Source-Receptor Relationships Between Precursor Emissions and O₃ and PM_2.5 Air Pollution Impacts

Area of Science:

• Environmental Science
• Atmospheric Chemistry
• Air Quality Modeling

Background:

• Reduced complexity models are crucial for assessing air pollution control scenarios.
• Existing tools often lack rigorous evaluation against sophisticated models for predicting pollution changes.
• Accurate estimation of particulate matter (PM2.5) and ozone (O3) is vital for public health and policy.

Purpose of the Study:

• To introduce and evaluate a new reduced complexity tool, Pattern Constructed Air Pollution Surfaces (PCAPS).
• To assess PCAPS's skill in estimating annual average PM2.5 and seasonal MDA8 O3 across the United States.
• To validate PCAPS's ability to predict air quality changes resulting from emission controls compared to photochemical grid models.

Main Methods:

• PCAPS was developed to estimate PM2.5 and O3 concentrations based on source locations.
• The tool was evaluated by comparing its predictions against emission control scenarios from state-of-the-science photochemical grid models.
• PCAPS was also applied retrospectively to predict PM2.5 chemical components for comparison with surface measurements.

Main Results:

• PCAPS accurately captured the magnitude and spatial variations of measured PM2.5 chemical components.
• Model performance for ambient measurements was comparable to other reduced complexity tools.
• PCAPS effectively reproduced the changes in O3 and PM2.5 predicted by photochemical transport models under various emission scenarios.

Conclusions:

• PCAPS is a flexible and reliable tool for estimating air quality and source-receptor relationships.
• Its ability to interpolate air pollution gradients makes it ideal for integration into larger air quality management frameworks.
• PCAPS provides valuable data for informing policy decisions, including the monetization of health effects from air pollution.