In the heart of South Asia, a silent invader is sweeping across borders, choking cities and islands alike. This isn’t a military force, but a plume of air pollution, originating from the Indo-Gangetic Plain, one of the most densely populated areas on Earth. A recent study, led by Benjamin de Foy from Saint Louis University’s Department of Earth, Environmental and Geospatial Science, sheds light on the long-range transport of aerosols and biomass burning smoke from the Bay of Bengal to the Indian Ocean, with significant implications for the energy sector.
The Indo-Gangetic Plain is a hotspot of air pollution, with extreme levels of PM2.5—fine particulate matter that can penetrate deep into the lungs and even enter the bloodstream. The region’s air quality is a toxic cocktail of mobile source emissions, industrial point sources, and agricultural biomass burning. But the impacts of this pollution aren’t confined to the plain itself. As de Foy explains, “The pollution doesn’t respect political boundaries. It’s a regional problem that requires a regional solution.”
De Foy and his team analyzed multiyear time series of hourly PM2.5 concentrations from five major cities around the Bay of Bengal and three locations in the Maldives. They used a sophisticated statistical model, a type of interpretable machine learning, to estimate the impacts of long-range transport, biomass burning, and local factors like wind and vertical mixing. The results were striking. Long-range transport accounts for about a third of the long-term average PM2.5 loading in Dhaka and the Maldives. Biomass burning, a significant source of pollution during certain times of the year, contributes substantially to PM2.5 levels in Dhaka, Kolkata, and even the distant Maldives.
For the energy sector, these findings are a wake-up call. The long-range transport of pollution means that efforts to improve air quality in one city or country can have benefits far beyond its borders. This could open up new opportunities for regional cooperation on air quality management, with potential benefits for public health and the environment. Moreover, understanding the sources of PM2.5 can help energy companies tailor their strategies for reducing emissions. For instance, if biomass burning is a significant source of pollution, there might be opportunities for promoting cleaner energy sources or improving agricultural practices.
The study also highlights the potential of interpretable machine learning in environmental science. By providing quantitative information on the sources of PM2.5, these models can help policymakers and businesses make informed decisions. As de Foy puts it, “We’re not just saying that pollution is a problem. We’re saying that we can pinpoint where it’s coming from and how it’s moving. That’s powerful information.”
The research, published in Environmental Research Communications, which translates to Environmental Research Letters, is a significant step forward in our understanding of regional air pollution. But it’s just the beginning. As de Foy notes, “This is a complex problem that will require a concerted effort to solve. But I’m optimistic. The more we understand about the problem, the better equipped we are to tackle it.”
The findings could shape future developments in air quality management, encouraging a more regional approach and leveraging advanced technologies like machine learning. For the energy sector, this means not just reducing emissions, but doing so in a way that’s informed by a deep understanding of how pollution moves and changes. It’s a challenging task, but as de Foy’s work shows, it’s also an opportunity—to improve air quality, protect public health, and promote sustainable development. The future of air quality management is regional, data-driven, and full of possibilities.