City-scale analysis of annual ambient PM_2.5 source contributions with the InMAP reduced-complexity air quality model: a case study of Madison, Wisconsin

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 with EVs. Similar percent reductions are calculated for population-weighted concentrations over Madison. Replacing 100% of light-duty vehicles 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.

Combining Satellite‐Derived PM2.5 Data and a Reduced‐Form Air Quality Model to Support Air Quality Analysis in US Cities

“…However, the statewide scaling approach does not improve the model's bias and error in predicting particulate nitrate. If InMAP users are interested in investigating state‐specific questions, it can be appropriate to apply state SFs (Jackson et al., 2023 ). We recommend considering particulate nitrate bias and error for that state to evaluate if this methodology is suitable.…”

“…For example, Jackson et al. ( 2023 ) used the data from five speciated monitors in Wisconsin to scale InMAP output, an appropriate approach due to the smaller domain of their research.…”

“…As such, the emissions inventory is out of date, particularily for the EGU sector (U.S. Environmental Protection Agency, 2017 ). Previous research with this model have updated the emissions inputs manually using more up‐to‐date available data, which could be an appropriate approach for an individual city and state analysis (Jackson et al., 2023 ). Future work could evaluate if applying a second set of satellite‐derived SFs could scale the emissions temporally and further ensure the applicability of the InMAP model to contemporary energy and air pollution inqueries.…”

“…However, it could be a suitable methodology if the number and location of regulatory air quality monitors fit the domain of the research question and calculated model performance statistics indicate as such. For example, Jackson et al (2023) used the data from five speciated monitors in Wisconsin to scale InMAP output, an appropriate approach due to the smaller domain of their research.…”

Environmental Justice and Systems Analysis for Air Quality Planning in the Port of Oakland in California

“…Overall, there are 1576 census tracts included in the exposure domain. We elected to use InMAP/the ISRM, which models the physical transport and chemical formation of pollutants using 2005 WRF-Chem based meteorological data, as our mechanistic air quality model because of its efficiency , and applications to exposure domains of similar scale. ,− InMAP has also been used outside of the United States, − making the methodological steps presented in this research adaptable for other locations. Despite some limitations in the InMAP/ISRM (e.g., based on 2005 meteorological data, use of annually averaged input data, parametrization of some chemical relationships, underestimation of particulate sulfate and overestimation of particulate ammonium formation), it has been shown to have similar performance to other reduced-complexity air quality models and is within published air quality model performance criteria used to quantify monetized health damages. ,− …”