Developing particle emission inventories using remote sensing (PEIRS)

ABSTRACT

Information regarding the magnitude and distribution of PM_2.5 emissions is crucial in establishing effective PM regulations and assessing the associated risk to human health and the ecosystem. At present, emission data is obtained from measured or estimated emission factors of various source types. Collecting such information for every known source is costly and time-consuming. For this reason, emission inventories are reported periodically and unknown or smaller sources are often omitted or aggregated at large spatial scale. To address these limitations, we have developed and evaluated a novel method that uses remote sensing data to construct spatially resolved emission inventories for PM_2.5. This approach enables us to account for all sources within a fixed area, which renders source classification unnecessary. We applied this method to predict emissions in the northeastern United States during the period 2002–2013 using high-resolution 1 km × 1 km aerosol optical depth (AOD). Emission estimates moderately agreed with the EPA National Emission Inventory (R² = 0.66–0.71, CV = 17.7–20%). Predicted emissions are found to correlate with land use parameters, suggesting that our method can capture emissions from land-use-related sources. In addition, we distinguished small-scale intra-urban variation in emissions reflecting distribution of metropolitan sources. In essence, this study demonstrates the great potential of remote sensing data to predict particle source emissions cost-effectively.

Implications: We present a novel method, particle emission inventories using remote sensing (PEIRS), using remote sensing data to construct spatially resolved PM_2.5 emission inventories. Both primary emissions and secondary formations are captured and predicted at a high spatial resolution of 1 km × 1 km. Using PEIRS, large and comprehensive data sets can be generated cost-effectively and can inform development of air quality regulations.

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Corrigendum

Acknowledgment

We especially thank Joy Lawrence and Alice Smythe for their comments and review of drafts.

Funding

This publication was made possible by U.S. EPA grant RD83479801. Its contents are solely the responsibility of the grantee and do not necessarily represent the official views of the U.S. EPA. Further, the U.S. EPA does not endorse the purchase of any commercial products or services mentioned in the publication.

Additional information

Funding

This publication was made possible by U.S. EPA grant RD83479801. Its contents are solely the responsibility of the grantee and do not necessarily represent the official views of the U.S. EPA. Further, the U.S. EPA does not endorse the purchase of any commercial products or services mentioned in the publication.

Notes on contributors

Chia-Hsi Tang

Chia-Hsi Tang is a Doctor of Science student in the Department of Environmental Health, Harvard School of Public Health.

Brent A. Coull

Brent A. Coull is a professor of the Department of Biostatistics, Harvard School of Public Health.

Joel Schwartz

Joel Schwartz is a professor of the Department of Environmental Health, Harvard School of Public Health.

Alexei I. Lyapustin

Alexei I. Lyapustin is a physical research scientist at the Climate and Radiation Laboratory at NASA.

Qian Di

Qian Di is a Doctor of Science student in the Department of Environmental Health, Harvard School of Public Health.

Petros Koutrakis

Petros Koutrakis is a professor of the Department of Environmental Health, Harvard School of Public Health.