A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Source-receptor reconciliation of fine-particulate emissions from residential wood combustion in the southeastern United States
Abstract An extensive collection of speciated PM2.5 measurements including organic tracers permitted a detailed examination of the emissions from residential wood combustion (RWC) in the southeastern United States over an entire year (2007). The Community Multiscale Air Quality model-based Integrated Source Apportionment Method (CMAQ-ISAM) was used in combination with the U.S. National Emissions Inventory (NEI) to compute source contributions from ten categories of biomass combustion, including RWC. A novel application of the receptor-based statistical model, Unmix, was used to subdivide the observed concentrations of levoglucosan, a unique tracer of biomass combustion. Using the CMAQ-ISAM and Unmix models together, we find that the emission-based RWC contribution to ambient carbonaceous PM2.5 predicted by the model is approximately a factor of two lower than indicated by observations. Recommendations for improving the temporal allocation of the emissions are proposed and tested to show a potential improvement in model RWC predictions, quantified by approximately 15% less bias. Further improvements in the sector predictions could be achieved with a survey-based analysis of detailed RWC emission patterns.
Highlights Air quality models underestimate PM contributions from residential wood combustion. Receptor- and source-based apportionment techniques allow to quantify levoglucosan bias. Levoglucosan bias can be improved with better temporal allocation of emissions.
Source-receptor reconciliation of fine-particulate emissions from residential wood combustion in the southeastern United States
Abstract An extensive collection of speciated PM2.5 measurements including organic tracers permitted a detailed examination of the emissions from residential wood combustion (RWC) in the southeastern United States over an entire year (2007). The Community Multiscale Air Quality model-based Integrated Source Apportionment Method (CMAQ-ISAM) was used in combination with the U.S. National Emissions Inventory (NEI) to compute source contributions from ten categories of biomass combustion, including RWC. A novel application of the receptor-based statistical model, Unmix, was used to subdivide the observed concentrations of levoglucosan, a unique tracer of biomass combustion. Using the CMAQ-ISAM and Unmix models together, we find that the emission-based RWC contribution to ambient carbonaceous PM2.5 predicted by the model is approximately a factor of two lower than indicated by observations. Recommendations for improving the temporal allocation of the emissions are proposed and tested to show a potential improvement in model RWC predictions, quantified by approximately 15% less bias. Further improvements in the sector predictions could be achieved with a survey-based analysis of detailed RWC emission patterns.
Highlights Air quality models underestimate PM contributions from residential wood combustion. Receptor- and source-based apportionment techniques allow to quantify levoglucosan bias. Levoglucosan bias can be improved with better temporal allocation of emissions.
Source-receptor reconciliation of fine-particulate emissions from residential wood combustion in the southeastern United States
Napelenok, Sergey L. (author) / Vedantham, Ram (author) / Bhave, Prakash V. (author) / Pouliot, George A. (author) / Kwok, Roger H.F. (author)
Atmospheric Environment ; 98 ; 454-460
2014-09-06
7 pages
Article (Journal)
Electronic Resource
English
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