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<p>This study investigates the spatial and temporal dynamics of air pollution in Guwahati, a rapidly urbanizing city in Northeast India, by integrating Geographic Information System (GIS)-based land-use analysis with advanced econometric techniques. Using daily air quality data from 1,461 observations across 2020–2023, the study examines major pollutants—PM2.5, PM10, NOₓ, SO₂, CO, NH₃, and O₃—through Robust Least Squares (RLS), Quantile Regression (QR), and Vector Autoregression (VAR) models. Spatial interpolation (IDW) and land-use overlays reveal high concentrations of PM2.5 and AQI values in traffic-heavy and construction-saturated zones such as GS Road and Beltola, coinciding with a 275% increase in built-up land over the past decade. Statistical findings confirm PM2.5 as the most consistent and dominant AQI predictor (RLS coefficient = 1.189; p &lt; 0.01), followed by PM10, NH₃, and SO₂. Seasonal analysis shows winter peaks in PM2.5 (mean = 113.05&#xa0;µg/m<sup>3</sup>) due to temperature inversions and limited dispersion. QR results reveal pollutant impacts intensify at higher AQI quantiles, especially for PM2.5, SO₂, and CO, indicating disproportionate health risks during severe episodes. These findings have direct implications for targeted air quality management, urban planning, and health policy. By linking pollutant behavior with land-use patterns, temporal feedbacks, and exposure risks, the study contributes a multidimensional framework for understanding air pollution in mid-sized South Asian cities and underscores the urgency of localized, data-informed interventions.</p>

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Spatial econometric assessment of urban form and seasonal variation impacts on ambient air quality index in Guwahati city of Assam

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月28日

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  • Bishal Das,
  • Priom Seal,
  • Mrinal Saikia,
  • Achintya Pran Hazarika

摘要

This study investigates the spatial and temporal dynamics of air pollution in Guwahati, a rapidly urbanizing city in Northeast India, by integrating Geographic Information System (GIS)-based land-use analysis with advanced econometric techniques. Using daily air quality data from 1,461 observations across 2020–2023, the study examines major pollutants—PM2.5, PM10, NOₓ, SO₂, CO, NH₃, and O₃—through Robust Least Squares (RLS), Quantile Regression (QR), and Vector Autoregression (VAR) models. Spatial interpolation (IDW) and land-use overlays reveal high concentrations of PM2.5 and AQI values in traffic-heavy and construction-saturated zones such as GS Road and Beltola, coinciding with a 275% increase in built-up land over the past decade. Statistical findings confirm PM2.5 as the most consistent and dominant AQI predictor (RLS coefficient = 1.189; p < 0.01), followed by PM10, NH₃, and SO₂. Seasonal analysis shows winter peaks in PM2.5 (mean = 113.05 µg/m3) due to temperature inversions and limited dispersion. QR results reveal pollutant impacts intensify at higher AQI quantiles, especially for PM2.5, SO₂, and CO, indicating disproportionate health risks during severe episodes. These findings have direct implications for targeted air quality management, urban planning, and health policy. By linking pollutant behavior with land-use patterns, temporal feedbacks, and exposure risks, the study contributes a multidimensional framework for understanding air pollution in mid-sized South Asian cities and underscores the urgency of localized, data-informed interventions.