Air pollution is a required environmental and public health issue in India, with multiple municipalities repeatedly ranking among the most polluted in the world. This study leverages large datasets to construct a predictive model for forecasting air quality trends using a novel approach that integrates the Recency Frequency Monetary (RFM) model with deep learning. The research aims to efficiently quantify pollution events frequency and assess the impact of air quality variations on public health, offering a more flexible and adaptive system for air quality monitoring. As a result, a large volume of air quality data provided by RFM (Recency, Frequency, and Monetary) will be flexible and frequently handled and analyzed. In this research, the performance of the integrated RFM technology is examined using Python and Google Colab, and the simulation results are compared to air pollution information from neural networks for structures in additional data using existing air quality monitoring systems in India. Performance examination of both regression and classification techniques in RFM. The execution of RFM can be one of the models and its potential to enhance air quality monitoring and urban sustainability

Landscapes are vital for ecological protection and cultural heritage, facing challenges from urbanization, agricultural modernization, and climate change. By setting legal boundaries, land use can be regulated to prevent unreasonable development and ensure the sustainable use of landscapes. This paper assesses the forest, geological, aquatic, cultural, and religious relic landscape resources of Xinchang County, Zhejiang Province, using the Analytic Hierarchy Process (AHP) and fuzzy evaluation methods to quantify their protection needs. The study finds that establishing nature reserves, ecological protection red lines, and historical and cultural villages can effectively maintain ecosystem stability and biodiversity, and protect cultural heritage. Legal protection has significantly improved forest coverage and water quality in Tianmu Mountain National Forest Park and Wozhou Lake Scenic Area, while Meizhu Ancient Village and Waipo Keng Village have excelled in cultural landscape protection. However, challenges such as inadequate law enforcement, low public participation, and insufficient funding hinder the execution of legal boundaries. Recommendations include strengthening law enforcement, raising public environmental awareness, and expanding funding sources. This paper provides a scientific basis and practical guidance for the formulation and implementation of landscape protection policies, contributing to the sustainable utilization and long-term protection of landscape resources in Xinchang County and other regions.

The construction industry has played a significant role in causing environmental degradation, primarily due to its substantial energy use. Focusing on green building development projects is gaining momentum as a sustainable solution for mitigating environmental challenges. This study assessed several environmental features of 22 green buildings in Dhaka, the capital of Bangladesh. In addition, the challenges were discussed, and mitigation strategies were recommended. The Leadership in Energy and Environmental Design (LEED) certification technique is widely acknowledged and globally accepted as the leading green building certification standard. Three LEED versions, v3 for new construction and major renovations and v4 and v4.1 for building design and construction, were investigated. Seven environmental features of three versions, including rainwater management, renewable energy, enhanced commissioning, optimized energy performance, construction and demolition waste management, water use reduction, and water efficient landscaping, were considered in this work. A survey questionnaire was prepared to receive information about these LEED-certified (or applied for certification) buildings. The findings of our study suggested that the general trend for seven environmental features of the selected green buildings was positive except for renewable energy, where 72.72% of buildings were in ‘very poor’ condition. Regarding rainwater management, enhanced commissioning, and optimized energy performance, 40.91% of buildings were in ‘very good’ condition. Despite satisfactory responses for several environmental features, the survey found that renewable energy integration remains challenging for all buildings. Solar energy should be extensively employed to enhance energy utilization efficiency, reduce energy demand, and minimize environmental impact. It was suggested that a few challenges, including the government’s lack of action and initiatives, financial incentives, investor hesitation, and knowledge gaps, must be overcome to create a truly green building market in Bangladesh. Bridging this disparity requires policy reforms, public awareness, industry development, and capacity building. This study provides a basic understanding of the green building situation and guides future research and policy initiatives to accelerate Bangladesh’s commitment to sustainable development goals.

In India’s economy, farming is crucial, making early detection of plant diseases an important task. This helps in reducing crop damage and preventing the diseases from spreading further. Numerous plants, such as corn, tomatoes, and potatoes, display evident symptoms of diseases on their leaves. These noticeable patterns can be employed to accurately predict the diseases and facilitate prompt intervention to reduce their impact. The customary method involves farmers or plant pathologists visually inspecting plant leaves and identifying the specific disease. This project involves a deep learning model designed for classifying plant diseases, utilizing CNNs for their proficiency in image classification. The model, which utilizes architectures like MobileNet, InceptionNet, ResNet, and ResNeXt, delivers faster and more accurate predictions than traditional manual methods. Notably, ResNeXt, with its added dimension of cardinality that aids in learning more complex features, achieved the highest accuracy, reaching 98.2%.

Diabetes mellitus is a prevalent chronic disease with significant implications for public health, including an expanded chance of coronary heart malady, stroke, persistent kidney illness, misery, and useful inability. In India, the predominance of diabetes among grownups matured 20 a long time and more seasoned rose from 5.5% in 1990 to 7.7% in 2016. Traditionally, diabetes management involves costly consultations and diagnostic tests, presenting challenges for timely diagnosis and treatment. Additionally, a comprehensive study was conducted to investigate the relationship between the incidence of type 2 diabetes mellitus (T2DM) and environmental exposure to arsenic in the form of air, water, and food pathways. The majority of the analyzed studies examined the levels of arsenic in water samples, with analyses of urine, blood, serum, and plasma samples coming next. Groundwater supplies may get contaminated by arsenic, especially in regions where arsenic deposits are naturally occurring or as a result of industrial activity. Additionally, various meals contain it, particularly rice, seafood, and poultry. Besides, it might be released into the environment by industrial processes such as coal combustion, smelting, and mining, which could lead to occupational exposure. There may be a genetic component to the association between arsenic exposure and the onset of diabetes. Ultimately, diabetes mellitus is enhanced by arsenic pollution through air, food, and drinking water. Advances in machine learning and telemedicine offer innovative solutions to address these challenges. Data mining, a crucial aspect of machine learning, facilitates the extraction of valuable insights from extensive datasets, enabling more efficient and effective diabetes management. This study explores a telemedicine-based system utilizing five classification techniques Tree, Naive Bayes, Support Vector Machine, and others to predict Type-2 diabetes. By leveraging real-time data analysis, the system aims to enhance early diagnosis and management of Type-2 diabetes, potentially preventing progression to critical conditions. The results evaluate the effectiveness of these models in a telemedicine context, identifying the bestperforming model to assist healthcare professionals in making informed decisions for early intervention and improved patient outcomes.

This research was initiated to study the groundwater potential zones using the Fuzzy logic technique at Lembangan Kadamaian, Kota Belud, Sabah, and its surroundings. The lithological units of this study mainly focus on the sedimentary rock of Wariu Formation, Crocker Formation, and Trusmadi Formation, including the quaternary alluvium deposition unit of Kota Belud. Based on the structural geology analysis results, the deformation trends are in the northwest-southeast direction. The interpretation of groundwater potential zones was made by using the ArcGIS Pro, R-studio Global Mapper, and several other mappingrelated software. Ten thematic maps that have been produced are lithological map, lineament density map, rainfall map, distant from river map, distant from lineament map, drainage density map, landform, and land cover map, Topographic Wetness Index (TWI) map, rock porosity map, curvature map, and slope steepness map. GIS techniques were used during the spatial analysis stage. All thematic maps have their class values and are based on field data, relevant department data, and remote sensing data. Further processes were done using R-studio, Fuzzy Toolset, and Raster Calculator. This process afterward will produce the groundwater potential map of the study area. The final result has been supported by the data of tube wells from the Department of Minerals and Geosciences, Sabah, and was validated using the ROC and AUC curve validation technique.

Heavy metal pollution released into the surface environment poses a significant threat, being hazardous to both the environment and living organisms. Phytoremediation thus appears as a viable technique to address heavy metal pollution in soils impacted by industrial effluents. To identify the growth performance of sunflower and cotton seeds under various concentrations of arsenic and chromium present in the tannery industrial wastewater in the Chengalpattu region, and to identify the accumulation of Arsenic(As)As and chromium (Cr) in the roots, shoots, and soil of these plants. This paper examined the usefulness of sunflower (Helianthus annuus) and cotton (Gossypium herbaceum) in eradicating Cr and As-polluted soils originating from tannery wastewater. In this experiment, Completely Randomized Block Design (CBRD) testing was performed, and the samples were analyzed using Inductively Coupled Plasma Mass Spectrometry (ICP-MS). The accumulation of Cr in sunflowers was 120 mg.kg-1 in the roots and 25 mg.kg-1 in the above-ground parts. As accumulated to 85 mg.kg-1 in the roots and 15 mg.kg-1 in the above-ground parts. Similarly, cotton plants accumulated 90 mg.kg-1 of Cr in the roots and 20 mg.kg-1 in the above-ground parts. As accumulation in cotton plants was 100 mg.kg-1 in the roots and 30 mg.kg-1 in the aboveground parts. The study inferred that, in comparison to the other plants, the concentrations of Cr in sunflower roots were significantly higher, but cotton was found to have a better ability to take up As in the roots as well as in the aerial parts of the plant. It hence demonstrates the applicability of sunflower and cotton to support phytoremediation efforts sustainably within industrial environments to mitigate pollution and improve the quality of the soil.

Understanding aridity trends is crucial for climate adaptation strategies. This study analyzes the spatial and temporal fluctuations in aridity across Karnataka, India, over 63 years from 1958 to 2020 using the Aridity Index (AI). Monthly, seasonal, and annual AI values were calculated using precipitation and potential evapotranspiration data sourced from TerraClimate. The results indicate that approximately 74% (142,464 sq. km) of Karnataka is classified as dryland, ranging from semi-arid to dry subhumid zones, while 26% (49,416 sq. km) falls under more humid non-dryland areas. The Malnad and coastal regions are more humid compared to the predominantly semi-arid northern inland Karnataka. Temporal analysis between the periods 1958–1990 and 1991–2020 revealed that 6.24% of the land area shifted from semi-arid to dry subhumid, indicating increased moisture availability, whereas 0.43% shifted from dry subhumid to semi-arid, suggesting localized aridification. During the post-monsoon season, 14.12% of dryland areas transitioned to non-dryland, with substantial improvements in moisture availability observed in districts such as Uttara Kannada (59.21%) and Mandya (82.97%). Conversely, 1.5% of non-dryland areas converted to dryland, indicating localized decreases in water resources. Seasonal analysis revealed that 99.92% of the summer aridity status remained constant, while during the monsoon season, only 2.42% of dryland areas changed to non-dryland, reflecting stable monsoonal rainfall patterns. These findings highlight the significant influence of topography, monsoonal patterns, and water management on aridity dynamics in Karnataka. The study provides valuable insights for developing policies on climate adaptation, sustainable agriculture, and regional water resource management. Addressing the increasing trends in aridity is essential to reduce desertification risks and enhance the State’s resilience to climate change.

Manganese (Mn) contamination in groundwater is a global concern due to its harmful effects. The high concentration of Mn2+ in humans creates memory issues, decreased fertility, appetite loss, sleeplessness, sperm abnormalities, and ‘Manganism’. In this study, the isolation of thermophiles was followed by their assessment for MIC (minimum inhibitory concentration) and Mn bioremediation. We have isolated a total of 11 Mn-resistant bacterial strains of thermophiles with the identification of their bioremediation potential from the Tapt Kund, Soldhar, and Gauri Kund hot springs of Uttarakhand, India. Out of 11 strains, three isolates (TA8, SA9, and GA7) were identified with the highest metal resistance properties for toxic Mn2+. The metal tolerance capabilities of the strains were evaluated through MIC and the metal biosorption rate was estimated by the live cells bioremediation through thermophilic bacteria. ICP-MS (inductively coupled plasma mass spectrometry) was used to assess the Mn2+ removal rate of bacterial bioremediation. It turned out that every strain exhibited promising bioremediation potential and proved Mn-resistant. The bacterial strain TA8 exhibits the highest MIC (600 µg.L-1.) with a bioremediation rate of 98.34% for Mn2+. The bacterial strain SA9 has a MIC value of 525 µg.L-1, with a biosorption rate of 77.74% for Mn2+. The bacterial strain GA7 has a MIC of 475 µg.L-1, with an efficiency rate of 61.17% for Mn2+ removal. The most promising strain of thermophilic bacteria for Mn2+ bioremediation is the TA8, which has demonstrated the highest potential (98.34%) out of all the tested strains. The findings may have public health implications, as reducing manganese levels in groundwater can help mitigate health risks associated with Mn exposure. Also, this research enriches our knowledge of microbial bioremediation and its potential applications in environmental management. Ultimately, this research could offer a novel, economical, and environmentally beneficial approach to managing metal toxicity

Ericoid mycorrhiza (ErM) is an unexplored and understudied member of the mycorrhizal world, surprisingly belonging to Ascomycota and Basidiomycota instead of Glomeromycota (the phylum comprising fungi forming associations with higher plants). ErM obtained its etymology due to its symbiotic relationship with members of the Ericaceae Family. Just like any other mycorrhiza, ErM also helps its hosts through nitrogen uptake and phosphorus bioavailability and provides defense to host plants against various phytopathogens. It also takes part in the decomposition of organic matter and depolymerization of complex substances. These mycorrhizae are distributed across all continents except Antarctica. The majority of culturable ErM is spread across England, Australia, Canada, the United States etc. This review focuses on the literature survey on ErM, its taxonomy, and diversity alongside its functions. Our review also sheds light on the host range of the ericoid fungi, wherein, out of all the hosts, Salal (Gautheria shallon) has been identified as one of the most promising ones