The stated goal of the research is to investigate the surface water quality of the Baitarani River in Odisha to ascertain its compatibility for various uses. Large, complex datasets generated during the one-year (2021-2022) monitoring program were collected from 13 locations and encompassed 22 parameters. To examine temporal and spatial fluctuations in and to interpret these datasets, MCDMs like TOPSIS and the Entropy-based Water Quality Index (EWQI) were utilized. The physical and chemical outcomes of the current experiment were compared to WHO standards. According to the analysis’s results, turbidity and total coliform (TC) are indicators that have a greater impact on water quality in all locations during both seasons and are directly linked to home and agricultural non-point source pollution. As per EWQI interpretation, 30.77 % of the observations in PRM and POM fall under the poor category. The findings showed how anthropogenic activities have harmed St. 8, 11, 12, and 13 and require effective management. A quantifiable approach was also carried out to decide the efficacy of TOPSIS. Farming attributes, including SAR, % Na, RSC, MR, KI, and PI, were estimated to delineate the agriculturally practicable zones. This work can offer a reference database for the betterment of water quality.

Air pollution affects public health and the environment, creating great concern in developed and developing countries. In India, there are numerous reasons for air pollution, and festivals like Diwali also contribute to air contamination. Determining the polluted region using several air contaminants is significant and should be analyzed carefully. This study aims to analyze the air quality in Tamil Nadu, India, during the Diwali festival from 2019 to 2021, based on multiple air pollutants. The study models the impact of air pollution as a Multi-Attribute Decision-Making (MADM) problem. It introduces a hybrid approach, namely the Analytical Hierarchy Process-Entropy-VlseKriterijumska Optimizacija I Kompromisno Resenje (AHP-Entropy-VIKOR) model, to analyze and rank the areas based on the quality of air. A combined approach of AHP and entropy is employed to determine the weights of multiple air pollutants. The VIKOR approach ranks the areas and identifies the areas with the worst air quality during the festival. The proposed model is validated by performing the Spearman’s rank correlation with two existing MADM methods: Combinative Distance Based Assessment (CODAS) and Weighted Aggregates Sum Product Assessment (WASPAS). Sensitivity analysis is carried out to assess the effects of the priority weights and the dependency of the pollutants in ranking the regions. The highest air pollution level during the festival was seen in Cellisini Colony (2019), Rayapuram (2020), T. Nagar and Triplicane (2021) in their respective year. The results demonstrate the consistency and efficiency of the proposed approach.

Air pollution affects public health and the environment, creating great concern in developed and developing countries. In India, there are numerous reasons for air pollution, and festivals like Diwali also contribute to air contamination. Determining the polluted region using several air contaminants is significant and should be analyzed carefully. This study aims to analyze the air quality in Tamil Nadu, India, during the Diwali festival from 2019 to 2021, based on multiple air pollutants. The study models the impact of air pollution as a Multi-Attribute Decision-Making (MADM) problem. It introduces a hybrid approach, namely the Analytical Hierarchy Process-Entropy-VlseKriterijumska Optimizacija I Kompromisno Resenje (AHP-Entropy-VIKOR) model, to analyze and rank the areas based on the quality of air. A combined approach of AHP and entropy is employed to determine the weights of multiple air pollutants. The VIKOR approach ranks the areas and identifies the areas with the worst air quality during the festival. The proposed model is validated by performing the Spearman’s rank correlation with two existing MADM methods: Combinative Distance Based Assessment (CODAS) and Weighted Aggregates Sum Product Assessment (WASPAS). Sensitivity analysis is carried out to assess the effects of the priority weights and the dependency of the pollutants in ranking the regions. The highest air pollution level during the festival was seen in Cellisini Colony (2019), Rayapuram (2020), T. Nagar and Triplicane (2021) in their respective year. The results demonstrate the consistency and efficiency of the proposed approach.

The stated goal of the research is to investigate the surface water quality of the Baitarani River in Odisha to ascertain its compatibility for various uses. Large, complex datasets generated during the one-year (2021-2022) monitoring program were collected from 13 locations and encompassed 22 parameters. To examine temporal and spatial fluctuations in and to interpret these datasets, MCDMs like TOPSIS and the Entropy-based Water Quality Index (EWQI) were utilized. The physical and chemical outcomes of the current experiment were compared to WHO standards. According to the analysis’s results, turbidity and total coliform (TC) are indicators that have a greater impact on water quality in all locations during both seasons and are directly linked to home and agricultural non-point source pollution. As per EWQI interpretation, 30.77 % of the observations in PRM and POM fall under the poor category. The findings showed how anthropogenic activities have harmed St. 8, 11, 12, and 13 and require effective management. A quantifiable approach was also carried out to decide the efficacy of TOPSIS. Farming attributes, including SAR, % Na, RSC, MR, KI, and PI, were estimated to delineate the agriculturally practicable zones. This work can offer a reference database for the betterment of water quality.