On the distribution of low-cost PM2.5 sensors in the US: demographic and air quality associations

Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning

Global fine particulate matter (PM2.5) assessment is impeded by a paucity of monitors. We improve estimation of the global distribution of PM2.5 concentrations by developing, optimizing, and applying a convolutional neural network with information from satellite-, simulation-, and monitor-based sources to predict the local bias in monthly geophysical a priori PM2.5 concentrations over 1998-2019. We develop a loss function that incorporates geophysical a priori estimates and apply it in model training to address the unrealistic results produced by mean-square-error loss functions in regions with few monitors. We introduce novel spatial cross-validation for air quality to examine the importance of considering spatial properties. We address the sharp decline in deep learning model performance in regions distant from monitors by incorporating the geophysical a priori PM2.5. The resultant monthly PM2.5 estimates are highly consistent with spatial cross-validation PM2.5 concentrations from monitors globally and regionally. We withheld 10% to 99% of monitors for testing to evaluate the sensitivity and robustness of model performance to the density of ground-based monitors. The model incorporating the geophysical a priori PM2.5 concentrations remains highly consistent with observations globally even under extreme conditions (e.g., 1% for training, R2 = 0.73), while the model without exhibits weaker performance (1% for training, R2 = 0.51).

Representativeness of the US EPA PM monitoring site locations to the US population: implications for air pollution prediction modeling

Air pollution prediction modeling establishes relationships between measurements and geographical and meteorological characteristics to infer concentrations at locations without measurements. Since air pollution monitors are limited in number, predictions may be generated for locations different than those used to train the model. The epidemiologic impacts of this potential mismatch hinge on whether the population resides in areas well-represented by monitoring sites. Here we quantify the fraction of the population with geographical characteristics not reflected by the 2000, 2010, and 2020 EPA PM2.5 and PM10 regulatory sites. We evaluated this measure nationwide, regionally, and by race. Nationally, the networks were very representative of the population experience; however, there was less overlap regionally and in regions stratified by race. This suggests that sub-national exposure modeling should carefully consider the representativeness of monitors for their populations. It also highlights that exposure models often borrow information from distal places to predict full population exposure.

Quantifying Disparities in Air Pollution Exposures across the United States Using Home and Work Addresses

While human mobility plays a crucial role in determining ambient air pollution exposures and health risks, research to date has assessed risks on the basis of almost solely residential location. Here, we leveraged a database of ∼128-144 million workers in the United States and published ambient PM2.5 data between 2011 and 2018 to explore how incorporating information on both workplace and residential location changes our understanding of disparities in air pollution exposure. In general, we observed higher workplace exposures relative to home exposures, as well as increased exposures for nonwhite and less educated workers relative to the national average. Workplace exposure disparities were higher among racial and ethnic groups and job types than by income, education, age, and sex. Not considering workplace exposures can lead to systematic underestimations in disparities in exposure among these subpopulations. We also quantified the error in assigning workers home instead of a weighted home-and-work exposure. We observed that biases in associations between PM2.5 and health impacts by using home instead of home-and-work exposure were the highest among urban, younger populations.

Local exposure misclassification in national models: relationships with urban infrastructure and demographics

BACKGROUND

National-scale linear regression-based modeling may mischaracterize localized patterns, including hyperlocal peaks and neighborhood- to regional-scale gradients. For studies focused on within-city differences, this mischaracterization poses a risk of exposure misclassification, affecting epidemiological and environmental justice conclusions.

OBJECTIVE

Characterize the difference between intraurban pollution patterns predicted by national-scale land use regression modeling and observation-based estimates within a localized domain and examine the relationship between that difference and urban infrastructure and demographics.

METHODS

We compare highly resolved (0.01 km2) observations of NO2 mixing ratio and ultrafine particle (UFP) count obtained via mobile monitoring with national model predictions in thirteen neighborhoods in the San Francisco Bay Area. Grid cell-level divergence between modeled and observed concentrations is termed "localized difference." We use a flexible machine learning modeling technique, Bayesian Additive Regression Trees, to investigate potentially nonlinear relationships between discrepancy between localized difference and known local emission sources as well as census block group racial/ethnic composition.

RESULTS

We find that observed local pollution extremes are not represented by land use regression predictions and that observed UFP count significantly exceeds regression predictions. Machine learning models show significant nonlinear relationships among localized differences between predictions and observations and the density of several types of pollution-related infrastructure (roadways, commercial and industrial operations). In addition, localized difference was greater in areas with higher population density and a lower share of white non-Hispanic residents, indicating that exposure misclassification by national models differs among subpopulations.

IMPACT

Comparing national-scale pollution predictions with hyperlocal observations in the San Francisco Bay Area, we find greater discrepancies near major roadways and food service locations and systematic underestimation of concentrations in neighborhoods with a lower share of non-Hispanic white residents. These findings carry implications for using national-scale models in intraurban epidemiological and environmental justice applications and establish the potential utility of supplementing large-scale estimates with publicly available urban infrastructure and pollution source information.

Assessing residential PM2.5 concentrations and infiltration factors with high spatiotemporal resolution using crowdsourced sensors

Building conditions, outdoor climate, and human behavior influence residential concentrations of fine particulate matter (PM2.5). To study PM2.5 spatiotemporal variability in residences, we acquired paired indoor and outdoor PM2.5 measurements at 3,977 residences across the United States totaling >10,000 monitor-years of time-resolved data (10-min resolution) from the PurpleAir network. Time-series analysis and statistical modeling apportioned residential PM2.5 concentrations to outdoor sources (median residential contribution = 52% of total, coefficient of variation = 69%), episodic indoor emission events such as cooking (28%, CV = 210%) and persistent indoor sources (20%, CV = 112%). Residences in the temperate marine climate zone experienced higher infiltration factors, consistent with expectations for more time with open windows in milder climates. Likewise, for all climate zones, infiltration factors were highest in summer and lowest in winter, decreasing by approximately half in most climate zones. Large outdoor-indoor temperature differences were associated with lower infiltration factors, suggesting particle losses from active filtration occurred during heating and cooling. Absolute contributions from both outdoor and indoor sources increased during wildfire events. Infiltration factors decreased during periods of high outdoor PM2.5, such as during wildfires, reducing potential exposures from outdoor-origin particles but increasing potential exposures to indoor-origin particles. Time-of-day analysis reveals that episodic emission events are most frequent during mealtimes as well as on holidays (Thanksgiving and Christmas), indicating that cooking-related activities are a strong episodic emission source of indoor PM2.5 in monitored residences.

Ethnoracial Disparities in Nitrogen Dioxide Pollution in the United States: Comparing Data Sets from Satellites, Models, and Monitors

In the United States (U.S.), studies on nitrogen dioxide (NO2) trends and pollution-attributable health effects have historically used measurements from in situ monitors, which have limited geographical coverage and leave 66% of urban areas unmonitored. Novel tools, including remotely sensed NO2 measurements and estimates of NO2 estimates from land-use regression and photochemical models, can aid in assessing NO2 exposure gradients, leveraging their complete spatial coverage. Using these data sets, we find that Black, Hispanic, Asian, and multiracial populations experience NO2 levels 15-50% higher than the national average in 2019, whereas the non-Hispanic White population is consistently exposed to levels that are 5-15% lower than the national average. By contrast, the in situ monitoring network indicates more moderate ethnoracial NO2 disparities and different rankings of the least- to most-exposed ethnoracial population subgroup. Validating these spatially complete data sets against in situ observations reveals similar performance, indicating that all these data sets can be used to understand spatial variations in NO2. Integrating in situ monitoring, satellite data, statistical models, and photochemical models can provide a semiobservational record, complete geospatial coverage, and increasingly high spatial resolution, enhancing future efforts to characterize, map, and track exposure and inequality for highly spatially heterogeneous pollutants like NO2.

State-of-the-Science Data and Methods Need to Guide Place-Based Efforts to Reduce Air Pollution Inequity

BACKGROUND

Recently enacted environmental justice policies in the United States at the state and federal level emphasize addressing place-based inequities, including persistent disparities in air pollution exposure and associated health impacts. Advances in air quality measurement, models, and analytic methods have demonstrated the importance of finer-scale data and analysis in accurately quantifying the extent of inequity in intraurban pollution exposure, although the necessary degree of spatial resolution remains a complex and context-dependent question.

OBJECTIVE

The objectives of this commentary were to a) discuss ways to maximize and evaluate the effectiveness of efforts to reduce air pollution disparities, and b) argue that environmental regulators must employ improved methods to project, measure, and track the distributional impacts of new policies at finer geographic and temporal scales.

DISCUSSION

The historic federal investments from the Inflation Reduction Act, the Infrastructure Investment and Jobs Act, and the Biden Administration's commitment to Justice40 present an unprecedented opportunity to advance climate and energy policies that deliver real reductions in pollution-related health inequities. In our opinion, scientists, advocates, policymakers, and implementing agencies must work together to harness critical advances in air quality measurements, models, and analytic methods to ensure success. https://doi.org/10.1289/EHP13063.

Evaluating the Performance of Low-Cost PM2.5 Sensors in Mobile Settings

Low-cost sensors (LCSs) for measuring air pollution are increasingly being deployed in mobile applications, but questions concerning the quality of the measurements remain unanswered. For example, what is the best way to correct LCS data in a mobile setting? Which factors most significantly contribute to differences between mobile LCS data and those of higher-quality instruments? Can data from LCSs be used to identify hotspots and generate generalizable pollutant concentration maps? To help address these questions, we deployed low-cost PM2.5 sensors (Alphasense OPC-N3) and a research-grade instrument (TSI DustTrak) in a mobile laboratory in Boston, MA, USA. We first collocated these instruments with stationary PM2.5 reference monitors (Teledyne T640) at nearby regulatory sites. Next, using the reference measurements, we developed different models to correct the OPC-N3 and DustTrak measurements and then transferred the corrections to the mobile setting. We observed that more complex correction models appeared to perform better than simpler models in the stationary setting; however, when transferred to the mobile setting, corrected OPC-N3 measurements agreed less well with the corrected DustTrak data. In general, corrections developed by using minute-level collocation measurements transferred better to the mobile setting than corrections developed using hourly-averaged data. Mobile laboratory speed, OPC-N3 orientation relative to the direction of travel, date, hour-of-the-day, and road class together explain a small but significant amount of variation between corrected OPC-N3 and DustTrak measurements during the mobile deployment. Persistent hotspots identified by the OPC-N3s agreed with those identified by the DustTrak. Similarly, maps of PM2.5 distribution produced from the mobile corrected OPC-N3 and DustTrak measurements agreed well. These results suggest that identifying hotspots and developing generalizable maps of PM2.5 are appropriate use-cases for mobile LCS data.

Evaluation of Calibration Approaches for Indoor Deployments of PurpleAir Monitors

Low-cost air quality monitors are growing in popularity among both researchers and community members to understand variability in pollutant concentrations. Several studies have produced calibration approaches for these sensors for ambient air. These calibrations have been shown to depend primarily on relative humidity, particle size distribution, and particle composition, which may be different in indoor environments. However, despite the fact that most people spend the majority of their time indoors, little is known about the accuracy of commonly used devices indoors. This stems from the fact that calibration data for sensors operating in indoor environments are rare. In this study, we sought to evaluate the accuracy of the raw data from PurpleAir fine particulate matter monitors and for published calibration approaches that vary in complexity, ranging from simply applying linear corrections to those requiring co-locating a filter sample for correction with a gravimetric concentration during a baseline visit. Our data includes PurpleAir devices that were co-located in each home with a gravimetric sample for 1-week periods (265 samples from 151 homes). Weekly-averaged gravimetric concentrations ranged between the limit of detection (3 μg/m3) and 330 μg/m3. We found a strong correlation between the PurpleAir monitor and the gravimetric concentration (R>0.91) using internal calibrations provided by the manufacturer. However, the PurpleAir data substantially overestimated indoor concentrations compared to the gravimetric concentration (mean bias error ≥ 23.6 μg/m3 using internal calibrations provided by the manufacturer). Calibrations based on ambient air data maintained high correlations (R ≥ 0.92) and substantially reduced bias (e.g. mean bias error = 10.1 μg/m3 using a US-wide calibration approach). Using a gravimetric sample from a baseline visit to calibrate data for later visits led to an improvement over the internal calibrations, but performed worse than the simpler calibration approaches based on ambient air pollution data. Furthermore, calibrations based on ambient air pollution data performed best when weekly-averaged concentrations did not exceed 30 μg/m3, likely because the majority of the data used to train these models were below this concentration.

Data-Driven Placement of PM2.5 Air Quality Sensors in the United States: An Approach to Target Urban Environmental Injustice

In the United States, citizens and policymakers heavily rely upon Environmental Protection Agency mandated regulatory networks to monitor air pollution; increasingly they also depend on low-cost sensor networks to supplement spatial gaps in regulatory monitor networks coverage. Although these regulatory and low-cost networks in tandem provide enhanced spatiotemporal coverage in urban areas, low-cost sensors are located often in higher income, predominantly White areas. Such disparity in coverage may exacerbate existing inequalities and impact the ability of different communities to respond to the threat of air pollution. Here we present a study using cost-constrained multiresolution dynamic mode decomposition (mrDMDcc) to identify the optimal and equitable placement of fine particulate matter (PM2.5) sensors in four U.S. cities with histories of racial or income segregation: St. Louis, Houston, Boston, and Buffalo. This novel approach incorporates the variation of PM2.5 on timescales ranging from 1 day to over a decade to capture air pollution variability. We also introduce a cost function into the sensor placement optimization that represents the balance between our objectives of capturing PM2.5 extremes and increasing pollution monitoring in low-income and nonwhite areas. We find that the mrDMDcc algorithm places a greater number of sensors in historically low-income and nonwhite neighborhoods with known environmental pollution problems compared to networks using PM2.5 information alone. Our work provides a roadmap for the creation of equitable sensor networks in U.S. cities and offers a guide for democratizing air pollution data through increasing spatial coverage of low-cost sensors in less privileged communities.

A nested machine learning approach to short-term PM2.5 prediction in metropolitan areas using PM2.5 data from different sensor networks

Many predictive models for ambient PM_2.5 concentrations rely on ground observations from a single monitoring network consisting of sparsely distributed sensors. Integrating data from multiple sensor networks for short-term PM_2.5 prediction remains largely unexplored. This paper presents a machine learning approach to predict ambient PM_2.5 concentration levels at any unmonitored location several hours ahead using PM_2.5 observations from nearby monitoring sites from two sensor networks and the location's social and environmental properties. Specifically, this approach first applies a Graph Neural Network and Long Short-Term Memory (GNN-LSTM) network to time series of daily observations from a regulatory monitoring network to make predictions of PM_2.5. This network produces feature vectors to store aggregated daily observations as well as dependency characteristics to predict daily PM_2.5. The daily feature vectors are then set as the precondition of the hourly level learning process. The hourly level learning again uses a GNN-LSTM network based on daily dependency information and hourly observations from a low-cost sensor network to produce spatiotemporal feature vectors capturing the combined dependency described by daily and hourly observations. Finally, the spatiotemporal feature vectors from the hourly learning process and social-environmental data are merged and used as the input to a single-layer Fully Connected (FC) network to output the predicted hourly PM_2.5 concentrations. To demonstrate the benefits of this novel prediction approach, we have conducted a case study using data collected from two sensor networks in Denver, CO, during 2021. Results show that the utilization of data from two sensor networks improves the overall performance of predicting fine-level, short-term PM_2.5 concentrations compared to other baseline models.

An analysis of degradation in low-cost particulate matter sensors

Low-cost sensors (LCS) are increasingly being used to measure fine particulate matter (PM2.5) concentrations in cities around the world. One of the most commonly deployed LCS is the PurpleAir with ~ 15,000 sensors deployed in the United States, alone. PurpleAir measurements are widely used by the public to evaluate PM2.5 levels in their neighborhoods. PurpleAir measurements are also increasingly being integrated into models by researchers to develop large-scale estimates of PM2.5. However, the change in sensor performance over time has not been well studied. It is important to understand the lifespan of these sensors to determine when they should be serviced or replaced, and when measurements from these devices should or should not be used for various applications. This paper fills this gap by leveraging the fact that: (1) Each PurpleAir sensor is comprised of two identical sensors and the divergence between their measurements can be observed, and (2) There are numerous PurpleAir sensors within 50 meters of regulatory monitors allowing for the comparison of measurements between these instruments. We propose empirically derived degradation outcomes for the PurpleAir sensors and evaluate how these outcomes change over time. On average, we find that the number of 'flagged' measurements, where the two sensors within each PurpleAir sensor disagree, increases with time to ~ 4% after 4 years of operation. Approximately 2 percent of all PurpleAir sensors were permanently degraded. The largest fraction of permanently degraded PurpleAir sensors appeared to be in the hot and humid climate zone, suggesting that sensors in these locations may need to be replaced more frequently. We also find that the bias of PurpleAir sensors, or the difference between corrected PM2.5 levels and the corresponding reference measurements, changed over time by -0.12 μg/m3(95% CI: -0.13 μg/m3, -0.10 μg/m3) per year. The average bias increases dramatically after 3.5 years. Further, climate zone is a significant modifier of the association between degradation outcomes and time.

Efficacy of Low-Cost Sensor Networks at Detecting Fine-Scale Variations in Particulate Matter in Urban Environments

The negative health impacts of air pollution are well documented. Not as well-documented, however, is how particulate matter varies at the hyper-local scale, and the role that proximal sources play in influencing neighborhood-scale patterns. We examined PM_2.5 variations in one airshed within Indianapolis (Indianapolis, IN, USA) by utilizing data from 25 active PurpleAir (PA) sensors involving citizen scientists who hosted all but one unit (the control), as well as one EPA monitor. PA sensors report live measurements of PM_2.5 on a crowd sourced map. After calibrating the data utilizing relative humidity and testing it against a mobile air-quality unit and an EPA monitor, we analyzed PM_2.5 with meteorological data, tree canopy coverage, land use, and various census variables. Greater proximal tree canopy coverage was related to lower PM_2.5 concentrations, which translates to greater health benefits. A 1% increase in tree canopy at the census tract level, a boundary delineated by the US Census Bureau, results in a ~0.12 µg/m³ decrease in PM_2.5, and a 1% increase in "heavy industry" results in a 0.07 µg/m³ increase in PM_2.5 concentrations. Although the overall results from these 25 sites are within the annual ranges established by the EPA, they reveal substantial variations that reinforce the value of hyper-local sensing technologies as a powerful surveillance tool.

Investigating Use of Low-Cost Sensors to Increase Accuracy and Equity of Real-Time Air Quality Information

U.S. Environmental Protection Agency (EPA) air quality (AQ) monitors, the "gold standard" for measuring air pollutants, are sparsely positioned across the U.S. Low-cost sensors (LCS) are increasingly being used by the public to fill in the gaps in AQ monitoring; however, LCS are not as accurate as EPA monitors. In this work, we investigate factors impacting the differences between an individual's true (unobserved) exposure to air pollution and the exposure reported by their nearest AQ instrument (which could be either an LCS or an EPA monitor). We use simulations based on California data to explore different combinations of hypothetical LCS placement strategies (e.g., at schools or near major roads), for different numbers of LCS, with varying plausible amounts of LCS device measurement errors. We illustrate how real-time AQ reporting could be improved (or, in some cases, worsened) by using LCS, both for the population overall and for marginalized communities specifically. This work has implications for the integration of LCS into real-time AQ reporting platforms.

Neighborhood Composition and Air Pollution in Chicago: Monitoring Inequities With a Dense, Low-Cost Sensing Network, 2021

Objectives. To evaluate the efficacy of a novel, real-time sensor network for routine monitoring of racial and economic disparities in fine particulate matter (PM_2.5; particulate matter ≤ 2.5 µm in diameter) exposures at the neighborhood level. Methods. We deployed a dense network of low-cost PM_2.5 sensors in Chicago, Illinois, to evaluate associations between neighborhood-level composition variables (percentage of Black residents, percentage of Hispanic/Latinx residents, and percentage of households below poverty) and interpolated PM_2.5. Relationships were assessed in spatial lag models after adjustment for all composition variables. Models were fit with data both from the overall period and during high-pollution episodes associated with social events (July 4, 2021) and wildfires (July 23, 2021). Results. The spatial lag models showed that racial/ethnic composition variables were associated with higher PM_2.5 levels. Levels were notably higher in neighborhoods with larger compositions of Hispanic/Latinx residents across the entire study period and notably higher in neighborhoods with larger Black populations during the July 4 episode. Conclusions. As a complement to sparse regulatory networks, dense, low-cost sensor networks can capture spatial variations during short-term air pollution episodes and enable monitoring of neighborhood-level inequities in air pollution exposures in real time. (Am J Public Health. 2022;112(12):1765-1773. https://doi.org/10.2105/AJPH.2022.307068).

Neighborhood Composition and Air Pollution in Chicago: Monitoring Inequities With a Dense, Low-Cost Sensing Network, 2021

Objectives. To evaluate the efficacy of a novel, real-time sensor network for routine monitoring of racial and economic disparities in fine particulate matter (PM2.5; particulate matter ≤ 2.5 µm in diameter) exposures at the neighborhood level. Methods. We deployed a dense network of low-cost PM2.5 sensors in Chicago, Illinois, to evaluate associations between neighborhood-level composition variables (percentage of Black residents, percentage of Hispanic/Latinx residents, and percentage of households below poverty) and interpolated PM2.5. Relationships were assessed in spatial lag models after adjustment for all composition variables. Models were fit with data both from the overall period and during high-pollution episodes associated with social events (July 4, 2021) and wildfires (July 23, 2021). Results. The spatial lag models showed that racial/ethnic composition variables were associated with higher PM2.5 levels. Levels were notably higher in neighborhoods with larger compositions of Hispanic/Latinx residents across the entire study period and notably higher in neighborhoods with larger Black populations during the July 4 episode. Conclusions. As a complement to sparse regulatory networks, dense, low-cost sensor networks can capture spatial variations during short-term air pollution episodes and enable monitoring of neighborhood-level inequities in air pollution exposures in real time. (Am J Public Health. 2022;112(12):1765-1773. https://doi.org/10.2105/AJPH.2022.307068).

Communities of color are disproportionately exposed to long-term and short-term PM2.5 in metropolitan America

We conducted a novel investigation of neighborhood-level racial/ethnic exposure disparities employing measures aligned with long-term and short-term PM_2.5 air pollution benchmarks across metropolitan contexts of the contiguous United States, 2012-2016. We used multivariable generalized estimating equations (GEE) to quantify PM_2.5 exposure disparities based on the census tract composition of people of color (POC) and POC groups (Hispanic/Latina/x/o, Black, Asian). We examined eight census tract-level measures of longer-to-shorter term exposures derived from data on modeled daily ambient PM_2.5 concentrations. We found associations between increased POC composition and greater exposure to all PM_2.5 measures, with associations strengthening across measures of longer-to-shorter term exposures. In a GEE with a negative binomial distribution, a standard deviation increase in POC composition predicted a 0.6% increase (incidence rate ratio (IRR): 1.006, 95% confidence interval (CI): 1.005-1.008) in the number of days PM_2.5 concentrations were ≥5 μg/m³ (longest-term benchmark). In a GEE with an inverse Gaussian distribution, a standard deviation increase in POC composition predicted a 0.110 μg/m³ (1.0%) increase (B: 0.110, 95% CI: 0.076-0.143) in mean PM_2.5 concentration. In GEEs with a negative binomial distribution, the effect of a standard deviation increase in POC composition on exposure strengthened to 2.6% (IRR:1.026, 95% CI:1.017-1.035), 3.4% (IRR:1.034, 95% CI:1.022-1.047), 4.2% (IRR:1.042, 95% CI:1.025-1.058), 16.2% (IRR:1.162, 95% CI:1.117-1.210), 22.7% (IRR:1.227, 95% CI:1.137-1.325) and 28.3% (IRR:1.283, 95% CI:1.144-1.439) with respect to the number of days PM_2.5 concentrations were ≥10, 12, 15, 25, 35 and 55.5 μg/m³. POC group models indicated exposure disparities based on greater Hispanic/Latina/x/o, Asian, and Black composition. Evidence for stronger POC associations with shorter-term (higher concentration) PM_2.5 exceedances suggests that reducing PM_2.5 would attenuate racial/ethnic exposure disparities.

Leveraging Citizen Science and Low-Cost Sensors to Characterize Air Pollution Exposure of Disadvantaged Communities in Southern California

Assessing exposure to fine particulate matter (PM_2.5) across disadvantaged communities is understudied, and the air monitoring network is inadequate. We leveraged emerging low-cost sensors (PurpleAir) and engaged community residents to develop a community-based monitoring program across disadvantaged communities (high proportions of low-income and minority populations) in Southern California. We recruited 22 households from 8 communities to measure residential outdoor PM_2.5 concentrations from June 2021 to December 2021. We identified the spatial and temporal patterns of PM_2.5 measurements as well as the relationship between the total PM_2.5 measurements and diesel PM emissions. We found that communities with a higher percentage of Hispanic and African American population and higher rates of unemployment, poverty, and housing burden were exposed to higher PM_2.5 concentrations. The average PM_2.5 concentrations in winter (25.8 µg/m³) were much higher compared with the summer concentrations (12.4 µg/m³). We also identified valuable hour-of-day and day-of-week patterns among disadvantaged communities. Our results suggest that the built environment can be targeted to reduce the exposure disparity. Integrating low-cost sensors into a citizen-science-based air monitoring program has promising applications to resolve monitoring disparity and capture “hotspots” to inform emission control and urban planning policies, thus improving exposure assessment and promoting environmental justice.

Exposures and behavioural responses to wildfire smoke

Pollution from wildfires constitutes a growing source of poor air quality globally. To protect health, governments largely rely on citizens to limit their own wildfire smoke exposures, but the effectiveness of this strategy is hard to observe. Using data from private pollution sensors, cell phones, social media posts and internet search activity, we find that during large wildfire smoke events, individuals in wealthy locations increasingly search for information about air quality and health protection, stay at home more and are unhappier. Residents of lower-income neighbourhoods exhibit similar patterns in searches for air quality information but not for health protection, spend less time at home and have more muted sentiment responses. During smoke events, indoor particulate matter (PM_2.5) concentrations often remain 3-4× above health-based guidelines and vary by 20× between neighbouring households. Our results suggest that policy reliance on self-protection to mitigate smoke health risks will have modest and unequal benefits.

Exploring the distributional environmental justice implications of an air quality monitoring network in Los Angeles County

Non-governmental air quality monitoring networks include low-cost, networked air pollution sensors hosted at homes and schools that display real-time pollutant concentration estimates on publicly accessible websites. Such networks can empower people to take health-protective actions, but their unplanned organization may produce an uneven spatial distribution of sensors. Barriers to acquiring sensors may disenfranchise particular social groups. To test this directly, we quantitatively examine if there are social inequalities in the distribution of sensors in a non-governmental air quality monitoring network (PurpleAir) in Los Angeles County, California. We paired sociodemographic data from the American Community Survey and estimates of PM_2.5 concentrations from the USEPA's Downscaler model at the census tract level (n = 2203) with a sensors per capita (SPC) variable, which is based on population proximity to PurpleAir sensors (n = 696) in Los Angeles County. Findings from multivariable generalized estimating equations (GEEs) controlling for clustering by housing age and value reveal patterns of environmental injustice in the distribution of PurpleAir sensors across Los Angeles County census tracts. Tracts with higher percentages of Hispanic/Latino/a and Black residents and lower median household income had decreased SPC. There was a curvilinear (concave) relationship between the percentage of renter-occupants and SPC. Sensors were concentrated in tracts with greater percentages of adults and seniors (vs. children), higher occupied housing density, and higher PM_2.5 pollution. Results reveal social inequalities in the self-organizing PurpleAir network, suggesting another layer of environmental injustice such that residents of low-income and minority neighborhoods have reduced access to information about local air pollution.

Socioeconomic Disparities of Low-Cost Air Quality Sensors in California, 2017-2020

Objectives. To (1) examine the disparity in availability of PurpleAir low-cost air quality sensors in California based on neighborhood socioeconomic status (SES) and exposure to fine particulate matter smaller than 2.5 micrometers (PM2.5), (2) investigate the temporal trend of sensor distribution and operation, and (3) identify priority communities for future sensor distribution. Methods. We obtained census tract-level SES variables and PM2.5 concentrations from the CalEnviroScreen4.0 data set. We obtained real-time PurpleAir sensor data (July 2017-September 2020) to examine sensor distribution and operation. We conducted spatial and temporal analyses at the census tract level to investigate neighborhood SES and PM2.5 concentrations in relation to sensor distribution and operation. Results. The spatial coverage and the number of PurpleAir sensors increased significantly in California. Fewer sensors were distributed in census tracts with lower SES, higher PM2.5, and higher proportions of racial/ethnic minority populations. Furthermore, a large proportion of existing sensors were not in operation at a given time, especially in disadvantaged communities. Conclusions. Disadvantaged communities should be given access to low-cost sensors to fill in spatial gaps of air quality monitoring and address environmental justice concerns. Sensor purchasing and deployment must be paired with regular maintenance to ensure their reliable performance. (Am J Public Health. 2022;112(3):434-442. https://doi.org/10.2105/AJPH.2021.306603).

Key Concerns and Drivers of Low-Cost Air Quality Sensor Use

Low-cost sensors are revolutionizing air pollution monitoring by providing real-time, highly localized air quality information. The relatively low-cost nature of these devices has made them accessible to the broader public. Although there have been several fitness-of-purpose appraisals of the various sensors on the market, little is known about what drives sensor usage and how the public interpret the data from their sensors. This article attempts to answer these questions by analyzing the key themes discussed in the user reviews of low-cost sensors on Amazon. The themes and use cases identified have the potential to spur interventions to support communities of sensor users and inform the development of actionable data-visualization strategies with the measurements from such instruments, as well as drive appropriate ‘fitness-of-purpose’ appraisals of such devices.