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Eom YJ, Chi H, Jung S, Kim J, Jeong J, Subramanian S, Kim R. Women's empowerment and child anthropometric failures across 28 sub-Saharan African countries: A cross-level interaction by Gender Inequality Index. SSM Popul Health 2024; 26:101651. [PMID: 38524893 PMCID: PMC10958109 DOI: 10.1016/j.ssmph.2024.101651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2023] [Revised: 02/01/2024] [Accepted: 03/02/2024] [Indexed: 03/26/2024] Open
Abstract
Background Child undernutrition remains a major global health issue, particularly in sub-Saharan Africa (SSA). Given the important role mothers play in early childhood health and development, we examined how individual-level women's empowerment and country-level Gender Inequality Index (GII) are jointly related with child undernutrition in SSA. Methods We pooled recent Demographic and Health Surveys from 28 SSA countries. For 137,699 children <5 years old, undernutrition was defined using anthropometric failures (stunting, underweight, wasting). Women's empowerment was assessed using three domains of Survey-based Women's EmPowERment (SWPER) index: attitude to violence, social independence, and decision-making; and country-level gender inequality was measured using GII from United Nations Development Programme. Three-level logistic regression was conducted to examine the joint associations of SWPER and GII as well as their interactions with child anthropometric failures, after adjusting for sociodemographic covariates. Results Overall, 32.85% of children were stunted, 17.63% were underweight, and 6.68% had wasting. Children of mothers with low-level of empowerment for all domains of SWPER had higher odds of stunting (attitude to violence: OR=1.15; 95% CI, 1.11-1.19; social independence: OR=1.21; 95% CI, 1.17-1.25; decision-making: OR=1.16; 95% CI, 1.12-1.20), and consistent results were found for underweight and wasting. Independent of women's empowerment, country-level GII increased the probability of underweight (ranging ORs=1.46; 95% CI, 1.15-1.85 to 1.50; 95% CI, 1.18-1.90) and wasting (ranging ORs=1.56; 95% CI, 1.24-1.97 to 1.61; 95% CI, 1.27-2.03). Significant interaction was found between women's empowerment and country-level GII for stunting and underweight (p<0.05). Conclusions In SSA countries with greater gender inequality, improving women's social independence and decision-making power in particular can reduce their children's risk of anthropometric failures. Policies and interventions targeted at strengthening women's empowerment should consider the degree of gender inequality in each country.
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Affiliation(s)
- Yun-Jung Eom
- Interdisciplinary Program in Precision Public Health, Department of Public Health Sciences, Graduate School of Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea
| | - Hyejun Chi
- Interdisciplinary Program in Precision Public Health, Department of Public Health Sciences, Graduate School of Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea
| | - Sohee Jung
- Interdisciplinary Program in Precision Public Health, Department of Public Health Sciences, Graduate School of Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea
| | - Jinseo Kim
- Interdisciplinary Program in Precision Public Health, Department of Public Health Sciences, Graduate School of Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea
| | - Joshua Jeong
- Hubert Department of Global Health, Rollins School of Public Health, Emory University, 1516 Clifton Road, NE, Atlanta, GA, 30322, USA
| | - S.V. Subramanian
- Harvard Center for Population and Development Studies, Cambridge, MA, USA
- Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Rockli Kim
- Interdisciplinary Program in Precision Public Health, Department of Public Health Sciences, Graduate School of Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea
- Division of Health Policy and Management, College of Health Sciences, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea
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deSouza PN, Anenberg S, Fann N, McKenzie LM, Chan E, Roy A, Jimenez JL, Raich W, Roman H, Kinney PL. Evaluating the sensitivity of mortality attributable to pollution to modeling Choices: A case study for Colorado. ENVIRONMENT INTERNATIONAL 2024; 185:108416. [PMID: 38394913 DOI: 10.1016/j.envint.2024.108416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/24/2023] [Revised: 12/14/2023] [Accepted: 01/02/2024] [Indexed: 02/25/2024]
Abstract
We evaluated the sensitivity of estimated PM2.5 and NO2 health impacts to varying key input parameters and assumptions including: 1) the spatial scale at which impacts are estimated, 2) using either a single concentration-response function (CRF) or using racial/ethnic group specific CRFs from the same epidemiologic study, 3) assigning exposure to residents based on home, instead of home and work locations for the state of Colorado. We found that the spatial scale of the analysis influences the magnitude of NO2, but not PM2.5, attributable deaths. Using county-level predictions instead of 1 km2 predictions of NO2 resulted in a lower estimate of mortality attributable to NO2 by ∼ 50 % for all of Colorado for each year between 2000 and 2020. Using an all-population CRF instead of racial/ethnic group specific CRFs results in a 130 % higher estimate of annual mortality attributable for the white population and a 40 % and 80 % lower estimate of mortality attributable to PM2.5 for Black and Hispanic residents, respectively. Using racial/ethnic group specific CRFs did not result in a different estimation of NO2 attributable mortality for white residents, but led to ∼ 50 % lower estimates of mortality for Black residents, and 290 % lower estimate for Hispanic residents. Using NO2 based on home instead of home and workplace locations results in a smaller estimate of annual mortality attributable to NO2 for all of Colorado by 2 % each year and 0.3 % for PM2.5. Our results should be interpreted as an exercise to make methodological recommendations for future health impact assessments of pollution.
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Affiliation(s)
- Priyanka N deSouza
- Department of Urban and Regional Planning, University of Colorado Denver, Denver, CO, USA; CU Population Center, University of Colorado Boulder, CO, USA; Senseable City Lab, Massachusetts Institute of Technology, USA.
| | - Susan Anenberg
- Milken Institute School of Public Health, George Washington University, Washington D.C., USA
| | - Neal Fann
- U.S. Environmental Protection Agency, USA
| | - Lisa M McKenzie
- Department of Environmental and Occupational Health, Colorado School of Public Health, University of Colorado Anschutz, Aurora, CO, USA
| | | | | | - Jose L Jimenez
- Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO, USA; Department of Chemistry, University of Colorado Boulder, Boulder, CO, USA
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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: 0] [Impact Index Per Article: 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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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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deSouza PN, Chaudhary E, Dey S, Ko S, Németh J, Guttikunda S, Chowdhury S, Kinney P, Subramanian SV, Bell ML, Kim R. An environmental justice analysis of air pollution in India. Sci Rep 2023; 13:16690. [PMID: 37794063 PMCID: PMC10551031 DOI: 10.1038/s41598-023-43628-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 09/26/2023] [Indexed: 10/06/2023] Open
Abstract
Due to the lack of timely data on socioeconomic factors (SES), little research has evaluated if socially disadvantaged populations are disproportionately exposed to higher PM2.5 concentrations in India. We fill this gap by creating a rich dataset of SES parameters for 28,081 clusters (villages in rural India and census-blocks in urban India) from the National Family and Health Survey (NFHS-4) using a precision-weighted methodology that accounts for survey-design. We then evaluated associations between total, anthropogenic and source-specific PM2.5 exposures and SES variables using fully-adjusted multilevel models. We observed that SES factors such as caste, religion, poverty, education, and access to various household amenities are important risk factors for PM2.5 exposures. For example, we noted that a unit standard deviation increase in the cluster-prevalence of Scheduled Caste and Other Backward Class households was significantly associated with an increase in total-PM2.5 levels corresponding to 0.127 μg/m3 (95% CI 0.062 μg/m3, 0.192 μg/m3) and 0.199 μg/m3 (95% CI 0.116 μg/m3, 0.283 μg/m3, respectively. We noted substantial differences when evaluating such associations in urban/rural locations, and when considering source-specific PM2.5 exposures, pointing to the need for the conceptualization of a nuanced EJ framework for India that can account for these empirical differences. We also evaluated emerging axes of inequality in India, by reporting associations between recent changes in PM2.5 levels and different SES parameters.
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Affiliation(s)
- Priyanka N deSouza
- Department of Urban and Regional Planning, University of Colorado Denver, Denver, CO, USA.
- Centre for Atmospheric Sciences, Indian Institute of Technology (IIT) Delhi, New Delhi, India.
| | - Ekta Chaudhary
- Centre for Atmospheric Sciences, Indian Institute of Technology (IIT) Delhi, New Delhi, India
| | - Sagnik Dey
- Centre for Atmospheric Sciences, Indian Institute of Technology (IIT) Delhi, New Delhi, India
- Centre of Excellence for Research on Clean Air, IIT Delhi, New Delhi, India
- School of Public Policy, IIT Delhi, New Delhi, India
| | - Soohyeon Ko
- Department of Public Health Sciences, Graduate School of Korea University, Seoul, South Korea
- Interdisciplinary Program in Precision Public Health, Department of Public Health Sciences, Graduate School of Korea University, Seoul, South Korea
| | - Jeremy Németh
- Department of Urban and Regional Planning, University of Colorado Denver, Denver, CO, USA
| | - Sarath Guttikunda
- Transportation Research and Injury Prevention (TRIP) Centre, Indian Institute of Technology, New Delhi, 110016, India
- Urban Emissions, New Delhi, 110019, India
| | | | - Patrick Kinney
- School of Public Health, Boston University, Boston, MA, USA
| | - S V Subramanian
- Harvard Center for Population and Development Studies, Bow Street, Cambridge, MA, 02138, USA
- Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, 02115, USA
| | - Michelle L Bell
- School of the Environment, Yale University, New Haven, CT, USA
| | - Rockli Kim
- Interdisciplinary Program in Precision Public Health, Department of Public Health Sciences, Graduate School of Korea University, Seoul, South Korea.
- Division of Health Policy and Management, College of Health Science, Korea University, Seoul, South Korea.
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Li X, Li Y, Yu B, Nima Q, Meng H, Shen M, Zhou Z, Liu S, Tian Y, Xing X, Yin L. Urban-rural differences in the association between long-term exposure to ambient particulate matter (PM) and malnutrition status among children under five years old: A cross-sectional study in China. J Glob Health 2023; 13:04112. [PMID: 37736866 PMCID: PMC10515095 DOI: 10.7189/jogh.13.04112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/23/2023] Open
Abstract
Background The evidence regarding the relationship between postnatal exposure of air pollution and child malnutrition indicators, as well as the corresponding urban-rural disparities, is limited, especially in low-pollution area of low- and middle-income countries (LMICs). Therefore, our aim was to contrast the effect estimates of varying ambient particulate matter (PM) on malnutrition indicators between urban and rural areas in Tibet, China. Methods Six malnutrition indicators were evaluated in this study, namely, Z-scores of height for age (HFA), Z-scores of weight for age (WFA), Z-scores of weight for height (WFH), stunting, underweight, and wasting. Exposure to particles with an aerodynamic diameter ≤2.5 micron (μm) (PM2.5), particles with an aerodynamic diameter ≤10 μm (PM10) and particles with an aerodynamic diameter between 2.5 and 10 μm (PMc) was estimated using satellite-based random forest models. Linear regression and logistic regression models were used to assess the associations between PM and the above malnutrition indicators. Furthermore, the effect estimates of different PM were contrasted between urban and rural areas. Results A total of 2511 children under five years old were included in this study. We found long-term exposure to PM2.5, PMc, and PM10 was associated with an increased risk of stunting and a decreased risk of underweight. Of these air pollutants, PMc had the strongest association for Z-scores of HFA and stunting, while PM2.5 had the strongest association for underweight. The results showed that the odds ratio (OR) for stunting were 1.36 (95% confidence interval (CI) = 1.06 to 1.75) per interquartile range (IQR) microgrammes per cubic metre (μg/m3) increase in PM2.5, 1.80 (95% CI = 1.30 to 2.50) per IQR μg/m3 increase in PMc and 1.55 (95% CI = 1.17 to 2.05) per IQR μg/m3 increase in PM10. The concentrations of PM were higher in urban areas, and the effects of PM on malnutrition indicators among urban children were higher than those of rural children. Conclusions Our results suggested that PM exposure might be an important trigger of child malnutrition. Further prospective researches are needed to provide important scientific literature for understanding child malnutrition risk concerning postnatal exposure of air pollutants and formulating synthetically social and environmental policies for malnutrition prevention.
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Affiliation(s)
- Xianzhi Li
- Meteorological Medical Research Center, Panzhihua Central Hospital, Panzhihua, Sichuan Province, China
- Clinical Medical Research Center, Panzhihua Central Hospital, Panzhihua, Sichuan Province, China
- Dali University, Dali, Yunnan Province, China
| | - Yajie Li
- Tibet Center for Disease Control and Prevention, Lhasa, Tibet Autonomous Region, China
| | - Bin Yu
- Institute for Disaster Management and Reconstruction, Sichuan University - Hong Kong Polytechnic University, Chengdu, Sichuan Province, China
| | - Qucuo Nima
- Tibet Center for Disease Control and Prevention, Lhasa, Tibet Autonomous Region, China
| | - Haorong Meng
- Yunnan Center for Disease Control and Prevention, Kunming, Yunnan Province, China
| | - Meiying Shen
- Nursing department, Panzhihua Central Hospital, Panzhihua, Sichuan Province, China
| | - Zonglei Zhou
- Department of Epidemiology, School of Public Health, Fudan University, Shanghai, China
| | - Shunjin Liu
- Meteorological Medical Research Center, Panzhihua Central Hospital, Panzhihua, Sichuan Province, China
- Clinical Medical Research Center, Panzhihua Central Hospital, Panzhihua, Sichuan Province, China
- Dali University, Dali, Yunnan Province, China
| | - Yunyun Tian
- Clinical Medical Research Center, Panzhihua Central Hospital, Panzhihua, Sichuan Province, China
- Dali University, Dali, Yunnan Province, China
| | - Xiangyi Xing
- Meteorological Medical Research Center, Panzhihua Central Hospital, Panzhihua, Sichuan Province, China
- Dali University, Dali, Yunnan Province, China
- Department of Pharmacy, Panzhihua Central Hospital, Panzhihua, Sichuan Province, China
| | - Li Yin
- Meteorological Medical Research Center, Panzhihua Central Hospital, Panzhihua, Sichuan Province, China
- Clinical Medical Research Center, Panzhihua Central Hospital, Panzhihua, Sichuan Province, China
- Dali University, Dali, Yunnan Province, China
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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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