Examining the Relationship Between MODIS Satellite Aerosol Optical Depth (AOD) Data at Different Hours and PM10 Air Pollution Index in Ahvaz City

In the past few decades, air pollution, especially PM10 particulate matter, has been a significant concern affecting people's health and is clearly visible as the most important environmental problem and air pollutant in Ahvaz, Iran. On the other hand, remote sensing (RS) has been introduced as a suitable source for air pollution monitoring by researchers in the last decade. Therefore, the aim of the present study was to determine and investigate the relationship between aerosol optical depth (AOD) data from MODIS sensor at different hours and PM10 air pollution index in Ahvaz, Iran, in 2022.

The present cross-sectional study was conducted in 2022. Given the critical role and widespread distribution of dust particles, the present study examined the relationship between AOD data from the MODIS sensor and PM10 data from the Environmental Protection Agency station in Ahvaz, Iran, over the course of one year. To evaluate the correlation between these two parameters, hourly PM10 values at 12:00, 13:00, 14:00, and 15:00 were analyzed for different seasons. The results of the present analysis allowed the researchers to assess seasonal and temporal effects on the correlation between these parameters. The collected data were analyzed using SPSS software and the statistical correlation coefficient test.

The correlation results revealed a significant relationship between the datasets, indicating a strong association between AOD and PM10 in the specified region. The daily correlations at different hours—12:00, 13:00, 14:00, and 15:00—showed coefficients of 0.41, 0.75, 0.72, 0.78, and 0.86, respectively, with the highest correlation observed at 15:00. The concentration of PM10 particles in the air is influenced by dust events, which intensify during the hot season, with the highest correlations mostly observed in spring and summer. Additionally, the correlation coefficient reached its lowest point during the cold season (fall and winter).

the present study examined various time intervals, each showing different levels of correlation. Based on the obtained correlation coefficients, remote sensing data can be used as a reliable source for air pollution monitoring.