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Vachon J, Buteau S, Liu Y, Van Ryswyk K, Hatzopoulou M, Smargiassi A. Spatial and spatiotemporal modelling of intra-urban ultrafine particles: A comparison of linear, nonlinear, regularized, and machine learning methods. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 954:176523. [PMID: 39326743 DOI: 10.1016/j.scitotenv.2024.176523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/11/2024] [Revised: 09/09/2024] [Accepted: 09/23/2024] [Indexed: 09/28/2024]
Abstract
BACKGROUND Machine learning methods are proposed to improve the predictions of ambient air pollution, yet few studies have compared ultrafine particles (UFP) models across a broad range of statistical and machine learning approaches, and only one compared spatiotemporal models. Most reported marginal differences between methods. This limits our ability to draw conclusions about the best methods to model ambient UFPs. OBJECTIVE To compare the performance and predictions of statistical and machine learning methods used to model spatial and spatiotemporal ambient UFPs. METHODS Daily and annual models were developed from UFP measurements from a year-long mobile monitoring campaign in Quebec City, Canada, combined with 262 geospatial and six meteorological predictors. Various road segment lengths were considered (100/300/500 m) for UFP data aggregation. Four statistical methods included linear, non-linear, and regularized regressions, whereas eight machine learning regressions utilized tree-based, neural networks, support vector, and kernel ridge algorithms. Nested cross-validation was used for model training, hyperparameter tuning and performance evaluation. RESULTS Mean annual UFP concentrations was 13,335 particles/cm3. Machine learning outperformed statistical methods in predicting UFPs. Tree-based methods performed best across temporal scales and segment lengths, with XGBoost producing the overall best performing models (annual R2 = 0.78-0.86, RMSE = 2163-2169 particles/cm3; daily R2 = 0.47-0.48, RMSE = 8651-11,422 particles/cm3). With 100 m segments, other annual models performed similarly well, but their prediction surfaces of annual mean UFP concentrations showed signs of overfitting. Spatial aggregation of monitoring data significantly impacted model performance. Longer segments yielded lower RMSE in all daily models and for annual statistical models, but not for annual machine learning models. CONCLUSIONS The use of tree-based methods significantly improved spatiotemporal predictions of UFP concentrations, and to a lesser extent annual concentrations. Segment length and hyperparameter tuning had notable impacts on model performance and should be considered in future studies.
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Affiliation(s)
- Julien Vachon
- Department of Environmental and Occupational Health, School of Public Health, University of Montreal, Montreal, Canada; Center for Public Health Research (CReSP), University of Montreal and CIUSSS du Centre-Sud-de-l'Île-de-Montréal, Montreal, Canada
| | - Stéphane Buteau
- Department of Environmental and Occupational Health, School of Public Health, University of Montreal, Montreal, Canada; Center for Public Health Research (CReSP), University of Montreal and CIUSSS du Centre-Sud-de-l'Île-de-Montréal, Montreal, Canada
| | - Ying Liu
- Department of Environmental and Occupational Health, School of Public Health, University of Montreal, Montreal, Canada
| | - Keith Van Ryswyk
- Air Pollution Exposure Science Section, Water and Air Quality Bureau, Health Canada, Ottawa, Canada
| | | | - Audrey Smargiassi
- Department of Environmental and Occupational Health, School of Public Health, University of Montreal, Montreal, Canada; Center for Public Health Research (CReSP), University of Montreal and CIUSSS du Centre-Sud-de-l'Île-de-Montréal, Montreal, Canada.
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Clark LP, Zilber D, Schmitt C, Fargo DC, Reif DM, Motsinger-Reif AA, Messier KP. A review of geospatial exposure models and approaches for health data integration. JOURNAL OF EXPOSURE SCIENCE & ENVIRONMENTAL EPIDEMIOLOGY 2024:10.1038/s41370-024-00712-8. [PMID: 39251872 DOI: 10.1038/s41370-024-00712-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 08/01/2024] [Accepted: 08/05/2024] [Indexed: 09/11/2024]
Abstract
BACKGROUND Geospatial methods are common in environmental exposure assessments and increasingly integrated with health data to generate comprehensive models of environmental impacts on public health. OBJECTIVE Our objective is to review geospatial exposure models and approaches for health data integration in environmental health applications. METHODS We conduct a literature review and synthesis. RESULTS First, we discuss key concepts and terminology for geospatial exposure data and models. Second, we provide an overview of workflows in geospatial exposure model development and health data integration. Third, we review modeling approaches, including proximity-based, statistical, and mechanistic approaches, across diverse exposure types, such as air quality, water quality, climate, and socioeconomic factors. For each model type, we provide descriptions, general equations, and example applications for environmental exposure assessment. Fourth, we discuss the approaches used to integrate geospatial exposure data and health data, such as methods to link data sources with disparate spatial and temporal scales. Fifth, we describe the landscape of open-source tools supporting these workflows.
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Affiliation(s)
- Lara P Clark
- National Institute of Environmental Health Sciences, Office of the Scientific Director, Office of Data Science, Durham, NC, USA
| | - Daniel Zilber
- National Institute of Environmental Health Sciences, Division of Translational Toxicology, Predictive Toxicology Branch, Durham, NC, USA
| | - Charles Schmitt
- National Institute of Environmental Health Sciences, Office of the Scientific Director, Office of Data Science, Durham, NC, USA
| | - David C Fargo
- National Institute of Environmental Health Sciences, Office of the Director, Office of Environmental Science Cyberinfrastructure, Durham, NC, USA
| | - David M Reif
- National Institute of Environmental Health Sciences, Division of Translational Toxicology, Predictive Toxicology Branch, Durham, NC, USA
| | - Alison A Motsinger-Reif
- National Institute of Environmental Health Sciences, Division of Intramural Research, Biostatistics and Computational Biology Branch, Durham, NC, USA
| | - Kyle P Messier
- National Institute of Environmental Health Sciences, Division of Translational Toxicology, Predictive Toxicology Branch, Durham, NC, USA.
- National Institute of Environmental Health Sciences, Division of Intramural Research, Biostatistics and Computational Biology Branch, Durham, NC, USA.
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Yuan Z, Kerckhoffs J, Li H, Khan J, Hoek G, Vermeulen R. Hyperlocal Air Pollution Mapping: A Scalable Transfer Learning LUR Approach for Mobile Monitoring. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:14372-14383. [PMID: 39082120 PMCID: PMC11325550 DOI: 10.1021/acs.est.4c06144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
Addressing the challenge of mapping hyperlocal air pollution in areas without local monitoring, we evaluated unsupervised transfer learning-based land-use regression (LUR) models developed using mobile monitoring data from other cities: CORrelation ALignment (Coral) and its inverse distance-weighted modification (IDW_Coral). These models mitigated domain shifts and transferred patterns learned from mobile air quality monitoring campaigns in Copenhagen and Rotterdam to estimate annual average air pollution levels in Amsterdam (50m road segments) without involving any Amsterdam measurements in model development. For nitrogen dioxide (NO2), IDW_Coral outperformed Copenhagen and Rotterdam LUR models directly applied to Amsterdam, achieving MAE (4.47 μg/m3) and RMSE (5.36 μg/m3) comparable to a locally fitted LUR model (AMS_SLR) developed using Amsterdam mobile measurements collected for 160 days. IDW_Coral yielded an R2 of 0.35, similar to that of the AMS_SLR based on 20 collection days, suggesting a minimum requirement of 20-day mobile monitoring to capture city-specific insights. For ultrafine particles (UFP), IDW_Coral's citywide predictions strongly correlated with previously published mixed-effect models fitted with 160-day Amsterdam measurements (Pearson correlation of 0.71 for UFP and 0.72 for NO2). IDW_Coral demands no direct measurements in the target area, showcasing its potential for large-scale applications and offering significant economic efficiencies in executing mobile monitoring campaigns.
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Affiliation(s)
- Zhendong Yuan
- Institute for Risk Assessment Sciences, Utrecht University, 3584 CM Utrecht, Netherlands
| | - Jules Kerckhoffs
- Institute for Risk Assessment Sciences, Utrecht University, 3584 CM Utrecht, Netherlands
| | - Hao Li
- Professorship of Big Geospatial Data Management, Technical University of Munich, 85521 Ottobrunn, Germany
| | - Jibran Khan
- Department of Environmental Science, Aarhus University, DK-4000 Roskilde, Denmark
- Danish Big Data Centre for Environment and Health (BERTHA), Aarhus University, DK-4000 Roskilde, Denmark
| | - Gerard Hoek
- Institute for Risk Assessment Sciences, Utrecht University, 3584 CM Utrecht, Netherlands
| | - Roel Vermeulen
- Institute for Risk Assessment Sciences, Utrecht University, 3584 CM Utrecht, Netherlands
- Julius Centre for Health Sciences and Primary Care, University Medical Centre, Utrecht University, 3584 CX Utrecht, The Netherlands
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Vachon J, Kerckhoffs J, Buteau S, Smargiassi A. Do Machine Learning Methods Improve Prediction of Ambient Air Pollutants with High Spatial Contrast? A Systematic Review. ENVIRONMENTAL RESEARCH 2024:119751. [PMID: 39117059 DOI: 10.1016/j.envres.2024.119751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 07/18/2024] [Accepted: 08/04/2024] [Indexed: 08/10/2024]
Abstract
BACKGROUND & OBJECTIVE The use of machine learning for air pollution modelling is rapidly increasing. We conducted a systematic review of studies comparing statistical and machine learning models predicting the spatiotemporal variation of ambient nitrogen dioxide (NO2), ultrafine particles (UFPs) and black carbon (BC) to determine whether and in which scenarios machine learning generates more accurate predictions. METHODS Web of Science and Scopus were searched up to June 13, 2024. All records were screened by two independent reviewers. Differences in the coefficient of determination (R2) and Root Mean Square Error (RMSE) between best statistical and machine learning methods were compared across categories of methodological elements. RESULTS A total of 38 studies with 46 model comparisons (30 for NO2, 8 for UFPs and 8 for BC) were included. Linear non-regularized methods and Random Forest were most frequently used. Machine learning outperformed statistical models in 34 comparisons. Mean differences (95% confidence intervals) in R2 and RMSE between best machine learning and statistical models were 0.12 (0.08, 0.17) and 20% (11%, 29%) respectively. Tree-based methods performed best in 12 of 17 multi-model comparisons. Nonlinear or regularization regression methods were used in only 12 comparisons and provided similar performance to machine learning methods. CONCLUSION This systematic review suggests that machine learning methods, especially tree-based methods, may be superior to linear non-regularized methods for predicting ambient concentrations of NO2, UFPs and BC. Additional comparison studies using nonlinear, regularized and a wider array of machine learning methods are needed to confirm their relative performance. Future air pollution studies would also benefit from more explicit and standardized reporting of methodologies and results.
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Affiliation(s)
- Julien Vachon
- Department of Environmental and Occupational Health, School of Public Health, University of Montreal, Montreal, Canada; Center for Public Health Research (CReSP), University of Montreal and CIUSSS du Centre-Sud-de-l'Île-de-Montréal, Montreal, Canada.
| | - Jules Kerckhoffs
- Institute for Risk Assessment Sciences, Utrecht University, Utrecht, the Netherlands
| | - Stéphane Buteau
- Department of Environmental and Occupational Health, School of Public Health, University of Montreal, Montreal, Canada; Center for Public Health Research (CReSP), University of Montreal and CIUSSS du Centre-Sud-de-l'Île-de-Montréal, Montreal, Canada
| | - Audrey Smargiassi
- Department of Environmental and Occupational Health, School of Public Health, University of Montreal, Montreal, Canada; Center for Public Health Research (CReSP), University of Montreal and CIUSSS du Centre-Sud-de-l'Île-de-Montréal, Montreal, Canada
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5
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Clark SN, Kulka R, Buteau S, Lavigne E, Zhang JJY, Riel-Roberge C, Smargiassi A, Weichenthal S, Van Ryswyk K. High-resolution spatial and spatiotemporal modelling of air pollution using fixed site and mobile monitoring in a Canadian city. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 356:124353. [PMID: 38866318 DOI: 10.1016/j.envpol.2024.124353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/23/2024] [Revised: 05/20/2024] [Accepted: 06/08/2024] [Indexed: 06/14/2024]
Abstract
The development of high-resolution spatial and spatiotemporal models of air pollutants is essential for exposure science and epidemiological applications. While fixed-site sampling has conventionally provided input data for statistical predictive models, the evolving mobile monitoring method offers improved spatial resolution, ideal for measuring pollutants with high spatial variability such as ultrafine particles (UFP). The Quebec Air Pollution Exposure and Epidemiology (QAPEE) study measured and modelled the spatial and spatiotemporal distributions of understudied pollutants, such as UFPs, black carbon (BC), and brown carbon (BrC), along with fine particulate matter (PM2.5), nitrogen dioxide (NO2), and ozone (O3) in Quebec City, Canada. We conducted a combined fixed-site (NO2 and O3) and mobile monitoring (PM2.5, BC, BrC, and UFPs) campaign over 10-months. Mobile monitoring routes were monitored on a weekly basis between 8am-10am and designed using location/allocation modelling. Seasonal fixed-site sampling campaigns captured continuous 24-h measurements over two-week periods. Generalized Additive Models (GAMs), which combined data on pollution concentrations with spatial, temporal, and spatiotemporal predictor variables were used to model and predict concentration surfaces. Annual models for PM2.5, NO2, O3 as well as seven of the smallest size fractions in the UFP range, had high out of sample predictive accuracy (range r2: 0.54-0.86). Varying spatial patterns were observed across UFP size ranges measured as Particle Number Counts (PNC). The monthly spatiotemporal models for PM2.5 (r2 = 0.49), BC (r2 = 0.27), BrC (r2 = 0.29), and PNC (r2 = 0.49) had moderate or moderate-low out of sample predictive accuracy. We conducted a sensitivity analysis and found that the minimum number of 'n visits' (mobile monitoring sessions) required to model annually representative air pollution concentrations was between 24 and 32 visits dependent on the pollutant. This study provides a single source of exposure models for a comprehensive set of air pollutants in Quebec City, Canada. These exposure models will feed into epidemiological research on the health impacts of ambient UFPs and other pollutants.
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Affiliation(s)
- Sierra Nicole Clark
- Environmental and Social Epidemiology Section, Population Health Research Institute, St. George's, University of London, London, UK; Air Pollution Exposure Science Section, Water and Air Quality Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Government of Canada, Ottawa, Ontario, Canada
| | - Ryan Kulka
- Air Pollution Exposure Science Section, Water and Air Quality Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Government of Canada, Ottawa, Ontario, Canada
| | - Stephane Buteau
- Institut National de sante publique du Quebec (INSPQ), Quebec, Canada; École de santé publique, Département de santé environnementale et santé au travail, Université de Montréal, Québec, Canada; Centre of Public Health Research, University of Montreal and CIUSSS du Centre-Sud-de-l'Île-de-Montréal, Montreal, Canada
| | - Eric Lavigne
- Populations Studies Division, Environmental Health Science and Research Bureau, Health Canada, Ottawa, Canada
| | - Joyce J Y Zhang
- Air Pollution Exposure Science Section, Water and Air Quality Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Government of Canada, Ottawa, Ontario, Canada
| | - Christian Riel-Roberge
- Direction de santé publique, Centre intégré universitaire de santé et de services sociaux (CIUSSS) de la Capitale-Nationale, Quebec City, Quebec, Canada
| | - Audrey Smargiassi
- Institut National de sante publique du Quebec (INSPQ), Quebec, Canada; École de santé publique, Département de santé environnementale et santé au travail, Université de Montréal, Québec, Canada; Centre of Public Health Research, University of Montreal and CIUSSS du Centre-Sud-de-l'Île-de-Montréal, Montreal, Canada
| | - Scott Weichenthal
- Air Pollution Exposure Science Section, Water and Air Quality Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Government of Canada, Ottawa, Ontario, Canada; Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec, Canada
| | - Keith Van Ryswyk
- Air Pollution Exposure Science Section, Water and Air Quality Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Government of Canada, Ottawa, Ontario, Canada.
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Yuan Z, Shen Y, Hoek G, Vermeulen R, Kerckhoffs J. LUR modeling of long-term average hourly concentrations of NO 2 using hyperlocal mobile monitoring data. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 922:171251. [PMID: 38417522 DOI: 10.1016/j.scitotenv.2024.171251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 02/22/2024] [Accepted: 02/22/2024] [Indexed: 03/01/2024]
Abstract
Mobile monitoring campaigns have effectively captured spatial hyperlocal variations in long-term average concentrations of regulated and unregulated air pollutants. However, their application in estimating spatiotemporally varying maps has rarely been investigated. Tackling this gap, we investigated whether mobile measurements can assess long-term average nitrogen dioxide (NO2) concentrations for each hour of the day. Using mobile NO2 data monitored for 10 months in Amsterdam, we examined the performance of two spatiotemporal land use regression (LUR) methods, Spatiotemporal-Kriging and GTWR (Geographical and Temporal Weighted Regression), alongside two classical spatial LUR models developed separately for each hour. We found that mobile measurements follow the general pattern of fixed-site measurements, but with considerable deviations (indicating collection uncertainty). Leveraging heterogeneous spatiotemporal autocorrelations, GTWR smoothed these deviations and achieved an overall performance of an R2 of 0.49 and a Mean Absolute Error of 6.33 μg/m3, validated by long-term fixed-site measurements (out-of-sample). The other models tested were more affected by the collection uncertainty. We highlighted that the spatiotemporal variations captured in mobile measurements can be used to reconstruct long-term average hourly air pollution maps. These maps facilitate dynamic exposure assessments considering spatiotemporal human activity patterns.
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Affiliation(s)
- Zhendong Yuan
- Institute for Risk Assessment Sciences, Utrecht University, Utrecht, the Netherlands.
| | - Youchen Shen
- Institute for Risk Assessment Sciences, Utrecht University, Utrecht, the Netherlands
| | - Gerard Hoek
- Institute for Risk Assessment Sciences, Utrecht University, Utrecht, the Netherlands
| | - Roel Vermeulen
- Institute for Risk Assessment Sciences, Utrecht University, Utrecht, the Netherlands; Julius Centre for Health Sciences and Primary Care, University Medical Centre, University of Utrecht, the Netherlands
| | - Jules Kerckhoffs
- Institute for Risk Assessment Sciences, Utrecht University, Utrecht, the Netherlands
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7
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Kerckhoffs J, Hoek G, Vermeulen R. Mobile monitoring of air pollutants; performance evaluation of a mixed-model land use regression framework in relation to the number of drive days. ENVIRONMENTAL RESEARCH 2024; 240:117457. [PMID: 37865326 DOI: 10.1016/j.envres.2023.117457] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 09/29/2023] [Accepted: 10/18/2023] [Indexed: 10/23/2023]
Abstract
We used black carbon data from a mobile monitoring campaign in Oakland, USA measuring street segments up to 40 times and compared a data-only, LUR model and mixed-model approach with a long-term average, represented by the average concentration based on 40 drive days on that street segment. The mixed model outperformed the data-only and LUR model estimates, with 80% explained variance after 5 drive days and 90% after 14 drive days. The data-only approach needed 8 and 15 to achieve an explained variance of 80% and 90%, respectively, The LUR model never achieved an explained variance higher than 70%. The mixed model is a scalable approach, as it can be used before all street segments in a domain are measured by developing a LUR model and adds information with increasing repeats per street segment.
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Affiliation(s)
- Jules Kerckhoffs
- Institute for Risk Assessment Sciences, Utrecht University, Utrecht, the Netherlands.
| | - Gerard Hoek
- Institute for Risk Assessment Sciences, Utrecht University, Utrecht, the Netherlands
| | - Roel Vermeulen
- Institute for Risk Assessment Sciences, Utrecht University, Utrecht, the Netherlands; Julius Centre for Health Sciences and Primary Care, University Medical Centre, University of Utrecht, the Netherlands
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8
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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: 6] [Impact Index Per Article: 6.0] [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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Doubleday A, Blanco MN, Austin E, Marshall JD, Larson TV, Sheppard L. Characterizing Ultrafine Particle Mobile Monitoring Data for Epidemiology. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:9538-9547. [PMID: 37326603 DOI: 10.1021/acs.est.3c00800] [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] [Indexed: 06/17/2023]
Abstract
Mobile monitoring is increasingly used to assess exposure to traffic-related air pollutants (TRAPs), including ultrafine particles (UFPs). Due to the rapid spatial decrease in the concentration of UFPs and other TRAPs with distance from roadways, mobile measurements may be non-representative of residential exposures, which are commonly used for epidemiologic studies. Our goal was to develop, apply, and test one possible approach for using mobile measurements in exposure assessment for epidemiology. We used an absolute principal component score model to adjust the contribution of on-road sources in mobile measurements to provide exposure predictions representative of cohort locations. We then compared UFP predictions at residential locations from mobile on-road plume-adjusted versus stationary measurements to understand the contribution of mobile measurements and characterize their differences. We found that predictions from mobile measurements are more representative of cohort locations after down-weighting the contribution of localized on-road plumes. Further, predictions at cohort locations derived from mobile measurements incorporate more spatial variation compared to those from short-term stationary data. Sensitivity analyses suggest that this additional spatial information captures features in the exposure surface not identified from the stationary data alone. We recommend the correction of mobile measurements to create exposure predictions representative of residential exposure for epidemiology.
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Affiliation(s)
- Annie Doubleday
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington 98195, United States
| | - Magali N Blanco
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington 98195, United States
| | - Elena Austin
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington 98195, United States
| | - Julian D Marshall
- Department of Civil & Environmental Engineering, University of Washington, Seattle, Washington 98195, United States
| | - Timothy V Larson
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington 98195, United States
- Department of Civil & Environmental Engineering, University of Washington, Seattle, Washington 98195, United States
| | - Lianne Sheppard
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington 98195, United States
- Department of Biostatistics, University of Washington, Seattle, Washington 98195, United States
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10
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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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11
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Abdillah SFI, Wang YF. Ambient ultrafine particle (PM 0.1): Sources, characteristics, measurements and exposure implications on human health. ENVIRONMENTAL RESEARCH 2023; 218:115061. [PMID: 36525995 DOI: 10.1016/j.envres.2022.115061] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Revised: 08/28/2022] [Accepted: 12/11/2022] [Indexed: 06/17/2023]
Abstract
The problem of ultrafine particles (UFPs; PM0.1) has been prevalent since the past decades. In addition to become easily inhaled by human respiratory system due to their ultrafine diameter (<100 nm), ambient UFPs possess various physicochemical properties which make it more toxic. These properties vary based on the emission source profile. The current development of UFPs studies is hindered by the problem of expensive instruments and the inexistence of standardized measurement method. This review provides detailed insights on ambient UFPs sources, physicochemical properties, measurements, and estimation models development. Implications on health impacts due to short-term and long-term exposure of ambient UFPs are also presented alongside the development progress of potentially low-cost UFPs sensors which can be used for future UFPs studies references. Current challenge and future outlook of ambient UFPs research are also discussed in this review. Based on the review results, ambient UFPs may originate from primary and secondary sources which include anthropogenic and natural activities. In addition to that, it is confirmed from various chemical content analysis that UFPs carry heavy metals, PAHs, BCs which are toxic in its nature. Measurement of ambient UFPs may be performed through stationary and mobile methods for environmental profiling and exposure assessment purposes. UFPs PNC estimation model (LUR) developed from measurement data could be deployed to support future epidemiological study of ambient UFPs. Low-cost sensors such as bipolar ion and ionization sensor from common smoke detector device may be further developed as affordable instrument to monitor ambient UFPs. Recent studies indicate that short-term exposure of UFPs can be associated with HRV change and increased cardiopulmonary effects. On the other hand, long-term UFPs exposure have positive association with COPD, CVD, CHF, pre-term birth, asthma, and also acute myocardial infarction cases.
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Affiliation(s)
- Sultan F I Abdillah
- Department of Environmental Engineering, Chung Yuan Christian University, Taoyuan, 32023, Taiwan; Center for Environmental Risk Management, Chung Yuan Christian University, Taoyuan, 32023, Taiwan
| | - Ya-Fen Wang
- Department of Environmental Engineering, Chung Yuan Christian University, Taoyuan, 32023, Taiwan; Center for Environmental Risk Management, Chung Yuan Christian University, Taoyuan, 32023, Taiwan.
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12
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Ganji A, Youssefi O, Xu J, Mallinen K, Lloyd M, Wang A, Bakhtari A, Weichenthal S, Hatzopoulou M. Design, calibration, and testing of a mobile sensor system for air pollution and built environment data collection: The urban scanner platform. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 317:120720. [PMID: 36442817 DOI: 10.1016/j.envpol.2022.120720] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 11/03/2022] [Accepted: 11/20/2022] [Indexed: 06/16/2023]
Abstract
This paper describes a mobile air pollution sampling system, the Urban Scanner, which aims at gathering dense spatiotemporal air quality data to support urban air quality and exposure science. Urban Scanner comprises custom vehicle-mounted sensors for air pollution, meteorology, and built environment data collection (low-cost sensors, wind anemometer, 360 deg camera, LIDAR, GPS) as well as a server to store, process, and map all gathered geo-referenced sensory information. Two levels of sensor calibration were implemented, both in a chamber and in the field, against reference instrumentation. Chamber tests and a set of mathematical tools were developed to correct for sensor noise (wavelet denoising), misalignment (linear and nonlinear), and hysteresis removal. Models based on chamber testing were further refined based on field co-location. While field co-location captures natural changes in air pollution and meteorology, chamber tests allow for simulating fast transitions in these variables, like the transitions experienced by a mobile sensor in an urban environment. The best suite of models achieved an R2 higher than 0.9 between sensor output and reference station observations and an RMSE of 2.88 ppb for nitrogen dioxide and 4.03 ppb for ozone. A mobile sampling campaign was conducted in the city of Toronto, Canada, to further test Urban Scanner. We observe that the platform adequately captures spatial and temporal variability in urban air pollution, leading to the development of land-use regression models with high explanatory power.
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Affiliation(s)
- Arman Ganji
- Department of Civil and Mineral Engineering, University of Toronto, Toronto, Ontario, Canada.
| | | | - Junshi Xu
- Department of Civil and Mineral Engineering, University of Toronto, Toronto, Ontario, Canada
| | - Keni Mallinen
- Department of Civil and Mineral Engineering, University of Toronto, Toronto, Ontario, Canada
| | - Marshall Lloyd
- Department of Epidemiology, Biostatistics & Occupational Health, McGill University, Canada
| | - An Wang
- Department of Civil and Mineral Engineering, University of Toronto, Toronto, Ontario, Canada
| | | | - Scott Weichenthal
- Department of Epidemiology, Biostatistics & Occupational Health, McGill University, Canada
| | - Marianne Hatzopoulou
- Department of Civil and Mineral Engineering, University of Toronto, Toronto, Ontario, Canada
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13
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Kerckhoffs J, Khan J, Hoek G, Yuan Z, Hertel O, Ketzel M, Jensen SS, Al Hasan F, Meliefste K, Vermeulen R. Hyperlocal variation of nitrogen dioxide, black carbon, and ultrafine particles measured with Google Street View cars in Amsterdam and Copenhagen. ENVIRONMENT INTERNATIONAL 2022; 170:107575. [PMID: 36306551 DOI: 10.1016/j.envint.2022.107575] [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: 03/18/2022] [Revised: 10/03/2022] [Accepted: 10/07/2022] [Indexed: 06/16/2023]
Abstract
Hyperlocal air quality maps are becoming increasingly common, as they provide useful insights into the spatial variation and sources of air pollutants. In this study, we produced several high-resolution concentration maps to assess the spatial differences of three traffic-related pollutants, Nitrogen dioxide (NO2), Black Carbon (BC) and Ultrafine Particles (UFP), in Amsterdam, the Netherlands, and Copenhagen, Denmark. All maps were based on a mixed-effect model approach by using state-of-the-art mobile measurements conducted by Google Street View (GSV) cars, during October 2018 - March 2020, and Land-use Regression (LUR) models based on several land-use and traffic predictor variables. We then explored the concentration ratio between the different normalised pollutants to understand possible contributing sources to the observed hyperlocal variations. The maps developed in this work reflect, (i) expected elevated pollution concentrations along busy roads, and (ii) similar concentration patterns on specific road types, e.g., motorways, for both cities. In the ratio maps, we observed a clear pattern of elevated concentrations of UFP near the airport in both cities, compared to BC and NO2. This is the first study to produce hyperlocal maps for BC and UFP using high-quality mobile measurements. These maps are important for policymakers and health-effect studies, trying to disentangle individual effects of key air pollutants of interest (e.g., UFP).
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Affiliation(s)
- Jules Kerckhoffs
- Institute for Risk Assessment Sciences, Utrecht University, Utrecht, 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
| | - Gerard Hoek
- Institute for Risk Assessment Sciences, Utrecht University, Utrecht, the Netherlands
| | - Zhendong Yuan
- Institute for Risk Assessment Sciences, Utrecht University, Utrecht, the Netherlands
| | - Ole Hertel
- Department of Ecoscience, Aarhus University, Roskilde, Denmark
| | - Matthias Ketzel
- Department of Environmental Science, Aarhus University, Roskilde, Denmark; Global Centre for Clean Air Research (GCARE), University of Surrey, Guildford, United Kingdom
| | | | - Fares Al Hasan
- Institute for Risk Assessment Sciences, Utrecht University, Utrecht, the Netherlands
| | - Kees Meliefste
- Institute for Risk Assessment Sciences, Utrecht University, Utrecht, the Netherlands
| | - Roel Vermeulen
- Institute for Risk Assessment Sciences, Utrecht University, Utrecht, the Netherlands; Julius Centre for Health Sciences and Primary Care, University Medical Centre, University of Utrecht, the Netherlands
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14
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Yuan Z, Kerckhoffs J, Hoek G, Vermeulen R. A Knowledge Transfer Approach to Map Long-Term Concentrations of Hyperlocal Air Pollution from Short-Term Mobile Measurements. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:13820-13828. [PMID: 36121846 PMCID: PMC9535937 DOI: 10.1021/acs.est.2c05036] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 09/06/2022] [Accepted: 09/06/2022] [Indexed: 05/06/2023]
Abstract
Mobile measurements are increasingly used to develop spatially explicit (hyperlocal) air quality maps using land-use regression (LUR) models. The prevailing design of mobile monitoring campaigns results in the collection of short-term, on-road air pollution measurements during daytime on weekdays. We hypothesize that LUR models trained with such mobile measurements are not optimized for estimating long-term average residential air pollution concentrations. To bridge the knowledge gaps in space (on-road versus near-road) and time (short- versus long-term), we propose transfer-learning techniques to adapt LUR models by transferring the mobile knowledge into long-term near-road knowledge in an end-to-end manner. We trained two transfer-learning LUR models by incorporating mobile measurements of nitrogen dioxide (NO2) and ultrafine particles (UFP) collected by Google Street View cars with long-term near-road measurements from regular monitoring networks in Amsterdam. We found that transfer-learning LUR models performed 55.2% better in predicting long-term near-road concentrations than the LUR model trained only with mobile measurements for NO2 and 26.9% for UFP, evaluated by normalized mean absolute errors. This improvement in model accuracy suggests that transfer-learning models provide a solution for narrowing the knowledge gaps and can improve the accuracy of mapping long-term near-road air pollution concentrations using short-term on-road mobile monitoring data.
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Affiliation(s)
- Zhendong Yuan
- Institute
for Risk Assessment Sciences, Utrecht University, 3584 CK Utrecht, The Netherlands
| | - Jules Kerckhoffs
- Institute
for Risk Assessment Sciences, Utrecht University, 3584 CK Utrecht, The Netherlands
| | - Gerard Hoek
- Institute
for Risk Assessment Sciences, Utrecht University, 3584 CK Utrecht, The Netherlands
| | - Roel Vermeulen
- Institute
for Risk Assessment Sciences, Utrecht University, 3584 CK Utrecht, The Netherlands
- Julius
Centre for Health Sciences and Primary Care, University Medical Centre, University of Utrecht, 3584 CK Utrecht, The Netherlands
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15
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Rovira J, Paredes-Ahumada JA, Barceló-Ordinas JM, García-Vidal J, Reche C, Sola Y, Fung PL, Petäjä T, Hussein T, Viana M. Non-linear models for black carbon exposure modelling using air pollution datasets. ENVIRONMENTAL RESEARCH 2022; 212:113269. [PMID: 35427594 DOI: 10.1016/j.envres.2022.113269] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 04/01/2022] [Accepted: 04/06/2022] [Indexed: 06/14/2023]
Abstract
Black carbon (BC) is a product of incomplete combustion, present in urban aerosols and sourcing mainly from road traffic. Epidemiological evidence reports positive associations between BC and cardiovascular and respiratory disease. Despite this, BC is currently not regulated by the EU Air Quality Directive, and as a result BC data are not available in urban areas from reference air quality monitoring networks in many countries. To fill this gap, a machine learning approach is proposed to develop a BC proxy using air pollution datasets as an input. The proposed BC proxy is based on two machine learning models, support vector regression (SVR) and random forest (RF), using observations of particle mass and number concentrations (N), gaseous pollutants and meteorological variables as the input. Experimental data were collected from a reference station in Barcelona (Spain) over a 2-year period (2018-2019). Two months of additional data were available from a second urban site in Barcelona, for model validation. BC concentrations estimated by SVR showed a high degree of correlation with the measured BC concentrations (R2 = 0.828) with a relatively low error (RMSE = 0.48 μg/m3). Model performance was dependent on seasonality and time of the day, due to the influence of new particle formation events. When validated at the second station, performance indicators decreased (R2 = 0.633; RMSE = 1.19 μg/m3) due to the lack of N data and PM2.5 and the smaller size of the dataset (2 months). New particle formation events critically impacted model performance, suggesting that its application would be optimal in environments where traffic is the main source of ultrafine particles. Due to its flexibility, it is concluded that the model can act as a BC proxy, even based on EU-regulatory air quality parameters only, to complement experimental measurements for exposure assessment in urban areas.
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Affiliation(s)
- J Rovira
- Barcelona University, Barcelona, Spain
| | - J A Paredes-Ahumada
- Department of Computer Architecture, Universitat Politècnica de Catalunya, UPC, Barcelona, Spain
| | - J M Barceló-Ordinas
- Department of Computer Architecture, Universitat Politècnica de Catalunya, UPC, Barcelona, Spain
| | - J García-Vidal
- Department of Computer Architecture, Universitat Politècnica de Catalunya, UPC, Barcelona, Spain
| | - C Reche
- Institute of Environmental Assessment and Water Research, Spanish Research Council, IDAEA-CSIC, Barcelona, Spain
| | - Y Sola
- Barcelona University, Barcelona, Spain
| | - P L Fung
- University of Helsinki, Institute for Atmospheric and Earth System Research (INAR/Physics), UHEL, Helsinki, Finland
| | - T Petäjä
- University of Helsinki, Institute for Atmospheric and Earth System Research (INAR/Physics), UHEL, Helsinki, Finland
| | - T Hussein
- University of Helsinki, Institute for Atmospheric and Earth System Research (INAR/Physics), UHEL, Helsinki, Finland; The University of Jordan, School of Science, Department of Physics, Amman, Jordan
| | - M Viana
- Institute of Environmental Assessment and Water Research, Spanish Research Council, IDAEA-CSIC, Barcelona, Spain.
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16
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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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17
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Kerckhoffs J, Khan J, Hoek G, Yuan Z, Ellermann T, Hertel O, Ketzel M, Jensen SS, Meliefste K, Vermeulen R. Mixed-Effects Modeling Framework for Amsterdam and Copenhagen for Outdoor NO 2 Concentrations Using Measurements Sampled with Google Street View Cars. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:7174-7184. [PMID: 35262348 PMCID: PMC9178915 DOI: 10.1021/acs.est.1c05806] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 12/23/2021] [Accepted: 02/15/2022] [Indexed: 05/22/2023]
Abstract
High-resolution air quality (AQ) maps based on street-by-street measurements have become possible through large-scale mobile measurement campaigns. Such campaigns have produced data-only maps and have been used to produce empirical models [i.e., land use regression (LUR) models]. Assuming that all road segments are measured, we developed a mixed model framework that predicts concentrations by an LUR model, while allowing road segments to deviate from the LUR prediction based on between-segment variation as a random effect. We used Google Street View cars, equipped with high-quality AQ instruments, and measured the concentration of NO2 on every street in Amsterdam (n = 46.664) and Copenhagen (n = 28.499) on average seven times over the course of 9 and 16 months, respectively. We compared the data-only mapping, LUR, and mixed model estimates with measurements from passive samplers (n = 82) and predictions from dispersion models in the same time window as mobile monitoring. In Amsterdam, mixed model estimates correlated rs (Spearman correlation) = 0.85 with external measurements, whereas the data-only approach and LUR model estimates correlated rs = 0.74 and 0.75, respectively. Mixed model estimates also correlated higher rs = 0.65 with the deterministic model predictions compared to the data-only (rs = 0.50) and LUR model (rs = 0.61). In Copenhagen, mixed model estimates correlated rs = 0.51 with external model predictions compared to rs = 0.45 and rs = 0.50 for data-only and LUR model, respectively. Correlation increased for 97 locations (rs = 0.65) with more detailed traffic information. This means that the mixed model approach is able to combine the strength of data-only mapping (to show hyperlocal variation) and LUR models by shrinking uncertain concentrations toward the model output.
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Affiliation(s)
- Jules Kerckhoffs
- Institute
for Risk Assessment Sciences, Utrecht University, 3584 CK Utrecht, Netherlands
| | - Jibran Khan
- Department
of Environmental Science, Aarhus University, DK-4000 Roskilde, Denmark
- Danish
Big Data Centre for Environment and Health (BERTHA), Aarhus University, DK-4000 Roskilde, Denmark
| | - Gerard Hoek
- Institute
for Risk Assessment Sciences, Utrecht University, 3584 CK Utrecht, Netherlands
| | - Zhendong Yuan
- Institute
for Risk Assessment Sciences, Utrecht University, 3584 CK Utrecht, Netherlands
| | - Thomas Ellermann
- Department
of Environmental Science, Aarhus University, DK-4000 Roskilde, Denmark
| | - Ole Hertel
- Department
of Bioscience, Aarhus University, DK-4000 Roskilde, Denmark
| | - Matthias Ketzel
- Department
of Environmental Science, Aarhus University, DK-4000 Roskilde, Denmark
- Global
Centre for Clean Air Research (GCARE), University
of Surrey, GU2 7XH Guildford, U.K.
| | - Steen Solvang Jensen
- Department
of Environmental Science, Aarhus University, DK-4000 Roskilde, Denmark
| | - Kees Meliefste
- Institute
for Risk Assessment Sciences, Utrecht University, 3584 CK Utrecht, Netherlands
| | - Roel Vermeulen
- Institute
for Risk Assessment Sciences, Utrecht University, 3584 CK Utrecht, Netherlands
- Julius Centre
for Health Sciences and Primary Care, University Medical Centre, University of Utrecht, 3584 CK Utrecht, The Netherlands
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18
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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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19
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Lenssen ES, Pieters RHH, Nijmeijer SM, Oldenwening M, Meliefste K, Hoek G. Short-term associations between barbecue fumes and respiratory health in young adults. ENVIRONMENTAL RESEARCH 2022; 204:111868. [PMID: 34453901 DOI: 10.1016/j.envres.2021.111868] [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: 03/05/2021] [Revised: 08/05/2021] [Accepted: 08/07/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND Epidemiological studies have associated biomass combustion with (respiratory) morbidity and mortality, primarily in indoor settings. Barbecuing results in high outdoor air pollution exposures, but the health effects are unknown. OBJECTIVE The objective was to investigate short-term changes in respiratory health in healthy adults, associated with exposure to barbecue fumes. METHODS 16 healthy, adult volunteers were exposed to barbecue smoke in outdoor air in rest during 1.5 h, using a repeated-measures design. Major air pollutants were monitored on-site, including particulate matter <2.5 μm (PM2.5), particle number concentrations (PNC) and black- and brown carbon. At the same place and time-of-day, subjects participated in a control session, during which they were not exposed to barbecue smoke. Before and immediately after all sessions lung function was measured. Before, immediately after, 4- and 18 h post-sessions nasal expression levels of interleukin (IL)-8, IL6 and Tumor Necrosis Factor alpha (TNFα) were determined in nasal swabs, using quantitative polymerase chain reaction. Associations between major air pollutants, lung function and inflammatory markers were assessed using mixed linear regression models. RESULTS High PM2.5 levels and PNCs were observed during barbecue sessions, with averages ranging from 553 to 1062 μg/m3 and 109,000-463,000 pt/cm3, respectively. Average black- and brown carbon levels ranged between 4.1-13.0 and 5.0-16.2 μg/m3. A 1000 μg/m3 increase in PM2.5 was associated with 2.37 (0.97, 4.67) and 2.21 (0.98, 5.00) times higher expression of IL8, immediately- and 18 h after exposure. No associations were found between air pollutants and lung function, or the expression of IL6 or TNFα. DISCUSSION Short-term exposure to air pollutants emitted from barbecuing was associated with a mild respiratory response in healthy young adults, including prolonged increase in nasal IL8 without a change in lung function and other measured inflammatory markers. The results might indicate prolonged respiratory inflammation, due to short-term exposure to barbecue fumes.
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Affiliation(s)
- Esther S Lenssen
- Institute for Risk Assessment Sciences (IRAS), Utrecht University, Utrecht, the Netherlands.
| | - Raymond H H Pieters
- Institute for Risk Assessment Sciences (IRAS), Utrecht University, Utrecht, the Netherlands.
| | - Sandra M Nijmeijer
- Institute for Risk Assessment Sciences (IRAS), Utrecht University, Utrecht, the Netherlands.
| | - Marieke Oldenwening
- Institute for Risk Assessment Sciences (IRAS), Utrecht University, Utrecht, the Netherlands.
| | - Kees Meliefste
- Institute for Risk Assessment Sciences (IRAS), Utrecht University, Utrecht, the Netherlands.
| | - Gerard Hoek
- Institute for Risk Assessment Sciences (IRAS), Utrecht University, Utrecht, the Netherlands.
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Xu X, Qin N, Qi L, Zou B, Cao S, Zhang K, Yang Z, Liu Y, Zhang Y, Duan X. Development of season-dependent land use regression models to estimate BC and PM 1 exposure. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 793:148540. [PMID: 34171802 DOI: 10.1016/j.scitotenv.2021.148540] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2021] [Revised: 06/11/2021] [Accepted: 06/15/2021] [Indexed: 06/13/2023]
Abstract
Reliable estimation of exposure to black carbon (BC) and sub-micrometer particles (PM1) within a city is challenging because of limited monitoring data as well as the lack of models suitable for assessing the intra-urban environment. In this study, to estimate exposure levels in the inner-city area, we developed land use regression (LUR) models for BC and PM1 based on specially designed mobile monitoring surveys conducted in 2019 and 2020 for three seasons. The daytime and nighttime LUR models were developed separately to capture additional details on the variation in pollutants. The results of mobile monitoring indicated similar temporal variation characteristics of BC and PM1. The mean concentrations of pollutants were higher in winter (BC: 4.72 μg/m3; PM1: 56.97 μg/m3) than in fall (BC: 3.74 μg/m3; PM1: 33.29 μg/m3) and summer (BC: 2.77 μg/m3; PM1: 27.04 μg/m3). For both BC and PM1, higher nighttime concentrations were found in winter and fall, whereas higher daytime concentrations were observed in the summer. A supervised forward stepwise regression method was used to select the predictors for the LUR models. The adjusted R2 of the LUR models for BC and PM1 ranged from 0.39 to 0.66 and 0.45 to 0.80, respectively. Traffic-related predictors were incorporated into all the models for BC. In contrast, more meteorology-related predictors were incorporated into the PM1 models. The concentration surface based on the LUR models was mapped at a spatial resolution of 100 m, and significant seasonal and diurnal trends were observed. PM1 was dominated by seasonal variations, whereas BC showed more spatial variation. In conclusion, the development of season-dependent diurnal LUR models based on mobile monitoring could provide a methodology for the estimation of exposure and screening of influencing factors of BC and PM1 in typical inner-city environments, and support pollution management.
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Affiliation(s)
- Xiangyu Xu
- School of Energy and Environmental Engineering, University of Science and Technology of Beijing, Beijing 100083, China
| | - Ning Qin
- School of Energy and Environmental Engineering, University of Science and Technology of Beijing, Beijing 100083, China
| | - Ling Qi
- School of Energy and Environmental Engineering, University of Science and Technology of Beijing, Beijing 100083, China
| | - Bin Zou
- School of Geosciences and Info-Physics, Central South University, Changsha, Hunan 410083, China
| | - Suzhen Cao
- School of Energy and Environmental Engineering, University of Science and Technology of Beijing, Beijing 100083, China
| | - Kai Zhang
- Department of Environmental Health Sciences, School of Public Health, University at Albany, State University of New York, Albany, NY 12144, USA
| | - Zhenchun Yang
- Global Health Research Center, Duke Kunshan University, Kunshan, Jiangsu Province 215316, China
| | - Yunwei Liu
- School of Energy and Environmental Engineering, University of Science and Technology of Beijing, Beijing 100083, China
| | - Yawei Zhang
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Xiaoli Duan
- School of Energy and Environmental Engineering, University of Science and Technology of Beijing, Beijing 100083, China.
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21
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Valencia A, Arunachalam S, Isakov V, Naess B, Serre M. Improving emissions inputs via mobile measurements to estimate fine-scale Black Carbon monthly concentrations through geostatistical space-time data fusion. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 793:148378. [PMID: 34171801 PMCID: PMC8457356 DOI: 10.1016/j.scitotenv.2021.148378] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2020] [Revised: 05/23/2021] [Accepted: 06/07/2021] [Indexed: 06/13/2023]
Abstract
Isolating air pollution sources in a complex transportation environment to quantify their contribution is challenging, particularly with sparse stationary measurements. Mobile measurements can add finer spatial resolution to support source apportionment, but they exhibit limitations when characterizing long term concentrations. Dispersion models can help overcome these limitations. However, they are only as reliable as their input emissions inventories. Herein, we developed an innovative method to revise emissions through inverse modeling and improve dispersion modeling predictions using stationary/mobile measurements. One specific revision estimated an adjustment factor of ~306 for warehouse emissions, indicating a significant underestimation of our initial estimates. This revised emission rate scaled up nationally would correspond to ~3.5% of the total Black Carbon emissions in the U.S. Nevertheless, domain-specific revisions only contribute to a 4% increase of area source emissions while improving R2 from monthly estimates at fixed sites by 38%. After revising emissions through inverse dispersion modeling, we combine this model with stationary/mobile measurements through Bayesian Maximum Entropy (I-DISP BME) to produce temporally coarse yet spatially fine data fusion. We compare this novel data fusion approach to BME using only measurements (Flat BME). A 10-fold conventional cross-validation (representative of months with mobile measurements) shows that all BME methods have R2 values that range from 0.787 to 0.798. A 2-fold cross-validation (representative of months with no mobile measurements) shows that the R2 for I-DISP BME increases by a factor 90 when compared to Flat BME. Furthermore, not only is our novel I-DISP BME method more accurate than the classic Flat BME method, but the area it detects as highly exposed can be up to 5 times larger than that detected by the less accurate Flat BME method.
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Affiliation(s)
- Alejandro Valencia
- Department of Environmental Sciences and Engineering, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Saravanan Arunachalam
- Institute for the Environment, The University of North Carolina at Chapel Hill, 100 Europa Drive, Suite 490, Chapel Hill, NC 27517, USA.
| | - Vlad Isakov
- Office of Research and Development, U.S. EPA, Research Triangle Park, NC 27711, USA
| | - Brian Naess
- Institute for the Environment, The University of North Carolina at Chapel Hill, 100 Europa Drive, Suite 490, Chapel Hill, NC 27517, USA
| | - Marc Serre
- Department of Environmental Sciences and Engineering, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
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Kerckhoffs J, Hoek G, Gehring U, Vermeulen R. Modelling nationwide spatial variation of ultrafine particles based on mobile monitoring. ENVIRONMENT INTERNATIONAL 2021; 154:106569. [PMID: 33866060 DOI: 10.1016/j.envint.2021.106569] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Revised: 03/10/2021] [Accepted: 04/08/2021] [Indexed: 05/22/2023]
Abstract
BACKGROUND Large nation- and region-wide epidemiological studies have provided important insights into the health effects of long-term exposure to outdoor air pollution. Evidence from these studies for the long-term effects of ultrafine particles (UFP), however is lacking. Reason for this is the shortage of empirical UFP land use regression models spanning large geographical areas including cities with varying topographies, peri-urban and rural areas. The aim of this paper is to combine targeted mobile monitoring and long-term regional background monitoring to develop national UFP models. METHOD We used an electric car to monitor UFP concentrations in selected cities and towns across the Netherlands over a 14-month period in 2016-2017. Routes were monitored 3 times and concentrations were averaged per road segment. In addition, we used kriging maps based on regional background monitoring (20 sites; 3 × 2 weeks) over the same period to assess annual average regional background concentrations. All road segments were used to model spatial variation of UFP with three different land-use (regression) approaches: supervised stepwise regression, LASSO and random forest. For each approach, we also tested a deconvolution method, which segregates the average concentration at each road segment into a local and background signal. Model performance was evaluated with short-term (400 sites across the Netherlands; 3 × 30 minutes) and external longer-term measurements (42 sites in two major cities; 3 × 24 hours). We also compared predictions of all six models at 1000 random addresses spread over the country. RESULTS We found similar predictive performance for the six models, with validation R2 values from 0.25 to 0.35 for short-term measurements and 0.52 to 0.60 for longer-term external measurements. Models with and without deconvolution had similar predictive performance. All models based on the deconvolution method included a regional background kriging map as important predictor. Correlations between predictions at random addresses were high with Pearson correlations from 0.84 to 0.99. Models overestimated exposure at the short-term and long-term sites by about 20-30% in all cases, with small differences between regions and road types. CONCLUSION We developed robust nation-wide models for long-term UFP exposure combining mobile monitoring with long-term regional background monitoring. Minor differences in predictive performance between different algorithms were found, but the deconvolution approach is considered more physically realistic. The models will be applied in Dutch nation-wide health 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
| | - Ulrike Gehring
- Institute for Risk Assessment Sciences (IRAS), Division of Environmental Epidemiology, Utrecht University, 3584 CK Utrecht, the Netherlands
| | - 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 Utrecht, the Netherlands
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23
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Talaat H, Xu J, Hatzopoulou M, Abdelgawad H. Mobile monitoring and spatial prediction of black carbon in Cairo, Egypt. ENVIRONMENTAL MONITORING AND ASSESSMENT 2021; 193:587. [PMID: 34415446 DOI: 10.1007/s10661-021-09351-0] [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: 03/21/2021] [Accepted: 07/30/2021] [Indexed: 06/13/2023]
Abstract
This study harnesses the power of mobile data in developing a spatial model for predicting black carbon (BC) concentrations within one of the most heavily populated regions in the Middle East and North Africa MENA region, Greater Cairo Region (GCR) in Egypt. A mobile data collection campaign was conducted in GCR to collect BC measurements along specific travel routes. In total, 3,300 km were travelled across a widespread 525 km of routes. Reported average BC values were around 20 µg/m3, announcing an alarming order of magnitude value when compared to the maximum reported values in similar studies. A bi-directional stepwise land use regression (LUR) model was developed to select the best combination of explanatory variables and generate an exposure surface for BC, in addition to a number of machine learning models (random forest gradient boost, light gradient boost model (LightGBM), Keras neural network (NN)). Data from 7 air quality (AQ) stations were compared-in terms of mean square error (MSE) and mean absolute error (MAE)-with predictions from the LUR and the NN model. The NN model estimated higher BC concentrations in the downtown areas, while lower concentrations are estimated for the peripheral area at the east side of the city. Such results shed light on the credibility of the LUR models in generating a general spatial trend of BC concentrations while the superiority of NN in BC accuracy estimation (0.023 vs 0.241 in terms of MSE and 0.12 vs 0.389 in terms of MAE; of NN vs LUR respectively).
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Affiliation(s)
- Hoda Talaat
- Faculty of Engineering, Cairo University, Giza, 12631, Egypt
| | - Junshi Xu
- Department of Civil & Mineral Engineering, University of Toronto, Toronto, Canada
| | - Marianne Hatzopoulou
- Department of Civil & Mineral Engineering, University of Toronto, Toronto, Canada
| | - Hossam Abdelgawad
- Faculty of Engineering, Cairo University, Giza, 12631, Egypt.
- Urban Transport Technologies, SETS International, Beirut, 113-7742, Lebanon.
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Mapping Urban Air Quality from Mobile Sensors Using Spatio-Temporal Geostatistics. SENSORS 2021; 21:s21144717. [PMID: 34300458 PMCID: PMC8309582 DOI: 10.3390/s21144717] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 06/26/2021] [Accepted: 07/02/2021] [Indexed: 11/30/2022]
Abstract
With the advancement of technology and the arrival of miniaturized environmental sensors that offer greater performance, the idea of building mobile network sensing for air quality has quickly emerged to increase our knowledge of air pollution in urban environments. However, with these new techniques, the difficulty of building mathematical models capable of aggregating all these data sources in order to provide precise mapping of air quality arises. In this context, we explore the spatio-temporal geostatistics methods as a solution for such a problem and evaluate three different methods: Simple Kriging (SK) in residuals, Ordinary Kriging (OK), and Kriging with External Drift (KED). On average, geostatistical models showed 26.57% improvement in the Root Mean Squared Error (RMSE) compared to the standard Inverse Distance Weighting (IDW) technique in interpolating scenarios (27.94% for KED, 26.05% for OK, and 25.71% for SK). The results showed less significant scores in extrapolating scenarios (a 12.22% decrease in the RMSE for geostatisical models compared to IDW). We conclude that univariable geostatistics is suitable for interpolating this type of data but is less appropriate for an extrapolation of non-sampled places since it does not create any information.
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25
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A Methodology for Designing Short-Term Stationary Air Quality Campaigns with Mobile Laboratories Using Different Possible Allocation Criteria. SUSTAINABILITY 2021. [DOI: 10.3390/su13137481] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Air quality monitoring and control are key issues for environmental assessment and management in order to protect public health and the environment. Local and central authorities have developed strategies and tools to manage environmental protection, which, for air quality, consist of monitoring networks with fixed and portable instrumentation and mathematical models. This study develops a methodology for designing short-term air quality campaigns with mobile laboratories (laboratories fully housed within or transported by a vehicle and maintained in a fixed location for a period of time) as a decision support system for environmental management and protection authorities. In particular, the study provides a methodology to identify: (i) the most representative locations to place mobile laboratories and (ii) the best time period to carry out the measurements in the case of short-term air quality campaigns. The approach integrates atmospheric dispersion models and allocation algorithms specifically developed for optimizing the measuring campaigns. The methodology is organized in two phases, each of them divided into several steps. Fourteen allocation algorithms dedicated to three type of receptors (population, vegetation and physical cultural heritage) have been proposed. The methodology has been applied to four short-term air quality campaigns in the Emilia-Romagna region.
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26
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Adam MG, Tran PTM, Cheong DKW, Chandra Sekhar S, Tham KW, Balasubramanian R. Assessment of Home-Based and Mobility-Based Exposure to Black Carbon in an Urban Environment: A Pilot Study. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18095028. [PMID: 34068742 PMCID: PMC8126254 DOI: 10.3390/ijerph18095028] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/24/2021] [Accepted: 05/05/2021] [Indexed: 01/20/2023]
Abstract
The combustion of fossil fuels is a significant source of particulate-bound black carbon (BC) in urban environments. The personal exposure (PE) of urban dwellers to BC and subsequent health impacts remain poorly understood due to a lack of observational data. In this study, we assessed and quantified the levels of PE to BC under two exposure scenarios (home-based and mobility-based exposure) in the city of Trivandrum in India. In the home-based scenario, the PE to BC was assessed in a naturally ventilated building over 24 h each day during the study period while in the mobility-based scenario, the PE to BC was monitored across diverse microenvironments (MEs) during the day using the same study protocol for consistency. Elevated BC concentrations were observed during the transport by motorcycle (26.23 ± 2.33 µg/m3) and car (17.49 ± 2.37 µg/m3). The BC concentrations observed in the MEs decreased in the following order: 16.58 ± 1.38 µg/m3 (temple), 13.78 ± 2.07 µg/m3 (restaurant), 11.44 ± 1.37 µg/m3 (bus stop), and 8.27 ± 1.88 µg/m3 (home); the standard deviations represent the temporal and spatial variations of BC concentrations. Overall, a relatively larger inhaled dose of BC in the range of 148.98–163.87 µg/day was observed for the mobility-based scenario compared to the home-based one (118.10–137.03 µg/day). This work highlights the importance of reducing PE to fossil fuel-related particulate emissions in cities for which BC is a good indicator. The study outcome could be used to formulate effective strategies to improve the urban air quality as well as public health.
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Affiliation(s)
- Max Gerrit Adam
- Department of Civil and Environmental Engineering, National University of Singapore, Singapore 117576, Singapore; (M.G.A.); (P.T.M.T.)
| | - Phuong Thi Minh Tran
- Department of Civil and Environmental Engineering, National University of Singapore, Singapore 117576, Singapore; (M.G.A.); (P.T.M.T.)
- Faculty of Environment, The University of Danang—University of Science and Technology, 54 Nguyen Luong Bang Street, Lien Chieu District, Danang City 50608, Vietnam
| | - David Kok Wai Cheong
- Department of Building, School of Design and Environment, National University of Singapore, Singapore 117566, Singapore; (D.K.W.C.); (S.C.S.); (K.W.T.)
| | - Sitaraman Chandra Sekhar
- Department of Building, School of Design and Environment, National University of Singapore, Singapore 117566, Singapore; (D.K.W.C.); (S.C.S.); (K.W.T.)
| | - Kwok Wai Tham
- Department of Building, School of Design and Environment, National University of Singapore, Singapore 117566, Singapore; (D.K.W.C.); (S.C.S.); (K.W.T.)
| | - Rajasekhar Balasubramanian
- Department of Civil and Environmental Engineering, National University of Singapore, Singapore 117576, Singapore; (M.G.A.); (P.T.M.T.)
- Correspondence: ; Tel.: +65-6516-5135; Fax: +65-6779-1635
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27
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Shairsingh KK, Brook JR, Mihele CM, Evans GJ. Characterizing long-term NO 2 concentration surfaces across a large metropolitan area through spatiotemporal land use regression modelling of mobile measurements. ENVIRONMENTAL RESEARCH 2021; 196:111010. [PMID: 33716024 DOI: 10.1016/j.envres.2021.111010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Revised: 01/12/2021] [Accepted: 03/08/2021] [Indexed: 06/12/2023]
Abstract
A spatiotemporal land use regression (LUR) model optimized to predict nitrogen dioxide (NO2) concentrations obtained from on-road, mobile measurements collected in 2015-16 was independently evaluated using concentrations observed at multiple sites across Toronto, Canada, obtained more than ten years earlier. This spatiotemporal LUR modelling approach improves upon estimates of historical NO2 concentrations derived from the previously used method of back-extrapolation. The optimal spatiotemporal LUR model (R2 = 0.71 for prediction of NO2 data in 2002 and 2004) uses daily average NO2 concentrations observed at multiple long-term monitoring sites and hourly average wind speed recorded at a single site, along with spatial predictors based on geographical information system data, to estimate NO2 levels for time periods outside of those used for model development. While the model tended to underestimate samplers located close to the roadway, it showed great accuracy when estimating samplers located beyond 100 m which are probably more relevant for exposure at residences. This study shows that spatiotemporal LUR models developed from strategic, multi-day (30 days in 3 different months) mobile measurements can enhance LUR model's ability to estimate long-term, intra-urban NO2 patterns. Furthermore, the mobile sampling strategy enabled this new LUR model to cover a larger domain of Toronto and outlying suburban communities, thereby increasing the potential population for future epidemiological studies.
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Affiliation(s)
- Kerolyn K Shairsingh
- Department of Chemical Engineering and Applied Chemistry. University of Toronto, Toronto, Ontario, M5S 3E5, Canada.
| | - Jeffrey R Brook
- Department of Chemical Engineering and Applied Chemistry. University of Toronto, Toronto, Ontario, M5S 3E5, Canada; Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, M5T 3M7, Canada.
| | - Cristian M Mihele
- Environment and Climate Change Canada, North York, Ontario, M3H 5T4, Canada
| | - Greg J Evans
- Department of Chemical Engineering and Applied Chemistry. University of Toronto, Toronto, Ontario, M5S 3E5, Canada
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28
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A Comparative Study of the Robustness and Resilience of Retail Areas in Seoul, Korea before and after the COVID-19 Outbreak, Using Big Data. SUSTAINABILITY 2021. [DOI: 10.3390/su13063302] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
This study aimed to assess the robustness and resilience of retail areas in Seoul, based on the changes in sales before and after the COVID-19 outbreak. The spatial range and temporal scope of the study were set as district- and community-level retail areas in Seoul, from January 2019 to August 2020, to consider the effect of the COVID-19 outbreak. The data used in this study comprised sales information from the retail sector, namely Shinhan Card sales data for domestic and foreigners by business type in Seoul, provided by Seoul Big Data Campus. We classified the retail areas based on the change in sales before and after the COVID-19 outbreak, using time series clustering. The results of this study showed that time series clustering based on the change in sales can be used to classify retail areas. The similarities and differences were confirmed by comparing the functional and structural characteristics of the district- and community-level retail areas by cluster and by retail area type. Furthermore, we derived knowledge on the decline and recovery of retail areas before and after a national crisis such as the emergence of a COVID-19 wave, which can provide significant information for sustainable retail area management and regional economic development.
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29
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Tessum MW, Sheppard L, Larson TV, Gould TR, Kaufman JD, Vedal S. Improving Air Pollution Predictions of Long-Term Exposure Using Short-Term Mobile and Stationary Monitoring in Two US Metropolitan Regions. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2021; 55:3530-3538. [PMID: 33635626 PMCID: PMC8729258 DOI: 10.1021/acs.est.0c04328] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Mobile monitoring is increasingly employed to measure fine spatial-scale variation in air pollutant concentrations. However, mobile measurement campaigns are typically conducted over periods much shorter than the decadal periods used for modeling chronic exposure for use in air pollution epidemiology. Using the regions of Los Angeles and Baltimore and the time period from 2005 to 2014 as our modeling domain, we investigate whether including mobile or stationary passive sampling device (PSD) monitoring data collected over a single 2-week period in one or two seasons using a unified spatio-temporal air pollution model can improve model performance in predicting NO2 and NOx concentrations throughout the 9-year study period beyond what is possible using only routine monitoring data. In this initial study, we use data from mobile measurement campaigns conducted contemporaneously with deployments of stationary PSDs and only use mobile data collected within 300 m of a stationary PSD location for inclusion in the model. We find that including either mobile or PSD data substantially improves model performance for pollutants and locations where model performance was initially the worst (with the most-improved R2 changing from 0.40 to 0.82) but does not meaningfully change performance in cases where performance was already very good. Results indicate that in many cases, additional spatial information from mobile monitoring and personal sampling is potentially cost-efficient inexpensive way of improving exposure predictions at both 2-week and decadal averaging periods, especially for the predictions that are located closer to features such as roadways targeted by the mobile short-term monitoring campaign.
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Affiliation(s)
- Mei W. Tessum
- University of Illinois at Urbana-Champaign, Department of Agricultural and Biological Engineering, Urbana, IL 61801, USA
- University of Washington, Department of Environmental and Occupational Health Sciences, Box 357234, Seattle, WA 98195, USA
| | - Lianne Sheppard
- University of Washington, Department of Environmental and Occupational Health Sciences, Box 357234, Seattle, WA 98195, USA
- University of Washington, Department of Biostatistics, Box 357232, Seattle, WA 98195, USA
| | - Timothy V. Larson
- University of Washington, Department of Environmental and Occupational Health Sciences, Box 357234, Seattle, WA 98195, USA
- University of Washington, Department of Civil & Environmental Engineering, Box 352700, Seattle, WA 98195, USA
| | - Timothy R. Gould
- University of Washington, Department of Civil & Environmental Engineering, Box 352700, Seattle, WA 98195, USA
| | - Joel D. Kaufman
- University of Washington, Department of Environmental and Occupational Health Sciences, Box 357234, Seattle, WA 98195, USA
| | - Sverre Vedal
- University of Washington, Department of Environmental and Occupational Health Sciences, Box 357234, Seattle, WA 98195, USA
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30
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Agyemang-Duah W, Oduro Appiah J, Adei D. Protecting the patches from the footprints: examining the land use factors associated with forest patches in Atewa range forest reserve. BMC Ecol Evol 2021; 21:28. [PMID: 33588761 PMCID: PMC7885454 DOI: 10.1186/s12862-021-01758-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 02/03/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Land use practices are noted to contribute to changes in forest landscape composition. However, whereas studies have reported the intermix of land uses and forest patches and measured the direct impacts of land uses on forest patches, little is known regarding the spatially-explicit association between the most recent forest patches and land use footprints in protected areas. In this study, we use methods from GIS, remote sensing, and statistics to model the spatial relationship between footprints of land uses and patches of forest cover by drawing on geospatial data from the Atewa range forest reserve (ARFR). RESULTS The study finds that forest patches that are within 1 km from agricultural land use footprints (AOR = 86.625, C.I. 18.057-415.563, P = 0.000), logging sites (AOR = 55.909, C.I. 12.032-259.804, P = 0.000), mine sites (53.571, C.I. 11.287-254.255, P = 0.000), access roads (AOR = 24.169, C.I. 5.544-105.357, P = 0.000), and human settlement footprints (AOR = 7.172, C.I. 1.969-26.128, P = 0.003) are significantly more likely to be less than the mean patch area (375,431.87 m2 = 37.54 ha) of forest cover. A ROC statistic of 0.995 achieved in this study suggests a high predictive power of the proposed model. CONCLUSION The study findings suggest that to ensure sustainable land uses and ecological integrity, there is a need for land use policies and land management strategies that ensure responsible livelihood activities as well as further restrictions on logging and mining in the globally significant biodiversity area.
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Affiliation(s)
- Williams Agyemang-Duah
- Department of Planning, Kwame Nkrumah University of Science and Technology, Private Mail Bag, Kumasi, Ghana.
| | - Joseph Oduro Appiah
- School of Environmental Planning, University of Northern British Columbia, 3333 University Way, Prince George, BC, V2N 4Z9, Canada
| | - Dina Adei
- Department of Planning, Kwame Nkrumah University of Science and Technology, Private Mail Bag, Kumasi, Ghana
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van de Beek E, Kerckhoffs J, Hoek G, Sterk G, Meliefste K, Gehring U, Vermeulen R. Spatial and Spatiotemporal Variability of Regional Background Ultrafine Particle Concentrations in the Netherlands. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2021; 55:1067-1075. [PMID: 33378199 DOI: 10.1021/acs.est.0c0680610.1021/acs.est.0c06806.s001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
Studies of the health effects of ultrafine particles (UFPs) in large nationwide cohorts are currently hampered by a lack of knowledge about spatial and spatiotemporal variations in regional background UFPs. We measured the UFP (10-300 nm) at 20 regional background locations (3 × 2 weeks) across the Netherlands and a reference site continuously over a total period of 14 months in 2016-2017. We compared the overall averages for each site and used kriging to create a regional background spatial map of the Netherlands. Spatiotemporal variability was analyzed by correlating time-series of 2 and 24 h average concentrations. The overall average measured UFP concentrations at the 20 locations ranged from 3814 to 7070 particles/cm3. We found the spatial correlation in the UFP concentrations up to 180 km and clear differences between the north and the more populated southern parts of the country. The average temporal correlation between 2 and 24 h average UFP concentrations was 0.50 (IQR: 0.36-0.61) and 0.58 (IQR: 0.44-0.75), respectively. Temporal correlation declined weakly with a distance between sites, from 0.58 for sites within 80 km of each other to 0.47 for sites farther away. The substantial spatial variation in the regional background UFP concentrations suggests that regional variation may contribute importantly to exposure contrast in nationwide health studies of UFP.
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Affiliation(s)
- Esther van de Beek
- Institute for Risk Assessment Sciences (IRAS), Division of Environmental Epidemiology, Utrecht University, 3584 CK Utrecht, The Netherlands
| | - 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
| | - Geert Sterk
- Department of Physical Geography, Utrecht University, 3508 TC Utrecht, The Netherlands
| | - Kees Meliefste
- Institute for Risk Assessment Sciences (IRAS), Division of Environmental Epidemiology, Utrecht University, 3584 CK Utrecht, The Netherlands
| | - Ulrike Gehring
- Institute for Risk Assessment Sciences (IRAS), Division of Environmental Epidemiology, Utrecht University, 3584 CK Utrecht, The Netherlands
| | - 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
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van de Beek E, Kerckhoffs J, Hoek G, Sterk G, Meliefste K, Gehring U, Vermeulen R. Spatial and Spatiotemporal Variability of Regional Background Ultrafine Particle Concentrations in the Netherlands. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2021; 55:1067-1075. [PMID: 33378199 PMCID: PMC7818655 DOI: 10.1021/acs.est.0c06806] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
Studies of the health effects of ultrafine particles (UFPs) in large nationwide cohorts are currently hampered by a lack of knowledge about spatial and spatiotemporal variations in regional background UFPs. We measured the UFP (10-300 nm) at 20 regional background locations (3 × 2 weeks) across the Netherlands and a reference site continuously over a total period of 14 months in 2016-2017. We compared the overall averages for each site and used kriging to create a regional background spatial map of the Netherlands. Spatiotemporal variability was analyzed by correlating time-series of 2 and 24 h average concentrations. The overall average measured UFP concentrations at the 20 locations ranged from 3814 to 7070 particles/cm3. We found the spatial correlation in the UFP concentrations up to 180 km and clear differences between the north and the more populated southern parts of the country. The average temporal correlation between 2 and 24 h average UFP concentrations was 0.50 (IQR: 0.36-0.61) and 0.58 (IQR: 0.44-0.75), respectively. Temporal correlation declined weakly with a distance between sites, from 0.58 for sites within 80 km of each other to 0.47 for sites farther away. The substantial spatial variation in the regional background UFP concentrations suggests that regional variation may contribute importantly to exposure contrast in nationwide health studies of UFP.
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Affiliation(s)
- Esther van de Beek
- Institute
for Risk Assessment Sciences (IRAS), Division of Environmental Epidemiology, Utrecht University, 3584 CK Utrecht, The Netherlands
| | - Jules Kerckhoffs
- Institute
for Risk Assessment Sciences (IRAS), Division of Environmental Epidemiology, Utrecht University, 3584 CK Utrecht, The Netherlands
- E-mail:
| | - Gerard Hoek
- Institute
for Risk Assessment Sciences (IRAS), Division of Environmental Epidemiology, Utrecht University, 3584 CK Utrecht, The Netherlands
| | - Geert Sterk
- Department
of Physical Geography, Utrecht University, 3508 TC Utrecht, The Netherlands
| | - Kees Meliefste
- Institute
for Risk Assessment Sciences (IRAS), Division of Environmental Epidemiology, Utrecht University, 3584 CK Utrecht, The Netherlands
| | - Ulrike Gehring
- Institute
for Risk Assessment Sciences (IRAS), Division of Environmental Epidemiology, Utrecht University, 3584 CK Utrecht, The Netherlands
| | - 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
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Xu X, Qin N, Yang Z, Liu Y, Cao S, Zou B, Jin L, Zhang Y, Duan X. Potential for developing independent daytime/nighttime LUR models based on short-term mobile monitoring to improve model performance. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2021; 268:115951. [PMID: 33162219 DOI: 10.1016/j.envpol.2020.115951] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Revised: 10/10/2020] [Accepted: 10/27/2020] [Indexed: 06/11/2023]
Abstract
Land use regression model (LUR) is a widespread method for predicting air pollution exposure. Few studies have explored the performance of independently developed daytime/nighttime LUR models. In this study, fine particulate matter (PM2.5), inhalable particulate matter (PM10), and nitrogen dioxide (NO2) concentrations were measured by mobile monitoring during non-heating and heating seasons in Taiyuan. Pollutant concentrations were higher in the nighttime than the daytime, and higher in the heating season than the non-heating season. Daytime/nighttime and full-day LUR models were developed and validated for each pollutant to examine variations in model performance. Adjusted coefficients of determination (adjusted R2) for the LUR models ranged from 0.53-0.87 (PM2.5), 0.53-0.85 (PM10), and 0.33-0.67 (NO2). The performance of the daytime/nighttime LUR models for PM2.5 and PM10 was better than that of the full-day models according to the results of model adjusted R2 and validation R2. Consistent results were confirmed in the non-heating and heating seasons. Effectiveness of developing independent daytime/nighttime models for NO2 to improve performance was limited. Surfaces based on the daytime/nighttime models revealed variations in concentrations and spatial distribution. In conclusion, the independent development of daytime/nighttime LUR models for PM2.5/PM10 has the potential to replace full-day models for better model performance. The modeling strategy is consistent with the residential activity patterns and contributes to achieving reliable exposure predictions for PM2.5 and PM10. Nighttime could be a critical exposure period, due to high pollutant concentrations.
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Affiliation(s)
- Xiangyu Xu
- School of Energy and Environmental Engineering, University of Science and Technology of Beijing, Beijing, 100083, China
| | - Ning Qin
- School of Energy and Environmental Engineering, University of Science and Technology of Beijing, Beijing, 100083, 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
| | - Yunwei Liu
- School of Energy and Environmental Engineering, University of Science and Technology of Beijing, Beijing, 100083, China
| | - Suzhen Cao
- School of Energy and Environmental Engineering, University of Science and Technology of Beijing, Beijing, 100083, China
| | - Bin Zou
- School of Geosciences and Info-Physics, Central South University, Changsha, Hunan, 410083, China
| | - Lan Jin
- Department of Surgery, Yale School of Medicine, New Haven, CT, 06520, USA
| | - Yawei Zhang
- Department of Surgery, Yale School of Medicine, New Haven, CT, 06520, USA; Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, 06510, USA
| | - Xiaoli Duan
- School of Energy and Environmental Engineering, University of Science and Technology of Beijing, Beijing, 100083, China.
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Air Pollution Measurements and Land-Use Regression in Urban Sub-Saharan Africa Using Low-Cost Sensors—Possibilities and Pitfalls. ATMOSPHERE 2020. [DOI: 10.3390/atmos11121357] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Air pollution is recognized as the most important environmental factor that adversely affects human and societal wellbeing. Due to rapid urbanization, air pollution levels are increasing in the Sub-Saharan region, but there is a shortage of air pollution monitoring. Hence, exposure data to use as a base for exposure modelling and health effect assessments is also lacking. In this study, low-cost sensors were used to assess PM2.5 (particulate matter) levels in the city of Adama, Ethiopia. The measurements were conducted during two separate 1-week periods. The measurements were used to develop a land-use regression (LUR) model. The developed LUR model explained 33.4% of the variance in the concentrations of PM2.5. Two predictor variables were included in the final model, of which both were related to emissions from traffic sources. Some concern regarding influential observations remained in the final model. Long-term PM2.5 and wind direction data were obtained from the city’s meteorological station, which should be used to validate the representativeness of our sensor measurements. The PM2.5 long-term data were however not reliable. Means of obtaining good reference data combined with longer sensor measurements would be a good way forward to develop a stronger LUR model which, together with improved knowledge, can be applied towards improving the quality of health. A health impact assessment, based on the mean level of PM2.5 (23 µg/m3), presented the attributable burden of disease and showed the importance of addressing causes of these high ambient levels in the area.
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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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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: 12] [Impact Index Per Article: 3.0] [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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Ganji A, Minet L, Weichenthal S, Hatzopoulou M. Predicting Traffic-Related Air Pollution Using Feature Extraction from Built Environment Images. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2020; 54:10688-10699. [PMID: 32786568 DOI: 10.1021/acs.est.0c00412] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
This study develops a set of algorithms to extract built environment features from Google aerial and street view images, reflecting the microcharacteristics of an urban location as well as the different functions of buildings. These features were used to train a Bayesian regularized artificial neural network (BRANN) model to predict near-road air quality based on measurements of ultrafine particles (UFPs) and black carbon (BC) in Toronto, Canada. The resulting models [adjusted R2 of 75.87 and 79.10% for UFP and BC and root mean squared error (RMSE) of 21,800 part/cm3 and 1300 ng/m3 for UFP and BC] were compared with similar ANN models developed using the same predictors, but extracted from traditional geographic information system (GIS) databases [adjusted R2 of 58.74 and 64.21% for UFP and BC and RMSE values of 23,000 part/cm3 and 1600 ng/m3 for UFP and BC]. The models based on feature extraction exhibited higher predictive power, thus highlighting the greater accuracy of the proposed methods compared to GIS layers that are solely based on aerial images. A comparison with other neural network approaches as well as with a traditional land-use regression model demonstrates the strength of the BRANN model for spatial interpolation of air quality.
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Affiliation(s)
- Arman Ganji
- Department of Civil and Mineral Engineering, University of Toronto, Toronto, Ontario M5S 1A1, Canada
| | - Laura Minet
- Department of Civil and Mineral Engineering, University of Toronto, Toronto, Ontario M5S 1A1, Canada
| | - Scott Weichenthal
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec H3A 0G4, Canada
| | - Marianne Hatzopoulou
- Department of Civil and Mineral Engineering, University of Toronto, Toronto, Ontario M5S 1A1, Canada
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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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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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Zuurbier M, Willems J, Schaap I, Van der Zee S, Hoek G. The contribution of moped emissions to ultrafine and fine particle concentrations on bike lanes. THE SCIENCE OF THE TOTAL ENVIRONMENT 2019; 686:191-198. [PMID: 31176818 DOI: 10.1016/j.scitotenv.2019.05.409] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2019] [Revised: 05/22/2019] [Accepted: 05/27/2019] [Indexed: 06/09/2023]
Abstract
Although not emitting air pollution themselves, cyclists are exposed to air pollution from motorised traffic. Because of the close proximity of mopeds to cyclists, moped emissions may affect cyclist exposure. However, the quantitative contribution of mopeds to cyclists' air pollution exposures is uncertain. The aim of the research was to quantify the contribution of moped emissions to air pollution concentrations on bike lanes. Measurements of Particle Number Concentrations (PNC),1 Particulate Matter (PM)2 and Black Carbon (BC)3 on bike lanes were performed in September 2016 in four Dutch cities. Passing two- and four-stroke mopeds and other traffic were recorded and distinguished by sound by the trained field worker. One-second PNC, PM and one-minute BC concentrations were measured. Using regression analyses the contribution of passing mopeds to air pollution exposure was analysed. At 18 non-tunnel locations, two-stroke and four-stroke mopeds contributed at average 12,000 and 3000 pt./cm3 PNC per second when passing by, respectively. In a tunnel, this was 92,000 and 12,000 pt./cm3. Two- and four-strokes added 3 to 19% to total PNC at non-tunnel sites and 58% in a tunnel. Four-strokes caused at average 54% of the contribution of moped emissions to total PNC. At non-tunnel sites, the contribution of mopeds to PM was 1.2 and 0.2 μg/m3 for two- and four-strokes, respectively. In a tunnel this was 3.9 and 2.3 μg/m3. Minute-measurements of BC did not show a relation between mopeds passing by and BC. Mopeds caused substantial short-term increases in air pollution levels on bike lanes, especially in a tunnel. Two-stroke mopeds caused higher concentration peaks than four-stroke mopeds. The contribution to total air pollution concentrations of four-stroke mopeds was larger, because of the higher share of four-strokes. Because of the close proximity of mopeds to cyclists, cyclists air pollution exposure can be largely influenced by moped emissions.
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Affiliation(s)
- Moniek Zuurbier
- Public Health Services Gelderland-Midden, P.O. Box 5364, 6802 EJ, the Netherlands.
| | - Jolanda Willems
- Public Health Services Gelderland-Midden, P.O. Box 5364, 6802 EJ, the Netherlands.
| | - Iris Schaap
- Institute for Risk Assessment Sciences, Utrecht University, P.O. Box 80.178, 3508 TD Utrecht, the Netherlands
| | - Saskia Van der Zee
- Public Health Services Amsterdam, Nieuwe Achtergracht 100, 1018 WT Amsterdam, the Netherlands.
| | - Gerard Hoek
- Institute for Risk Assessment Sciences, Utrecht University, P.O. Box 80.178, 3508 TD Utrecht, the Netherlands.
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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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42
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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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43
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Kerckhoffs J, Hoek G, Portengen L, Brunekreef B, Vermeulen RCH. Performance of Prediction Algorithms for Modeling Outdoor Air Pollution Spatial Surfaces. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2019; 53:1413-1421. [PMID: 30609353 DOI: 10.1021/acs.est.8b06038] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2023]
Abstract
Land use regression (LUR) models for air pollutants are often developed using multiple linear regression techniques. However, in the past decade linear (stepwise) regression methods have been criticized for their lack of flexibility, their ignorance of potential interaction between predictors, and their limited ability to incorporate highly correlated predictors. We used two training sets of ultrafine particles (UFP) data (mobile measurements (8200 segments, 25 s monitoring per segment), and short-term stationary measurements (368 sites, 3 × 30 min per site)) to evaluate different modeling approaches to estimate long-term UFP concentrations by estimating precision and bias based on an independent external data set (42 sites, average of three 24-h measurements). Higher training data R2 did not equate to higher test R2 for the external long-term average exposure estimates, making the argument that external validation data are critical to compare model performance. Machine learning algorithms trained on mobile measurements explained only 38-47% of external UFP concentrations, whereas multivariable methods like stepwise regression and elastic net explained 56-62%. Some machine learning algorithms (bagging, random forest) trained on short-term measurements explained modestly more variability of external UFP concentrations compared to multiple linear regression and regularized regression techniques. In conclusion, differences in predictive ability of algorithms depend on the type of training data and are generally modest.
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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
| | - Lützen Portengen
- Institute for Risk Assessment Sciences (IRAS), Division of Environmental Epidemiology , Utrecht University , 3584 CK Utrecht , The Netherlands
| | - 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 , 358 CK Utrecht , The Netherlands
| | - Roel C H 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 , 358 CK Utrecht , The Netherlands
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44
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Niedzwiecki MM, Walker DI, Vermeulen R, Chadeau-Hyam M, Jones DP, Miller GW. The Exposome: Molecules to Populations. Annu Rev Pharmacol Toxicol 2019; 59:107-127. [PMID: 30095351 DOI: 10.1146/annurev-pharmtox-010818-021315] [Citation(s) in RCA: 117] [Impact Index Per Article: 23.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Derived from the term exposure, the exposome is an omic-scale characterization of the nongenetic drivers of health and disease. With the genome, it defines the phenome of an individual. The measurement of complex environmental factors that exert pressure on our health has not kept pace with genomics and historically has not provided a similar level of resolution. Emerging technologies make it possible to obtain detailed information on drugs, toxicants, pollutants, nutrients, and physical and psychological stressors on an omic scale. These forces can also be assessed at systems and network levels, providing a framework for advances in pharmacology and toxicology. The exposome paradigm can improve the analysis of drug interactions and detection of adverse effects of drugs and toxicants and provide data on biological responses to exposures. The comprehensive model can provide data at the individual level for precision medicine, group level for clinical trials, and population level for public health.
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Affiliation(s)
- Megan M Niedzwiecki
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; ,
| | - Douglas I Walker
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; ,
- Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia 30322, USA
- Clinical Biomarkers Laboratory, Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Department of Medicine, Emory University, Atlanta, Georgia 30322, USA;
| | - Roel Vermeulen
- Institute for Risk Assessment Sciences, Utrecht University, 3584 CM Utrecht, Netherlands;
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, 3584 Utrecht, Netherlands
- MRC/PHE Centre for Environmental Health, Department of Epidemiology and Public Health, Imperial College London, W2 1PG London, United Kingdom;
| | - Marc Chadeau-Hyam
- Institute for Risk Assessment Sciences, Utrecht University, 3584 CM Utrecht, Netherlands;
- MRC/PHE Centre for Environmental Health, Department of Epidemiology and Public Health, Imperial College London, W2 1PG London, United Kingdom;
| | - Dean P Jones
- Clinical Biomarkers Laboratory, Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Department of Medicine, Emory University, Atlanta, Georgia 30322, USA;
| | - Gary W Miller
- Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia 30322, USA
- Current affiliation: Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University Medical Center, New York, NY 10032, USA;
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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: 26] [Impact Index Per Article: 4.3] [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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Tessum MW, Larson T, Gould TR, Simpson CD, Yost MG, Vedal S. Mobile and Fixed-Site Measurements To Identify Spatial Distributions of Traffic-Related Pollution Sources in Los Angeles. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2018; 52:2844-2853. [PMID: 29382190 PMCID: PMC5843188 DOI: 10.1021/acs.est.7b04889] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Mobile monitoring and fixed-site monitoring using passive sampling devices (PSD) are popular air pollutant measurement techniques with complementary strengths and weaknesses. This study investigates the utility of combining data from concurrent 2-week mobile monitoring and fixed-site PSD campaigns in Los Angeles in the summer and early spring to identify sources of traffic-related air pollutants (TRAP) and their spatial distributions. There were strong to moderate correlations between mobile and fixed-site PSD measurements of both NO2 and NO x in the summer and spring (Pearson's r between 0.43 and 0.79), suggesting that the two data sets can be reliably combined for source apportionment. PCA identified the major TRAP sources as light-duty vehicle emissions, diesel exhaust, crankcase vent emissions, and an independent source of combustion-derived ultrafine particle emissions. The component scores of those four sources at each site were significantly correlated across the two seasons (Pearson's r between 0.58 and 0.79). Spatial maps of absolute principal component scores showed all sources to be most prominent near major roadways and the central business district and the ultrafine particle source being, in addition, more prominent near the airport. Mobile monitoring combined with fixed-site PSD sampling can provide high spatial resolution estimates of TRAP and can reveal underlying sources of exposure variability.
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Affiliation(s)
- Mei W. Tessum
- Department of Environmental and Occupational Health Sciences, University of Washington, Box 357234, Seattle, Washington 98198, United States
| | - Timothy Larson
- Department of Environmental and Occupational Health Sciences, University of Washington, Box 357234, Seattle, Washington 98198, United States
- Department of Civil & Environmental Engineering, University of Washington, Box 352700, Seattle, Washington 98198, United States
| | - Timothy R. Gould
- Department of Civil & Environmental Engineering, University of Washington, Box 352700, Seattle, Washington 98198, United States
| | - Christopher D. Simpson
- Department of Environmental and Occupational Health Sciences, University of Washington, Box 357234, Seattle, Washington 98198, United States
| | - Michael G. Yost
- Department of Environmental and Occupational Health Sciences, University of Washington, Box 357234, Seattle, Washington 98198, United States
| | - Sverre Vedal
- Department of Environmental and Occupational Health Sciences, University of Washington, Box 357234, Seattle, Washington 98198, United States
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Vedal S, Han B, Xu J, Szpiro A, Bai Z. Design of an Air Pollution Monitoring Campaign in Beijing for Application to Cohort Health Studies. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2017; 14:ijerph14121580. [PMID: 29244738 PMCID: PMC5750998 DOI: 10.3390/ijerph14121580] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/17/2017] [Revised: 12/08/2017] [Accepted: 12/12/2017] [Indexed: 12/25/2022]
Abstract
No cohort studies in China on the health effects of long-term air pollution exposure have employed exposure estimates at the fine spatial scales desirable for cohort studies with individual-level health outcome data. Here we assess an array of modern air pollution exposure estimation approaches for assigning within-city exposure estimates in Beijing for individual pollutants and pollutant sources to individual members of a cohort. Issues considered in selecting specific monitoring data or new monitoring campaigns include: needed spatial resolution, exposure measurement error and its impact on health effect estimates, spatial alignment and compatibility with the cohort, and feasibility and expense. Sources of existing data largely include administrative monitoring data, predictions from air dispersion or chemical transport models and remote sensing (specifically satellite) data. New air monitoring campaigns include additional fixed site monitoring, snapshot monitoring, passive badge or micro-sensor saturation monitoring and mobile monitoring, as well as combinations of these. Each of these has relative advantages and disadvantages. It is concluded that a campaign in Beijing that at least includes a mobile monitoring component, when coupled with currently available spatio-temporal modeling methods, should be strongly considered. Such a campaign is economical and capable of providing the desired fine-scale spatial resolution for pollutants and sources.
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Affiliation(s)
- Sverre Vedal
- Department of Environmental and Occupational Health Sciences, University of Washington School of Public Health, Seattle, WA 98105, USA.
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100112, China.
| | - Bin Han
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100112, China.
| | - Jia Xu
- Department of Environmental and Occupational Health Sciences, University of Washington School of Public Health, Seattle, WA 98105, USA.
| | - Adam Szpiro
- Department of Biostatistics, University of Washington School of Public Health, Seattle, WA 98195, USA.
| | - Zhipeng Bai
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100112, China.
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