Spatial PM<sub>2.5</sub> mobile source impacts using a calibrated indicator method
Creators: name: Xinxin Zhai
name: Mulholland, James A.
name: Friberg, Mariel D.
name: Holmes, Heather A.
name: Armistead G. Russell
name: Yongtao Hu
Publisher: name: Taylor & Francis
Year: 2018
Subjects: subject: Medicine
subject: Microbiology
subject: FOS: Biological sciences
subject: FOS: Biological sciences
subject: Biotechnology
subject: 59999 Environmental Sciences not elsewhere classified
subject: FOS: Earth and related environmental sciences
subject: FOS: Earth and related environmental sciences
subject: Sociology
subject: FOS: Sociology
subject: FOS: Sociology
subject: 69999 Biological Sciences not elsewhere classified
subject: 80699 Information Systems not elsewhere classified
subject: FOS: Computer and information sciences
subject: FOS: Computer and information sciences
subject: 19999 Mathematical Sciences not elsewhere classified
subject: FOS: Mathematics
subject: FOS: Mathematics
subject: Inorganic Chemistry
subject: FOS: Chemical sciences
subject: FOS: Chemical sciences
subject: Plant Biology

description: Motor vehicles are major sources of fine particulate matter (PM<sub>2.5</sub>), and the PM<sub>2.5</sub> from mobile vehicles is associated with adverse health effects. Traditional methods for estimating source impacts that employ receptor models are limited by the availability of observational data. To better estimate temporally and spatially resolved mobile source impacts on PM<sub>2.5</sub>, we developed an approach based on a method that uses elemental carbon (EC), carbon monoxide (CO), and nitrogen oxide (NO<sub>x</sub>) measurements as an indicator of mobile source impacts. We extended the original integrated mobile source indicator (IMSI) method in three aspects. First, we generated spatially resolved indicators using 24-hr average concentrations of EC, CO, and NO<sub>x</sub> estimated at 4 km resolution by applying a method developed to fuse chemical transport model (Community Multiscale Air Quality Model [CMAQ]) simulations and observations. Second, we used spatially resolved emissions instead of county-level emissions in the IMSI formulation. Third, we spatially calibrated the unitless indicators to annually-averaged mobile source impacts estimated by the receptor model Chemical Mass Balance (CMB). Daily total mobile source impacts on PM<sub>2.5</sub>, as well as separate gasoline and diesel vehicle impacts, were estimated at 12 km resolution from 2002 to 2008 and 4 km resolution from 2008 to 2010 for Georgia. The total mobile and separate vehicle source impacts compared well with daily CMB results, with high temporal correlation (e.g., <i>R</i> ranges from 0.59 to 0.88 for total mobile sources with 4 km resolution at nine locations). The total mobile source impacts had higher correlation and lower error than the separate gasoline and diesel sources when compared with observation-based CMB estimates. Overall, the enhanced approach provides spatially resolved mobile source impacts that are similar to observation-based estimates and can be used to improve assessment of health effects. <i>Implications:</i> An approach is developed based on an integrated mobile source indicator method to estimate spatiotemporal PM<sub>2.5</sub> mobile source impacts. The approach employs three air pollutant concentration fields that are readily simulated at 4 and 12 km resolutions, and is calibrated using PM<sub>2.5</sub> source apportionment modeling results to generate daily mobile source impacts in the state of Georgia. The estimated source impacts can be used in investigations of traffic pollution and health.