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Barbalat G, Hough I, Dorman M, Lepeule J, Kloog I. A multi-resolution ensemble model of three decision-tree-based algorithms to predict daily NO 2 concentration in France 2005-2022. ENVIRONMENTAL RESEARCH 2024; 257:119241. [PMID: 38810827 DOI: 10.1016/j.envres.2024.119241] [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: 01/19/2024] [Revised: 05/13/2024] [Accepted: 05/26/2024] [Indexed: 05/31/2024]
Abstract
Understanding and managing the health effects of Nitrogen Dioxide (NO2) requires high resolution spatiotemporal exposure maps. Here, we developed a multi-stage multi-resolution ensemble model that predicts daily NO2 concentration across continental France from 2005 to 2022. Innovations of this work include the computation of daily predictions at a 200 m resolution in large urban areas and the use of a spatio-temporal blocking procedure to avoid data leakage and ensure fair performance estimation. Predictions were obtained after three cascading stages of modeling: (1) predicting NO2 total column density from Ozone Monitoring Instrument satellite; (2) predicting daily NO2 concentrations at a 1 km spatial resolution using a large set of potential predictors such as predictions obtained from stage 1, land-cover and road traffic data; and (3) predicting residuals from stage 2 models at a 200 m resolution in large urban areas. The latter two stages used a generalized additive model to ensemble predictions of three decision-tree algorithms (random forest, extreme gradient boosting and categorical boosting). Cross-validated performances of our ensemble models were overall very good, with a ten-fold cross-validated R2 for the 1 km model of 0.83, and of 0.69 for the 200 m model. All three basis learners participated in the ensemble predictions to various degrees depending on time and space. In sum, our multi-stage approach was able to predict daily NO2 concentrations with a relatively low error. Ensembling the predictions maximizes the chance of obtaining accurate values if one basis learner fails in a specific area or at a particular time, by relying on the other learners. To the best of our knowledge, this is the first study aiming to predict NO2 concentrations in France with such a high spatiotemporal resolution, large spatial extent, and long temporal coverage. Exposure estimates are available to investigate NO2 health effects in epidemiological studies.
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Affiliation(s)
- Guillaume Barbalat
- University Grenoble Alpes, Inserm, CNRS, Team of Environmental Epidemiology Applied to Development and Respiratory Health, Institute for Advanced Biosciences (IAB), Grenoble, France; Centre Ressource de Réhabilitation Psychosociale et de Remédiation Cognitive, Hôpital Le Vinatier, Pôle Centre Rive Gauche, UMR, 5229, CNRS & Université Claude Bernard Lyon 1, France.
| | - Ian Hough
- Université Grenoble Alpes, CNRS, INRAE, IRD, INP-G, IGE (UMR 5001), Grenoble, France
| | - Michael Dorman
- The Department of Environmental, Geoinformatics and Urban Planning Sciences, Ben-Gurion University of the Negev, Israel
| | - Johanna Lepeule
- University Grenoble Alpes, Inserm, CNRS, Team of Environmental Epidemiology Applied to Development and Respiratory Health, Institute for Advanced Biosciences (IAB), Grenoble, France.
| | - Itai Kloog
- The Department of Environmental, Geoinformatics and Urban Planning Sciences, Ben-Gurion University of the Negev, Israel; Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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Wu Y, Bi J, Gassett AJ, Young MT, Szpiro AA, Kaufman JD. Integrating traffic pollution dispersion into spatiotemporal NO 2 prediction. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 925:171652. [PMID: 38485010 PMCID: PMC11027090 DOI: 10.1016/j.scitotenv.2024.171652] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 02/18/2024] [Accepted: 03/09/2024] [Indexed: 03/25/2024]
Abstract
Accurately predicting ambient NO2 concentrations has great public health importance, as traffic-related air pollution is of major concern in urban areas. In this study, we present a novel approach incorporating traffic contribution to NO2 prediction in a fine-scale spatiotemporal model. We used nationally available traffic estimate dataset in a scalable dispersion model, Research LINE source dispersion model (RLINE). RLINE estimates then served as an additional input for a validated spatiotemporal pollution modeling approach. Our analysis uses measurement data collected by the Multi-Ethnic Study of Atherosclerosis and Air Pollution in the greater Los Angeles area between 2006 and 2009. We predicted road-type-specific annual average daily traffic (AADT) on road segments via national-level spatial regression models with nearest-neighbor Gaussian processes (spNNGP); the spNNGP models were trained based on over half a million point-level traffic volume measurements nationwide. AADT estimates on all highways were combined with meteorological data in RLINE models. We evaluated two strategies to integrate RLINE estimates into spatiotemporal NO2 models: 1) incorporating RLINE estimates as a space-only covariate and, 2) as a spatiotemporal covariate. The results showed that integrating the RLINE estimates as a space-only covariate improved overall cross-validation R2 from 0.83 to 0.84, and root mean squared error (RMSE) from 3.58 to 3.48 ppb. Incorporating the estimates as a spatiotemporal covariate resulted in similar model improvement. The improvement of our spatiotemporal model was more profound in roadside monitors alongside highways, with R2 increasing from 0.56 to 0.66 and RMSE decreasing from 3.52 to 3.11 ppb. The observed improvement indicates that the RLINE estimates enhanced the model's predictive capabilities for roadside NO2 concentration gradients even after considering a comprehensive list of geographic covariates including the distance to roads. Our proposed modeling framework can be generalized to improve high-resolution prediction of NO2 exposure - especially near major roads in the U.S.
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Affiliation(s)
- Yunhan Wu
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Jianzhao Bi
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA.
| | - Amanda J Gassett
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA
| | - Michael T Young
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA
| | - Adam A Szpiro
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Joel D Kaufman
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA
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Wang J, Alli AS, Clark SN, Ezzati M, Brauer M, Hughes AF, Nimo J, Moses JB, Baah S, Nathvani R, D V, Agyei-Mensah S, Baumgartner J, Bennett JE, Arku RE. Inequalities in urban air pollution in sub-Saharan Africa: an empirical modeling of ambient NO and NO 2 concentrations in Accra, Ghana. ENVIRONMENTAL RESEARCH LETTERS : ERL [WEB SITE] 2024; 19:034036. [PMID: 38419692 PMCID: PMC10897512 DOI: 10.1088/1748-9326/ad2892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 02/04/2024] [Accepted: 02/12/2024] [Indexed: 03/02/2024]
Abstract
Road traffic has become the leading source of air pollution in fast-growing sub-Saharan African cities. Yet, there is a dearth of robust city-wide data for understanding space-time variations and inequalities in combustion related emissions and exposures. We combined nitrogen dioxide (NO2) and nitric oxide (NO) measurement data from 134 locations in the Greater Accra Metropolitan Area (GAMA), with geographical, meteorological, and population factors in spatio-temporal mixed effects models to predict NO2 and NO concentrations at fine spatial (50 m) and temporal (weekly) resolution over the entire GAMA. Model performance was evaluated with 10-fold cross-validation (CV), and predictions were summarized as annual and seasonal (dusty [Harmattan] and rainy [non-Harmattan]) mean concentrations. The predictions were used to examine population distributions of, and socioeconomic inequalities in, exposure at the census enumeration area (EA) level. The models explained 88% and 79% of the spatiotemporal variability in NO2 and NO concentrations, respectively. The mean predicted annual, non-Harmattan and Harmattan NO2 levels were 37 (range: 1-189), 28 (range: 1-170) and 50 (range: 1-195) µg m-3, respectively. Unlike NO2, NO concentrations were highest in the non-Harmattan season (41 [range: 31-521] µg m-3). Road traffic was the dominant factor for both pollutants, but NO2 had higher spatial heterogeneity than NO. For both pollutants, the levels were substantially higher in the city core, where the entire population (100%) was exposed to annual NO2 levels exceeding the World Health Organization (WHO) guideline of 10 µg m-3. Significant disparities in NO2 concentrations existed across socioeconomic gradients, with residents in the poorest communities exposed to levels about 15 µg m-3 higher compared with the wealthiest (p < 0.001). The results showed the important role of road traffic emissions in air pollution concentrations in the GAMA, which has major implications for the health of the city's poorest residents. These data could support climate and health impact assessments as well as policy evaluations in the city.
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Affiliation(s)
- Jiayuan Wang
- Department of Environmental Health Sciences, School of Public Health and Health Sciences, University of Massachusetts, Amherst, MA, United States of America
| | - Abosede S Alli
- Department of Environmental Health Sciences, School of Public Health and Health Sciences, University of Massachusetts, Amherst, MA, United States of America
| | - Sierra N Clark
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, United Kingdom
| | - Majid Ezzati
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, United Kingdom
- Regional Institute for Population Studies, University of Ghana, Accra, Ghana
- Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, United Kingdom
| | - Michael Brauer
- School of Population and Public Health, The University of British Columbia, Vancouver, Canada
| | | | - James Nimo
- Department of Physics, University of Ghana, Accra, Ghana
| | | | - Solomon Baah
- Department of Physics, University of Ghana, Accra, Ghana
| | - Ricky Nathvani
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, United Kingdom
| | - Vishwanath D
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, United Kingdom
| | - Samuel Agyei-Mensah
- Department of Geography and Resource Development, University of Ghana, Accra, Ghana
- Department of Civil and Environmental Engineering, Imperial College London, London, United Kingdom
| | - Jill Baumgartner
- Institute for Health and Social Policy, McGill University, Montreal, Canada
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Canada
| | - James E Bennett
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, United Kingdom
| | - Raphael E Arku
- Department of Environmental Health Sciences, School of Public Health and Health Sciences, University of Massachusetts, Amherst, MA, United States of America
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AlShehhi A, Welsch R. Artificial intelligence for improving Nitrogen Dioxide forecasting of Abu Dhabi environment agency ground-based stations. JOURNAL OF BIG DATA 2023; 10:92. [PMID: 37303479 PMCID: PMC10236404 DOI: 10.1186/s40537-023-00754-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 05/08/2023] [Indexed: 06/13/2023]
Abstract
Nitrogen Dioxide (NO2 ) is a common air pollutant associated with several adverse health problems such as pediatric asthma, cardiovascular mortality,and respiratory mortality. Due to the urgent society's need to reduce pollutant concentration, several scientific efforts have been allocated to understand pollutant patterns and predict pollutants' future concentrations using machine learning and deep learning techniques. The latter techniques have recently gained much attention due it's capability to tackle complex and challenging problems in computer vision, natural language processing, etc. In the NO2 context, there is still a research gap in adopting those advanced methods to predict the concentration of pollutants. This study fills in the gap by comparing the performance of several state-of-the-art artificial intelligence models that haven't been adopted in this context yet. The models were trained using time series cross-validation on a rolling base and tested across different periods using NO2 data from 20 monitoring ground-based stations collected by Environment Agency- Abu Dhabi, United Arab Emirates. Using the seasonal Mann-Kendall trend test and Sen's slope estimator, we further explored and investigated the pollutants trends across the different stations. This study is the first comprehensive study that reported the temporal characteristic of NO2 across seven environmental assessment points and compared the performance of the state-of-the-art deep learning models for predicting the pollutants' future concentration. Our results reveal a difference in the pollutants concentrations level due to the geographic location of the different stations, with a statistically significant decrease in the NO2 annual trend for the majority of the stations. Overall, NO2 concentrations exhibit a similar daily and weekly pattern across the different stations, with an increase in the pollutants level during the early morning and the first working day. Comparing the state-of-the-art model performance transformer model demonstrate the superiority of ( MAE:0.04 (± 0.04),MSE:0.06 (± 0.04), RMSE:0.001 (± 0.01), R2 : 0.98 (± 0.05)), compared with LSTM (MAE:0.26 (± 0.19), MSE:0.31 (± 0.21), RMSE:0.14 (± 0.17), R2 : 0.56 (± 0.33)), InceptionTime (MAE: 0.19 (± 0.18), MSE: 0.22 (± 0.18), RMSE:0.08 (± 0.13), R2 :0.38 (± 1.35) ), ResNet (MAE:0.24 (± 0.16), MSE:0.28 (± 0.16), RMSE:0.11 (± 0.12), R2 :0.35 (± 1.19) ), XceptionTime (MAE:0.7 (± 0.55), MSE:0.79 (± 0.54), RMSE:0.91 (± 1.06), R2 : - 4.83 (± 9.38) ), and MiniRocket (MAE:0.21 (± 0.07), MSE:0.26 (± 0.08), RMSE:0.07 (± 0.04), R2 : 0.65 (± 0.28) ) to tackle this challenge. The transformer model is a powerful model for improving the accurate forecast of the NO2 levels and could strengthen the current monitoring system to control and manage the air quality in the region. Supplementary Information The online version contains supplementary material available at 10.1186/s40537-023-00754-z.
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Affiliation(s)
- Aamna AlShehhi
- Biomedical Engineering, Khalifa University, Abu Dhabi, United Arab Emirates
| | - Roy Welsch
- Sloan School of Management and Statistics, Massachusetts Institute of Technology, Cambridge, Massachusetts USA
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He C, Yang H, Xi M, Fu L, Huo J, Zhao C. Efficient electrocatalytic reduction of NO to ammonia on BC 3 nanosheets. ENVIRONMENTAL RESEARCH 2022; 212:113479. [PMID: 35588777 DOI: 10.1016/j.envres.2022.113479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 05/10/2022] [Accepted: 05/11/2022] [Indexed: 06/15/2023]
Abstract
Searching for an economical and highly efficient electrocatalytic reduction catalyst for ammonia synthesis under controllable conditions is a very attractive and challenging subject in chemistry. In this study, we systematically studied the electrocatalytic performance of BC3 nanosheets as potential NO reduction reaction (NORR) electrocatalysts using density functional theory (DFT) calculations. It was found that BC3 two-dimensional (2D) materials exhibit excellent catalytic activity with a very low limiting potential of -0.29/-0.11 V along three reaction paths. The total reaction is NO (g)+5H++5e-→NH3(g)+ H2O. The density of states of adsorbed NO, NH3, and the corresponding crystal orbital hamiltonian population (COHP) analysis revealed the mechanism of NO being activated and the reasons for NH3 adsorption/desorption on the surface of BC3. The reaction path, limiting potential, and Gibbs free energy calculations of BC3 catalyzed NO to ammonia synthesis revealed that for path 1, the potential-determining step is *NO+H++e-→*NOH, and for path 2/3 the potential-determining step is *NO+(H++e-)→*HNO. Calculation of the thermodynamic energy barriers for NO dissociation at the BC3 surface and NO hydrogenation reveals that NO is more likely to be hydrogenated rather than dissociated. The influences of the proton-electron hydrogenation site on the process of ammonia synthesis in the key reduction step were analyzed by Bader charge analysis and charge density, it is pointed out that the electronic structure and affects the reaction process can be controlled by hydrogenation at different sites of intermediates. These results pave the way for using nitrogen oxides not just nitrogen as raw materials for ammonia synthesis with 2D materials.
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Affiliation(s)
- Chaozheng He
- Institute of Environment and Energy Catalysis, Shaanxi Key Laboratory of Optoelectronic Functional Materials and Devices, School of Materials Science and Chemical Engineering, Xi'an Technological University, Xi'an, 710021, China
| | - Houyong Yang
- Institute of Environment and Energy Catalysis, Shaanxi Key Laboratory of Optoelectronic Functional Materials and Devices, School of Materials Science and Chemical Engineering, Xi'an Technological University, Xi'an, 710021, China
| | - Menghui Xi
- Institute of Environment and Energy Catalysis, Shaanxi Key Laboratory of Optoelectronic Functional Materials and Devices, School of Materials Science and Chemical Engineering, Xi'an Technological University, Xi'an, 710021, China
| | - Ling Fu
- College of Resources and Environmental Engineering, Tianshui Normal University, Tianshui, 741001, China.
| | - Jinrong Huo
- School of Sciences, Xi'an Technological University, Xi'an, Shaanxi, 710021, China
| | - Chenxu Zhao
- Institute of Environment and Energy Catalysis, Shaanxi Key Laboratory of Optoelectronic Functional Materials and Devices, School of Materials Science and Chemical Engineering, Xi'an Technological University, Xi'an, 710021, China; Department of Materials Science and Engineering, Jilin University, 130022, Changchun, China
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Huang C, Sun K, Hu J, Xue T, Xu H, Wang M. Estimating 2013-2019 NO 2 exposure with high spatiotemporal resolution in China using an ensemble model. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2022; 292:118285. [PMID: 34634409 PMCID: PMC8616822 DOI: 10.1016/j.envpol.2021.118285] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 09/29/2021] [Accepted: 10/03/2021] [Indexed: 05/30/2023]
Abstract
Air pollution has become a major issue in China, especially for traffic-related pollutants such as nitrogen dioxide (NO2). Current studies in China at the national scale were less focused on NO2 exposure and consequent health effects than fine particulate exposure, mainly due to a lack of high-quality exposure models for accurate NO2 predictions over a long period. We developed an advanced modeling framework that incorporated multisource, high-quality predictor data (e.g., satellite observations [Ozone Monitoring Instrument NO2, TROPOspheric Monitoring Instrument NO2, and Multi-Angle Implementation of Atmospheric Correction aerosol optical depth], chemical transport model simulations, high-resolution geographical variables) and three independent machine learning algorithms into an ensemble model. The model contains three stages: (1) filling missing satellite data; (2) building an ensemble model and predicting daily NO2 concentrations from 2013 to 2019 across China at 1×1 km2 resolution; (3) downscaling the predictions to finer resolution (100 m) at the urban scale. Our model achieves a high performance in terms of cross-validation to assess the agreement of the overall (R2 = 0.72) and the spatial (R2 = 0.85) variations of the NO2 predictions over the observations. The model performance remains moderately good when the predictions are extrapolated to the previous years without any monitoring data (CV R2 > 0.68) or regions far away from monitors (CV R2 > 0.63). We identified a clear decreasing trend of NO2 exposure from 2013 to 2019 across the country with the largest reduction in suburban and rural areas. Our downscaled model further improved the prediction ability by 4%-14% in some megacities and captured substantial NO2 variations within 1-km grids in the urban areas, especially near major roads. Our model provides flexibility at both temporal and spatial scales and can be applied to exposure assessment and epidemiological studies with various study domains (e.g., national or citywide) and settings (e.g., long-term and short-term).
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Affiliation(s)
- Conghong Huang
- Department of Epidemiology and Environmental Health, School of Public Health and Health Professions, University at Buffalo, Buffalo, NY, USA
| | - Kang Sun
- Department of Civil, Structural and Environmental Engineering, University at Buffalo, Buffalo, USA; Research and Education in Energy, Environment and Water Institute, University at Buffalo, Buffalo, NY, USA
| | - Jianlin Hu
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Engineering Technology Research Center of Environmental Cleaning Materials, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, 219 Ningliu Road, Nanjing, 210044, China
| | - Tao Xue
- Institute of Reproductive and Child Health/Ministry of Health Key Laboratory of Reproductive Health and Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
| | - Hao Xu
- The Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China
| | - Meng Wang
- Department of Epidemiology and Environmental Health, School of Public Health and Health Professions, University at Buffalo, Buffalo, NY, USA; Research and Education in Energy, Environment and Water Institute, University at Buffalo, Buffalo, NY, USA; Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA.
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