New research has found that our global understanding of black carbon pollution is still incomplete, especially in parts of the world where people are most vulnerable to its health effects.
Black carbon (aka soot) is a byproduct of the incomplete combustion of fossil fuels and a major contributor to climate change and human health impacts. As such it is important that we have a good idea of how is being emitted. And where.
Traditionally, however tracking its presence has been difficult, especially in developing countries, because of limited on-the-ground data and dated computer models.
Researchers at the McKelvey School of Engineering at Washington University in St. Louis set out to establish how accurately we estimating black carbon pollution around the world by using high-quality black carbon measurements from a global network called SPARTAN (Surface Particulate Matter Network).
The Spartan network includes monitoring sites not only in wealthy countries but also in fast-growing cities in the Global South, such as Bangladesh, Ethiopia and Nigeria.
These real-world measurements were compared with computer model predictions that use different emissions databases, such as the Community Emissions Data System (CEDS), the Emissions Database for Global Atmospheric Research (EDGAR) and the Hemispheric Transport of Air Pollution initiative (HTAP). The models were run using advanced tools that can now simulate air pollution at much finer scales than before.
The researchers found that in developed countries like the U.S., Canada, and Australia, the models did a good job of predicting the amount of black carbon in the air, but in the Global South, they underestimated it by about 38% on average. In some cities, like Dhaka and Addis Ababa, the model estimates were off by more than half.
The problem, say the team, isn’t with the measurement methods or weather patterns, but rather with the incomplete or outdated data about pollution sources in these countries. For example, many emissions come from sources such as cooking with firewood, open waste burning or unregulated diesel engines, sources that are often left out or poorly estimated in official data.
Interestingly, the models overestimated black carbon in Beijing, China. It is speculated that this is because recent efforts have greatly reduced pollution there but the emissions data used by the models hasn’t been fully updated to reflect those changes.
Yuxuan Ren, a doctoral student in the McKelvey School of Engineering at Washington University explained: ‘There are different burning activities in this region, such as household burning of wood and charcoal for cooking and heating. Emission inventory developers will summarise the total amount of fuel used from all these sources and estimate the total black carbon emission in the air.
‘But comparing different studies from different regions, estimating emissions from these diffuse and inefficient sources is challenging and may lead to a certain degree of bias. We think this is the primary reason for the underestimation of black carbon.’
The full research can be read here.
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