Publication: The Relationship between PM2.5 and Solar Cell Electricity Generation Using Aerosol Optical Depth (AOD)
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Issued Date
2025-01-01
Resource Type
ISSN
16866576
eISSN
26730014
Scopus ID
2-s2.0-85214022048
Journal Title
International Journal of Geoinformatics
Volume
21
Issue
1
Start Page
83
End Page
96
Rights Holder(s)
SCOPUS
Bibliographic Citation
International Journal of Geoinformatics Vol.21 No.1 (2025) , 83-96
Suggested Citation
Lalaeng S., Thanasang T., Puttinet P., Srireuan N., Chavanavesskul S. The Relationship between PM2.5 and Solar Cell Electricity Generation Using Aerosol Optical Depth (AOD). International Journal of Geoinformatics Vol.21 No.1 (2025) , 83-96. 96. doi:10.52939/ijg.v21i1.3797 Retrieved from: https://hdl.handle.net/20.500.14740/20113
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Abstract
This study analyses the relationship between PM2.5 concentrations, derived from Aerosol Optical Depth (AOD), and solar power generation at a solar farm owned by a “Private Owner" in Samut Prakan Province, Thailand. Most existing research emphasizes directly measuring dust accumulation on panels or converting AOD values into particulate matter levels, with limited focus on seasonal variations or applying remote sensing data, such as AOD, to assess solar energy impacts. This research seeks to address these gaps by examining the effects of dust across seasons using satellite-derived data. PM2.5 data from pollution monitoring stations of the Pollution Control Department, AOD data from the MCD19A2.061 product, and solar power generation data from the Electricity Generating Authority of Thailand (EGAT) in 2022 are utilized. The results indicate a negative correlation between PM2.5 concentrations and solar power generation during the summer (R² =-0.7), meaning that as PM2.5 levels increase, solar power generation decreases. A regression equation used for power prediction achieved an accuracy of R² = 0.97. In contrast, a positive correlation is observed during the winter (R² = 0.6), suggesting that as PM2.5 levels increase, solar power generation increases, with a prediction accuracy of R² = 0.93. No significant correlation is found during the rainy season (R² =-0.07), likely due to other influencing factors. When predicting solar power generation in different areas, the distinct physical and seasonal factors unique to each location should be considered.