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Climate TRACE and Clean Air Fund explore estimating PM2.5 and other non-GHG co-pollutant emissions from assets globally - Climate TRACE

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Climate TRACE and Clean Air Fund explore estimating PM2.5 and other non-GHG co-pollutant emissions from assets globally

Jun 28, 2024

By Dan Cavallari


smog obscures a city skyline

Airborne particulate pollution causes a variety of adverse health and environmental impacts. Yet reliable and current data to mitigate that pollution are conspicuously absent. Current datasets are often out of date, inaccurate, or otherwise lacking in specificity. 

Climate TRACE has independently modeled greenhouse gas (GHG) emissions estimates since 2021 and asset-level emissions estimates since 2022. Our methodology employs a combination of satellite imagery and other forms of remote sensing, a variety of additional data sources, and machine learning. 

We’re now poised to apply and adapt that methodology to other non-GHG forms of air pollution, thanks to an initial scoping study supported by the Clean Air Fund and undertaken by Climate TRACE coalition member Johns Hopkins University Applied Physics Laboratory (JHUAPL). The scoping study explores how Climate TRACE might model particulate matter (PM2.5) and other non-GHG co-pollutant emissions from assets around the world in our global dataset.

The scoping study notes that gaps in knowledge of sources and magnitude of ambient air pollution are significant impediments to addressing critical health and environmental problems. Climate TRACE aims to quickly fill that gap. 

While air pollution datasets do exist, they can lack source specificity, distribution, and global coverage; recency is also often an issue. Climate TRACE’s methodology addresses those major gaps. Integration of particulate emissions data into Climate TRACE could enable new solutions for improving human health and overall climate modeling.

Complementary nature of GHG tracking and non-GHG co-pollutant tracking


At a high level, Climate TRACE’s approach across sectors models asset emissions via three fundamental steps: 

1. Remotely observe an asset’s activity, such as steam plumes from a power plant’s smokestacks, an infrared heat signature from a steel factory, or a ship’s course and speed across the ocean

2. Associate emissions factors with those assets and their activities

3. Estimate absolute emissions based on activity data across a specific timeframe, such as monthly or annual

Our researchers have vetted a similar process to independently estimate non-GHG related emissions from specific sources — particularly inhalable airborne particulate matter (PM2.5). Fossil fuel combustion and other anthropogenic activities are major contributors to PM2.5. Thus the same assets and activities that Climate TRACE already tracks for GHG emissions modeling could also be tapped for insights into PM2.5 air pollution.

While there are some limitations to this approach, Climate TRACE’s bottom-up approach to data collection will substantially advance the state of the art amongst global emissions datasets. “Most gridded datasets utilize spatial and temporal proxies in order to distribute country-level emissions onto a grid,” noted the JUHAPL team in the Clean Air Fund scoping study. Climate TRACE’s data, on the other hand, computes data at the facility level, eliminating “the need for spatial proxies for disaggregation.” And Climate TRACE’s data are continually updated (moving toward monthly granularity), providing a much clearer estimate of non-GHG emissions throughout the year. 

That data can be directly attributed to a specific source. Its movement from the source to other locations locally or globally can be tracked and estimated within a specific timeframe to give a better sense of that pollutant’s impacts on stakeholders and environments.

The specificity and recency of Climate TRACE’s data, “would be a substantial contribution and improvement over current sets of inventories available,” according to the JHUAPL scoping study.

Approach to the scoping study


The scope of the study falls into six facets to identify potential high-impact applications of global air quality data. 

1. Applying general emissions factors to estimate non-GHG emissions for assets in the Climate TRACE database.

2. Conducting detailed comparisons of satellite-derived activity data estimates with ground sensor data to generate even higher-quality emissions modeling for a subset of assets in a subset of regions.

3. Calculating the distance from each emitting source to nearby population centers, particularly hospitals, schools, low-income housing, and vulnerable communities.

4. Understanding how Climate TRACE data can be used for source apportionment.

5. Using air quality emissions measurement to calibrate and improve GHG estimates. 

6. Using inversion modeling to supplement and improve air quality estimates and apportion to emissions sources. 

In these ways, GHG emissions modeling from Climate TRACE could contribute to better understanding of PM2.5 and other air quality concerns.

Future potential for non-GHG pollutant tracking


Climate TRACE’s ability to immediately impact non-GHG pollution data collection shortens the timeline during which the impacts of airborne particulates can be assessed across demographics and regions. 

The World Health Organization estimates that globally 6.7 million premature deaths are caused by poor air quality, with 4.2 million of those deaths caused by ambient outdoor air pollution. Greater availability of reliable and current reporting that tracks the origin, path, and specific environmental impact of such air pollution could help to bend that arc. With Climate TRACE non-GHG pollution data, it becomes possible to attribute specific pollution sources to entities, thereby creating pathways for action toward accountability and remediation. 

Another application poised for immediate impact lies within the financial sector. The Joint Impact Model (JIM), which helps financial institutions assess the environmental risks associated with their financial portfolios and maintain regulation compliance in Europe, notes that its clients often struggle with regulation compliance due to a lack of data. Climate TRACE’s data would help financial institutions comply with regulations, and track the impact of financial interventions year over year. (Note: JIM and Climate TRACE previously announced a collaboration to bring greater transparency to financed GHG emissions.)

For entities responsible for airborne pollution, current and constantly-updated data would allow better decision-making around project siting. Renewable energy developers are already using similar data to build large clean energy projects in locations where they will displace the most GHG emissions from dirty power generators. Pairing this with new airborne pollution data would allow them to consider locations that would reduce non-GHG pollution and improve human health in nearby communities, as well. 

The expansion of Climate TRACE’s methodology into PM2.5 measurement estimation has clear and immediate implications for the improvement of global pollution data. The resulting data present high value to a wide variety of environmental stakeholders and improve understanding of GHG and non-GHG pollutant production, movement, and impact. 

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