Air Quality Assessment Methods: A Comprehensive Overview
Air quality assessment is crucial for estimating population exposures to air pollutants. These exposures can be defined as the product of the pollutant concentration and the time over which a person is in contact with this pollutant. Multiple methods are needed for a comprehensive air quality management knowledge base and capability. This document summarizes several air quality measurement and modeling methods that can be used to estimate ground-level air pollutant concentrations and presents multiple approaches to monitoring ambient air pollution at different spatial and temporal scales. Policy-makers and government officials can use the available methods summarized in this document to assess their country’s baseline air quality levels as well as monitor progress resulting from air pollution reduction policies. The document can further help officials develop plans for air quality monitoring and data management. It is also relevant in assisting national and local authorities responsible for protecting public health from the adverse effects of air pollution.
When deciding on how to best develop or improve their air pollution monitoring capability, countries can assess the ease of implementation within constraints: cost (capital and operating); human/ technical resources; and computational and energy requirements. Ideally, every nation should have access to at least one reference-grade monitor - opening the door to many other air quality methods. More importantly, no single method can address the entirety of a country’s air quality problem, and nations may want to employ a mixture of measurements and modeling methods to address their local air quality issues while balancing their national priorities and resource availability.
This article explores various air quality measurement and modeling methods, highlighting their strengths, limitations, and applications. It also delves into source-specific exposure assessment approaches and their role in understanding health impacts and environmental justice.
Ambient Air Pollution Monitoring Methods
Air quality measurements and models are presented in order of increasing complexity/technology, starting with the least complex. For each method a brief description is provided followed by its strengths and limitations as well as a few examples of global or regional applications. The Ambient Air Quality Directives also allow for objective estimation as an air quality assessment method for air quality zones with very good air quality. By identifying local pollution sources and information on regional air quality, an estimation of the concentration of a regulated pollutant is made.
For example, a country that has no monitoring may consider setting up a reference-grade monitor and complementing this monitor with passive samplers (low cost, low human resources for deployment, no energy requirement) or low-cost sensors (LCS) (low capital cost but medium operating cost, medium technical resource for calibration and modeller expertise, low energy but medium computing needs for big data) and dispersion modelling (medium capital cost, medium modeller expertise, medium computing needs). Countries with a sparse monitoring network may consider increasing the density of their reference-grade monitors as well as developing locally calibrated chemical transport models (CTMs) (medium capital cost, high modeller expertise, high computing needs).
Source-Specific Air Pollution Exposure Assessment Approaches
Identifying sources of air pollution exposure is crucial for addressing their health impacts and associated inequities. Researchers have developed modeling approaches to resolve source‐specific exposure for application in exposure assessments, epidemiology, risk assessments, and environmental justice. We explore six source‐specific air pollution exposure assessment approaches: Photochemical Grid Models (PGMs), Data‐Driven Statistical Models, Dispersion Models, Reduced Complexity chemical transport Models (RCMs), Receptor Models, and Proximity Exposure Estimation Models. These models have been applied to estimate exposure from sources such as on‐road vehicles, power plants, industrial sources, and wildfires.
We categorize these models based on their approaches for assessing emissions and atmospheric processes (e.g., statistical or first principles), their exposure units (direct physical measures or indirect measures/scaled indices), and their temporal and spatial scales. While most of the studies we discuss are from the United States, the methodologies and models are applicable to other countries and regions. We recommend identifying the key physical processes that determine exposure from a given source and using a model that sufficiently accounts for these processes. For instance, PGMs use first principles parameterizations of atmospheric processes and provide source impacts exposure variability in concentration units, although approaches within PGMs for source attribution introduce uncertainties relative to the base model and are difficult to evaluate.
The Role of Air Quality Exposure Assessments
Air quality exposure assessments involve quantifying the degree of exposure to pollutant sources, pathways, and chemical concentrations and identifying when and how such exposure occurs (Shaddick et al., 2018; Committee on Human and Environmental Exposure Science in the 21st Century et al., 2012). A traditional exposure assessment typically involves identifying the source of interest and its emissions, as well as estimating the contributions to air pollution concentrations in the environment.
We explore the application of source‐specific exposure assessments in four domains: epidemiological studies, risk assessments, and environmental justice (Table 1). Epidemiologists identify patterns, causes, and impacts of diseases in populations (Déglin et al., 2021; Shaddick et al., 2018). In air pollution epidemiology, this typically involves quantifying the association between exposure and health outcomes to establish exposure [or concentration]‐response functions (Alexeeff et al., 2015; Dionisio et al., 2016), which are often presented as relative risks or odds ratios. Source‐specific exposure metrics evaluate factors leading to health outcomes from specific pollutants or sources.
Air pollution risk assessments combine information from exposure assessments, epidemiological studies and baseline health information to estimate a health burden (Déglin et al., 2021). In combination with economic valuation, risk assessments can be extended to estimate monetary burdens or benefits of specific actions. Such analysis aids in formulating preventive strategies, for example, by developing cost‐benefit analyses of proposed regulations. This practice is required for EPA regulations, even though the Clean Air Act prohibits consideration of regulatory cost (Popp, 2003).
Environmental justice in air pollution quantifies disparities in exposure and health burdens across populations of different age, race, income, and education levels, and other factors (D’Evelyn et al., 2022; Gallagher & Holloway, 2022). Results from exposure assessments, risk assessments, and epidemiology may be used directly in environmental justice assessments (Johnston & Cushing, 2020). Researchers have argued that exposure assessments, risk assessments, epidemiological analyses, and environmental justice studies targeting individual sources yield more actionable results than quantifying impacts from air pollution from all sources (Gardner‐Frolick et al., 2022; Wambebe & Duan, 2020).
We have identified air pollution modeling techniques that are used as source‐specific air pollution exposure metrics and have summarized (a) the approach used by each model to account for emissions and atmospheric processes, and (b) the units produced by the metrics. Our primary goal is to classify these metrics, understand the rationale for their selection, evaluate efforts made to assess their uncertainty, and make recommendations for each of the four types of studies.
Three categories of processes dictate exposure to air pollution sources: emissions processes, physical processes, and chemical processes (Thakrar et al., 2020; Q. Wang et al., 2020; B. Xu et al., 2020). Emissions can be directly measured, as with large power plants under EPA mandates (Lavoie et al., 2017) or estimated using emission factors and activity data (Shen et al., 2021; Tainio et al., 2021). Estimated emissions generally have more uncertainty than measured emissions. Atmospheric processes that dictate pollution spread include plume rise, transport influenced by meteorology, aerosol microphysics, and wet and dry deposition (Seinfeld & Pandis, 2016).
We group models based on the approach they use to incorporate atmospheric processes into three categories: (a) first principles, (b) statistical, or (c) not explicitly considered. First principles approaches, also known as deterministic modeling, are based on a fundamental scientific understanding of the process; for example, the advection‐diffusion‐reaction equation represents a full characterization of the known processes that dictate pollutant transport and reactions in the atmosphere (Seinfeld & Pandis, 2016). First principles models include the Community Multiscale Air Quality (CMAQ), Comprehensive Air Quality Model with Extensions (CAMx) and Weather Research and Forecasting model with Chemistry (WRF‐Chems). Distinct from deterministic models, which are directly grounded in the physical understanding of atmospheric transport and chemistry, statistical models-including traditional land use regression and more flexible machine learning techniques that can better identify non‐linear relationships between inputs variables-use observed data to establish correlations between pollutant exposure and variables such as population density, land use, and proximity to emission sources (Wilkins et al., 2022; Yao et al., 2023).
We have identified two types of metrics used to quantify exposure: (a) air pollution concentration units and (b) relative non‐concentration indices (Table 2). Physical air pollution concentration units may be volumetric (e.g., parts per million by volume, ppmV) or mass‐based, such as micrograms per cubic meter (μg/m3). In the United States, gas concentrations are typically reported in volumetric units while particulate matter concentrations are reported in mass units (other countries tend to use mass units for gases and particles). Employing concentration units in source‐specific exposures allows for direct comparison against regulatory standards, ground‐based measurements, satellite observations, and/or outputs from other models. These comparisons should be made with an important limitation in mind: ambient air pollution observations generally do not measure source‐specific concentrations.
Under a second framework, exposure assessments do not use concentration units. Instead, researchers use relative indices assumed to reflect exposure to a source. An advantage of using these metrics in air pollution is their ability to translate the potentially abstract nature of air pollutant concentrations into more accessible descriptors of specific sources (e.g., number of nearby sources, or distance from a source), which may be of interest to regulators or the public. A disadvantage is the inability to compare exposure to existing standards or observed or modeled concentrations, leading to additional exposure misclassification above existing uncertainties in the model. For instance, a population living within 1 mile of a refinery may be assigned the same exposure as another population living nearby a different refinery; in contrast, a population assigned an exposure of 5 ppmV has the same exposure as a separate population being assigned 5 ppmV by the same model.
Exposure to individual sources varies in time and space. In contrast, while environmental justice studies typically rely more on spatial variability, recent advances in satellite remote sensing have allowed for the investigation of both spatial and temporal changes in air pollution related to environmental justice. As finer resolution satellite data become more available, these spatiotemporal analyses in environmental justice are likely to expand.
Air Quality Models and Their Applications
Atmospheric modeling is used by air quality managers to make decisions on effective and efficient ways to implement the National Ambient Air Quality Standards (NAAQS) and improve air quality. Advances in modeling enables users to better estimate the relationship between sources of pollution and their effects on ambient air quality, predict the impacts from potential emission sources, and simulate ambient pollution concentrations under different policy scenarios. This research is also enhancing the ability to conduct multipollutant air quality assessments at local, regional, national, and global scales in addition to developing multimedia and multi-stressor models to address complex environmental issues.
The Community Multiscale Air Quality (CMAQ) Modeling System is EPA’s premier modeling system for studying air pollution from local to hemispheric scales. For more than two decades, EPA and states have used CMAQ to translate fundamental atmospheric science principles to policy scenarios to support air quality management decisions. CMAQ combines meteorological, emissions, and air chemistry transport and deposition models to explore the estimated short- and long-term impacts of different policy and regulatory options, including actions to attain the NAAQS, and long-term impacts of the changing environment. Researchers lead efforts to conduct and apply fundamental physical science that improves CMAQ’s representation of complex atmospheric chemistry and dynamics pertinent to emerging problems and contaminants. Currently, CMAQ developers are broadening the model’s scope to enhance its ability to consider atmospheric phenomena from the global scale to the neighborhood scale.
Air quality dispersion models predict the impact of pollutants released from various sources such as power plants and roadways. EPA researchers, in collaboration with other scientists, developed the Air Quality Dispersion Model (AERMOD), which is the Agency-preferred and recommended dispersion modeling system used today. Research continues to provide updates to the model. To improve modeling capability, there is a need for more information on the influence that buildings, roadways and other structures have on the flow and dispersion of air pollution. Studies are conducted in the meteorological wind tunnel at the Fluid Modeling Facility in Research Triangle Park, North Carolina, and in the field. The tunnel is large enough to simulate pollution dispersion of a scaled replica of a building, power plant or other object of interest and surrounding topography.
Indoor Air Quality Models
Estimation of emissions, fate and transport of indoor air pollutants is an essential part of multi-pathway exposure assessment since most people spend a large portion of their time indoors. Researchers at EPA have developed indoor air modeling programs to assist with understanding indoor air pollution. These programs are Microsoft Windows-based and user friendly.
PARAMS implements 30 methods for estimating the parameters in indoor emissions source models, which are an essential component of indoor air quality and exposure models. IAQX and i-SVOC are used for dynamic modeling of the emissions, transport, and absorption of pollutants in the indoor environment. IAQX consists of five stand-alone simulation programs. A general-purpose simulation program performs multi-zone, multipollutant simulations and allows gas-phase chemical reactions. The other four programs implement fundamentally based models for special purposes. The i-SVOC program estimates the emissions, transport, and absorption of semi-volatile organic compounds (SVOCs) in the indoor environment as functions of time when a series of initial conditions are given. The program covers six types of indoor compartments: air (gas phase), air (particle phase), sources, sinks (i.e., absorption by interior surfaces), contaminant barriers, and settled dust. Using these indoor air modeling programs, scientists can gain a deeper understanding of the hazards and risks of many chemicals.
In 2020, EPA awarded nine Science to Achieve Results (STAR) grants to conduct research to improve air quality models by providing a better representation of atmospheric chemical reactions, which is known as chemical mechanisms. New insights on atmospheric chemistry and advancement in chemical mechanisms will improve air quality model predictions, which may inform the development of more effective strategies for improving air quality.
The Interconnectedness of the Environment
The natural world that consists of our atmosphere, land, water, and ecosystems is interconnected in many ways. Because of this connectivity, when a contaminant or pollutant is introduced into the environment, there can be a cascade of multiple impacts. Many of the nation’s environmental problems require an understanding of how contaminants move across air, land and water and how they may transform. For example, deposition of gases and particles from the atmosphere to the water and land surfaces results in acidification and eutrophication, which, in turn, impacts the abundance and diversity of aquatic species.
EPA scientists work to understand the interconnectedness of the environment across media by collecting and evaluating observations (e.g. pollutant concentrations in various media, dissolved oxygen levels, etc.) and developing, evaluating and applying multi-media modeling tools. The modeling enables simulation of the transport and transformation of chemicals through different media (e.g., air, water, soil) to provide data that can be used to address environmental challenges such as acid rain, nitrogen deposition and other environmental problems. Scientists are developing a coupled modeling system that includes exchange processes between existing air quality, meteorology, hydrology, and water temperature models to identify hotspots of high-water temperatures areas, which are correlated to high-nutrient loads in the Mississippi River Basin. In another project using meteorological and hydrology modeling, scientists are exploring methods of projecting the risks to ecosystems and stormwater management from changes in extreme rainfall.
Ensuring the quality of assessment information either generated through monitoring, modelling, or objective estimation is one of the paramount provisions of the Directive. Data quality objectives are prescribed with the aim of defining maximum allowed uncertainty and data coverage (proportion of the calendar year for which there is valid data). Member States are responsible for ensuring appropriate quality assurance of the assessment as well as the appropriate quality control of the information provided to the public and through the assessment reports. The Commission has set-up a community-wide process, managed by the Joint Research Centre (JRC). AQUILA is a network of national reference laboratories that provides expert advice to the Commission.
Table 1: Source‐Specific Exposure Assessments in Epidemiology, Risk Assessments, and Environmental Justice
| Domain | Description | Application |
|---|---|---|
| Epidemiological Studies | Quantifying the association between exposure and health outcomes. | Establishing exposure-response functions. |
| Risk Assessments | Estimating health burdens and monetary impacts of specific actions. | Formulating preventive strategies and cost-benefit analyses. |
| Environmental Justice | Quantifying disparities in exposure and health burdens across different populations. | Identifying and addressing environmental inequities. |
Figure 1: Information flow among source‐specific air pollution exposure assessment, risk assessment, epidemiology, and environmental justice research.