Atmospheric Environment: X (Jan 2024)

Spatio-temporal assessment of aerosol and cloud properties using MODIS satellite data and a HYSPLIT model: Implications for climate and agricultural systems

  • Muhammad Haseeb,
  • Zainab Tahir,
  • Syed Amer Mahmood,
  • Saira Batool,
  • Aqil Tariq,
  • Linlin Lu,
  • Walid Soufan

Journal volume & issue
Vol. 21
p. 100242

Abstract

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Understanding the spatiotemporal dynamics of aerosol optical characteristics is crucial for assessing their impact on the climate system. This study focuses on Aerosol Optical Depth (AOD) at 550 nm, measured by the Moderate-resolution Imaging Spectroradiometer (MODIS) aboard the Terra satellite, over a decade (2011–2021) in ten major cities across Pakistan. Our primary objectives were to investigate AOD variability, assess its correlation with cloud parameters, examine the source and trajectory of aerosol-laden air masses, and analyze the relationship between AOD and the Angstrom exponent. We employed the Hybrid single-particle Lagrangian Integrated Trajectory (HYSPLIT) model to trace air mass origins and paths. AOD exhibited its highest values in low-latitude urban areas, reflecting significant human activity. Conversely, high-altitude and mountainous regions displayed the lowest AOD levels. In summer (June–August), AOD peaked at 1.19, while in winter (December–February), it dropped to 0.24. The negative correlation between AOD and the Angstrom exponent, particularly in southern and western Pakistan, highlighted aerosol particle size variations. We further explored the relationships between AOD and five cloud parameters: water vapor (WV), cloud fraction (CF), cloud optical thickness (COT), cloud top temperature (CTT), and cloud top pressure (CTP). These relationships were found to be weather-dependent. This study provides valuable insights into the spatio-temporal dynamics of AOD in Pakistan, contributing to a better understanding of its impact on climate. This information is essential for climate scientists, meteorologists, and environmental departments, facilitating informed decision-making and climate modeling in the region.

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