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Mohammadi F, Teiri H, Hajizadeh Y, Abdolahnejad A, Ebrahimi A. Prediction of atmospheric PM 2.5 level by machine learning techniques in Isfahan, Iran. Sci Rep 2024; 14:2109. [PMID: 38267539 PMCID: PMC10808097 DOI: 10.1038/s41598-024-52617-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 01/21/2024] [Indexed: 01/26/2024] Open
Abstract
With increasing levels of air pollution, air quality prediction has attracted more attention. Mathematical models are being developed by researchers to achieve precise predictions. Monitoring and prediction of atmospheric PM2.5 levels, as a predominant pollutant, is essential in emission mitigation programs. In this study, meteorological datasets from 9 years in Isfahan city, a large metropolis of Iran, were applied to predict the PM2.5 levels, using four machine learning algorithms including Artificial Neural |Networks (ANNs), K-Nearest-Neighbors (KNN), Support Vector |Machines (SVMs) and ensembles of classification trees Random Forest (RF). The data from 7 air quality monitoring stations located in Isfahan City were taken into consideration. The Confusion Matrix and Cross-Entropy Loss were used to analyze the performance of classification models. Several parameters, including sensitivity, specificity, accuracy, F1 score, precision, and the area under the curve (AUC), are computed to assess model performance. Finally, by introducing the predicted data for 2020 into ArcGIS software and using the IDW (Inverse Distance Weighting) method, interpolation was conducted for the area of Isfahan city and the pollution map was illustrated for each month of the year. The results showed that, based on the accuracy percentage, the ANN model has a better performance (90.1%) in predicting PM2.5 grades compared to the other models for the applied meteorological dataset, followed by RF (86.1%), SVM (84.6%) and KNN (82.2%) models, respectively. Therefore, ANN modelling provides a feasible procedure for the managerial planning of air pollution control.
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Affiliation(s)
- Farzaneh Mohammadi
- Department of Environmental Health Engineering, Faculty of Health, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Hakimeh Teiri
- Department of Environmental Health Engineering, Faculty of Health, Isfahan University of Medical Sciences, Isfahan, Iran.
| | - Yaghoub Hajizadeh
- Environment Research Center, Research Institute for Primordial Prevention of Non-Communicable Diseases, Isfahan University of Medical Sciences, Hezar Jerib Street, Isfahan, 8174673461, Iran.
- Department of Environmental Health Engineering, Faculty of Health, Isfahan University of Medical Sciences, Isfahan, Iran.
| | - Ali Abdolahnejad
- Department of Environmental Health Engineering, School of Public Health, Maragheh University of Medical Sciences, Maragheh, Iran
| | - Afshin Ebrahimi
- Environment Research Center, Research Institute for Primordial Prevention of Non-Communicable Diseases, Isfahan University of Medical Sciences, Hezar Jerib Street, Isfahan, 8174673461, Iran
- Department of Environmental Health Engineering, Faculty of Health, Isfahan University of Medical Sciences, Isfahan, Iran
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Huang J, Gao Y, Chang Y, Peng J, Yu Y, Wang B. Machine Learning in Bioelectrocatalysis. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2306583. [PMID: 37946709 PMCID: PMC10787072 DOI: 10.1002/advs.202306583] [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: 09/12/2023] [Indexed: 11/12/2023]
Abstract
At present, the global energy crisis and environmental pollution coexist, and the demand for sustainable clean energy has been highly concerned. Bioelectrocatalysis that combines the benefits of biocatalysis and electrocatalysis produces high-value chemicals, clean biofuel, and biodegradable new materials. It has been applied in biosensors, biofuel cells, and bioelectrosynthesis. However, there are certain flaws in the application process of bioelectrocatalysis, such as low accuracy/efficiency, poor stability, and limited experimental conditions. These issues can possibly be solved using machine learning (ML) in recent reports although the combination of them is still not mature. To summarize the progress of ML in bioelectrocatalysis, this paper first introduces the modeling process of ML, then focuses on the reports of ML in bioelectrocatalysis, and ultimately makes a summary and outlook about current issues and future directions. It is believed that there is plenty of scope for this interdisciplinary research direction.
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Affiliation(s)
- Jiamin Huang
- Department of Environmental Science and Engineering, University of Science and Technology Beijing, Beijing, 100083, China
- CAS Key Laboratory of Nanosystem and Hierarchical Fabrication, National Center for Nanoscience and Technology, Beijing, 100190, China
| | - Yang Gao
- CAS Key Laboratory of Nanosystem and Hierarchical Fabrication, National Center for Nanoscience and Technology, Beijing, 100190, China
| | - Yanhong Chang
- Department of Environmental Science and Engineering, University of Science and Technology Beijing, Beijing, 100083, China
| | - Jiajie Peng
- School of Computer Science, Northwestern Polytechnical University, Xi'an, 710072, China
| | - Yadong Yu
- College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, Nanjing, 211816, China
| | - Bin Wang
- CAS Key Laboratory of Nanosystem and Hierarchical Fabrication, National Center for Nanoscience and Technology, Beijing, 100190, China
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Lukyanets A, Gura D, Savinova O, Kondratenko L, Lushkov R. Industrial emissions effect into atmospheric air quality: mathematical modeling. REVIEWS ON ENVIRONMENTAL HEALTH 2023; 38:385-393. [PMID: 35447020 DOI: 10.1515/reveh-2022-0005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Accepted: 03/28/2022] [Indexed: 06/02/2023]
Abstract
This paper presents the results of modeling the distribution process of industrial emission components at specified distances from the emission source along the normal. The model uses a system of differential diffusion equations to compute the concentration profiles of aerosols, industrial gases, and fine particles in the atmosphere. In order to investigate the regularity of the emitter propagation into the atmosphere, a theory of impurity dispersion was developed. The model is constrained by the effect of particle interactions. The partial derivative equations are presented to calculate the concentrations of aerosols and fine particles under the turbulent airflow in the atmosphere, dispersion of inert impurities, and distribution of chemically active compounds. The adequacy of the mathematical model for a series of theoretical calculations was checked by contrasting the data of the atmospheric air monitoring for the cities of Almaty, Ust-Kamenogorsk, Pavlodar, Atyrau, Krasnodar, Chelyabinsk, Beijing, and Shanghai. Air monitoring data included PM10, SO2, and NO2 levels. The mathematical model solutions for the relative values of the emitter concentration in the direction along the normal of the pollution source at the surface were obtained. Graphical interpretation of the calculation results over the 0…200 m distance for time intervals ranging from 3 to 600 min was provided. According to the multiple factor cluster analysis, the critical values of SO2 concentrations in Atyrau exceeded MPC in 26.2% of cases. The level of NO2 for Shanghai was 15.6%, and those for PM10 concentrations in Almaty and Atyrau amounted to 16.4%. A comparison of theoretical values and results obtained from official sources showed arithmetic mean of 49.4 mg/m3 and maximum value of 823.0 mg/m3. Standard deviation comprised 48.9 mg/m3. Results were considered statistically significant at p≤0.005. The mathematical model developed in this study can be used to predict the status of atmospheric air.
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Affiliation(s)
- Artem Lukyanets
- Department of Demographic and Migration Policy, Moscow State Institute of International Relations (MGIMO University), Moscow, Russian Federation
- Laboratory of International Demographic Research, Institute for Demographic Research FCTAS RAS (IDR FCTAC RAS), Moscow, Russian Federation
| | - Dmitry Gura
- Department of Cadastre and Geoengineering, Kuban State Technological University, Krasnodar, Russian Federation
- Department of Geodesy, Kuban State Agrarian University, Krasnodar, Russian Federation
| | - Olga Savinova
- Department of Pharmacy, Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russian Federation
| | - Larisa Kondratenko
- Department of Advanced Mathematics, Kuban State Agrarian University named after I.T. Trubilin, Krasnodar, Russian Federation
| | - Richard Lushkov
- Department of Orthopedic Dentistry, Institute of Dentistry named after E.V. Borovsky, Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russian Federation
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Li J, Crooks J, Murdock J, de Souza P, Hohsfield K, Obermann B, Stockman T. A nested machine learning approach to short-term PM 2.5 prediction in metropolitan areas using PM 2.5 data from different sensor networks. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 873:162336. [PMID: 36813194 DOI: 10.1016/j.scitotenv.2023.162336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 01/26/2023] [Accepted: 02/15/2023] [Indexed: 06/18/2023]
Abstract
Many predictive models for ambient PM2.5 concentrations rely on ground observations from a single monitoring network consisting of sparsely distributed sensors. Integrating data from multiple sensor networks for short-term PM2.5 prediction remains largely unexplored. This paper presents a machine learning approach to predict ambient PM2.5 concentration levels at any unmonitored location several hours ahead using PM2.5 observations from nearby monitoring sites from two sensor networks and the location's social and environmental properties. Specifically, this approach first applies a Graph Neural Network and Long Short-Term Memory (GNN-LSTM) network to time series of daily observations from a regulatory monitoring network to make predictions of PM2.5. This network produces feature vectors to store aggregated daily observations as well as dependency characteristics to predict daily PM2.5. The daily feature vectors are then set as the precondition of the hourly level learning process. The hourly level learning again uses a GNN-LSTM network based on daily dependency information and hourly observations from a low-cost sensor network to produce spatiotemporal feature vectors capturing the combined dependency described by daily and hourly observations. Finally, the spatiotemporal feature vectors from the hourly learning process and social-environmental data are merged and used as the input to a single-layer Fully Connected (FC) network to output the predicted hourly PM2.5 concentrations. To demonstrate the benefits of this novel prediction approach, we have conducted a case study using data collected from two sensor networks in Denver, CO, during 2021. Results show that the utilization of data from two sensor networks improves the overall performance of predicting fine-level, short-term PM2.5 concentrations compared to other baseline models.
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Affiliation(s)
- Jing Li
- Department of Geography and the Environment, University of Denver, United States of America.
| | - James Crooks
- Division of Biostatistics and Bioinformatics, National Jewish Health, United States of America; Department of Epidemiology, Colorado School of Public Health, United States of America
| | - Jennifer Murdock
- Department of Geography and the Environment, University of Denver, United States of America
| | - Priyanka de Souza
- Department of Urban and Regional Planning, University of Colorado - Denver, United States of America; CU Population Center, University of Colorado - Boulder, United States of America
| | - Kirk Hohsfield
- University of Colorado, School of Medicine, United States of America
| | - Bill Obermann
- Department of Public Health and Environment, City and County of Denver, United States of America
| | - Tehya Stockman
- Department of Public Health and Environment, City and County of Denver, United States of America; Civil, Environmental and Architectural Engineering Department, University of Colorado - Boulder, United States of America
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Zhang W, Huang W, Tan J, Guo Q, Wu B. Heterogeneous catalysis mediated by light, electricity and enzyme via machine learning: Paradigms, applications and prospects. CHEMOSPHERE 2022; 308:136447. [PMID: 36116627 DOI: 10.1016/j.chemosphere.2022.136447] [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: 08/09/2022] [Revised: 09/08/2022] [Accepted: 09/11/2022] [Indexed: 06/15/2023]
Abstract
Energy crisis and environmental pollution have become the bottleneck of human sustainable development. Therefore, there is an urgent need to develop new catalysts for energy production and environmental remediation. Due to the high cost caused by blind screening and limited valuable computing resources, the traditional experimental methods and theoretical calculations are difficult to meet with the requirements. In the past decades, computer science has made great progress, especially in the field of machine learning (ML). As a new research paradigm, ML greatly accelerates the theoretical calculation methods represented by first principal calculation and molecular dynamics, and establish the physical picture of heterogeneous catalytic processes for energy and environment. This review firstly summarized the general research paradigms of ML in the discovery of catalysts. Then, the latest progresses of ML in light-, electricity- and enzyme-mediated heterogeneous catalysis were reviewed from the perspective of catalytic performance, operating conditions and reaction mechanism. The general guidelines of ML for heterogeneous catalysis were proposed. Finally, the existing problems and future development trend of ML in heterogeneous catalysis mediated by light, electricity and enzyme were summarized. We highly expect that this review will facilitate the interaction between ML and heterogeneous catalysis, and illuminate the development prospect of heterogeneous catalysis.
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Affiliation(s)
- Wentao Zhang
- Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, People's Republic of China
| | - Wenguang Huang
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment of PRC, Guangzhou, 510655, People's Republic of China.
| | - Jie Tan
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment of PRC, Guangzhou, 510655, People's Republic of China
| | - Qingwei Guo
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment of PRC, Guangzhou, 510655, People's Republic of China
| | - Bingdang Wu
- School of Environmental Science and Engineering, Suzhou University of Science and Technology, Suzhou, 215009, People's Republic of China; Key Laboratory of Suzhou Sponge City Technology, Suzhou, 215002, People's Republic of China.
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Tong C, Shi Z, Shi W, Zhang A. Estimation of On-Road PM 2.5 Distributions by Combining Satellite Top-of-Atmosphere With Microscale Geographic Predictors for Healthy Route Planning. GEOHEALTH 2022; 6:e2022GH000669. [PMID: 36101834 PMCID: PMC9453924 DOI: 10.1029/2022gh000669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 08/30/2022] [Accepted: 09/01/2022] [Indexed: 06/15/2023]
Abstract
How to reduce the health risks for commuters, caused by air pollution such as PM2.5 has always been an urgent issue needing to be solved. Proposed in this study, is a novel framework which enables greater avoidance of pollution and hence assists the provision of healthy travel. This framework is based on the estimation of on-road PM2.5 throughout the whole city. First, the micro-scale PM2.5 is predicted by land use regression (LUR) modeling enhanced by the use of the Landsat-8 top-of-atmosphere (TOA) data and microscale geographic predictors. In particular, the green view index (GVI) factor derived, the sky view factor, and the index-based built-up index, are incorporated within the TOA-LUR modeling. On-road PM2.5 distributions are then mapped in high-spatial-resolution. The maps obtained can be used to find healthy travel routes with less PM2.5. The proposed framework was applied in high-density Hong Kong by Landsat 8 images. External testing was based on mobile measurements. The results showed that the estimation performance of the proposed seasonal TOA-LUR Geographical and Temporal Weighted Regression models is at a high-level with an R 2 of 0.70-0.90. The newly introduced GVI index played an important role in these estimations. The PM2.5 distribution maps at high-spatial-resolution were then used to develop an application providing Hong Kong residents with healthy route planning services. The proposed framework can, likewise, be applied in other cities to better ensure people's health when traveling, especially those in high-density cities.
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Affiliation(s)
- Chengzhuo Tong
- Otto Poon Charitable Foundation Smart Cities Research Institute and Department of Land Surveying and Geo‐InformaticsThe Hong Kong Polytechnic UniversityHong KongChina
| | - Zhicheng Shi
- Research Institute for Smart CitiesSchool of Architecture and Urban PlanningShenzhen UniversityShenzhenChina
| | - Wenzhong Shi
- Otto Poon Charitable Foundation Smart Cities Research Institute and Department of Land Surveying and Geo‐InformaticsThe Hong Kong Polytechnic UniversityHong KongChina
| | - Anshu Zhang
- Otto Poon Charitable Foundation Smart Cities Research Institute and Department of Land Surveying and Geo‐InformaticsThe Hong Kong Polytechnic UniversityHong KongChina
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Man J, Guo Y, Zhou Q, Yao Y. Database examination, multivariate analysis, and machine learning: Predictions of vapor intrusion attenuation factors. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2022; 242:113874. [PMID: 35843107 DOI: 10.1016/j.ecoenv.2022.113874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/19/2022] [Revised: 06/26/2022] [Accepted: 07/08/2022] [Indexed: 06/15/2023]
Abstract
Traditional soil vapor intrusion (VI) models usually rely on preset conceptual scenarios, simplifying the influences of limiting environmental covariates in determining indoor attenuation factors relative to subsurface sources. This study proposed a technical framework and applied it to predict VI attenuation factors based on site-specific parameters recorded in the United States Environmental Protection Agency (USEPA)'s and the California Environmental Protection Agency (CalEPA)'s VI databases, which can overcome the limitations of traditional VI models. We examined the databases with multivariate analysis of variance to identify effective covariates, which were then employed to develop VI models with three machine learning algorithms. The results of multivariate analysis show that the effective covariates include soil texture, source depth, foundation type, lateral separation, surface cover, and land use. Based on these covariates, the predicted attenuation factors by these new models are generally within one order of magnitude of the observations recorded in the databases. Then the developed models were employed to generate the generic indoor attenuation factors to subsurface vapor (i.e., the 95th percentile of selected dataset), the values of which are different between the USEPA's and CalEPA's databases by one order of magnitude, although comparable to recommendations by the USEPA and literature, respectively. Such a difference may reflect the significant regional disparity in factors such as building structures or operational conditions (e.g., indoor air exchange rates), which necessitates generating generic VI attenuation factors on a state-specific basis. This study provides an alternative for VI risk screens on a site-specific basis, especially in states with a good collection of datasets. Although the proposed technical framework is used for the VI databases, it can be equally applied to other environmental science problems.
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Affiliation(s)
- Jun Man
- Key Laboratory of Soil Environment and Pollution Remediation, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yuanming Guo
- Nanjing University of Science and Technology, Nanjing 210094, China
| | - Qing Zhou
- Key Laboratory of Soil Environment and Pollution Remediation, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yijun Yao
- Key Laboratory of Soil Environment and Pollution Remediation, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China; University of Chinese Academy of Sciences, Beijing 100049, China.
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Ju T, Lei M, Guo G, Xi J, Zhang Y, Xu Y, Lou Q. A new prediction method of industrial atmospheric pollutant emission intensity based on pollutant emission standard quantification. FRONTIERS OF ENVIRONMENTAL SCIENCE & ENGINEERING 2022; 17:8. [PMID: 36061489 PMCID: PMC9419144 DOI: 10.1007/s11783-023-1608-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 05/22/2022] [Accepted: 07/27/2022] [Indexed: 06/15/2023]
Abstract
UNLABELLED Industrial emissions are the main source of atmospheric pollutants in China. Accurate and reasonable prediction of the emission of atmospheric pollutants from single enterprise can determine the exact source of atmospheric pollutants and control atmospheric pollution precisely. Based on China's coking enterprises in 2020, we proposed a quantitative method for pollutant emission standards and introduced the quantification results of pollutant emission standards (QRPES) into the construction of support vector regression (SVR) and random forest regression (RFR) prediction methods for SO2 emission of coking enterprises in China. The results show that, affected by the types of coke ovens and regions, China's current coking enterprises have implemented a total of 21 emission standards, with marked differences. After adding QRPES, it was found that the root mean squared error (RMSE) of SVR and RFR decreased from 0.055 kt/a and 0.059 kt/a to 0.045 kt/a and 0.039 kt/a, and the R 2 increased from 0.890 and 0.881 to 0.926 and 0.945, respectively. This shows that the QRPES can greatly improve the prediction accuracy, and the SO2 emissions of each enterprise are highly correlated with the strictness of standards. The predicted result shows that 45% of SO2 emissions from Chinese coking enterprises are concentrated in Shanxi, Shaanxi and Hebei provinces in central China. The method created in this paper fills in the blank of forecasting method of air pollutant emission intensity of single enterprise and is of great help to the accurate control of air pollutants. ELECTRONIC SUPPLEMENTARY MATERIAL Supplementary material is available in the online version of this article at 10.1007/s11783-023-1608-1 and is accessible for authorized users.
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Affiliation(s)
- Tienan Ju
- Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
| | - Mei Lei
- Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
| | - Guanghui Guo
- Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
| | - Jinglun Xi
- Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
| | - Yang Zhang
- Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
| | - Yuan Xu
- Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
| | - Qijia Lou
- Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
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Kow PY, Chang LC, Lin CY, Chou CCK, Chang FJ. Deep neural networks for spatiotemporal PM 2.5 forecasts based on atmospheric chemical transport model output and monitoring data. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2022; 306:119348. [PMID: 35487466 DOI: 10.1016/j.envpol.2022.119348] [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: 03/08/2022] [Revised: 04/13/2022] [Accepted: 04/20/2022] [Indexed: 06/14/2023]
Abstract
Reliable long-horizon PM2.5 forecasts are crucial and beneficial for health protection through early warning against air pollution. However, the dynamic nature of air quality makes PM2.5 forecasts at long horizons very challenging. This study proposed a novel machine learning-based model (MCNN-BP) that fused multiple convolutional neural networks (MCNN) with a back-propagation neural network (BPNN) for making spatiotemporal PM2.5 forecasts for the next 72 h at 74 stations covering the whole Taiwan simultaneously. Model configuration involved an ensemble of massive hourly air quality and meteorological monitoring datasets and the existing publicly-available PM2.5 simulated (forecasted) datasets from an atmospheric chemical transport (ACT) model. The proposed methodology collaboratively constructed two CNNs to mine the observed data (the past) and the forecasted data from ACT (the future) separately. The results showed that the MCNN-BP model could significantly improve the accuracy of spatiotemporal PM2.5 forecasts and substantially reduce the forecast biases of the ACT model. We demonstrated that the proposed MCNN-BP model with effective feature extraction and good denoising ability could overcome the curse of dimensionality and offer satisfactory regional long-horizon PM2.5 forecasts. Moreover, the MCNN-BP model has considerably shorter computational time (5 min) and lower computational load than the compute-intensive ACT model. The proposed approach hits a milestone in multi-site and multi-horizon forecasting, which significantly contributes to early warning against regional air pollution.
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Affiliation(s)
- Pu-Yun Kow
- Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei, 10617, Taiwan
| | - Li-Chiu Chang
- Department of Water Resources and Environmental Engineering, Tamkang University, New Taipei City, 25137, Taiwan
| | - Chuan-Yao Lin
- Research Center for Environmental Changes, Academia Sinica, Taipei, 11529, Taiwan
| | - Charles C-K Chou
- Research Center for Environmental Changes, Academia Sinica, Taipei, 11529, Taiwan
| | - Fi-John Chang
- Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei, 10617, Taiwan.
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Random Forest Estimation and Trend Analysis of PM2.5 Concentration over the Huaihai Economic Zone, China (2000–2020). SUSTAINABILITY 2022. [DOI: 10.3390/su14148520] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Consisting of ten cities in four Chinese provinces, the Huaihai Economic Zone has suffered serious air pollution over the last two decades, particularly of fine particulate matter (PM2.5). In this study, we used multi-source data, namely MAIAC AOD (at a 1 km spatial resolution), meteorological, topographic, date, and location (latitude and longitude) data, to construct a regression model using random forest to estimate the daily PM2.5 concentration over the Huaihai Economic Zone from 2000 to 2020. It was found that the variable expressing time (date) had the greatest characteristic importance when estimating PM2.5. By averaging the modeled daily PM2.5 concentration, we produced a yearly PM2.5 concentration dataset, at a 1 km resolution, for the study area from 2000 to 2020. On comparing modeled daily PM2.5 with observational data, the coefficient of determination (R2) of the modeling was 0.85, the root means square error (RMSE) was 14.63 μg/m3, and the mean absolute error (MAE) was 10.03 μg/m3. The quality assessment of the synthesized yearly PM2.5 concentration dataset shows that R2 = 0.77, RMSE = 6.92 μg/m3, and MAE = 5.42 μg/m3. Despite different trends from 2000–2010 and from 2010–2020, the trend of PM2.5 concentration over the Huaihai Economic Zone during the 21 years was, overall, decreasing. The area of the significantly decreasing trend was small and mainly concentrated in the lake areas of the Zone. It is concluded that PM2.5 can be well-estimated from the MAIAC AOD dataset, when incorporating spatiotemporal variability using random forest, and that the resultant PM2.5 concentration data provide a basis for environmental monitoring over large geographic areas.
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Middya AI, Roy S. Pollutant specific optimal deep learning and statistical model building for air quality forecasting. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2022; 301:118972. [PMID: 35183666 DOI: 10.1016/j.envpol.2022.118972] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 01/30/2022] [Accepted: 02/06/2022] [Indexed: 06/14/2023]
Abstract
Poor air quality is becoming a critical environmental concern in different countries over the last several years. Most of the air pollutants have serious consequences on human health and wellbeing. In this context, efficient forecasting of air pollutants is currently crucial to predict future events with a view to taking corrective actions and framing effective environmental policies. Although deep learning (DL) as well as statistical forecasting models are investigated in the literature, they have rarely used in air pollutant-specific optimal model building for long-term forecasting. In this paper, our aim is to develop the pollutant-specific optimal forecasting models for the phases spanning from preprocessing to model building by investigating a set of predictive techniques. In this regard, this paper presents a methodology for long-term forecasting of some important air pollutants. More specifically, a total of eight best performing models such as stacked LSTM, LSTM auto-encoder, Bi-LSTM, convLSTM, Holt-Winters, auto-regressive (AR), SARIMA, and Prophet are investigated for developing pollutant-specific optimal forecasting models. The study is carried out based on the real-world data obtained from government-run air quality monitoring units in Kolkata over a period of 4 years. The models such as Holt-Winters, Bi-LSTM, and ConvLSTM achieve high forecasting accuracy with respect to MAE and RMSE values for majority of the pollutants.
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Affiliation(s)
- Asif Iqbal Middya
- Department of Computer Science and Engineering, Jadavpur University, India.
| | - Sarbani Roy
- Department of Computer Science and Engineering, Jadavpur University, India.
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12
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Structural Differences of PM2.5 Spatial Correlation Networks in Ten Metropolitan Areas of China. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 2022. [DOI: 10.3390/ijgi11040267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
Abstract
The cross-impact of environmental pollution among cities has been reported in more research works recently. To implement the coordinated control of environmental pollution, it is necessary to explore the structural characteristics and influencing factors of the PM2.5 spatial correlation network from the perspective of the metropolitan area. This paper utilized the gravity model to construct the PM2.5 spatial correlation network of ten metropolitan areas in China from 2019 to 2020. After analyzing the overall characteristics and node characteristics of each spatial correlation network based on the social network analysis (SNA) method, the quadratic assignment procedure (QAP) regression analysis method was used to explore the influence mechanism of each driving factor. Patent granted differences, as a new indicator, were also considered during the above. The results showed that: (1) In the overall network characteristics, the network density of Chengdu and the other three metropolitan areas displayed a downward trend in two years, and the network density of Wuhan and Chengdu was the lowest. The network density and network grade of Hangzhou and the other four metropolitan areas were high and stable, and the network structure of each metropolitan area was unstable. (2) From the perspective of the node characteristics, the PM2.5 spatial correlation network all performed trends of centralization and marginalization. Beijing-Tianjin-Hebei and South Central Liaoning were “multi-core” metropolitan areas, and the other eight were “single-core” metropolitan areas. (3) The analysis results of QAP regression illustrated that the top three influencing factors of the six metropolitan areas were geographical locational relationship, the secondary industrial proportion differences, respectively, and patent granted differences, and the other metropolitan areas had no dominant influencing factors.
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Forecast of Hourly Airport Visibility Based on Artificial Intelligence Methods. ATMOSPHERE 2022. [DOI: 10.3390/atmos13010075] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Based on the hourly visibility data, visibility and its changes during 2010–2020 at monthly and annual time scales over 47 international airports in China are investigated, and nine artificial-intelligence-based hourly visibility prediction models are trained (hourly data in 2018–2019) and tested (hourly data in 2020) at these airports. The analyses show that the visibility of airports in eastern and central China is at a poor level all year round, and LXA (in Lhasa) has good visibility all year round. Airports in south and the northwest China have better visibility from May to October and poorer visibility from November to April. In all months, the increasing visibility mainly occurs in the central, northeast and coastal areas of China, while decreasing visibility mainly appears in the western and northern parts of China. In spring, summer and autumn, the changes difference between east and west is particularly obvious. This East–West distribution of trends is obviously different from the North–South distribution shown by the mean. For all airports, good visibility mainly occurs from 14:00–18:00 p.m. Beijing Time, while poor visibility mainly concentrates from 22:00 p.m. to 12:00 p.m. the next day, especially between 3:00–9:00 a.m. Our proposed artificial intelligence algorithm models can be reasonably used in airport visibility prediction. In particular, most algorithm models have the best results in the visibility prediction over HFE (in Hefei) and SJW (in Shijiazhuang). On the contrary, the worst forecast results appear at LXA and LHW (in Lanzhou) airports. The prediction results of airport visibility in the cold season (October–December) are better than those in the warm season (May–September). Among the algorithm models, the prediction performance of the RF-based model is the best.
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Razavi-Termeh SV, Sadeghi-Niaraki A, Farhangi F, Choi SM. COVID-19 Risk Mapping with Considering Socio-Economic Criteria Using Machine Learning Algorithms. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:9657. [PMID: 34574582 PMCID: PMC8471719 DOI: 10.3390/ijerph18189657] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 09/02/2021] [Accepted: 09/09/2021] [Indexed: 12/15/2022]
Abstract
The reduction of population concentration in some urban land uses is one way to prevent and reduce the spread of COVID-19 disease. Therefore, the objective of this study is to prepare the risk mapping of COVID-19 in Tehran, Iran, using machine learning algorithms according to socio-economic criteria of land use. Initially, a spatial database was created using 2282 locations of patients with COVID-19 from 2 February 2020 to 21 March 2020 and eight socio-economic land uses affecting the disease-public transport stations, supermarkets, banks, automated teller machines (ATMs), bakeries, pharmacies, fuel stations, and hospitals. The modeling was performed using three machine learning algorithms that included random forest (RF), adaptive neuro-fuzzy inference system (ANFIS), and logistic regression (LR). Feature selection was performed using the OneR method, and the correlation between land uses was obtained using the Pearson coefficient. We deployed 70% and 30% of COVID-19 patient locations for modeling and validation, respectively. The results of the receiver operating characteristic (ROC) curve and the area under the curve (AUC) showed that the RF algorithm, which had a value of 0.803, had the highest modeling accuracy, which was followed by the ANFIS algorithm with a value of 0.758 and the LR algorithm with a value of 0.747. The results showed that the central and the eastern regions of Tehran are more at risk. Public transportation stations and pharmacies were the most correlated with the location of COVID-19 patients in Tehran, according to the results of the OneR technique, RF, and LR algorithms. The results of the Pearson correlation showed that pharmacies and banks are the most incompatible in distribution, and the density of these land uses in Tehran has caused the prevalence of COVID-19.
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Affiliation(s)
- Seyed Vahid Razavi-Termeh
- Geoinformation Technology Center of Excellence, Faculty of Geodesy and Geomatics Engineering, K.N. Toosi University of Technology, Tehran 19697, Iran; (S.V.R.-T.); (F.F.)
| | - Abolghasem Sadeghi-Niaraki
- Geoinformation Technology Center of Excellence, Faculty of Geodesy and Geomatics Engineering, K.N. Toosi University of Technology, Tehran 19697, Iran; (S.V.R.-T.); (F.F.)
- Department of Computer Science and Engineering, and Convergence Engineering for Intelligent Drone, Sejong University, Seoul 143-747, Korea;
| | - Farbod Farhangi
- Geoinformation Technology Center of Excellence, Faculty of Geodesy and Geomatics Engineering, K.N. Toosi University of Technology, Tehran 19697, Iran; (S.V.R.-T.); (F.F.)
| | - Soo-Mi Choi
- Department of Computer Science and Engineering, and Convergence Engineering for Intelligent Drone, Sejong University, Seoul 143-747, Korea;
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Spatial Modeling of Asthma-Prone Areas Using Remote Sensing and Ensemble Machine Learning Algorithms. REMOTE SENSING 2021. [DOI: 10.3390/rs13163222] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
In this study, asthma-prone area modeling of Tehran, Iran was provided by employing three ensemble machine learning algorithms (Bootstrap aggregating (Bagging), Adaptive Boosting (AdaBoost), and Stacking). First, a spatial database was created with 872 locations of asthma patients and affecting factors (particulate matter (PM10 and PM2.5), ozone (O3), sulfur dioxide (SO2), carbon monoxide (CO), nitrogen dioxide (NO2), rainfall, wind speed, humidity, temperature, distance to street, traffic volume, and a normalized difference vegetation index (NDVI)). We created four factors using remote sensing (RS) imagery, including air pollution (O3, SO2, CO, and NO2), altitude, and NDVI. All criteria were prepared using a geographic information system (GIS). For modeling and validation, 70% and 30% of the data were used, respectively. The weight of evidence (WOE) model was used to assess the spatial relationship between the dependent and independent data. Finally, three ensemble algorithms were used to perform asthma-prone areas mapping. According to the Gini index, the most influential factors on asthma occurrence were distance to the street, NDVI, and traffic volume. The area under the curve (AUC) of receiver operating characteristic (ROC) values for the AdaBoost, Bagging, and Stacking algorithms was 0.849, 0.82, and 0.785, respectively. According to the findings, the AdaBoost algorithm outperforms the Bagging and Stacking algorithms in spatial modeling of asthma-prone areas.
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