Demonstration of a mobile Flux Laboratory for the Atmospheric Measurement of Emissions (FLAME) to assess emissions inventories.

J Environ Monit

Via Department of Civil and Environmental Engineering, Virginia Polytechnic Institute and State University, 418 Durham Hall (0246), Blacksburg, VA 24061, USA.

Published: February 2009

The advancement of air quality science and the development of effective air quality management plans require accurate estimates of emissions. In response to the need for new approaches to quantifying emissions, we have designed a mobile Flux Lab for the Atmospheric Measurement of Emissions (FLAME) that uses eddy covariance for the direct measurement of anthropogenic emissions at the neighborhood scale. To demonstrate the FLAME's capabilities, we have deployed it in the Huntington-Ashland region at the borders of Ohio, Kentucky and West Virginia. This area routinely experiences high ozone and fine particulate matter (PM(2.5)) concentrations and is home to a significant amount of industrial activity, including coal storage and transport. Experiments focused on carbon dioxide (CO(2)), nitrogen oxides (NO(x)) and fine particulate matter (PM(2.5)). Spikes in CO(2) and NO(x) concentrations were correlated with the passage of trains and barges through the FLAME's footprint. Calculated barge emission factors ranged from 49 to 76 kg NO(x) tonne(-1) fuel and agreed well with previously published values. Fluxes measured at three sites in the town of Worthington were mainly positive. They ranged between -6.5 to 29 mg m(-2) s(-1) for CO(2) and -9.7 x 10(-5) to 9.1 x 10(-5) mg m(-2) s(-1) for PM(2.5). We illustrate how the measurements can be compared to emissions inventories on a per capita basis for greenhouse gases and countywide for other pollutants. The results show that a mobile eddy covariance system can be used successfully to measure fluxes of multiple pollutants in a variety of settings. This alternative method for estimating emissions can be a useful tool for assessing uncertainties in emissions inventories and for improving their accuracy.

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http://dx.doi.org/10.1039/b810798jDOI Listing

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