<p>Satellite data from the Sentinel-5P/TROPOMI sensor are used to monitor major air pollutants across Rajasthan in a consistent way. This study uses these observations to examine trends in six pollutants (NO<sub>2</sub>, SO<sub>2</sub>, CO, O<sub>3</sub>, CH<sub>4</sub> and HCHO) from 2018 to 2024 and to find statistically significant hotspot areas. An Analytic Hierarchy Process (AHP) is applied to assign weights to the six pollutants after checking that the consistency ratio is within the accepted limit (CR ≤ 0.10). The weighted layers are combined to create a Composite Burden (CB) map. Hot and cold spots are then detected using the Getis–Ord Gi* statistic, based on a regular grid, an optimized neighborhood distance, row-standardized spatial weights, and false-discovery-rate correction. The state-level trend analysis shows that ozone (O<sub>3</sub>) and methane (CH<sub>4</sub>) are increasing steadily and significantly during the study period. Sulfur dioxide (SO<sub>2</sub>) shows a small decline, while nitrogen dioxide (NO<sub>2</sub>), carbon monoxide (CO) and formaldehyde (HCHO) show only small or non-significant changes. The CB and Gi* results highlight three main hotspot belts. The first is in the northern canal region, including Sri Ganganagar, Hanumangarh and Churu. The second follows the north-eastern industrial and transport corridor from Khairthal–Tijara through Bhiwadi, Neemrana, Kotputli, Behror, Alwar, Bharatpur and Dholpur. The third lies in the south-central and south-eastern industrial belt around Chittorgarh and the Kota–Baran–Jhalawar region, where many power plants and cement units are located. District-level analysis also shows similar increases in O<sub>3</sub> and CH<sub>4</sub> across major urban and industrial centres. This combined AHP and Gi* approach gives a clear, decision-oriented picture of Rajasthan’s multi-pollutant burden and points to priority regions for actions such as NOₓ/VOC control, SO<sub>2</sub> reduction at large point sources, and CH<sub>4</sub> management in agricultural and waste-rich districts.</p>

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AHP-Weighted multi-pollutant composite with Getis–Ord Gi* hotspot analysis from sentinel-5P over Rajasthan, India

  • Saurabh Singh,
  • Wafa Saleh Alkhuraiji,
  • Rabin Chakrabortty,
  • Arkadeep Dutta,
  • Ali Raza,
  • Mohamed Zhran

摘要

Satellite data from the Sentinel-5P/TROPOMI sensor are used to monitor major air pollutants across Rajasthan in a consistent way. This study uses these observations to examine trends in six pollutants (NO2, SO2, CO, O3, CH4 and HCHO) from 2018 to 2024 and to find statistically significant hotspot areas. An Analytic Hierarchy Process (AHP) is applied to assign weights to the six pollutants after checking that the consistency ratio is within the accepted limit (CR ≤ 0.10). The weighted layers are combined to create a Composite Burden (CB) map. Hot and cold spots are then detected using the Getis–Ord Gi* statistic, based on a regular grid, an optimized neighborhood distance, row-standardized spatial weights, and false-discovery-rate correction. The state-level trend analysis shows that ozone (O3) and methane (CH4) are increasing steadily and significantly during the study period. Sulfur dioxide (SO2) shows a small decline, while nitrogen dioxide (NO2), carbon monoxide (CO) and formaldehyde (HCHO) show only small or non-significant changes. The CB and Gi* results highlight three main hotspot belts. The first is in the northern canal region, including Sri Ganganagar, Hanumangarh and Churu. The second follows the north-eastern industrial and transport corridor from Khairthal–Tijara through Bhiwadi, Neemrana, Kotputli, Behror, Alwar, Bharatpur and Dholpur. The third lies in the south-central and south-eastern industrial belt around Chittorgarh and the Kota–Baran–Jhalawar region, where many power plants and cement units are located. District-level analysis also shows similar increases in O3 and CH4 across major urban and industrial centres. This combined AHP and Gi* approach gives a clear, decision-oriented picture of Rajasthan’s multi-pollutant burden and points to priority regions for actions such as NOₓ/VOC control, SO2 reduction at large point sources, and CH4 management in agricultural and waste-rich districts.