Advanced statistical analysis of air quality and its health impacts in India: Quantifying significance by detangling weather-driven effects
Air quality has emerged as a significant concern due to its direct impact on human health. Over recent decades, India has witnessed a marked deterioration in air quality due to rising anthropogenic emissions and climate change. The COVID-19 lockdown offered a unique opportunity to examine air pollut...
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Elsevier
2025-01-01
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author | Akshansha Chauhan Guggilla Pavan Sai Chin-Yu Hsu |
author_facet | Akshansha Chauhan Guggilla Pavan Sai Chin-Yu Hsu |
author_sort | Akshansha Chauhan |
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description | Air quality has emerged as a significant concern due to its direct impact on human health. Over recent decades, India has witnessed a marked deterioration in air quality due to rising anthropogenic emissions and climate change. The COVID-19 lockdown offered a unique opportunity to examine air pollutant reductions under restricted human activities. This study conducted a long-term analysis of air quality in five major Indian cities—Delhi, Kolkata, Bengaluru, Hyderabad, and Visakhapatnam—by analysing variations in PM2.5, PM10, NOx, NH3, SO2, CO, and O3, incorporating a de-weathering strategy to isolate meteorological influences. In Delhi, we observed significant reductions in PM10 (92.50–136.70 μg/m³), NOx (62.13–151.91 ppb), and CO (0.53–0.88 mg/m³), which shifted health risks from the 'extreme' to 'low' category. Visakhapatnam also experienced notable declines in NOx levels (7.50–17.13 ppb). Conversely, Hyderabad exhibited no significant reductions, and AQHI increased (+0.97) due to rising NOx concentrations. Ozone concentrations showed a significant increase across cities, attributed to VOC-limited effects. The analysis revealed that meteorological variability and long-range transport of airmass played critical roles in shaping pollutant concentrations. These findings highlight the complexity of urban air quality dynamics and underscore the benefits of emission reductions for public health. |
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spelling | doaj-art-cf7aa6518b8847118b2c384b01dbe73d2025-02-02T05:28:07ZengElsevierHeliyon2405-84402025-01-01112e41762Advanced statistical analysis of air quality and its health impacts in India: Quantifying significance by detangling weather-driven effectsAkshansha Chauhan0Guggilla Pavan Sai1Chin-Yu Hsu2Department of Safety, Health and Environmental Engineering, Ming Chi University of Technology, 84 Gungjuan Rd., Taishan Dist., New Taipei City, 24301, Taiwan; School of Minerals and Energy Resources Engineering, University of New South Wales, Sydney, NSW, AustraliaDepartment of Safety, Health and Environmental Engineering, Ming Chi University of Technology, 84 Gungjuan Rd., Taishan Dist., New Taipei City, 24301, TaiwanDepartment of Safety, Health and Environmental Engineering, Ming Chi University of Technology, 84 Gungjuan Rd., Taishan Dist., New Taipei City, 24301, Taiwan; Center for Environmental Sustainability and Human Health, Ming Chi University of Technology, 84 Gungjuan Rd., Taishan Dist., New Taipei City, 24301, Taiwan; Corresponding author. Department of Safety, Health and Environmental Engineering, Ming Chi University of Technology, 84 Gungjuan Rd., Taishan Dist., New Taipei City, 24301, Taiwan. Tel.: +886 2 290 89899x6204; fax: +886 2 290 82201.Air quality has emerged as a significant concern due to its direct impact on human health. Over recent decades, India has witnessed a marked deterioration in air quality due to rising anthropogenic emissions and climate change. The COVID-19 lockdown offered a unique opportunity to examine air pollutant reductions under restricted human activities. This study conducted a long-term analysis of air quality in five major Indian cities—Delhi, Kolkata, Bengaluru, Hyderabad, and Visakhapatnam—by analysing variations in PM2.5, PM10, NOx, NH3, SO2, CO, and O3, incorporating a de-weathering strategy to isolate meteorological influences. In Delhi, we observed significant reductions in PM10 (92.50–136.70 μg/m³), NOx (62.13–151.91 ppb), and CO (0.53–0.88 mg/m³), which shifted health risks from the 'extreme' to 'low' category. Visakhapatnam also experienced notable declines in NOx levels (7.50–17.13 ppb). Conversely, Hyderabad exhibited no significant reductions, and AQHI increased (+0.97) due to rising NOx concentrations. Ozone concentrations showed a significant increase across cities, attributed to VOC-limited effects. The analysis revealed that meteorological variability and long-range transport of airmass played critical roles in shaping pollutant concentrations. These findings highlight the complexity of urban air quality dynamics and underscore the benefits of emission reductions for public health.http://www.sciencedirect.com/science/article/pii/S2405844025001422Urban air qualityDe-weathering analysisGAM modelStatistical analysisAir quality health indexCOVID-19 lockdown |
spellingShingle | Akshansha Chauhan Guggilla Pavan Sai Chin-Yu Hsu Advanced statistical analysis of air quality and its health impacts in India: Quantifying significance by detangling weather-driven effects Heliyon Urban air quality De-weathering analysis GAM model Statistical analysis Air quality health index COVID-19 lockdown |
title | Advanced statistical analysis of air quality and its health impacts in India: Quantifying significance by detangling weather-driven effects |
title_full | Advanced statistical analysis of air quality and its health impacts in India: Quantifying significance by detangling weather-driven effects |
title_fullStr | Advanced statistical analysis of air quality and its health impacts in India: Quantifying significance by detangling weather-driven effects |
title_full_unstemmed | Advanced statistical analysis of air quality and its health impacts in India: Quantifying significance by detangling weather-driven effects |
title_short | Advanced statistical analysis of air quality and its health impacts in India: Quantifying significance by detangling weather-driven effects |
title_sort | advanced statistical analysis of air quality and its health impacts in india quantifying significance by detangling weather driven effects |
topic | Urban air quality De-weathering analysis GAM model Statistical analysis Air quality health index COVID-19 lockdown |
url | http://www.sciencedirect.com/science/article/pii/S2405844025001422 |
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