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Reid CE, Considine EM, Watson GL, Telesca D, Pfister GG, Jerrett M. Effect modification of the association between fine particulate air pollution during a wildfire event and respiratory health by area-level measures of socio-economic status, race/ethnicity, and smoking prevalence. Environ Res Health 2023; 1:025005. [PMID: 38332844 PMCID: PMC10852067 DOI: 10.1088/2752-5309/acc4e1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/10/2024]
Abstract
Fine particulate air pollution (PM2.5) is decreasing in most areas of the United States, except for areas most affected by wildfires, where increasing trends in PM2.5 can be attributed to wildfire smoke. The frequency and duration of large wildfires and the length of the wildfire season have all increased in recent decades, partially due to climate change, and wildfire risk is projected to increase further in many regions including the western United States. Increasingly, empirical evidence suggests differential health effects from air pollution by class and race; however, few studies have investigated such differential health impacts from air pollution during a wildfire event. We investigated differential risk of respiratory health impacts during the 2008 northern California wildfires by a comprehensive list of socio-economic status (SES), race/ethnicity, and smoking prevalence variables. Regardless of SES level across nine measures of SES, we found significant associations between PM2.5 and asthma hospitalizations and emergency department (ED) visits during these wildfires. Differential respiratory health risk was found by SES for ED visits for chronic obstructive pulmonary disease where the highest risks were in ZIP codes with the lowest SES levels. Findings for differential effects by race/ethnicity were less consistent across health outcomes. We found that ZIP codes with higher prevalence of smokers had greater risk of ED visits for asthma and pneumonia. Our study suggests that public health efforts to decrease exposures to high levels of air pollution during wildfires should focus on lower SES communities.
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Affiliation(s)
- C E Reid
- Department of Geography, University of Colorado Boulder, Boulder, CO, United States of America
| | - E M Considine
- Department of Applied Math, University of Colorado Boulder, Boulder, CO, United States of America
- Current address: Department of Biostatistics, Harvard T.H. Chan School of Public Health, Harvard University., Boston, MA, United States of America
| | - G L Watson
- Department of Biostatistics, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA, United States of America
| | - D Telesca
- Department of Biostatistics, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA, United States of America
| | - G G Pfister
- National Center for Atmospheric Research, Boulder, CO, United States of America
| | - M Jerrett
- Department of Environmental Health Sciences, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA, United States of America
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Considine EM, Hao J, deSouza P, Braun D, Reid CE, Nethery RC. Evaluation of Model-Based PM 2.5 Estimates for Exposure Assessment during Wildfire Smoke Episodes in the Western U.S. Environ Sci Technol 2023; 57:2031-2041. [PMID: 36693177 PMCID: PMC10288567 DOI: 10.1021/acs.est.2c06288] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Investigating the health impacts of wildfire smoke requires data on people's exposure to fine particulate matter (PM2.5) across space and time. In recent years, it has become common to use machine learning models to fill gaps in monitoring data. However, it remains unclear how well these models are able to capture spikes in PM2.5 during and across wildfire events. Here, we evaluate the accuracy of two sets of high-coverage and high-resolution machine learning-derived PM2.5 data sets created by Di et al. and Reid et al. In general, the Reid estimates are more accurate than the Di estimates when compared to independent validation data from mobile smoke monitors deployed by the US Forest Service. However, both models tend to severely under-predict PM2.5 on high-pollution days. Our findings complement other recent studies calling for increased air pollution monitoring in the western US and support the inclusion of wildfire-specific monitoring observations and predictor variables in model-based estimates of PM2.5. Lastly, we call for more rigorous error quantification of machine-learning derived exposure data sets, with special attention to extreme events.
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Affiliation(s)
- Ellen M. Considine
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, 02115, USA
| | - Jiayuan Hao
- 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, University of Colorado Denver, Denver, Colorado, 80202, USA
- CU Population Center, University of Colorado Boulder, Boulder, Colorado, 80309, 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
| | - Colleen E. Reid
- CU Population Center, University of Colorado Boulder, Boulder, Colorado, 80309, USA
- Department of Geography, University of Colorado Boulder, Boulder, Colorado, 80302, USA
- Earth Lab, University of Colorado Boulder, Boulder, Colorado, 80303, USA
| | - Rachel C. Nethery
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, 02115, USA
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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. Environ Sci Technol 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] [What about the content of this article? (0)] [Affiliation(s)] [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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Bell JM, Considine EM, McCallen LM, Chatfield KC. The Prevalence of Noonan Spectrum Disorders in Pediatric Patients with Pulmonary Valve Stenosis. J Pediatr 2021; 234:134-141.e5. [PMID: 33794220 DOI: 10.1016/j.jpeds.2021.03.050] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 03/23/2021] [Accepted: 03/25/2021] [Indexed: 12/18/2022]
Abstract
OBJECTIVE To investigate the prevalence of Noonan spectrum disorders in a pediatric population with pulmonary valve stenosis (PVS) and explore other characteristics of Noonan spectrum disorders associated with PVS. STUDY DESIGN A retrospective medical record review was completed for patients with a diagnosis of PVS seen at the Children's Hospital Colorado Cardiology clinic between 2009 and 2019. Syndromic diagnoses, genotypes, cardiac characteristics, and extracardiac characteristics associated with Noonan spectrum disorders were recorded; statistical analysis was conducted using R. RESULTS Syndromic diagnoses were made in 16% of 686 pediatric patients with PVS, with Noonan spectrum disorders accounting for 9% of the total diagnoses. Individuals with Noonan spectrum disorders were significantly more likely to have an atrial septal defect and/or hypertrophic cardiomyopathy than the non-Noonan spectrum disorder individuals. Supravalvar pulmonary stenosis was also correlated significantly with Noonan spectrum disorders. Extracardiac clinical features presenting with PVS that were significantly associated with Noonan spectrum disorders included feeding issues, failure to thrive, developmental delay, short stature, and ocular findings. The strongest predictors of a Noonan spectrum disorder diagnosis were cryptorchidism (70%), pectus abnormalities (66%), and ocular findings (48%). The presence of a second characteristic further increased this likelihood, with the highest probability occurring with cryptorchidism combined with ocular findings (92%). CONCLUSIONS The 9% prevalence of Noonan spectrum disorder in patients with PVS should alert clinicians to consider Noonan spectrum disorders when encountering a pediatric patient with PVS. The presence of PVS with 1 or more Noonan spectrum disorder-related features should prompt a genetic evaluation and genetic testing for RAS pathway defects. Noonan spectrum disorders should also be included in the differential when a patient presents with supravalvar pulmonary stenosis.
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Affiliation(s)
- Janet M Bell
- Department of Pediatrics, University of Colorado School of Medicine, Children's Hospital Colorado, Aurora, CO
| | - Ellen M Considine
- Department of Applied Mathematics, University of Colorado College of Engineering & Applied Science, Laboratory for Interdisciplinary Statistical Analysis (LISA), Boulder, CO
| | - Leslie M McCallen
- Department of Pediatrics, University of Colorado School of Medicine, Children's Hospital Colorado, Aurora, CO
| | - Kathryn C Chatfield
- Department of Pediatrics, University of Colorado School of Medicine, Children's Hospital Colorado, Aurora, CO.
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Reid CE, Considine EM, Maestas MM, Li G. Daily PM 2.5 concentration estimates by county, ZIP code, and census tract in 11 western states 2008-2018. Sci Data 2021; 8:112. [PMID: 33875665 PMCID: PMC8055869 DOI: 10.1038/s41597-021-00891-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Accepted: 03/04/2021] [Indexed: 11/20/2022] Open
Abstract
We created daily concentration estimates for fine particulate matter (PM2.5) at the centroids of each county, ZIP code, and census tract across the western US, from 2008-2018. These estimates are predictions from ensemble machine learning models trained on 24-hour PM2.5 measurements from monitoring station data across 11 states in the western US. Predictor variables were derived from satellite, land cover, chemical transport model (just for the 2008-2016 model), and meteorological data. Ten-fold spatial and random CV R2 were 0.66 and 0.73, respectively, for the 2008-2016 model and 0.58 and 0.72, respectively for the 2008-2018 model. Comparing areal predictions to nearby monitored observations demonstrated overall R2 of 0.70 for the 2008-2016 model and 0.58 for the 2008-2018 model, but we observed higher R2 (>0.80) in many urban areas. These data can be used to understand spatiotemporal patterns of, exposures to, and health impacts of PM2.5 in the western US, where PM2.5 levels have been heavily impacted by wildfire smoke over this time period.
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Affiliation(s)
- Colleen E Reid
- Geography Department, Campus Box 260, University of Colorado Boulder, Boulder, CO, 80309, USA.
- Earth Lab, 4001 Discovery Drive Suite S348 - UCB 611, University of Colorado Boulder, Boulder, CO, 80309, USA.
- Institute of Behavioral Sciences, 483 UCB, University of Colorado Boulder, Boulder, CO, 80309, USA.
| | - Ellen M Considine
- Earth Lab, 4001 Discovery Drive Suite S348 - UCB 611, University of Colorado Boulder, Boulder, CO, 80309, USA
- Applied Mathematics Department, Engineering Center, ECOT 225, 526 UCB, University of Colorado Boulder, Boulder, CO, 80309, USA
| | - Melissa M Maestas
- Earth Lab, 4001 Discovery Drive Suite S348 - UCB 611, University of Colorado Boulder, Boulder, CO, 80309, USA
| | - Gina Li
- Geography Department, Campus Box 260, University of Colorado Boulder, Boulder, CO, 80309, USA
- Earth Lab, 4001 Discovery Drive Suite S348 - UCB 611, University of Colorado Boulder, Boulder, CO, 80309, USA
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Considine EM, Reid CE, Ogletree MR, Dye T. Improving accuracy of air pollution exposure measurements: Statistical correction of a municipal low-cost airborne particulate matter sensor network. Environ Pollut 2021; 268:115833. [PMID: 33120139 DOI: 10.1016/j.envpol.2020.115833] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Revised: 10/09/2020] [Accepted: 10/10/2020] [Indexed: 06/11/2023]
Abstract
Low-cost air quality sensors can help increase spatial and temporal resolution of air pollution exposure measurements. These sensors, however, most often produce data of lower accuracy than higher-end instruments. In this study, we investigated linear and random forest models to correct PM2.5 measurements from the Denver Department of Public Health and Environment (DDPHE)'s network of low-cost sensors against measurements from co-located U.S. Environmental Protection Agency Federal Equivalence Method (FEM) monitors. Our training set included data from five DDPHE sensors from August 2018 through May 2019. Our testing set included data from two newly deployed DDPHE sensors from September 2019 through mid-December 2019. In addition to PM2.5, temperature, and relative humidity from the low-cost sensors, we explored using additional temporal and spatial variables to capture unexplained variability in sensor measurements. We evaluated results using spatial and temporal cross-validation techniques. For the long-term dataset, a random forest model with all time-varying covariates and length of arterial roads within 500 m was the most accurate (testing RMSE = 2.9 μg/m3 and R2 = 0.75; leave-one-location-out (LOLO)-validation metrics on the training set: RMSE = 2.2 μg/m3 and R2 = 0.93). For on-the-fly correction, we found that a multiple linear regression model using the past eight weeks of low-cost sensor PM2.5, temperature, and humidity data plus a near-highway indicator predicted each new week of data best (testing RMSE = 3.1 μg/m3 and R2 = 0.78; LOLO-validation metrics on the training set: RMSE = 2.3 μg/m3 and R2 = 0.90). The statistical methods detailed here will be used to correct low-cost sensor measurements to better understand PM2.5 pollution within the city of Denver. This work can also guide similar implementations in other municipalities by highlighting the improved accuracy from inclusion of variables other than temperature and relative humidity to improve accuracy of low-cost sensor PM2.5 data.
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Affiliation(s)
| | - Colleen E Reid
- Department of Geography, University of Colorado Boulder, USA.
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Reid CE, Considine EM, Watson GL, Telesca D, Pfister GG, Jerrett M. Associations between respiratory health and ozone and fine particulate matter during a wildfire event. Environ Int 2019; 129:291-298. [PMID: 31146163 DOI: 10.1016/j.envint.2019.04.033] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2018] [Revised: 03/24/2019] [Accepted: 04/14/2019] [Indexed: 05/20/2023]
Abstract
Wildfires have been increasing in frequency in the western United States (US) with the 2017 and 2018 fire seasons experiencing some of the worst wildfires in terms of suppression costs and air pollution that the western US has seen. Although growing evidence suggests respiratory exacerbations from elevated fine particulate matter (PM2.5) during wildfires, significantly less is known about the impacts on human health of ozone (O3) that may also be increased due to wildfires. Using machine learning, we created daily surface concentration maps for PM2.5 and O3 during an intense wildfire in California in 2008. We then linked these daily exposures to counts of respiratory hospitalizations and emergency department visits at the ZIP code level. We calculated relative risks of respiratory health outcomes using Poisson generalized estimating equations models for each exposure in separate and mutually-adjusted models, additionally adjusted for pertinent covariates. During the active fire periods, PM2.5 was significantly associated with exacerbations of asthma and chronic obstructive pulmonary disease (COPD) and these effects remained after controlling for O3. Effect estimates of O3 during the fire period were non-significant for respiratory hospitalizations but were significant for ED visits for asthma (RR = 1.05 and 95% CI = (1.022, 1.078) for a 10 ppb increase in O3). In mutually-adjusted models, the significant findings for PM2.5 remained whereas the associations with O3 were confounded. Adjusted for O3, the RR for asthma ED visits associated with a 10 μg/m3 increase in PM2.5 was 1.112 and 95% CI = (1.087, 1.138). The significant findings for PM2.5 but not for O3 in mutually-adjusted models is likely due to the fact that PM2.5 levels during these fires exceeded the 24-hour National Ambient Air Quality Standard (NAAQS) of 35 μg/m3 for 4976 ZIP-code days and reached levels up to 6.073 times the NAAQS, whereas our estimated O3 levels during the fire period only occasionally exceeded the NAAQS of 70 ppb with low exceedance levels. Future studies should continue to investigate the combined role of O3 and PM2.5 during wildfires to get a more comprehensive assessment of the cumulative burden on health from wildfire smoke.
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Affiliation(s)
- Colleen E Reid
- Geography Department, University of Colorado Boulder Campus Box 260, Boulder, CO 80309, United States of America.
| | | | - Gregory L Watson
- Department of Biostatistics, Fielding School of Public Health, University of California Los Angeles, United States of America
| | - Donatello Telesca
- Department of Biostatistics, Fielding School of Public Health, University of California Los Angeles, United States of America
| | | | - Michael Jerrett
- Department of Environmental Health Sciences, Fielding School of Public Health, University of California Los Angeles, United States of America
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