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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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Araki S, Shimadera H, Chatani S, Kitayama K, Shima M. Long-term spatiotemporal variation of benzo[a]pyrene in Japan: Significant decrease in ambient concentrations, human exposure, and health risk. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 360:124650. [PMID: 39111529 DOI: 10.1016/j.envpol.2024.124650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Revised: 07/24/2024] [Accepted: 07/30/2024] [Indexed: 08/15/2024]
Abstract
Although Benzo[a]pyrene (BaP) is considered carcinogenic to humans, the health effects of exposure to ambient levels have not been sufficiently investigated. This study estimated the long-term spatiotemporal variation of BaP in Japan over nearly two decades at a fine spatial resolution of 1 km. This study aimed to obtain an accurate spatiotemporal distribution of BaP that can be used in epidemiological studies on the health effects of ambient BaP exposure. The annual BaP concentrations were estimated using an ensemble machine learning approach using various predictors, including the concentrations and emission intensities of the criteria air pollutants, and meteorological, land use, and traffic-related variables. The model performance, evaluated by location-based cross-validation, exhibited satisfactory accuracy (R2 of 0.693). Densely populated areas showed higher BaP levels and greater temporal reduction, whereas BaP levels remained higher in some industrial areas. The population-weighted BaP in 2018 was 0.12 ng m-3, a decrease of approximately 70% from its 2000 value of 0.44 ng m-3, which was also reflected in the estimated excess number of lung cancer incidences. Accordingly, the proportion of BaP exposure below 0.12 ng m-3, which is the BaP concentration associated with an excess lifetime cancer risk of 10-5, reached 67% in 2018. Our estimates can be used in epidemiological studies to assess the health effects of BaP exposure at ambient concentrations.
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Affiliation(s)
- Shin Araki
- Graduate School of Engineering, Osaka University, Suita, 565-0871, Japan.
| | - Hikari Shimadera
- Graduate School of Engineering, Osaka University, Suita, 565-0871, Japan.
| | - Satoru Chatani
- National Institute for Environmental Studies, Tsukuba, 305-8506, Japan.
| | - Kyo Kitayama
- National Institute for Environmental Studies, Tsukuba, 305-8506, Japan.
| | - Masayuki Shima
- Department of Public Health, School of Medicine, Hyogo Medical University, Nishinomiya, 663-8501, Japan.
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Zalzal J, Minet L, Brook J, Mihele C, Chen H, Hatzopoulou M. Capturing Exposure Disparities with Chemical Transport Models: Evaluating the Suitability of Downscaling Using Land Use Regression. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024. [PMID: 39092553 DOI: 10.1021/acs.est.4c03725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/04/2024]
Abstract
High resolution exposure surfaces are essential to capture disparities in exposure to traffic-related air pollution in urban areas. In this study, we develop an approach to downscale Chemical Transport Model (CTM) simulations to a hyperlocal level (∼100m) in the Greater Toronto Area (GTA) under three scenarios where emissions from cars, trucks and buses are zeroed out, thus capturing the burden of each transportation mode. This proposed approach statistically fuses CTMs with Land-Use Regression using machine learning techniques. With this proposed downscaling approach, changes in air pollutant concentrations under different scenarios are appropriately captured by downscaling factors that are trained to reflect the spatial distribution of emission reductions. Our validation analysis shows that high-resolution models resulted in better performance than coarse models when compared with observations at reference stations. We used this downscaling approach to assess disparities in exposure to nitrogen dioxide (NO2) for populations composed of renters, low-income households, recent immigrants, and visible minorities. Individuals in all four categories were disproportionately exposed to the burden of cars, trucks, and buses. We conducted this analysis at spatial resolutions of 12, 4, 1 km, and 100 m and observed that disparities were significantly underestimated when using coarse spatial resolutions. This reinforces the need for high-spatial resolution exposure surfaces for environmental justice analyses.
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Affiliation(s)
- Jad Zalzal
- Department of Civil & Mineral Engineering, University of Toronto, 35 St George Street, Toronto, Ontario M5S 1A4, Canada
| | - Laura Minet
- Department of Civil Engineering, University of Victoria, 3800 Finnerty Road, Victoria, British Columbia V8P 5C2, Canada
| | - Jeffrey Brook
- Dalla Lana School of Public Health, University of Toronto, 155 College Street, Toronto, Ontario M5T 3M7, Canada
| | - Cristian Mihele
- Air Quality Research Division, Environment and Climate Change Canada, 4905 Dufferin Street, North York, Ontario M3H 5T4, Canada
| | - Hong Chen
- Dalla Lana School of Public Health, University of Toronto, 155 College Street, Toronto, Ontario M5T 3M7, Canada
- Environmental Health Science and Research Bureau, Health Canada, 50 Colombine Driveway, Ottawa, Ontario K1A 0K9, Canada
- Public Health Ontario, 480 University Avenue, Toronto, Ontario M5G 1 V2, Canada
- ICES, 2075 Bayview Avenue, Toronto, Ontario M4N 3M5, Canada
| | - Marianne Hatzopoulou
- Department of Civil & Mineral Engineering, University of Toronto, 35 St George Street, Toronto, Ontario M5S 1A4, Canada
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Deng L, Chen G, Duan T, Xie J, Huang G, Li X, Huang S, Zhang J, Luo Z, Liu C, Zhu S, He G, Dong X, Liu T, Ma W, Gong Y, Shen X, Yang P. Mixed effects of ambient air pollutants on oocyte-related outcomes: A novel insight from women undergoing assisted reproductive technology. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2024; 280:116525. [PMID: 38852468 DOI: 10.1016/j.ecoenv.2024.116525] [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: 12/05/2023] [Revised: 05/19/2024] [Accepted: 05/28/2024] [Indexed: 06/11/2024]
Abstract
Air pollution is widely acknowledged as a significant risk factor for human health, especially reproductive health. Nevertheless, many studies have disregarded the potentially mixed effects of air pollutants on reproductive outcomes. We performed a retrospective cohort study involving 8048 women with 9445 cycles undergoing In Vitro Fertilization (IVF) and Intracytoplasmic Sperm Injection (ICSI) in China, from 2017 to 2021. A land-use random forest model was applied to estimate daily residential exposure to air pollutants, including sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), ozone (O3), and fine particulate matter (PM2.5). Individual and joint associations between air pollutants and oocyte-related outcomes of ART were evaluated. In 90 days prior to oocyte pick-up to oocyte pick-up (period A), NO2, O3 and CO was negatively associated with total oocyte yield. In the 90 days prior to oocyte pick-up to start of gonadotropin medication (Gn start, period B), there was a negative dose-dependent association of exposure to five air pollutants with total oocyte yield and mature oocyte yield. In Qgcomp analysis, increasing the multiple air pollutants mixtures by one quartile was related to reducing the number of oocyte pick-ups by -2.00 % (95 %CI: -2.78 %, -1.22 %) in period A, -2.62 % (95 %CI: -3.40 %, -1.84 %) in period B, and -0.98 % (95 %CI: -1.75 %, -0.21 %) in period C. During period B, a 1-unit increase in the WQS index of multiple air pollutants exposure was associated with fewer number of total oocyte (-1.27 %, 95 %CI: -2.16 %, -0.36 %) and mature oocyte (-1.42 %, 95 %CI: -2.41 %, -0.43 %). O3 and NO2 were major contributors with adverse effects on the mixed associations. Additionally, period B appears to be the susceptible window. Our study implies that exposure to air pollution adversely affects oocyte-related outcomes, which raises concerns about the potential adverse impact of air pollution on women's reproductive health.
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Affiliation(s)
- Langjing Deng
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangdong, Guangzhou 510632, PR China; China Greater Bay Area Research Center of Environmental Health, School of Medicine, Jinan University, Guangdong, Guangzhou 510632, PR China
| | - Guimin Chen
- State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong 510060, PR China
| | - Tiantian Duan
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangdong, Guangzhou 510632, PR China; China Greater Bay Area Research Center of Environmental Health, School of Medicine, Jinan University, Guangdong, Guangzhou 510632, PR China
| | - Jinying Xie
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangdong, Guangzhou 510632, PR China; China Greater Bay Area Research Center of Environmental Health, School of Medicine, Jinan University, Guangdong, Guangzhou 510632, PR China
| | - Guangtong Huang
- School of Medicine, Jinan University, Guangdong, Guangzhou 510632, PR China
| | - Xiaojie Li
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangdong, Guangzhou 510632, PR China; China Greater Bay Area Research Center of Environmental Health, School of Medicine, Jinan University, Guangdong, Guangzhou 510632, PR China
| | - Songyi Huang
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangdong, Guangzhou 510632, PR China; China Greater Bay Area Research Center of Environmental Health, School of Medicine, Jinan University, Guangdong, Guangzhou 510632, PR China
| | - Jinglei Zhang
- Reproductive Medicine Center, the First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong 510080, PR China
| | - Zicong Luo
- Reproductive Medicine Center, the First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong 510080, PR China
| | - Chaoqun Liu
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangdong, Guangzhou 510632, PR China; China Greater Bay Area Research Center of Environmental Health, School of Medicine, Jinan University, Guangdong, Guangzhou 510632, PR China
| | - Sui Zhu
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangdong, Guangzhou 510632, PR China; China Greater Bay Area Research Center of Environmental Health, School of Medicine, Jinan University, Guangdong, Guangzhou 510632, PR China
| | - Guanhao He
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangdong, Guangzhou 510632, PR China; China Greater Bay Area Research Center of Environmental Health, School of Medicine, Jinan University, Guangdong, Guangzhou 510632, PR China
| | - Xiaomei Dong
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangdong, Guangzhou 510632, PR China; China Greater Bay Area Research Center of Environmental Health, School of Medicine, Jinan University, Guangdong, Guangzhou 510632, PR China
| | - Tao Liu
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangdong, Guangzhou 510632, PR China; China Greater Bay Area Research Center of Environmental Health, School of Medicine, Jinan University, Guangdong, Guangzhou 510632, PR China
| | - Wenjun Ma
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangdong, Guangzhou 510632, PR China; China Greater Bay Area Research Center of Environmental Health, School of Medicine, Jinan University, Guangdong, Guangzhou 510632, PR China
| | - Yajie Gong
- School of Public Health, Guangdong Pharmaceutical University, Guangzhou, Guangdong 510006, PR China.
| | - Xiaoting Shen
- Reproductive Medicine Center, the First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong 510080, PR China.
| | - Pan Yang
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangdong, Guangzhou 510632, PR China; China Greater Bay Area Research Center of Environmental Health, School of Medicine, Jinan University, Guangdong, Guangzhou 510632, PR China; Guangdong Key Laboratory of Environmental Pollution and Health, School of Environment, Jinan University, Guangdong, Guangzhou 510632, PR China.
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Ma X, Zou B, Deng J, Gao J, Longley I, Xiao S, Guo B, Wu Y, Xu T, Xu X, Yang X, Wang X, Tan Z, Wang Y, Morawska L, Salmond J. A comprehensive review of the development of land use regression approaches for modeling spatiotemporal variations of ambient air pollution: A perspective from 2011 to 2023. ENVIRONMENT INTERNATIONAL 2024; 183:108430. [PMID: 38219544 DOI: 10.1016/j.envint.2024.108430] [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: 09/03/2023] [Revised: 11/26/2023] [Accepted: 01/04/2024] [Indexed: 01/16/2024]
Abstract
Land use regression (LUR) models are widely used in epidemiological and environmental studies to estimate humans' exposure to air pollution within urban areas. However, the early models, developed using linear regressions and data from fixed monitoring stations and passive sampling, were primarily designed to model traditional and criteria air pollutants and had limitations in capturing high-resolution spatiotemporal variations of air pollution. Over the past decade, there has been a notable development of multi-source observations from low-cost monitors, mobile monitoring, and satellites, in conjunction with the integration of advanced statistical methods and spatially and temporally dynamic predictors, which have facilitated significant expansion and advancement of LUR approaches. This paper reviews and synthesizes the recent advances in LUR approaches from the perspectives of the changes in air quality data acquisition, novel predictor variables, advances in model-developing approaches, improvements in validation methods, model transferability, and modeling software as reported in 155 LUR studies published between 2011 and 2023. We demonstrate that these developments have enabled LUR models to be developed for larger study areas and encompass a wider range of criteria and unregulated air pollutants. LUR models in the conventional spatial structure have been complemented by more complex spatiotemporal structures. Compared with linear models, advanced statistical methods yield better predictions when handling data with complex relationships and interactions. Finally, this study explores new developments, identifies potential pathways for further breakthroughs in LUR methodologies, and proposes future research directions. In this context, LUR approaches have the potential to make a significant contribution to future efforts to model the patterns of long- and short-term exposure of urban populations to air pollution.
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Affiliation(s)
- Xuying Ma
- College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China; College of Safety Science and Engineering, Xi'an University of Science and Technology, Xi'an 710054, China; International Laboratory for Air Quality and Health, Queensland University of Technology, Brisbane, Queensland 4000, Australia.
| | - Bin Zou
- School of Geosciences and Info-Physics, Central South University, Changsha, Hunan 410083, China.
| | - Jun Deng
- College of Safety Science and Engineering, Xi'an University of Science and Technology, Xi'an 710054, China; Shaanxi Key Laboratory of Prevention and Control of Coal Fire, Xi'an University of Science and Technology, Xi'an 710054, China
| | - Jay Gao
- School of Environment, Faculty of Science, University of Auckland, Auckland 1010, New Zealand
| | - Ian Longley
- National Institute of Water and Atmospheric Research, Auckland 1010, New Zealand
| | - Shun Xiao
- School of Geography and Tourism, Shaanxi Normal University, Xi'an 710119, China
| | - Bin Guo
- College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China
| | - Yarui Wu
- College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China
| | - Tingting Xu
- School of Software Engineering, Chongqing University of Post and Telecommunications, Chongqing 400065, China
| | - Xin Xu
- Xi'an Institute for Innovative Earth Environment Research, Xi'an 710061, China
| | - Xiaosha Yang
- Shandong Nova Fitness Co., Ltd., Baoji, Shaanxi 722404, China
| | - Xiaoqi Wang
- College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China
| | - Zelei Tan
- College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China
| | - Yifan Wang
- College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China
| | - Lidia Morawska
- International Laboratory for Air Quality and Health, Queensland University of Technology, Brisbane, Queensland 4000, Australia.
| | - Jennifer Salmond
- School of Environment, Faculty of Science, University of Auckland, Auckland 1010, New Zealand
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6
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Wu Y, Liu H, Liu S, Lou C. Estimate of near-surface NO 2 concentrations in Fenwei Plain, China, based on TROPOMI data and random forest model. ENVIRONMENTAL MONITORING AND ASSESSMENT 2023; 195:1379. [PMID: 37882903 DOI: 10.1007/s10661-023-11993-1] [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: 06/24/2023] [Accepted: 10/12/2023] [Indexed: 10/27/2023]
Abstract
Nitrogen dioxide (NO2) concentration is a crucial indicator of ground-level air quality, and elevated concentrations can adversely affect human health and the atmospheric environment. In this study, we utilized Tropospheric Monitoring Instrument (TROPOMI) tropospheric NO2 vertical column density data (VCD) and multi-source geographic data to establish a random forest regression (RF) model that accurately estimates NO2 concentrations near the ground in the Fenwei Plain. The model addresses the inherent limitations of traditional ground-based monitoring and provides data support for analyzing regional pollution spatial and temporal characteristics. (1) The RF model based on TROPOMI and geographic data demonstrates high estimation accuracy, with monthly average RF model fit and validation coefficient of determination (R2) reaching 0.949 and 0.875, respectively. (2) A complex nonlinear relationship exists between near-surface NO2 concentration and multi-source geographic data. The RF model's estimations reveal clear seasonal and regional variations in near-surface NO2 concentration. Concentrations are generally highest in winter, followed by spring and autumn, and lowest in summer. The high NO2 concentrations are primarily mainly distributed in the plains and river valleys with low elevation and dense population density. The model estimation results also indicate that the estimated effect is better when the NO2 concentration fluctuates less and anthropogenic emission reduction measures significantly impact the NO2 concentration near the ground. (3) The population exposure risk results indicate that most cities in the Fenwei Plain face varying exposure risks. These findings offer valuable insights for regional NO2 pollution management.
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Affiliation(s)
- Yarui Wu
- College of Geomatics, Xi'an University of Science and Technology, Xi'an, 710054, Shaanxi, China.
| | - Honglei Liu
- College of Geomatics, Xi'an University of Science and Technology, Xi'an, 710054, Shaanxi, China
| | - Shuangyue Liu
- College of Geomatics, Xi'an University of Science and Technology, Xi'an, 710054, Shaanxi, China
| | - Chunhui Lou
- College of Geomatics, Xi'an University of Science and Technology, Xi'an, 710054, Shaanxi, China
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Li Y, Hong T, Gu Y, Li Z, Huang T, Lee HF, Heo Y, Yim SHL. Assessing the Spatiotemporal Characteristics, Factor Importance, and Health Impacts of Air Pollution in Seoul by Integrating Machine Learning into Land-Use Regression Modeling at High Spatiotemporal Resolutions. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:1225-1236. [PMID: 36630679 DOI: 10.1021/acs.est.2c03027] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Previous studies have characterized spatial patterns of air pollution with land-use regression (LUR) models. However, the spatiotemporal characteristics of air pollution, the contribution of various factors to them, and the resultant health impacts have yet to be evaluated comprehensively. This study integrates machine learning (random forest) into LUR modeling (LURF) with intensive evaluations to develop high spatiotemporal resolution prediction models to estimate daily and diurnal PM2.5 and NO2 in Seoul, South Korea, at the spatial resolution of 500 m for a year (2019) and to then evaluate the contribution of driving factors and quantify the resultant premature mortality. Our results show that incorporating the random forest algorithm into our LUR model improves the model performance. Meteorological conditions have a great influence on daily models, while land-use factors play important roles in diurnal models. Our health assessment using dynamic population data estimates that PM2.5 and NO2 pollution, when combined, causes a total of 11,183 (95% CI: 5837-16,354) premature mortalities in Seoul in 2019, of which 64.9% are due to PM2.5, while the remaining are attributable to NO2. The air pollution-attributable health impacts in Seoul are largely caused by cardiovascular diseases including stroke. This study pinpoints the significant spatiotemporal variations and health impact of PM2.5 and NO2 in Seoul, providing essential data for epidemiological research and air quality management.
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Affiliation(s)
- Yue Li
- Department of Geography and Resource Management, The Chinese University of Hong Kong, Sha Tin 999077, Hong Kong, China
- Institute of Environment, Energy and Sustainability, The Chinese University of Hong Kong, Sha Tin 999077, Hong Kong, China
| | - Tageui Hong
- School of Civil, Environmental and Architectural Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea
| | - Yefu Gu
- Department of Geography and Resource Management, The Chinese University of Hong Kong, Sha Tin 999077, Hong Kong, China
| | - Zhiyuan Li
- Institute of Environment, Energy and Sustainability, The Chinese University of Hong Kong, Sha Tin 999077, Hong Kong, China
| | - Tao Huang
- Department of Geography and Resource Management, The Chinese University of Hong Kong, Sha Tin 999077, Hong Kong, China
| | - Harry Fung Lee
- Department of Geography and Resource Management, The Chinese University of Hong Kong, Sha Tin 999077, Hong Kong, China
| | - Yeonsook Heo
- School of Civil, Environmental and Architectural Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea
| | - Steve H L Yim
- Asian School of the Environment, Nanyang Technological University, Singapore 639798, Singapore
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 639798, Singapore
- Earth Observatory of Singapore, Nanyang Technological University, Singapore 639798, Singapore
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Huang Z, Xu X, Ma M, Shen J. Assessment of NO 2 population exposure from 2005 to 2020 in China. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:80257-80271. [PMID: 35713829 PMCID: PMC9204072 DOI: 10.1007/s11356-022-21420-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 06/08/2022] [Indexed: 05/30/2023]
Abstract
Nitrogen dioxide (NO2) is a major air pollutant with serious environmental and human health impacts. A random forest model was developed to estimate ground-level NO2 concentrations in China at a monthly time scale based on ground-level observed NO2 concentrations, tropospheric NO2 column concentration data from the Ozone Monitoring Instrument (OMI), and meteorological covariates (the MAE, RMSE, and R2 of the model were 4.16 µg/m3, 5.79 µg/m3, and 0.79, respectively, and the MAE, RMSE, and R2 of the cross-validation were 4.3 µg/m3, 5.82 µg/m3, and 0.77, respectively). On this basis, this article analyzed the spatial and temporal variation in NO2 population exposure in China from 2005 to 2020, which effectively filled the gap in the long-term NO2 population exposure assessment in China. NO2 population exposure over China has significant spatial aggregation, with high values mainly distributed in large urban clusters in the north, east, south, and provincial capitals in the west. The NO2 population exposure in China shows a continuous increasing trend before 2012 and a continuous decreasing trend after 2012. The change in NO2 population exposure in western and southern cities is more influenced by population density compared to northern cities. NO2 pollution in China has substantially improved from 2013 to 2020, but Urumqi, Lanzhou, and Chengdu still maintain high NO2 population exposure. In these cities, the Environmental Protection Agency (EPA) could reduce NO2 population exposure through more monitoring instruments and limiting factory emissions.
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Affiliation(s)
- Zhongyu Huang
- Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Sciences, Southwest University, Chongqing, 400715, China
- Chongqing Engineering Research Center for Remote Sensing Big Data Application, School of Geographical Sciences, Southwest University, Chongqing, 400715, China
| | - Xiankang Xu
- Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Sciences, Southwest University, Chongqing, 400715, China
- Chongqing Engineering Research Center for Remote Sensing Big Data Application, School of Geographical Sciences, Southwest University, Chongqing, 400715, China
| | - Mingguo Ma
- Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Sciences, Southwest University, Chongqing, 400715, China
- Chongqing Engineering Research Center for Remote Sensing Big Data Application, School of Geographical Sciences, Southwest University, Chongqing, 400715, China
| | - Jingwei Shen
- Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Sciences, Southwest University, Chongqing, 400715, China.
- Chongqing Engineering Research Center for Remote Sensing Big Data Application, School of Geographical Sciences, Southwest University, Chongqing, 400715, China.
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9
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Yu X, Zeng Q. Random forest algorithm-based classification model of pesticide aquatic toxicity to fishes. AQUATIC TOXICOLOGY (AMSTERDAM, NETHERLANDS) 2022; 251:106265. [PMID: 36030712 DOI: 10.1016/j.aquatox.2022.106265] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 08/12/2022] [Accepted: 08/13/2022] [Indexed: 06/15/2023]
Abstract
Aquatic toxicity of pesticides can result in poisoning of many non-target organisms, of which various fishes are the most prominent one. It is a challenge to predict the toxicity (LC50) classes of organic pesticides to various fish species from global QSAR models with a larger applicability domain. In this paper, by applying the random forest (RF) algorithm for a two-class problem, only eight molecular descriptors were used to develop a quantitative structure-activity relationship (QSAR) model for 1106 toxicity data (96 h, LC50) of organic pesticides to various fish species including Oncorhynchus mykiss, Lepomis macrochirus, Pimephales promelas, Brachydanio rerio, Cyprinodon, Cyprinus carpio, etc. By the prediction of the optimal RF Model I (ntree =280, mtry = 3 and nodesize = 5), the training set (885 organic pesticides) has the prediction accuracies of 99.6% for Class 1 (LC50 ≤ 10) and 96.7% for Class 2 (LC50 > 10); the test set (221 organic pesticides) has the accuracies being 90.8% for Class 1 and 91.2% for Class 2. The optimal RF Model I is satisfactory compared with other QSAR model reported in the literature, although its descriptor subset is small.
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Affiliation(s)
- Xinliang Yu
- Hunan Provincial Key Laboratory of Environmental Catalysis & Waste Regeneration, College of Materials and Chemical Engineering, Hunan Institute of Engineering, Xiangtan, Hunan 411104, China.
| | - Qun Zeng
- Department of Neurosurgery, Central Hospital of Xiangtan, Xiangtan, Hunan 411100, China
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10
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Ayalew AD, Wagner PD, Sahlu D, Fohrer N. Land use change and climate dynamics in the Rift Valley Lake Basin, Ethiopia. ENVIRONMENTAL MONITORING AND ASSESSMENT 2022; 194:791. [PMID: 36107274 PMCID: PMC9477955 DOI: 10.1007/s10661-022-10393-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 08/12/2022] [Indexed: 06/15/2023]
Abstract
Land use and climate dynamics have a pronounced impact on water resources, biodiversity, land degradation, and productivity at all scales. Thus, in this study, we present the spatio-temporal dynamics of land use change and climate aiming to provide a scientific evidence about gains and losses in major land use categories and associated drivers and significancy and homogeneity of climate change. To this end, Landsat images and historical climate data have been used to determine the dynamics. In addition, population census data and land use policy have been considered to assess the potential drivers of land use change. The spatio-temporal land use dynamics have been evaluated using transition matrix and dynamics index. Likewise, shifts in the climate data were analyzed using change point analysis and three homogenous climate zones have been identified using principal component analysis. The results show that, from 1989 to 2019, the areal percentage of agricultural land increased by 27.5%, settlement by 0.8%, and barren land 0.4% while the natural vegetation, wetland, water body, and grass land decreased by 24.5%, 1.6%, 0.5%, and 2.1%, respectively. The land use dynamics have been stronger in the first decade of the study period. An abrupt shift of climate has occurred in the 1980s. In the last four decades, rainfall shows a not significant decreasing trend. However, a significant increasing trend has been observed for temperature. Rapid population growth, agricultural expansion policy, and climate variability have been identified as the underlying drivers of land use dynamics.
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Affiliation(s)
- Ayenew D Ayalew
- Department of Hydrology and Water Resources Management, Christian-Albrechts-University, Kiel, Germany.
| | - Paul D Wagner
- Department of Hydrology and Water Resources Management, Christian-Albrechts-University, Kiel, Germany
| | - Dejene Sahlu
- Department of Hydrology and Water Resources Management, Christian-Albrechts-University, Kiel, Germany
| | - Nicola Fohrer
- Department of Hydrology and Water Resources Management, Christian-Albrechts-University, Kiel, Germany
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11
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Liu B, Fang X, Strodl E, He G, Ruan Z, Wang X, Liu L, Chen W. Fetal Exposure to Air Pollution in Late Pregnancy Significantly Increases ADHD-Risk Behavior in Early Childhood. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph191710482. [PMID: 36078201 PMCID: PMC9518584 DOI: 10.3390/ijerph191710482] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 07/18/2022] [Accepted: 07/20/2022] [Indexed: 05/25/2023]
Abstract
BACKGROUND Air pollution nowadays has seriously threatened the health of the Chinese population, especially in the vulnerable groups of fetuses, infants and toddlers. In particular, the effects of air pollution on children's neurobehavioral development have attracted widespread attention. Moreover, the early detection of a sensitive period is very important for the precise intervention of the disease. However, such studies focusing on hyperactive behaviors and susceptible window identification are currently lacking in China. OBJECTIVES The study aims to explore the correlation between air pollution exposure and hyperactive behaviors during the early life stage and attempt to identify whether a susceptible exposure window exists that is crucial for further precise intervention. METHODS Based on the Longhua Child Cohort Study, we collected the basic information and hyperactivity index of 26,052 children using a questionnaire conducted from 2015 to 2017, and the Conners' Parent Rating Scale-revised (CPRS-48) was used to assess hyperactive behaviors. Moreover, the data of air pollution concentration (PM10, PM2.5, NO2, CO, O3 and SO2) were collected from the monitoring station between 2011 to 2017, and a land-use random forest model was used to evaluate the exposure level of each subject. Furthermore, Distributed lag non-linear models (DLNMs) were applied for statistic analysis. RESULTS The risk of child hyperactivity was found to be positively associated with early life exposure to PM10, PM2.5 and NO2. In particular, for an increase of per 10 µg/m3 in PM10, PM2.5 and NO2 exposure concentration during early life, the risk of child hyperactivity increased significantly during the seventh month of pregnancy to the fourth month after birth, with the strongest association in the ninth month of pregnancy (PM10: OR = 1.043, 95% CI: 1.016-1.071; PM2.5: OR = 1.062, 95% CI: 1.024-1.102; NO2: OR = 1.043, 95% CI: 1.016-1.071). However, no significant associations among early life exposure to CO, O3 and SO2 and child hyperactive behaviors were observed. CONCLUSIONS Early life exposure to PM10, PM2.5 and NO2 is associated with an increased risk of child ADHD-like behaviors at the age around 3 years, and the late-prenatal and early postnatal periods might be the susceptible exposure windows.
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Affiliation(s)
- Binquan Liu
- Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210006, China
| | - Xinyu Fang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei 230032, China
- Department of Public Health and Health Administration, Clincial College of Anhui Medical University, Hefei 230031, China
- Anhui Province Laboratory of Inflammation and Immune Mediated Disease, Hefei 230032, China
| | - Esben Strodl
- School of Psychology and Counselling, Queensland University of Technology, Brisbane, QLD 4059, Australia
| | - Guanhao He
- Department of Biostatistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Zengliang Ruan
- Department of Biostatistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Ximeng Wang
- Department of Biostatistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Li Liu
- Department of Biostatistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Weiqing Chen
- Department of Biostatistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
- Department of Information Management, Xinhua College of Sun Yat-sen University, Guangzhou 510080, China
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12
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Mousavi SZ, Momeni SA, Dehdashti B, Dehdashti D, Fatehizadeh A, Amin MM. Advanced treatment of industrial estate effluent using ozone-membrane processes based on optimized random forest model. JOURNAL OF WATER PROCESS ENGINEERING 2022; 48:102897. [DOI: 10.1016/j.jwpe.2022.102897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/21/2023]
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13
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Cukjati J, Mongus D, Žalik KR, Žalik B. IoT and Satellite Sensor Data Integration for Assessment of Environmental Variables: A Case Study on NO 2. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22155660. [PMID: 35957214 PMCID: PMC9371219 DOI: 10.3390/s22155660] [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: 05/19/2022] [Revised: 07/25/2022] [Accepted: 07/25/2022] [Indexed: 05/14/2023]
Abstract
This paper introduces a novel approach to increase the spatiotemporal resolution of an arbitrary environmental variable. This is achieved by utilizing machine learning algorithms to construct a satellite-like image at any given time moment, based on the measurements from IoT sensors. The target variables are calculated by an ensemble of regression models. The observed area is gridded, and partitioned into Voronoi cells based on the IoT sensors, whose measurements are available at the considered time. The pixels in each cell have a separate regression model, and take into account the measurements of the central and neighboring IoT sensors. The proposed approach was used to assess NO2 data, which were obtained from the Sentinel-5 Precursor satellite and IoT ground sensors. The approach was tested with three different machine learning algorithms: 1-nearest neighbor, linear regression and a feed-forward neural network. The highest accuracy yield was from the prediction models built with the feed-forward neural network, with an RMSE of 15.49 ×10-6 mol/m2.
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14
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Balogun AL, Tella A. Modelling and investigating the impacts of climatic variables on ozone concentration in Malaysia using correlation analysis with random forest, decision tree regression, linear regression, and support vector regression. CHEMOSPHERE 2022; 299:134250. [PMID: 35318016 DOI: 10.1016/j.chemosphere.2022.134250] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/21/2021] [Revised: 12/01/2021] [Accepted: 03/05/2022] [Indexed: 06/14/2023]
Abstract
Climate change is generally known to impact ozone concentration globally. However, the intensity varies across regions and countries. Therefore, local studies are essential to accurately assess the correlation of climate change and ozone concentration in different countries. This study investigates the effects of climatic variables on ozone concentration in Malaysia in order to understand the nexus between climate change and ozone concentration. The selected data was obtained from ten (10) air monitoring stations strategically mounted in urban-industrial and residential areas with significant emissions of pollutants. Correlation analysis and four machine learning algorithms (random forest, decision tree regression, linear regression, and support vector regression) were used to analyze ozone and meteorological dataset in the study area. The analysis was carried out during the southwest monsoon due to the rise of ozone in the dry season. The results show a very strong correlation between temperature and ozone. Wind speed also exhibits a moderate to strong correlation with ozone, while relative humidity is negatively correlated. The highest correlation values were obtained at Bukit Rambai, Nilai, Jaya II Perai, Ipoh, Klang and Petaling Jaya. These locations have high industries and are well urbanized. The four machine learning algorithms exhibit high predictive performances, generally ascertaining the predictive accuracy of the climatic variables. The random forest outperformed other algorithms with a very high R2 of 0.970, low RMSE of 2.737 and MAE of 1.824, followed by linear regression, support vector regression and decision tree regression, respectively. This study's outcome indicates a linkage between temperature and wind speed with ozone concentration in the study area. An increase of these variables will likely increase the ozone concentration posing threats to lives and the environment. Therefore, this study provides data-driven insights for decision-makers and other stakeholders in ensuring good air quality for sustainable cities and communities. It also serves as a guide for the government for necessary climate actions to reduce the effect of climate change on air pollution and enabling sustainable cities in accordance with the UN's SDGs 13 and 11, respectively.
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Affiliation(s)
- Abdul-Lateef Balogun
- Professional Services Department (Resources), Esri Australia, 613 King Street, West Melbourne, VIC, 3003, Australia; Geospatial Analysis and Modelling (GAM) Research Laboratory, Department of Civil and Environmental Engineering, Universiti Teknologi PETRONAS (UTP), 32610, Seri Iskandar, Perak, Malaysia
| | - Abdulwaheed Tella
- Earth, Environment and Space Division, Foresight Institute of Research and Translation, Ibadan, Nigeria; Geospatial Analysis and Modelling (GAM) Research Laboratory, Department of Civil and Environmental Engineering, Universiti Teknologi PETRONAS (UTP), 32610, Seri Iskandar, Perak, Malaysia.
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15
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Fine-Grained Urban Air Quality Mapping from Sparse Mobile Air Pollution Measurements and Dense Traffic Density. REMOTE SENSING 2022. [DOI: 10.3390/rs14112613] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Urban air quality mapping has been widely applied in urban planning, air pollution control and personal air pollution exposure assessment. Urban air quality maps are traditionally derived using measurements from fixed monitoring stations. Due to high cost, these stations are generally sparsely deployed in a few representative locations, leading to a highly generalized air quality map. In addition, urban air quality varies rapidly over short distances (<1 km) and is influenced by meteorological conditions, road network and traffic flow. These variations are not well represented in coarse-grained air quality maps generated by conventional fixed-site monitoring methods but have important implications for characterizing heterogeneous personal air pollution exposures and identifying localized air pollution hotspots. Therefore, fine-grained urban air quality mapping is indispensable. In this context, supplementary low-cost mobile sensors make mobile air quality monitoring a promising alternative. Using sparse air quality measurements collected by mobile sensors and various contextual factors, especially traffic flow, we propose a context-aware locally adapted deep forest (CLADF) model to infer the distribution of NO2 by 100 m and 1 h resolution for fine-grained air quality mapping. The CLADF model exploits deep forest to construct a local model for each cluster consisting of nearest neighbor measurements in contextual feature space, and considers traffic flow as an important contextual feature. Extensive validation experiments were conducted using mobile NO2 measurements collected by 17 postal vans equipped with low-cost sensors operating in Antwerp, Belgium. The experimental results demonstrate that the CLADF model achieves the lowest RMSE as well as advances in accuracy and correlation, compared with various benchmark models, including random forest, deep forest, extreme gradient boosting and support vector regression.
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16
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Spatio-Temporal Evolution Dynamic, Effect and Governance Policy of Construction Land Use in Urban Agglomeration: Case Study of Yangtze River Delta, China. SUSTAINABILITY 2022. [DOI: 10.3390/su14106204] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
The urban construction land change is the most obvious and complex spatial phenomenon in urban agglomerations which has attracted extensive attention of scholars in different fields. Yangtze River Delta Urban Agglomeration is the most mature urban agglomeration in China, a typical representative in both China and the world. This paper analyzes the evolution dynamic, effect and governance policy of urban construction land in Yangtze River Delta Urban Agglomeration 2011–2020 using a combination of BCG model, decoupling model and GIS tools. The findings are as follows. (1) There are large intercity differences in urban construction land in urban agglomerations, but the spatial heterogeneity is gradually decreasing. (2) The change trends and evolution patterns of urban construction land in urban agglomerations are increasingly diversified, with emergence of a variety of types such as rapid growth, slow growth, inverted U-shape, stars, cows, question and dogs. (3) The population growth, economic development and income improvement corresponding to the change of urban construction land in urban agglomerations have no desirable effect, with most cities in the expansive negative decoupling state. (4) The decoupling types show increasingly complex changes, in evolution, degeneration and unchanged states. Affected by economic transformation and the outbreak of COVID-19, an increasing number of cities are in strong negative decoupling and degeneration states, threatening the sustainable development of urban agglomerations. (5) Based on the division of urban agglomerations into three policy areas of Transformation Leading, Land Dependent, and Land Reduction, the response strategies for each are proposed, and a differentiated land use zoning management system is established.
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17
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Predicting Daily PM2.5 Exposure with Spatially Invariant Accuracy Using Co-Existing Pollutant Concentrations as Predictors. ATMOSPHERE 2022. [DOI: 10.3390/atmos13050782] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The spatiotemporal variation of PM2.5 should be accurately estimated for epidemiological studies. However, the accuracy of prediction models may change over geographical space, which is not conducive for proper exposure assessment. In this study, we developed a prediction model to estimate daily PM2.5 concentrations from 2010 to 2017 in the Kansai region of Japan with co-existing pollutant concentrations as predictors. The overall objective was to obtain daily estimates over the study domain with spatially homogeneous accuracy. We used random forest algorithm to model the relationship between the daily PM2.5 concentrations and various predictors. The model performance was evaluated via spatial and temporal cross-validation and the daily PM2.5 surface was estimated from 2010 to 2017 at a 1 km × 1 km resolution. We achieved R2 values of 0.91 and 0.92 for spatial and temporal cross-validation, respectively. The prediction accuracy for each monitoring site was found to be consistently high, regardless of the distance to the nearest monitoring location, up to 10 km. Even for distances greater than 10 km, the mean R2 value was 0.88. Our approach yielded spatially homogeneous prediction accuracy, which is beneficial for epidemiological studies. The daily PM2.5 estimates will be used in a related birth cohort study to evaluate the potential impact on human health.
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18
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Dharmalingam S, Senthilkumar N, D'Souza RR, Hu Y, Chang HH, Ebelt S, Yu H, Kim CS, Rohr A. Developing air pollution concentration fields for health studies using multiple methods: Cross-comparison and evaluation. ENVIRONMENTAL RESEARCH 2022; 207:112207. [PMID: 34653409 DOI: 10.1016/j.envres.2021.112207] [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: 02/06/2021] [Revised: 09/14/2021] [Accepted: 10/09/2021] [Indexed: 06/13/2023]
Abstract
Past air pollution epidemiological studies have used a wide range of methods to develop concentration fields for health analyses. The fields developed differ considerably among these methods. The reasons for these differences and comparisons of their strengths, as well as the limitations for estimating exposures, remains under-investigated. Here, we applied nine methods to develop fields of eight pollutants (carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (O3), fine particulate matter (PM2.5), and three speciated PM2.5 constituents including elemental carbon (EC), organic carbon (OC), and sulfate (SO4)) for the metropolitan Atlanta region for five years. The nine methods are Central Monitor (CM), Site Average (SA), Inverse Distance Weighting (IDW), Kriging (KRIG), Land Use Regression (LUR), satellite Aerosol Optical Depth (AOD), CMAQ model, CMAQ with kriging adjustment (CMAQ-KRIG), and CMAQ based data fusion (CMAQ-DF). Additionally, we applied an increasingly popular method, Random Forest (RF), and compared its results for NO2 and PM2.5 with other methods. For statistical evaluation, we focused our discussion on the temporal coefficient of determination, although other metrics are also calculated. Raw output from the CMAQ model contains modeling biases and errors, which are partially mitigated by fusing observational data in the CMAQ-KRIG and CMAQ-DF methods. Based on analyses that simulated model responses to more limited input data, the RF model is more robust and outperforms LUR for PM2.5. These results suggest RF may have potential in air pollution health studies, especially when limited measurement data are available. The RF method has several important weaknesses, including a relatively poor performance for NO2, diagnostic challenges, and computational intensiveness. The results of this study will help to improve our understanding of the strengths and weaknesses of different methods for estimating air pollutant exposures in epidemiological studies.
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Affiliation(s)
- Selvaraj Dharmalingam
- Department of Civil, Environmental and Construction Engineering, University of Central Florida, Orlando, FL, USA
| | - Nirupama Senthilkumar
- School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Rohan Richard D'Souza
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Yongtao Hu
- School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Howard H Chang
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Stefanie Ebelt
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Haofei Yu
- Department of Civil, Environmental and Construction Engineering, University of Central Florida, Orlando, FL, USA.
| | - Chloe S Kim
- Electric Power Research Institute, Palo Alto, CA, USA
| | - Annette Rohr
- Electric Power Research Institute, Palo Alto, CA, USA
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Dimakopoulou K, Samoli E, Analitis A, Schwartz J, Beevers S, Kitwiroon N, Beddows A, Barratt B, Rodopoulou S, Zafeiratou S, Gulliver J, Katsouyanni K. Development and Evaluation of Spatio-Temporal Air Pollution Exposure Models and Their Combinations in the Greater London Area, UK. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19095401. [PMID: 35564796 PMCID: PMC9103954 DOI: 10.3390/ijerph19095401] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 04/26/2022] [Accepted: 04/27/2022] [Indexed: 11/21/2022]
Abstract
Land use regression (LUR) and dispersion/chemical transport models (D/CTMs) are frequently applied to predict exposure to air pollution concentrations at a fine scale for use in epidemiological studies. Moreover, the use of satellite aerosol optical depth data has been a key predictor especially for particulate matter pollution and when studying large populations. Within the STEAM project we present a hybrid spatio-temporal modeling framework by (a) incorporating predictions from dispersion modeling of nitrogen dioxide (NO2), ozone (O3) and particulate matter with an aerodynamic diameter equal or less than 10 μm (PM10) and less than 2.5 μm (PM2.5) into a spatio-temporal LUR model; and (b) combining the predictions LUR and dispersion modeling and additionally, only for PM2.5, from an ensemble machine learning approach using a generalized additive model (GAM). We used air pollution measurements from 2009 to 2013 from 62 fixed monitoring sites for O3, 115 for particles and up to 130 for NO2, obtained from the dense network in the Greater London Area, UK. We assessed all models following a 10-fold cross validation (10-fold CV) procedure. The hybrid models performed better compared to separate LUR models. Incorporation of the dispersion estimates in the LUR models as a predictor, improved the LUR model fit: CV-R2 increased to 0.76 from 0.71 for NO2, to 0.79 from 0.57 for PM10, to 0.81 to 0.66 for PM2.5 and to 0.75 from 0.62 for O3. The CV-R2 obtained from the hybrid GAM framework was also increased compared to separate LUR models (CV-R2 = 0.80 for NO2, 0.76 for PM10, 0.79 for PM2.5 and 0.75 for O3). Our study supports the combined use of different air pollution exposure assessment methods in a single modeling framework to improve the accuracy of spatio-temporal predictions for subsequent use in epidemiological studies.
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Affiliation(s)
- Konstantina Dimakopoulou
- Department of Hygiene, Epidemiology and Medical Statistics, Medical School, National and Kapodistrian University of Athens, 115 27 Athens, Greece; (K.D.); (E.S.); (A.A.); (S.R.); (S.Z.)
| | - Evangelia Samoli
- Department of Hygiene, Epidemiology and Medical Statistics, Medical School, National and Kapodistrian University of Athens, 115 27 Athens, Greece; (K.D.); (E.S.); (A.A.); (S.R.); (S.Z.)
| | - Antonis Analitis
- Department of Hygiene, Epidemiology and Medical Statistics, Medical School, National and Kapodistrian University of Athens, 115 27 Athens, Greece; (K.D.); (E.S.); (A.A.); (S.R.); (S.Z.)
| | - Joel Schwartz
- Department of Environmental Health, Harvard TH Chan School of Public Health, Boston, MA 02115, USA;
- Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA 02115, USA
| | - Sean Beevers
- MRC Centre for Environment and Health, Imperial College London, London SE1 9NH, UK; (S.B.); (N.K.); (A.B.); (B.B.)
| | - Nutthida Kitwiroon
- MRC Centre for Environment and Health, Imperial College London, London SE1 9NH, UK; (S.B.); (N.K.); (A.B.); (B.B.)
| | - Andrew Beddows
- MRC Centre for Environment and Health, Imperial College London, London SE1 9NH, UK; (S.B.); (N.K.); (A.B.); (B.B.)
| | - Benjamin Barratt
- MRC Centre for Environment and Health, Imperial College London, London SE1 9NH, UK; (S.B.); (N.K.); (A.B.); (B.B.)
- National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Health Impact of Environmental Hazards, Imperial College London, London SW7 2AZ, UK
| | - Sophia Rodopoulou
- Department of Hygiene, Epidemiology and Medical Statistics, Medical School, National and Kapodistrian University of Athens, 115 27 Athens, Greece; (K.D.); (E.S.); (A.A.); (S.R.); (S.Z.)
| | - Sofia Zafeiratou
- Department of Hygiene, Epidemiology and Medical Statistics, Medical School, National and Kapodistrian University of Athens, 115 27 Athens, Greece; (K.D.); (E.S.); (A.A.); (S.R.); (S.Z.)
| | - John Gulliver
- Centre for Environmental Health and Sustainability, School of Geography, Geology and the Environment, University of Leicester, University Road, Leicester LE1 7RH, UK;
| | - Klea Katsouyanni
- Department of Hygiene, Epidemiology and Medical Statistics, Medical School, National and Kapodistrian University of Athens, 115 27 Athens, Greece; (K.D.); (E.S.); (A.A.); (S.R.); (S.Z.)
- MRC Centre for Environment and Health, Imperial College London, London SE1 9NH, UK; (S.B.); (N.K.); (A.B.); (B.B.)
- Correspondence:
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20
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Chi Y, Fan M, Zhao C, Yang Y, Fan H, Yang X, Yang J, Tao J. Machine learning-based estimation of ground-level NO 2 concentrations over China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 807:150721. [PMID: 34619217 DOI: 10.1016/j.scitotenv.2021.150721] [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: 09/02/2021] [Revised: 09/27/2021] [Accepted: 09/27/2021] [Indexed: 05/16/2023]
Abstract
Most current scientific research on NO2 remote sensing focuses on tropospheric NO2 column concentrations rather than ground-level NO2 concentrations; however, ground-level NO2 concentrations are more related to anthropogenic emissions and human health. This study proposes a machine learning estimation method for retrieving the ground-level NO2 concentrations throughout China based on the tropospheric NO2 column concentrations from the TROPOspheric Monitoring Instrument (TROPOMI) and multisource geographic data from 2018 to 2020. This method adopts the XGBoost machine learning model characterized by a strong fitting ability and complex model structure, which can explain the complex nonlinear and high-order relationships between ground-measured NO2 and its influencing factors. The R2 values between the retrievals and the validation and test datasets are 0.67 and 0.73, respectively, which suggests that the proposed method can reliably retrieve the ground-level NO2 concentrations across China. The distribution characteristics, seasonal variations and interannual differences in ground-level NO2 concentrations are further analyzed based on the retrieval results, demonstrating that the ground-level NO2 concentrations exhibit significant geographical and seasonal variations, with high concentrations in winter and low concentrations in summer, and the highly polluted regions are concentrated mainly in Beijing-Tianjin-Hebei (BTH), the Yangtze River Delta (YRD), the Pearl River Delta (PRD), Cheng-Yu District (CY) and other urban agglomerations. Finally, the interannual variation in the ground-level NO2 concentrations indicates that pollution decreased continuously from 2018 to 2020.
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Affiliation(s)
- Yulei Chi
- State Key Laboratory of Earth Surface Processes and Resource Ecology, College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China; State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
| | - Meng Fan
- State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
| | - Chuanfeng Zhao
- State Key Laboratory of Earth Surface Processes and Resource Ecology, College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China.
| | - Yikun Yang
- State Key Laboratory of Earth Surface Processes and Resource Ecology, College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China
| | - Hao Fan
- State Key Laboratory of Earth Surface Processes and Resource Ecology, College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China
| | - Xingchuan Yang
- State Key Laboratory of Earth Surface Processes and Resource Ecology, College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China
| | - Jie Yang
- State Key Laboratory of Earth Surface Processes and Resource Ecology, College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China
| | - Jinhua Tao
- State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
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21
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Interpolation-Based Fusion of Sentinel-5P, SRTM, and Regulatory-Grade Ground Stations Data for Producing Spatially Continuous Maps of PM2.5 Concentrations Nationwide over Thailand. ATMOSPHERE 2022. [DOI: 10.3390/atmos13020161] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Atmospheric pollution has recently drawn significant attention due to its proven adverse effects on public health and the environment. This concern has been aggravated specifically in Southeast Asia due to increasing vehicular use, industrial activity, and agricultural burning practices. Consequently, elevated PM2.5 concentrations have become a matter of intervention for national authorities who have addressed the needs of monitoring air pollution by operating ground stations. However, their spatial coverage is limited and the installation and maintenance are costly. Therefore, alternative approaches are necessary at national and regional scales. In the current paper, we investigated interpolation models to fuse PM2.5 measurements from ground stations and satellite data in an attempt to produce spatially continuous maps of PM2.5 nationwide over Thailand. Four approaches are compared, namely the inverse distance weighted (IDW), ordinary kriging (OK), random forest (RF), and random forest combined with OK (RFK) leveraging on the NO2, SO2, CO, HCHO, AI, and O3 products from the Sentinel-5P satellite, regulatory-grade ground PM2.5 measurements, and topographic parameters. The results suggest that RFK is the most robust, especially when the pollution levels are moderate or extreme, achieving an RMSE value of 7.11 μg/m3 and an R2 value of 0.77 during a 10-day long period in February, and an RMSE of 10.77 μg/m3 and R2 and 0.91 during the entire month of March. The proposed approach can be adopted operationally and expanded by leveraging regulatory-grade stations, low-cost sensors, as well as upcoming satellite missions such as the GEMS and the Sentinel-5.
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22
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Boniardi L, Nobile F, Stafoggia M, Michelozzi P, Ancona C. A multi-step machine learning approach to assess the impact of COVID-19 lockdown on NO 2 attributable deaths in Milan and Rome, Italy. Environ Health 2022; 21:17. [PMID: 35034644 PMCID: PMC8761378 DOI: 10.1186/s12940-021-00825-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Accepted: 12/23/2021] [Indexed: 06/14/2023]
Abstract
BACKGROUND Air pollution is one of the main concerns for the health of European citizens, and cities are currently striving to accomplish EU air pollution regulation. The 2020 COVID-19 lockdown measures can be seen as an unintended but effective experiment to assess the impact of traffic restriction policies on air pollution. Our objective was to estimate the impact of the lockdown measures on NO2 concentrations and health in the two largest Italian cities. METHODS NO2 concentration datasets were built using data deriving from a 1-month citizen science monitoring campaign that took place in Milan and Rome just before the Italian lockdown period. Annual mean NO2 concentrations were estimated for a lockdown scenario (Scenario 1) and a scenario without lockdown (Scenario 2), by applying city-specific annual adjustment factors to the 1-month data. The latter were estimated deriving data from Air Quality Network stations and by applying a machine learning approach. NO2 spatial distribution was estimated at a neighbourhood scale by applying Land Use Random Forest models for the two scenarios. Finally, the impact of lockdown on health was estimated by subtracting attributable deaths for Scenario 1 and those for Scenario 2, both estimated by applying literature-based dose-response function on the counterfactual concentrations of 10 μg/m3. RESULTS The Land Use Random Forest models were able to capture 41-42% of the total NO2 variability. Passing from Scenario 2 (annual NO2 without lockdown) to Scenario 1 (annual NO2 with lockdown), the population-weighted exposure to NO2 for Milan and Rome decreased by 15.1% and 15.3% on an annual basis. Considering the 10 μg/m3 counterfactual, prevented deaths were respectively 213 and 604. CONCLUSIONS Our results show that the lockdown had a beneficial impact on air quality and human health. However, compliance with the current EU legal limit is not enough to avoid a high number of NO2 attributable deaths. This contribution reaffirms the potentiality of the citizen science approach and calls for more ambitious traffic calming policies and a re-evaluation of the legal annual limit value for NO2 for the protection of human health.
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Affiliation(s)
- Luca Boniardi
- EPIGET - Epidemiology, Epigenetics, and Toxicology Lab, Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy.
- Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Environmental and Industrial Toxicology Unit, Milan, Italy.
| | - Federica Nobile
- Department of Epidemiology, Lazio Regional Health Service/ASL, Roma 1, Via C. Colombo 112, 00147, Rome, Italy
| | - Massimo Stafoggia
- Department of Epidemiology, Lazio Regional Health Service/ASL, Roma 1, Via C. Colombo 112, 00147, Rome, Italy
| | - Paola Michelozzi
- Department of Epidemiology, Lazio Regional Health Service/ASL, Roma 1, Via C. Colombo 112, 00147, Rome, Italy
| | - Carla Ancona
- Department of Epidemiology, Lazio Regional Health Service/ASL, Roma 1, Via C. Colombo 112, 00147, Rome, Italy
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23
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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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24
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Abstract
Abstract
Due to the recent climate changes such as floods and droughts, there is a need for Land Use Land Cover (LULC) mapping to monitor environmental changes that have effects on ecology, policy management, health and disaster management. As such, in this study, two well-known machine learning classifiers, namely, Support Vector Machine (SVM) and Random Forest (RF), are used for land cover mapping. In addition, two advanced deep learning algorithms, namely, the GAMLP and FSMLP, that are based on the Multi-layer Perceptron (MLP) function are developed in MATLAB programming language. The GAMLP uses a Genetic Algorithm (GA) to optimise parameters of the MLP function and, on the other hand, the FSMLP uses a derivative-free function for optimisation of the MLP function parameters. Three different scenarios using Landsat-8 imagery with spatial resolutions of 30 and 15 m are defined to investigate the effects of data pre-processing on the final predicted LULC map. Results based on the statistical indices, including overall accuracy (OA) and kappa index, show that the developed MLP-based algorithms have relatively high accuracies with higher than 98% correct classification. Besides the statistical indices, final LULC maps are interpreted visually where the GAMLP and FSMLP give the best results for the pre-processed Landsat-8 imagery with a spatial resolution of 15 m, but they have the worst outcomes for the unprocessed Landsat-8 imagery compared to SVM and RF classifiers visually and statistically.
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25
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Araki S, Hasunuma H, Yamamoto K, Shima M, Michikawa T, Nitta H, Nakayama SF, Yamazaki S. Estimating monthly concentrations of ambient key air pollutants in Japan during 2010-2015 for a national-scale birth cohort. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2021; 284:117483. [PMID: 34261212 DOI: 10.1016/j.envpol.2021.117483] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 05/12/2021] [Accepted: 05/27/2021] [Indexed: 06/13/2023]
Abstract
Exposure to ambient air pollution is associated with maternal and child health. Some air pollutants exhibit similar behavior in the atmosphere, and some interact with each other; thus, comprehensive assessments of individual air pollutants are required. In this study, we developed national-scale monthly models for six air pollutants (NO, NO2, SO2, O3, PM2.5, and suspended particulate matter (SPM)) to obtain accurate estimates of pollutant concentrations at 1 km × 1 km resolution from 2010 through 2015 for application to the Japan Environment and Children's Study (JECS), which is a large-scale birth cohort study. We developed our models in the land use regression framework using random forests in conjunction with kriging. We evaluated the model performance via 5-fold location-based cross-validation. We successfully predicted monthly NO (r2 = 0.65), NO2 (r2 = 0.84), O3 (r2 = 0.86), PM2.5 (r2 = 0.79), and SPM (r2 = 0.64) concentrations. For SO2, a satisfactory model could not be developed (r2 = 0.45) because of the low SO2 concentrations in Japan. The performance of our models is comparable to those reported in previous studies at similar temporal and spatial scales. The model predictions in conjunction with the JECS will reveal the critical windows of prenatal and infancy exposure to ambient air pollutants, thus contributing to the development of environmental policies on air pollution.
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Affiliation(s)
- Shin Araki
- Japan Environment and Children's Study Programme Office, National Institute for Environmental Studies, Tsukuba, 305-8506, Japan; Graduate School of Engineering, Osaka University, Osaka, 565-0871, Japan.
| | - Hideki Hasunuma
- Department of Public Health, Hyogo College of Medicine, Hyogo, 663-8501, Japan.
| | - Kouhei Yamamoto
- Graduate School of Energy Science, Kyoto University, Kyoto, 606-8501, Japan.
| | - Masayuki Shima
- Department of Public Health, Hyogo College of Medicine, Hyogo, 663-8501, Japan.
| | - Takehiro Michikawa
- Japan Environment and Children's Study Programme Office, National Institute for Environmental Studies, Tsukuba, 305-8506, Japan; Department of Environmental and Occupational Health, School of Medicine, Toho University, Tokyo, 143-8540, Japan.
| | - Hiroshi Nitta
- Japan Environment and Children's Study Programme Office, National Institute for Environmental Studies, Tsukuba, 305-8506, Japan.
| | - Shoji F Nakayama
- Japan Environment and Children's Study Programme Office, National Institute for Environmental Studies, Tsukuba, 305-8506, Japan.
| | - Shin Yamazaki
- Japan Environment and Children's Study Programme Office, National Institute for Environmental Studies, Tsukuba, 305-8506, Japan.
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26
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Coker ES, Amegah AK, Mwebaze E, Ssematimba J, Bainomugisha E. A land use regression model using machine learning and locally developed low cost particulate matter sensors in Uganda. ENVIRONMENTAL RESEARCH 2021; 199:111352. [PMID: 34043968 DOI: 10.1016/j.envres.2021.111352] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 04/22/2021] [Accepted: 05/17/2021] [Indexed: 06/12/2023]
Abstract
The application of land use regression (LUR) modeling for estimating air pollution exposure has been used only rarely in sub-Saharan Africa (SSA). This is generally due to a lack of air quality monitoring networks in the region. Low cost air quality sensors developed locally in sub-Saharan Africa presents a sustainable operating mechanism that may help generate the air monitoring data needed for exposure estimation of air pollution with LUR models. The primary objective of our study is to investigate whether a network of locally developed low-cost air quality sensors can be used in LUR modeling for accurately predicting monthly ambient fine particulate matter (PM2.5) air pollution in urban areas of central and eastern Uganda. Secondarily, we aimed to explore whether the application of machine learning (ML) can improve LUR predictions compared to ordinary least squares (OLS) regression. We used data for the entire year of 2020 from a network of 23 PM2.5 low-cost sensors located in urban municipalities of eastern and central Uganda. Between January 1, 2020 and December 31, 2020, these sensors collected highly time-resolved measurement data of PM2.5 air concentrations. We used monthly-averaged PM2.5 concentration data for LUR prediction modeling of monthly PM2.5 concentrations. We used eight different ML base-learner algorithms as well as ensemble modeling. We applied 5-fold cross validation (80% training/20% test random splits) to evaluate the models with resampling and Root mean squared error (RMSE). The relative explanatory power and accuracy of the ML algorithms were evaluated by comparing coefficient of determination (R2) and RMSE, using OLS as the reference approach. The overall average PM2.5 concentration during the study period was 52.22 μg/m3 (IQR: 38.11, 62.84 μg/m3)-well above World Health Organization PM2.5 ambient air guidelines. From the base-learner and ensemble models, RMSE and R2 values ranged between 7.65 μg/m3 - 16.85 μg/m3 and 0.24-0.84, respectively. Extreme gradient boosting (xgbTree) performed best out of the base learner algorithms (R2 = 0.84; RMSE = 7.65 μg/m3). Model performance from ensemble modeling with Lasso and Elastic-Net Regularized Generalized Linear Models (glmnet) did not outperform xgbTree, but prediction performance was comparable to that of xgbTree. The most important temporal and spatial predictors of monthly PM2.5 levels were monthly precipitation, percent of the population using solid fuels for cooking, distance to Lake Victoria, and greenspace (NDVI) within a 500-m buffer of air monitors. In conclusion, data from locally developed low-cost PM sensors provide evidence that they can be used for spatio-temporal prediction modeling of air pollution exposures in Uganda. Moreover, the non-parametric ML and ensemble approaches to LUR modeling clearly outperformed OLS regression algorithm for the prediction of monthly PM2.5 concentrations. Deploying low-cost air quality sensors in concert with implementation of data quality control measures, can help address the critical need for expanding and improving air quality monitoring in resource-constrained settings of sub-Saharan Africa. These low-cost sensors, in conjunction with non-parametric ML algorithms, may provide a rapid path forward for PM2.5 exposure assessment and to spur air pollution epidemiology research in the region.
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Affiliation(s)
- Eric S Coker
- University of Florida, College of Public Health and Health Professions, Department of Environmental and Global Health, University of Florida, Gainesville, FL, USA.
| | - A Kofi Amegah
- Public Health Research Group, Department of Biomedical Sciences, University of Cape Coast, Cape Coast, Ghana
| | | | - Joel Ssematimba
- AirQo, Department of Computer Science, College of Computing and Information Sciences, Makerere University, Plot 56 Pool Road, Kampala, Uganda
| | - Engineer Bainomugisha
- Sunbird AI, P.O. Box 11296, Kampala, Uganda; AirQo, Department of Computer Science, College of Computing and Information Sciences, Makerere University, Plot 56 Pool Road, Kampala, Uganda
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27
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Li X, Yang T, Zeng Z, Li X, Zeng G, Liang J, Xiao R, Chen X. Underestimated or overestimated? Dynamic assessment of hourly PM 2.5 exposure in the metropolitan area based on heatmap and micro-air monitoring stations. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 779:146283. [PMID: 33752001 DOI: 10.1016/j.scitotenv.2021.146283] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 02/22/2021] [Accepted: 03/01/2021] [Indexed: 06/12/2023]
Abstract
Spatio-temporal distributions of air pollution and population are two important factors influencing the patterns of mortality and diseases. Past studies have quantified the adverse effects of long-term exposure to air pollution. However, the dynamic changes of air pollution levels and population mobility within a day are rarely taken into consideration, especially in metropolitan areas. In this study, we use the high-resolution PM2.5 data from the micro-air monitoring stations, and hourly population mobility simulated by the heatmap based on Location Based Service (LBS) big data to evaluate the hourly active PM2.5 exposure in a typical Chinese metropolis. The dynamic "active population exposure" is compared spatiotemporally with the static "census population exposure" based on census data. The results show that over 12 h on both study periods, 45.83% of suburbs' population-weighted exposure (PWE) is underestimated, while 100% of rural PWE and more than 34.78% of downtown's PWE are overestimated, with the relative difference reaching from -11 μg/m3 to 7 μg/m3. More notably, the total PWE of the active population at morning peak hours on weekdays is worse than previously realized, about 12.41% of people are exposed to PM2.5 over 60 μg/m3, about twice as much as that in census scenario. The commuters who live in the suburbs and work in downtown may suffer more from PM2.5 exposure and uneven environmental resource distribution. This study proposes a new approach of calculating population exposure which can also be extended to quantify other environmental issues and related health burdens.
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Affiliation(s)
- Xin Li
- College of Environmental Science and Engineering, Hunan University, Changsha 410082, PR China.
| | - Tao Yang
- School of Architecture, Hunan University, Changsha 410082, PR China.
| | - Zhuotong Zeng
- Department of Dermatology, Second Xiangya Hospital, Central South University, Changsha 410011, PR China.
| | - Xiaodong Li
- College of Environmental Science and Engineering, Hunan University, Changsha 410082, PR China.
| | - Guangming Zeng
- College of Environmental Science and Engineering, Hunan University, Changsha 410082, PR China.
| | - Jie Liang
- College of Environmental Science and Engineering, Hunan University, Changsha 410082, PR China.
| | - Rong Xiao
- Department of Dermatology, Second Xiangya Hospital, Central South University, Changsha 410011, PR China.
| | - Xuwu Chen
- College of Environmental Science and Engineering, Hunan University, Changsha 410082, PR China.
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28
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Abstract
Air pollutant forecasting can be used to quantitatively estimate pollutant reduction trends. Combining bibliometrics with the evolutionary tree and Markov chain methods can achieve a superior quantitative analysis of research hotspots and trends. In this work, we adopted a bibliometric method to review the research status of statistical prediction methods for air pollution, used evolutionary trees to analyze the development trend of such research, and applied the Markov chain to predict future research trends for major air pollutants. The results indicate that papers mainly focused on the effects of air pollution on human diseases, urban pollution exposure models, and land use regression (LUR) methods. Particulate matter (PM), nitrogen oxides (NOx), and ozone (O3) were the most investigated pollutants. Artificial neural network (ANN) methods were preferred in studies of PM and O3, while LUR were more widely used in studies of NOx. Additionally, multi-method hybrid techniques gradually became the most widely used approach between 2010 and 2018. In the future, the statistical prediction of air pollution is expected to be based on a mixed method to simultaneously predict multiple pollutants, and the interaction between pollutants will be the most challenging aspect of research on air pollution prediction. The research results summarized in this paper provide technical support for the accurate prediction of atmospheric pollution and the emergency management of regional air quality.
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29
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Wong PY, Lee HY, Chen YC, Zeng YT, Chern YR, Chen NT, Candice Lung SC, Su HJ, Wu CD. Using a land use regression model with machine learning to estimate ground level PM 2.5. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2021; 277:116846. [PMID: 33735646 DOI: 10.1016/j.envpol.2021.116846] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 01/30/2021] [Accepted: 02/24/2021] [Indexed: 06/12/2023]
Abstract
Ambient fine particulate matter (PM2.5) has been ranked as the sixth leading risk factor globally for death and disability. Modelling methods based on having access to a limited number of monitor stations are required for capturing PM2.5 spatial and temporal continuous variations with a sufficient resolution. This study utilized a land use regression (LUR) model with machine learning to assess the spatial-temporal variability of PM2.5. Daily average PM2.5 data was collected from 73 fixed air quality monitoring stations that belonged to the Taiwan EPA on the main island of Taiwan. Nearly 280,000 observations from 2006 to 2016 were used for the analysis. Several datasets were collected to determine spatial predictor variables, including the EPA environmental resources dataset, a meteorological dataset, a land-use inventory, a landmark dataset, a digital road network map, a digital terrain model, MODIS Normalized Difference Vegetation Index (NDVI) database, and a power plant distribution dataset. First, conventional LUR and Hybrid Kriging-LUR were utilized to identify the important predictor variables. Then, deep neural network, random forest, and XGBoost algorithms were used to fit the prediction model based on the variables selected by the LUR models. Data splitting, 10-fold cross validation, external data verification, and seasonal-based and county-based validation methods were used to verify the robustness of the developed models. The results demonstrated that the proposed conventional LUR and Hybrid Kriging-LUR models captured 58% and 89% of PM2.5 variations, respectively. When XGBoost algorithm was incorporated, the explanatory power of the models increased to 73% and 94%, respectively. The Hybrid Kriging-LUR with XGBoost algorithm outperformed the other integrated methods. This study demonstrates the value of combining Hybrid Kriging-LUR model and an XGBoost algorithm for estimating the spatial-temporal variability of PM2.5 exposures.
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Affiliation(s)
- Pei-Yi Wong
- Department of Environmental and Occupational Health, National Cheng Kung University, Tainan, Taiwan
| | - Hsiao-Yun Lee
- Department of Leisure Industry and Health Promotion, National Taipei University of Nursing and Health Sciences, Taipei, Taiwan
| | - Yu-Cheng Chen
- National Institute of Environmental Health Sciences, National Health Research Institutes, Miaoli, Taiwan
| | - Yu-Ting Zeng
- Department of Geomatics, National Cheng Kung University, Tainan, Taiwan
| | - Yinq-Rong Chern
- Department of Geomatics, National Cheng Kung University, Tainan, Taiwan
| | - Nai-Tzu Chen
- Research Center of Environmental Trace Toxic Substances, National Cheng Kung University, Tainan, Taiwan
| | - Shih-Chun Candice Lung
- Research Center for Environmental Changes, Academia Sinica, Taipei, Taiwan; Department of Atmospheric Sciences, National Taiwan University, Taipei, Taiwan; Institute of Environmental Health, National Taiwan University, Taipei, Taiwan
| | - Huey-Jen Su
- Department of Environmental and Occupational Health, National Cheng Kung University, Tainan, Taiwan
| | - Chih-Da Wu
- National Institute of Environmental Health Sciences, National Health Research Institutes, Miaoli, Taiwan; Department of Geomatics, National Cheng Kung University, Tainan, Taiwan.
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Jiang Y, Zhu X, Chen C, Ge Y, Wang W, Zhao Z, Cai J, Kan H. On-field test and data calibration of a low-cost sensor for fine particles exposure assessment. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2021; 211:111958. [PMID: 33503545 DOI: 10.1016/j.ecoenv.2021.111958] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Revised: 01/09/2021] [Accepted: 01/17/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND Accurate individual exposure assessment is crucial for evaluating the health effects of particulate matter (PM). Various portable monitors built upon low-cost optical sensors have emerged. However, the main challenge for their application is to guarantee accuracy of measurements. OBJECTIVE To assess the performance of a newly developed PM sensor, and to develop methods for post-hoc data calibration to optimize its data quality. METHOD We conducted a series of laboratory experiments and field evaluations to quantify the reproducibility within Plantower PM sensors 7003 (PMS 7003) and the consistency between sensors and two established PM2.5 measurement methods [tapered element oscillating microbalances (TEOM) and gravimetric method (GM)]. Post-hoc data calibration methods for sensors were based on a multiple linear regression model (MLRM) and a random forest model (RFM). Ratios of raw and calibrated readings over the data of reference methods were calculated to examine the improvement after calibration. RESULTS Strong correlations (≥0.82) and relatively small relative standard deviations (16-21%) between sensors were found during the laboratory and the field sampling. Compared with the reference methods, moderate to strong coefficients of determination (0.56-0.83) were observed; however, significant deviations were presented. After calibration, the ratios of PMS measurements over that of two reference methods both became convergent. CONCLUSIONS Our study validated low-cost optical PM sensors under a wide range of PM2.5 concentrations (8-167 μg/m3). Our findings indicated potential applicability of PM sensors in PM2.5 exposure assessment, and confirmed a need of calibration. Linear calibration methods may be sufficient for ambient monitoring using TEOM as a reference, while nonlinear calibration methods may be more appropriate for indoor monitoring using GM as a reference.
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Affiliation(s)
- Yixuan Jiang
- 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
| | - Xinlei Zhu
- 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
| | - Chen Chen
- 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
| | - 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
| | - Weidong Wang
- 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
| | - Zhuohui Zhao
- 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; Shanghai Typhoon Institute/CMA, Shanghai Key Laboratory of Meteorology and Health, Shanghai 200030, 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; Children's Hospital of Fudan University, National Center for Children's Health, Shanghai 201102, China.
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Estimating the Daily NO2 Concentration with High Spatial Resolution in the Beijing–Tianjin–Hebei Region Using an Ensemble Learning Model. REMOTE SENSING 2021. [DOI: 10.3390/rs13040758] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Nitrogen dioxide (NO2) is an important pollutant related to human activities, which has short-term and long-term effects on human health. An ensemble learning model was constructed and applied to estimate daily NO2 concentrations in the Beijing–Tianjin–Hebei region between 2010 and 2016. A variety of predictive variables included satellite-based troposphere NO2 vertical column concentration, meteorology, elevation, gross domestic product (GDP), population, land-use variables, and road network. The ensemble learning model achieved two things: a 0.01° × 0.01° grid resolution and the estimation of historical data for the years 2010–2013. The ensemble model showed good performance, whereby the R2 of tenfold cross-validation was 0.72 and the R2 of test validation was 0.71. Meteorological hysteretic effects were incorporated into the model, where the one-day lagged boundary layer height contributed the most. The annual NO2 estimation showed little change from 2010 to 2016. The seasonal NO2 estimation from highest to lowest occurred in winter, autumn, spring, and summer. In the annual maps and seasonal maps, the NO2 estimations in the northwest region were lower than those in the southeast region, and there was a heavily polluted band in the south of the Taihang Mountains. In coastal areas, the annual NO2 estimations were higher than the NO2 monitored values. The drawback of the model is underestimation at high values and overestimation at low values. This study indicates that the ensemble learning model has excellent performance in the simulation of NO2 with high spatial and temporal resolution. Furthermore, the research framework in this study can be a generally applied for drawing implications for other regions, especially for other cities in China.
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Gordon S, Jones DK, Blazer VS, Iwanowicz L, Williams B, Smalling K. Modeling estrogenic activity in streams throughout the Potomac and Chesapeake Bay watersheds. ENVIRONMENTAL MONITORING AND ASSESSMENT 2021; 193:105. [PMID: 33527185 DOI: 10.1007/s10661-021-08899-1] [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: 05/01/2020] [Accepted: 01/17/2021] [Indexed: 06/12/2023]
Abstract
Endocrine-disrupting compounds (EDCs), specifically estrogenic endocrine-disrupting compounds, vary in concentration and composition in surface waters under the influence of different landscape sources and landcover gradients. Estrogenic activity in surface waters may lead to adverse effects in aquatic species at both individual and population levels, often observed through the presence of intersex and vitellogenin induction in male fish. In the Chesapeake Bay Watershed, located on the mid-Atlantic coast of the USA, intersex has been observed in several sub-watersheds where previous studies have identified specific landscape sources of EDCs in tandem with observed fish health effects. Previous work in the Potomac River Watershed (PRW), the largest basin within the Chesapeake Bay Watershed, was leveraged to build random forest regression models to predict estrogenic activity at unsampled reaches in both the Potomac River and larger Chesapeake Bay Watersheds (CBW). Model outputs including important variables, partial dependence plots, and predicted values of estrogenic activity at unsampled reaches provide insight into drivers of estrogenic activity at different seasons and scales. Using the US Environmental Protection Agency effects-based threshold of 1.0 ng/L 17 β-estradiol equivalents, catchments predicted to exceed this value were categorized as at risk for adverse effects from exposure to estrogenic compounds and evaluated relative to healthy watersheds and recreation access locations throughout the PRW. Results show immediate catchment scale models are more reliable than upstream models, and the best predictive variables differ by season and scale. A small percentage of healthy watersheds (< 13%) and public access sites were classified as at risk using the "Total" (annual) model in the CBW. This study is the first Potomac River Watershed assessment of estrogenic activity, providing a new foundation for future risk assessment and management design efforts, with additional context provided for the entire Chesapeake Bay Watershed.
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Affiliation(s)
- Stephanie Gordon
- U.S. Geological Survey Leetown Science Center Aquatic Ecology Laboratory, Kearneysville, WV, USA.
| | - Daniel K Jones
- U.S. Geological Survey Utah Water Science Center, West Valley City, UT, USA
| | - Vicki S Blazer
- U.S. Geological Survey Leetown Science Center Fish Health Laboratory, Kearneysville, WV, USA
| | - Luke Iwanowicz
- U.S. Geological Survey Leetown Science Center Fish Health Laboratory, Kearneysville, WV, USA
| | - Brianna Williams
- U.S. Geological Survey New Jersey Water Science Center, Lawrenceville, NJ, USA
| | - Kelly Smalling
- U.S. Geological Survey New Jersey Water Science Center, Lawrenceville, NJ, USA
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Head JR, Collender PA, Lewnard JA, Skaff NK, Li L, Cheng Q, Baker JM, Li C, Chen D, Ohringer A, Liang S, Yang C, Hubbard A, Lopman B, Remais JV. Early Evidence of Inactivated Enterovirus 71 Vaccine Impact Against Hand, Foot, and Mouth Disease in a Major Center of Ongoing Transmission in China, 2011-2018: A Longitudinal Surveillance Study. Clin Infect Dis 2020; 71:3088-3095. [PMID: 31879754 PMCID: PMC7819528 DOI: 10.1093/cid/ciz1188] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2019] [Accepted: 12/11/2019] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND Enterovirus 71 (EV71) is a major causative agent of hand, foot, and mouth disease (HFMD), associated with severe manifestations of the disease. Pediatric immunization with inactivated EV71 vaccine was initiated in 2016 in the Asia-Pacific region, including China. We analyzed a time series of HFMD cases attributable to EV71, coxsackievirus A16 (CA16), and other enteroviruses in Chengdu, a major transmission center in China, to assess early impacts of immunization. METHODS Reported HFMD cases were obtained from China's notifiable disease surveillance system. We compared observed postvaccination incidence rates during 2017-2018 with counterfactual predictions made from a negative binomial regression and a random forest model fitted to prevaccine years (2011-2015). We fit a change point model to the full time series to evaluate whether the trend of EV71 HFMD changed following vaccination. RESULTS Between 2011 and 2018, 279 352 HFMD cases were reported in the study region. The average incidence rate of EV71 HFMD in 2017-2018 was 60% (95% prediction interval [PI], 41%-72%) lower than predicted in the absence of immunization, corresponding to an estimated 6911 (95% PI, 3246-11 542) EV71 cases averted over 2 years. There were 52% (95% PI, 42%-60%) fewer severe HFMD cases than predicted. However, the incidence rate of non-CA16 and non-EV71 HFMD was elevated in 2018. We identified a significant decline in the trend of EV71 HFMD 4 months into the postvaccine period. CONCLUSIONS We provide the first real-world evidence that programmatic vaccination against EV71 is effective against childhood HFMD and present an approach to detect early vaccine impact or intended consequences from surveillance data.
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Affiliation(s)
- Jennifer R Head
- Division of Epidemiology, School of Public Health, University of California, Berkeley, Berkeley, California, USA
| | - Philip A Collender
- Division of Environmental Health Sciences, School of Public Health, University of California, Berkeley, Berkeley, California, USA
| | - Joseph A Lewnard
- Division of Epidemiology, School of Public Health, University of California, Berkeley, Berkeley, California, USA
- Center for Computational Biology, College of Engineering, University of California, Berkeley, Berkeley, California, USA
| | - Nicholas K Skaff
- Division of Environmental Health Sciences, School of Public Health, University of California, Berkeley, Berkeley, California, USA
| | - Ling Li
- Institute for Public Health Information, Sichuan Center for Disease Control and Prevention, Chengdu, China
| | - Qu Cheng
- Division of Environmental Health Sciences, School of Public Health, University of California, Berkeley, Berkeley, California, USA
| | - Julia M Baker
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA
| | - Charles Li
- Division of Environmental Health Sciences, School of Public Health, University of California, Berkeley, Berkeley, California, USA
| | - Dehao Chen
- Department of Environmental and Global Health, College of Public Health and Health Professions, University of Florida, Gainesville, Florida, USA
| | - Alison Ohringer
- Division of Environmental Health Sciences, School of Public Health, University of California, Berkeley, Berkeley, California, USA
| | - Song Liang
- Department of Environmental and Global Health, College of Public Health and Health Professions, University of Florida, Gainesville, Florida, USA
| | - Changhong Yang
- Institute for Public Health Information, Sichuan Center for Disease Control and Prevention, Chengdu, China
| | - Alan Hubbard
- Division of Biostatistics, School of Public Health, University of California, Berkeley, Berkeley, California, USA
| | - Benjamin Lopman
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA
| | - Justin V Remais
- Division of Environmental Health Sciences, School of Public Health, University of California, Berkeley, Berkeley, California, USA
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Lag Variables in Nitrogen Oxide Concentration Modelling: A Case Study in Wrocław, Poland. ATMOSPHERE 2020. [DOI: 10.3390/atmos11121293] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Due to the unwavering interest of both residents and authorities in the air quality of urban agglomerations, we pose the following question in this paper: What impact do current and past meteorological factors and traffic flow intensity have on air quality? What is the impact of lagged variables on the fit of an explanation model, and how do they affect its ability to predict? We focused on NO2 and NOx concentrations, and conducted this research using hourly data from the city of Wrocław (western Poland) from 2015 to 2017; we used multi-objective optimization to determine the optimal delays. It turned out that for both NO2 and NOx, the past values for traffic flow, wind speed, and sunshine duration are more important than the current ones. We built random forest models on each of the pollutants for both the current and past values and discovered that including a lagged variable increases the resulting R2 from 0.51 to 0.56 for NO2 and from 0.46 to 0.52 for NOx. We also analyzed the feature importance in each model, and found that for NO2, a wind speed delay of more than three hours causes a significant decrease, while the importance of relative humidity increases with a seven-hour delay; likewise, wind speed increases the importance for NOx prediction with a two-hour delay. We concluded that, in pollutant concentration modeling, the possibility of a delayed effect of the independent variables should always be considered, because it can significantly increase the performance of the model and suggest unexpected relationships or dependencies.
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Abstract
The concentration of surface ozone (O3) strongly depends on environmental and meteorological variables through a series of complex and non-linear functions. This study aims to explore the performances of an advanced machine learning (ML) method, the boosted regression trees (BRT) technique, in exploring the relationships between surface O3 and its driving factors, and in predicting the levels of O3 concentrations. To this end, a BRT model was trained on hourly data of air pollutants and meteorological parameters, acquired, over the 2016–2018 period, in a rural area affected by an anthropic source of air pollutants. The abilities of the BRT model in ranking, visualizing, and predicting the relationship between ground-level O3 concentrations and its driving factors were analyzed and illustrated. A comparison with a multiple linear regression (MLR) model was performed based on several statistical indicators. The results obtained indicated that the BRT model was able to account for 81% of changes in O3 concentrations; it slightly outperforms the MLR model in terms of the predictions accuracy and allows a better identification of the main factors influencing O3 variability on a local scale. This knowledge is expected to be useful in defining effective measures to prevent and/or mitigate the health damages associated with O3 exposure.
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Shao Y, Ma Z, Wang J, Bi J. Estimating daily ground-level PM 2.5 in China with random-forest-based spatiotemporal kriging. THE SCIENCE OF THE TOTAL ENVIRONMENT 2020; 740:139761. [PMID: 32559526 DOI: 10.1016/j.scitotenv.2020.139761] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Revised: 04/29/2020] [Accepted: 05/26/2020] [Indexed: 06/11/2023]
Abstract
Ambient fine particulate matter (PM2.5) plays an important role in cardiovascular- and respiratory-related death. Empirical statistical models have been widely applied to estimate ambient PM2.5 concentrations with correlated variables. However, empirical statistical models ignore the nonlinear relationship between PM2.5 and covariates and assume that residuals are independent and identically distributed random variables. Here, a hybrid approach, which integrates random forest (RF) model and spatiotemporal kriging, is proposed to estimate the daily PM2.5 concentration. The proposed RF-based spatiotemporal kriging (RFSTK) model effectively captures nonlinear interactions among different predictors and accounts for the detailed spatiotemporal dependence of the PM2.5 concentration. The RFSTK model performs well in predicting the daily PM2.5 concentration. The 10-fold overall cross-validation R2 value is 0.881, the mean absolute error (MAE) is 6.89 μg/m3 and the root-mean-square error (RMSE) is 11.48 μg/m3, indicating better performance than the original RF model (R2 = 0.848, MAE = 7.88 μg/m3 and RMSE = 13.26 μg/m3). The spatiotemporal prediction of the PM2.5 concentration shows that approximately 90.04% of China had a daily exposure to PM2.5 in 2018 that was below the nation's air quality standard of 75 μg/m3. The proposed hybrid method is entirely general and can be applied to map the ambient PM2.5 concentration over a large spatiotemporal domain.
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Affiliation(s)
- Yanchuan Shao
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, Jiangsu 210023, China
| | - Zongwei Ma
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, Jiangsu 210023, China; Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science & Technology, Nanjing, Jiangsu 210044, China.
| | - Jianghao Wang
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences & Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China.
| | - Jun Bi
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, Jiangsu 210023, China
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Kriging-Based Land-Use Regression Models That Use Machine Learning Algorithms to Estimate the Monthly BTEX Concentration. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:ijerph17196956. [PMID: 32977562 PMCID: PMC7579284 DOI: 10.3390/ijerph17196956] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Revised: 09/16/2020] [Accepted: 09/20/2020] [Indexed: 01/10/2023]
Abstract
This paper uses machine learning to refine a Land-use Regression (LUR) model and to estimate the spatial–temporal variation in BTEX concentrations in Kaohsiung, Taiwan. Using the Taiwanese Environmental Protection Agency (EPA) data of BTEX (benzene, toluene, ethylbenzene, and xylenes) concentrations from 2015 to 2018, which includes local emission sources as a result of Asian cultural characteristics, a new LUR model is developed. The 2019 data was then used as external data to verify the reliability of the model. We used hybrid Kriging-land-use regression (Hybrid Kriging-LUR) models, geographically weighted regression (GWR), and two machine learning algorithms—random forest (RF) and extreme gradient boosting (XGBoost)—for model development. Initially, the proposed Hybrid Kriging-LUR models explained each variation in BTEX from 37% to 52%. Using machine learning algorithms (XGBoost) increased the explanatory power of the models for each BTEX, between 61% and 79%. This study compared each combination of the Hybrid Kriging-LUR model and (i) GWR, (ii) RF, and (iii) XGBoost algorithm to estimate the spatiotemporal variation in BTEX concentration. It is shown that a combination of Hybrid Kriging-LUR and the XGBoost algorithm gives better performance than other integrated methods.
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Sánchez-Balseca J, Pérez-Foguet A. Spatio-temporal air pollution modelling using a compositional approach. Heliyon 2020; 6:e04794. [PMID: 32984572 PMCID: PMC7495062 DOI: 10.1016/j.heliyon.2020.e04794] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Revised: 06/26/2020] [Accepted: 08/24/2020] [Indexed: 01/04/2023] Open
Abstract
Air pollutant data are compositional in character because they describe quantitatively the parts of a whole (atmospheric composition). However, it is common to use air pollutant concentrations in statistical models without considering this characteristic of the data and, therefore, without control of common statistical problems, such as spurious correlations and subcompositional incoherence. This paper now proposes a daily multivariate spatio-temporal model with a compositional approach. The air pollution spatio-temporal model is based on a dynamic linear modelling framework with Bayesian inference. The novel modelling methodology was applied in an urban area for carbon monoxide (CO, mg·m-3), sulfur dioxide (SO2, μg·m-3), ozone (O3, μg·m-3), nitrogen dioxide (NO2, μg·m-3), and particulate matter less than 2.5 μm in aerodynamic diameter (PM2.5, μg·m-3). The proposal complemented and improved the conventional approach in air pollution modelling. The main improvements come from a fast multivariate data description, high spatial-correlation, and adequate modelling of air pollutants with high variability.
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Affiliation(s)
- Joseph Sánchez-Balseca
- Research Group on Engineering Sciences and Global Development (EScGD), Civil and Environmental Engineering Department, Universitat Politècnica de Catalunya – BarcelonaTech (UPC), Spain
| | - Agustí Pérez-Foguet
- Research Group on Engineering Sciences and Global Development (EScGD), Civil and Environmental Engineering Department, Universitat Politècnica de Catalunya – BarcelonaTech (UPC), Spain
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Araki S, Shima M, Yamamoto K. Estimating historical PM 2.5 exposures for three decades (1987-2016) in Japan using measurements of associated air pollutants and land use regression. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2020; 263:114476. [PMID: 33618487 DOI: 10.1016/j.envpol.2020.114476] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/02/2019] [Revised: 03/25/2020] [Accepted: 03/25/2020] [Indexed: 06/12/2023]
Abstract
Accurate estimation of historical PM2.5 exposures for epidemiological studies is challenging when extensive monitoring data are limited in duration. Here, we develop a national-scale PM2.5 exposure model for Japan using measurements recorded between 2014 and 2016 to estimate monthly means for 1987 through 2016. Our objective is to obtain accurate PM2.5 estimates for years prior to implementation of extensive PM2.5 monitoring, using observations from a limited period. We utilize a neural network to convey the non-linear relationship between the target pollutant and predictors, while incorporating the associated air pollutants. We obtain high R2 values of 0.76 and 0.73 through spatial and temporal cross validation, respectively. We evaluate estimation accuracy using an independent data set and achieve an R2 of 0.75. Moreover, monthly variations for 2000-2013 are well reproduced with correlation coefficients of greater than 0.78, obtained through a comparison with observations. We estimate monthly means at 1 × 1 km resolution from 1987 through 2016. The estimates show decreases in the area and population weighted means beginning in the 1990s. We successfully estimate monthly mean PM2.5 concentrations over three decades with outstanding predictive accuracy. Our findings illustrate that the presented approach achieves accurate long-term historical estimations using observations limited in duration.
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Affiliation(s)
- Shin Araki
- Graduate School of Engineering, Osaka University, Yamadaoka 2-1, Suita, Osaka, 565-0871, Japan.
| | - Masayuki Shima
- Department of Public Health, Hyogo College of Medicine, Mukogawa-cho 1-1, Nishinomiya, Hyogo, 663-8501, Japan
| | - Kouhei Yamamoto
- Graduate School of Energy Science, Kyoto University, Yoshidahonmachi, Sakyo, Kyoto, 606-8501, Japan
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40
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Gariazzo C, Carlino G, Silibello C, Renzi M, Finardi S, Pepe N, Radice P, Forastiere F, Michelozzi P, Viegi G, Stafoggia M. A multi-city air pollution population exposure study: Combined use of chemical-transport and random-Forest models with dynamic population data. THE SCIENCE OF THE TOTAL ENVIRONMENT 2020; 724:138102. [PMID: 32268284 DOI: 10.1016/j.scitotenv.2020.138102] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2019] [Revised: 03/13/2020] [Accepted: 03/20/2020] [Indexed: 06/11/2023]
Abstract
Cities are severely affected by air pollution. Local emissions and urban structures can produce large spatial heterogeneities. We aim to improve the estimation of NO2, O3, PM2.5 and PM10 concentrations in 6 Italian metropolitan areas, using chemical-transport and machine learning models, and to assess the effect on population exposure by using information on urban population mobility. Three years (2013-2015) of simulations were performed by the Chemical-Transport Model (CTM) FARM, at 1 km resolution, fed by boundary conditions provided by national-scale simulations, local emission inventories and meteorological fields. A downscaling of daily air pollutants at higher resolution (200 m) was then carried out by means of a machine learning Random-Forest (RF) model, considering CTM and spatial-temporal predictors, such as population, land-use, surface greenness and vehicular traffic, as input. RF achieved mean cross-validation (CV) R2 of 0.59, 0.72, 0.76 and 0.75 for NO2, PM10, PM2.5 and O3, respectively, improving results from CTM alone. Mean concentration fields exhibited clear geographical gradients caused by climate conditions, local emission sources and photochemical processes. Time series of population weighted exposure (PWE) were estimated for two months of the year 2015 and for five cities, by combining population mobility data (derived from mobile phone traffic volumes data), and concentration levels from the RF model. PWE_RF metric better approximated the observed concentrations compared with the predictions from either CTM alone or CTM and RF combined, especially for pollutants exhibiting strong spatial gradients, such as NO2. 50% of the population was estimated to be exposed to NO2 concentrations between 12 and 38 μg/m3 and PM10 between 20 and 35 μg/m3. This work supports the potential of machine learning methods in predicting air pollutant levels in urban areas at high spatial and temporal resolutions.
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Affiliation(s)
- Claudio Gariazzo
- Occupational and Environmental Medicine, Epidemiology and Hygiene Department, Italian Workers' Compensation Authority (INAIL), Monte Porzio Catone (RM), Italy.
| | | | | | - Matteo Renzi
- Department of Epidemiology, Lazio Regional Health Service, ASL Roma 1, Rome, Italy
| | | | | | | | - Francesco Forastiere
- CNR Institute of Biomedicine and Molecular Immunology "Alberto Monroy", National Research Council Palermo, Italy; Environmental Research Group, King's College, London, UK
| | - Paola Michelozzi
- Department of Epidemiology, Lazio Regional Health Service, ASL Roma 1, Rome, Italy
| | - Giovanni Viegi
- CNR Institute of Biomedicine and Molecular Immunology "Alberto Monroy", National Research Council Palermo, Italy
| | - Massimo Stafoggia
- Department of Epidemiology, Lazio Regional Health Service, ASL Roma 1, Rome, Italy
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Di Q, Amini H, Shi L, Kloog I, Silvern R, Kelly J, Sabath MB, Choirat C, Koutrakis P, Lyapustin A, Wang Y, Mickley LJ, Schwartz J. Assessing NO 2 Concentration and Model Uncertainty with High Spatiotemporal Resolution across the Contiguous United States Using Ensemble Model Averaging. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2020; 54:1372-1384. [PMID: 31851499 PMCID: PMC7065654 DOI: 10.1021/acs.est.9b03358] [Citation(s) in RCA: 129] [Impact Index Per Article: 32.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
NO2 is a combustion byproduct that has been associated with multiple adverse health outcomes. To assess NO2 levels with high accuracy, we propose the use of an ensemble model to integrate multiple machine learning algorithms, including neural network, random forest, and gradient boosting, with a variety of predictor variables, including chemical transport models. This NO2 model covers the entire contiguous U.S. with daily predictions on 1-km-level grid cells from 2000 to 2016. The ensemble produced a cross-validated R2 of 0.788 overall, a spatial R2 of 0.844, and a temporal R2 of 0.729. The relationship between daily monitored and predicted NO2 is almost linear. We also estimated the associated monthly uncertainty level for the predictions and address-specific NO2 levels. This NO2 estimation has a very high spatiotemporal resolution and allows the examination of the health effects of NO2 in unmonitored areas. We found the highest NO2 levels along highways and in cities. We also observed that nationwide NO2 levels declined in early years and stagnated after 2007, in contrast to the trend at monitoring sites in urban areas, where the decline continued. Our research indicates that the integration of different predictor variables and fitting algorithms can achieve an improved air pollution modeling framework.
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Affiliation(s)
- Qian Di
- Research Center for Public Health, Tsinghua University, Beijing, China, 100084
- Department of Environmental Health, Harvard T.H. Chan School of Public Heath, Boston, Massachusetts, United States, 02215
- Corresponding author: Qian Di ()
| | - Heresh Amini
- Department of Environmental Health, Harvard T.H. Chan School of Public Heath, Boston, Massachusetts, United States, 02215
| | - Liuhua Shi
- Department of Environmental Health, Harvard T.H. Chan School of Public Heath, Boston, Massachusetts, United States, 02215
- Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, United States, 30322
| | - Itai Kloog
- Department of Geography and Environmental Development, Ben-Gurion University of the Negev, Beer Sheva, Israel, P.O.Box 653
| | - Rachel Silvern
- Department of Earth and Planetary Sciences, Harvard University, Cambridge, Massachusetts, United States, 02138
| | - James Kelly
- U.S. Environmental Protection Agency, Office of Air Quality Planning & Standards, Research Triangle Park, North Carolina, United States, 27711
| | - M. Benjamin Sabath
- Department of Biostatistics, Harvard T.H. Chan School of Public Heath, Boston, Massachusetts, United States, 02115
| | - Christine Choirat
- Department of Biostatistics, Harvard T.H. Chan School of Public Heath, Boston, Massachusetts, United States, 02115
| | - Petros Koutrakis
- Department of Environmental Health, Harvard T.H. Chan School of Public Heath, Boston, Massachusetts, United States, 02215
| | - Alexei Lyapustin
- NASA Goddard Space Flight Center, Greenbelt, Maryland, United States, 20771
| | - Yujie Wang
- University of Maryland, Baltimore County, Baltimore, Maryland, United States, 21250
| | - Loretta J. Mickley
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge Massachusetts, United States, 02138
| | - Joel Schwartz
- Department of Environmental Health, Harvard T.H. Chan School of Public Heath, Boston, Massachusetts, United States, 02215
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Srivastava R, Tiwari AN, Giri VK. Solar radiation forecasting using MARS, CART, M5, and random forest model: A case study for India. Heliyon 2019; 5:e02692. [PMID: 31720460 PMCID: PMC6838948 DOI: 10.1016/j.heliyon.2019.e02692] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2019] [Revised: 06/19/2019] [Accepted: 10/16/2019] [Indexed: 10/26/2022] Open
Abstract
Solar radiation is a critical requirement for all solar power plants. As it is a time-varying quantity, the power output of any solar power plant is also time variant in nature. Hence, for the prediction of probable electricity generation for a few days in advance, for any solar power plant, forecasting solar radiation a few days into the future is vital. Hourly forecasting for a few days in advance may help a utility or ISO in the bidding process. In this study, 1-day-ahead to 6-day-ahead hourly solar radiation forecasting was been performed using the MARS, CART, M5 and random forest models. The data required for the forecasting were collected from a solar radiation resource setup, commissioned by an autonomous body of the Government of India in Gorakhpur, India. From the results, it was determined that, for the present study, the random forest model provided the best results, whereas the CART model presented the worst results among all four models considered.
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Affiliation(s)
- Rachit Srivastava
- Department of Electrical Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur 273010, Uttar Pradesh, India
| | - A N Tiwari
- Department of Electrical Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur 273010, Uttar Pradesh, India
| | - V K Giri
- Department of Electrical Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur 273010, Uttar Pradesh, India
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Chen ZY, Zhang R, Zhang TH, Ou CQ, Guo Y. A kriging-calibrated machine learning method for estimating daily ground-level NO 2 in mainland China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2019; 690:556-564. [PMID: 31301496 DOI: 10.1016/j.scitotenv.2019.06.349] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/02/2019] [Revised: 06/19/2019] [Accepted: 06/22/2019] [Indexed: 05/16/2023]
Abstract
It is unclear how to develop a model based on the combined satellite data and ground monitoring data to accurately estimate daily NO2 levels. Furthermore, the conventional cross-validation (CV) results represent average levels but the model performance may vary greatly from grid to grid. It is an essential issue to evaluate model's prediction ability in different grids and determine the factors affecting model extrapolating ability, which have never been well examined to date. The aim of this study was to compare the ability of three different methods to estimate the daily NO2 across mainland China during 2014-2016; and to develop a novel two-stage meta-analysis method for exploring the influence of the number and the distribution of nearby sites on grid-level prediction accuracy. For better estimating the daily NO2 level, we developed and compared three methods, including universal kriging model, satellite-based method (Non-linear exposure-lag-response model & Extreme gradient boosting combined technique) and the kriging-calibrated satellite method. For exploring influencing factors, the two-stage meta-analysis method was purposed. The kriging-calibrated satellite method had an overall CV R-square and root mean square error (RMSE) of 0.85 and 7.87μg/m3, better than the Universal Kriging model and the satellite-based method (CV R2 = 0.57 and 0.81). The two-stage meta-analysis method revealed that the model performance did decrease with the sparser distribution of nearby sites. And adding 5 sites within 50 km in the random mode can bring 17.51% improvement in model extrapolating ability. The kriging-calibration can help satellite-based machine learning to provide more accurate NO2 prediction. Our novel evaluation method can provide the suggestion of adding new sites effectively within a limit budget.
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Affiliation(s)
- Zhao-Yue Chen
- State Key Laboratory of Organ Failure Research, Department of Biostatistics, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou 510515, China
| | - Rong Zhang
- State Key Laboratory of Organ Failure Research, Department of Biostatistics, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou 510515, China
| | - Tian-Hao Zhang
- State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
| | - Chun-Quan Ou
- State Key Laboratory of Organ Failure Research, Department of Biostatistics, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou 510515, China.
| | - Yuming Guo
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Melbourne, VIC 3004, Australia
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Chen J, de Hoogh K, Gulliver J, Hoffmann B, Hertel O, Ketzel M, Bauwelinck M, van Donkelaar A, Hvidtfeldt UA, Katsouyanni K, Janssen NAH, Martin RV, Samoli E, Schwartz PE, Stafoggia M, Bellander T, Strak M, Wolf K, Vienneau D, Vermeulen R, Brunekreef B, Hoek G. A comparison of linear regression, regularization, and machine learning algorithms to develop Europe-wide spatial models of fine particles and nitrogen dioxide. ENVIRONMENT INTERNATIONAL 2019; 130:104934. [PMID: 31229871 DOI: 10.1016/j.envint.2019.104934] [Citation(s) in RCA: 83] [Impact Index Per Article: 16.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2019] [Revised: 05/21/2019] [Accepted: 06/13/2019] [Indexed: 05/12/2023]
Abstract
Empirical spatial air pollution models have been applied extensively to assess exposure in epidemiological studies with increasingly sophisticated and complex statistical algorithms beyond ordinary linear regression. However, different algorithms have rarely been compared in terms of their predictive ability. This study compared 16 algorithms to predict annual average fine particle (PM2.5) and nitrogen dioxide (NO2) concentrations across Europe. The evaluated algorithms included linear stepwise regression, regularization techniques and machine learning methods. Air pollution models were developed based on the 2010 routine monitoring data from the AIRBASE dataset maintained by the European Environmental Agency (543 sites for PM2.5 and 2399 sites for NO2), using satellite observations, dispersion model estimates and land use variables as predictors. We compared the models by performing five-fold cross-validation (CV) and by external validation (EV) using annual average concentrations measured at 416 (PM2.5) and 1396 sites (NO2) from the ESCAPE study. We further assessed the correlations between predictions by each pair of algorithms at the ESCAPE sites. For PM2.5, the models performed similarly across algorithms with a mean CV R2 of 0.59 and a mean EV R2 of 0.53. Generalized boosted machine, random forest and bagging performed best (CV R2~0.63; EV R2 0.58-0.61), while backward stepwise linear regression, support vector regression and artificial neural network performed less well (CV R2 0.48-0.57; EV R2 0.39-0.46). Most of the PM2.5 model predictions at ESCAPE sites were highly correlated (R2 > 0.85, with the exception of predictions from the artificial neural network). For NO2, the models performed even more similarly across different algorithms, with CV R2s ranging from 0.57 to 0.62, and EV R2s ranging from 0.49 to 0.51. The predicted concentrations from all algorithms at ESCAPE sites were highly correlated (R2 > 0.9). For both pollutants, biases were low for all models except the artificial neural network. Dispersion model estimates and satellite observations were two of the most important predictors for PM2.5 models whilst dispersion model estimates and traffic variables were most important for NO2 models in all algorithms that allow assessment of the importance of variables. Different statistical algorithms performed similarly when modelling spatial variation in annual average air pollution concentrations using a large number of training sites.
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Affiliation(s)
- Jie Chen
- Institute for Risk Assessment Sciences (IRAS), Utrecht University, Postbus 80125, 3508 TC, Utrecht, the Netherlands.
| | - Kees de Hoogh
- Swiss Tropical and Public Health Institute, Socinstrasse 57, 4051 Basel, Switzerland; University of Basel, Petersplatz 1, Postfach 4001 Basel, Switzerland.
| | - John Gulliver
- Centre for Environmental Health and Sustainability, School of Geography, Geology and the Environment, University of Leicester, University Road, Leicester LE1 7RH, UK.
| | - Barbara Hoffmann
- Institute for Occupational, Social and Environmental Medicine, Centre for Health and Society, Medical Faculty, Heinrich Heine University Düsseldorf, Universitätsstraße 1, 40225 Düsseldorf, Germany.
| | - Ole Hertel
- Department of Environmental Science, Aarhus University, P.O. Box 358, Frederiksborgvej 399, 4000 Roskilde, Denmark.
| | - Matthias Ketzel
- Department of Environmental Science, Aarhus University, P.O. Box 358, Frederiksborgvej 399, 4000 Roskilde, Denmark; Global Centre for Clean Air Research (GCARE), Department of Civil and Environmental Engineering, University of Surrey, Guildford GU2 7XH, UK.
| | - Mariska Bauwelinck
- Interface Demography, Department of Sociology, Vrije Universiteit Brussel, Pleinlaan 2, 1050, Brussels, Belgium.
| | - Aaron van Donkelaar
- Department of Physics and Atmospheric Science, Dalhousie University, B3H 4R2 Halifax, Nova Scotia, Canada.
| | - Ulla A Hvidtfeldt
- Danish Cancer Society Research Center, Strandboulevarden 49, 2100 Copenhagen, Denmark.
| | - Klea Katsouyanni
- Department of Hygiene, Epidemiology and Medical Statistics, Medical School, National and Kapodistrian University of Athens, 75 Mikras Asias Str, 115 27 Athens, Greece; Department Population Health Sciences and Department of Analytical, Environmental and Forensic Sciences, School of Population Health & Environmental Sciences, King's College Strand, London WC2R 2LS, UK.
| | - Nicole A H Janssen
- National Institute for Public Health and the Environment (RIVM), PO Box 1, 3720 BA, Bilthoven, the Netherlands.
| | - Randall V Martin
- Department of Physics and Atmospheric Science, Dalhousie University, B3H 4R2 Halifax, Nova Scotia, Canada; Atomic and Molecular Physics Division, Harvard-Smithsonian Center for Astrophysics, 60 Garden St, Cambridge, MA 02138, USA.
| | - Evangelia Samoli
- Department of Hygiene, Epidemiology and Medical Statistics, Medical School, National and Kapodistrian University of Athens, 75 Mikras Asias Str, 115 27 Athens, Greece.
| | - Per E Schwartz
- Division of Environmental Medicine, Norwegian Institute of Public Health, PO Box 4404 Nydalen, N-0403 Oslo, Norway.
| | - Massimo Stafoggia
- Department of Epidemiology, Lazio Region Health Service/ASL Roma 1, Via Cristoforo Colombo, 112, 00147, Rome, Italy; Institute of Environmental Medicine, Karolinska Institutet, SE-171 77 Stockholm, Sweden.
| | - Tom Bellander
- Institute of Environmental Medicine, Karolinska Institutet, SE-171 77 Stockholm, Sweden.
| | - Maciek Strak
- Institute for Risk Assessment Sciences (IRAS), Utrecht University, Postbus 80125, 3508 TC, Utrecht, the Netherlands.
| | - Kathrin Wolf
- Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Institute of Epidemiology, Ingolstädter Landstr. 1, D-85764 Neuherberg, Germany.
| | - Danielle Vienneau
- Swiss Tropical and Public Health Institute, Socinstrasse 57, 4051 Basel, Switzerland; University of Basel, Petersplatz 1, Postfach 4001 Basel, Switzerland.
| | - Roel Vermeulen
- Institute for Risk Assessment Sciences (IRAS), Utrecht University, Postbus 80125, 3508 TC, Utrecht, the Netherlands; Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, the Netherlands.
| | - Bert Brunekreef
- Institute for Risk Assessment Sciences (IRAS), Utrecht University, Postbus 80125, 3508 TC, Utrecht, the Netherlands; Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, the Netherlands.
| | - Gerard Hoek
- Institute for Risk Assessment Sciences (IRAS), Utrecht University, Postbus 80125, 3508 TC, Utrecht, the Netherlands.
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Feng R, Zheng HJ, Zhang AR, Huang C, Gao H, Ma YC. Unveiling tropospheric ozone by the traditional atmospheric model and machine learning, and their comparison:A case study in hangzhou, China. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2019; 252:366-378. [PMID: 31158665 DOI: 10.1016/j.envpol.2019.05.101] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2018] [Revised: 05/19/2019] [Accepted: 05/20/2019] [Indexed: 06/09/2023]
Abstract
Tropospheric ozone in the surface air has become the primary atmospheric pollutant in Hangzhou, China, in recent years. Previous analysis is not enough to decode it for better regulation. Therefore, we use the traditional atmospheric model, Weather Research and Forecasting coupled with Community Multi-scale Air Quality (WRF-CMAQ), and machine learning models, Extreme Learning Machine (ELM), Multi-layer Perceptron (MLP), Random Forest (RF) and Recurrent Neural Network (RNN) to analyze and predict the ozone in the surface air in Hangzhou, China, using meteorology and air pollutants as input. We firstly quantitatively demonstrate that the dew-point deficit, instead of temperature and relative humidity, is the predominant meteorological factor in shaping tropospheric ozone. Urban heat island, daily direct solar radiation time, wind speed and wind direction play trivial role in impacting tropospheric ozone. NO2 is the primary influential factors both for hourly ozone and daily O3-8 h due to the titration effect. The most environmental-friendly way to mitigate the ozone pollution is to lower the volatile organic compounds (VOCs) with the highest ozone formation potentials. We deduce that the tropospheric ozone formation process tends to be not only non-linear but also non-smooth. Compared with the traditional atmospheric models, machine learning, whose characteristics are rapid convergence, short calculating time, adaptation of forecasting episodes, small program memory, higher accuracy and less cost, is able to predict tropospheric ozone more accurately.
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Affiliation(s)
- Rui Feng
- State Key Laboratory of Clean Energy Utilization, Zhejiang University, Hangzhou, 310027, PR China.
| | - Hui-Jun Zheng
- Department of Intensive Care Unit, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310020, PR China.
| | - An-Ran Zhang
- Zhejiang Tongji Vocational College of Science and Technology, Hangzhou, 311215, PR China
| | - Chong Huang
- Hangzhou Netease Zaigu Technology Co., Ltd., Hangzhou, 310052, PR China
| | - Han Gao
- Zhejiang Construction Investment Environment Engineering Co, Ltd., Hangzhou, 310013, PR China
| | - Yu-Cheng Ma
- School of Electronics & Control Engineering, Chang'an University, Xi'an, 710064, PR China.
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Miskell G, Pattinson W, Weissert L, Williams D. Forecasting short-term peak concentrations from a network of air quality instruments measuring PM 2.5 using boosted gradient machine models. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2019; 242:56-64. [PMID: 31026803 DOI: 10.1016/j.jenvman.2019.04.010] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2018] [Revised: 02/21/2019] [Accepted: 04/05/2019] [Indexed: 06/09/2023]
Abstract
Machine learning algorithms are used successfully in this paper to forecast reliably upcoming short-term high concentration episodes, or peaks (<60-min) of fine particulate air pollution (PM2.5) 1 h in advance. Results are from a network around Christchurch, New Zealand, with an objective to forecast the occurrence of short-term peaks using a gradient boosted machine with a binary classifier as the response (1 = peak, 0 = no peak). Results are successful, with 80-90% accurate forecasting of whether a peak in PM2.5 would occur within the next 60-min period. Elevated and variable nitrogen monoxide, nitrogen dioxide, and lower temperatures and wind gusts are found to be important precursors to the occurrence of PM2.5 peaks. The use of meteorological data from a network of personal weather stations across the monitored area and from the measurement instruments was able to identify local-scale peak differences in the network. Boosted models using hourly-averaged and daily-averaged peaks as the response are developed separately to showcase differences in precursors between short-term and long-term peaks, with recent wind gusts and nitrogen oxides linked to hourly-averaged peaks and aloft air temperatures and atmospheric pressure linked to daily-averaged peaks. Results could prove useful in exposure mitigation strategies (e.g. as a short-term warning system).
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Affiliation(s)
- Georgia Miskell
- School of Chemical Sciences, University of Auckland, 23 Symonds Street, Auckland, 1010, New Zealand.
| | | | - Lena Weissert
- School of Chemical Sciences, University of Auckland, 23 Symonds Street, Auckland, 1010, New Zealand
| | - David Williams
- School of Chemical Sciences, University of Auckland, 23 Symonds Street, Auckland, 1010, New Zealand
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Remote Sensing of Wetland Flooding at a Sub-Pixel Scale Based on Random Forests and Spatial Attraction Models. REMOTE SENSING 2019. [DOI: 10.3390/rs11101231] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Wetland flooding is significant for the flora and fauna of wetlands. High temporal resolution remote sensing images are widely used for the timely mapping of wetland flooding but have a limitation of their relatively low spatial resolutions. In this study, a novel method based on random forests and spatial attraction models (RFSAM) was proposed to improve the accuracy of sub-pixel mapping of wetland flooding (SMWF) using remote sensing images. A random forests-based SMWF algorithm (RM-SMWF) was developed firstly, and a comprehensive complexity index of a mixed pixel was formulated. Then the RFSAM-SMWF method was developed. Landsat 8 Operational Land Imager (OLI) images of two wetlands of international importance included in the Ramsar List were used to evaluate RFSAM-SMWF against three other SMWF methods, and it consistently achieved more accurate sub-pixel mapping results in terms of visual and quantitative assessments in the two wetlands. The effects of the number of trees in random forests and the complexity threshold on the mapping accuracy of RFSAM-SMWF were also discussed. The results of this study improve the mapping accuracy of wetland flooding from medium-low spatial resolution remote sensing images and therefore benefit the environmental studies of wetlands.
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48
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Stafoggia M, Bellander T, Bucci S, Davoli M, de Hoogh K, De' Donato F, Gariazzo C, Lyapustin A, Michelozzi P, Renzi M, Scortichini M, Shtein A, Viegi G, Kloog I, Schwartz J. Estimation of daily PM 10 and PM 2.5 concentrations in Italy, 2013-2015, using a spatiotemporal land-use random-forest model. ENVIRONMENT INTERNATIONAL 2019; 124:170-179. [PMID: 30654325 DOI: 10.1016/j.envint.2019.01.016] [Citation(s) in RCA: 128] [Impact Index Per Article: 25.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2018] [Revised: 01/04/2019] [Accepted: 01/06/2019] [Indexed: 05/28/2023]
Abstract
Particulate matter (PM) air pollution is one of the major causes of death worldwide, with demonstrated adverse effects from both short-term and long-term exposure. Most of the epidemiological studies have been conducted in cities because of the lack of reliable spatiotemporal estimates of particles exposure in nonurban settings. The objective of this study is to estimate daily PM10 (PM < 10 μm), fine (PM < 2.5 μm, PM2.5) and coarse particles (PM between 2.5 and 10 μm, PM2.5-10) at 1-km2 grid for 2013-2015 using a machine learning approach, the Random Forest (RF). Separate RF models were defined to: predict PM2.5 and PM2.5-10 concentrations in monitors where only PM10 data were available (stage 1); impute missing satellite Aerosol Optical Depth (AOD) data using estimates from atmospheric ensemble models (stage 2); establish a relationship between measured PM and satellite, land use and meteorological parameters (stage 3); predict stage 3 model over each 1-km2 grid cell of Italy (stage 4); and improve stage 3 predictions by using small-scale predictors computed at the monitor locations or within a small buffer (stage 5). Our models were able to capture most of PM variability, with mean cross-validation (CV) R2 of 0.75 and 0.80 (stage 3) and 0.84 and 0.86 (stage 5) for PM10 and PM2.5, respectively. Model fitting was less optimal for PM2.5-10, in summer months and in southern Italy. Finally, predictions were equally good in capturing annual and daily PM variability, therefore they can be used as reliable exposure estimates for investigating long-term and short-term health effects.
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Affiliation(s)
- Massimo Stafoggia
- Department of Epidemiology, Lazio Regional Health Service/ASL Roma 1, Via C. Colombo 112, 00147 Rome, Italy; Karolinska Institutet, Institute of Environmental Medicine, Stockholm, Sweden.
| | - Tom Bellander
- Karolinska Institutet, Institute of Environmental Medicine, Stockholm, Sweden
| | - Simone Bucci
- Department of Epidemiology, Lazio Regional Health Service/ASL Roma 1, Via C. Colombo 112, 00147 Rome, Italy
| | - Marina Davoli
- Department of Epidemiology, Lazio Regional Health Service/ASL Roma 1, Via C. Colombo 112, 00147 Rome, Italy
| | - Kees de Hoogh
- Swiss Tropical and Public Health Institute, Basel, Switzerland; University of Basel, Basel, Switzerland
| | - Francesca De' Donato
- Department of Epidemiology, Lazio Regional Health Service/ASL Roma 1, Via C. Colombo 112, 00147 Rome, Italy
| | - Claudio Gariazzo
- INAIL, Department of Occupational & Environmental Medicine, Monteporzio Catone, Italy
| | - Alexei Lyapustin
- National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GSFC), Greenbelt, MD, USA
| | - Paola Michelozzi
- Department of Epidemiology, Lazio Regional Health Service/ASL Roma 1, Via C. Colombo 112, 00147 Rome, Italy
| | - Matteo Renzi
- Department of Epidemiology, Lazio Regional Health Service/ASL Roma 1, Via C. Colombo 112, 00147 Rome, Italy
| | - Matteo Scortichini
- Department of Epidemiology, Lazio Regional Health Service/ASL Roma 1, Via C. Colombo 112, 00147 Rome, Italy
| | - Alexandra Shtein
- Department of Geography and Environmental Development, Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Giovanni Viegi
- Institute of Biomedicine and Molecular Immunology "Alberto Monroy", National Research Council, Palermo, Italy
| | - Itai Kloog
- Department of Geography and Environmental Development, Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Joel Schwartz
- Department of Environmental Health, Harvard T. H. Chan School of Public Health, Cambridge, MA, USA
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Machine Learning Approaches for Outdoor Air Quality Modelling: A Systematic Review. APPLIED SCIENCES-BASEL 2018. [DOI: 10.3390/app8122570] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Current studies show that traditional deterministic models tend to struggle to capture the non-linear relationship between the concentration of air pollutants and their sources of emission and dispersion. To tackle such a limitation, the most promising approach is to use statistical models based on machine learning techniques. Nevertheless, it is puzzling why a certain algorithm is chosen over another for a given task. This systematic review intends to clarify this question by providing the reader with a comprehensive description of the principles underlying these algorithms and how they are applied to enhance prediction accuracy. A rigorous search that conforms to the PRISMA guideline is performed and results in the selection of the 46 most relevant journal papers in the area. Through a factorial analysis method these studies are synthetized and linked to each other. The main findings of this literature review show that: (i) machine learning is mainly applied in Eurasian and North American continents and (ii) estimation problems tend to implement Ensemble Learning and Regressions, whereas forecasting make use of Neural Networks and Support Vector Machines. The next challenges of this approach are to improve the prediction of pollution peaks and contaminants recently put in the spotlights (e.g., nanoparticles).
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