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Ouma YO, Keitsile A, Lottering L, Nkwae B, Odirile P. Spatiotemporal empirical analysis of particulate matter PM 2.5 pollution and air quality index (AQI) trends in Africa using MERRA-2 reanalysis datasets (1980-2021). THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 912:169027. [PMID: 38056664 DOI: 10.1016/j.scitotenv.2023.169027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 11/27/2023] [Accepted: 11/29/2023] [Indexed: 12/08/2023]
Abstract
In this study, the spatial-temporal trends of PM2.5 pollution were analyzed for subregions in Africa and the entire continent from 1980 to 2021. The distributions and trends of PM2.5 were derived from the monthly concentrations of the aerosol species from MERRA-2 reanalysis datasets comprising of sulphates (SO4), organic carbon (OC), black carbon (BC), Dust2.5 and Sea Salt (SS2.5). The resulting PM2.5 trends were compared with the climate factors, socio-economic indicators, and terrain characteristics. Using the Mann-Kendall (M-K) test, the continent and its subregions showed positive trends in PM2.5 concentrations, except for western and central Africa which exhibited marginal negative trends. The M-K trends also determined Dust2.5 as the dominant contributing aerosol factor responsible for the high PM2.5 concentrations in the northern, western and central regions of Africa, while SO4 and OC were respectively the most significant contributors to PM2.5 in the eastern and southern Africa regions. For the climate factors, the PM2.5 trends were determined to be positively correlated with the wind speed trends, while precipitation and temperature trends exhibited low and sometimes negative correlations with PM2.5. Socio-economically, highly populated, and bare/sparse vegetated areas showed higher PM2.5 concentrations, while vegetated areas tended to have lower PM2.5 concentrations. Topographically, low laying regions were observed to retain the deposited PM2.5 especially in the northern and western regions of Africa. The Air Quality Index (AQI) results showed that 94 % of the continent had an average PM2.5 of 12-35 μg/m3 hence classified as "Moderate" AQI, and the rest of the continent's PM2.5 levels was between 35 and 55 μg/m3 implying AQI classification of "Unhealthy for Sensitive People". Northern and western Africa regions had the highest AQI, while southern Africa had the lowest AQI. The approach and findings in this study can be used to complement the evaluation and management of air quality in Africa.
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Affiliation(s)
- Yashon O Ouma
- Department of Civil Engineering, University of Botswana, Private Bag UB 0061, Gaborone, Botswana.
| | - Amantle Keitsile
- Department of Civil Engineering, University of Botswana, Private Bag UB 0061, Gaborone, Botswana
| | - Lone Lottering
- Department of Civil Engineering, University of Botswana, Private Bag UB 0061, Gaborone, Botswana
| | - Boipuso Nkwae
- Department of Civil Engineering, University of Botswana, Private Bag UB 0061, Gaborone, Botswana
| | - Phillimon Odirile
- Department of Civil Engineering, University of Botswana, Private Bag UB 0061, Gaborone, Botswana
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2
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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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3
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An Estimation Method for PM2.5 Based on Aerosol Optical Depth Obtained from Remote Sensing Image Processing and Meteorological Factors. REMOTE SENSING 2022. [DOI: 10.3390/rs14071617] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Understanding the spatiotemporal variations in the mass concentrations of particulate matter ≤2.5 µm (PM2.5) in size is important for controlling environmental pollution. Currently, ground measurement points of PM2.5 in China are relatively discrete, thereby limiting spatial coverage. Aerosol optical depth (AOD) data obtained from satellite remote sensing provide insights into spatiotemporal distributions for regional pollution sources. In this study, data from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD (1 km resolution) product from Moderate Resolution Imaging Spectroradiometer (MODIS) and hourly PM2.5 concentration ground measurements from 2015 to 2020 in Dalian, China were used. Although trends in PM2.5 and AOD were consistent over time, there were seasonal differences. Spatial distributions of AOD and PM2.5 were consistent (R2 = 0.922), with higher PM2.5 values in industrial areas. The method of cross-dividing the test set by year was adopted, with AOD and meteorological factors as the input variable and PM2.5 as the output variable. A backpropagation neural network (BPNN) model of joint cross-validation was established; the stability of the model was evaluated. The trend in the predicted values of BPNN was consistent with the monitored values; the estimation result of the BPNN with the introduction of meteorological factors is better; coefficient of determination (R2) and RMSE standard deviation (SD) between the predicted values and the monitored values in the test set were 0.663–0.752 and 0.01–0.05 μg/m3, respectively. The BPNN was simpler and the training time was shorter compared with those of a regression model and support vector regression (SVR). This study demonstrated that BPNN could be effectively applied to the MAIAC AOD data to estimate PM2.5 concentrations.
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Vu BN, Bi J, Wang W, Huff A, Kondragunta S, Liu Y. Application of geostationary satellite and high-resolution meteorology data in estimating hourly PM 2.5 levels during the Camp Fire episode in California. REMOTE SENSING OF ENVIRONMENT 2022; 271:112890. [PMID: 37033879 PMCID: PMC10081518 DOI: 10.1016/j.rse.2022.112890] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Wildland fire smoke contains large amounts of PM2.5 that can traverse tens to hundreds of kilometers, resulting in significant deterioration of air quality and excess mortality and morbidity in downwind regions. Estimating PM2.5 levels while considering the impact of wildfire smoke has been challenging due to the lack of ground monitoring coverage near the smoke plumes. We aim to estimate total PM2.5 concentration during the Camp Fire episode, the deadliest wildland fire in California history. Our random forest (RF) model combines calibrated low-cost sensor data (PurpleAir) with regulatory monitor measurements (Air Quality System, AQS) to bolster ground observations, Geostationary Operational Environmental Satellite-16 (GOES-16)'s high temporal resolution to achieve hourly predictions, and oversampling techniques (Synthetic Minority Oversampling Technique, SMOTE) to reduce model underestimation at high PM2.5 levels. In addition, meteorological fields at 3 km resolution from the High-Resolution Rapid Refresh model and land use variables were also included in the model. Our AQS-only model achieved an out of bag (OOB) R2 (RMSE) of 0.84 (12.00 μg/m3) and spatial and temporal cross-validation (CV) R2 (RMSE) of 0.74 (16.28 μg/m3) and 0.73 (16.58 μg/m3), respectively. Our AQS + Weighted PurpleAir Model achieved OOB R2 (RMSE) of 0.86 (9.52 μg/m3) and spatial and temporal CV R2 (RMSE) of 0.75 (14.93 μg/m3) and 0.79 (11.89 μg/m3), respectively. Our AQS + Weighted PurpleAir + SMOTE Model achieved OOB R2 (RMSE) of 0.92 (10.44 μg/m3) and spatial and temporal CV R2 (RMSE) of 0.84 (12.36 μg/m3) and 0.85 (14.88 μg/m3), respectively. Hourly predictions from our model may aid in epidemiological investigations of intense and acute exposure to PM2.5 during the Camp Fire episode.
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Affiliation(s)
- Bryan N. Vu
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, United States
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, United States
| | - Jianzhao Bi
- Department of Environmental & Occupational Health Sciences, School of Public Health, University of Washington, Seattle, WA, United States
| | - Wenhao Wang
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, United States
| | - Amy Huff
- I.M. Systems Group, 5825 University Research Ct, Suite 3250, College Park, MD, United States
| | - Shobha Kondragunta
- Satellite Meteorology and Climatology Division, STAR Center for Satellite Applications and Research, National Oceanic and Atmospheric Administration, Washington, DC, United States
| | - Yang Liu
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, United States
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5
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Jin Z, Ma Y, Chu L, Liu Y, Dubrow R, Chen K. Predicting spatiotemporally-resolved mean air temperature over Sweden from satellite data using an ensemble model. ENVIRONMENTAL RESEARCH 2022; 204:111960. [PMID: 34464620 DOI: 10.1016/j.envres.2021.111960] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 07/29/2021] [Accepted: 08/23/2021] [Indexed: 06/13/2023]
Abstract
Mapping of air temperature (Ta) at high spatiotemporal resolution is critical to reducing exposure assessment errors in epidemiological studies on the health effects of air temperature. In this study, we applied a three-stage ensemble model to estimate daily mean Ta from satellite-based land surface temperature (Ts) over Sweden during 2001-2019 at a high spatial resolution of 1 × 1 km2. The ensemble model incorporated four base models, including a generalized additive model (GAM), a generalized additive mixed model (GAMM), and two machine learning models (random forest [RF] and extreme gradient boosting [XGBoost]), and allowed the weights for each model to vary over space, with the best-performing model for each grid cell assigned the highest weight. Various spatial predictors were included as adjustment variables in all the base models, including land cover type, normalized difference vegetation index (NDVI), and elevation. The ensemble model showed high performance with an overall R2 of 0.98 and a root mean square error of 1.38 °C in the ten-fold cross-validation, and outperformed each of the four base models. Although each base model performed well, the two machine learning models (RF [R2 = 0.97], XGBoost [R2 = 0.98]) had better performance than the two regression models (GAM [R2 = 0.95], GAMM [R2 = 0.96]). In the machine learning models, Ts was the dominant predictor of Ta, followed by day of year, NDVI, latitude, elevation, and longitude. The highly spatiotemporally-resolved Ta can improve temperature exposure assessment in future epidemiological studies.
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Affiliation(s)
- Zhihao Jin
- Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, USA; Yale Center on Climate Change and Health, Yale School of Public Health, New Haven, CT, USA
| | - Yiqun Ma
- Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, USA; Yale Center on Climate Change and Health, Yale School of Public Health, New Haven, CT, USA
| | - Lingzhi Chu
- Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, USA; Yale Center on Climate Change and Health, Yale School of Public Health, New Haven, CT, USA
| | - Yang Liu
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Robert Dubrow
- Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, USA; Yale Center on Climate Change and Health, Yale School of Public Health, New Haven, CT, USA
| | - Kai Chen
- Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, USA; Yale Center on Climate Change and Health, Yale School of Public Health, New Haven, CT, USA.
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6
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Evaluating the Utility of High-Resolution Spatiotemporal Air Pollution Data in Estimating Local PM2.5 Exposures in California from 2015–2018. ATMOSPHERE 2022. [DOI: 10.3390/atmos13010085] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Air quality management is increasingly focused not only on across-the-board reductions in ambient pollution concentrations but also on identifying and remediating elevated exposures that often occur in traditionally disadvantaged communities. Remote sensing of ambient air pollution using data derived from satellites has the potential to better inform management decisions that address environmental disparities by providing increased spatial coverage, at high-spatial resolutions, compared to air pollution exposure estimates based on ground-based monitors alone. Daily PM2.5 estimates for 2015–2018 were estimated at a 1 km2 resolution, derived from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instrument and the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm in order to assess the utility of highly refined spatiotemporal air pollution data in 92 California cities and in the 13 communities included in the California Community Air Protection Program. The identification of pollution hot-spots within a city is typically not possible relying solely on the regulatory monitoring networks; however, day-to-day temporal variability was shown to be generally well represented by nearby ground-based monitoring data even in communities with strong spatial gradients in pollutant concentrations. An assessment of within-ZIP Code variability in pollution estimates indicates that high-resolution pollution estimates (i.e., 1 km2) are not always needed to identify spatial differences in exposure but become increasingly important for larger geographic areas (approximately 50 km2). Taken together, these findings can help inform strategies for use of remote sensing data for air quality management including the screening of locations with air pollution exposures that are not well represented by existing ground-based air pollution monitors.
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Chen CC, Wang YR, Yeh HY, Lin TH, Huang CS, Wu CF. Estimating monthly PM 2.5 concentrations from satellite remote sensing data, meteorological variables, and land use data using ensemble statistical modeling and a random forest approach. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2021; 291:118159. [PMID: 34543952 DOI: 10.1016/j.envpol.2021.118159] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 09/06/2021] [Accepted: 09/09/2021] [Indexed: 06/13/2023]
Abstract
Fine particulate matter (PM2.5) is associated with various adverse health outcomes and poses serious concerns for public health. However, ground monitoring stations for PM2.5 measurements are mostly installed in population-dense or urban areas. Thus, satellite retrieved aerosol optical depth (AOD) data, which provide spatial and temporal surrogates of exposure, have become an important tool for PM2.5 estimates in a study area. In this study, we used AOD estimates of surface PM2.5 together with meteorological and land use variables to estimate monthly PM2.5 concentrations at a spatial resolution of 3 km2 over Taiwan Island from 2015 to 2019. An ensemble two-stage estimation procedure was proposed, with a generalized additive model (GAM) for temporal-trend removal in the first stage and a random forest model used to assess residual spatiotemporal variations in the second stage. We obtained a model-fitting R2 of 0.98 with a root mean square error (RMSE) of 1.40 μg/m3. The leave-one-out cross-validation (LOOCV) R2 with seasonal stratification was 0.82, and the RMSE was 3.85 μg/m3, whereas the R2 and RMSE obtained by using the pure random forest approach produced R2 and RMSE values of 0.74 and 4.60 μg/m3, respectively. The results indicated that the ensemble modeling approach had a higher predictive ability than the pure machine learning method and could provide reliable PM2.5 estimates over the entire island, which has complex terrain in terms of land use and topography.
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Affiliation(s)
- Chu-Chih Chen
- Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Taiwan; Research Center for Environmental Medicine, Kaohsiung Medical University, Taiwan.
| | - Yin-Ru Wang
- Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Taiwan
| | - Hung-Yi Yeh
- Center for Space and Remote Sensing Research, National Central University, Taiwan
| | - Tang-Huang Lin
- Center for Space and Remote Sensing Research, National Central University, Taiwan.
| | - Chun-Sheng Huang
- Institute of Environmental and Occupational Health Sciences, School of Public Health, National Taiwan University, Taiwan
| | - Chang-Fu Wu
- Institute of Environmental and Occupational Health Sciences, School of Public Health, National Taiwan University, Taiwan.
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8
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Malings C, Knowland KE, Keller CA, Cohn SE. Sub-City Scale Hourly Air Quality Forecasting by Combining Models, Satellite Observations, and Ground Measurements. EARTH AND SPACE SCIENCE (HOBOKEN, N.J.) 2021; 8:e2021EA001743. [PMID: 34435082 PMCID: PMC8365697 DOI: 10.1029/2021ea001743] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 05/03/2021] [Accepted: 05/27/2021] [Indexed: 05/19/2023]
Abstract
While multiple information sources exist concerning surface-level air pollution, no individual source simultaneously provides large-scale spatial coverage, fine spatial and temporal resolution, and high accuracy. It is, therefore, necessary to integrate multiple data sources, using the strengths of each source to compensate for the weaknesses of others. In this study, we propose a method incorporating outputs of NASA's GEOS Composition Forecasting model system with satellite information from the TROPOMI instrument and ground measurement data on surface concentrations. Although we use ground monitoring data from the Environmental Protection Agency network in the continental United States, the model and satellite data sources used have the potential to allow for global application. This method is demonstrated using surface measurements of nitrogen dioxide as a test case in regions surrounding five major US cities. The proposed method is assessed through cross-validation against withheld ground monitoring sites. In these assessments, the proposed method demonstrates major improvements over two baseline approaches which use ground-based measurements only. Results also indicate the potential for near-term updating of forecasts based on recent ground measurements.
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Affiliation(s)
- C. Malings
- Goddard Space Flight CenterNASA Postdoctoral Program FellowGreenbeltMDUSA
- Goddard Space Flight CenterGlobal Modeling and Assimilation OfficeGreenbeltMDUSA
- Universities Space Research AssociationColumbiaMDUSA
| | - K. E. Knowland
- Goddard Space Flight CenterGlobal Modeling and Assimilation OfficeGreenbeltMDUSA
- Universities Space Research AssociationColumbiaMDUSA
| | - C. A. Keller
- Goddard Space Flight CenterGlobal Modeling and Assimilation OfficeGreenbeltMDUSA
- Universities Space Research AssociationColumbiaMDUSA
| | - S. E. Cohn
- Goddard Space Flight CenterGlobal Modeling and Assimilation OfficeGreenbeltMDUSA
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9
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Zhang Y, Li Z, Bai K, Wei Y, Xie Y, Zhang Y, Ou Y, Cohen J, Zhang Y, Peng Z, Zhang X, Chen C, Hong J, Xu H, Guang J, Lv Y, Li K, Li D. Satellite remote sensing of atmospheric particulate matter mass concentration: Advances, challenges, and perspectives. FUNDAMENTAL RESEARCH 2021. [DOI: 10.1016/j.fmre.2021.04.007] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022] Open
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10
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Imputing Satellite-Derived Aerosol Optical Depth Using a Multi-Resolution Spatial Model and Random Forest for PM2.5 Prediction. REMOTE SENSING 2021. [DOI: 10.3390/rs13010126] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
A task for environmental health research is to produce complete pollution exposure maps despite limited monitoring data. Satellite-derived aerosol optical depth (AOD) is frequently used as a predictor in various models to improve PM2.5 estimation, despite significant gaps in coverage. We analyze PM2.5 and AOD from July 2011 in the contiguous United States. We examine two methods to aid in gap-filling AOD: (1) lattice kriging, a spatial statistical method adapted to handle large amounts data, and (2) random forest, a tree-based machine learning method. First, we evaluate each model’s performance in the spatial prediction of AOD, and we additionally consider ensemble methods for combining the predictors. In order to accurately assess the predictive performance of these methods, we construct spatially clustered holdouts to mimic the observed patterns of missing data. Finally, we assess whether gap-filling AOD through one of the proposed ensemble methods can improve prediction of PM2.5 in a random forest model. Our results suggest that ensemble methods of combining lattice kriging and random forest can improve AOD gap-filling. Based on summary metrics of performance, PM2.5 predictions based on random forest models were largely similar regardless of the inclusion of gap-filled AOD, but there was some variability in daily model predictions.
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11
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Xiao Q, Liang F, Ning M, Zhang Q, Bi J, He K, Lei Y, Liu Y. The long-term trend of PM 2.5-related mortality in China: The effects of source data selection. CHEMOSPHERE 2021; 263:127894. [PMID: 32814138 DOI: 10.1016/j.chemosphere.2020.127894] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Revised: 07/22/2020] [Accepted: 07/31/2020] [Indexed: 05/22/2023]
Abstract
Quantification of PM2.5 exposure and associated mortality is critical to inform policy making. Previous studies estimated varying PM2.5-related mortality in China due to the usage of different source data, but rarely justify the data selection. To quantify the sensitivity of mortality assessment to source data, we first constructed state-of-the-art PM2.5 predictions during 2000-2018 at a 1-km resolution with an ensemble machine learning model that filled missing data explicitly. We also calibrated and fused various gridded population data with a geostatistical method. Then we assessed the PM2.5-related mortality with various PM2.5 predictions, population distributions, exposure-response functions, and baseline mortalities. We found that in addition to the well documented uncertainties in the exposure-response functions, missingness in PM2.5 prediction, PM2.5 prediction error, and prediction error in population distribution resulted to a 40.5%, 25.2% and 15.9% lower mortality assessment compared to the mortality assessed with the best-performed source data, respectively. With the best-performed source data, we estimated a total of approximately 25 million PM2.5-related mortality during 2001-2017 in China. From 2001 to 2017, The PM2.5 variations, growth and aging of population, decrease in baseline mortality led to a 7.8% increase, a 42.0% increase and a 24.6% decrease in PM2.5-related mortality, separately. We showed that with the strict clean air policies implemented in 2013, the population-weighted PM2.5 concentration decreased remarkably at an annual rate of 4.5 μg/m3, leading to a decrease of 179 thousand PM2.5-related deaths nationwide during 2013-2017. The mortality decrease due to PM2.5 reduction was offset by the population growth and aging population.
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Affiliation(s)
- Qingyang Xiao
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
| | - Fengchao Liang
- Department of Epidemiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, ChineseAcademy of Medical Sciences and Peking Union Medical College, Beijing, 100037, China
| | - Miao Ning
- Atmospheric Environment Institute, Chinese Academy of Environmental Planning, Beijing, 100012, China
| | - Qiang Zhang
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China
| | - Jianzhao Bi
- Rollins School of Public Health, Emory University, Atlanta, 30032, USA
| | - Kebin He
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
| | - Yu Lei
- Atmospheric Environment Institute, Chinese Academy of Environmental Planning, Beijing, 100012, China.
| | - Yang Liu
- Rollins School of Public Health, Emory University, Atlanta, 30032, USA; State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China.
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12
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Chen J, de Hoogh K, Gulliver J, Hoffmann B, Hertel O, Ketzel M, Weinmayr G, Bauwelinck M, van Donkelaar A, Hvidtfeldt UA, Atkinson R, Janssen NAH, Martin RV, Samoli E, Andersen ZJ, Oftedal BM, Stafoggia M, Bellander T, Strak M, Wolf K, Vienneau D, Brunekreef B, Hoek G. Development of Europe-Wide Models for Particle Elemental Composition Using Supervised Linear Regression and Random Forest. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2020; 54:15698-15709. [PMID: 33237771 PMCID: PMC7745532 DOI: 10.1021/acs.est.0c06595] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
We developed Europe-wide models of long-term exposure to eight elements (copper, iron, potassium, nickel, sulfur, silicon, vanadium, and zinc) in particulate matter with diameter <2.5 μm (PM2.5) using standardized measurements for one-year periods between October 2008 and April 2011 in 19 study areas across Europe, with supervised linear regression (SLR) and random forest (RF) algorithms. Potential predictor variables were obtained from satellites, chemical transport models, land-use, traffic, and industrial point source databases to represent different sources. Overall model performance across Europe was moderate to good for all elements with hold-out-validation R-squared ranging from 0.41 to 0.90. RF consistently outperformed SLR. Models explained within-area variation much less than the overall variation, with similar performance for RF and SLR. Maps proved a useful additional model evaluation tool. Models differed substantially between elements regarding major predictor variables, broadly reflecting known sources. Agreement between the two algorithm predictions was generally high at the overall European level and varied substantially at the national level. Applying the two models in epidemiological studies could lead to different associations with health. If both between- and within-area exposure variability are exploited, RF may be preferred. If only within-area variability is used, both methods should be interpreted equally.
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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, LE1 7RH Leicester, U.K.
| | - 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, GU2 7XH Guildford, U.K.
| | - Gudrun Weinmayr
- Institute
of Epidemiology and Medical Biometry, Ulm
University, Helmholtzstr.
22, 89081 Ulm, Germany
| | - 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
- Department of Energy, Environmental &
Chemical Engineering, Washington University
in St. Louis, 63130 St. Louis, Missouri, United States
| | - Ulla A. Hvidtfeldt
- Danish Cancer Society Research Center, Strandboulevarden 49, 2100 Copenhagen, Denmark
| | | | - Nicole A. H. Janssen
- National Institute for Public Health and
the Environment (RIVM), P.O. Box 1, 3720 BA Bilthoven, The Netherlands
| | - Randall V. Martin
- Department
of Physics and Atmospheric Science, Dalhousie
University, B3H 4R2 Halifax, Nova Scotia, Canada
- Danish Cancer Society Research Center, Strandboulevarden 49, 2100 Copenhagen, Denmark
- Atomic and Molecular Physics Division, Harvard-Smithsonian Center for Astrophysics, Cambridge, 60 Garden Street, 02138 Cambridge, Massachusetts, United States
| | - 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
| | | | - Bente M. Oftedal
- Department of Environmental Health, Norwegian
Institute of Public Health, P.O. 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
| | - Maciej Strak
- Institute
for Risk Assessment Sciences (IRAS), Utrecht
University, Postbus 80125, 3508 TC Utrecht, The Netherlands
- Atomic and Molecular Physics Division, Harvard-Smithsonian Center for Astrophysics, Cambridge, 60 Garden Street, 02138 Cambridge, Massachusetts, United States
| | - 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
| | - 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, Netherlands
| | - Gerard Hoek
- Institute
for Risk Assessment Sciences (IRAS), Utrecht
University, Postbus 80125, 3508 TC Utrecht, The Netherlands
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13
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Application of Bayesian Additive Regression Trees for Estimating Daily Concentrations of PM 2.5 Components. ATMOSPHERE 2020; 11. [PMID: 34322279 PMCID: PMC8315111 DOI: 10.3390/atmos11111233] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
Abstract
Bayesian additive regression tree (BART) is a recent statistical method that combines ensemble learning and nonparametric regression. BART is constructed under a probabilistic framework that also allows for model-based prediction uncertainty quantification. We evaluated the application of BART in predicting daily concentrations of four fine particulate matter (PM2.5) components (elemental carbon, organic carbon, nitrate, and sulfate) in California during the period 2005 to 2014. We demonstrate in this paper how BART can be tuned to optimize prediction performance and how to evaluate variable importance. Our BART models included, as predictors, a large suite of land-use variables, meteorological conditions, satellite-derived aerosol optical depth parameters, and simulations from a chemical transport model. In cross-validation experiments, BART demonstrated good out-of-sample prediction performance at monitoring locations (R2 from 0.62 to 0.73). More importantly, prediction intervals associated with concentration estimates from BART showed good coverage probability at locations with and without monitoring data. In our case study, major PM2.5 components could be estimated with good accuracy, especially when collocated PM2.5 total mass observations were available. In conclusion, BART is an attractive approach for modeling ambient air pollution levels, especially for its ability to provide uncertainty in estimates that may be useful for subsequent health impact and health effect analyses.
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14
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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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15
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Peng Z, Liu W, An S. Haze pollution causality mining and prediction based on multi-dimensional time series with PS-FCM. Inf Sci (N Y) 2020. [DOI: 10.1016/j.ins.2020.03.012] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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16
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Liu X, Bai X, Tian H, Wang K, Hua S, Liu H, Liu S, Wu B, Wu Y, Liu W, Luo L, Wang Y, Hao J, Lin S, Zhao S, Zhang K. Fine particulate matter pollution in North China: Seasonal-spatial variations, source apportionment, sector and regional transport contributions. ENVIRONMENTAL RESEARCH 2020; 184:109368. [PMID: 32192990 DOI: 10.1016/j.envres.2020.109368] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2019] [Revised: 03/03/2020] [Accepted: 03/09/2020] [Indexed: 05/22/2023]
Abstract
Large areas of mainland China have been suffering frequently from heavy haze pollution during the past years, which feature high concentrations of fine particulate matter (PM2.5, particulate matters with aerodynamic diameters less than 2.5 μm) and low visibility. Moreover, these areas manifested strong regional complex pollution characteristics, particularly in North China including Beijing and the five surrounding provinces (BSFP). In this study, by using the localized comprehensive emission inventory of BSFP region in 2012 as an input, the Comprehensive Air Quality Model with Extensions-Particulate Matter Source Apportionment Technology (CAMx/PSAT) was used to assess the seasonal variations and source apportionment of PM2.5 in the highly polluted BSFP region, with a specific focus on the sectoral and sub-regional contributions to PM2.5 in Beijing during winter and summer. Results showed that the PM2.5 concentrations of BSFP region was higher in winter than that in summer. And the heavily polluted area in BSFP region shrinked noticeably in summer, compared with winter. As for source apportionment of PM2.5, residential and remaining industrial sectors constituted the top two sources of PM2.5 mass concentrations in Beijing. In addition, agricultural source represented a major contributor to ammonium, whereas transportation and power sectors constituted major sources to nitrates. In terms of contributions from sub-regions, the local sources ranked as the dominant contributors to PM2.5 in Beijing, while the main external contributions originated from the surrounding areas, such as Hebei and Shandong. Results of daily source apportionment to PM2.5 in Beijing showed that sub-regional long-distance transport became stronger when haze pollution was severe, in which contribution from remaining industrial sector would be higher than that of other periods. The results will allow for an improved understanding of the causes and origins of heavy regional PM2.5 pollution, and thus will benefit the development of effective joint control policies and identification of key polluting emission categories in North China and ultimately serve as references for other highly polluted megacities in the world.
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Affiliation(s)
- Xiangyang Liu
- State Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing, 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing, 100875, China
| | - Xiaoxuan Bai
- State Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing, 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing, 100875, China
| | - Hezhong Tian
- State Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing, 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing, 100875, China.
| | - Kun Wang
- Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing, 100875, China; Beijing Municipal Institute of Labor Protection, Beijing, 100054, China
| | - Shenbing Hua
- Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing, 100875, China; China Electric Power Research Institute, Beijing, 100192, China
| | - Huanjia Liu
- State Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing, 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing, 100875, China
| | - Shuhan Liu
- State Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing, 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing, 100875, China
| | - Bobo Wu
- State Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing, 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing, 100875, China
| | - Yiming Wu
- State Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing, 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing, 100875, China
| | - Wei Liu
- State Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing, 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing, 100875, China
| | - Lining Luo
- State Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing, 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing, 100875, China
| | - Yuxuan Wang
- Department of Earth and Atmospheric Sciences, University of Houston, Houston, TX, USA
| | - Jiming Hao
- School of Environment, Tsinghua University, Beijing, 100084, China
| | - Shumin Lin
- State Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing, 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing, 100875, China
| | - Shuang Zhao
- State Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing, 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing, 100875, China
| | - Kai Zhang
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center Houston, Houston, TX, USA
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