Atmospheric particulate matter (PM) can diminish solar energy production through both ambient PM and PM deposited on solar photovoltaics (PVs). The global solar energy loss due to PM pollution is equivalent to tens of billions of US dollars annually. Regions such as northern India and eastern central China with the most expanding solar PV energy production are also having the highest PM pollution. Solar energy loss was estimated to be an enormous ~25–35% in these regions with almost equal contributions from ambient and deposited PM. Even though the importance of both types of PM pollution on solar PV energy production has been soaring given a projected 4-fold increase of global solar power capacity over the next 20 years, to date few studies have quantified their respective and combined impacts at a granular level on individual solar farms in a cost-effective way. In this study, we combined global-coverage, daily, 3 m resolution, 3-band (RGB) micro-satellite imagery with energy production data at a solar farm in Pune, India to predict solar energy loss due to PM. We measured the probability distribution difference in pixel values between each solar farm’s solar panel image and background image by computing the Kullback-Leibler (KL)-divergences in all RGB channels. We show that a random forest using RGB KL-divergences and several ancillary variables as features can on average over a period of 2.5 years predict the solar farm’s daily solar energy production and as a result the total solar energy loss from both sources within 5.5%. We assessed the total loss in non-monsoon seasons from both sources to be ~11-17% over the sampling period. We further used both standard and ambient PM-corrected satellite images to estimate the contributions from ambient and deposited PM and obtained an average of ~30-40% contribution from deposited PM. Our approach can inform solar companies about the breakdown of their daily solar energy production loss due to PM and help them determine the optimal solar panel cleaning strategies.

Assessing impact of particulate matter air pollution on solar energy production using a micro-satellite-based remote sensing approach

Macromolecular Chemistry: The Second Century

[Author Benefits](https://scimeetings.acs.org/?utm_source=pubs_content_marketing&utm_medium=website&utm_campaign=0320_MCF_NPI_Launch_Spring_Homepage&ref=pubs_content_marketing)

[How to use SciMeetings](https://storage.googleapis.com/dl.morressier.com/acs/SciMeetings-ACSSpring2020-HowToGuide_M3020-Final.pdf)

[Frequently Asked Questions](https://storage.googleapis.com/dl.morressier.com/acs/SciMtng_ACSNMSp2020_FAQ_M3120_Final.pdf)

ACS Spring 2020 National Meeting & Expo

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.