Abstract

Air quality modeling for research and regulatory applications often involves executing many emissions sensitivity cases to quantify impacts of hypothetical scenarios, estimate source contributions, or quantify uncertainties. Despite the prevalence of this task, conventional approaches for perturbing emissions in chemical transport models like the Community Multiscale Air Quality (CMAQ) model require extensive offline creation and finalization of alternative emissions input files. This workflow is often time-consuming, error-prone, inconsistent among model users, difficult to document, and dependent on increased hard disk resources. The Detailed Emissions Scaling, Isolation, and Diagnostic (DESID) module, a component of CMAQv5.3 and beyond, addresses these limitations by performing these modifications online during the air quality simulation. Further, the model contains an Emission Control Interface which allows users to prescribe both simple and highly complex emissions scaling operations with control over individual or multiple chemical species, emissions sources, and spatial areas of interest. DESID further enhances the transparency of its operations with extensive error-checking and optional gridded output of processed emission fields. These new features are of high value to many air quality applications including routine perturbation studies, atmospheric chemistry research, and coupling with external models (e.g., energy system models, reduced-form models).

Highlights

  • Air pollution causes significant adverse health effects, including premature mortality, with more than 4 million deaths attributed to PM2.5 and ozone exposure globally in 2015 (US EPA, 2019a; Cohen et al, 2017; Burnett et al, 2018)

  • This paper describes the version of DESID as it exists in Community Multiscale Air Quality (CMAQ) version 5.3.2 (US EPA, 2020a)

  • We conclude with some thoughts on potential future directions in emissions modeling for air quality applications

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Summary

Introduction

Air pollution causes significant adverse health effects, including premature mortality, with more than 4 million deaths attributed to PM2.5 (particulate matter with diameter less than 2.5 μm) and ozone exposure globally in 2015 (US EPA, 2019a; Cohen et al, 2017; Burnett et al, 2018). Air pollution research studies are often designed to characterize the fate and transport of novel pollutants, evaluate emerging chemical mechanism configurations, or quantify the impact of updates to emissions speciation profiles (Qin et al, 2021; Lu et al, 2020) These kinds of detailed studies either do not warrant or cannot afford the effort required to generate entirely new bottom-up emission datasets, and the procedures required to introduce emissions of new species to existing input files are available but are again expensive and error-prone. In response to these and other motivations, modules have been developed for other modeling systems to process emissions inventories with activity data and chemical speciation within the CTM simulation. We conclude with some thoughts on potential future directions in emissions modeling for air quality applications

Algorithm framework
Interface
Emissions scaling
Region definitions
Family definitions
Aerosol Size Distribution definitions
Diagnostics
Relevant applications
Findings
Conclusions and future directions
Full Text
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