A Systematic Literature Review Assessing the Inequities in Air Quality Monitor Placement

1. Introduction

Prior studies have shown that sparse regulatory monitoring networks make it challenging to gather local data on air quality, especially within historically disadvantaged communities (Stuart et al., 2009; Krupnick & Sullivan, 2018; Wang et al., 2024). Only about one in three counties in the United States has a monitor that is approved by the Environmental Protection Agency (EPA), and many of these counties are located in metropolitan areas where air quality monitors are required for every 500,000 people (EPA, 2024; EPA, 2025). The absence of air quality monitors contributes to the uncertainty of what air pollution levels are for both the public and regulators. Currently, about 100,000-200,000 people in the U.S. die annually because of air pollution (Thakrar et al., 2020). Short term and long term air pollution exposure have been linked to a wide range of adverse health conditions (Rau et al., 2022).

Black and Hispanic individuals, on average, consume fewer goods and services than Whites, but still face disproportionately high levels of air pollution exposure (Tessum et al., 2019). Existing literature thoroughly addresses the relationship between air quality exposure and racial minorities (Bullard, 1983; Mohai et al., 2009; Brender et al., 2011; Hajat et al., 2015; Mohai and Saha, 2015; Tessum et al., 2019; Tessum et al., 2021; Jbaily et al., 2022). Prior studies have not assessed if the placement of regulatory monitoring provides equitable monitoring of air quality across demographic, environmental, and structural equity measures. Addressing disparities in air quality monitoring requires targeted policy interventions and increased investment in monitoring infrastructure. Bridging these gaps requires a unified approach that prioritizes justice and equity with a focus on assessing the fairness and coverage of air quality monitoring to better protect underserved populations from health risks.

This systematic review assesses the current placement of outdoor air quality monitors to determine if there is equitable placement of monitoring in the U.S. Summarizing existing literature and assessing monitor placement provides justification for equity centered reforms in air quality monitoring. This review may serve as a resource to local regulators and policymakers by informing them of the equity metrics identified in the literature to assess the current placement of regulatory monitors and which demographics and environments could benefit from monitoring in the future.

2. Methodology

A systematic review is a structured process for identifying, analyzing, and synthesizing evidence from existing primary sources to address a defined set of research questions. To examine the existing literature assessing the current placement of outdoor air quality monitors for equitable monitoring, I conducted a systematic literature review using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) procedures laid out by Pai et al. (2004).

2.1. Review Question

This systematic review addresses the following review question: Does the current placement of EPA regulatory air quality monitors provide equitable monitoring of air quality across all social groups in the United States?

2.2. Information Sources

Searches for eligible studies were conducted in four sources: (1) key academic databases including Google Scholar, JSTOR, PubMed, Agricultural & Environmental Science Collection, Web of Science, Academic Search Ultimate, GreenFILE, and Pollution Abstracts; (2) hand searching of the following ten journals: American Journal of Public Health, Journal of Exposure Science and Environmental Epidemiology, Atmosphere, Environmental Sociology, Applied Geography, GeoHealth, JAMA Network Open, International Journal of Environmental Research and Public Health, Environmental Research Letters, and Environmental Science and Technology Letters; (3) the references of articles that met the inclusion criteria in the database and journal search; and (4) the articles cited by articles found from the databases, journals, and references sections through forward searching.

2.3. Search Strategy

My search string for electronic databases consisted of the following keywords with a Boolean AND: “air quality monitoring” AND “air quality sensors” AND “environmental justice” AND “disparities” AND “united states.” The database search was conducted in June of 2025 for the study period January 1999 to June 2025. After applying the search procedures, the collected articles were compiled into a database and checked for duplicates and language of publication. After completing the database search, manual searches were performed in the journals listed above. Additionally, the references of eligible articles identified in the initial search were reviewed. Lastly, with the selected articles from the three previous search types, a forward search was performed to identify articles that cited included studies. Articles were screened based on their title and abstract, then methodology, and finally followed by a screening of the full text to determine study inclusion.

2.4. Inclusion Criteria

After removing 5 duplicate records and one non-English manuscript, 219 articles were screened by title and abstract. Of these, 214 manuscripts were selected for further assessment based on their methodology. Five articles were retained for full text review, all of which met the inclusion criteria. Additionally, two were found through forward searching the articles cited by those selected in the database, journal, and reference review search. In total, 7 peer reviewed manuscripts were included in the final review depicted in Figure 1.

The following criteria were used to select studies for analysis in this review:

• Type and Year of Publication: Studies published in a peer reviewed journal between January 1, 1999, and June 8, 2025, to ensure comprehensive coverage from the database search and manual journal searches.
• Study Focus: Research related to EPA regulatory air quality monitoring in the United States.
• Language: Only studies published in English.
• Issue of Interest: Equitable air quality monitoring categorized by sociodemographic and environmental/structural equity metrics.
• In the methodology section each article had to have:

  a.

  At least one or both equity metrics

  b.

  EPA regulatory air quality monitoring

3. Results

3.1. Data Abstraction

Information extracted from each eligible article included general information such as the authors and year published, demographic equity measures, and environmental and structural equity measures.

Table 1 Summarizes the regulatory focused articles from the systematic review, which are central to evaluating monitoring equity. Five studies rely exclusively on regulatory grade monitors, and two use both regulatory and low-cost sensors to assess monitor placement. Figure 1. PRISMA CHART.

3.2. Study Characteristics

Across the 7 studies included in my review, researchers combined diverse data sources (PurpleAir, EPA Air Quality System (AQS) monitors, U.S. Census/American Community Survey (ACS) sociodemographics) with spatial statistics to assess who is monitored, who is missed, and why. Studies included in Table 2 applied a range of spatial statistical techniques and spatial overlay analyses to compare the locations of government and community sensors against socioeconomic indicators from the U.S. Census to assess disparities in air quality monitoring coverage. These spatial statistics enabled researchers to probe not only where monitors are placed, but also who is monitored, and who remains overlooked in current monitoring systems.

Articles used models for algorithmic siting for future equitable monitor placement in addition to EPA monitor data. “Clustering” groups areas with similar pollution trends together to make sure the chosen sensor locations provide good coverage across the whole area, especially in places that are often overlooked (Kelp et al., 2022; 2023; Kelly et al., 2024). Grainger and Schreiber (2019) linked satellite and ground data to show that regulators in attainment counties steer monitors away from poor or minority hotspots. Bell and Ebisu (2012) extended inequity analysis to PM_2.5 (fine particulate matter less than 2.5 μm in diameter) chemistry, finding 14 PM_2.5 chemical components (sulfate, nitrate, ammonium, organic carbon matter, elemental carbon, sodium ion, aluminum, calcium, chlorine, nickel, silicon, titanium, vanadium, and zinc) measured by the EPA’s network having specific burdens highest for Hispanics, Blacks, children, and the poor.

Table 2 This table synthesizes peer reviewed studies and lists each study’s authors and publication year, analytical methods, equity or demographic measures examined and the primary findings related to the distribution of regulatory air quality monitors.

3.3. Inequities in EPA Monitor Placement

The articles call for identifying disparities in exposure and access, particularly among vulnerable populations, to advance environmental justice outcomes. A summary of the included studies and their findings is described in Table 2.

3.4. Findings Based on Demographic Equity Measures

Across the reviewed studies, a consistent theme emerges: demographic equity measures reveal significant disparities in the placement of air quality monitors. Demographic indicators most often examine income levels, race and ethnicity, and other markers of social vulnerability. Five of seven studies point to the underrepresentation of low income and racially minoritized communities in EPA’s regulatory network represented in Table 1 Grainger and Schreiber (2019) report that high income or predominantly White unpolluted areas were more likely to receive monitors, while low income and minority communities were systematically less likely to be covered. This pattern aligns with Kelly et al. (2024), who found that census block groups with larger proportions of Black, Hispanic, and multiracial residents had fewer monitors than expected relative to population size. Similarly, Roque et al. (2025) identified “monitoring deserts” concentrated in counties with higher proportions of African American and Hispanic residents, as well as in low income and rural regions (signaling a persistent inequity in collection of air quality monitoring data). In contrast, Bell and Ebisu (2012) find that census tracts with monitors were more likely to be low income and majority Black, suggesting that siting decisions have historically concentrated in these areas. This study stands out as an exception compared to more recent literature. The monitoring approach in Bell and Ebisu (2012) differs substantially from the other studies in the review because they rely on the small subset of EPA monitors that can measure individual PM_2.5 chemical components, rather than the full PM_2.5 mass monitoring network which measures the overall amount of PM_2.5 particles in the air (not broken down into chemical components). These component monitors are sparse and are strategically placed in areas where pollution levels are expected to be highest, such as neighborhoods near industrial facilities, heavy traffic corridors, or large combustion sources. As a result, they are disproportionately located in urban communities with higher proportions of minority and low income residents. Bell and Ebisu (2012) found more PM_2.5 component monitors in these neighborhoods because regulators historically prioritize monitoring in areas with elevated emissions and potential health risks.

3.5. Findings Based on Environmental and Structural Equity Measures

Environmental equity measures examine whether different communities experience unequal environmental conditions such as higher pollution levels or fewer environmental benefits. Structural equity measures focus on the underlying systems, policies, and historical patterns (like zoning, redlining, or land-use decisions) that create and reinforce those environmental inequalities. Table 1 shows that environmental and structural equity measures, in contrast, displayed greater variability in their findings compared to demographic equity measures. Some studies accounted for geographic features, land use patterns, and climatic conditions that shape air pollution exposure and dispersion. These measures revealed that physical and environmental factors (such as topography, regional climate, or proximity to industrial point sources) also influence where monitors are sited. While demographic indicators highlight systematic inequities in monitor placement, environmental and structural equity measures reveal a more complex and variable picture. These measures shed light on how physical and environmental factors shape monitoring networks. For instance, Kelp et al. (2022) document that communities facing wildfire smoke or industrial emissions were often left outside the reach of the regulatory monitoring system. Their study emphasized that wildfires, which generate significant PM_2.5 episodes, are poorly tracked by the existing EPA infrastructure in the Pacific Northwest (suggesting a structural gap in coverage that exacerbates exposure misclassification in fire prone areas).

Other studies use regression analysis approaches to evaluate the reach of monitors. Kelly et al. (2024) employed buffer analyses to measure the effective coverage of EPA monitors, finding that many communities of color were located outside the spatial range of monitors, even when geographically proximate to polluted areas. Roque et al. (2025) described monitoring deserts not only in terms of demographics in rural, southern, and economically disadvantaged regions. Similarly, Kelp et al. (2023) pointed out that EPA monitors remain unevenly distributed due to their original regulatory purpose: to track compliance near high emitting point sources, rather than to capture community level exposures. deSouza and Kinney (2021) shows that the monitoring gaps they identify reflect structural features of how the EPA’s PM_2.5 monitoring network is designed and implemented, rather than simply incomplete geographic coverage. They demonstrate that regulatory monitors are sited through processes that prioritize compliance assessment, existing infrastructure, and historical siting decisions, resulting in concentrations of monitors in certain areas while leaving large portions of the country without regulatory coverage. These structural constraints mean that some communities lack monitors not because of temporary data gaps, but because the regulatory framework and legacy siting patterns systematically favor certain locations over others. This structural perspective clarifies why EPA monitoring coverage remains uneven and strengthens the conclusion that gaps in the network stem from institutional siting practices rather than random omission.

Taken together, these findings demonstrate that environmental and structural measures do not always align with demographic analyses, but they reinforce the conclusion that physical geography, climate, and the regulatory rationale of monitor siting have created uneven monitoring landscapes. These inequities mean that certain hazards, such as wildfire smoke, or certain regions, such as rural and low income areas, remain systematically underserved by EPA’s regulatory network.

4. Discussion

4.1. Disparities in EPA Regulatory Monitoring Coverage

Across the body of literature reviewed, five of the seven studies consistently reveal that EPA regulatory monitoring is sparse, particularly in communities that are racially or ethnically minoritized (Grainger and Schreiber, 2019; deSouza and Kinney, 2021; Kelp et al., 2023; Kelly et al., 2024; Roque et al., 2025). Kelp et al. (2022) did not assess demographic equity measures, and Bell and Ebisu (2012) did not find evidence of inequitable monitor coverage in the specific set of component monitor sites included in their analysis, even though they examined this question directly. Bell and Ebisu (2012) conclude that populations at higher risk of exposure to PM_2.5 components do not seem to be underrepresented in areas with monitors relative to areas without them. Together, these findings underscore that the spatial distribution of regulatory monitors is not only a technical or logistical matter but also deeply intertwined with issues of equity and justice.

The literature identifies two major categories of equity measures (demographic and environmental/structural) that scholars use to evaluate where monitors are placed and whose exposures are being captured. Six of the seven studies incorporate demographic equity measures, while five also include environmental or structural measures. Across the majority of studies, these measures consistently revealed a pattern of fewer monitors being located in low income and minoritized communities. This uneven coverage raises concerns that the health risks faced by these groups, who are already disproportionately burdened by environmental hazards, may be underestimated or overlooked by regulatory monitoring systems.

Since the selection and application of these measures varied across studies, their results were less uniform than those based on demographic equity. The variability highlights the complexity of balancing environmental representativeness with community level equity: while some regions may be covered with monitoring stations due to geographic or meteorological considerations, others (often home to vulnerable populations) remain underserved.

Taken together, this literature demonstrates that inequities in air quality monitoring are multidimensional. Demographic analyses make clear that marginalized communities are disproportionately left without sufficient monitoring, while environmental and structural assessments suggest that geography and climate may further entrench these disparities. This dual perspective underscores the need for monitoring strategies that explicitly integrate both social and environmental equity considerations in order to provide comprehensive and just protection of public health.

4.2. Inequities in Sensor Distribution

While the EPA monitoring network provides regulatory grade data, its spatial distribution is uneven (particularly across rural and lower income areas). Findings revealed that many areas with elevated pollution concentrations lack monitors, aligning with previous research documenting inequities in sensor placement. Studies such as deSouza and Kinney (2021) and Kelp et al. (2023) underscore how low-cost sensors, which could help fill these EPA gaps, are often concentrated in wealthier and Whiter neighborhoods because local regulatory decisions may be shaped by pressure from influential people or businesses (Grainger and Schreiber, 2019). This pattern reinforces environmental monitoring disparities and suggests that even community led data collection may inadvertently mirror systemic inequities in resource access. The geographic and demographic disparities seen in EPA monitor coverage are thus compounded by inequitable distribution of low-cost sensors, emphasizing the need for coordinated strategies that intentionally expand monitoring in socially and environmentally burdened areas.

4.3. The Role of Community Science and Localized Monitoring

Lu et al. (2022) demonstrated the power of community installed sensors in addressing regulatory monitoring gaps by showing that PM_2.5 exposure varied substantially across neighborhoods in Southern California. Their work underscores how dense, community driven networks can uncover micro scale pollution patterns that regulatory sites overlook. Integrating localized data from community or mobile sensors could also enhance temporal and spatial resolution in the neighborhoods where EPA data indicate inconsistencies or underrepresentation. Additionally, community involvement in data collection promotes public awareness, strengthens trust in environmental science, and can guide policymakers in prioritizing regions for new monitor installations.

4.4. Modeling and Optimization for Equitable Monitoring

Recent studies have turned to optimization frameworks to address inequities in monitoring coverage. Lin et al. (2025) developed the GCLM model in Chicago to balance environmental exposure and sociodemographic vulnerability, showing significantly better coverage than existing low-cost sensor networks (though this improvement was demonstrated only through model simulation, not real world deployment). Kelp et al. (2023) used the mrDMDcc framework to improve sensor siting in cities like St. Louis and Houston, showing that incorporating race and income leads to more equitable placements. Likewise, Kelp et al. (2022) applied the mrDMD model nationwide to identify wildfire driven PM_2.5 hotspots in the West that are poorly covered by the current EPA network, advocating for a westward shift in monitoring infrastructure. These modeling approaches demonstrate that equity focused sensor siting strategies can significantly improve environmental justice outcomes and public health surveillance.

4.5. Study Limitations

Across the reviewed studies on this article, many studies rely on short term data making it difficult to determine whether observed monitoring disparities are persistent or part of temporary patterns, limiting our ability to assess long term trends or causality. Other limitations include a lack of attention to how factors like race, income, and housing overlap and influence sensor placement. Optimization studies, such as Kelp et al. (2023), focus mostly on improving technical performance without addressing how their plans would work in practice. And while many studies show gaps in monitoring in disadvantaged areas, most do not explore how this affects health or policy. Together, these gaps suggest that future research should include longer time periods, combine data and community input, and consider both technical and social factors to build fairer air quality monitoring systems.

5. Conclusions

The evidence synthesized in this review underscores the need for equity centered reforms in air quality monitoring. Of the seven studies examining regulatory placement, five concluded that areas lacking sufficient monitoring were disproportionately minoritized. From the disproportionate placement of air quality monitors to the lack of coverage in high risk communities, current monitoring systems remain misaligned with the principles of environmental justice. Investments in community led sensor networks, greater transparency in siting decisions, and federal standards for equitable monitor distribution are all needed to improve environmental health outcomes in marginalized communities. Future research should aim to use nationally representative data, integrate high resolution pollution mapping, and prioritize intersectional analyses of environmental risk. Doing so will ensure that the burden of air pollution is more accurately measured, more fairly addressed, and more justly distributed. These findings reveal critical gaps in who gets monitored and where environmental interventions are targeted. As such, they call for an intentional, equity focused redesign of air quality monitoring systems that prioritizes vulnerable populations and advances environmental justice outcomes.