• PM₂.₅ and PM 10 have emerged as the primary air quality pollutants in Muzaffarnagar, consistently surpassing CPCB standards, whereas SO₂ and NOₓ levels remained compliant. • The crushing period (November-April) shows significantly higher pollutant levels than non-crushing months (May-October), with PM₂.₅ at 95-98 µg/m³ and PM₁₀ at 180-220 µg/m³ exceeding permissible limits due to bagasse combustion and sugarcane transportation. • Relative humidity emerged as the primary AQI driver (coefficient - 224.48), with temperature and wind speed also significant, and winter's low wind speeds and thermal inversions creating conditions that trap and concentrate pollutants. • Central and southern Muzaffarnagar exhibited persistent pollution hotspots throughout 2018-2024, particularly near sugar mills during crushing seasons when AQI values regularly ranged from 165-280 (Poor to Very Poor). • Significant geographic clustering of pollution was confirmed through Moran's I analysis (autocorrelation values - 0.612 non-crushing, 0.499 crushing), supporting targeted, location-based intervention approaches in critical zones. This study evaluates air quality in the Muzaffarnagar district. From 2018 to 2024, geo-spatial and geo-statistical methods were used to measure how gaseous and particulate pollutants changed over time and by season. Data was obtained from two stationary monitoring stations, provided by the Central Pollution Control Board (CPCB) and the Uttar Pradesh Pollution Control Board (UPPCB). A thorough analysis of the correlation between air quality and local meteorological parameters was performed for the Muzaffarnagar district. The findings indicate that PM₁₀ and Suspended Particulate Matter (SPM) are the primary pollutants contributing to air quality deterioration across Muzaffarnagar. In contrast, concentrations of NOX and SO2 remained beneath the regulatory standards set by the CPCB. The results indicate that the levels of air pollutants are significantly elevated in scenarios involving crushing operations compared to those without such activities. The spatiotemporal distribution of the Air Quality Index (AQI) clearly illustrates the acute air pollution affecting the Muzaffarnagar district. Ordinary Least Squares (OLS) regression analysis demonstrates a robust correlation between AQI and climatic conditions, specifically temperature, relative humidity, and wind velocity across different seasons. Furthermore, a spatial analysis was conducted to evaluate the extent of spatial association between AQI and weather conditions in the Muzaffarnagar district using Moran's I and Hot Spot Analysis. The results derived from Moran's I signify a strong positive autocorrelation between AQI and relative humidity, followed by temperature and wind speed. The integration of Geographic Information Systems (GIS) with statistical analyses helps elucidate the spatial impacts of meteorological conditions on air pollutants, providing a concise overview of the relatively critical zones that require increased attention from policymakers to implement strategies aimed at mitigating air pollution.
Neehat et al. (Wed,) studied this question.