Developing particle emission inventories using remote sensing (PEIRS)

Autor: Qian Di, Chia-Hsi Tang, Joel Schwartz, Petros Koutrakis, Alexei Lyapustin, Brent A. Coull
Rok vydání: 2016
Předmět:
Zdroj: Journal of the Air & Waste Management Association. 67:53-63
ISSN: 2162-2906
1096-2247
DOI: 10.1080/10962247.2016.1214630
Popis: Information regarding the magnitude and distribution of PM2.5 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 PM2.5. 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 northeast United States during the period of 2002–2013 using high- resolution 1 km × 1km 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.
Databáze: OpenAIRE
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