Predictive Modelling and Spatial Analysis of M3 Motorway-Induced Air Quality Impacts in Punjab
Keywords:
Air quality, predictive modeling, environmental impacts, spatial analysisAbstract
There is an increasing global concern about the potential environmental impacts of the continuous expansion of highways and transportation infrastructure, particularly regarding air quality. This research provides a comprehensive analysis of the effects of highway development, specifically the M3 Motorway, on air quality in the Punjab region by integrating innovative air impact prediction models and spatial analysis techniques. This research uses ground-based data from 2006 to 2022 and Remote Sensing data (Sentinel-5P and MODIS) from 2018 to 2022. The analysis is focused on the concentrations of key air pollutants, including sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), methane (CH4), aerosol optical depth (AOD), formaldehyde (HCHO), and ozone (O3). The results revealed that SO2 concentrations fluctuated noticeably, ranging from 0.08 to 0.26 µmol/m². Higher amounts were regularly noted in the Renala Khurd and Kabir Wala regions. Similarly, NO2 values ranged from 78.65 to 97.54 µmol/m² in the Renala Khurd and Sahiwal regions. The concentrations of CO varied between 36.30 and 40.30 µmol/m², with higher values found in urban areas. The concentration of CH4 was noted from 188058 to 1920961.12 µmol/m², reflecting the effects of industry and agricultural pollutants. Predictive modelling coupled with spatial analysis provides a thorough framework for evaluating the complex links between highway construction and air quality. This study helps to balance contemporary transportation networks and environmental stewardship by advancing a more nuanced knowledge of the environmental impact of highway developments in Punjab.
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