Chromium contamination in water bodies poses severe risks to both the environment and human health. This research introduces an innovative solution to this challenge by creating a vapor-activated carbosorbent from biodegradable household waste. The efficacy of this adsorbent in removing total chromium through batch methods from aqueous solutions was investigated. Surface analysis using scanning electron microscopy (SEM) exhibited a porous structure, while Fourier-transform infrared spectroscopy (FTIR) identified distinct functional groups on the surface. The point of zero charge (PZC), determined at 6.95, revealed the adsorbent’s surface chemistry. Impressively, the synthesized carbosorbent exhibited significant adsorption capacities of 23.08 mg.g-1 for Cr(III) and 24.84 mg.g-1 for Cr(VI) under optimal conditions. The Langmuir isotherm model illustrated a monolayer adsorption mechanism aligned with the pseudo-second-order kinetic model, confirming chemisorption. Thermodynamic analysis disclosed favorable and spontaneous chromium adsorption. Negative ΔG° values affirmed the spontaneity, while the exothermic nature of the process was signified by the positive ΔH° value, indicating heat release. Increased randomness at the solid-liquid interface, indicated by the positive ΔS° value, underscored the enhanced affinity between the adsorbent and adsorbate. This study exemplifies the potential of the vapor-activated carbosorbent as an efficient and sustainable remedy for chromium-contaminated water bodies.

This research investigates the potential of two Plant Growth-Promoting Bacteria (PGPB) strains, EP1-AS and EP1-BM, isolated from the halophyte Euphorbia prostrata, to enhance plant growth and provide abiotic stress resilience. The study addresses the urgent need for sustainable agricultural practices in the face of challenges like soil salinization and heavy metal contamination. The investigation comprehensively analyzes the heavy metal and salt tolerance of the PGPB strains, revealing their potential applications in promoting plant growth under adverse environmental conditions. The research further explores the impact of these PGPB strains on wheat plants subjected to varying concentrations of heavy metals and salts. Results indicate that both PGPB strains, especially EP1-BM, exhibit significant tolerance to heavy metals and salt stress. EP1-BM demonstrates remarkable resilience even under high concentrations of these stressors. The study extends its findings to in vitro testing on wheat plants, revealing the positive influence of PGPB strains on germination, shoot length, and root length in the presence of salt and heavy metals. This research underscores the significance of understanding plant-microbe interactions, particularly in the context of promoting sustainable agriculture in challenging environments. The identified resilience of PGPB strains, especially EP1-BM, suggests their potential application as bio-remediators and plant growth promoters in soils affected by salinity and heavy metal stress. The promising results observed will be followed-up field trials. They will highlight the translational potential of these PGPB strains, offering a novel avenue for developing biofertilizer formulations with a cautious approach to safety concerns. Overall, this study contributes valuable insights into harnessing the untapped potential of resilient plants and their associated microbial communities for sustainable agriculture. It addresses key global challenges outlined by the United Nations Sustainable Development Goals.

Dual concerns involving the rise in airborne pollutant levels and bulging need to protect-preserve human health have propelled the search for innovative means for air quality monitoring to aid in evidence-based decision-making (pollution prevention-mitigation). In this regard, moss bags have gathered a great deal of attention as active biomonitors. In this reflective discourse, we systematically review the world literature to present a bird’s eye view of moss bag applications and advances while highlighting potential concerns. We begin with a brief note on mosses as biomonitors, highlighting the advantages of moss bags over the passive technique (native moss), other living organisms (lichens, vascular plants), and instrument-based measurements. A major strand of moss bag research involves urban ecosystem sustainability studies (e.g., street tunnels and canyons, parks), while others include event-specific monitoring and change detection (e.g., SARS-CoV-2 Lockdown), indoor-outdoor air quality assessment, and change detection in land use patterns. Recent advances include biomagnetic studies, radioisotopic investigations, and mobile applications. Efforts are currently underway to couple moss bag results with a suite of indicators [e.g., relative accumulation factor (RAF), contamination factor (CF), pollution load index (PLI), enrichment factor (EF)] and spatially map the results for holistic appraisal of environmental quality (hot spot detection). However, while moss bag innovations and applications continue to grow over time, we point to fundamental concerns/uncertainties (e.g., lack of concordance in operational procedures and parameterization, ideal species selection, moss vitality) that still need to be addressed by targeted case studies, before the moss results could be considered in regulatory interventions.

SCS-curve number (CN) is one of the most well-liked and commonly applied methods for estimating surface runoff. The present study aims to calculate surface runoff using SCS-CN watershed-based calculation and geospatial technology in the Kurumballi sub-watershed Shivamogga District of Karnataka, India. The study area covers about an area of 47.67 sq. km. The union of land use/land cover classification with hydrological soil groups (HSG) yields the runoff estimation by the SCS-CN curve approach. This method calculates the runoff volume from the land surface flows into the river or streams. Moreover, the study area’s delineation of runoff potential zones was done using the thematic integration method. Different thematic layers were used, including lithology, geomorphology, soil, slope, land use and land cover, drainage, surface water bodies, groundwater contour, and isohyetal maps. Furthermore, associating it with the SCS-CN technique, the total surface runoff volume of the study area was estimated. The total surface runoff volume in the study area is 21065849.7 m3. To this study, thematic integration with the SCS-CN approach to estimate runoff for watersheds is valuable for improving water management and soil conservation.

This study evaluated the seasonal variability of water quality in the Tilacancha River, the water source that supplies Chachapoyas, and the rural communities of Levanto and San Isidro del Maino of Perú. Eighteen physical, chemical, and microbiological water parameters were evaluated at five sampling points in two seasons (rainy and dry). To determine water quality, the results obtained for the parameters evaluated were compared with the Maximum Permissible Limits (MPL) established in the Regulation on Water Quality for Human Consumption (DS Nº 031-2010-SA), approved by the Environmental Health Directorate of the Ministry of Health. In addition, a Pearson correlation was performed to estimate the correlation between the variables evaluated. The results showed that microbiological parameters exceeded the MPLs in both periods evaluated, such as the case of total coliforms (44 MPN.100 mL-1), fecal coliforms (25 MPN.100 mL-1), and E. coli (5.45 MPN.100 mL-1), these microbiological parameters reported a positive correlation with turbidity, temperature, total dissolved solids, and flow rate. In addition, aluminum (Al) and manganese (Mn) exceeded the MPL in the rainy (0.26 mg Al.L-1) and dry (1.41 mg.Mn-1.L-1) seasons, respectively. The results indicated that the water of the Tilacancha River is not suitable for human consumption. Therefore, it must be treated in drinking water treatment plants to be used as drinking water.

Over time, predictive models tend to become more accurate but also more complex, thus achieving better predictive accuracy. When the data is improved by increasing its quantity and availability, the models are also better, which implies that the data must be processed to filter and adapt it for initial analysis and then modeling. This work aims to apply the Random Forest model to predict PM10 particles. For this purpose, data were obtained from environmental monitoring stations in Mexico City, which operates 29 stations of which 12 belong to the State of Mexico. The pollutants analyzed were CO carbon monoxide, NO nitrogen oxide, and PM10 particulate matter equal to or less than 10 μg.m-3, NOx nitrogen oxide, NO2 nitrogen dioxide, SO2 sulfur dioxide, O3 ozone, and PM2.5 particulate matter equal to or less than 2.5 μg.m-3. The result was that when calculating the certainty of our model, we have a value of 80.40% when calculating the deviation from the mean, using 15 reference variables.

The present study attempted to observe the perception and adaptation strategies of farmers in the context of climate change. It observes that the majority of the farmers are aware of climate change and understand that they are facing problems due to it. The major problems faced by the farmers are the long duration of dryness due to lack of rainfall, weed pressure, very high temperatures, and crop disease. However, farmers are not very aware of technological adaptation and have changed the cropping time due to changes in the time of monsoon. The study recommends that there is a need for intensive micro and macro policy initiatives in terms of modern green sustainable technology along with awareness and skill development of the farmers. The government should also focus more on policy initiatives for sustainable agricultural practices in line with sustainable development goals.

A United Nations (UN) report on the severity of pollution in cities around the world in 2020 rated Aba City, Nigeria, as the most polluted city in the world. This has become a source of worry and embarrassment for environmental policymakers in the country. The matter of whether industries are efficiently managing their wastes came to the fore, and policymakers questioned the compliance of these industries with environmental laws and Environmental Impact Assessment (EIA) guidelines and the reasons behind the seemingly non-compliance of the industries with these guidelines. The study aimed to investigate the determinants that influence compliance with EIA guidelines by industries in Aba. A survey research method was employed in the study. Questionnaires and interviews were also used to elicit data from industrialists and environmentalists in the study area. 384 industries were sampled in the study. Principal Component Analysis was used to test the hypothesis. The study revealed seven factors that influenced the compliance rate of EIA guidelines by industries, and they include weak public participation (65.5%), ignorance (54.5%), an effective legal system and legislation (42.4%), the cost of compliance (40.5%), weak coordination along the line of departments (town planning officers and consultants) (35.5%), delay in approval (30.5%), and limited scope (28.9%). It was recommended that the government strengthen the legal system as it relates to the implementation of EIA; then, there is a need to involve affected stakeholders in the preparation of EIA documents.

Assessing the dynamics and patterns of Land Use and Land Cover (LULC) and its transformation is an important practice of urban planners and environmentalists for a variety of applications, including land management, urban climate modeling, and sustainability of any urban region. Monitoring changes in LULC using geospatial techniques can help to identify areas at risk for indefensible land use, low-grade environment, and especially for sustainable urban planning. This study aims to analyze the changing pattern, dynamics, and alteration of LULC using Google Earth Engine (GEE) and Machine Learning Applications for the years 1991, 2001, 2011, and 2022 in the Varanasi City Development Region (VCDR). The LULC classification was divided into seven classes using random forest classification, and Landsat-5(TM) and 9(OLI-2) satellite data were used. Saga GIS has been utilized for the detection of LULC change during the 1991-2022 period. For validation of classification results, accuracy assessment was estimated using error matrices and through user, producer, and overall accuracy estimation. The Kappa statistics were applied for the reliability of the accuracy assessment result. As a result, the built-up area increased by 507.8 percent, and other classes like agricultural, barren, fallow land, and vegetation cover rapidly declined and altered into concrete areas over the period. Water bodies and river sand classes have been slightly converted into different classes. The finding explains that 114.8 km2 of fertile agricultural land, 14.81 km2 barren land, and 12.93 km2 of vegetation cover transformed into impervious surface, which is unsustainable and causes various problems like food scarcity, environmental degradation, and low quality of urban life. This study can be a useful guide for urban planners, academicians, and policymakers by providing a scientific background for sustainable urban planning and management of VCDR and other cities as well.

Heavy metals like cadmium (Cd), mercury (Hg), bismuth (Bi), and arsenic (As) are potent and harmful poisonous sources that cause havoc on health conditions for the population of the world. However, the response of our crop species to these potent heavy metals-related toxicity is still left to be fully understood. It is a matter of great concern, as we are heavily dependent on crop species like rice, wheat, peas, etc. Our study here aims to learn about the defensive mechanism of Pisum sativum L. aided with putrescine and mycorrhiza against the stress created by Cd-related toxicity. We quantified physiological parameters such as the membrane-related injury and stability index. We further measured the total free proline content, lipid peroxidation content, and SOD activity. We executed our quantitative experiments on the stressed pea plants due to the exogenously applied Cd-toxicity in the presence and absence of mycorrhiza and putrescine. Insights of our significant results will improve the understanding of readers of the role of mycorrhiza and putrescine in improvising the tolerance level of a pea plant over Cd-related toxicity.