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<p>Air pollution and climate-driven stressors increasingly threaten environmental sustainability in South Asia, yet few studies have jointly examined their effects on vegetation and surface thermal dynamics. This study uses a multi-sensor satellite framework to analyze the spatiotemporal interactions among atmospheric pollutants, vegetation indices, and land surface temperature (LST) across five representative districts of Punjab, Pakistan: Lahore, Faisalabad, Sialkot, Okara, and Bahawalpur, from 2019 to 2023. Datasets included PM₂.₅ (SatPM<sub>2.5</sub>), trace gases (NO₂, SO₂, HCHO from Sentinel-5P), vegetation indices (NDVI, EVI from MODIS), and LST (MODIS). Seasonal composites for summer and winter were generated, followed by grid-based sampling and Kendall’s tau correlation to capture non-linear associations. Results revealed strong seasonal contrasts: winter showed higher pollutant concentrations due to temperature inversions and atmospheric stagnation, while summer had better dispersion but elevated photochemical activity, particularly for HCHO. Vegetation indices were negatively correlated with PM₂.₅ and HCHO (τ = − 0.30 to − 0.50 in urban districts), indicating significant vegetation stress during peak pollution. In contrast, LST positively correlated with PM₂.₅ and NO₂ (τ = 0.25–0.45), especially in Lahore and Faisalabad, highlighting the interaction between urban heat island (UHI) effects and pollution accumulation. Semi-arid Bahawalpur recorded extreme summer LST (&gt; 50&#xa0;°C), while Sialkot and Okara showed cooling effects from vegetation. From 2019 to 2021, vegetation health declined by 12.3%, followed by a 7.8% recovery in 2023 due to favourable monsoon rains and improved irrigation practices. Urban centers exhibited the highest pollutant loads, while agricultural and semi-arid districts demonstrated contrasting resilience. These findings provide a scientific foundation for region-specific emission control, urban greening, and climate-smart agriculture, promoting environmental sustainability in rapidly urbanizing South Asia.</p>

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Spatio-temporal interactions of air pollution, vegetation health, and land surface temperature: Insights from multi-sensor satellite observations

  • 期刊论文

  • 发表日期：

    2026年4月24日

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  • Muhammad Haseeb,
  • Zainab Tahir,
  • Syed Amer Mahmood,
  • Aqil Tariq,
  • Nazih Y. Rebouh,
  • Khadeijah Yahya Faqeih,
  • Somayah Moshrif Alamri,
  • Eman Rafi Alamery

摘要

Air pollution and climate-driven stressors increasingly threaten environmental sustainability in South Asia, yet few studies have jointly examined their effects on vegetation and surface thermal dynamics. This study uses a multi-sensor satellite framework to analyze the spatiotemporal interactions among atmospheric pollutants, vegetation indices, and land surface temperature (LST) across five representative districts of Punjab, Pakistan: Lahore, Faisalabad, Sialkot, Okara, and Bahawalpur, from 2019 to 2023. Datasets included PM₂.₅ (SatPM2.5), trace gases (NO₂, SO₂, HCHO from Sentinel-5P), vegetation indices (NDVI, EVI from MODIS), and LST (MODIS). Seasonal composites for summer and winter were generated, followed by grid-based sampling and Kendall’s tau correlation to capture non-linear associations. Results revealed strong seasonal contrasts: winter showed higher pollutant concentrations due to temperature inversions and atmospheric stagnation, while summer had better dispersion but elevated photochemical activity, particularly for HCHO. Vegetation indices were negatively correlated with PM₂.₅ and HCHO (τ = − 0.30 to − 0.50 in urban districts), indicating significant vegetation stress during peak pollution. In contrast, LST positively correlated with PM₂.₅ and NO₂ (τ = 0.25–0.45), especially in Lahore and Faisalabad, highlighting the interaction between urban heat island (UHI) effects and pollution accumulation. Semi-arid Bahawalpur recorded extreme summer LST (> 50 °C), while Sialkot and Okara showed cooling effects from vegetation. From 2019 to 2021, vegetation health declined by 12.3%, followed by a 7.8% recovery in 2023 due to favourable monsoon rains and improved irrigation practices. Urban centers exhibited the highest pollutant loads, while agricultural and semi-arid districts demonstrated contrasting resilience. These findings provide a scientific foundation for region-specific emission control, urban greening, and climate-smart agriculture, promoting environmental sustainability in rapidly urbanizing South Asia.