Furthering a partnership: Air quality modeling and improving public health

ABSTRACT

Air pollution is one of the top five risk factors for population health globally. In recent years, advances in air pollution data and modeling have occurred simultaneously with advances in data and methods available for health studies. To realize the potential of such advances, the air quality modeling and public health communities should continue to strengthen their engagements and build effective interdisciplinary teams. These partnerships recognize the tight coupling between air quality and health data and methods and the value of expertise from multiple fields to ensure that this information is applied appropriately with a deep understanding of its capabilities and limitations. Building effective multidisciplinary teams takes a sustained commitment to engage with partners with different expertise to establish working partnerships and collaborations to better address public exposures to air pollution. Effective partnerships enable better targeting of research resources to answer important questions and provide essential information to protect public health.

Implications: Air quality models are an effective tool that can be used to estimate air pollution exposure in epidemiologic studies and risk assessments. Working together in collaborative multidisciplinary teams will lead to greater advancements in understanding of air pollution impacts and in useful information informing actions to improve public health.

Introduction

Air pollution is one of the top five risk factors for population health globally (Health Effects Institute 2019). The public health community is interested in identifying what sources contribute to population exposures and health impacts and understanding how such exposures vary across an area or through time. Air pollution monitoring data is generally insufficient to achieve the spatial and temporal resolution desired in health studies and the reliability of emerging sensor data has not been fully established. Air quality models have been used for decades in conducting environmental policy assessments and have proven capability to inform all of these things!

Air quality models applied in health assessments vary from Gaussian dispersion models that predict primary pollutant concentrations at high resolution to chemical-transport models (CTMs) that predict primary and secondary pollutants at coarser resolution. Most advanced air quality models trace their origin to the urban air quality model developed for the Los Angeles basin in California (Reynolds, Roth, and Seinfeld 1973). Many improvements in modeling have occurred over the past five decades through focused research. For example, some advancements include improvements to emissions inventories and spatial and temporal allocation of those emissions, finer scale spatial and temporal resolution, improved chemical mechanisms, inclusion of aerosols, addition of heterogenous chemistry, more complex land/atmosphere interactions, and inclusion of tools that track source contribution to pollution levels. As a result of these advancements, air quality models have improved over time and can be applied to answer more complicated questions, making them even more valuable for providing key information for the public health community.

Air quality models are increasingly being used in health assessments to predict exposure. An air quality model can best do this when it has the ability to accurately predict important pollutant concentrations and how those concentrations respond to changing emissions or meteorological conditions. To do this, it is important that these models have accurate representation of emissions and meteorological inputs as well as state of the science atmospheric processes. Work to develop and advance air quality models continues within the United States Environmental Protection Agency (U.S. EPA), other federal agencies, academia, and across the modeling community. The Community Multiscale Air Quality Modeling System (CMAQ) is an open-source modeling system that has been used for over two decades by the U.S. EPA and State governments to inform air quality management decisions (https://epa.gov/CMAQ). The core science in CMAQ has been validated, e.g. Kang et al. (2019) and CMAQ has been extensively evaluated against observational data, e.g. Dennis et al. (2010); Appel et al. (2017); Kelly et al. (2019b). Because much of the work to improve air quality models focuses on increasing their correlation with measurements of components of the air pollution mixture, they are valuable tools for informing air quality management decisions. The development of these models for air quality management applications provides a strong foundation for their use to improve characterization of air pollution exposure in the health studies that ultimately drive the air quality management system (Bachmann, 2007).

Using air quality models to better understand the public health impacts of air pollution

Decades of research in multiple health disciplines have established a strong understanding of the impact of air pollution on public health. For the “criteria” pollutants, like particulate matter (PM) and ozone, the body of evidence is extensive and shows that exposure to these pollutants is linked to multiple health impacts, e.g., Bachmann (2007). EPA synthesizes and summarizes this body of evidence in the Integrated Science Assessments (ISAs) as part of their periodic reviews of the national ambient air quality standards (NAAQS), e.g., U.S. EPA (2019); U.S. EPA (2020a). For other pollutants, such as hazardous air pollutants (HAPs), such as benzene, evidence supports the link between exposure and serious health effects such as cancer (https://www.epa.gov/haps/health-effects-notebook-hazardous-air-pollutants). Scientists and policymakers remain interested in further increasing the understanding of connections between air pollutant concentrations and health impacts, which in many cases, requires improved understanding of where, when, and to what pollutants people are exposed.

As we continue to strive for greater understanding of health impacts of PM, ozone, and other air pollutants, when used appropriately by taking into account precision and accuracy, air quality models are a valuable tool to investigate these questions. Air quality models, particularly when combined with measurement data (e.g., ground-based monitoring and satellite data) or in a hybrid combination of model outputs, have increasingly been used to improve our understanding of human exposure to air pollution and the impact on public health. Depending on the particular health assessment, the demands that are placed on the air quality modeling system may vary with respect to accuracy and precision in space and time, as well as responsiveness to changes in emissions. Often these needs are influenced by the underlying health information and the pollutant or pollutants of interest.

In combination with ambient monitoring and other data, photochemical and dispersion models can be used to estimate exposure to pollutants in various types of epidemiology studies to estimate health risks associated with air pollution impacts on human health (e.g., asthma incidence, hospital admissions, mortality). For example, as illustrated in Figure 1, time-series epidemiological studies typically investigate associations between population-level, day-to-day changes in air pollutants and short-term changes in acute health outcomes (e.g., hospital admissions for respiratory disease). Cohort studies, on the other hand, are often used to investigate longer term health impacts associated with exposure to air pollution and are concerned with spatial and temporal variability of air pollutants. For these studies, individuals are grouped within cohorts, according to the degree of exposure, and the incidence of disease or mortality is evaluated over time. Generally, for time-series and cohort studies a priority is placed on predicting the spatial exposure gradient across the area of interest (cohort studies) and/or the relative changes in concentration over time (time-series studies), rather than the absolute value of the concentration. For a panel study, however, more extensive health information is known about the participants. The panel of initially healthy people are considered along with the amount of their exposure over a particular period of time and this is compared to how many of them are more/less healthy after this period of time. For these types of studies, exposure estimates are needed for specific individuals so ambient monitoring data are currently the most often used.

Figure 1. Simple representation of time series, cohort, and panel epidemiology studies

Major advances in the use of CTMs to produce exposure data for epidemiology research have occurred over the past 15 years, from the description of the potential by Jerrett et al. (2005) to the major review of currently available techniques and data by Diao et al. (2019). Using these models in combination with either satellite or ground-based measurement data is currently one of the most promising methods for developing exposure estimates and health studies. Several research groups have recently used techniques for data fusion of measurements and air quality model output to produce the air pollution exposure data for epidemiologic studies (Laurent et al. 2014; Ostro et al. 2015; Özkaynak et al. 2013; Weber et al. 2016). For example, Laurent et al. (2016) use a nested matched case–control approach with three different methods to estimate air pollution exposure. Analysis of their results showed that interpolation of ambient measurements provides acceptable estimates of monthly temporal trends and general spatial trends. However, the chemical transport models do a better job with the spatial variability and provide exposure estimates for more pollutants and components (Friberg et al. 2016). The fact that the temporal variability is not predicted by the air quality model as well as the interpolated measurements highlights the need for careful understanding of both the model capabilities and the requirements of the health analysis.

As air quality models continue to improve their capabilities to provide estimates of concentrations for finer and finer spatial scales, questions arise regarding the level of resolution needed for reliable exposure estimates. In epidemiological studies, the resolution needed depends on a number of considerations and finer spatial resolution alone may not be sufficient to provide better exposure information. For epidemiologic studies that focus on health impacts in groups of people who generally do not travel far from their homes (e.g. elderly) a higher spatial resolution may improve exposure estimates, while for studies that consider a population with extensive mobility across a region due to regular travel or commuting patterns, greater spatial resolution may lead to a requirement for additional information on time-activity-mobility patterns. For larger time series and cohort studies, health data may only be available at the county or census tract level; therefore, exposure data should also be considered at a similar scale. In some cases, improved estimates of aggregated exposure can be created by combining finer spatial and temporal scale air quality estimates from models with time-activity-mobility data and aggregating to the county or census tract. Air quality modeling estimates at a finer spatial scale, when informed with reliable emission and meteorological inputs, have been found to be beneficial in many health assessments, which seek to estimate the risks or health impacts of pollutants by combining known information about risk factors or the relationship of pollutant concentration to a particular health response with air pollutant concentration estimates. For example, EPA’s 2014 National Air Toxics Assessment used a hybrid modeling approach to estimate concentrations of 40 HAPS at a local scale across the US (Scheffe et al. 2016; U.S. EPA 2018a). Additionally, EPA has used air quality models to provide spatially refined concentrations, that when used in conjunction with the Air Pollutants Exposure Model (APEX), estimate population exposure across various urban areas in the NAAQS reviews for ozone and sulfur dioxide (SO₂) (U.S. EPA 2018b, 2020b). Lastly, EPA’s Detroit Multipollutant Pilot Project used both photochemical and dispersion air quality models to estimate the risks and exposures associated with several multipollutant control strategies and to demonstrate how this type of information can help inform cost-effective decisions (Fann et al. 2011; Wesson et al. 2010). Note, however, that evaluating very high-resolution modeling is challenging because of the sparse nature of monitoring networks.

Due to the limited spatial and temporal coverage of ambient monitoring networks, air quality models are especially useful tools for informing health studies or risk assessments that cover areas with limited measurement data. For instance, recent epidemiologic studies have characterized exposure for the full Medicare cohort of roughly 61 million people (Di et al. 2017) using predictions of machine learning models that combine information from monitoring networks with CTM predictions and other data (Di et al. 2016). In sensitivity tests using a nearest-monitor exposure assignment, the cohort was necessarily limited to members within 50-km of a monitoring site (Di et al. 2017), thereby reducing the study population. CMAQ PM_2.5 (PM with a diameter of less than 2.5 microns) predictions were identified as important predictor variables in the ensemble of model of Diao et al. (2019), demonstrating the value of geophysical process modeling for informing the machine learning and statistical models used in major epidemiologic studies. However, McGuinn et al. (2017) reported some disagreement in the variation in PM_2.5-health associations between urban and rural areas using exposure assignments based on different modeling approaches. Also, studies have reported increasing differences among model predictions and degradation in performance statistics with increasing distance from the nearest monitoring site (Berrocal et al. 2020; Huang et al. 2018; Jin et al. 2019; Kelly et al. 2019a). These results motivate additional work to improve air quality model predictions in sparsely monitored areas and to develop model evaluation approaches that target the features of most importance for specific health studies. Such developments will require experts in air quality modeling and public health working together.

Some of the current, and much of the future needs, placed on air quality models are influenced by the health community’s research directions and the outcomes of that research. These needs will further stress and stretch the capabilities of these models in order to appropriately inform health studies. For instance, limitations in monitoring of ultrafine particles have led to the use of air quality models for characterizing ultrafine particle exposure in health studies (Ostro et al. 2015). Additionally, the health community has wrestled with the relationship between individual components of PM and health outcomes for many years. As new air quality measurement and modeling methods emerge, health researchers continue to use this new information to assess the relationship between PM components and specific health outcomes. While this continues to be a significant research question, some studies suggest that no specific component of PM can be excluded as a possible contributor to its toxicity (Vedal et al. 2013) and the 2019 PM ISA concluded that health effects of PM cannot be linked to any single PM component (U.S. EPA 2019). However, the details of chemical composition of particles that can be estimated with air quality models may be useful to investigate health effects to a particular pollution source or to show how composition may modify PM toxicity. Indeed, recent studies have used CTMs in developing fields of PM_2.5 component concentrations for use in health studies (van Donkelaar et al. 2019) and NASA has partnered with epidemiologists and health organizations through the Multi-Angle Imager for Aerosols (MAIA; https://maia.jpl.nasa.gov/) investigation to understand how different types of particulate matter pollution affect human health.

Research needs

Air quality models can be powerful tools for investigations into the health impacts of air pollution. However, the value of these models is greatest when combined with a clear understanding of how health studies will utilize their output. Because the health research questions can vary in the demand placed on the accuracy of the air quality modeling system, it is important that air quality modelers work collaboratively with health researchers. In addition, air quality modelers should communicate both the capabilities and limitations of information that these models provide for current studies and to inform needed science improvements.

By developing a clear understanding of the information needs for health studies and assessments, air quality modelers can ensure they effectively provide appropriate data. Additionally, a clear understanding of the exposure requirements and health assessment needs can guide future improvements to air quality models. Greater collaborations with health researchers will allow the air quality modeling community to go beyond pursuing interesting chemistry or meteorology questions to better consider what aspects of spatial and temporal resolution, pollutant composition, accuracy, and precision are important to public health decision-makers to guide model improvement investments.

Cleaner air leads to new and different science questions. As air quality has improved in the United States, understanding the impacts of exposures at lower concentrations becomes increasingly important. Decreased emissions from the transportation and power generation sectors have increased the importance of emissions from commercial/residential sectors (Dedoussi et al. 2020) and consumer products (McDonald et al. 2018). Similarly, as regional concentrations of pollutants such as ozone and PM2.5 decrease, local air quality problems such as air toxics become the next challenge for improving public health. Health risk assessments and air quality model results are building the case for the importance of emerging contaminants such as ethylene oxide and perfluoroalkyl and polyfluoroalkyl substances (PFAS) by helping to identify sources, understand transport in the atmosphere, and estimate potential exposure through air or other media. For reliable risk assessments, it is key to continue to improve the capabilities and accuracy of air quality model predictions. For example, the air quality model predictions of pollutant deposition are very important for capturing pollutant transport in the atmosphere before contributing to chemical exposures through soil and water.

During the past several months of the global pandemic, activity patterns have changed dramatically and the combined health impacts from exposure to multiple stressors (e.g. air pollution and the SARS-CoV-2 virus) have become further recognized. Collaborative efforts between air quality modelers and health experts are needed to help determine the strength of these interactions and where they are greatest. Additionally, collaborative effort may provide insight into the impact of cumulative exposures over longer time periods and series of repeated exposures over multiple days or weeks. Importantly, questions investigating the impact of neighborhoods and issues related to environmental justice would benefit from researchers with a range of expertise working together.

Rapid increases in the availability of air quality and health data provide opportunities to better understand the large public health burden associated with air pollution. However, more finely resolved health studies with advanced algorithms place greater demands on the technical information and warrant a renewed effort to coordinate expertise across the air quality modeling and public health communities. Such partnerships could be expanded in the future to include experts from social sciences and other disciplines as health studies identify new factors (e.g., green space, Heo and Bell (2019)) for elucidating the relationships between air pollution and health. By working collaboratively across multiple disciplines, the scientific community can provide the most useful information to understand air pollution impacts and inform actions to improve public health.

Disclaimer

This document has been reviewed in accordance with U.S. Environmental Protection Agency policy and approved for publication. Any mention of trade names, manufacturers or products does not imply an endorsement by the United States Government or the U.S. Environmental Protection Agency.

Acknowledgment

The authors thank their colleagues Lars Perlmutt, Bryan Hubbell, Pat Dolwick and Tyler Fox for helpful feedback on this document. They also thank S.T. Rao for many years of encouraging communication aimed at increasing our ability to understand air pollution health impacts.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

Sherri W. Hunt

Sherri W. Hunt is the Principal Associate National Program Director with the U.S. Environmental Protection Agency, Office of Research and Development, Air & Energy National Research Program located in Washington, DC, USA.

Darrell A. Winner

Darrell A. Winner is a Senior Science Advisor with the U.S. Environmental Protection Agency, Office of Research and Development, Center for Public Health and Environmental Assessment located in Washington, DC, USA.

Karen Wesson

Karen Wesson is the Group Leader of the Ambient Standards Group at the U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards located in Research Triangle Park, NC, USA.

James T. Kelly

James T. Kelly is an Environmental Scientist with the U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards located in Research Triangle Park, NC, USA.