City-scale analysis of annual ambient PM2.5 source contributions with the InMAP reduced-complexity air quality model: a case study of Madison, Wisconsin. (1st March 2023)

• Record Type: Journal Article
• Authors: Jackson, Clara M; Holloway, Tracey; Tessum, Christopher W
• Is Part Of: Environmental research: infrastructure and sustainability. Volume 3:Number 1(2023)
• Publication Date: 2023-03-01
• Subjects: air quality -- energy -- public health -- electric vehicles -- coal power plants; Human ecology -- Periodicals; Sustainable development -- Periodicals; Infrastructure (Economics) -- Periodicals; Economic development -- Environmental aspects -- Periodicals; 333.707
• DOI: 10.1088/2634-4505/acb0fa
• Languages: English
• ISSNs: 2634-4505

Abstract

Abstract: Air pollution is highly variable, such that source contributions to air pollution can vary even within a single city. However, few tools exist to support city-scale air quality analyses, including impacts of energy system changes. We present a methodology that utilizes regional ground-based monitor measurements to scale speciation data from the Intervention Model for Air Pollution (InMAP), a national-scale reduced-complexity model. InMAP, like all air quality models, has biases in its concentration estimates; these biases may be pronounced when examining a single city. We apply the bias correction methodology to Madison, Wisconsin and estimate the relative contributions of sources to annual-average fine particulate matter (PM2.5 ), as well as the impacts of coal power plant retirements and electric vehicle (EV) adoption. We find that the largest contributors to ambient PM2.5 concentrations in Madison are on-road transportation, contributing 21% of total PM2.5 ; non-point sources, 16%; and electricity generating units, 14%. State-wide coal power plant closures from 2014 to 2020 and planned closures through 2025 were modeled to assess air quality benefits. The largest relative reductions are seen in areas north of Milwaukee (up to 7%), though population-weighted PM2.5 was reduced by only 3.8% across the state. EV adoption scenarios lead to a relative reduction in PM2.5 over Madison of 0.5% to 13.7% or a 9.3% reduction in total PM2.5 from a total replacement of light-duty vehicles (LDVs) with EVs. Similar percent reductions are calculated for population-weighted concentrations over Madison. Replacing 100% of LDVs with EVs reduced CO2 emissions by over 50%, highlighting the potential benefits of EVs to both climate and air quality. This work illustrates the potential of combining data from models and monitors to inform city-scale air quality analyses, supporting local decision-makers working to reduce air pollution and improve public health.