Temporalization of peak electric generation particulate matter emissions during high energy demand days

Caroline M. Farkas, Michael D. Moeller, Frank A. Felder, Kirk R. Baker, Mark Rodgers, Annmarie G. Carlton

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)


Underprediction of peak ambient pollution by air quality models hinders development of effective strategies to protect health and welfare. The U.S. Environmental Protection Agencys community multiscale air quality (CMAQ) model routinely underpredicts peak ozone and fine particulate matter (PM2.5) concentrations. Temporal misallocation of electricity sector emissions contributes to this modeling deficiency. Hourly emissions are created for CMAQ by use of temporal profiles applied to annual emission totals unless a source is matched to a continuous emissions monitor (CEM) in the National Emissions Inventory (NEI). More than 53% of CEMs in the Pennsylvania-New Jersey-Maryland (PJM) electricity market and 45% nationally are unmatched in the 2008 NEI. For July 2006, a United States heat wave with high electricity demand, peak electric sector emissions, and elevated ambient PM2.5 mass, we match hourly emissions for 267 CEM/NEI pairs in PJM (approximately 49% and 12% of unmatched CEMs in PJM and nationwide) using state permits, electricity dispatch modeling and CEMs. Hourly emissions for individual facilities can differ up to 154% during the simulation when measurement data is used rather than default temporalization values. Maximum CMAQ PM2.5 mass, sulfate, and elemental carbon predictions increase up to 83%, 103%, and 310%, at the surface and 51%, 75%, and 38% aloft (800 mb), respectively.

Original languageEnglish
Pages (from-to)4696-4704
Number of pages9
JournalEnvironmental Science and Technology
Issue number7
Publication statusPublished - Apr 7 2015

ASJC Scopus subject areas

  • Chemistry(all)
  • Environmental Chemistry

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