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Babaan J, Wong PY, Chen PC, Chen HL, Lung SCC, Chen YC, Wu CD. Geospatial artificial intelligence for estimating daytime and nighttime nitrogen dioxide concentration variations in Taiwan: A spatial prediction model. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 360:121198. [PMID: 38772239 DOI: 10.1016/j.jenvman.2024.121198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Revised: 05/16/2024] [Accepted: 05/16/2024] [Indexed: 05/23/2024]
Abstract
Nitrogen dioxide (NO2) is a major air pollutant primarily emitted from traffic and industrial activities, posing health risks. However, current air pollution models often underestimate exposure risks by neglecting the bimodal pattern of NO2 levels throughout the day. This study aimed to address this gap by developing ensemble mixed spatial models (EMSM) using geo-artificial intelligence (Geo-AI) to examine the spatial and temporal variations of NO2 concentrations at a high resolution of 50m. These EMSMs integrated spatial modelling methods, including kriging, land use regression, machine learning, and ensemble learning. The models utilized 26 years of observed NO2 measurements, meteorological parameters, geospatial layers, and social and season-dependent variables as representative of emission sources. Separate models were developed for daytime and nighttime periods, which achieved high reliability with adjusted R2 values of 0.92 and 0.93, respectively. The study revealed that mean NO2 concentrations were significantly higher at nighttime (9.60 ppb) compared to daytime (5.61 ppb). Additionally, winter exhibited the highest NO2 levels regardless of time period. The developed EMSMs were utilized to generate maps illustrating NO2 levels pre and during COVID restrictions in Taiwan. These findings could aid epidemiological research on exposure risks and support policy-making and environmental planning initiatives.
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Affiliation(s)
- Jennieveive Babaan
- Department of Geodetic Engineering, University of the Philippines Diliman, Quezon City, Philippines
| | - Pei-Yi Wong
- Department of Environmental and Occupational Health, National Cheng Kung University, Tainan City, Taiwan
| | - Pau-Chung Chen
- National Institute of Environmental Health Sciences, National Health Research Institutes, Miaoli County, Taiwan; Institute of Environmental and Occupational Health Sciences, National Taiwan University College of Public Health, Taipei City, Taiwan; Department of Environmental and Occupational Medicine, National Taiwan University Hospital, Taipei City, Taiwan; Department of Public Health, National Taiwan University College of Public Health, Taipei City, Taiwan
| | - Hsiu-Ling Chen
- Department of Food Safety/Hygiene and Risk Management, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Shih-Chun Candice Lung
- Research Center for Environmental Changes, Academia Sinica, Taipei City, Taiwan; Department of Atmospheric Sciences, National Taiwan University, Taipei, Taiwan; Institute of Environmental Health, School of Public Health, National Taiwan University, Taipei, Taiwan
| | - Yu-Cheng Chen
- National Institute of Environmental Health Sciences, National Health Research Institutes, Miaoli County, Taiwan
| | - Chih-Da Wu
- National Institute of Environmental Health Sciences, National Health Research Institutes, Miaoli County, Taiwan; Department of Geomatics, National Cheng Kung University, Tainan City, Taiwan; Innovation and Development Center of Sustainable Agriculture, National Chung Hsing University, Taichung City, Taiwan; Research Center for Precision Environmental Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan.
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2
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Yadav N, Sorek-Hamer M, Von Pohle M, Asanjan AA, Sahasrabhojanee A, Suel E, E Arku R, Lingenfelter V, Brauer M, Ezzati M, Oza N, Ganguly AR. Using deep transfer learning and satellite imagery to estimate urban air quality in data-poor regions. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 342:122914. [PMID: 38000726 PMCID: PMC7615387 DOI: 10.1016/j.envpol.2023.122914] [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: 08/25/2023] [Revised: 11/08/2023] [Accepted: 11/09/2023] [Indexed: 11/26/2023]
Abstract
Urban air pollution is a critical public health challenge in low-and-middle-income countries (LMICs). At the same time, LMICs tend to be data-poor, lacking adequate infrastructure to monitor air quality (AQ). As LMICs undergo rapid urbanization, the socio-economic burden of poor AQ will be immense. Here we present a globally scalable two-step deep learning (DL) based approach for AQ estimation in LMIC cities that mitigates the need for extensive AQ infrastructure on the ground. We train a DL model that can map satellite imagery to AQ in high-income countries (HICs) with sufficient ground data, and then adapt the model to learn meaningful AQ estimates in LMIC cities using transfer learning. The trained model can explain up to 54% of the variation in the AQ distribution of the target LMIC city without the need for target labels. The approach is demonstrated for Accra in Ghana, Africa, with AQ patterns learned and adapted from two HIC cities, specifically Los Angeles and New York.
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Affiliation(s)
- Nishant Yadav
- Sustainability and Data Sciences Laboratory, Northeastern University, Boston, USA; University Space Research Association (USRA), Mountain View, USA.
| | - Meytar Sorek-Hamer
- University Space Research Association (USRA), Mountain View, USA; NASA Ames Research Center, Moffett Field, USA
| | - Michael Von Pohle
- University Space Research Association (USRA), Mountain View, USA; NASA Ames Research Center, Moffett Field, USA
| | - Ata Akbari Asanjan
- University Space Research Association (USRA), Mountain View, USA; NASA Ames Research Center, Moffett Field, USA
| | - Adwait Sahasrabhojanee
- University Space Research Association (USRA), Mountain View, USA; NASA Ames Research Center, Moffett Field, USA
| | | | | | - Violet Lingenfelter
- Sustainability and Data Sciences Laboratory, Northeastern University, Boston, USA; University Space Research Association (USRA), Mountain View, USA
| | | | | | - Nikunj Oza
- NASA Ames Research Center, Moffett Field, USA
| | - Auroop R Ganguly
- Sustainability and Data Sciences Laboratory, Northeastern University, Boston, USA; Pacific Northwest National Laboratory (PNNL), Richland, USA; The Institute for Experiential AI, Northeastern University, Boston, USA
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Li Z, Yim SHL, He X, Xia X, Ho KF, Yu JZ. High spatial resolution estimates of major PM 2.5 components and their associated health risks in Hong Kong using a coupled land use regression and health risk assessment approach. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 907:167932. [PMID: 37863225 DOI: 10.1016/j.scitotenv.2023.167932] [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: 09/05/2023] [Revised: 10/07/2023] [Accepted: 10/17/2023] [Indexed: 10/22/2023]
Abstract
Few studies have focused on the spatial distribution of the typical components and source tracers of PM2.5 and their associated health risks, despite the fact that the chemical components of PM2.5 pose potentially significant and independent risks to human health. The main objective of this study was to evaluate the spatial distribution of major PM2.5 components and their associated health risks in Hong Kong using a coupled land use regression and health risk assessment modeling approach. The established land use regression models of the major PM2.5 components and source tracers achieved a relatively high statistical performance, with training and leave-one-out cross-validation R2 values of 0.85-0.96 and 0.62-0.88, respectively. The high spatial resolution (500 m × 500 m) distribution patterns of the chemical components of PM2.5 showed the heterogeneity of population exposure to different components and the related potential health risks, as evidenced by the weak spatial correlations between the mass of PM2.5 and some components. Elemental carbon, nickel, arsenic, and chromium from PM2.5 made major contributions to the total health risk and should therefore be reduced further. Our results will enable researchers to determine independent associations between exposure to the various components of PM2.5 and health endpoints in epidemiological studies.
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Affiliation(s)
- Zhiyuan Li
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou 510275, China; Institute of Environment, Energy and Sustainability, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong, China.
| | - Steve Hung Lam Yim
- Asian School of the Environment, Nanyang Technological University, Singapore; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore; Earth Observatory of Singapore, Nanyang Technological University, Singapore
| | - Xiao He
- College of Chemistry and Environmental Engineering, Shenzhen University, Shenzhen 518060, China
| | - Xi Xia
- School of Public Health, Shaanxi University of Chinese Medicine, Xi'an, China
| | - Kin-Fai Ho
- The Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong, China
| | - Jian Zhen Yu
- Department of Chemistry and Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
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4
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Xu X, Qin N, Zhao W, Tian Q, Si Q, Wu W, Iskander N, Yang Z, Zhang Y, Duan X. A three-dimensional LUR framework for PM 2.5 exposure assessment based on mobile unmanned aerial vehicle monitoring. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2022; 301:118997. [PMID: 35176409 DOI: 10.1016/j.envpol.2022.118997] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 02/11/2022] [Accepted: 02/12/2022] [Indexed: 06/14/2023]
Abstract
Land use regression (LUR) models have been widely used in epidemiological studies and risk assessments related to air pollution. Although efforts have been made to improve the performance of LUR models so that they capture the spatial heterogeneity of fine particulate matter (PM2.5) in high-density cities, few studies have revealed the vertical differences in PM2.5 exposure. This study proposes a three-dimensional LUR (3-D LUR) assessment framework for PM2.5 exposure that combines a high-resolution LUR model with a vertical PM2.5 variation model to investigate the results of horizontal and vertical mobile PM2.5 monitoring campaigns. High-resolution LUR models that were developed independently for daytime and nighttime were found to explain 51% and 60% of the PM2.5 variation, respectively. Vertical measurements of PM2.5 from three regions were first parameterized to produce a coefficient of variation for the concentration (CVC) to define the rate at which PM2.5 changes at a certain height relative to the ground. The vertical variation model for PM2.5 was developed based on a spline smoothing function in a generalized additive model (GAM) framework with an adjusted R2 of 0.91 and explained 92.8% of the variance. PM2.5 exposure levels for the population in the study area were estimated based on both the LUR models and the 3-D LUR framework. The 3-D LUR framework was found to improve the accuracy of exposure estimation in the vertical direction by avoiding exposure estimation errors of up to 5%. Although the 3-D LUR-based assessment did not indicate significant variation in estimates of premature mortality that could be attributed to PM2.5, exposure to this pollutant was found to differ in the vertical direction. The 3-D LUR framework has the potential to provide accurate exposure estimates for use in future epidemiological studies and health risk assessments.
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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
| | - Wenjing Zhao
- School of Energy and Environmental Engineering, University of Science and Technology of Beijing, Beijing 100083, China
| | - Qi Tian
- School of Energy and Environmental Engineering, University of Science and Technology of Beijing, Beijing 100083, China
| | - Qi Si
- School of Energy and Environmental Engineering, University of Science and Technology of Beijing, Beijing 100083, China
| | - Weiqi Wu
- School of Energy and Environmental Engineering, University of Science and Technology of Beijing, Beijing 100083, China
| | - Nursiya Iskander
- School of Energy and Environmental Engineering, University of Science and Technology of Beijing, Beijing 100083, China
| | - Zhenchun Yang
- Duke Global Health Institute, Duke University, Durham, NC 27708, United States
| | - 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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Zhang P, Yang L, Ma W, Wang N, Wen F, Liu Q. Spatiotemporal estimation of the PM 2.5 concentration and human health risks combining the three-dimensional landscape pattern index and machine learning methods to optimize land use regression modeling in Shaanxi, China. ENVIRONMENTAL RESEARCH 2022; 208:112759. [PMID: 35077716 DOI: 10.1016/j.envres.2022.112759] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Revised: 01/05/2022] [Accepted: 01/16/2022] [Indexed: 06/14/2023]
Abstract
PM2.5 pollution endangers human health and urban sustainable development. Land use regression (LUR) is one of the most important methods to reveal the temporal and spatial heterogeneity of PM2.5, and the introduction of characteristic variables of geographical factors and the improvement of model construction methods are important research directions for its optimization. However, the complex non-linear correlation between PM2.5 and influencing indicators is always unrecognized by the traditional regression model. The two-dimensional landscape pattern index is difficult to reflect the real information of the surface, and the research accuracy cannot meet the requirements. As such, a novel integrated three-dimensional landscape pattern index (TDLPI) and machine learning extreme gradient boosting (XGBOOST) improved LUR model (LTX) are developed to estimate the spatiotemporal heterogeneity in the fine particle concentration in Shaanxi, China, and health risks of exposure and inhalation of PM2.5 were explored. The LTX model performed well with R2 = 0.88, RMSE of 8.73 μg/m3 and MAE of 5.85 μg/m3. Our findings suggest that integrated three-dimensional landscape pattern information and XGBOOST approaches can accurately estimate annual and seasonal variations of PM2.5 pollution The Guanzhong Plain and northern Shaanxi always feature high PM2.5 values, which exhibit similar distribution trends to those of the observed PM2.5 pollution. This study demonstrated the outstanding performance of the LTX model, which outperforms most models in past researches. On the whole, LTX approach is reliable and can improve the accuracy of pollutant concentration prediction. The health risks of human exposure to fine particles are relatively high in winter. Central part is a high health risk area, while northern area is low. Our study provides a new method for atmospheric pollutants assessing, which is important for LUR model optimization, high-precision PM2.5 pollution prediction and landscape pattern planning. These results can also contribute to human health exposure risks and future epidemiological studies of air pollution.
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Affiliation(s)
- Ping Zhang
- School of Environmental and Chemical Engineering, Xi'an Polytechnic University, Xi'an, 710048, China; Shaanxi Key Laboratory of Land Consolidation, Xi'an, 710075, China.
| | - Lianwei Yang
- School of Environmental and Chemical Engineering, Xi'an Polytechnic University, Xi'an, 710048, China
| | - Wenjie Ma
- School of Environmental and Chemical Engineering, Xi'an Polytechnic University, Xi'an, 710048, China
| | - Ning Wang
- School of Environmental and Chemical Engineering, Xi'an Polytechnic University, Xi'an, 710048, China
| | - Feng Wen
- School of Environmental and Chemical Engineering, Xi'an Polytechnic University, Xi'an, 710048, China.
| | - Qi Liu
- School of Environmental and Chemical Engineering, Xi'an Polytechnic University, Xi'an, 710048, China; The First Institute of Photogrammetry and Remote Sensing, MNR, Xi'an, 710054, China.
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Sorek-Hamer M, von Pohle M, Sahasrabhojanee A, Asanjan AA, Deardorff E, Suel E, Lingenfelter V, Das K, Oza N, Ezzati M, Brauer M. A Deep Learning Approach for Meter-Scale Air Quality Estimation in Urban Environments Using Very High-Spatial-Resolution Satellite Imagery. ATMOSPHERE 2022; 13:696. [PMID: 37724306 PMCID: PMC7615102 DOI: 10.3390/atmos13050696] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
High-spatial-resolution air quality (AQ) mapping is important for identifying pollution sources to facilitate local action. Some of the most populated cities in the world are not equipped with the infrastructure required to monitor AQ levels on the ground and must rely on other sources, like satellite derived estimates, to monitor AQ. Current satellite-data-based models provide AQ mapping on a kilometer scale at best. In this study we focus on producing hundred-meter-scale AQ maps for urban environments in developed cities. We examined the feasibility of an image-based object-detection analysis approach using very high-spatial-resolution (2.5 m) commercial satellite imagery. We fed the satellite imagery to a deep neural network (DNN) to learn the association between visual urban features and air pollutants. The developed model, which solely uses satellite imagery, was tested and evaluated using both ground monitoring observations and land-use regression modeled PM2.5 and NO2 concentrations over London, Vancouver (BC), Los Angeles, and New York City. The results demonstrate a low error with a total RMSE < 2 µg/m3 and highlight the contribution of specific urban features, such as green areas and roads, to continuous hundred-meter-scale AQ estimation. This approach offers promise for scaling to global applications in developed and developing urban environments. Further analysis on domain transferability will enable application of a parsimonious model based merely on satellite images to create hundred-meter-scale AQ maps in developing cities, where current and historical ground data is limited.
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Affiliation(s)
- Meytar Sorek-Hamer
- Universities Space Research Association (USRA), Mountain View, CA
- NASA Ames Research Center, Mountain View, CA
| | - Michael von Pohle
- Universities Space Research Association (USRA), Mountain View, CA
- NASA Ames Research Center, Mountain View, CA
| | - Adwait Sahasrabhojanee
- Universities Space Research Association (USRA), Mountain View, CA
- NASA Ames Research Center, Mountain View, CA
| | - Ata Akbari Asanjan
- Universities Space Research Association (USRA), Mountain View, CA
- NASA Ames Research Center, Mountain View, CA
| | - Emily Deardorff
- Universities Space Research Association (USRA), Mountain View, CA
- NASA Ames Research Center, Mountain View, CA
| | | | - Violet Lingenfelter
- Universities Space Research Association (USRA), Mountain View, CA
- NASA Ames Research Center, Mountain View, CA
| | - Kamalika Das
- Universities Space Research Association (USRA), Mountain View, CA
- NASA Ames Research Center, Mountain View, CA
| | - Nikunj Oza
- NASA Ames Research Center, Mountain View, CA
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Kang J, Jung JY, Huh JY, Ji HW, Kim HC, Lee SW. Behavioral interventions to reduce particulate matter exposure in patients with COPD. Medicine (Baltimore) 2021; 100:e28119. [PMID: 34889270 PMCID: PMC8663843 DOI: 10.1097/md.0000000000028119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Accepted: 11/16/2021] [Indexed: 01/05/2023] Open
Abstract
INTRODUCTION Chronic obstructive pulmonary disease (COPD) is commonly affected by particulate matter (PM) exposure. In this study, we aimed to evaluate whether behavioral interventions to reduce PM exposure improve clinical outcomes in patients with COPD. METHODS A multicenter randomized controlled trial will be conducted involving 120 participants recruited from 3 hospitals in the capital region of the Republic of Korea. Patients aged 40 to 80 years with a diagnosis of COPD and a forced expiratory volume at 1 s <80% of the predicted value are eligible for inclusion. The participants will be randomized to either the intervention group or the usual care group (2:1). The behavioral interventions will comprise the following activities: checking air quality forecast; operating indoor air cleaners and regular check-ups of filters; ventilating the home regularly by opening windows; adhering to inhaler treatment; and refraining from going out on high air pollution days. "Internet-of-things"-based, gravimetric, and light-scattering methods will be used to measure indoor and outdoor PM concentrations. To estimate the degree of individual PM exposure, a time-activity diary and land use regression modeling will be used. The efficacy of the behavioral interventions on the following outcomes will be analyzed: amount of PM exposure, changes in forced expiratory volume at 1 s from the baseline, changes in respiratory symptoms and quality of life, risks of exacerbation, hospitalization, and death. DISCUSSION Given the harmful effect of air pollutants, individual-level interventions to reduce exposure may be significant. However, there is a lack of evidence on how effective such interventions are to date. This study will be able to provide physicians and patients with evidence-based strategies to reduce PM exposure in daily life. TRIAL REGISTRATION NUMBER NCT04878367.
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Affiliation(s)
- Jieun Kang
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Ilsan Paik Hospital, Inje University College of Medicine, Goyang, Republic of Korea
| | - Ji Ye Jung
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Jin-Young Huh
- Department of Pulmonary and Critical Care Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Hyun Woo Ji
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Hwan-Cheol Kim
- Department of Occupational and Environmental Medicine, Inha University Hospital, Inha University College of Medicine, Incheon, Republic of Korea
| | - Sei Won Lee
- Department of Pulmonary and Critical Care Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
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Popovic I, Magalhães RJS, Yang S, Yang Y, Ge E, Yang B, Dong G, Wei X, Marks GB, Knibbs LD. Development and Validation of a Sub-National, Satellite-Based Land-Use Regression Model for Annual Nitrogen Dioxide Concentrations in North-Western China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph182412887. [PMID: 34948497 PMCID: PMC8701972 DOI: 10.3390/ijerph182412887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 12/01/2021] [Accepted: 12/03/2021] [Indexed: 11/16/2022]
Abstract
Existing national- or continental-scale models of nitrogen dioxide (NO2) exposure have a limited capacity to capture subnational spatial variability in sparsely-populated parts of the world where NO2 sources may vary. To test and validate our approach, we developed a land-use regression (LUR) model for NO2 for Ningxia Hui Autonomous Region (NHAR) and surrounding areas, a small rural province in north-western China. Using hourly NO2 measurements from 105 continuous monitoring sites in 2019, a supervised, forward addition, linear regression approach was adopted to develop the model, assessing 270 potential predictor variables, including tropospheric NO2, optically measured by the Aura satellite. The final model was cross-validated (5-fold cross validation), and its historical performance (back to 2014) assessed using 41 independent monitoring sites not used for model development. The final model captured 63% of annual NO2 in NHAR (RMSE: 6 ppb (21% of the mean of all monitoring sites)) and contiguous parts of Inner Mongolia, Gansu, and Shaanxi Provinces. Cross-validation and independent evaluation against historical data yielded adjusted R2 values that were 1% and 10% lower than the model development values, respectively, with comparable RMSE. The findings suggest that a parsimonious, satellite-based LUR model is robust and can be used to capture spatial contrasts in annual NO2 in the relatively sparsely-populated areas in NHAR and neighbouring provinces.
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Affiliation(s)
- Igor Popovic
- Faculty of Medicine, School of Public Health, University of Queensland, Herston 4006, Australia
- UQ Spatial Epidemiology Laboratory, School of Veterinary Science, University of Queensland, Gatton 4343, Australia;
- Correspondence:
| | - Ricardo J. Soares Magalhães
- UQ Spatial Epidemiology Laboratory, School of Veterinary Science, University of Queensland, Gatton 4343, Australia;
- Children’s Health and Environment Program, UQ Children’s Health Research Center, The University of Queensland, South Brisbane 4101, Australia
| | - Shukun Yang
- Department of Radiology, The Second Affiliated Hospital of Ningxia Medical University, The First People’s Hospital in Yinchuan, Yinchuan 750004, China;
| | - Yurong Yang
- Department of Pathogenic Biology & Medical Immunology, School of Basic Medical Science, Ningxia Medical University, Yinchuan 750004, China;
| | - Erjia Ge
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON M5S 1A1, Canada; (E.G.); (X.W.)
| | - Boyi Yang
- Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou 510085, China;
| | - Guanghui Dong
- Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Department of Preventive Medicine, School of Public Health, Sun Yat-sen University, Guangzhou 510085, China;
| | - Xiaolin Wei
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON M5S 1A1, Canada; (E.G.); (X.W.)
| | - Guy B. Marks
- South Western Sydney Clinical School, University of New South Wales, Liverpool 2170, Australia;
- Woolcock Institute of Medical Research, Glebe 2037, Australia
- Centre for Air Pollution, Energy and Health Research, Glebe 2037, Australia;
| | - Luke D. Knibbs
- Centre for Air Pollution, Energy and Health Research, Glebe 2037, Australia;
- Faculty of Medicine and Health, School of Public Health, The University of Sydney, Camperdown 2006, Australia
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9
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Luminati O, Ledebur de Antas de Campos B, Flückiger B, Brentani A, Röösli M, Fink G, de Hoogh K. Land use regression modelling of NO 2 in São Paulo, Brazil. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2021; 289:117832. [PMID: 34340182 DOI: 10.1016/j.envpol.2021.117832] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 06/30/2021] [Accepted: 07/21/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND Air pollution is a major global public health problem. The situation is most severe in low- and middle-income countries, where pollution control measures and monitoring systems are largely lacking. Data to quantify the exposure to air pollution in low-income settings are scarce. METHODS In this study, land use regression models (LUR) were developed to predict the outdoor nitrogen dioxide (NO2) concentration in the study area of the Western Region Birth Cohort in São Paulo. NO2 measurements were performed for one week in winter and summer at eighty locations. Additionally, weekly measurements at one regional background location were performed over a full one-year period to create an annual prediction. RESULTS Three LUR models were developed (annual, summer, winter) by using a supervised stepwise linear regression method. The winter, summer and annual models explained 52 %, 75 % and 66 % of the variance (R2) respectively. Cross-holdout validation tests suggest robust models. NO2 levels ranged from 43.2 μg/m3 to 93.4 μg/m3 in the winter and between 28.1 μg/m3 and 72.8 μg/m3 in summer. Based on our annual prediction, about 67 % of the population living in the study area is exposed to NO2 values over the WHO suggested annual guideline of 40 μg/m3 annual average. CONCLUSION In this study we were able to develop robust models to predict NO2 residential exposure. We could show that average measures, and therefore the predictions of NO2, in such a complex urban area are substantially high and that a major variability within the area and especially within the season is present. These findings also suggest that in general a high proportion of the population is exposed to high NO2 levels.
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Affiliation(s)
- Ornella Luminati
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Socinstrasse 57, P.O.Box, 4002 Basel, Switzerland; University of Basel, Petersplatz 1, P. O. Box, 4001, Basel, Switzerland
| | - Bartolomeu Ledebur de Antas de Campos
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Socinstrasse 57, P.O.Box, 4002 Basel, Switzerland; University of Basel, Petersplatz 1, P. O. Box, 4001, Basel, Switzerland
| | - Benjamin Flückiger
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Socinstrasse 57, P.O.Box, 4002 Basel, Switzerland; University of Basel, Petersplatz 1, P. O. Box, 4001, Basel, Switzerland
| | - Alexandra Brentani
- Department of Pediatrics at the Medical School of São Paulo University, São Paulo, Brazil
| | - Martin Röösli
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Socinstrasse 57, P.O.Box, 4002 Basel, Switzerland; University of Basel, Petersplatz 1, P. O. Box, 4001, Basel, Switzerland
| | - Günther Fink
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Socinstrasse 57, P.O.Box, 4002 Basel, Switzerland; University of Basel, Petersplatz 1, P. O. Box, 4001, Basel, Switzerland
| | - Kees de Hoogh
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Socinstrasse 57, P.O.Box, 4002 Basel, Switzerland; University of Basel, Petersplatz 1, P. O. Box, 4001, Basel, Switzerland.
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10
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Mo Y, Booker D, Zhao S, Tang J, Jiang H, Shen J, Chen D, Li J, Jones KC, Zhang G. The application of land use regression model to investigate spatiotemporal variations of PM 2.5 in Guangzhou, China: Implications for the public health benefits of PM 2.5 reduction. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 778:146305. [PMID: 34030351 DOI: 10.1016/j.scitotenv.2021.146305] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2020] [Revised: 02/28/2021] [Accepted: 03/02/2021] [Indexed: 06/12/2023]
Abstract
Understanding the intra-city variation of PM2.5 is important for air quality management and exposure assessment. In this study, to investigate the spatiotemporal variation of PM2.5 in Guangzhou, we developed land use regression (LUR) models using data from 49 routine air quality monitoring stations. The R2, adjust R2 and 10-fold cross validation R2 for the annual PM2.5 LUR model were 0.78, 0.72 and 0.66, respectively, indicating the robustness of the model. In all the LUR models, traffic variables (e.g., length of main road and the distance to nearest ancillary) were the most common variables in the LUR models, suggesting vehicle emission was the most important contributor to PM2.5 and controlling vehicle emissions would be an effective way to reduce PM2.5. The predicted PM2.5 exhibited significant variations with different land uses, with the highest value for impervious surfaces, followed by green land, cropland, forest and water areas. Guangzhou as the third largest city that PM2.5 concentration has achieved CAAQS Grade II guideline in China, it represents a useful case study city to examine the health and economic benefits of further reduction of PM2.5 to the lower concentration ranges. So, the health and economic benefits of reducing PM2.5 in Guangzhou was further estimated using the BenMAP model, based on the annual PM2.5 concentration predicted by the LUR model. The results showed that the avoided all cause mortalities were 992 cases (95% CI: 221-2140) and the corresponding economic benefits were 1478 million CNY (95% CI: 257-2524) (willingness to pay approach) if the annual PM2.5 concentration can be reduced to the annual CAAQS Grade I guideline value of 15 μg/m3. Our results are expected to provide valuable information for further air pollution control strategies in China.
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Affiliation(s)
- Yangzhi Mo
- State Key Laboratory of Organic Geochemistry and Guangdong-Hong Kong-Macao Joint Laboratory for Environmental Pollution and Control, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China; CAS Center for Excellence in Deep Earth Science, Guangzhou 510640, China; National Air Quality Testing Services, Lancaster Environment Centre, Lancaster University, Lancaster LA1 4YQ, United Kingdom
| | - Douglas Booker
- National Air Quality Testing Services, Lancaster Environment Centre, Lancaster University, Lancaster LA1 4YQ, United Kingdom; Lancaster Environment Centre, Lancaster University, Lancaster LA1 4YQ, United Kingdom
| | - Shizhen Zhao
- State Key Laboratory of Organic Geochemistry and Guangdong-Hong Kong-Macao Joint Laboratory for Environmental Pollution and Control, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China; CAS Center for Excellence in Deep Earth Science, Guangzhou 510640, China
| | - Jiao Tang
- State Key Laboratory of Organic Geochemistry and Guangdong-Hong Kong-Macao Joint Laboratory for Environmental Pollution and Control, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China; CAS Center for Excellence in Deep Earth Science, Guangzhou 510640, China
| | - Hongxing Jiang
- State Key Laboratory of Organic Geochemistry and Guangdong-Hong Kong-Macao Joint Laboratory for Environmental Pollution and Control, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China; CAS Center for Excellence in Deep Earth Science, Guangzhou 510640, China
| | - Jin Shen
- Guangdong Environmental Protection Key Laboratory of Secondary Air Pollution Research, Guangdong Environmental Monitoring Center, Guangzhou, China
| | - Duohong Chen
- Guangdong Environmental Protection Key Laboratory of Secondary Air Pollution Research, Guangdong Environmental Monitoring Center, Guangzhou, China
| | - Jun Li
- State Key Laboratory of Organic Geochemistry and Guangdong-Hong Kong-Macao Joint Laboratory for Environmental Pollution and Control, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China; CAS Center for Excellence in Deep Earth Science, Guangzhou 510640, China
| | - Kevin C Jones
- Lancaster Environment Centre, Lancaster University, Lancaster LA1 4YQ, United Kingdom
| | - Gan Zhang
- State Key Laboratory of Organic Geochemistry and Guangdong-Hong Kong-Macao Joint Laboratory for Environmental Pollution and Control, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China; CAS Center for Excellence in Deep Earth Science, Guangzhou 510640, China.
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11
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Zhang Y, Cheng H, Huang D, Fu C. High Temporal Resolution Land Use Regression Models with POI Characteristics of the PM 2.5 Distribution in Beijing, China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:6143. [PMID: 34200158 PMCID: PMC8201188 DOI: 10.3390/ijerph18116143] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 05/10/2021] [Accepted: 05/11/2021] [Indexed: 12/03/2022]
Abstract
PM2.5 is one of the primary components of air pollutants, and it has wide impacts on human health. Land use regression models have the typical disadvantage of low temporal resolution. In this study, various point of interests (POIs) variables are added to the usual predictive variables of the general land use regression (LUR) model to improve the temporal resolution. Hourly PM2.5 concentration data from 35 monitoring stations in Beijing, China, were used. Twelve LUR models were developed for working days and non-working days of the heating season and non-heating season, respectively. The results showed that these models achieved good fitness in winter and summer, and the highest R2 of the winter and summer models were 0.951 and 0.628, respectively. Meteorological factors, POIs, and roads factors were the most critical predictive variables in the models. This study also showed that POIs had time characteristics, and different types of POIs showed different explanations ranging from 5.5% to 41.2% of the models on working days or non-working days, respectively. Therefore, this study confirmed that POIs can greatly improve the temporal resolution of LUR models, which is significant for high precision exposure studies.
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Affiliation(s)
| | - Hongguang Cheng
- School of Environment, Beijing Normal University, Beijing 100875, China; (Y.Z.); (D.H.); (C.F.)
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12
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WARREN JOSHUAL, MIRANDA MARIELYNN, TOOTOO JOSHUAL, OSGOOD CLAIREE, BELL MICHELLEL. SPATIAL DISTRIBUTED LAG DATA FUSION FOR ESTIMATING AMBIENT AIR POLLUTION. Ann Appl Stat 2021; 15:323-342. [PMID: 34113416 PMCID: PMC8189329 DOI: 10.1214/20-aoas1399] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
We introduce spatial (DLfuse) and spatiotemporal (DLfuseST) distributed lag data fusion methods for predicting point-level ambient air pollution concentrations, using, as input, gridded average pollution estimates from a deterministic numerical air quality model. The methods incorporate predictive information from grid cells surrounding the prediction location of interest and are shown to collapse to existing downscaling approaches when this information adds no benefit. The spatial lagged parameters are allowed to vary spatially/spatiotemporally to accommodate the setting where surrounding geographic information is useful in one area/time but not in another. We apply the new methods to predict ambient concentrations of eight-hour maximum ozone and 24-hour average PM2.5 at unobserved spatial locations and times, and compare the predictions with those from several state-of-the-art data fusion approaches. Results show that DLfuse and DLfuseST often provide improved model fit and predictive accuracy when the lagged information is shown to be beneficial. Code to apply the methods is available in the R package DLfuse.
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Affiliation(s)
| | - MARIE LYNN MIRANDA
- Department of Applied and Computational Mathematics and Statistics, University of Notre Dame
| | - JOSHUA L. TOOTOO
- Children’s Environmental Health Initiative, University of Notre Dame
| | - CLAIRE E. OSGOOD
- Children’s Environmental Health Initiative, University of Notre Dame
| | - MICHELLE L. BELL
- School of Forestry and Environmental Studies, Department of Environmental Health Sciences, Yale University
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13
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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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