[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125289-en":3,"doc-seo-125289-105":30,"detail-sidebar-cat-0-en-105":95},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125289,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Forecasting and Assessment of Air Quality Dynamics in Northeast India Using Machine Learning Models","This study investigates air quality dynamics in Northeast India, emphasizing the role of local terrestrial features, ecological sensitivity, and the need for sustained monitoring. It forecasts emissions and gas/aerosol concentrations for aerosols, SO2, NO2, CO, HCHO, O3, and CH4 using TROPOMI satellite data from 2019–2023 over a 9‑month horizon. Five machine learning models are compared and evaluated with R2, MSE, and MAE, revealing strong agreement and identifying the most effective approach for multiple pollutants.","PHILIPP AGRIC SCIENTIST Vol. 108 No. 1, 25 – 40  \nMarch 2025  \nISSN 0031-7454 [https://doi.org/10.62550/GW18043024](https://doi.org/10.62550/GW18043024)  \nForecasting and Assessment of Air Quality Dynamics in Northeast India Using Machine Learning Models  \nKumar Shubham1,*, Gopikrishnan T¹, and Anshuman Singh¹  \n1 Department of Civil Engineering, National Institute of Technology Patna, Patna, Bihar-800005, India  \n*Author for [correspondence: ks.civil.17@gmail.com](correspondence: ks.civil.17@gmail.com)  \nReceived: July 09, 2024/ Revised: November 21, 2024/ Accepted: February 06, 2025  \nThis study investigated air quality dynamics in Northeast India, a region with unique terrestrial features, includingthe Eastern Himalayas. While air quality varies across districts, pollution impacts the entire area. Northeast India’s rich ecology is crucial for Himalayan climate regulation. Robust air quality monitoring and pollution control are essential to preserve environmental balance. This work focused on forecasting emissions of aerosols, SO2, NO2, CO, HCHO, O3, and CH4 primarily associated with human activities. Utilizing data from the Tropospheric Monitoring Instrument (TROPOMI) satellite instrument from 2019 to 2023, a 9-mo forecast was conducted using 5 machine learning models: random forest, cubic regression, linear regression, quadratic regression, and k-nearest neighbors algorithm (KNN) models. The effectiveness of models was evaluated through R2, mean square error (MSE), and mean absolute error (MAE) . The results showed a strong alignment between regional dynamics and models with low MSE and high R2 values. Perpetual air quality monitoring is crucial for region-specific modeling and solutions. Gas concentration variations emphasize the need for regularly updated air quality reports. The random forest model was found to be most effective with high R2 values: UV aerosol index (0.97 in Imphal, Aizawl), CO (0.96 in Imphal), NO2 (0.92 in Gangtok), O3 (0.98 in Gangtok), SO2 (0.92 in Gangtok), and CH4 (1.00 in Itanagar, Shillong) . Correlation analysis with Central Pollution Control Board (CPCB) data showed notable results for Aerosol-PM2.5 (0.76 in Imphal) and Aerosol-PM10 (0.79 in Imphal) . Findings from this study may help identify effective machine learning models for forecasting and assessing air quality.  \nKeywords: air pollution, climate, Eastern Himalayas, machine learning, Northeast India  \nIntroduction  \nAir pollution has become a critical environmental and health concern posing significant challenges in various regions of India (Kaur and Pandey 2021). Higher air pollution is attributed to population growth, increased traffic and congestion, rapid developments, and the adoption of conventional sources such as brick kilns, cookstoves, mining businesses, forest fires, and waste burning (Guttikunda et al. 2014) . Aerosols and gases are diverse forms of air pollutants present in the environment. Gases of chemicals exist in a gaseous state (Francis and Peters 1980). Aerosols are tiny particles that float in the air which are liquefied or compacted and are produced by industry, natural processes, and the burning of fossil fuels (Ito and Penner 2005) . According to the United States Environmental Protection Agency (USEPA) (2024), particulate matter (PM) (particles with a diameter of 10 microns or less) and PM2.5 (defined as particles with a diameter of 2.5 microns or less) are considered  \naerosols. Both gases and aerosols contribute to air pollution and affect human health and the environment (Manisalidiset al. 2020) . Gases are typically molecular substances that can freely mix in the air, while aerosols consist of microscopic particles or liquid droplets that can be suspended in the atmosphere for varying durations (Bellouin 2024) .  \nRoughly 1.3 billion people rely on the Himalayas for water, electricity, and economic dependencies (Prakash 2020) . This region’s fragile biota is becoming steadily susceptible to the effects of in","cbCaia293ZzulmOn","https://ap.wps.com/l/cbCaia293ZzulmOn","pdf",2970717,1,16,"English","en",105,"# Introduction\n## Air pollution background and regional relevance\n## Sources and pollutants considered\n# Methodology\n## Data source and study period\n## Machine learning models and evaluation metrics\n# Results and Discussion\n## Model performance across districts\n## Correlation with CPCB measurements\n# Conclusion\n## Implications for monitoring and pollutant forecasting","[{\"question\":\"Which pollutants and emissions are forecast in the study?\",\"answer\":\"The study forecasts emissions and concentrations for aerosols, SO2, NO2, CO, HCHO, O3, and CH4, focusing on primarily human-activity related sources.\"},{\"question\":\"What data source and forecasting period are used?\",\"answer\":\"TROPOMI satellite data from 2019 to 2023 are used to produce a 9‑month forecast.\"},{\"question\":\"How are the machine learning models evaluated?\",\"answer\":\"Model effectiveness is assessed using R2, mean square error (MSE), and mean absolute error (MAE).\"},{\"question\":\"Which model performs best for multiple pollutants?\",\"answer\":\"Random forest is reported as the most effective overall, showing high R2 values for several pollutants across different locations.\"}]","Forecasting and Assessment of Air Quality Dynamics in Northeast India Using Machine Learning Models | 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pollutants and emissions are forecast in the study?","Question",{"text":75,"@type":76},"The study forecasts emissions and concentrations for aerosols, SO2, NO2, CO, HCHO, O3, and CH4, focusing on primarily human-activity related sources.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data source and forecasting period are used?",{"text":80,"@type":76},"TROPOMI satellite data from 2019 to 2023 are used to produce a 9‑month forecast.",{"name":82,"@type":73,"acceptedAnswer":83},"How are the machine learning models evaluated?",{"text":84,"@type":76},"Model effectiveness is assessed using R2, mean square error (MSE), and mean absolute error (MAE).",{"name":86,"@type":73,"acceptedAnswer":87},"Which model performs best for multiple pollutants?",{"text":88,"@type":76},"Random forest is reported as the most effective overall, showing high R2 values for several pollutants across different 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