to the study, there is a strong correlation between the soil and crop/food matrices at the different locations, like 0.97 for Fe and Ni, 0.94 for Mg, 0.95 for Mn, and 0.55 for Pb and Zn, and very little correlation between K, P, and Ca, while a negative correlation between Al, Cr, Cu, and Na. The DTPA extractable method was used for the elemental analysis, and analysis was done using the ICP-OES instrument following microwave-assisted digestion. The results show that metal contamination transforms from soil to crop/food matrices, which represents a serious concern and requires action to address the metal contamination by industrialization.

Water pollution caused by oil spills at sea and industrial and daily wastewater discharges are causing serious damage to the ecological environment, not only in terms of economic losses but also in terms of human health and survival, a problem that needs to be addressed urgently. Oil/water separation is a global challenge, and while these problems are frequent, various oil/water separation strategies have been extensively investigated in recent years. The efficiency of the materials prepared is a key factor, as are the environmental friendliness and low cost of the methods and raw materials used in the experiments. This work reviews methods and materials applied in oil/water separation in recent years, including natural textile materials, metal meshes, synthetic membranes, particulate adsorbent materials, foams, sponges, aerogels, smart controllable special wettable separation materials.

Environmental issues have continued to spur discussions, debates, public outrages, and awareness campaigns, inciting interest in emerging technologies such as Artificial Intelligence. Its usage is spread across many environmental industries, including wildlife protection, natural resource conservation, clean energy, agriculture, energy management, pollution control, and waste management. In 2017, at the United Nations Artificial Intelligence Summit in Geneva, the UN acknowledged that AI could be an enabler in the sustainable development process towards peace, prosperity, and dignified life for humankind and proposed to refocus on the application of AI in assisting global efforts on sustainable development to eradicate poverty, hunger and to protect the environment as well as to conserve natural resources. It is vital to address environmental sustainability concerns; however, with the advent of AI, most common environmental issues are now solvable by prioritizing human interests. Sustainability encompasses the interrelated areas of the environment, society, and economy. According to the United Nations’ “Our Common Future,” also known as the “Brundtland Report,” it is defined as “development that satisfies current needs without compromising the ability of future generations to meet their own needs.” Unfortunately, the Earth is currently facing serious consequences from global warming and climate change, and immediate action is required to encourage the use of environmentally friendly and sustainable products to address these issues. Environmental degradation and climate change are numerous environmental concerns requiring novel and intelligent artificial intelligence solutions. The literature on AI and environmental sustainability encompasses various domains. Notably, AI is being used to address the bulk of regional and global environmental concerns, including energy, water, biodiversity, and transportation, even though many of these sectors have permeated and evolved. However, there is a need to combine current literature on the application of AI, particularly in relation to environmental sustainability in areas such as energy, water, biodiversity, and transportation. There is a significant lack of research on how AI can promote environmental sustainability. This research aims to explore how AI can be applied to address environmental issues in various sectors to achieve the Sustainable Development Goals (SDGs).

There is now a worldwide collective obligation, a reality, to acknowledge environmental challenges. The paper discusses and analyses the principles of international environmental laws and how those are applied in international conventions and treaties, and the effectiveness and weaknesses of those laws. This discussion mainly focuses on the principles’ backdrop, what they mean, and how they have been adopted in international environmental law. Furthermore, the paper focuses on the outcomes of formulating the principles and enforcement of the legal framework. It analyses their prospect to strengthen the legal framework to achieve the objective of these principles. Besides, some recommendations have been made to strengthen these legal frameworks. It shows why it is essential to form efficient environmental platforms in present climate issues and how all nations can be brought under a common platform where they can take decisions regarding the safeguard amid the evolving environmental situations.