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Xu J, Saeedi M, Zalzal J, Zhang M, Ganji A, Mallinen K, Wang A, Lloyd M, Venuta A, Simon L, Weichenthal S, Hatzopoulou M. Exploring the triple burden of social disadvantage, mobility poverty, and exposure to traffic-related air pollution. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 920:170947. [PMID: 38367734 DOI: 10.1016/j.scitotenv.2024.170947] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 01/26/2024] [Accepted: 02/11/2024] [Indexed: 02/19/2024]
Abstract
Understanding the relationships between ultrafine particle (UFP) exposure, socioeconomic status (SES), and sustainable transportation accessibility in Toronto, Canada is crucial for promoting public health, addressing environmental justice, and ensuring transportation equity. We conducted a large-scale mobile measurement campaign and employed a gradient boost model to generate exposure surfaces using land use, built environment, and meteorological conditions. The Ontario Marginalization Index was used to quantify various indicators of social disadvantage for Toronto's neighborhoods. Our findings reveal that people in socioeconomically disadvantaged areas experience elevated UFP exposures. We highlight significant disparities in accessing sustainable transportation, particularly in areas with higher ethnic concentrations. When factoring in daily mobility, UFP exposure disparities in disadvantaged populations are further exacerbated. Furthermore, individuals who do not generate emissions themselves are consistently exposed to higher UFPs, with active transportation users experiencing the highest UFP exposures both at home and at activity locations. Finally, we proposed a novel index, the Community Prioritization Index (CPI), incorporating three indicators, including air quality, social disadvantage, and sustainable transportation. This index identifies neighborhoods experiencing a triple burden, often situated near major infrastructure hubs with high diesel truck activity and lacking greenspace, marking them as high-priority areas for policy action and targeted interventions.
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Affiliation(s)
- Junshi Xu
- Civil and Mineral Engineering, University of Toronto, Canada.
| | - Milad Saeedi
- Civil and Mineral Engineering, University of Toronto, Canada.
| | - Jad Zalzal
- Civil and Mineral Engineering, University of Toronto, Canada.
| | - Mingqian Zhang
- Civil and Mineral Engineering, University of Toronto, Canada
| | - Arman Ganji
- Civil and Mineral Engineering, University of Toronto, Canada.
| | - Keni Mallinen
- Civil and Mineral Engineering, University of Toronto, Canada.
| | - An Wang
- Urban Lab, Massachusetts Institute of Technology, United States.
| | - Marshall Lloyd
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Canada.
| | - Alessya Venuta
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Canada.
| | - Leora Simon
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Canada.
| | - Scott Weichenthal
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Canada.
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Amini H, Bergmann ML, Taghavi Shahri SM, Tayebi S, Cole-Hunter T, Kerckhoffs J, Khan J, Meliefste K, Lim YH, Mortensen LH, Hertel O, Reeh R, Gaarde Nielsen C, Loft S, Vermeulen R, Andersen ZJ, Schwartz J. Harnessing AI to unmask Copenhagen's invisible air pollutants: A study on three ultrafine particle metrics. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 346:123664. [PMID: 38431246 DOI: 10.1016/j.envpol.2024.123664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 02/08/2024] [Accepted: 02/25/2024] [Indexed: 03/05/2024]
Abstract
Ultrafine particles (UFPs) are airborne particles with a diameter of less than 100 nm. They are emitted from various sources, such as traffic, combustion, and industrial processes, and can have adverse effects on human health. Long-term mean ambient average particle size (APS) in the UFP range varies over space within cities, with locations near UFP sources having typically smaller APS. Spatial models for lung deposited surface area (LDSA) within urban areas are limited and currently there is no model for APS in any European city. We collected particle number concentration (PNC), LDSA, and APS data over one-year monitoring campaign from May 2021 to May 2022 across 27 locations and estimated annual mean in Copenhagen, Denmark, and obtained additionally annual mean PNC data from 6 state-owned continuous monitors. We developed 94 predictor variables, and machine learning models (random forest and bagged tree) were developed for PNC, LDSA, and APS. The annual mean PNC, LDSA, and APS were, respectively, 5523 pt/cm3, 12.0 μm2/cm3, and 46.1 nm. The final R2 values by random forest (RF) model were 0.93 for PNC, 0.88 for LDSA, and 0.85 for APS. The 10-fold, repeated 10-times cross-validation R2 values were 0.65, 0.67, and 0.60 for PNC, LDSA, and APS, respectively. The root mean square error for final RF models were 296 pt/cm3, 0.48 μm2/cm3, and 1.60 nm for PNC, LDSA, and APS, respectively. Traffic-related variables, such as length of major roads within buffers 100-150 m and distance to streets with various speed limits were amongst the highly-ranked predictors for our models. Overall, our ML models achieved high R2 values and low errors, providing insights into UFP exposure in a European city where average PNC is quite low. These hyperlocal predictions can be used to study health effects of UFPs in the Danish Capital.
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Affiliation(s)
- Heresh Amini
- Department of Environmental Medicine and Public Health, Institute for Climate Change, Environmental Health, and Exposomics, Icahn School of Medicine at Mount Sinai, New York, United States; Department of Public Health, University of Copenhagen, Copenhagen, Denmark; Department of Environmental Health, Harvard TH Chan School of Public Health, Boston, MA, United States.
| | - Marie L Bergmann
- Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | | | - Shali Tayebi
- Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Thomas Cole-Hunter
- Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Jules Kerckhoffs
- Institute for Risk Assessment Sciences (IRAS), Utrecht University, the Netherlands
| | - Jibran Khan
- Department of Environmental Science, Aarhus University, Roskilde, Denmark; Danish Big Data Centre for Environment and Health (BERTHA), Aarhus University, Roskilde, Denmark
| | - Kees Meliefste
- Institute for Risk Assessment Sciences (IRAS), Utrecht University, the Netherlands
| | - Youn-Hee Lim
- Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Laust H Mortensen
- Department of Public Health, University of Copenhagen, Copenhagen, Denmark; Statistics Denmark, Copenhagen, Denmark
| | - Ole Hertel
- Faculty of Technical Sciences, Aarhus University, Denmark
| | | | | | - Steffen Loft
- Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Roel Vermeulen
- Institute for Risk Assessment Sciences (IRAS), Utrecht University, the Netherlands
| | - Zorana J Andersen
- Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Joel Schwartz
- Department of Environmental Health, Harvard TH Chan School of Public Health, Boston, MA, United States
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Wang J, Alli AS, Clark SN, Ezzati M, Brauer M, Hughes AF, Nimo J, Moses JB, Baah S, Nathvani R, D V, Agyei-Mensah S, Baumgartner J, Bennett JE, Arku RE. Inequalities in urban air pollution in sub-Saharan Africa: an empirical modeling of ambient NO and NO 2 concentrations in Accra, Ghana. ENVIRONMENTAL RESEARCH LETTERS : ERL [WEB SITE] 2024; 19:034036. [PMID: 38419692 PMCID: PMC10897512 DOI: 10.1088/1748-9326/ad2892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 02/04/2024] [Accepted: 02/12/2024] [Indexed: 03/02/2024]
Abstract
Road traffic has become the leading source of air pollution in fast-growing sub-Saharan African cities. Yet, there is a dearth of robust city-wide data for understanding space-time variations and inequalities in combustion related emissions and exposures. We combined nitrogen dioxide (NO2) and nitric oxide (NO) measurement data from 134 locations in the Greater Accra Metropolitan Area (GAMA), with geographical, meteorological, and population factors in spatio-temporal mixed effects models to predict NO2 and NO concentrations at fine spatial (50 m) and temporal (weekly) resolution over the entire GAMA. Model performance was evaluated with 10-fold cross-validation (CV), and predictions were summarized as annual and seasonal (dusty [Harmattan] and rainy [non-Harmattan]) mean concentrations. The predictions were used to examine population distributions of, and socioeconomic inequalities in, exposure at the census enumeration area (EA) level. The models explained 88% and 79% of the spatiotemporal variability in NO2 and NO concentrations, respectively. The mean predicted annual, non-Harmattan and Harmattan NO2 levels were 37 (range: 1-189), 28 (range: 1-170) and 50 (range: 1-195) µg m-3, respectively. Unlike NO2, NO concentrations were highest in the non-Harmattan season (41 [range: 31-521] µg m-3). Road traffic was the dominant factor for both pollutants, but NO2 had higher spatial heterogeneity than NO. For both pollutants, the levels were substantially higher in the city core, where the entire population (100%) was exposed to annual NO2 levels exceeding the World Health Organization (WHO) guideline of 10 µg m-3. Significant disparities in NO2 concentrations existed across socioeconomic gradients, with residents in the poorest communities exposed to levels about 15 µg m-3 higher compared with the wealthiest (p < 0.001). The results showed the important role of road traffic emissions in air pollution concentrations in the GAMA, which has major implications for the health of the city's poorest residents. These data could support climate and health impact assessments as well as policy evaluations in the city.
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Affiliation(s)
- Jiayuan Wang
- Department of Environmental Health Sciences, School of Public Health and Health Sciences, University of Massachusetts, Amherst, MA, United States of America
| | - Abosede S Alli
- Department of Environmental Health Sciences, School of Public Health and Health Sciences, University of Massachusetts, Amherst, MA, United States of America
| | - Sierra N Clark
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, United Kingdom
| | - Majid Ezzati
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, United Kingdom
- Regional Institute for Population Studies, University of Ghana, Accra, Ghana
- Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, United Kingdom
| | - Michael Brauer
- School of Population and Public Health, The University of British Columbia, Vancouver, Canada
| | | | - James Nimo
- Department of Physics, University of Ghana, Accra, Ghana
| | | | - Solomon Baah
- Department of Physics, University of Ghana, Accra, Ghana
| | - Ricky Nathvani
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, United Kingdom
| | - Vishwanath D
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, United Kingdom
| | - Samuel Agyei-Mensah
- Department of Geography and Resource Development, University of Ghana, Accra, Ghana
- Department of Civil and Environmental Engineering, Imperial College London, London, United Kingdom
| | - Jill Baumgartner
- Institute for Health and Social Policy, McGill University, Montreal, Canada
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Canada
| | - James E Bennett
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, United Kingdom
| | - Raphael E Arku
- Department of Environmental Health Sciences, School of Public Health and Health Sciences, University of Massachusetts, Amherst, MA, United States of America
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Lloyd M, Ganji A, Xu J, Venuta A, Simon L, Zhang M, Saeedi M, Yamanouchi S, Apte J, Hong K, Hatzopoulou M, Weichenthal S. Predicting spatial variations in annual average outdoor ultrafine particle concentrations in Montreal and Toronto, Canada: Integrating land use regression and deep learning models. ENVIRONMENT INTERNATIONAL 2023; 178:108106. [PMID: 37544265 DOI: 10.1016/j.envint.2023.108106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 06/28/2023] [Accepted: 07/19/2023] [Indexed: 08/08/2023]
Abstract
BACKGROUND Concentrations of outdoor ultrafine particles (UFP; <0.1 µm) and black carbon (BC) can vary greatly within cities and long-term exposures to these pollutants have been associated with a variety of adverse health outcomes. OBJECTIVE This study integrated multiple approaches to develop new models to estimate within-city spatial variations in annual median (i.e. average) outdoor UFP and BC concentrations as well as mean UFP size in Canada's two largest cities, Montreal and Toronto. METHODS We conducted year-long mobile monitoring campaigns in each city that included evenings and weekends. We developed generalized additive models trained on land use parameters and deep Convolutional Neural Network (CNN) models trained on satellite-view images. Using predictions from these models, we developed final combined models. RESULTS In Toronto, the median observed UFP concentration, UFP size, and BC concentration values were 16,172pt/cm3, 33.7 nm, and 1225 ng/m3, respectively. In Montreal, the median observed UFP concentration, UFP size, and BC concentration values were 14,702pt/cm3, 29.7 nm, and 1060 ng/m3, respectively. For all pollutants in both cities, the proportion of spatial variation explained (i.e., R2) was slightly greater (1-2 percentage points) for the combined models than the generalized additive models and a greater (approximately 10 percentage points) than the deep CNN models. The Toronto combined model R2 values in the test set were 0.73, 0.55, and 0.61 for UFP concentrations, UFP size, and BC concentration, respectively. The Montreal combined model R2 values were 0.60, 0.49, and 0.60 for UFP concentration, UFP size, and BC concentration models respectively. For each pollutant, predictions from the combined, deep CNN, and generalized additive models were highly correlated with each other and differences between models were explored in sensitivity analyses. CONCLUSION Predictions from these models are available to support future epidemiological research examining long-term health impacts of outdoor UFPs and BC.
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Affiliation(s)
- Marshall Lloyd
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Québec H3A 1G1, Canada.
| | - Arman Ganji
- Department of Civil and Mineral Engineering, University of Toronto, Toronto, Ontario M5S 1A4, Canada.
| | - Junshi Xu
- Department of Civil and Mineral Engineering, University of Toronto, Toronto, Ontario M5S 1A4, Canada.
| | - Alessya Venuta
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Québec H3A 1G1, Canada.
| | - Leora Simon
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Québec H3A 1G1, Canada.
| | - Mingqian Zhang
- Department of Civil and Mineral Engineering, University of Toronto, Toronto, Ontario M5S 1A4, Canada.
| | - Milad Saeedi
- Department of Civil and Mineral Engineering, University of Toronto, Toronto, Ontario M5S 1A4, Canada.
| | - Shoma Yamanouchi
- Department of Civil and Mineral Engineering, University of Toronto, Toronto, Ontario M5S 1A4, Canada.
| | - Joshua Apte
- Department of Civil and Environmental Engineering, University of California at Berkeley, Berkeley, CA 94720, United States; School of Public Health, University of California, Berkeley, CA 94720, United States.
| | - Kris Hong
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Québec H3A 1G1, Canada.
| | - Marianne Hatzopoulou
- Department of Civil and Mineral Engineering, University of Toronto, Toronto, Ontario M5S 1A4, Canada.
| | - Scott Weichenthal
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Québec H3A 1G1, Canada.
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Blanco MN, Doubleday A, Austin E, Marshall JD, Seto E, Larson TV, Sheppard L. Design and evaluation of short-term monitoring campaigns for long-term air pollution exposure assessment. JOURNAL OF EXPOSURE SCIENCE & ENVIRONMENTAL EPIDEMIOLOGY 2023; 33:465-473. [PMID: 36045136 PMCID: PMC9971335 DOI: 10.1038/s41370-022-00470-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 08/12/2022] [Accepted: 08/15/2022] [Indexed: 06/02/2023]
Abstract
BACKGROUND Short-term mobile monitoring campaigns to estimate long-term air pollution levels are becoming increasingly common. Still, many campaigns have not conducted temporally-balanced sampling, and few have looked at the implications of such study designs for epidemiologic exposure assessment. OBJECTIVE We carried out a simulation study using fixed-site air quality monitors to better understand how different short-term monitoring designs impact the resulting exposure surfaces. METHODS We used Monte Carlo resampling to simulate three archetypal short-term monitoring sampling designs using oxides of nitrogen (NOx) monitoring data from 69 regulatory sites in California: a year-around Balanced Design that sampled during all seasons of the year, days of the week, and all or various hours of the day; a temporally reduced Rush Hours Design; and a temporally reduced Business Hours Design. We evaluated the performance of each design's land use regression prediction model. RESULTS The Balanced Design consistently yielded the most accurate annual averages; while the reduced Rush Hours and Business Hours Designs generally produced more biased results. SIGNIFICANCE A temporally-balanced sampling design is crucial for short-term campaigns such as mobile monitoring aiming to assess long-term exposure in epidemiologic cohorts. IMPACT STATEMENT Short-term monitoring campaigns to assess long-term air pollution trends are increasingly common, though they rarely conduct temporally balanced sampling. We show that this approach produces biased annual average exposure estimates that can be improved by collecting temporally-balanced samples.
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Affiliation(s)
- Magali N Blanco
- Department of Environmental and Occupational Health Sciences, School of Public Health, University of Washington, Hans Rosling Center for Population Health, 3980 15th Ave NE, Seattle, WA, 98195, USA.
| | - Annie Doubleday
- Department of Environmental and Occupational Health Sciences, School of Public Health, University of Washington, Hans Rosling Center for Population Health, 3980 15th Ave NE, Seattle, WA, 98195, USA
| | - Elena Austin
- Department of Environmental and Occupational Health Sciences, School of Public Health, University of Washington, Hans Rosling Center for Population Health, 3980 15th Ave NE, Seattle, WA, 98195, USA
| | - Julian D Marshall
- Department of Civil & Environmental Engineering, College of Engineering, University of Washington, 201 More Hall, Box 352700, Seattle, WA, 98195, USA
| | - Edmund Seto
- Department of Environmental and Occupational Health Sciences, School of Public Health, University of Washington, Hans Rosling Center for Population Health, 3980 15th Ave NE, Seattle, WA, 98195, USA
| | - Timothy V Larson
- Department of Environmental and Occupational Health Sciences, School of Public Health, University of Washington, Hans Rosling Center for Population Health, 3980 15th Ave NE, Seattle, WA, 98195, USA
- Department of Civil & Environmental Engineering, College of Engineering, University of Washington, 201 More Hall, Box 352700, Seattle, WA, 98195, USA
| | - Lianne Sheppard
- Department of Environmental and Occupational Health Sciences, School of Public Health, University of Washington, Hans Rosling Center for Population Health, 3980 15th Ave NE, Seattle, WA, 98195, USA.
- Department of Biostatistics, School of Public Health, University of Washington, Hans Rosling Center for Population Health, 3980 15th Ave NE, Seattle, WA, 98195, USA.
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Jung CR, Chen WT, Young LH, Hsiao TC. A hybrid model for estimating the number concentration of ultrafine particles based on machine learning algorithms in central Taiwan. ENVIRONMENT INTERNATIONAL 2023; 175:107937. [PMID: 37088007 DOI: 10.1016/j.envint.2023.107937] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Revised: 04/12/2023] [Accepted: 04/13/2023] [Indexed: 05/03/2023]
Abstract
Modeling is a cost-effective measure to estimate ultrafine particle (UFP) levels. Previous UFP estimates generally relied on land-use regression with insufficient temporal resolution. We carried out in-situ measurements for UFP in central Taiwan and developed a model incorporating satellite-based measurements, meteorological variables, and land-use data to estimate daily UFP levels at a 1-km resolution. Two sampling campaigns were conducted for measuring hourly UFP concentrations at six sites between 2008-2010 and 2017-2021, respectively, using scanning mobility particle sizers. Three machine learning algorithms, namely random forest, eXtreme gradient boosting (XGBoost), and deep neural network, were used to develop UFP estimation models. The performances were evaluated with a 10-fold cross-validation, temporal, and spatial validation. A total of 1,022 effective sampling days were conducted. The XGBoost model had the best performance with a training coefficient of determination (R2) of 0.99 [normalized root mean square error (nRMSE): 6.52%] and a cross-validation R2 of 0.78 (nRMSE: 31.0%). The ten most important variables were surface pressure, distance to the nearest road, temperature, calendar year, day of the year, NO2, meridional wind, the total length of roads, PM2.5, and zonal wind. The UFP levels were elevated along the main roads across different seasons, suggesting that traffic emission is an important contributor to UFP. This hybrid model outperformed prior land use regression models and thus can provide more accurate estimates of UFP for epidemiological studies.
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Affiliation(s)
- Chau-Ren Jung
- Department of Public Health, College of Public Health, China Medical University, Taichung, Taiwan; Japan Environment and Children's Study Programme Office, Health and Environmental Risk Division, National Institute for Environmental Studies, Tsukuba, Japan.
| | - Wei-Ting Chen
- Department of Atmospheric Sciences, National Taiwan University, Taipei, Taiwan
| | - Li-Hao Young
- Department of Occupational Safety and Health, China Medical University, Taichung, Taiwan
| | - Ta-Chih Hsiao
- Graduate Institute of Environmental Engineering, National Taiwan University, Taipei, Taiwan
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Kim SY, Blanco MN, Bi J, Larson TV, Sheppard L. Exposure assessment for air pollution epidemiology: A scoping review of emerging monitoring platforms and designs. ENVIRONMENTAL RESEARCH 2023; 223:115451. [PMID: 36764437 PMCID: PMC9992293 DOI: 10.1016/j.envres.2023.115451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 01/10/2023] [Accepted: 02/07/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND Both exposure monitoring and exposure prediction have played key roles in assessing individual-level long-term exposure to air pollutants and their associations with human health. While there have been notable advances in exposure prediction methods, improvements in monitoring designs are also necessary, particularly given new monitoring paradigms leveraging low-cost sensors and mobile platforms. OBJECTIVES We aim to provide a conceptual summary of novel monitoring designs for air pollution cohort studies that leverage new paradigms and technologies, to investigate their characteristics in real-world examples, and to offer practical guidance to future studies. METHODS We propose a conceptual summary that focuses on two overarching types of monitoring designs, mobile and non-mobile, as well as their subtypes. We define mobile designs as monitoring from a moving platform, and non-mobile designs as stationary monitoring from permanent or temporary locations. We only consider non-mobile studies with cost-effective sampling devices. Then we discuss similarities and differences across previous studies with respect to spatial and temporal representation, data comparability between design classes, and the data leveraged for model development. Finally, we provide specific suggestions for future monitoring designs. RESULTS Most mobile and non-mobile monitoring studies selected monitoring sites based on land use instead of residential locations, and deployed monitors over limited time periods. Some studies applied multiple design and/or sub-design classes to the same area, time period, or instrumentation, to allow comparison. Even fewer studies leveraged monitoring data from different designs to improve exposure assessment by capitalizing on different strengths. In order to maximize the benefit of new monitoring technologies, future studies should adopt monitoring designs that prioritize residence-based site selection with comprehensive temporal coverage and leverage data from different designs for model development in the presence of good data compatibility. DISCUSSION Our conceptual overview provides practical guidance on novel exposure assessment monitoring for epidemiological applications.
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Affiliation(s)
- Sun-Young Kim
- Department of Cancer AI and Digital Health, Graduate School of Cancer Science and Policy, National Cancer Center, Goyang-si, Gyeonggi-do, Republic of Korea; Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA.
| | - Magali N Blanco
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA
| | - Jianzhao Bi
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA
| | - Timothy V Larson
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA; Department of Civil and Environmental Engineering, University of Washington, Seattle, WA, USA
| | - Lianne Sheppard
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA; Department of Biostatistics, University of Washington, Seattle, WA, USA
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8
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Blanco MN, Bi J, Austin E, Larson TV, Marshall JD, Sheppard L. Impact of Mobile Monitoring Network Design on Air Pollution Exposure Assessment Models. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:440-450. [PMID: 36508743 PMCID: PMC10615227 DOI: 10.1021/acs.est.2c05338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
Short-term mobile monitoring campaigns are increasingly used to assess long-term air pollution exposure in epidemiology. Little is known about how monitoring network design features, including the number of stops and sampling temporality, impacts exposure assessment models. We address this gap by leveraging an extensive mobile monitoring campaign conducted in the greater Seattle area over the course of a year during all days of the week and most hours. The campaign measured total particle number concentration (PNC; sheds light on ultrafine particulate (UFP) number concentration), black carbon (BC), nitrogen dioxide (NO2), fine particulate matter (PM2.5), and carbon dioxide (CO2). In Monte Carlo sampling of 7327 total stops (278 sites × 26 visits each), we restricted the number of sites and visits used to estimate annual averages. Predictions from the all-data campaign performed well, with cross-validated R2s of 0.51-0.77. We found similar model performances (85% of the all-data campaign R2) with ∼1000 to 3000 randomly selected stops for NO2, PNC, and BC, and ∼4000 to 5000 stops for PM2.5 and CO2. Campaigns with additional temporal restrictions (e.g., business hours, rush hours, weekdays, or fewer seasons) had reduced model performances and different spatial surfaces. Mobile monitoring campaigns wanting to assess long-term exposure should carefully consider their monitoring designs.
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Affiliation(s)
- Magali N Blanco
- Department of Environmental and Occupational Health Sciences, School of Public Health, Hans Rosling Center for Population Health, University of Washington, 3980 15th Avenue NE, Seattle, Washington98195, United States
| | - Jianzhao Bi
- Department of Environmental and Occupational Health Sciences, School of Public Health, Hans Rosling Center for Population Health, University of Washington, 3980 15th Avenue NE, Seattle, Washington98195, United States
| | - Elena Austin
- Department of Environmental and Occupational Health Sciences, School of Public Health, Hans Rosling Center for Population Health, University of Washington, 3980 15th Avenue NE, Seattle, Washington98195, United States
| | - Timothy V Larson
- Department of Environmental and Occupational Health Sciences, School of Public Health, Hans Rosling Center for Population Health, University of Washington, 3980 15th Avenue NE, Seattle, Washington98195, United States
- Department of Civil & Environmental Engineering, College of Engineering, University of Washington, 201 More Hall, Box 352700, Seattle, Washington98195, United States
| | - Julian D Marshall
- Department of Civil & Environmental Engineering, College of Engineering, University of Washington, 201 More Hall, Box 352700, Seattle, Washington98195, United States
| | - Lianne Sheppard
- Department of Environmental and Occupational Health Sciences, School of Public Health, Hans Rosling Center for Population Health, University of Washington, 3980 15th Avenue NE, Seattle, Washington98195, United States
- Department of Biostatistics, School of Public Health, Hans Rosling Center for Population Health, University of Washington, 3980 15th Avenue NE, Seattle, Washington98195, United States
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Shah RU, Padilla LE, Peters DR, Dupuy-Todd M, Fonseca ER, Ma GQ, Popoola OAM, Jones RL, Mills J, Martin NA, Alvarez RA. Identifying Patterns and Sources of Fine and Ultrafine Particulate Matter in London Using Mobile Measurements of Lung-Deposited Surface Area. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:96-108. [PMID: 36548159 PMCID: PMC9835830 DOI: 10.1021/acs.est.2c08096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 12/06/2022] [Accepted: 12/06/2022] [Indexed: 06/17/2023]
Abstract
We performed more than a year of mobile, 1 Hz measurements of lung-deposited surface area (LDSA, the surface area of 20-400 nm diameter particles, deposited in alveolar regions of lungs) and optically assessed fine particulate matter (PM2.5), black carbon (BC), and nitrogen dioxide (NO2) in central London. We spatially correlated these pollutants to two urban emission sources: major roadways and restaurants. We show that optical PM2.5 is an ineffective indicator of tailpipe emissions on major roadways, where we do observe statistically higher LDSA, BC, and NO2. Additionally, we find pollutant hot spots in commercial neighborhoods with more restaurants. A low LDSA (15 μm2 cm-3) occurs in areas with fewer major roadways and restaurants, while the highest LDSA (25 μm2 cm-3) occurs in areas with more of both sources. By isolating areas that are higher in one source than the other, we demonstrate the comparable impacts of traffic and restaurants on LDSA. Ratios of hyperlocal enhancements (ΔLDSA:ΔBC and ΔLDSA:ΔNO2) are higher in commercial neighborhoods than on major roadways, further demonstrating the influence of restaurant emissions on LDSA. We demonstrate the added value of using particle surface in identifying hyperlocal patterns of health-relevant PM components, especially in areas with strong vehicular emissions where the high LDSA does not translate to high PM2.5.
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Affiliation(s)
- Rishabh U. Shah
- Environmental
Defense Fund, 301 Congress Avenue, #1300, Austin, Texas78701, United
States
| | - Lauren E. Padilla
- Environmental
Defense Fund, 301 Congress Avenue, #1300, Austin, Texas78701, United
States
| | - Daniel R. Peters
- Environmental
Defense Fund, 301 Congress Avenue, #1300, Austin, Texas78701, United
States
| | - Megan Dupuy-Todd
- Environmental
Defense Fund, 301 Congress Avenue, #1300, Austin, Texas78701, United
States
| | | | - Geoffrey Q. Ma
- National
Physical Laboratory, Hampton Road, Teddington, MiddlesexTW11 0LW, U.K.
- Yusuf
Hamied Department of Chemistry, University
of Cambridge, CambridgeCB2 1EW, U.K.
| | | | - Roderic L. Jones
- Yusuf
Hamied Department of Chemistry, University
of Cambridge, CambridgeCB2 1EW, U.K.
| | - Jim Mills
- ACOEM UK Ltd., TewkesburyGL20 8GD, U.K.
| | - Nicholas A. Martin
- National
Physical Laboratory, Hampton Road, Teddington, MiddlesexTW11 0LW, U.K.
| | - Ramón A. Alvarez
- Environmental
Defense Fund, 301 Congress Avenue, #1300, Austin, Texas78701, United
States
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10
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Davis Z, de Groh M, Rainham DG. The Canadian Environmental Quality Index (Can-EQI): Development and calculation of an index to assess spatial variation of environmental quality in Canada's 30 largest cities. ENVIRONMENT INTERNATIONAL 2022; 170:107633. [PMID: 36413927 DOI: 10.1016/j.envint.2022.107633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 11/08/2022] [Accepted: 11/12/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND Multiple characteristics of the urban environment have been shown to influence population health and health-related behaviours, though the distribution and combined effects of these characteristics on health is less understood. A composite measure of multiple environmental conditions would allow for comparisons among different urban areas; however, this measure is not available in Canada. OBJECTIVES To develop an index of environmental quality for Canada's largest urban areas and to assess the influence of population size on index values. METHODS We conducted a systematic search of potential datasets and consulted with experts to refine and select datasets for inclusion. We identified and selected nine datasets across five domains (outdoor air pollution, natural environments, built environments, radiation, and climate/weather). Datasets were chosen based on known impacts on human health across the life course, complete geographic coverage of the cities of interest, and temporal alignment with the 2016 Canadian census. Each dataset was then summarized into dissemination areas (DAs). The Canadian Environmental Quality Index (Can-EQI) was created by summing decile ranks of each variable based on hypothesized relationships to health outcomes. RESULTS We selected 30 cities with a population of more than 100,000 people which included 28,026 DAs and captured approximately 55% of the total Canadian population. Can-EQI scores ranged from 21.1 to 88.9 out of 100, and in Canada's largest cities were 10.2 (95% CI: -10.7, -9.7) points lower than the smallest cities. Mapping the Can-EQI revealed high geographic variability within and between cities. DISCUSSION Our work demonstrates a valuable methodology for exploring variations in environmental conditions in Canada's largest urban areas and provides a means for exploring the role of environmental factors in explaining urban health inequalities and disparities. Additionally, the Can-EQI may be of value to municipal planners and decision makers considering the allocation of investments to improve urban conditions.
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Affiliation(s)
- Zoë Davis
- School of Ecosystem and Forest Sciences, Faculty of Science, University of Melbourne, Richmond, VIC 3121, Australia
| | - Margaret de Groh
- Centre for Surveillance and Applied Research, Public Health Agency of Canada, Ottawa, ON K1A 0K9, Canada
| | - Daniel G Rainham
- School of Health and Human Performance, Faculty of Health, Dalhousie University, Halifax, NS B3H 4R2, Canada; Healthy Populations Institute, Dalhousie University, Halifax, NS B3H 4R2, Canada.
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11
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Blanco MN, Gassett A, Gould T, Doubleday A, Slager DL, Austin E, Seto E, Larson TV, Marshall JD, Sheppard L. Characterization of Annual Average Traffic-Related Air Pollution Concentrations in the Greater Seattle Area from a Year-Long Mobile Monitoring Campaign. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:11460-11472. [PMID: 35917479 PMCID: PMC9396693 DOI: 10.1021/acs.est.2c01077] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Growing evidence links traffic-related air pollution (TRAP) to adverse health effects. We designed an innovative and extensive mobile monitoring campaign to characterize TRAP exposure levels for the Adult Changes in Thought (ACT) study, a Seattle-based cohort. The campaign measured particle number concentration (PNC) to capture ultrafine particles (UFP), black carbon (BC), nitrogen dioxide (NO2), fine particulate matter (PM2.5), and carbon dioxide (CO2) at 309 roadside sites within a large, 1200 land km2 (463 mi2) area representative of the cohort. We collected about 29 two-minute measurements at each site during all seasons, days of the week, and most times of the day over a 1-year period. Validation showed good agreement between our BC, NO2, and PM2.5 measurements and monitoring agency sites (R2 = 0.68-0.73). Universal kriging-partial least squares models of annual average pollutant concentrations had cross-validated mean square error-based R2 (and root mean square error) values of 0.77 (1177 pt/cm3) for PNC, 0.60 (102 ng/m3) for BC, 0.77 (1.3 ppb) for NO2, 0.70 (0.3 μg/m3) for PM2.5, and 0.51 (4.2 ppm) for CO2. Overall, we found that the design of this extensive campaign captured the spatial pollutant variations well and these were explained by sensible land use features, including those related to traffic.
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Affiliation(s)
- Magali N. Blanco
- Department of Environmental and Occupational Health Sciences, School of Public Health, University of Washington, Hans Rosling Center for Population Health, 3980 15th Ave NE, Seattle, WA 98195, United States of America
| | - Amanda Gassett
- Department of Environmental and Occupational Health Sciences, School of Public Health, University of Washington, Hans Rosling Center for Population Health, 3980 15th Ave NE, Seattle, WA 98195, United States of America
| | - Timothy Gould
- Department of Civil & Environmental Engineering, College of Engineering, University of Washington, 201 More Hall, Box 352700, Seattle, WA 98195, United States of America
| | - Annie Doubleday
- Department of Environmental and Occupational Health Sciences, School of Public Health, University of Washington, Hans Rosling Center for Population Health, 3980 15th Ave NE, Seattle, WA 98195, United States of America
| | - David L. Slager
- Department of Environmental and Occupational Health Sciences, School of Public Health, University of Washington, Hans Rosling Center for Population Health, 3980 15th Ave NE, Seattle, WA 98195, United States of America
| | - Elena Austin
- Department of Environmental and Occupational Health Sciences, School of Public Health, University of Washington, Hans Rosling Center for Population Health, 3980 15th Ave NE, Seattle, WA 98195, United States of America
| | - Edmund Seto
- Department of Environmental and Occupational Health Sciences, School of Public Health, University of Washington, Hans Rosling Center for Population Health, 3980 15th Ave NE, Seattle, WA 98195, United States of America
| | - Timothy V. Larson
- Department of Environmental and Occupational Health Sciences, School of Public Health, University of Washington, Hans Rosling Center for Population Health, 3980 15th Ave NE, Seattle, WA 98195, United States of America
- Department of Civil & Environmental Engineering, College of Engineering, University of Washington, 201 More Hall, Box 352700, Seattle, WA 98195, United States of America
| | - Julian D. Marshall
- Department of Civil & Environmental Engineering, College of Engineering, University of Washington, 201 More Hall, Box 352700, Seattle, WA 98195, United States of America
| | - Lianne Sheppard
- Department of Environmental and Occupational Health Sciences, School of Public Health, University of Washington, Hans Rosling Center for Population Health, 3980 15th Ave NE, Seattle, WA 98195, United States of America
- Department of Biostatistics, School of Public Health, University of Washington, Hans Rosling Center for Population Health, 3980 15th Ave NE, Seattle, WA 98195, United States of America
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12
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Alazmi A, Rakha H. Assessing and Validating the Ability of Machine Learning to Handle Unrefined Particle Air Pollution Mobile Monitoring Data Randomly, Spatially, and Spatiotemporally. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:10098. [PMID: 36011733 PMCID: PMC9408314 DOI: 10.3390/ijerph191610098] [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: 03/24/2022] [Revised: 06/19/2022] [Accepted: 06/20/2022] [Indexed: 06/15/2023]
Abstract
Many epidemiological studies have evaluated the accuracy of machine learning models in predicting levels of particulate number (PN) and black carbon (BC) pollutant concentrations. However, few studies have investigated the ability of machine learning to predict the pollutant concentration with using unrefined mobile measurement data and explore the reliability of the prediction models. Additionally, researchers are moving away from using fixed-site data in favor of using mobile monitoring data in a variety of locations to develop hourly empirical models of particulate air pollution. This study compared the differences between long-term (daily average) and short-term (hourly average and 1 s unrefined data) model performance in three different classes of cross validation: randomly, spatially, and spatially temporally. This study used secondary data describing BC and PN pollutant levels in the rural location of Blacksburg (VA). Our results show that the model based on unrefined data was able to detect the pollutant hot spot areas with similar accuracy compared to the aggregated model. Moreover, the performance was found to improve when temporal data added to the model: the 10-fold MAE for the BC and PN were 0.44 μg/m3 and 3391 pt/cm3, respectively, for the unrefined data (one second data) model. The findings detailed here will add to the literature on the correlation between data (pre)processing and the efficacy of machine learning models in predicting pollution levels while also enhancing our understanding of more reliable validation strategies.
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Affiliation(s)
- Asmaa Alazmi
- Department of Construction Project, Ministry of Public Work of Kuwait, Kuwait City 12011, Kuwait
| | - Hesham Rakha
- Department of Civil and Environmental Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA
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13
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Wai TH, Apte JS, Harris MH, Kirchstetter TW, Portier CJ, Preble CV, Roy A, Szpiro AA. Insights from Application of a Hierarchical Spatio-Temporal Model to an Intensive Urban Black Carbon Monitoring Dataset. ATMOSPHERIC ENVIRONMENT (OXFORD, ENGLAND : 1994) 2022; 277:119069. [PMID: 35462958 PMCID: PMC9031477 DOI: 10.1016/j.atmosenv.2022.119069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
Existing regulatory pollutant monitoring networks rely on a small number of centrally located measurement sites that are purposefully sited away from major emission sources. While informative of general air quality trends regionally, these networks often do not fully capture the local variability of air pollution exposure within a community. Recent technological advancements have reduced the cost of sensors, allowing air quality monitoring campaigns with high spatial resolution. The 100×100 black carbon (BC) monitoring network deployed 100 low-cost BC sensors across the 15 km2 West Oakland, CA community for 100 days in the summer of 2017, producing a nearly continuous site-specific time series of BC concentrations which we aggregated to one-hour averages. Leveraging this dataset, we employed a hierarchical spatio-temporal model to accurately predict local spatio-temporal concentration patterns throughout West Oakland, at locations without monitors (average cross-validated hourly temporal R 2=0.60). Using our model, we identified spatially varying temporal pollution patterns associated with small-scale geographic features and proximity to local sources. In a sub-sampling analysis, we demonstrated that fine scale predictions of nearly comparable accuracy can be obtained with our modeling approach by using ~30% of the 100×100 BC network supplemented by a shorter-term high-density campaign.
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Affiliation(s)
- Travis Hee Wai
- Department of Medicine, Division of Pulmonary, Critical Care, and Sleep Medicine, University of Washington, Seattle, WA
| | - Joshua S Apte
- Department of Civil and Environmental Engineering, University of California, Berkeley, Berkeley, CA
- School of Public Health, University of California, Berkeley, Berkeley, CA
| | | | - Thomas W Kirchstetter
- Department of Civil and Environmental Engineering, University of California, Berkeley, Berkeley, CA
- Energy Technologies Area, Lawrence Berkeley National Laboratory, Berkeley, CA
| | | | - Chelsea V Preble
- Department of Civil and Environmental Engineering, University of California, Berkeley, Berkeley, CA
- Energy Technologies Area, Lawrence Berkeley National Laboratory, Berkeley, CA
| | - Ananya Roy
- Environmental Defense Fund, Washington, DC
| | - Adam A Szpiro
- Department of Biostatistics, University of Washington, Seattle, WA
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14
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Ge Y, Fu Q, Yi M, Chao Y, Lei X, Xu X, Yang Z, Hu J, Kan H, Cai J. High spatial resolution land-use regression model for urban ultrafine particle exposure assessment in Shanghai, China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 816:151633. [PMID: 34785221 DOI: 10.1016/j.scitotenv.2021.151633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 11/04/2021] [Accepted: 11/08/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND Little is currently known about long-term health effects of ambient ultrafine particles (UFPs) due to the lack of exposure assessment metrics suitable for use in large population-based studies. Land use regression (LUR) models have been used increasingly for modeling small-scale spatial variation in UFPs concentrations in European and American, but have never been applied in developing countries with heavy air pollution. OBJECTIVE This study developed a land-use regression (LUR) model for UFP exposure assessment in Shanghai, a typic mega city of China, where dense population resides. METHOD A 30-minute measurement of particle number concentrations of UFPs was collected at each visit at 144 fixed sites, and each was visited three times in each season of winter, spring, and summer. The annual adjusted average was calculated and regressed against pre-selected geographic information system-derived predictor variables using a stepwise variable selection method. RESULT The final LUR model explained 69% of the spatial variability in UFP with a root mean square error of 6008 particles cm-3. The 10-fold cross validation R2 reached 0.68, revealing the robustness of the model. The final predictors included traffic-related NOx emissions, number of restaurants, building footprint area, and distance to the nearest national road. These predictors were within a relatively small buffer size, ranging from 50 m to 100 m, indicating great spatial variations of UFP particle number concentration and the need of high-resolution models for UFP exposure assessment in urban areas. CONCLUSION We concluded that based on a purpose-designed short-term monitoring network, LUR model can be applied to predict UFPs spatial surface in a mega city of China. Majority of the spatial variability in the annual mean of ambient UFP was explained in the model comprised primarily of traffic-, building-, and restaurant-related predictors.
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Affiliation(s)
- Yihui Ge
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Lab of Health Technology Assessment, Fudan University, Shanghai 200032, China
| | - Qingyan Fu
- Shanghai Environmental Monitoring Center, Shanghai 200233, China
| | - Min Yi
- Shanghai Environmental Monitoring Center, Shanghai 200233, China
| | - Yuan Chao
- Shanghai Environmental Monitoring Center, Shanghai 200233, China
| | - Xiaoning Lei
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Lab of Health Technology Assessment, Fudan University, Shanghai 200032, China
| | - Xueyi Xu
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Lab of Health Technology Assessment, Fudan University, Shanghai 200032, China
| | - Zhenchun Yang
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, St Mary's Campus, London, United Kingdom
| | - Jianlin Hu
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing 210044, China
| | - Haidong Kan
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Lab of Health Technology Assessment, Fudan University, Shanghai 200032, China.
| | - Jing Cai
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Lab of Health Technology Assessment, Fudan University, Shanghai 200032, China.
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15
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An exploratory analysis of sociodemographic characteristics with ultrafine particle concentrations in Boston, MA. PLoS One 2022; 17:e0263434. [PMID: 35353820 PMCID: PMC8967040 DOI: 10.1371/journal.pone.0263434] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Accepted: 01/19/2022] [Indexed: 11/19/2022] Open
Abstract
Little is known of the relationship between exposure to the smallest particles of air pollution and socio-demographic characteristics. This paper explores linkages between ultrafine particle (UFP) concentrations and indicators of both race/ethnicity and socioeconomic status in Boston, Massachusetts, USA. We used estimates of UFP based on a highly-resolved land-use regression model of concentrations. In multivariate linear regression models census block groups with high proportions of Asians were associated with higher levels of UFP in comparison to block groups with majority White or other minority groups. Lower UFP concentrations were associated with higher homeownership (indicating higher SES) and with higher female head of household (indicating lower socioeconomic status). One explanation for the results include the proximity of specific groups to traffic corridors that are the main sources of UFP in Boston. Additional studies, especially at higher geographic resolution, are needed in Boston and other major cities to better characterize UFP concentrations by sociodemographic factors.
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16
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Zhang JJY, Sun L, Rainham D, Dummer TJB, Wheeler AJ, Anastasopolos A, Gibson M, Johnson M. Predicting intraurban airborne PM 1.0-trace elements in a port city: Land use regression by ordinary least squares and a machine learning algorithm. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 806:150149. [PMID: 34583078 DOI: 10.1016/j.scitotenv.2021.150149] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 08/31/2021] [Accepted: 09/01/2021] [Indexed: 06/13/2023]
Abstract
Airborne particulate matter (PM) has been associated with cardiovascular and respiratory morbidity and mortality, and there is some evidence that spatially varying metals found in PM may contribute to adverse health effects. We developed spatially refined models for PM trace elements using ordinary least squares land use regression (OLS-LUR) and machine leaning random forest land-use regression (RF-LUR). Two-week integrated measurements of PM1.0 (median aerodiameter < 1.0 μm) were collected at 50 sampling sites during fall (2010), winter (2011), and summer (2011) in the Halifax Regional Municipality, Nova Scotia, Canada. PM1.0 filters were analyzed for metals and trace elements using inductively coupled plasma-mass spectrometry. OLS- and RF-LUR models were developed for approximately 30 PM1.0 trace elements in each season. Model predictors included industrial, commercial, and institutional/ government/ military land use, roadways, shipping, other transportation sources, and wind rose information. RF generated more accurate models than OLS for most trace elements based on 5-fold cross validation. On average, summer models had the highest cross validation R2 (OLS-LUR = 0.40, RF-LUR = 0.46), while fall had the lowest (OLS-LUR = 0.27, RF-LUR = 0.31). Many OLS-LUR models displayed overprediction in the final exposure surface. In contrast, RF-LUR models did not exhibit overpredictions. Taking overpredictions and cross validation performances into account, OLS-LUR performed better than RF-LUR in roughly 20% of the seasonal trace element models. RF-LUR models provided more interpretable predictors in most cases. Seasonal predictors varied, likely due to differences in seasonal distribution of trace elements related to source activity, and meteorology.
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Affiliation(s)
- Joyce J Y Zhang
- Air Health Science Division, Health Canada, Ottawa, ON, Canada
| | - Liu Sun
- Air Health Science Division, Health Canada, Ottawa, ON, Canada
| | - Daniel Rainham
- Healthy Populations Institute and the School of Health and Human Performance, Dalhousie University, Halifax, NS, Canada
| | - Trevor J B Dummer
- School of Population and Public Health, University of British Columbia, Vancouver, BC, , Canada
| | - Amanda J Wheeler
- Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne, VIC, Australia; Menzies Institute for Medical Research, University of Tasmania, Hobart, TAS, Australia
| | | | - Mark Gibson
- Division of Air Quality and Exposure Science, AirPhoton, Baltimore, MD, USA
| | - Markey Johnson
- Air Health Science Division, Health Canada, Ottawa, ON, Canada.
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17
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Lloyd M, Carter E, Diaz FG, Magara-Gomez KT, Hong KY, Baumgartner J, Herrera G VM, Weichenthal S. Predicting Within-City Spatial Variations in Outdoor Ultrafine Particle and Black Carbon Concentrations in Bucaramanga, Colombia: A Hybrid Approach Using Open-Source Geographic Data and Digital Images. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2021; 55:12483-12492. [PMID: 34498865 DOI: 10.1021/acs.est.1c01412] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Outdoor ultrafine particles (UFP, <0.1 μm) and black carbon (BC) vary greatly within cities and may have adverse impacts on human health. In this study, we used a hybrid approach to develop new models to estimate within-city spatial variations in outdoor UFP and BC concentrations across Bucaramanga, Colombia. We conducted a mobile monitoring campaign over 20 days in 2019. Regression models were trained on land use data and combined with predictions from convolutional neural networks (CNN) trained to predict UFP and BC concentrations using satellite and street-level images. The combined UFP model (R2 = 0.54) outperformed the CNN (R2 = 0.47) and land use regression (LUR) models (R2 = 0.47) on their own. Similarly, the combined BC model also outperformed the CNN and LUR BC models (R2 = 0.51 vs 0.43 and 0.45, respectively). Spatial variations in model performance were more stable for the CNN and combined models compared to the LUR models, suggesting that the combined approach may be less likely to contribute to differential exposure measurement error in epidemiological studies. In general, our findings demonstrated that satellite and street-level images can be combined with a traditional LUR modeling approach to improve predictions of within-city spatial variations in outdoor UFP and BC concentrations.
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Affiliation(s)
- Marshall Lloyd
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal H3A 1A2, Canada
| | - Ellison Carter
- Department of Civil and Environmental Engineering, Walter Scott, Jr. College of Engineering, Colorado State University, Fort Collins 80523, United States
| | - Florencio Guzman Diaz
- Department of Civil and Environmental Engineering, Walter Scott, Jr. College of Engineering, Colorado State University, Fort Collins 80523, United States
| | | | - Kris Y Hong
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal H3A 1A2, Canada
| | - Jill Baumgartner
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal H3A 1A2, Canada
- Institute for Health and Social Policy, McGill University, Montreal H3A 1A2, Canada
| | - Víctor M Herrera G
- Facultad de Ciencias de la Salud, Universidad Autónoma de Bucaramanga, Bucaramanga 680006, Colombia
| | - Scott Weichenthal
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal H3A 1A2, Canada
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18
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Saha PK, Hankey S, Marshall JD, Robinson AL, Presto AA. High-Spatial-Resolution Estimates of Ultrafine Particle Concentrations across the Continental United States. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2021; 55:10320-10331. [PMID: 34284581 DOI: 10.1021/acs.est.1c03237] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
There is growing evidence that ultrafine particles (UFP; particles smaller than 100 nm) are likely more toxic than larger particles. However, the health effects of UFP remain uncertain due in part to the lack of large-scale population-based exposure assessment. We develop a national-scale empirical model of particle number concentration (PNC; a measure of UFP) using data from mobile monitoring and fixed sites across the United States and a land-use regression (LUR) modeling framework. Traffic, commercial land use, and urbanicity-related variables explain much of the spatial variability of PNC (base model R2 = 0.77, RMSE = 2400 cm-3). Model predictions are robust across a diverse set of evaluations [random 10-fold holdout cross-validation (HCV): R2 = 0.72, RMSE = 2700 cm-3; spatially defined HCV: R2 = 0.66, RMSE = 3000 cm-3; evaluation against an independent data set: R2 = 0.54, RMSE = 2600 cm-3]. We apply our model to predict PNC at ∼6 million residential census blocks in the contiguous United States. Our estimates are annual average concentrations for 2016-2017. The predicted national census-block-level mean PNC ranges between 1800 and 26 600 cm-3 (population-weighted average: 6500 cm-3), with hotspots in cities and near highways. Our national PNC model predicts large urban-rural, intra-, and inter-city contrasts. PNC and PM2.5 are moderately correlated at the city scale, but uncorrelated at the regional/national scale. Our high-spatial-resolution national PNC estimates are useful for analyzing population exposure (socioeconomic disparity, epidemiological health impact) and environmental policy and regulation.
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Affiliation(s)
- Provat K Saha
- Center for Atmospheric Particle Studies, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
- Department of Mechanical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, Pennsylvania 15213, United States
| | - Steve Hankey
- School of Public and International Affairs, Virginia Tech, Blacksburg, Virginia 24061, United States
| | - Julian D Marshall
- Department of Civil and Environmental Engineering, University of Washington, Seattle, Washington 98195, United States
| | - Allen L Robinson
- Center for Atmospheric Particle Studies, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
- Department of Mechanical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, Pennsylvania 15213, United States
| | - Albert A Presto
- Center for Atmospheric Particle Studies, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
- Department of Mechanical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, Pennsylvania 15213, United States
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19
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Zhang Y, Cheng H, Huang D, Fu C. High Temporal Resolution Land Use Regression Models with POI Characteristics of the PM 2.5 Distribution in Beijing, China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:6143. [PMID: 34200158 PMCID: PMC8201188 DOI: 10.3390/ijerph18116143] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 05/10/2021] [Accepted: 05/11/2021] [Indexed: 12/03/2022]
Abstract
PM2.5 is one of the primary components of air pollutants, and it has wide impacts on human health. Land use regression models have the typical disadvantage of low temporal resolution. In this study, various point of interests (POIs) variables are added to the usual predictive variables of the general land use regression (LUR) model to improve the temporal resolution. Hourly PM2.5 concentration data from 35 monitoring stations in Beijing, China, were used. Twelve LUR models were developed for working days and non-working days of the heating season and non-heating season, respectively. The results showed that these models achieved good fitness in winter and summer, and the highest R2 of the winter and summer models were 0.951 and 0.628, respectively. Meteorological factors, POIs, and roads factors were the most critical predictive variables in the models. This study also showed that POIs had time characteristics, and different types of POIs showed different explanations ranging from 5.5% to 41.2% of the models on working days or non-working days, respectively. Therefore, this study confirmed that POIs can greatly improve the temporal resolution of LUR models, which is significant for high precision exposure studies.
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Affiliation(s)
| | - Hongguang Cheng
- School of Environment, Beijing Normal University, Beijing 100875, China; (Y.Z.); (D.H.); (C.F.)
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Karumanchi S, Siemiatycki J, Richardson L, Hatzopoulou M, Lequy E. Spatial and temporal variability of airborne ultrafine particles in the Greater Montreal area: Results of monitoring campaigns in two seasons. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 771:144652. [PMID: 33545464 DOI: 10.1016/j.scitotenv.2020.144652] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Revised: 12/16/2020] [Accepted: 12/16/2020] [Indexed: 06/12/2023]
Abstract
It has been hypothesized that ultrafine particles (UFP) in air pollution may cause lung cancer. In preparation for an epidemiologic case-control study to assess this hypothesis in Montreal, Canada, we conducted a UFP measurement campaign in order to create an exposure surface with which we could assign UFP exposure to subjects corresponding to their residential addresses. The purpose of this paper is to describe the temporal and spatial variability that underlies the creation of an exposure surface in the Montreal area, and to consider the implications for epidemiological exposure assessment. We identified 249 fixed sampling sites, selected to provide a dense spatial representation of the areas of residence of Montreal residents. We conducted a winter campaign and a summer campaign, and each of the sites was visited three times during each seasonal campaign. Each visit entailed a 20-minute measurement period for UFPs with a separate measurement each second. This provided data for temporal comparisons at each site between seasons, between visits and between seconds. The median of UFP measurements was 16,593 particles/cm3 in winter and 8919 particles/cm3 in summer. Across the 249 sampling sites the Spearman correlation coefficient between the UFP measurements of winter and summer was 0.35. Within each visit, correlation was below 0.50 between pairs of UFP measurements taken more than 60 s apart, and there was hardly any correlation among measurements taken more than 300 s apart. When sites were grouped by proximity to certain types of pollution sources, and the seven resulting groups compared, there were modest, albeit statistically significant, differences in UFP levels. There was moderate positive spatial autocorrelation in UFPs over the study area. High temporal variability of UFPs from short-term measurements campaigns will likely compromise the predictive validity of the exposure surface, and will eventually attenuate the epidemiologic risk estimates.
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Affiliation(s)
- Shilpa Karumanchi
- Carrefour de l'innovation, Centre de recherche du centre hospitalier de l'université de Montréal, 850 St-Denis, Montréal, Québec H2X 0A9, Canada; School of Public Health, Université de Montréal, Montréal, Canada.
| | - Jack Siemiatycki
- Carrefour de l'innovation, Centre de recherche du centre hospitalier de l'université de Montréal, 850 St-Denis, Montréal, Québec H2X 0A9, Canada; School of Public Health, Université de Montréal, Montréal, Canada
| | - Lesley Richardson
- Carrefour de l'innovation, Centre de recherche du centre hospitalier de l'université de Montréal, 850 St-Denis, Montréal, Québec H2X 0A9, Canada
| | - Marianne Hatzopoulou
- Department of Civil & Mineral Engineering, University of Toronto, 35 St. George Street, Toronto, Ontario M5S 1A4, Canada
| | - Emeline Lequy
- Carrefour de l'innovation, Centre de recherche du centre hospitalier de l'université de Montréal, 850 St-Denis, Montréal, Québec H2X 0A9, Canada; Institut national de la santé et de la recherche médicale (INSERM), UMS 011, 16 avenue Paul Vaillant Couturier, Villejuif F-94807, France
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21
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Ren Z, Liu X, Liu T, Chen D, Jiao K, Wang X, Suo J, Yang H, Liao J, Ma L. Effect of ambient fine particulates (PM 2.5) on hospital admissions for respiratory and cardiovascular diseases in Wuhan, China. Respir Res 2021; 22:128. [PMID: 33910560 PMCID: PMC8080330 DOI: 10.1186/s12931-021-01731-x] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Accepted: 04/22/2021] [Indexed: 11/18/2022] Open
Abstract
Background Positive associations between ambient PM2.5 and cardiorespiratory disease have been well demonstrated during the past decade. However, few studies have examined the adverse effects of PM2.5 based on an entire population of a megalopolis. In addition, most studies in China have used averaged data, which results in variations between monitoring and personal exposure values, creating an inherent and unavoidable type of measurement error.
Methods This study was conducted in Wuhan, a megacity in central China with about 10.9 million people. Daily hospital admission records, from October 2016 to December 2018, were obtained from the Wuhan Information center of Health and Family Planning, which administrates all hospitals in Wuhan. Daily air pollution concentrations and weather variables in Wuhan during the study period were collected. We developed a land use regression model (LUR) to assess individual PM2.5 exposure. Time-stratified case-crossover design and conditional logistic regression models were adopted to estimate cardiorespiratory hospitalization risks associated with short-term exposure to PM2.5. We also conducted stratification analyses by age, sex, and season. Results A total of 2,806,115 hospital admissions records were collected during the study period, from which we identified 332,090 cardiovascular disease admissions and 159,365 respiratory disease admissions. Short-term exposure to PM2.5 was associated with an increased risk of a cardiorespiratory hospital admission. A 10 μg/m3 increase in PM2.5 (lag0–2 days) was associated with an increase in hospital admissions of 1.23% (95% CI 1.01–1.45%) and 1.95% (95% CI 1.63–2.27%) for cardiovascular and respiratory diseases, respectively. The elderly were at higher PM-induced risk. The associations appeared to be more evident in the cold season than in the warm season. Conclusions This study contributes evidence of short-term effects of PM2.5 on cardiorespiratory hospital admissions, which may be helpful for air pollution control and disease prevention in Wuhan. Supplementary Information The online version contains supplementary material available at 10.1186/s12931-021-01731-x.
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Affiliation(s)
- Zhan Ren
- Wuhan University School of Health Sciences, No. 115 Donghu Road, Wuchang district, Wuhan, 430071, Hubei, China
| | - Xingyuan Liu
- Wuhan Information Center of Health and Family Planning, Wuhan, 430021, China
| | - Tianyu Liu
- Wuhan University School of Health Sciences, No. 115 Donghu Road, Wuchang district, Wuhan, 430071, Hubei, China
| | - Dieyi Chen
- Department of Biostatistics, Yale University, New Haven, CT, 06520, USA
| | - Kuizhuang Jiao
- Wuhan University School of Health Sciences, No. 115 Donghu Road, Wuchang district, Wuhan, 430071, Hubei, China
| | - Xiaodie Wang
- Wuhan University School of Health Sciences, No. 115 Donghu Road, Wuchang district, Wuhan, 430071, Hubei, China
| | - Jingdong Suo
- Wuhan University School of Health Sciences, No. 115 Donghu Road, Wuchang district, Wuhan, 430071, Hubei, China
| | - Haomin Yang
- Wuhan University School of Health Sciences, No. 115 Donghu Road, Wuchang district, Wuhan, 430071, Hubei, China
| | - Jingling Liao
- Department of Nutrition and Food Hygiene, School of Public Health, Medical College, Wuhan University of Science and Technology, No. 2 Huangjiahu West Road, Hongshan district, Wuhan, 430081, Hubei, China.
| | - Lu Ma
- Wuhan University School of Health Sciences, No. 115 Donghu Road, Wuchang district, Wuhan, 430071, Hubei, China.
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22
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McIsaac MA, Sanders E, Kuester T, Aronson KJ, Kyba CCM. The impact of image resolution on power, bias, and confounding: A simulation study of ambient light at night exposure. Environ Epidemiol 2021; 5:e145. [PMID: 33870017 PMCID: PMC8043729 DOI: 10.1097/ee9.0000000000000145] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Accepted: 02/22/2021] [Indexed: 11/26/2022] Open
Abstract
Studies of the impact of environmental pollutants on health outcomes can be compromised by mismeasured exposures or unmeasured confounding with other environmental exposures. Both problems can be exacerbated by measuring exposure from data sources with low spatial resolution. Artificial light at night, for example, is often estimated from low-resolution satellite images, which may result in substantial measurement error and increased correlation with air or noise pollution. METHODS Light at night exposure was considered in simulated epidemiologic studies in Vancouver, British Columbia. First, we assessed statistical power and bias for hypothetical studies that replaced true light exposure with estimates from sources with low resolution. Next, health status was simulated based on pollutants other than light exposure, and we assessed the frequency with which studies might incorrectly attribute negative health impacts to light exposure as a result of unmeasured confounding by the other environmental exposures. RESULTS When light was simulated to be the causal agent, studies relying on low-resolution data suffered from lower statistical power and biased estimates. Additionally, correlations between light and other pollutants increased as the spatial resolution of the light exposure map decreased, so studies estimating light exposure from images with lower spatial resolution were more prone to confounding. CONCLUSIONS Studies estimating exposure to pollutants from data with lower spatial resolution are prone to increased bias, increased confounding, and reduced power. Studies examining effects of light at night should avoid using exposure estimates based on low-resolution maps, and should consider potential confounding with other environmental pollutants.
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Affiliation(s)
- Michael A. McIsaac
- School of Mathematical and Computational Sciences, University of Prince Edward Island, Charlottetown, PEI, Canada
- Department of Public Health Sciences, Queen’s University, Kingston, ON, Canada
| | - Eric Sanders
- Department of Statistics, University of British Columbia, Vancouver, BC, Canada
| | - Theres Kuester
- GFZ German Research Centre for Geosciences, Potsdam, Germany
| | - Kristan J. Aronson
- Department of Public Health Sciences, Queen’s University, Kingston, ON, Canada
- Division of Cancer Care and Epidemiology, Cancer Research Institute, Queen’s University, Kingston, ON, Canada
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23
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Yang Z, Freni-Sterrantino A, Fuller GW, Gulliver J. Development and transferability of ultrafine particle land use regression models in London. THE SCIENCE OF THE TOTAL ENVIRONMENT 2020; 740:140059. [PMID: 32927570 DOI: 10.1016/j.scitotenv.2020.140059] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2020] [Revised: 05/06/2020] [Accepted: 06/05/2020] [Indexed: 06/11/2023]
Abstract
Due to a lack of routine monitoring, bespoke measurements are required to develop ultrafine particle (UFP) land use regression (LUR) models, which is especially challenging in megacities due to their large area. As an alternative, for London, we developed separate models for three urban residential areas, models combining two areas, and models using all three areas. Models were developed against annual mean ultrafine particle count cm-3 estimated from repeated 30-min fixed-site measurements, in different seasons (2016-2018), at forty sites per area, that were subsequently temporally adjusted using continuous measurements from a single reference site within or close to each area. A single model and 10 models were developed for each individual area and combination of areas. Within each area, sites were split into 10 groups using stratified random sampling. Each of the 10 models were developed using 90% of sites. Hold-out validation was performed by pooling the 10% of sites held-out each time. The transferability of models was tested by applying individual and two-area models to external area(s). In model evaluation, within-area mean squared error (MSE) R2 ranged from 14% to 48%. Transferring individual- and combined-area models to external areas without calibration yielded MSE-R2 ranging from -18 to 0. MSE-R2 was in the range 21% to 41% when using particle number count (PNC) measurements in external areas to calibrate models. Our results suggest that the UFP models could be transferred to other areas without calibration in London to assess relative ranking in exposures but not for estimating absolute values of PNC.
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Affiliation(s)
- Zhenchun Yang
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, St Mary's Campus, London, United Kingdom..
| | - Anna Freni-Sterrantino
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, St Mary's Campus, London, United Kingdom
| | - Gary W Fuller
- MRC Centre for Environment and Health, School of Population Health & Environmental Sciences, Faculty of Life Sciences & Medicine, King's College London, United Kingdom
| | - John Gulliver
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, St Mary's Campus, London, United Kingdom.; Centre for Environmental Health and Sustainability & School of Geography, Geology and the Environment, University of Leicester, Leicester, United Kingdom
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Laguerre A, George LA, Gall ET. High-Efficiency Air Cleaning Reduces Indoor Traffic-Related Air Pollution and Alters Indoor Air Chemistry in a Near-Roadway School. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2020; 54:11798-11808. [PMID: 32841011 DOI: 10.1021/acs.est.0c02792] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
Schools in proximity to roadways expose students to traffic-related air pollution (TRAP). We investigate impacts of air-cleaning on indoor TRAP levels and indoor chemistry in a renovated school adjacent an interstate highway. We monitor air pollutants pre- and post-renovation and quantify efficiency of particle (MERV8 and 16 filters) and gas (functionalized activated carbon) air-cleaning. Time-resolved measurements show air-cleaning systems are effective, with in situ particle removal efficiency >94% across 10 nm to 10 μm. Activated carbon removed BTEX and NO2 with variability in removal efficiency. Over eight months of monitoring, NO2 removal efficiency was 96% initially and decreased to 61%; and BTEX removal efficiency was >80% or increased to >80%. Air-cleaning reduced indoor TRAP to below or near urban background. Air-cleaning systems suppressed indoor chemistry by reducing indoor levels of oxidants (NO2, O3) and reactive organics of indoor origin. When the air cleaning system was inactive, our data show that indoor SOA formation within the school was elevated. Loss rates of NO2 and O3 through the air-cleaning system were ∼1.5-2.4 h-1 and ∼2.3 h-1, respectively. Air-cleaning was 83% and 69% efficient, respectively, in removing monoterpenes and isoprene. By suppressing precursors, scaling calculations show air-cleaning prevented ∼3.4 mg/h of indoor SOA formation due to indoor ozone-monoterpene chemistry. For comparison, we estimate that filtration removed ∼130 mg/h of PM0.01-0.3.
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Affiliation(s)
- Aurélie Laguerre
- Department of Mechanical and Materials Engineering, Portland State University, 1930 SW 4th Avenue, Suite 400, Portland, Oregon 97201, United States
| | - Linda A George
- Department of Environmental Science and Management, Portland State University, P.O. Box 751, Portland, Oregon 97201, United States
| | - Elliott T Gall
- Department of Mechanical and Materials Engineering, Portland State University, 1930 SW 4th Avenue, Suite 400, Portland, Oregon 97201, United States
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Xu J, Wang A, Schmidt N, Adams M, Hatzopoulou M. A gradient boost approach for predicting near-road ultrafine particle concentrations using detailed traffic characterization. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2020; 265:114777. [PMID: 32540592 DOI: 10.1016/j.envpol.2020.114777] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Revised: 05/07/2020] [Accepted: 05/07/2020] [Indexed: 06/11/2023]
Abstract
This study investigates the influence of meteorology, land use, built environment, and traffic characteristics on near-road ultrafine particle (UFP) concentrations. To achieve this objective, minute-level UFP concentrations were measured at various locations along a major arterial road in the Greater Toronto Area (GTA) between February and May 2019. Each location was visited five times, at least once in the morning, mid-day, and afternoon. Each visit lasted for 30 min, resulting in 2.5 h of minute-level data collected at each location. Local traffic information, including vehicle class and turning movements, were processed using computer vision techniques. The number of fast-food restaurants, cafes, trees, traffic signals, and building footprint, were found to have positive impacts on the mean UFP, while distance to the closest major road was negatively associated with UFP. We employed the Extreme Gradient Boosting (XGBoost) method to develop prediction models for UFP concentrations. The Shapley additive explanation (SHAP) measures were used to capture the influence of each feature on model output. The model results demonstrated that minute-level counts of local traffic from different directions had significant impacts on near-road UFP concentrations, model performance was robust under random cross-validation as coefficients of determination (R2) ranged from 0.63 to 0.69, but it revealed weaknesses when data at specific locations were eliminated from the training dataset. This result indicates that proper cross-validation techniques should be developed to better evaluate machine learning models for air quality predictions.
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Affiliation(s)
- Junshi Xu
- Civil and Mineral Engineering, University of Toronto, 35 St George Street, Toronto, ON, M5S 1A4., Canada.
| | - An Wang
- Civil and Mineral Engineering, University of Toronto, 35 St George Street, Toronto, ON, M5S 1A4., Canada.
| | - Nicole Schmidt
- Civil and Mineral Engineering, University of Toronto, 35 St George Street, Toronto, ON, M5S 1A4., Canada.
| | - Matthew Adams
- Department of Geography, University of Toronto Mississauga., Canada.
| | - Marianne Hatzopoulou
- Civil and Mineral Engineering, University of Toronto, 35 St George Street, Toronto, ON, M5S 1A4., Canada.
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26
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Smith KE, Weis D. Evaluating Spatiotemporal Resolution of Trace Element Concentrations and Pb Isotopic Compositions of Honeybees and Hive Products as Biomonitors for Urban Metal Distribution. GEOHEALTH 2020; 4:e2020GH000264. [PMID: 32671313 PMCID: PMC7340846 DOI: 10.1029/2020gh000264] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Revised: 06/05/2020] [Accepted: 06/08/2020] [Indexed: 06/01/2023]
Abstract
Assessing metal distributions in cities is an important aspect of urban environmental quality management. Western honeybees (Apis mellifera) and their products are biomonitors that can elucidate small-scale metal distribution within a city. We compare range and variations in trace element (TE) concentrations and lead (Pb) isotopic compositions of honey, bee tissue, bee pollen, and propolis collected throughout Metro Vancouver (BC, Canada). Honey, bee, and bee pollen results have similar TE and isotopic trends; samples collected in urban and industrialized areas exhibit elevated concentrations of anthropogenically influenced TE (e.g., Pb, Zn, V, and Ti) and a less radiogenic Pb isotopic composition (i.e., lower 206Pb/207Pb and elevated 208Pb/206Pb) relative to their suburban and rural counterparts. For example, 206Pb/207Pb, 208Pb/206Pb in honey range from 1.126, 2.131 and 1.184, 2.063; extremes measured in honey from urban and suburban/rural areas, respectively. Except for propolis, measured and interpolated (kriged) results in all materials reflect the immediate zoning or land use setting near the hive, providing kilometer-scale geospatial resolution, suitable for monitoring urban systems. Statistical analysis reveals that no systematic variations or intra- or inter-annual trends exist in TE concentrations or Pb isotopic compositions, including among sampling and field methods (i.e., old vs. new hive equipment and honey from the brood nest box vs. honey super). The results of this systematic study using honeybees and hive products in Metro Vancouver provide a robust, current baseline for future comparison of local land use and environmental policy change.
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Affiliation(s)
- Kate E. Smith
- Pacific Centre for Isotopic and Geochemical Research, Department of Earth, Ocean and Atmospheric SciencesUniversity of British ColumbiaVancouverBritish ColumbiaCanada
| | - Dominique Weis
- Pacific Centre for Isotopic and Geochemical Research, Department of Earth, Ocean and Atmospheric SciencesUniversity of British ColumbiaVancouverBritish ColumbiaCanada
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27
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Zalzal J, Alameddine I, El-Fadel M, Weichenthal S, Hatzopoulou M. Drivers of seasonal and annual air pollution exposure in a complex urban environment with multiple source contributions. ENVIRONMENTAL MONITORING AND ASSESSMENT 2020; 192:415. [PMID: 32500382 DOI: 10.1007/s10661-020-08345-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/09/2019] [Accepted: 05/04/2020] [Indexed: 06/11/2023]
Abstract
Outdoor air pollution is a global health concern, but detailed exposure information is still limited for many parts of the world. In this study, high-resolution exposure surfaces were generated for annual and seasonal fine particulate matter (PM2.5), coarse particulate matter (PM10), and carbon monoxide (CO) for the Greater Beirut Area (GBA), Lebanon, an urban zone with a complex topography and multiple source contributions. Land use regression models (LUR) were calibrated and validated with monthly data collected from 58 locations between March 2017 and March 2018. The annual mean (±1 SD) concentrations of PM2.5, PM10, and CO across the monitoring locations were 68.1 (±15.7) μg/m3, 83.5 (±19.5) μg/m3, and 2.48 (±1.12) ppm, respectively. The coefficients of determination for LUR models ranged from 56 to 67% for PM2.5, 44 to 63% for the PM10 models, and 50 to 60% for the CO. LUR model structures varied significantly by season for both PM2.5 and PM10 but not for CO. Traffic emissions were consistently the main source of CO emissions throughout the year. The relative importance of industrial emissions and power generation sources towards predicted PM levels increased during the hot season while the contribution of the international airport diminished. Moreover, the complex topography of the study area along with the seasonal changes in the predominant wind directions affected the spatial predicted concentrations of all three pollutants. Overall, the predicted exposure surfaces were able to conserve the inter-pollution correlations determined from the field monitoring campaign, with the exception of the cold season. Our pollution surfaces suggest that the entire population of Beirut is regularly exposed to concentrations exceeding the World Health Organization (WHO) air quality standards for both PM2.5 and PM10.
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Affiliation(s)
- Jad Zalzal
- Department of Civil and Environmental Engineering, Maroun Semaan Faculty of Engineering and Architecture, American University of Beirut, Beirut, Lebanon
| | - Ibrahim Alameddine
- Department of Civil and Environmental Engineering, Maroun Semaan Faculty of Engineering and Architecture, American University of Beirut, Beirut, Lebanon.
| | - Mutasem El-Fadel
- Department of Civil and Environmental Engineering, Maroun Semaan Faculty of Engineering and Architecture, American University of Beirut, Beirut, Lebanon
| | - Scott Weichenthal
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada
| | - Marianne Hatzopoulou
- Department of Civil & Mineral Engineering, University of Toronto, Toronto, ON, Canada
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28
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Simon MC, Naumova EN, Levy JI, Brugge D, Durant JL. Ultrafine Particle Number Concentration Model for Estimating Retrospective and Prospective Long-Term Ambient Exposures in Urban Neighborhoods. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2020; 54:1677-1686. [PMID: 31934748 PMCID: PMC8374642 DOI: 10.1021/acs.est.9b03369] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Short-term exposure to ultrafine particles (UFP; <100 nm in diameter), which are present at high concentrations near busy roadways, is associated with markers of cardiovascular and respiratory disease risk. To date, few long-term studies (months to years) have been conducted due to the challenges of long-term exposure assignment. To address this, we modified hybrid land-use regression models of particle number concentrations (PNCs; a proxy for UFP) for two study areas in Boston (MA) by replacing the measured PNC term with an hourly model and adjusting for overprediction. The hourly PNC models used covariates for meteorology, traffic, and sulfur dioxide concentrations (a marker of secondary particle formation). We compared model performance against long-term PNC data collected continuously from 9 years before and up to 3 years after the model-development period. Model predictions captured the major temporal variations in the data and model performance remained relatively stable retrospectively and prospectively. The Pearson correlation of modeled versus measured hourly log-transformed PNC at a long-term monitoring site for 9 years prior was 0.74. Our results demonstrate that highly resolved spatial-temporal PNC models are capable of estimating ambient concentrations retrospectively and prospectively with generally good accuracy, giving us confidence in using these models in epidemiological studies.
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Affiliation(s)
- Matthew C Simon
- Department of Environmental Health , Boston University School of Public Health , 715 Albany Street , Boston , Massachusetts 02118 , United States
- Department of Civil and Environmental Engineering , Tufts University , 200 College Avenue , Medford , Massachusetts 02155 , United States
| | - Elena N Naumova
- Department of Civil and Environmental Engineering , Tufts University , 200 College Avenue , Medford , Massachusetts 02155 , United States
- Friedman School of Nutrition Science and Policy , Tufts University , 150 Harrison Avenue , Boston , Massachusetts 02111 , United States
| | - Jonathan I Levy
- Department of Environmental Health , Boston University School of Public Health , 715 Albany Street , Boston , Massachusetts 02118 , United States
| | - Doug Brugge
- Department of Civil and Environmental Engineering , Tufts University , 200 College Avenue , Medford , Massachusetts 02155 , United States
- Department of Public Health and Community Medicine , Tufts University , 136 Harrison Avenue , Boston , Massachusetts 02111 , United States
- Department of Community Medicine and Health Care , University of Connecticut , 195 Farmington Avenue , Farmington , Connecticut 06032 , United States
| | - John L Durant
- Department of Civil and Environmental Engineering , Tufts University , 200 College Avenue , Medford , Massachusetts 02155 , United States
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29
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Rahman MM, Karunasinghe J, Clifford S, Knibbs LD, Morawska L. New insights into the spatial distribution of particle number concentrations by applying non-parametric land use regression modelling. THE SCIENCE OF THE TOTAL ENVIRONMENT 2020; 702:134708. [PMID: 31715399 DOI: 10.1016/j.scitotenv.2019.134708] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/11/2019] [Revised: 09/27/2019] [Accepted: 09/27/2019] [Indexed: 06/10/2023]
Abstract
Ambient particle number concentration (PNC) varies significantly in time and space within cities, yet complexity and cost prohibit large-scale routine monitoring; as a consequence, there is not enough data for assessment of human exposure to, or risk from the particles. The quality of assessments can be augmented by modelling; however, models are generally less capable of predicting PNC spatial variation than predicting variations in other ambient pollutants. To advance modelling of PNC, we aimed to develop and compare the performance of parametric and non-parametric machine learning land-use regression (LUR) models to predict hourly average PNC. We used data from 25 short-term stationary campaigns and five long-term sites during 2009-2012 in the Brisbane Metropolitan Area, Australia. We analysed three particle size ranges of total PNC (<30 nm, <414 nm and <3000 nm) as response variables, and over 150 independent variables, including land use, roads and traffic, population, distance, elevation, meteorology and time of day as potential predictors of PNC. The LUR models were developed separately for All Days, Nuc Days (when particle nucleation occurred), and No-nuc Days (when no particle nucleation occurred). We selected two algorithms to develop LUR models for PNC: a random forest (RF) model, and a generalised additive model (GAM) based on the least angle regression (LARS). The best LARS model for <30 nm, <414 nm and <3000 nm explained 30%, 31%, and 34%, respectively, whereas the best RF models were significantly better, explaining 73%, 64%, and 88%, respectively. Using this novel approach, we provided new insights into spatial variation in PNC and also demonstrated that the non-parametric RF model is a better choice for developing a LUR model for PNCs because of its robust predictive performance in comparison with the LARS parametric regression model.
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Affiliation(s)
- Md Mahmudur Rahman
- International Laboratory for Air Quality and Health, Queensland University of Technology, GPO Box 2434, Brisbane, QLD 4001, Australia; Climate and Atmospheric Science, Department of Planning, Industry and Environment, 480 Weeroona Road, Lidcombe, NSW 2141, Australia
| | - Jayanandana Karunasinghe
- International Laboratory for Air Quality and Health, Queensland University of Technology, GPO Box 2434, Brisbane, QLD 4001, Australia
| | - Sam Clifford
- International Laboratory for Air Quality and Health, Queensland University of Technology, GPO Box 2434, Brisbane, QLD 4001, Australia; Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, Keppel Street, London WC1E 7HT, UK
| | - Luke D Knibbs
- School of Public Health, The University of Queensland, Herston, QLD 4006, Australia
| | - Lidia Morawska
- International Laboratory for Air Quality and Health, Queensland University of Technology, GPO Box 2434, Brisbane, QLD 4001, Australia.
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Jones RR, Hoek G, Fisher JA, Hasheminassab S, Wang D, Ward MH, Sioutas C, Vermeulen R, Silverman DT. Land use regression models for ultrafine particles, fine particles, and black carbon in Southern California. THE SCIENCE OF THE TOTAL ENVIRONMENT 2020; 699:134234. [PMID: 31793436 DOI: 10.1016/j.scitotenv.2019.134234] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Revised: 08/31/2019] [Accepted: 08/31/2019] [Indexed: 05/26/2023]
Abstract
Exposure models are needed to evaluate health effects of long-term exposure to ambient ultrafine particles (UFP; <0.1 μm) and to disentangle their association from other pollutants, particularly PM2.5 (<2.5 μm). We developed land use regression (LUR) models to support UFP exposure assessment in the Los Angeles Ultrafines Study, a cohort in Southern California. We conducted a short-term measurement campaign in Los Angeles and parts of Riverside and Orange counties to measure UFP, PM2.5, and black carbon (BC), collecting three 30-minute average measurements at 215 sites across three seasons. We averaged concentrations for each site and evaluated geographic predictors including traffic intensity, distance to airports, land use, and population and building density by supervised stepwise selection to develop models. UFP and PM2.5 measurements (r = 0.001) and predictions (r = 0.05) were uncorrelated at the sites. UFP model explained variance was robust (R2 = 0.66) and 10-fold cross-validation indicated good performance (R2 = 0.59). Explained variation was moderate for PM2.5 (R2 = 0.47) and BC (R2 = 0.38). In the cohort, we predicted a 2.3-fold exposure contrast from the 5th to 95th percentiles for all three pollutants. The correlation between modeled UFP and PM2.5 at cohort residences was weak (r = 0.28), although higher than between measured levels. LUR models, particularly for UFP, were successfully developed and predicted reasonable exposure contrasts.
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Affiliation(s)
- Rena R Jones
- Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, United States.
| | - Gerard Hoek
- Institute for Risk Assessment Sciences, Division of Environmental Epidemiology, Utrecht University, Utrecht, Netherlands
| | - Jared A Fisher
- Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, United States
| | - Sina Hasheminassab
- Department of Civil and Environmental Engineering, University of Southern California, Los Angeles, CA, United States
| | - Dongbin Wang
- Department of Civil and Environmental Engineering, University of Southern California, Los Angeles, CA, United States
| | - Mary H Ward
- Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, United States
| | - Constantinos Sioutas
- Department of Civil and Environmental Engineering, University of Southern California, Los Angeles, CA, United States
| | - Roel Vermeulen
- Institute for Risk Assessment Sciences, Division of Environmental Epidemiology, Utrecht University, Utrecht, Netherlands; University Medical Center, Utrecht University, Utrecht, Netherlands
| | - Debra T Silverman
- Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, United States
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Robinson ES, Shah RU, Messier K, Gu P, Li HZ, Apte JS, Robinson AL, Presto AA. Land-Use Regression Modeling of Source-Resolved Fine Particulate Matter Components from Mobile Sampling. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2019; 53:8925-8937. [PMID: 31313910 DOI: 10.1021/acs.est.9b01897] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
This study presents land-use regression (LUR) models for submicron particulate matter (PM1) components from an urban area. Models are presented for mass concentrations of inorganic species (SO4, NO3, NH4), organic aerosol (OA) factors, and total PM1. OA is source-apportioned using positive matrix factorization (PMF) of data collected from aerosol mass spectrometry deployed on a mobile laboratory. PMF yielded a three-factor solution: cooking OA (COA), hydrocarbon-like OA (HOA), and less-oxidized oxygenated OA (LO-OOA). This study represents the first time that LUR has been applied to source-resolved OA factors. We sampled a roughly 20 km2 area of West Oakland, California, USA, over 1 month (mid-July to mid-August, 2017). The road network of the sampling domain was comprehensively sampled each day using a randomized driving route to minimize temporal and spatial bias. Mobile measurements were aggregated both spatially and temporally for use as discrete spatial observations for LUR model building. LUR model performance was highest for those species with more spatial variability (primary OA factors: COA R2 = 0.80, HOA R2 = 0.67) and lowest for secondary inorganic species (SO4 R2 = 0.47, NH4 R2 = 0.43) that were more spatially homogeneous. Notably, the stepwise selective LUR algorithm largely selected predictors for primary OA factors that correspond to the associated land-use categories (e.g., cooking land-use variables were selected in cooking-related PM models). This finding appears to be robust, as we demonstrate the predictive link between land-use variables and the corresponding source-resolved PM1 components through a subsampling analysis.
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Affiliation(s)
- Ellis Shipley Robinson
- Department of Mechanical Engineering , Carnegie Mellon University , Pittsburgh , Pennsylvania 15213 , United States
- Center for Atmospheric Particle Studies , Carnegie Mellon University , Pittsburgh , Pennsylvania 15213 , United States
| | - Rishabh Urvesh Shah
- Department of Mechanical Engineering , Carnegie Mellon University , Pittsburgh , Pennsylvania 15213 , United States
- Center for Atmospheric Particle Studies , Carnegie Mellon University , Pittsburgh , Pennsylvania 15213 , United States
| | - Kyle Messier
- Department of Environmental and Molecular Toxicology , Oregon State University , Corvallis , Oregon 97333 , United States
| | - Peishi Gu
- Department of Mechanical Engineering , Carnegie Mellon University , Pittsburgh , Pennsylvania 15213 , United States
- Center for Atmospheric Particle Studies , Carnegie Mellon University , Pittsburgh , Pennsylvania 15213 , United States
| | - Hugh Z Li
- Department of Mechanical Engineering , Carnegie Mellon University , Pittsburgh , Pennsylvania 15213 , United States
- Center for Atmospheric Particle Studies , Carnegie Mellon University , Pittsburgh , Pennsylvania 15213 , United States
| | - Joshua Schulz Apte
- Department of Civil, Architectural & Environmental Engineering , University of Texas at Austin , Austin , Texas 78705 , United States
| | - Allen L Robinson
- Department of Mechanical Engineering , Carnegie Mellon University , Pittsburgh , Pennsylvania 15213 , United States
- Center for Atmospheric Particle Studies , Carnegie Mellon University , Pittsburgh , Pennsylvania 15213 , United States
| | - Albert A Presto
- Department of Mechanical Engineering , Carnegie Mellon University , Pittsburgh , Pennsylvania 15213 , United States
- Center for Atmospheric Particle Studies , Carnegie Mellon University , Pittsburgh , Pennsylvania 15213 , United States
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Saha PK, Li HZ, Apte JS, Robinson AL, Presto AA. Urban Ultrafine Particle Exposure Assessment with Land-Use Regression: Influence of Sampling Strategy. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2019; 53:7326-7336. [PMID: 31150214 DOI: 10.1021/acs.est.9b02086] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/15/2023]
Abstract
Sampling strategies in the collection of ultrafine particle (UFP) data to develop land-use regression (LUR) models can strongly influence the resulting exposure estimates. Here, we systematically examine how much sampling is needed to develop robust and stable UFP LUR models. To address this question, we collected 3-6 weeks of continuous measurements of UFP concentrations at 32 sites in Pittsburgh, Pennsylvania covering a wide range of urban land-use attributes. Through systematic subsampling of this data set, we evaluate the performance of hundreds of LUR models with varying numbers of sampling days and daily sampling durations. Our base LUR model derived from wintertime average concentrations explained about 80% of the spatial variability in the data (adjusted R2 ∼ 0.8). The performance of the LUR models degrades with decreasing number of sampling days and sampling duration per day. For our data set, 1-3 h of sampling per day for 10-15 days provided UFP concentration estimates comparable to models derived from the entire data set. Small numbers of repeated sampling per site (1-3 days) at short duration (∼15-60 min per day) result in poor performance ( R2 < 0.5), similar to previous UFP LUR models. This study provides guidelines for the design of future measurement campaigns and monitoring networks to generate robust UFP LUR models for exposure assessments. Further study in other locations with more sites is needed to evaluate these guidelines over a broader range of conditions.
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Affiliation(s)
- Provat K Saha
- Center for Atmospheric Particle Studies , Carnegie Mellon University , 5000 Forbes Avenue , Pittsburgh , Pennsylvania 15213 , United States
- Department of Mechanical Engineering , Carnegie Mellon University , 5000 Forbes Avenue , Pittsburgh , Pennsylvania 15213 , United States
| | - Hugh Z Li
- Center for Atmospheric Particle Studies , Carnegie Mellon University , 5000 Forbes Avenue , Pittsburgh , Pennsylvania 15213 , United States
| | - Joshua S Apte
- Department of Civil, Architectural and Environmental Engineering , University of Texas at Austin , Austin , Texas 78712 , United States
| | - Allen L Robinson
- Center for Atmospheric Particle Studies , Carnegie Mellon University , 5000 Forbes Avenue , Pittsburgh , Pennsylvania 15213 , United States
- Department of Mechanical Engineering , Carnegie Mellon University , 5000 Forbes Avenue , Pittsburgh , Pennsylvania 15213 , United States
| | - Albert A Presto
- Center for Atmospheric Particle Studies , Carnegie Mellon University , 5000 Forbes Avenue , Pittsburgh , Pennsylvania 15213 , United States
- Department of Mechanical Engineering , Carnegie Mellon University , 5000 Forbes Avenue , Pittsburgh , Pennsylvania 15213 , United States
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Chastko K, Adams M. Assessing the accuracy of long-term air pollution estimates produced with temporally adjusted short-term observations from unstructured sampling. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2019; 240:249-258. [PMID: 30952045 DOI: 10.1016/j.jenvman.2019.03.108] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/24/2018] [Revised: 03/12/2019] [Accepted: 03/24/2019] [Indexed: 06/09/2023]
Abstract
More commonly air pollution observations are obtained with unstructured monitoring, where either a research grade monitor or low-cost sensor is irregularly relocated throughout the study area. This unstructured data is commonly observed in community science programs. Often the objective is to apply these data to estimate a long-term concentration, which is achieved using a temporal adjustment to correct for the irregular sampling. Temporal adjustments leverage information from a stationary continuous reference monitor, in combination with short-term monitoring data, to estimate long-term pollutant concentrations. We assess the performance of temporal adjustment approaches to predict long-term pollutant concentrations using data representing unstructured sampling. A series of monitoring campaigns are simulated from air pollution data obtained from regulatory monitoring networks in four different cities (Paris, France; Taipei, Taiwan; Toronto, Canada; and Vancouver, Canada) for eight different pollutants (CO, NO, NOx, NO2, O3, PM10, PM2.5, and SO2). These simulated campaigns have randomized monitoring locations and sampling times to simulate the irregular nature of crowd sourced or mobile monitoring data. The number of consecutive samples reported, and selection of the reference monitor used to adjust observations, are varied in this study. The accuracy of estimates is assessed by comparing the estimated long-term concentration to the observed long-term concentration from the complete regulatory monitoring dataset. This study found that a common temporal adjustment applied in research performed significantly worse than other adjustments including a Naïve Temporal Approach where no data adjustment occurred. Increasing the sample size improved the accuracy of estimates, which showed decreasing benefit with increased sample lengths. Lastly, controlling for land use conditions of the reference monitor did not consistently improve the long-term estimates, which suggests that land use pairing of mobile and reference monitors does not significantly influence the predictive power of temporal adjustment approaches. Temporal adjustments can reduce the error in long-term concentration estimates of air pollution using incomplete data, but this benefit cannot be assumed across all approaches, pollutants or sampling programs.
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Affiliation(s)
- Karl Chastko
- Department of Geography, University of Toronto Mississauga, Ontario, Canada
| | - Matthew Adams
- Department of Geography, University of Toronto Mississauga, Ontario, Canada.
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Zalzal J, Alameddine I, El Khoury C, Minet L, Shekarrizfard M, Weichenthal S, Hatzopoulou M. Assessing the transferability of landuse regression models for ultrafine particles across two Canadian cities. THE SCIENCE OF THE TOTAL ENVIRONMENT 2019; 662:722-734. [PMID: 30703730 DOI: 10.1016/j.scitotenv.2019.01.123] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/24/2018] [Revised: 01/03/2019] [Accepted: 01/11/2019] [Indexed: 06/09/2023]
Abstract
Land use regression (LUR) models have been increasingly used to predict intra-city variations in the concentrations of different air pollutants. However, limited research assessing the transferability of these models between cities has been published to date. In this study, LUR models were generated for Ultra-Fine Particles (UFP) (<0.1 um) using data collected from mobile monitoring campaigns in two Canadian cities, Montreal and Toronto. City-specific models were first generated for each city before the models were transferred to the second city with and without recalibration. The calibrated transferred models showed only a slight decrease in performance, with the coefficient of determination (R2), dropping from 0.49 to 0.36 for Toronto and from 0.41 to 0.38 for Montreal. Transferring models between cities with no calibration resulted in low R2; 0.11 in Toronto and 0.18 in Montreal. Moreover, two additional models were generated by combining data from the two cities. The first combined model (CM1) assumed a spatially invariant effect of the predictors, while the second (CM2) relaxed the assumption of spatial invariance for some of the model coefficients. The performance of both combined models (R2 ranged between 0.41 for CM1 and 0.43 for CM2; root mean squared error (RMSE) ranged between 0.34 for CM1 and 0.33 for CM2) was found to be on par with the Toronto city-specific model and outperformed the Montreal model. The results of this study highlight that the UFP LUR models appear to support transferability of model structures between cities with similar geographical characteristics, with a minor drop in model fit and predictive skill.
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Affiliation(s)
- Jad Zalzal
- Department of Civil and Environmental Engineering, American University of Beirut, Beirut, Lebanon
| | - Ibrahim Alameddine
- Department of Civil and Environmental Engineering, American University of Beirut, Beirut, Lebanon.
| | - Celine El Khoury
- Department of Civil and Environmental Engineering, American University of Beirut, Beirut, Lebanon
| | - Laura Minet
- Department of Civil & Mineral Engineering, University of Toronto, Toronto, Ontario, Canada
| | - Maryam Shekarrizfard
- Department of Civil & Mineral Engineering, University of Toronto, Toronto, Ontario, Canada
| | - Scott Weichenthal
- Department of Epidemiology, Biostatistics & Occupational Health, McGill University, Montreal, QC, Canada
| | - Marianne Hatzopoulou
- Department of Civil & Mineral Engineering, University of Toronto, Toronto, Ontario, Canada
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Hankey S, Sforza P, Pierson M. Using Mobile Monitoring to Develop Hourly Empirical Models of Particulate Air Pollution in a Rural Appalachian Community. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2019; 53:4305-4315. [PMID: 30871316 DOI: 10.1021/acs.est.8b05249] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Most empirical air quality models (e.g., land use regression) focus on urban areas. Mobile monitoring for model development offers the opportunity to explore smaller, rural communities - an understudied population. We use mobile monitoring to systematically sample all daylight hours (7 am to 7 pm) to develop empirical models capable of estimating hourly concentrations in Blacksburg, VA, a small town in rural Appalachia (population: 182 635). We collected ∼120 h of mobile monitoring data for particle number (PN) and black carbon (BC). We developed (1) daytime (12-h average) models that approximate long-term concentrations and (2) spatiotemporal models for estimating hourly concentrations. Model performance for the daytime models is consistent with previous fixed-site and short-term sampling studies; adjusted R2 (10-fold CV R2) was 0.80 (0.69) for the PN model and 0.67 (0.58) for the BC model. The spatiotemporal models had comparable performance (10-fold CV R2 for the PN [BC] models: 0.42 [0.25]) to previous mobile monitoring studies that isolate specific time periods. Temporal and spatial model coefficients had similar magnitudes in the spatiotemporal models suggesting both factors are important for exposure. We observed similar spatial patterns in Blacksburg (e.g., roadway gradients) as in other studies in urban areas suggesting similar exposure disparities exist in small, rural communities.
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Affiliation(s)
- Steve Hankey
- School of Public and International Affairs , Virginia Tech , 140 Otey Street , Blacksburg , Virginia 24061 , United States
| | - Peter Sforza
- Center for Geospatial Information Technology , Virginia Tech , 620 Drillfield Drive , Blacksburg , Virginia 24061 , United States
| | - Matt Pierson
- Center for Geospatial Information Technology , Virginia Tech , 620 Drillfield Drive , Blacksburg , Virginia 24061 , United States
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Núñez-Alonso D, Pérez-Arribas LV, Manzoor S, Cáceres JO. Statistical Tools for Air Pollution Assessment: Multivariate and Spatial Analysis Studies in the Madrid Region. JOURNAL OF ANALYTICAL METHODS IN CHEMISTRY 2019; 2019:9753927. [PMID: 30881728 PMCID: PMC6387705 DOI: 10.1155/2019/9753927] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/24/2018] [Accepted: 12/16/2018] [Indexed: 05/13/2023]
Abstract
The present work reports the distribution of pollutants in the Madrid city and province from 22 monitoring stations during 2010 to 2017. Statistical tools were used to interpret and model air pollution data. The data include the annual average concentrations of nitrogen oxides, ozone, and particulate matter (PM10), collected in Madrid and its suburbs, which is one of the largest metropolitan places in Europe, and its air quality has not been studied sufficiently. A mapping of the distribution of these pollutants was done, in order to reveal the relationship between them and also with the demography of the region. The multivariate analysis employing correlation analysis, principal component analysis (PCA), and cluster analysis (CA) resulted in establishing a correlation between different pollutants. The results obtained allowed classification of different monitoring stations on the basis of each of the four pollutants, revealing information about their sources and mechanisms, visualizing their spatial distribution, and monitoring their levels according to the average annual limits established in the legislation. The elaboration of contour maps by the geostatistical method, ordinary kriging, also supported the interpretation derived from the multivariate analysis demonstrating the levels of NO2 exceeding the annual limit in the centre, south, and east of the Madrid province.
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Affiliation(s)
- David Núñez-Alonso
- Laser-Chemical-Group, Department of Analytical Chemistry, Faculty of Chemical Sciences, Complutense University, 28040 Madrid, Spain
| | - Luis Vicente Pérez-Arribas
- Laser-Chemical-Group, Department of Analytical Chemistry, Faculty of Chemical Sciences, Complutense University, 28040 Madrid, Spain
| | - Sadia Manzoor
- Laser-Chemical-Group, Department of Analytical Chemistry, Faculty of Chemical Sciences, Complutense University, 28040 Madrid, Spain
| | - Jorge O. Cáceres
- Laser-Chemical-Group, Department of Analytical Chemistry, Faculty of Chemical Sciences, Complutense University, 28040 Madrid, Spain
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van den Bosch M, Brauer M, Burnett R, Davies HW, Davis Z, Guhn M, Jarvis I, Nesbitt L, Oberlander T, Rugel E, Sbihi H, Su JG, Jerrett M. Born to be Wise: a population registry data linkage protocol to assess the impact of modifiable early-life environmental exposures on the health and development of children. BMJ Open 2018; 8:e026954. [PMID: 30552286 PMCID: PMC6303566 DOI: 10.1136/bmjopen-2018-026954] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
INTRODUCTION Deficiencies in childhood development is a major global issue and inequalities are large. The influence of environmental exposures on childhood development is currently insufficiently explored. This project will analyse the impact of various modifiable early life environmental exposures on different dimensions of childhood development. METHODS Born to be Wise will study a Canadian cohort of approximately 34 000 children who have completed an early development test at the age of 5. Land use regression models of air pollution and spatially defined noise models will be linked to geocoded data on early development to analyse any harmful effects of these exposures. The potentially beneficial effect on early development of early life exposure to natural environments, as measured by fine-grained remote sensing data and various land use indexes, will also be explored. The project will use data linkages and analyse overall and age-specific impact, including variability depending on cumulative exposure by assigning time-weighted exposure estimates and by studying subsamples who have changed residence and exposure. Potentially moderating effects of natural environments on air pollution or noise exposures will be studied by mediation analyses. A matched case-control design will be applied to study moderating effects of natural environments on the association between low socioeconomic status and early development. The main statistical approach will be mixed effects models, applying a specific software to deal with multilevel random effects of nested data. Extensive confounding control will be achieved by including data on a range of detailed health and sociodemographic variables. ETHICS AND DISSEMINATION The study protocol has been ethically approved by the Behavioural Research Ethics Board at the University of British Columbia. The findings will be published in peer-reviewed journals and presented at scholarly conferences. Through stakeholder engagement, the results will also reach policy and a broader audience.
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Affiliation(s)
- Matilda van den Bosch
- The School of Population and Public Health, The University of British Columbia, Vancouver, British Columbia, Canada
- The Department of Forest and Conservation Sciences, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Michael Brauer
- The School of Population and Public Health, The University of British Columbia, Vancouver, British Columbia, Canada
| | | | - Hugh W Davies
- The School of Population and Public Health, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Zoe Davis
- The School of Population and Public Health, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Martin Guhn
- The School of Population and Public Health, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Ingrid Jarvis
- The School of Population and Public Health, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Lorien Nesbitt
- The School of Population and Public Health, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Tim Oberlander
- The School of Population and Public Health, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Emily Rugel
- The School of Population and Public Health, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Hind Sbihi
- The School of Population and Public Health, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Jason G Su
- Department of Statistics, University of California, Berkeley, California, USA
| | - Michael Jerrett
- Fielding School of Public Health, University of California, Los Angeles, California, USA
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Investigating the Effects of the Built Environment on PM2.5 and PM10: A Case Study of Seoul Metropolitan City, South Korea. SUSTAINABILITY 2018. [DOI: 10.3390/su10124552] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Air pollution has a major impact on human health and quality of life; therefore, its determinants should be studied to promote effective management and reduction. Here, we examined the influence of the built environment on air pollution by analyzing the relationship between the built environment and particulate matter (i.e., PM2.5 and PM10). Air pollution data collected in Seoul in 2014 were spatially mapped using geographic information system tools, and PM2.5 and PM10 concentrations were determined in individual neighborhoods using an interpolation method. PM2.5 and PM10 failed to show spatial autocorrelation; therefore, we analyzed the associations between PM fractions and built environment characteristics using an ordinary least squares regression model. PM2.5 and PM10 exhibited some differences in spatial distributions, suggesting that the built environment has different effects on these fractions. For instance, high PM10 concentrations were associated with neighborhoods with more bus routes, bus stops, and river areas. Meanwhile, both PM2.5 and PM10 were more likely to be high in areas with more commercial areas and multi-family housing, but low in areas with more main roads, more single-family housing, and high average gross commercial floor area. This study is expected to contribute to establishing policies and strategies to promote sustainability in Seoul, Korea.
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Cole CA, Carlsten C, Koehle M, Brauer M. Particulate matter exposure and health impacts of urban cyclists: a randomized crossover study. Environ Health 2018; 17:78. [PMID: 30428890 PMCID: PMC6237024 DOI: 10.1186/s12940-018-0424-8] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2018] [Accepted: 10/30/2018] [Indexed: 05/25/2023]
Abstract
BACKGROUND Cycling and other forms of active transportation provide health benefits via increased physical activity. However, direct evidence of the extent to which these benefits may be offset by exposure and intake of traffic-related air pollution is limited. The purpose of this study is to measure changes in endothelial function, measures of oxidative stress and inflammation, and lung function in healthy participants before and after cycling along a high- and low- traffic route. METHODS Participants (n = 38) bicycled for 1 h along a Downtown and a Residential designated bicycle route in a randomized crossover trial. Heart rate, power output, particulate matter air pollution (PM10, PM2.5, and PM1) and particle number concentration (PNC) were measured. Lung function, endothelial function (reactive hyperemia index, RHI), C-reactive protein, interleukin-6, and 8-hydroxy-2'-deoxyguanosine were assessed within one hour pre- and post-trial. RESULTS Geometric mean PNC exposures and intakes were higher along the Downtown (exposure = 16,226 particles/cm3; intake = 4.54 × 1010 particles) compared to the Residential route (exposure = 9367 particles/cm3; intake = 3.13 × 1010 particles). RHI decreased following cycling along the Downtown route and increased on the Residential route; in mixed linear regression models, the (post-pre) change in RHI was 21% lower following cycling on the Downtown versus the Residential route (-0.43, 95% CI: -0.79, -0.079) but RHI decreases were not associated with measured exposure or intake of air pollutants. The differences in RHI by route were larger amongst females and older participants. No consistent associations were observed for any of the other outcome measures. CONCLUSIONS Although PNC exposures and intakes were higher along the Downtown route, the lack of association between air pollutant exposure or intake with RHI and other measures suggests other exposures related to cycling on the Downtown route may have been influential in the observed differences between routes in RHI. TRIAL REGISTRATION ClinicalTrials.gov, NCT01708356 . Registered 16 October 2012.
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Affiliation(s)
- Christie A. Cole
- School of Population and Public Health, University of British Columbia, 2206 East Mall, Vancouver, BC V6T 1Z3 Canada
| | - Christopher Carlsten
- Air Pollution Exposure Lab, Department of Medicine, University of British Columbia, 2775 Laurel Street 7th Floor, Vancouver, BC V5Z 1M9 Canada
| | - Michael Koehle
- School of Kinesiology and Division of Sport & Exercise Medicine, University of British Columbia, 2176 Health Sciences Mall, Vancouver, BC V6T 1Z3 Canada
| | - Michael Brauer
- School of Population and Public Health, University of British Columbia, 2206 East Mall, Vancouver, BC V6T 1Z3 Canada
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40
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Messier KP, Chambliss SE, Gani S, Alvarez R, Brauer M, Choi JJ, Hamburg SP, Kerckhoffs J, LaFranchi B, Lunden MM, Marshall JD, Portier CJ, Roy A, Szpiro AA, Vermeulen RCH, Apte JS. Mapping Air Pollution with Google Street View Cars: Efficient Approaches with Mobile Monitoring and Land Use Regression. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2018; 52:12563-12572. [PMID: 30354135 DOI: 10.1021/acs.est.8b03395] [Citation(s) in RCA: 56] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2023]
Abstract
Air pollution measurements collected through systematic mobile monitoring campaigns can provide outdoor concentration data at high spatial resolution. We explore approaches to minimize data requirements for mapping a city's air quality using mobile monitors with "data-only" versus predictive modeling approaches. We equipped two Google Street View cars with 1-Hz instruments to collect nitric oxide (NO) and black carbon (BC) measurements in Oakland, CA. We explore two strategies for efficiently mapping spatial air quality patterns through Monte Carlo analyses. First, we explore a "data-only" approach where we attempt to minimize the number of repeated visits needed to reliably estimate concentrations for all roads. Second, we combine our data with a land use regression-kriging (LUR-K) model to predict at unobserved locations; here, measurements from only a subset of roads or repeat visits are considered. Although LUR-K models did not capture the full variability of on-road concentrations, models trained with minimal data consistently captured important covariates and general spatial air pollution trends, with cross-validation R2 for log-transformed NO and BC of 0.65 and 0.43. Data-only mapping performed poorly with few (1-2) repeated drives but obtained better cross-validation R2 than the LUR-K approach within 4 to 8 repeated drive days per road segment.
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Affiliation(s)
- Kyle P Messier
- Department of Civil, Architectural and Environmental Engineering , University of Texas at Austin , Austin , Texas 78712 , United States
- Environmental Defense Fund, New York , New York 10010 , United States
| | - Sarah E Chambliss
- Department of Civil, Architectural and Environmental Engineering , University of Texas at Austin , Austin , Texas 78712 , United States
| | - Shahzad Gani
- Department of Civil, Architectural and Environmental Engineering , University of Texas at Austin , Austin , Texas 78712 , United States
| | - Ramon Alvarez
- Environmental Defense Fund, New York , New York 10010 , United States
| | - Michael Brauer
- School of Population and Public Health , University of British Columbia , Vancouver , British Columbia V6T 1Z3 , Canada
| | - Jonathan J Choi
- Environmental Defense Fund, New York , New York 10010 , United States
| | - Steven P Hamburg
- Environmental Defense Fund, New York , New York 10010 , United States
| | - Jules Kerckhoffs
- Institute for Risk Assessment Science , Utrecht University , Utrecht 3584 CM , Netherlands
| | - Brian LaFranchi
- Aclima, Inc., 10 Lombard Street , San Francisco , California 94111 , United States
| | - Melissa M Lunden
- Aclima, Inc., 10 Lombard Street , San Francisco , California 94111 , United States
| | - Julian D Marshall
- Department of Civil and Environmental Engineering , University of Washington , Seattle , Washington 98195 , United States
| | | | - Ananya Roy
- Environmental Defense Fund, New York , New York 10010 , United States
| | - Adam A Szpiro
- Department of Biostatistics , University of Washington , Seattle , Washington 98195 , United States
| | - Roel C H Vermeulen
- Institute for Risk Assessment Science , Utrecht University , Utrecht 3584 CM , Netherlands
| | - Joshua S Apte
- Department of Civil, Architectural and Environmental Engineering , University of Texas at Austin , Austin , Texas 78712 , United States
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Robinson ES, Gu P, Ye Q, Li HZ, Shah RU, Apte JS, Robinson AL, Presto AA. Restaurant Impacts on Outdoor Air Quality: Elevated Organic Aerosol Mass from Restaurant Cooking with Neighborhood-Scale Plume Extents. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2018; 52:9285-9294. [PMID: 30070466 DOI: 10.1021/acs.est.8b02654] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Organic aerosol (OA) is a major component of fine particulate matter (PM2.5) in urban environments. We performed in-motion ambient sampling from a mobile platform with an aerosol mass spectrometer (AMS) to investigate the spatial variability and sources of OA concentrations in Pittsburgh, Pennsylvania, a midsize, largely postindustrial American city. To characterize the relative importance of cooking and traffic sources, we sampled in some of the most populated areas (∼18 km2) in and around Pittsburgh during afternoon rush hour and evening mealtime, including congested highways, major local roads, areas with high densities of restaurants, and urban background locations. We found greatly elevated OA concentrations (10s of μg m-3) in the vicinity of numerous individual restaurants and commercial districts containing multiple restaurants. The AMS mass spectral information indicates that majority of the high concentration plumes (71%) were from cooking sources. Areas containing both busy roads and restaurants had systematically higher OA concentrations than areas with only busy roads and urban background locations. Elevated OA concentrations were measured hundreds of meters downwind of some restaurants, indicating that these sources can influence air quality on neighborhood scales. Approximately 20% of the population (∼250 000 people) in the Pittsburgh area lives within 200 m of a restaurant; therefore, restaurant emissions are potentially an important source of outdoor PM exposures for this large population.
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Affiliation(s)
- Ellis Shipley Robinson
- Department of Mechanical Engineering , Carnegie Mellon University , Pittsburgh , Pennsylvania 15213 , United States
- Center for Atmospheric Particle Studies , Carnegie Mellon University , Pittsburgh , Pennsylvania 15213 , United States
| | - Peishi Gu
- Department of Mechanical Engineering , Carnegie Mellon University , Pittsburgh , Pennsylvania 15213 , United States
- Center for Atmospheric Particle Studies , Carnegie Mellon University , Pittsburgh , Pennsylvania 15213 , United States
| | - Qing Ye
- Center for Atmospheric Particle Studies , Carnegie Mellon University , Pittsburgh , Pennsylvania 15213 , United States
- Department of Chemistry , Carnegie Mellon University , Pittsburgh , Pennsylvania 15213 , United States
- Department of Engineering & Public Policy , Carnegie Mellon University , Pittsburgh , Pennsylvania 15213 , United States
| | - Hugh Z Li
- Department of Mechanical Engineering , Carnegie Mellon University , Pittsburgh , Pennsylvania 15213 , United States
- Center for Atmospheric Particle Studies , Carnegie Mellon University , Pittsburgh , Pennsylvania 15213 , United States
| | - Rishabh Urvesh Shah
- Department of Mechanical Engineering , Carnegie Mellon University , Pittsburgh , Pennsylvania 15213 , United States
- Center for Atmospheric Particle Studies , Carnegie Mellon University , Pittsburgh , Pennsylvania 15213 , United States
| | - Joshua Schulz Apte
- Department of Civil, Architectural & Environmental Engineering , University of Texas at Austin , Austin , Texas 78705 , United States
| | - Allen L Robinson
- Department of Mechanical Engineering , Carnegie Mellon University , Pittsburgh , Pennsylvania 15213 , United States
- Center for Atmospheric Particle Studies , Carnegie Mellon University , Pittsburgh , Pennsylvania 15213 , United States
- Department of Engineering & Public Policy , Carnegie Mellon University , Pittsburgh , Pennsylvania 15213 , United States
| | - Albert A Presto
- Department of Mechanical Engineering , Carnegie Mellon University , Pittsburgh , Pennsylvania 15213 , United States
- Center for Atmospheric Particle Studies , Carnegie Mellon University , Pittsburgh , Pennsylvania 15213 , United States
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Yang Y, Tang R, Qiu H, Lai PC, Wong P, Thach TQ, Allen R, Brauer M, Tian L, Barratt B. Long term exposure to air pollution and mortality in an elderly cohort in Hong Kong. ENVIRONMENT INTERNATIONAL 2018; 117:99-106. [PMID: 29730535 DOI: 10.1016/j.envint.2018.04.034] [Citation(s) in RCA: 81] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/24/2017] [Revised: 04/19/2018] [Accepted: 04/19/2018] [Indexed: 05/06/2023]
Abstract
BACKGROUND Several studies have reported associations between long term exposure to air pollutants and cause-specific mortality. However, since the concentrations of air pollutants in Asia are much higher compared to those reported in North American and European cohort studies, cohort studies on long term effects of air pollutants in Asia are needed for disease burden assessment and to inform policy. OBJECTIVES To assess the effects of long-term exposure to particulate matter with aerodynamic diameter < 2.5 μm (PM2.5), black carbon (BC) and nitrogen dioxide (NO2) on cause-specific mortality in an elderly cohort in Hong Kong. METHODS In a cohort of 66,820 participants who were older than or equal to 65 years old in Hong Kong from 1998 to 2011, air pollutant concentrations were estimated by land use regression and assigned to the residential addresses of all participants at baseline and for each year during a 11 year follow up period. Hazard ratios (HRs) of cause-specific mortality (including all natural cause, cardiovascular and respiratory mortality) associated with air pollutants were estimated with Cox models, including a number of personal and area-level socioeconomic, demographic, and lifestyle factors. RESULTS The median concentration of PM2.5 during the baseline period was 42.2 μg/m3 with an IQR of 5.5 μg/m3, 12.1 (9.6) μg/m3 for BC and 104 (25.6) μg/m3 for NO2. For PM2.5, adjusted HR per IQR increase and per 10 μg/m3 for natural cause mortality was 1.03 (95%CI: 1.01, 1.06) and 1.06 (95%CI: 1.02, 1.11) respectively. The corresponding HR were 1.06 (95%CI: 1.02, 1.10) and 1.01 (95%CI: 0.96, 1.06) for cardiovascular disease and respiratory disease mortality, respectively. For BC, the HR of an interquartile range increase for all natural cause mortality was 1.03 (95%CI: 1.00, 1.05). The corresponding HR was 1.07 (95%CI: 1.03, 1.11) and 0.99 (95%CI: 0.94, 1.04) for cardiovascular disease and respiratory disease mortality. For NO2, almost all HRs were approximately 1.0, except for IHD (ischemic heart disease) mortality. CONCLUSION Long-term exposure to ambient PM2.5 and BC was associated with an elevated risk of cardiovascular mortality. Despite far higher air pollution exposure concentrations, HRs per unit increase in PM2.5 were similar to those from recent comparable studies in North America.
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Affiliation(s)
- Yang Yang
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Robert Tang
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Hong Qiu
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Poh-Chin Lai
- Department of Geography, The University of Hong Kong, Hong Kong, China
| | - Paulina Wong
- Lingnan University, Science Unit, Hong Kong, China
| | - Thuan-Quoc Thach
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Ryan Allen
- Faculty of Health Sciences, Simon Fraser University, Burnaby, British Columbia, Canada
| | - Michael Brauer
- School of Population and Public Health, University of British Columbia, Vancouver, Canada
| | - Linwei Tian
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Benjamin Barratt
- MRC-PHE Centre for Environment and Health, Faculty of Life Sciences & Medicine, King's College London, London, UK; HPRU Health Impact of Environmental Hazards, King's College London, London, UK.
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Pollutant composition modification of the effect of air pollution on progression of coronary artery calcium: the Multi-Ethnic Study of Atherosclerosis. Environ Epidemiol 2018; 2. [PMID: 30854505 PMCID: PMC6402342 DOI: 10.1097/ee9.0000000000000024] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
Background: Differences in traffic-related air pollution (TRAP) composition may cause heterogeneity in associations between air pollution exposure and cardiovascular health outcomes. Clustering multipollutant measurements allows investigation of effect modification by TRAP profiles. Methods: We measured TRAP components with fixed-site and on-road instruments for two 2-week periods in Baltimore, Maryland. We created representative TRAP profiles for cold and warm seasons using predictive k-means clustering. We predicted cluster membership for 1005 participants in the Multi-Ethnic Study of Atherosclerosis and Air Pollution with follow-up between 2000 and 2012. We estimated cluster-specific relationships between coronary artery calcification (CAC) progression and long-term exposure to fine particulate matter (PM2.5) and oxides of nitrogen (NOX). Results: We identified two clusters in the cold season, notable for higher ratios of gases and ultrafine particles, respectively. A 5-μg/m3 difference in PM2.5 was associated with 17.0 (95% confidence interval [CI] = 7.2, 26.7) and 42.6 (95% CI = 25.7, 59.4) Agatston units/year CAC progression among participants in clusters 1 and 2, respectively (effect modification P = 0.006). A 40 ppb difference in NOX was associated with 22.2 (95% CI = 7.7, 36.7) and 41.9 (95% CI = 23.7, 60.2) Agatston units/year CAC progression in clusters 1 and 2, respectively (P = 0.08). Similar trends occurred using clusters identified from warm season measurements. Clusters correlated highly with baseline pollution level. Conclusions: Clustering TRAP measurements identified spatial differences in composition. We found evidence of greater CAC progression rates per unit PM2.5 exposures among people living in areas characterized by high ratios of ultrafine particle counts relative to NOX concentrations.
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Simon MC, Patton AP, Naumova EN, Levy JI, Kumar P, Brugge D, Durant JL. Combining Measurements from Mobile Monitoring and a Reference Site To Develop Models of Ambient Ultrafine Particle Number Concentration at Residences. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2018; 52:6985-6995. [PMID: 29762018 PMCID: PMC8371457 DOI: 10.1021/acs.est.8b00292] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Significant spatial and temporal variation in ultrafine particle (UFP; <100 nm in diameter) concentrations creates challenges in developing predictive models for epidemiological investigations. We compared the performance of land-use regression models built by combining mobile and stationary measurements (hybrid model) with a regression model built using mobile measurements only (mobile model) in Chelsea and Boston, MA (USA). In each study area, particle number concentration (PNC; a proxy for UFP) was measured at a stationary reference site and with a mobile laboratory driven along a fixed route during an ∼1-year monitoring period. In comparing PNC measured at 20 residences and PNC estimates from hybrid and mobile models, the hybrid model showed higher Pearson correlations of natural log-transformed PNC ( r = 0.73 vs 0.51 in Chelsea; r = 0.74 vs 0.47 in Boston) and lower root-mean-square error in Chelsea (0.61 vs 0.72) but no benefit in Boston (0.72 vs 0.71). All models overpredicted log-transformed PNC by 3-6% at residences, yet the hybrid model reduced the standard deviation of the residuals by 15% in Chelsea and 31% in Boston with better tracking of overnight decreases in PNC. Overall, the hybrid model considerably outperformed the mobile model and could offer reduced exposure error for UFP epidemiology.
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Affiliation(s)
- Matthew C. Simon
- Department of Environmental Health, Boston University School of Public Health, 715 Albany Street, Boston, Massachusetts 02118, United States
- Department of Civil and Environmental Engineering, Tufts University, 200 College Avenue, Medford, Massachusetts 02155, United States
- Corresponding Author:
| | - Allison P. Patton
- Health Effects Institute, 75 Federal Street, Suite 1400, Boston, Massachusetts 02110, United States
| | - Elena N. Naumova
- Department of Civil and Environmental Engineering, Tufts University, 200 College Avenue, Medford, Massachusetts 02155, United States
- Friedman School of Nutrition Science and Policy, Tufts University, 150 Harrison Avenue, Boston, Massachusetts 02111, United States
| | - Jonathan I. Levy
- Department of Environmental Health, Boston University School of Public Health, 715 Albany Street, Boston, Massachusetts 02118, United States
| | - Prashant Kumar
- Global Centre for Clean Air Research (GCARE), Department of Civil and Environmental Engineering, University of Surrey, Guildford GU2 7XH, United Kingdom
| | - Doug Brugge
- Department of Civil and Environmental Engineering, Tufts University, 200 College Avenue, Medford, Massachusetts 02155, United States
- Department of Public Health and Community Medicine, Tufts University, 136 Harrison Avenue, Boston, Massachusetts 02111, United States
- Jonathan M. Tisch College of Civil Life, Tufts University, 10 Upper Campus Road, Medford, Massachusetts 02155, United States
| | - John L. Durant
- Department of Civil and Environmental Engineering, Tufts University, 200 College Avenue, Medford, Massachusetts 02155, United States
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Corlin L, Woodin M, Hart JE, Simon MC, Gute DM, Stowell J, Tucker KL, Durant JL, Brugge D. Longitudinal associations of long-term exposure to ultrafine particles with blood pressure and systemic inflammation in Puerto Rican adults. Environ Health 2018; 17:33. [PMID: 29622024 PMCID: PMC5887259 DOI: 10.1186/s12940-018-0379-9] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2017] [Accepted: 03/28/2018] [Indexed: 05/20/2023]
Abstract
BACKGROUND Few longitudinal studies have examined the association between ultrafine particulate matter (UFP, particles < 0.1 μm aerodynamic diameter) exposure and cardiovascular disease (CVD) risk factors. We used data from 791 adults participating in the longitudinal Boston Puerto Rican Health Study (Massachusetts, USA) between 2004 and 2015 to assess whether UFP exposure was associated with blood pressure and high sensitivity C-reactive protein (hsCRP, a biomarker of systemic inflammation). METHODS Residential annual average UFP exposure (measured as particle number concentration, PNC) was assigned using a model accounting for spatial and temporal trends. We also adjusted PNC values for participants' inhalation rate to obtain the particle inhalation rate (PIR) as a secondary exposure measure. Multilevel linear models with a random intercept for each participant were used to examine the association of UFP with blood pressure and hsCRP. RESULTS Overall, in adjusted models, an inter-quartile range increase in PNC was associated with increased hsCRP (β = 6.8; 95% CI = - 0.3, 14.0%) but not with increased systolic blood pressure (β = 0.96; 95% CI = - 0.33, 2.25 mmHg), pulse pressure (β = 0.70; 95% CI = - 0.27, 1.67 mmHg), or diastolic blood pressure (β = 0.55; 95% CI = - 0.20, 1.30 mmHg). There were generally stronger positive associations among women and never smokers. Among men, there were inverse associations of PNC with systolic blood pressure and pulse pressure. In contrast to the primary findings, an inter-quartile range increase in the PIR was positively associated with systolic blood pressure (β = 1.03; 95% CI = 0.00, 2.06 mmHg) and diastolic blood pressure (β = 1.01; 95% CI = 0.36, 1.66 mmHg), but not with pulse pressure or hsCRP. CONCLUSIONS We observed that exposure to PNC was associated with increases in measures of CVD risk markers, especially among certain sub-populations. The exploratory PIR exposure metric should be further developed.
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Affiliation(s)
- Laura Corlin
- Department of Civil and Environmental Engineering, Tufts University, 200 College Avenue, Medford, MA 02155 USA
| | - Mark Woodin
- Department of Civil and Environmental Engineering, Tufts University, 200 College Avenue, Medford, MA 02155 USA
- Department of Public Health and Community Medicine, Tufts University, 145 Harrison Ave, Boston, MA 02111 USA
| | - Jaime E. Hart
- Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, 401 Park Drive, Landmark 3rd Floor West, Boston, MA 02215 USA
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, 401 Park Drive, Landmark 3rd Floor West, Boston, MA 02215 USA
| | - Matthew C. Simon
- Department of Civil and Environmental Engineering, Tufts University, 200 College Avenue, Medford, MA 02155 USA
| | - David M. Gute
- Department of Civil and Environmental Engineering, Tufts University, 200 College Avenue, Medford, MA 02155 USA
- Department of Public Health and Community Medicine, Tufts University, 145 Harrison Ave, Boston, MA 02111 USA
| | - Joanna Stowell
- Department of Civil and Environmental Engineering, Tufts University, 200 College Avenue, Medford, MA 02155 USA
| | - Katherine L. Tucker
- Department of Biomedical and Nutritional Sciences, University of Massachusetts-Lowell, 3 Solomont Way Suite 4, Lowell, MA 01854 USA
| | - John L. Durant
- Department of Civil and Environmental Engineering, Tufts University, 200 College Avenue, Medford, MA 02155 USA
| | - Doug Brugge
- Department of Civil and Environmental Engineering, Tufts University, 200 College Avenue, Medford, MA 02155 USA
- Department of Public Health and Community Medicine, Tufts University, 145 Harrison Ave, Boston, MA 02111 USA
- Tisch College of Civic Life, Tufts University, 10 Upper Campus Rd, Medford, MA 02155 USA
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Miskell G, Salmond JA, Williams DE. Use of a handheld low-cost sensor to explore the effect of urban design features on local-scale spatial and temporal air quality variability. THE SCIENCE OF THE TOTAL ENVIRONMENT 2018; 619-620:480-490. [PMID: 29156268 DOI: 10.1016/j.scitotenv.2017.11.024] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2017] [Revised: 10/30/2017] [Accepted: 11/02/2017] [Indexed: 06/07/2023]
Abstract
Portable low-cost instruments have been validated and used to measure ambient nitrogen dioxide (NO2) at multiple sites over a small urban area with 20min time resolution. We use these results combined with land use regression (LUR) and rank correlation methods to explore the effects of traffic, urban design features, and local meteorology and atmosphere chemistry on small-scale spatio-temporal variations. We measured NO2 at 45 sites around the downtown area of Vancouver, BC, in spring 2016, and constructed four different models: i) a model based on averaging concentrations observed at each site over the whole measurement period, and separate temporal models for ii) morning, iii) midday, and iv) afternoon. Redesign of the temporal models using the average model predictors as constants gave three 'hybrid' models that used both spatial and temporal variables. These accounted for approximately 50% of the total variation with mean absolute error±5ppb. Ranking sites by concentration and by change in concentration across the day showed a shift of high NO2 concentrations across the central city from morning to afternoon. Locations could be identified in which NO2 concentration was determined by the geography of the site, and others as ones in which the concentration changed markedly from morning to afternoon indicating the importance of temporal controls. Rank correlation results complemented LUR in identifying significant urban design variables that impacted NO2 concentration. High variability across a relatively small space was partially described by predictor variables related to traffic (bus stop density, speed limits, traffic counts, distance to traffic lights), atmospheric chemistry (ozone, dew point), and environment (land use, trees). A high-density network recording continuously would be needed fully to capture local variations.
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Affiliation(s)
- Georgia Miskell
- MacDiarmid Institute for Advanced Materials and Nanotechnology, School of Chemical Sciences, School of Environment, The University of Auckland, Private Bag 92019, Auckland 1142, New Zealand.
| | - Jennifer A Salmond
- MacDiarmid Institute for Advanced Materials and Nanotechnology, School of Chemical Sciences, School of Environment, The University of Auckland, Private Bag 92019, Auckland 1142, New Zealand
| | - David E Williams
- MacDiarmid Institute for Advanced Materials and Nanotechnology, School of Chemical Sciences, School of Environment, The University of Auckland, Private Bag 92019, Auckland 1142, New Zealand
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Abstract
Purpose of Review Epidemiological studies of health effects of long-term exposure to outdoor air pollution rely on different exposure assessment methods. This review discusses widely used methods with a special focus on new developments. Recent Findings New data and study designs have been applied, including satellite measurements of fine particles and nitrogen dioxide (NO2). The methods to apply satellite data for epidemiological studies are improving rapidly and have already contributed significantly to national-, continental- and global-scale models. Spatiotemporal models have been developed allowing more detailed temporal resolution compared to spatial models. The development of hybrid models combining dispersion models, satellite observations, land use and surface monitoring has improved models substantially. Mobile monitoring designs to develop models for long-term UFP exposure have been conducted. Summary Methods to assess long-term exposure to outdoor air pollution have improved significantly over the past decade. Application of satellite data and mobile monitoring designs is promising new methods.
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Gulliver J, Morley D, Dunster C, McCrea A, van Nunen E, Tsai MY, Probst-Hensch N, Eeftens M, Imboden M, Ducret-Stich R, Naccarati A, Galassi C, Ranzi A, Nieuwenhuijsen M, Curto A, Donaire-Gonzalez D, Cirach M, Vermeulen R, Vineis P, Hoek G, Kelly FJ. Land use regression models for the oxidative potential of fine particles (PM 2.5) in five European areas. ENVIRONMENTAL RESEARCH 2018; 160:247-255. [PMID: 29031214 DOI: 10.1016/j.envres.2017.10.002] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/12/2017] [Revised: 09/22/2017] [Accepted: 10/03/2017] [Indexed: 06/07/2023]
Abstract
Oxidative potential (OP) of particulate matter (PM) is proposed as a biologically-relevant exposure metric for studies of air pollution and health. We aimed to evaluate the spatial variability of the OP of measured PM2.5 using ascorbate (AA) and (reduced) glutathione (GSH), and develop land use regression (LUR) models to explain this spatial variability. We estimated annual average values (m-3) of OPAA and OPGSH for five areas (Basel, CH; Catalonia, ES; London-Oxford, UK (no OPGSH); the Netherlands; and Turin, IT) using PM2.5 filters. OPAA and OPGSH LUR models were developed using all monitoring sites, separately for each area and combined-areas. The same variables were then used in repeated sub-sampling of monitoring sites to test sensitivity of variable selection; new variables were offered where variables were excluded (p > .1). On average, measurements of OPAA and OPGSH were moderately correlated (maximum Pearson's maximum Pearson's R = = .7) with PM2.5 and other metrics (PM2.5absorbance, NO2, Cu, Fe). HOV (hold-out validation) R2 for OPAA models was .21, .58, .45, .53, and .13 for Basel, Catalonia, London-Oxford, the Netherlands and Turin respectively. For OPGSH, the only model achieving at least moderate performance was for the Netherlands (R2 = .31). Combined models for OPAA and OPGSH were largely explained by study area with weak local predictors of intra-area contrasts; we therefore do not endorse them for use in epidemiologic studies. Given the moderate correlation of OPAA with other pollutants, the three reasonably performing LUR models for OPAA could be used independently of other pollutant metrics in epidemiological studies.
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Affiliation(s)
- John Gulliver
- MRC-PHE Centre for Environment and Health, the Department of Epidemiology and Biostatistics, Imperial College London, London, United Kingdom.
| | - David Morley
- MRC-PHE Centre for Environment and Health, the Department of Epidemiology and Biostatistics, Imperial College London, London, United Kingdom
| | - Chrissi Dunster
- MRC-PHE Centre for Environment and Health, Environmental Research Group (ERG), King's College London, London, United Kingdom
| | - Adrienne McCrea
- MRC-PHE Centre for Environment and Health, the Department of Epidemiology and Biostatistics, Imperial College London, London, United Kingdom
| | - Erik van Nunen
- Institute for Risk Assessment Sciences (IRAS), division of Environmental Epidemiology (EEPI), Utrecht University, Utrecht, The Netherlands
| | - Ming-Yi Tsai
- Swiss Tropical and Public Health (TPH) Institute, University of Basel, Basel, Switzerland; University of Basel, Basel, Switzerland
| | - Nicoltae Probst-Hensch
- Swiss Tropical and Public Health (TPH) Institute, University of Basel, Basel, Switzerland; University of Basel, Basel, Switzerland
| | - Marloes Eeftens
- Swiss Tropical and Public Health (TPH) Institute, University of Basel, Basel, Switzerland; University of Basel, Basel, Switzerland
| | - Medea Imboden
- Swiss Tropical and Public Health (TPH) Institute, University of Basel, Basel, Switzerland; University of Basel, Basel, Switzerland
| | - Regina Ducret-Stich
- Swiss Tropical and Public Health (TPH) Institute, University of Basel, Basel, Switzerland; University of Basel, Basel, Switzerland
| | | | - Claudia Galassi
- Unit of Cancer Epidemiology, Citta' della Salute e della Scienza University Hospital and Centre for Cancer Prevention, Turin, Italy
| | - Andrea Ranzi
- Environmental Health Reference Centre, Regional Agency for Prevention, Environment and Energy of Emilia-Romagna, Modena, Italy
| | - Mark Nieuwenhuijsen
- ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain; Department of Experimental and Health Sciences, Pompeu Fabra University (UPF), Barcelona, Spain; CIBER Epidemiologia y Salud Pública (CIBERESP), Barcelona, Spain
| | - Ariadna Curto
- ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain; Department of Experimental and Health Sciences, Pompeu Fabra University (UPF), Barcelona, Spain; CIBER Epidemiologia y Salud Pública (CIBERESP), Barcelona, Spain
| | - David Donaire-Gonzalez
- ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain; Department of Experimental and Health Sciences, Pompeu Fabra University (UPF), Barcelona, Spain; CIBER Epidemiologia y Salud Pública (CIBERESP), Barcelona, Spain
| | - Marta Cirach
- ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain; Department of Experimental and Health Sciences, Pompeu Fabra University (UPF), Barcelona, Spain; CIBER Epidemiologia y Salud Pública (CIBERESP), Barcelona, Spain
| | - Roel Vermeulen
- Institute for Risk Assessment Sciences (IRAS), division of Environmental Epidemiology (EEPI), Utrecht University, Utrecht, The Netherlands
| | - Paolo Vineis
- MRC-PHE Centre for Environment and Health, the Department of Epidemiology and Biostatistics, Imperial College London, London, United Kingdom
| | - Gerard Hoek
- Institute for Risk Assessment Sciences (IRAS), division of Environmental Epidemiology (EEPI), Utrecht University, Utrecht, The Netherlands
| | - Frank J Kelly
- MRC-PHE Centre for Environment and Health, Environmental Research Group (ERG), King's College London, London, United Kingdom
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Kerckhoffs J, Hoek G, Vlaanderen J, van Nunen E, Messier K, Brunekreef B, Gulliver J, Vermeulen R. Robustness of intra urban land-use regression models for ultrafine particles and black carbon based on mobile monitoring. ENVIRONMENTAL RESEARCH 2017; 159:500-508. [PMID: 28866382 DOI: 10.1016/j.envres.2017.08.040] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/24/2017] [Revised: 08/04/2017] [Accepted: 08/23/2017] [Indexed: 05/22/2023]
Abstract
Land-use regression (LUR) models for ultrafine particles (UFP) and Black Carbon (BC) in urban areas have been developed using short-term stationary monitoring or mobile platforms in order to capture the high variability of these pollutants. However, little is known about the comparability of predictions of mobile and short-term stationary models and especially the validity of these models for assessing residential exposures and the robustness of model predictions developed in different campaigns. We used an electric car to collect mobile measurements (n = 5236 unique road segments) and short-term stationary measurements (3 × 30min, n = 240) of UFP and BC in three Dutch cities (Amsterdam, Utrecht, Maastricht) in 2014-2015. Predictions of LUR models based on mobile measurements were compared to (i) measured concentrations at the short-term stationary sites, (ii) LUR model predictions based on short-term stationary measurements at 1500 random addresses in the three cities, (iii) externally obtained home outdoor measurements (3 × 24h samples; n = 42) and (iv) predictions of a LUR model developed based upon a 2013 mobile campaign in two cities (Amsterdam, Rotterdam). Despite the poor model R2 of 15%, the ability of mobile UFP models to predict measurements with longer averaging time increased substantially from 36% for short-term stationary measurements to 57% for home outdoor measurements. In contrast, the mobile BC model only predicted 14% of the variation in the short-term stationary sites and also 14% of the home outdoor sites. Models based upon mobile and short-term stationary monitoring provided fairly high correlated predictions of UFP concentrations at 1500 randomly selected addresses in the three Dutch cities (R2 = 0.64). We found higher UFP predictions (of about 30%) based on mobile models opposed to short-term model predictions and home outdoor measurements with no clear geospatial patterns. The mobile model for UFP was stable over different settings as the model predicted concentration levels highly correlated to predictions made by a previously developed LUR model with another spatial extent and in a different year at the 1500 random addresses (R2 = 0.80). In conclusion, mobile monitoring provided robust LUR models for UFP, valid to use in epidemiological studies.
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Affiliation(s)
- Jules Kerckhoffs
- Institute for Risk Assessment Sciences (IRAS), Division of Environmental Epidemiology, Utrecht University, 3584 CK Utrecht, The Netherlands.
| | - Gerard Hoek
- Institute for Risk Assessment Sciences (IRAS), Division of Environmental Epidemiology, Utrecht University, 3584 CK Utrecht, The Netherlands
| | - Jelle Vlaanderen
- Institute for Risk Assessment Sciences (IRAS), Division of Environmental Epidemiology, Utrecht University, 3584 CK Utrecht, The Netherlands
| | - Erik van Nunen
- Institute for Risk Assessment Sciences (IRAS), Division of Environmental Epidemiology, Utrecht University, 3584 CK Utrecht, The Netherlands
| | - Kyle Messier
- Dept. of Civil, Architectural and Environmental Engineering, University of Texas at Austin, USA; Environmental Defense Fund, Austin, TX, USA
| | - Bert Brunekreef
- Institute for Risk Assessment Sciences (IRAS), Division of Environmental Epidemiology, Utrecht University, 3584 CK Utrecht, The Netherlands; Julius Center for Health Sciences and Primary Care, University Medical Center, University of Utrecht, 3584 CK Utrecht, The Netherlands
| | - John Gulliver
- MRC-PHE Centre for Environment and Health, Department of Epidemiology and Biostatistics, Imperial College London, St Mary's Campus, London, United Kingdom
| | - Roel Vermeulen
- Institute for Risk Assessment Sciences (IRAS), Division of Environmental Epidemiology, Utrecht University, 3584 CK Utrecht, The Netherlands; Julius Center for Health Sciences and Primary Care, University Medical Center, University of Utrecht, 3584 CK Utrecht, The Netherlands; MRC-PHE Centre for Environment and Health, Department of Epidemiology and Biostatistics, Imperial College London, St Mary's Campus, London, United Kingdom
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Simon MC, Hudda N, Naumova EN, Levy JI, Brugge D, Durant JL. Comparisons of Traffic-Related Ultrafine Particle Number Concentrations Measured in Two Urban Areas by Central, Residential, and Mobile Monitoring. ATMOSPHERIC ENVIRONMENT (OXFORD, ENGLAND : 1994) 2017; 169:113-127. [PMID: 29333080 PMCID: PMC5761336 DOI: 10.1016/j.atmosenv.2017.09.003] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Traffic-related ultrafine particles (UFP; <100 nanometers diameter) are ubiquitous in urban air. While studies have shown that UFP are toxic, epidemiological evidence of health effects, which is needed to inform risk assessment at the population scale, is limited due to challenges of accurately estimating UFP exposures. Epidemiologic studies often use empirical models to estimate UFP exposures; however, the monitoring strategies upon which the models are based have varied between studies. Our study compares particle number concentrations (PNC; a proxy for UFP) measured by three different monitoring approaches (central-site, short-term residential-site, and mobile on-road monitoring) in two study areas in metropolitan Boston (MA, USA). Our objectives were to quantify ambient PNC differences between the three monitoring platforms, compare the temporal patterns and the spatial heterogeneity of PNC between the monitoring platforms, and identify factors that affect correlations across the platforms. We collected >12,000 hours of measurements at the central sites, 1,000 hours of measurements at each of 20 residential sites in the two study areas, and >120 hours of mobile measurements over the course of ~1 year in each study area. Our results show differences between the monitoring strategies: mean one-minute PNC on-roads were higher (64,000 and 32,000 particles/cm3 in Boston and Chelsea, respectively) compared to central-site measurements (23,000 and 19,000 particles/cm3) and both were higher than at residences (14,000 and 15,000 particles/cm3). Temporal correlations and spatial heterogeneity also differed between the platforms. Temporal correlations were generally highest between central and residential sites, and lowest between central-site and on-road measurements. We observed the greatest spatial heterogeneity across monitoring platforms during the morning rush hours (06:00-09:00) and the lowest during the overnight hours (18:00-06:00). Longer averaging times (days and hours vs. minutes) increased temporal correlations (Pearson correlations were 0.69 and 0.60 vs. 0.39 in Boston; 0.71 and 0.61 vs. 0.45 in Chelsea) and reduced spatial heterogeneity (coefficients of divergence were 0.24 and 0.29 vs. 0.33 in Boston; 0.20 and 0.27 vs. 0.31 in Chelsea). Our results suggest that combining stationary and mobile monitoring may lead to improved characterization of UFP in urban areas and thereby lead to improved exposure assignment for epidemiology studies.
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Affiliation(s)
- Matthew C. Simon
- Department of Civil and Environmental Engineering, Tufts University,
200 College Avenue, Medford, MA 02155, USA
| | - Neelakshi Hudda
- Department of Civil and Environmental Engineering, Tufts University,
200 College Avenue, Medford, MA 02155, USA
| | - Elena N. Naumova
- Department of Civil and Environmental Engineering, Tufts University,
200 College Avenue, Medford, MA 02155, USA
- Friedman School of Nutrition Science and Policy, Tufts University,
150 Harrison Avenue, Boston, MA 02111, USA
| | - Jonathan I. Levy
- School of Public Health, Boston University, 715 Albany Street,
Boston, MA 02118, USA
| | - Doug Brugge
- Department of Public Health and Community Medicine, Tufts
University, 136 Harrison Avenue, Boston, MA 02111, USA
| | - John L. Durant
- Department of Civil and Environmental Engineering, Tufts University,
200 College Avenue, Medford, MA 02155, USA
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