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This paper explores the application of remote sensing in the analysis of air quality in the lower atmosphere (troposphere) over Sarajevo. The primary objective is to examine the relationship between suspended particulate matter (PM 2.5) measured at ground stations and satellite-derived aerosol optical depth (AOD) measurements. PM 2.5 data were collected from the US Embassy’s automatic monitoring station, while AOD measurements were obtained using the MODIS instrument on the Terra satellite. The data, processed using three different algorithms in Python, were analyzed through a linear regression model to assess the correlation between AOD and PM 2.5 values. Results for 2019 showed that the algorithm with a spatial resolution of 3 km produced the best correlation between AOD and PM 2.5. The study also compared two different algorithms with a spatial resolution of 10 km, revealing that the combined “Deep Blue” and “Dark Target” algorithms provided better correlation results than the “Deep Blue” algorithm alone. However, the linear regression model could not fully explain the correlation between AOD and PM 2.5. Therefore, the study suggests that additional factors, such as wind speed, humidity, temperature, and tropospheric gases (Ozone (O3), Carbon Dioxide (CO2), and Nitrogen Dioxide (NO2)), should be incorporated into future models to enhance accuracy.

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Remote Sensing Applications for Analyzing Air Quality in Sarajevo’s Lower Atmosphere

  • 会议论文

  • 发表日期：

    2026年

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  • Admir Mulahusić,
  • Jusuf Topoljak,
  • Ivan Regoje,
  • Muamer Đidelija,
  • Nedim Kulo,
  • Adis Hamzić,
  • Nedim Tuno

摘要

This paper explores the application of remote sensing in the analysis of air quality in the lower atmosphere (troposphere) over Sarajevo. The primary objective is to examine the relationship between suspended particulate matter (PM 2.5) measured at ground stations and satellite-derived aerosol optical depth (AOD) measurements. PM 2.5 data were collected from the US Embassy’s automatic monitoring station, while AOD measurements were obtained using the MODIS instrument on the Terra satellite. The data, processed using three different algorithms in Python, were analyzed through a linear regression model to assess the correlation between AOD and PM 2.5 values. Results for 2019 showed that the algorithm with a spatial resolution of 3 km produced the best correlation between AOD and PM 2.5. The study also compared two different algorithms with a spatial resolution of 10 km, revealing that the combined “Deep Blue” and “Dark Target” algorithms provided better correlation results than the “Deep Blue” algorithm alone. However, the linear regression model could not fully explain the correlation between AOD and PM 2.5. Therefore, the study suggests that additional factors, such as wind speed, humidity, temperature, and tropospheric gases (Ozone (O3), Carbon Dioxide (CO2), and Nitrogen Dioxide (NO2)), should be incorporated into future models to enhance accuracy.