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Apte JS, Manchanda C. High-resolution urban air pollution mapping. Science 2024; 385:380-385. [PMID: 39052801 DOI: 10.1126/science.adq3678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2024] [Accepted: 06/07/2024] [Indexed: 07/27/2024]
Abstract
Variation in urban air pollution arises because of complex spatial, temporal, and chemical processes, which profoundly affect population exposure, human health, and environmental justice. This Review highlights insights from two popular in situ measurement methods-mobile monitoring and dense sensor networks-that have distinct but complementary strengths in characterizing the dynamics and impacts of the multidimensional urban air quality system. Mobile monitoring can measure many pollutants at fine spatial scales, thereby informing about processes and control strategies. Sensor networks excel at providing temporal resolution at many locations. Increasingly sophisticated studies leveraging both methods can vividly identify spatial and temporal patterns that affect exposures and disparities and offer mechanistic insight toward effective interventions. This Review summarizes the strengths and limitations of these methods and discusses their implications for understanding fine-scale processes and impacts.
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Affiliation(s)
- Joshua S Apte
- Department of Civil and Environmental Engineering, University of California, Berkeley, Berkeley, CA 94720, USA
- School of Public Health, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Chirag Manchanda
- Department of Civil and Environmental Engineering, University of California, Berkeley, Berkeley, CA 94720, USA
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2
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Shen S, Li C, van Donkelaar A, Jacobs N, Wang C, Martin RV. Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning. ACS ES&T AIR 2024; 1:332-345. [PMID: 38751607 PMCID: PMC11092969 DOI: 10.1021/acsestair.3c00054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 03/11/2024] [Accepted: 03/12/2024] [Indexed: 05/18/2024]
Abstract
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).
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Affiliation(s)
- Siyuan Shen
- Department
of Energy, Environmental, and Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Chi Li
- Department
of Energy, Environmental, and Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Aaron van Donkelaar
- Department
of Energy, Environmental, and Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Nathan Jacobs
- Department
of Computer Science and Engineering, Washington
University in St. Louis, St. Louis, Missouri 63130, United
States
| | - Chenguang Wang
- Department
of Computer Science and Engineering, Washington
University in St. Louis, St. Louis, Missouri 63130, United
States
| | - Randall V. Martin
- Department
of Energy, Environmental, and Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, United States
- Department
of Computer Science and Engineering, Washington
University in St. Louis, St. Louis, Missouri 63130, United
States
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3
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Pedde M, Adar SD. Representativeness of the US EPA PM monitoring site locations to the US population: implications for air pollution prediction modeling. JOURNAL OF EXPOSURE SCIENCE & ENVIRONMENTAL EPIDEMIOLOGY 2024:10.1038/s41370-024-00644-3. [PMID: 38316907 DOI: 10.1038/s41370-024-00644-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 01/17/2024] [Accepted: 01/18/2024] [Indexed: 02/07/2024]
Abstract
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.
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Affiliation(s)
- Meredith Pedde
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, USA.
| | - Sara D Adar
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, USA
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4
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de Souza P, Anenberg S, Makarewicz C, Shirgaokar M, Duarte F, Ratti C, Durant JL, Kinney PL, Niemeier D. Quantifying Disparities in Air Pollution Exposures across the United States Using Home and Work Addresses. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:280-290. [PMID: 38153403 DOI: 10.1021/acs.est.3c07926] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/29/2023]
Abstract
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.
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Affiliation(s)
- Priyanka de Souza
- Department of Urban and Regional Planning, University of Colorado Denver, Denver, Colorado 80202, United States
- CU Population Center, University of Colorado Boulder, Boulder, Colorado 80302, United States
- Senseable City Lab, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Susan Anenberg
- Milken Institute School of Public Health, George Washington University, Washington, D.C. 20037, United States
| | - Carrie Makarewicz
- Department of Urban and Regional Planning, University of Colorado Denver, Denver, Colorado 80202, United States
| | - Manish Shirgaokar
- Department of Urban and Regional Planning, University of Colorado Denver, Denver, Colorado 80202, United States
| | - Fabio Duarte
- Senseable City Lab, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Carlo Ratti
- Senseable City Lab, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - John L Durant
- Department of Civil and Environmental Engineering, Tufts University, Medford, Massachusetts 02155, United States
| | - Patrick L Kinney
- Boston University School of Public Health, Boston, Massachusetts 02118, United States
| | - Deb Niemeier
- Department of Civil and Environmental Engineering, University of Maryland, College Park, Maryland 20742, United States
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5
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Chambliss SE, Campmier MJ, Audirac M, Apte JS, Zigler CM. Local exposure misclassification in national models: relationships with urban infrastructure and demographics. JOURNAL OF EXPOSURE SCIENCE & ENVIRONMENTAL EPIDEMIOLOGY 2023:10.1038/s41370-023-00624-z. [PMID: 38135708 DOI: 10.1038/s41370-023-00624-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Revised: 11/27/2023] [Accepted: 11/28/2023] [Indexed: 12/24/2023]
Abstract
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.
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Affiliation(s)
- Sarah E Chambliss
- Department of Statistics and Data Sciences, The University of Texas at Austin, Austin, TX, 78712, USA.
| | - Mark Joseph Campmier
- Department of Civil and Environmental Engineering, University of California, Berkeley, Berkeley, CA, 94720, USA
| | - Michelle Audirac
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Joshua S Apte
- Department of Civil and Environmental Engineering, University of California, Berkeley, Berkeley, CA, 94720, USA
- School of Public Health, University of California, Berkeley, Berkeley, CA, 94720, USA
| | - Corwin M Zigler
- Department of Statistics and Data Sciences, The University of Texas at Austin, Austin, TX, 78712, USA
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Lunderberg DM, Liang Y, Singer BC, Apte JS, Nazaroff WW, Goldstein AH. Assessing residential PM 2.5 concentrations and infiltration factors with high spatiotemporal resolution using crowdsourced sensors. Proc Natl Acad Sci U S A 2023; 120:e2308832120. [PMID: 38048461 PMCID: PMC10723120 DOI: 10.1073/pnas.2308832120] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 09/21/2023] [Indexed: 12/06/2023] Open
Abstract
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.
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Affiliation(s)
- David M. Lunderberg
- Department of Environmental Science, Policy, and Management, University of California, Berkeley, CA94720
- Department of Chemistry, University of California, Berkeley, CA94720
| | - Yutong Liang
- Department of Environmental Science, Policy, and Management, University of California, Berkeley, CA94720
- College of Engineering, School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA30332
| | - Brett C. Singer
- Indoor Environment Group, Energy Technologies Area, Lawrence Berkeley National Laboratory, Berkeley, CA94720
| | - Joshua S. Apte
- Department of Civil and Environmental Engineering, University of California, Berkeley, CA94720
- Environmental Health Sciences Division, School of Public Health, University of California, Berkeley, CA94720
| | - William W. Nazaroff
- Department of Civil and Environmental Engineering, University of California, Berkeley, CA94720
| | - Allen H. Goldstein
- Department of Environmental Science, Policy, and Management, University of California, Berkeley, CA94720
- Department of Civil and Environmental Engineering, University of California, Berkeley, CA94720
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Kerr GH, Goldberg DL, Harris MH, Henderson BH, Hystad P, Roy A, Anenberg SC. Ethnoracial Disparities in Nitrogen Dioxide Pollution in the United States: Comparing Data Sets from Satellites, Models, and Monitors. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:19532-19544. [PMID: 37934506 DOI: 10.1021/acs.est.3c03999] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2023]
Abstract
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.
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Affiliation(s)
- Gaige Hunter Kerr
- Department of Environmental and Occupational Health, Milken Institute School of Public Health, George Washington University, Washington, District of Columbia 20052, United States
| | - Daniel L Goldberg
- Department of Environmental and Occupational Health, Milken Institute School of Public Health, George Washington University, Washington, District of Columbia 20052, United States
| | - Maria H Harris
- Environmental Defense Fund, 257 Park Avenue South, New York, New York 10010, United States
| | - Barron H Henderson
- U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27711, United States
| | - Perry Hystad
- College of Public Health and Human Sciences, Oregon State University, Corvallis, Oregon 97333, United States
| | - Ananya Roy
- Environmental Defense Fund, 257 Park Avenue South, New York, New York 10010, United States
| | - Susan C Anenberg
- Department of Environmental and Occupational Health, Milken Institute School of Public Health, George Washington University, Washington, District of Columbia 20052, United States
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8
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Gohlke JM, Harris MH, Roy A, Thompson TM, DePaola M, Alvarez RA, Anenberg SC, Apte JS, Demetillo MAG, Dressel IM, Kerr GH, Marshall JD, Nowlan AE, Patterson RF, Pusede SE, Southerland VA, Vogel SA. State-of-the-Science Data and Methods Need to Guide Place-Based Efforts to Reduce Air Pollution Inequity. ENVIRONMENTAL HEALTH PERSPECTIVES 2023; 131:125003. [PMID: 38109120 PMCID: PMC10727036 DOI: 10.1289/ehp13063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 11/19/2023] [Accepted: 11/27/2023] [Indexed: 12/19/2023]
Abstract
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.
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Affiliation(s)
- Julia M. Gohlke
- Environmental Defense Fund, Washington, District of Columbia, USA
- Department of Population Health Sciences, Virginia Tech, Blacksburg, Virginia, USA
| | - Maria H. Harris
- Environmental Defense Fund, Washington, District of Columbia, USA
| | - Ananya Roy
- Environmental Defense Fund, Washington, District of Columbia, USA
| | | | - Mindi DePaola
- Environmental Defense Fund, Washington, District of Columbia, USA
| | - Ramón A. Alvarez
- Environmental Defense Fund, Washington, District of Columbia, USA
| | - Susan C. Anenberg
- Department of Environmental and Occupational Health, George Washington University, Washington, District of Columbia, USA
| | - Joshua S. Apte
- Department of Civil and Environmental Engineering, University of California, Berkeley, Berkeley, California, USA
- School of Public Health, University of California, Berkeley, Berkeley, California, USA
| | | | - Isabella M. Dressel
- Department of Environmental Sciences, University of Virginia, Charlottesville, Virginia, USA
| | - Gaige H. Kerr
- Department of Environmental and Occupational Health, George Washington University, Washington, District of Columbia, USA
| | - Julian D. Marshall
- Department of Civil and Environmental Engineering, University of Washington, Seattle, Washington, USA
| | - Aileen E. Nowlan
- Environmental Defense Fund, Washington, District of Columbia, USA
| | - Regan F. Patterson
- Department of Civil and Environmental Engineering, University of California, Los Angeles, Los Angeles, California, USA
| | - Sally E. Pusede
- Department of Environmental Sciences, University of Virginia, Charlottesville, Virginia, USA
| | - Veronica A. Southerland
- Environmental Defense Fund, Washington, District of Columbia, USA
- Department of Environmental and Occupational Health, George Washington University, Washington, District of Columbia, USA
| | - Sarah A. Vogel
- Environmental Defense Fund, Washington, District of Columbia, USA
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Darweesh SKL, Burke JF, Peters S. Air Pollution and Parkinson Disease: Increasing Our Risk Each Breath We Take? Neurology 2023; 101:921-922. [PMID: 37903643 DOI: 10.1212/wnl.0000000000207972] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 09/09/2023] [Indexed: 11/01/2023] Open
Affiliation(s)
- Sirwan K L Darweesh
- From the Radboud University Medical Center (S.K.L.D.), Nijmegen, the Netherlands; Ohio State University Medical Center (J.F.B.), Columbus; and Institute for Risk Assessment Sciences (S.P.), Utrecht University, the Netherlands.
| | - James F Burke
- From the Radboud University Medical Center (S.K.L.D.), Nijmegen, the Netherlands; Ohio State University Medical Center (J.F.B.), Columbus; and Institute for Risk Assessment Sciences (S.P.), Utrecht University, the Netherlands
| | - Susan Peters
- From the Radboud University Medical Center (S.K.L.D.), Nijmegen, the Netherlands; Ohio State University Medical Center (J.F.B.), Columbus; and Institute for Risk Assessment Sciences (S.P.), Utrecht University, the Netherlands
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deSouza P, Wang A, Machida Y, Duhl T, Mora S, Kumar P, Kahn R, Ratti C, Durant JL, Hudda N. Evaluating the Performance of Low-Cost PM 2.5 Sensors in Mobile Settings. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:15401-15411. [PMID: 37789620 DOI: 10.1021/acs.est.3c04843] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
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.
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Affiliation(s)
- Priyanka deSouza
- Department of Urban and Regional Planning, University of Colorado Denver, Denver, Colorado 80217, United States
- CU Population Center, University of Colorado Boulder, Boulder, Colorado 80309, United States
| | - An Wang
- MIT Senseable City Lab, Cambridge, Massachusetts 02139, United States
| | - Yuki Machida
- MIT Senseable City Lab, Cambridge, Massachusetts 02139, United States
| | - Tiffany Duhl
- Department of Civil and Environmental Engineering, Tufts University, Medford, Massachusetts 02155, United States
| | - Simone Mora
- MIT Senseable City Lab, Cambridge, Massachusetts 02139, United States
| | - Prashant Kumar
- Global Centre for Clean Air Research (GCARE), School of Sustainability, Civil and Environmental Engineering, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, GU2 7XH Surrey, U.K
- Institute for Sustainability, University of Surrey, Guildford, GU2 7XH Surrey, U.K
| | - Ralph Kahn
- NASA Goddard Space Flight Center, Greenbelt, Maryland 20771, United States
| | - Carlo Ratti
- MIT Senseable City Lab, Cambridge, Massachusetts 02139, United States
| | - John L Durant
- Department of Civil and Environmental Engineering, Tufts University, Medford, Massachusetts 02155, United States
| | - Neelakshi Hudda
- Department of Civil and Environmental Engineering, Tufts University, Medford, Massachusetts 02155, United States
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11
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Koehler K, Wilks M, Green T, Rule AM, Zamora ML, Buehler C, Datta A, Gentner DR, Putcha N, Hansel NN, Kirk GD, Raju S, McCormack M. Evaluation of Calibration Approaches for Indoor Deployments of PurpleAir Monitors. ATMOSPHERIC ENVIRONMENT (OXFORD, ENGLAND : 1994) 2023; 310:119944. [PMID: 37901719 PMCID: PMC10609655 DOI: 10.1016/j.atmosenv.2023.119944] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/31/2023]
Abstract
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.
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Affiliation(s)
- Kirsten Koehler
- Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Megan Wilks
- Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Tim Green
- Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Ana M Rule
- Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Misti L Zamora
- Department of Public Health Sciences UConn School of Medicine, University of Connecticut Health Center, Farmington, CT, USA
| | - Colby Buehler
- Chemical and Environmental Engineering, Yale University, New Haven, CT, USA
| | - Abhirup Datta
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Drew R Gentner
- Chemical and Environmental Engineering, Yale University, New Haven, CT, USA
| | - Nirupama Putcha
- Department of Pulmonary and Critical Care Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Nadia N Hansel
- Department of Pulmonary and Critical Care Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Gregory D Kirk
- Department of Epidemiology and Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Sarath Raju
- Department of Pulmonary and Critical Care Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Meredith McCormack
- Department of Pulmonary and Critical Care Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA
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12
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Kelp MM, Fargiano TC, Lin S, Liu T, Turner JR, Kutz JN, Mickley LJ. Data-Driven Placement of PM 2.5 Air Quality Sensors in the United States: An Approach to Target Urban Environmental Injustice. GEOHEALTH 2023; 7:e2023GH000834. [PMID: 37711364 PMCID: PMC10499371 DOI: 10.1029/2023gh000834] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 08/01/2023] [Accepted: 08/04/2023] [Indexed: 09/16/2023]
Abstract
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.
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Affiliation(s)
- Makoto M. Kelp
- Department of Earth and Planetary SciencesHarvard UniversityCambridgeMAUSA
| | | | - Samuel Lin
- Department of Computer ScienceHarvard UniversityCambridgeMAUSA
| | - Tianjia Liu
- Department of Earth System ScienceUniversity of California, IrvineIrvineCAUSA
| | - Jay R. Turner
- Department of EnergyEnvironmental and Chemical EngineeringWashington UniversitySt. LouisMOUSA
| | - J. Nathan Kutz
- Department of Applied MathematicsUniversity of WashingtonSeattleWAUSA
| | - Loretta J. Mickley
- John A. Paulson School of Engineering and Applied SciencesHarvard UniversityCambridgeMAUSA
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13
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Li J, Crooks J, Murdock J, de Souza P, Hohsfield K, Obermann B, Stockman T. A nested machine learning approach to short-term PM 2.5 prediction in metropolitan areas using PM 2.5 data from different sensor networks. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 873:162336. [PMID: 36813194 DOI: 10.1016/j.scitotenv.2023.162336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 01/26/2023] [Accepted: 02/15/2023] [Indexed: 06/18/2023]
Abstract
Many predictive models for ambient PM2.5 concentrations rely on ground observations from a single monitoring network consisting of sparsely distributed sensors. Integrating data from multiple sensor networks for short-term PM2.5 prediction remains largely unexplored. This paper presents a machine learning approach to predict ambient PM2.5 concentration levels at any unmonitored location several hours ahead using PM2.5 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 PM2.5. This network produces feature vectors to store aggregated daily observations as well as dependency characteristics to predict daily PM2.5. 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 PM2.5 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 PM2.5 concentrations compared to other baseline models.
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Affiliation(s)
- Jing Li
- Department of Geography and the Environment, University of Denver, United States of America.
| | - James Crooks
- Division of Biostatistics and Bioinformatics, National Jewish Health, United States of America; Department of Epidemiology, Colorado School of Public Health, United States of America
| | - Jennifer Murdock
- Department of Geography and the Environment, University of Denver, United States of America
| | - Priyanka de Souza
- Department of Urban and Regional Planning, University of Colorado - Denver, United States of America; CU Population Center, University of Colorado - Boulder, United States of America
| | - Kirk Hohsfield
- University of Colorado, School of Medicine, United States of America
| | - Bill Obermann
- Department of Public Health and Environment, City and County of Denver, United States of America
| | - Tehya Stockman
- Department of Public Health and Environment, City and County of Denver, United States of America; Civil, Environmental and Architectural Engineering Department, University of Colorado - Boulder, United States of America
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14
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deSouza P, Barkjohn K, Clements A, Lee J, Kahn R, Crawford B, Kinney P. An analysis of degradation in low-cost particulate matter sensors. ENVIRONMENTAL SCIENCE: ATMOSPHERES 2023; 3:521-536. [PMID: 37234229 PMCID: PMC10208317 DOI: 10.1039/d2ea00142j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
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.
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Affiliation(s)
- Priyanka deSouza
- Department of Urban and Regional Planning, University of Colorado Denver, Denver CO, 80202, USA
- CU Population Center, University of Colorado Boulder, Boulder CO, 80302, USA
| | - Karoline Barkjohn
- Office of Research and Development, US Environmental Protection Agency, 109 T.W. Alexander Drive, Research Triangle Park, NC 27711, USA
| | - Andrea Clements
- Office of Research and Development, US Environmental Protection Agency, 109 T.W. Alexander Drive, Research Triangle Park, NC 27711, USA
| | - Jenny Lee
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Ralph Kahn
- NASA Goddard Space Flight Center, Greenbelt, MD, 20771, USA
| | - Ben Crawford
- Department of Geography and Environmental Sciences, University of Colorado Denver, 80202, USA
| | - Patrick Kinney
- Boston University School of Public Health, Boston, MA, 02118 USA
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15
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Heintzelman A, Filippelli GM, Moreno-Madriñan MJ, Wilson JS, Wang L, Druschel GK, Lulla VO. Efficacy of Low-Cost Sensor Networks at Detecting Fine-Scale Variations in Particulate Matter in Urban Environments. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:1934. [PMID: 36767298 PMCID: PMC9915248 DOI: 10.3390/ijerph20031934] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Revised: 01/11/2023] [Accepted: 01/15/2023] [Indexed: 06/18/2023]
Abstract
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 PM2.5 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 PM2.5 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 PM2.5 with meteorological data, tree canopy coverage, land use, and various census variables. Greater proximal tree canopy coverage was related to lower PM2.5 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 PM2.5, and a 1% increase in "heavy industry" results in a 0.07 µg/m3 increase in PM2.5 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.
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Affiliation(s)
- Asrah Heintzelman
- Department of Earth Sciences, Indiana University-Purdue University Indianapolis (IUPUI), Indianapolis, IN 46202, USA
- Environmental Resilience Institute, Indiana University, Bloomington, IN 47408, USA
| | - Gabriel M. Filippelli
- Department of Earth Sciences, Indiana University-Purdue University Indianapolis (IUPUI), Indianapolis, IN 46202, USA
- Environmental Resilience Institute, Indiana University, Bloomington, IN 47408, USA
| | | | - Jeffrey S. Wilson
- Department of Geography, Indiana University-Purdue University Indianapolis (IUPUI), Indianapolis, IN 46202, USA
| | - Lixin Wang
- Department of Earth Sciences, Indiana University-Purdue University Indianapolis (IUPUI), Indianapolis, IN 46202, USA
| | - Gregory K. Druschel
- Department of Earth Sciences, Indiana University-Purdue University Indianapolis (IUPUI), Indianapolis, IN 46202, USA
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16
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Considine EM, Braun D, Kamareddine L, Nethery RC, deSouza P. Investigating Use of Low-Cost Sensors to Increase Accuracy and Equity of Real-Time Air Quality Information. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:10.1021/acs.est.2c06626. [PMID: 36623253 PMCID: PMC10329730 DOI: 10.1021/acs.est.2c06626] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
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.
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Affiliation(s)
- Ellen M. Considine
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, 02115, USA
| | - Danielle Braun
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, 02115, USA
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts, 02215, USA
| | - Leila Kamareddine
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, 02115, USA
| | - Rachel C. Nethery
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, 02115, USA
| | - Priyanka deSouza
- Department of Urban and Regional Planning, University of Colorado Denver, Denver, Colorado, 80202, USA
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17
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Esie P, Daepp MIG, Roseway A, Counts S. Neighborhood Composition and Air Pollution in Chicago: Monitoring Inequities With a Dense, Low-Cost Sensing Network, 2021. Am J Public Health 2022. [PMID: 36383946 DOI: 10.2105/ajph.022.307068] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
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).
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Affiliation(s)
- Precious Esie
- At the time of the study, the authors were with Microsoft Research, Redmond, WA
| | - Madeleine I G Daepp
- At the time of the study, the authors were with Microsoft Research, Redmond, WA
| | - Asta Roseway
- At the time of the study, the authors were with Microsoft Research, Redmond, WA
| | - Scott Counts
- At the time of the study, the authors were with Microsoft Research, Redmond, WA
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18
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Esie P, Daepp MIG, Roseway A, Counts S. Neighborhood Composition and Air Pollution in Chicago: Monitoring Inequities With a Dense, Low-Cost Sensing Network, 2021. Am J Public Health 2022; 112:1765-1773. [PMID: 36383946 PMCID: PMC9670210 DOI: 10.2105/ajph.2022.307068] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
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).
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Affiliation(s)
- Precious Esie
- At the time of the study, the authors were with Microsoft Research, Redmond, WA
| | - Madeleine I G Daepp
- At the time of the study, the authors were with Microsoft Research, Redmond, WA
| | - Asta Roseway
- At the time of the study, the authors were with Microsoft Research, Redmond, WA
| | - Scott Counts
- At the time of the study, the authors were with Microsoft Research, Redmond, WA
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19
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Collins TW, Grineski SE, Shaker Y, Mullen CJ. Communities of color are disproportionately exposed to long-term and short-term PM 2.5 in metropolitan America. ENVIRONMENTAL RESEARCH 2022; 214:114038. [PMID: 35961542 DOI: 10.1016/j.envres.2022.114038] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 08/01/2022] [Accepted: 08/02/2022] [Indexed: 06/15/2023]
Abstract
We conducted a novel investigation of neighborhood-level racial/ethnic exposure disparities employing measures aligned with long-term and short-term PM2.5 air pollution benchmarks across metropolitan contexts of the contiguous United States, 2012-2016. We used multivariable generalized estimating equations (GEE) to quantify PM2.5 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 PM2.5 concentrations. We found associations between increased POC composition and greater exposure to all PM2.5 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 PM2.5 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 PM2.5 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 PM2.5 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) PM2.5 exceedances suggests that reducing PM2.5 would attenuate racial/ethnic exposure disparities.
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Affiliation(s)
- Timothy W Collins
- Department of Geography, University of Utah; 260 Central Campus Dr., Rm. 4625, Salt Lake City, UT, 84112, USA; Center for Natural & Technological Hazards, University of Utah; 260 Central Campus Dr., Rm. 4625, Salt Lake City, UT, 84112, USA.
| | - Sara E Grineski
- Center for Natural & Technological Hazards, University of Utah; 260 Central Campus Dr., Rm. 4625, Salt Lake City, UT, 84112, USA; Department of Sociology, University of Utah; 380 S 1530 E, Rm. 301, Salt Lake City, UT, 84112, USA
| | - Yasamin Shaker
- Center for Natural & Technological Hazards, University of Utah; 260 Central Campus Dr., Rm. 4625, Salt Lake City, UT, 84112, USA; Department of Sociology, University of Utah; 380 S 1530 E, Rm. 301, Salt Lake City, UT, 84112, USA
| | - Casey J Mullen
- Center for Natural & Technological Hazards, University of Utah; 260 Central Campus Dr., Rm. 4625, Salt Lake City, UT, 84112, USA; Department of Sociology, University of Utah; 380 S 1530 E, Rm. 301, Salt Lake City, UT, 84112, USA
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20
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Burke M, Heft-Neal S, Li J, Driscoll A, Baylis P, Stigler M, Weill JA, Burney JA, Wen J, Childs ML, Gould CF. Exposures and behavioural responses to wildfire smoke. Nat Hum Behav 2022; 6:1351-1361. [PMID: 35798884 DOI: 10.1038/s41562-022-01396-6] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 05/18/2022] [Indexed: 11/10/2022]
Abstract
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 (PM2.5) 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.
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Affiliation(s)
- Marshall Burke
- Department of Earth System Science, Stanford University, Stanford, CA, USA.
- Center on Food Security and the Environment, Stanford University, Stanford, CA, USA.
- National Bureau of Economic Research, Cambridge, MA, USA.
| | - Sam Heft-Neal
- Center on Food Security and the Environment, Stanford University, Stanford, CA, USA
| | - Jessica Li
- Center on Food Security and the Environment, Stanford University, Stanford, CA, USA
| | - Anne Driscoll
- Center on Food Security and the Environment, Stanford University, Stanford, CA, USA
| | - Patrick Baylis
- Department of Economics, University of British Columbia, Vancouver, British Columbia, Canada
| | - Matthieu Stigler
- Center on Food Security and the Environment, Stanford University, Stanford, CA, USA
| | - Joakim A Weill
- Department of Agricultural and Resource Economics, University of California, Davis, Davis, CA, USA
| | - Jennifer A Burney
- Global Policy School, University of California, San Diego, San Diego, CA, USA
| | - Jeff Wen
- Department of Earth System Science, Stanford University, Stanford, CA, USA
| | - Marissa L Childs
- Emmett Interdisciplinary Program in Environment and Resources, Stanford University, Stanford, CA, USA
| | - Carlos F Gould
- Department of Earth System Science, Stanford University, Stanford, CA, USA
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21
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Lu T, Liu Y, Garcia A, Wang M, Li Y, Bravo-villasenor G, Campos K, Xu J, Han B. Leveraging Citizen Science and Low-Cost Sensors to Characterize Air Pollution Exposure of Disadvantaged Communities in Southern California. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:8777. [PMID: 35886628 PMCID: PMC9322770 DOI: 10.3390/ijerph19148777] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 07/13/2022] [Accepted: 07/17/2022] [Indexed: 12/02/2022]
Abstract
Assessing exposure to fine particulate matter (PM2.5) 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 PM2.5 concentrations from June 2021 to December 2021. We identified the spatial and temporal patterns of PM2.5 measurements as well as the relationship between the total PM2.5 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 PM2.5 concentrations. The average PM2.5 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.
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Affiliation(s)
- Tianjun Lu
- Department of Earth Science and Geography, California State University, Dominguez Hills, Carson, CA 90747, USA; (A.G.); (G.B.-v.); (K.C.)
| | - Yisi Liu
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA;
| | - Armando Garcia
- Department of Earth Science and Geography, California State University, Dominguez Hills, Carson, CA 90747, USA; (A.G.); (G.B.-v.); (K.C.)
| | - Meng Wang
- Department of Epidemiology and Environmental Health, School of Public and Health Professions, University at Buffalo, Buffalo, NY 14214, USA;
| | - Yang Li
- Department of Environmental Science, Baylor University, Waco, TX 76798, USA;
| | - German Bravo-villasenor
- Department of Earth Science and Geography, California State University, Dominguez Hills, Carson, CA 90747, USA; (A.G.); (G.B.-v.); (K.C.)
| | - Kimberly Campos
- Department of Earth Science and Geography, California State University, Dominguez Hills, Carson, CA 90747, USA; (A.G.); (G.B.-v.); (K.C.)
| | - Jia Xu
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China; (J.X.); (B.H.)
| | - Bin Han
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China; (J.X.); (B.H.)
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22
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Mullen C, Flores A, Grineski S, Collins T. Exploring the distributional environmental justice implications of an air quality monitoring network in Los Angeles County. ENVIRONMENTAL RESEARCH 2022; 206:112612. [PMID: 34953883 DOI: 10.1016/j.envres.2021.112612] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 12/03/2021] [Accepted: 12/20/2021] [Indexed: 06/14/2023]
Abstract
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 PM2.5 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 PM2.5 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.
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Affiliation(s)
- Casey Mullen
- Department of Sociology, University of Utah, 380 S 1530 E, Rm. 301, Salt Lake City, UT, 84112, United States.
| | - Aaron Flores
- Department of Geography, University of Utah, 260 Central Campus Dr., Rm. 4625, Salt Lake City, UT, 84112, United States
| | - Sara Grineski
- Department of Sociology, University of Utah, 380 S 1530 E, Rm. 301, Salt Lake City, UT, 84112, United States
| | - Timothy Collins
- Department of Geography, University of Utah, 260 Central Campus Dr., Rm. 4625, Salt Lake City, UT, 84112, United States
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23
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Grineski SE, Collins TW, Mullen CJ. When Not Implemented Communally, Citizen Science Efforts May Reflect, Reinforce, and Potentially Exacerbate Environmental Injustice. Am J Public Health 2022; 112:348-350. [PMID: 35196036 PMCID: PMC8887147 DOI: 10.2105/ajph.2021.306646] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/25/2021] [Indexed: 11/04/2022]
Affiliation(s)
- Sara E Grineski
- Sara E. Grineski and Casey J. Mullen are with the Department of Sociology, and Timothy W. Collins is with the Department of Geography, at the University of Utah, Salt Lake City. S. E. Grineski and T. W. Collins are co-directors of the Center for Natural and Technological Hazards at the University of Utah, where C. J. Mullen is a graduate research associate
| | - Timothy W Collins
- Sara E. Grineski and Casey J. Mullen are with the Department of Sociology, and Timothy W. Collins is with the Department of Geography, at the University of Utah, Salt Lake City. S. E. Grineski and T. W. Collins are co-directors of the Center for Natural and Technological Hazards at the University of Utah, where C. J. Mullen is a graduate research associate
| | - Casey J Mullen
- Sara E. Grineski and Casey J. Mullen are with the Department of Sociology, and Timothy W. Collins is with the Department of Geography, at the University of Utah, Salt Lake City. S. E. Grineski and T. W. Collins are co-directors of the Center for Natural and Technological Hazards at the University of Utah, where C. J. Mullen is a graduate research associate
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Sun Y, Mousavi A, Masri S, Wu J. Socioeconomic Disparities of Low-Cost Air Quality Sensors in California, 2017-2020. Am J Public Health 2022; 112:434-442. [PMID: 35196049 PMCID: PMC8887182 DOI: 10.2105/ajph.2021.306603] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/18/2021] [Indexed: 11/04/2022]
Abstract
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).
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Affiliation(s)
- Yi Sun
- All of the authors are with the Department of Environmental and Occupational Health, Program in Public Health, University of California, Irvine
| | - Amirhosein Mousavi
- All of the authors are with the Department of Environmental and Occupational Health, Program in Public Health, University of California, Irvine
| | - Shahir Masri
- All of the authors are with the Department of Environmental and Occupational Health, Program in Public Health, University of California, Irvine
| | - Jun Wu
- All of the authors are with the Department of Environmental and Occupational Health, Program in Public Health, University of California, Irvine
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25
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Abstract
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.
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