Literature DB >> 18318344

Fine particulate matter emissions inventories: comparisons of emissions estimates with observations from recent field programs.

Heather Simon1, David T Allen, Ann E Wittig.   

Abstract

Emissions inventories of fine particulate matter (PM2.5) were compared with estimates of emissions based on data emerging from U.S. Environment Protection Agency Particulate Matter Supersites and other field programs. Six source categories for PM2.5 emissions were reviewed: on-road mobile sources, nonroad mobile sources, cooking, biomass combustion, fugitive dust, and stationary sources. Ammonia emissions from all of the source categories were also examined. Regional emissions inventories of PM in the exhaust from on-road and nonroad sources were generally consistent with ambient observations, though uncertainties in some emission factors were twice as large as the emission factors. In contrast, emissions inventories of road dust were up to an order of magnitude larger than ambient observations, and estimated brake wear and tire dust emissions were half as large as ambient observations in urban areas. Although comprehensive nationwide emissions inventories of PM2.5 from cooking sources and biomass burning are not yet available, observational data in urban areas suggest that cooking sources account for approximately 5-20% of total primary emissions (excluding dust), and biomass burning sources are highly dependent on region. Finally, relatively few observational data were available to assess the accuracy of emission estimates for stationary sources. Overall, the uncertainties in primary emissions for PM2.s are substantial. Similar uncertainties exist for ammonia emissions. Because of these uncertainties, the design of PM2.5 control strategies should be based on inventories that have been refined by a combination of bottom-up and top-down methods.

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Year:  2008        PMID: 18318344     DOI: 10.3155/1047-3289.58.2.320

Source DB:  PubMed          Journal:  J Air Waste Manag Assoc        ISSN: 1096-2247            Impact factor:   2.235


  2 in total

1.  Measuring Spatial and Temporal PM2.5 Variations in Sacramento, California, Communities Using a Network of Low-Cost Sensors.

Authors:  Anondo Mukherjee; Steven G Brown; Michael C McCarthy; Nathan R Pavlovic; Levi G Stanton; Janice Lam Snyder; Stephen D'Andrea; Hilary R Hafner
Journal:  Sensors (Basel)       Date:  2019-10-29       Impact factor: 3.576

2.  Variability in Observation-based Onroad Emission Constraints from a Near-road Environment.

Authors:  Heather Simon; Barron H Henderson; R Chris Owen; Kristen M Foley; Michelle G Snyder; Sue Kimbrough
Journal:  Atmosphere (Basel)       Date:  2020-11-18       Impact factor: 2.686

  2 in total

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