1
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Fang C, Zhou L, Gu X, Liu X, Werner M. A data driven approach to urban area delineation using multi source geospatial data. Sci Rep 2025; 15:8708. [PMID: 40082559 PMCID: PMC11906633 DOI: 10.1038/s41598-025-93366-x] [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: 09/04/2024] [Accepted: 03/06/2025] [Indexed: 03/16/2025] Open
Abstract
This study introduces a data-driven, bottom-up approach to urban delineation, integrating feature engineering with the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, which represents a significant improvement in precision and methodology compared to traditional approaches that rely on simplistic OpenStreetMap (OSM) road node data aggregations. By employing a broad array of OSM categories and refining data selection through feature engineering, our research significantly enhances the precision and relevance of urban clustering. Using Bavaria, Germany, as a case study, we demonstrate that feature engineering effectively reduces noise and mitigates common DBSCAN clustering pitfalls by filtering out irrelevant and autocorrelated data. The robustness of the proposed method is validated through a comprehensive assessment involving three key elements: (1) a 5% improvement in average accuracy, (2) optimal clustering selections based on entropy values that eliminate the need for prior knowledge, and (3) validation through nighttime light data and Zipf's law, where a high p-value of 0.99 confirms a good fit, supporting the power law. This study contributes to urban studies by providing a scalable, replicable model that incorporates advanced data processing techniques and multidimensional data sources, supporting improved urban planning and policy-making while effectively delineating urban areas in varied settings.
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Affiliation(s)
- Chenyu Fang
- Department of Aerospace and Geodesy, Professorship for Big Geospatial Data Management, Technical University of Munich, 85521, Munich, Germany.
| | - Lin Zhou
- School of Public Policy, University of Massachusetts Amherst, Amherst, USA
| | - Xinyue Gu
- Department of Land Surveying and Geo-Informatics (LSGI), The Hong Kong Polytechnic University, Hong Kong, China
| | - Xing Liu
- China Academy of Urban Planning & Design Shenzhen, Shenzhen, 518000, Guangdong, China
| | - Martin Werner
- Department of Aerospace and Geodesy, Professorship for Big Geospatial Data Management, Technical University of Munich, 85521, Munich, Germany
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2
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Chatterjee K, Thara MN, Reddy MS, Selvamuthukumaran N, Priyadharshini M, Reddy TAV, Chakraborty S, Mallik S, Shah MA, Li A. Ecosense: a revolution in urban air quality forecasting for smart cities. BMC Res Notes 2025; 18:62. [PMID: 39934850 PMCID: PMC11817920 DOI: 10.1186/s13104-025-07099-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Accepted: 01/10/2025] [Indexed: 02/13/2025] Open
Abstract
The Smart City (SC) framework is popular due to its advancement in enhancing lives and public safety. However, these advancements lead to many challenges due to the dependency of Internet of Things (IoT) devices in terms of electronic waste and resource consumption. To address those challenges, the integration of a weather-smart grid (WSG) with SC becomes crucial to safeguard the environment and residents' well-being. Along with these concepts, this study proposes a novel approach, EcoSense: A Revolution in Urban Air Quality Forecasting for Smart Cities, which incorporates Bi-directional Stacked LSTM with a Weather-Smart Grid (BlaSt). BlaSt innovatively integrates several key components: (i) the model captures intricate temporal dependencies and trends in air quality data by incorporating historical air pollutant and meteorological data. (ii) integration of the WSG component enhances the model's capability to incorporate weather data, which is critical for accurate air quality forecasting. (iii) the model computes 12-hour predictions by designing 1-hour prediction models, enabling it to provide timely forecasts with high precision. BlaSt demonstrates significant improvements over existing models, with enhancements of 36%, 26%, 21%, 46%, 14%, 10%, and 6% in accuracy compared to SVR, MLP, RAQP, Vlachogianni, LSTM, BLSTM, and SLSTM models, respectively. It achieves a mean absolute error (MAE) of 0.10 and a mean squared error (MSE) of 0.08. Additionally, BlaSt reduces computational complexity by 25%, making it more efficient in processing large-scale air quality data. The experimental results demonstrate BlaSt's superior accuracy and efficiency, showcasing its potential to advance urban air quality forecasting in SCs.
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Affiliation(s)
- Kalyan Chatterjee
- Computer Science & Engineering, Nalla Malla Reddy Engineering College, Hyderabad, 500088, Telangana, India
| | - Machakanti Navya Thara
- Artificial Engineering & Machine Learning, Nalla Malla Reddy Engineering College, Hyderabad, 500088, Telangana, India
| | - Mandadi Sriya Reddy
- Computer Science & Engineering, Indian Institute of Information Technology, Vadodara, Gandhinagar, 362520, Gujarat, India
| | - N Selvamuthukumaran
- Artificial Engineering & Machine Learning, Nalla Malla Reddy Engineering College, Hyderabad, 500088, Telangana, India
| | - M Priyadharshini
- Computer Science and Information Systems, Faculty of Science and Technology (IcfaiTech), ICFAI Foundation for Higher Education, Hyderabad, 501203, India
| | - Tummala Abhinav Vardhan Reddy
- Artificial Engineering & Machine Learning, Nalla Malla Reddy Engineering College, Hyderabad, 500088, Telangana, India
| | - Somenath Chakraborty
- Computer Science and Information Systems, The West Virginia University Institute of Technology, Beckley, West Virginia, USA
| | - Saurav Mallik
- Department of Environmental Health, Harvard T H Chan School of Public Health, Boston, MA, 02115, USA.
- Department of Pharmacology & Toxicology, The University of Arizona, Tucson, MA, 85721, USA.
| | - Mohd Asif Shah
- Department of Economics, Kardan University, Kabul, 1001, Afghanistan.
- Centre for Research Impact & Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, 140401, Punjab, India.
- Chitkara Centre for Research and Development, Chitkara University, Baddi, Himachal Pradesh, 174103, India.
- Division of Research and Development, Lovely Professional University, Phagwara, Punjab, 144001, India.
| | - Aimin Li
- School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, 710048, Shaanxi, China
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3
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Jayaraman S, T N, S A, G S. Enhancing urban air quality prediction using time-based-spatial forecasting framework. Sci Rep 2025; 15:4139. [PMID: 39900952 PMCID: PMC11791089 DOI: 10.1038/s41598-024-83248-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2024] [Accepted: 12/12/2024] [Indexed: 02/05/2025] Open
Abstract
Air quality forecasting plays a pivotal role in environmental management, public health and urban planning. This research presents a comprehensive approach for forecasting the Air Quality Index (AQI). The proposed Time-Based-Spatial (TBS) forecasting framework is integrated with spatial and temporal information using machine learning techniques on data collected from a wide range of cities. The TBS employs Convolutional Neural Networks (CNNs) to capture spatial dependencies based on normalized latitude and longitude coordinates of the cities. Simultaneously, time series model, specifically the ARIMA (AutoRegressive Integrated Moving Average) was employed to capture temporal dependencies using pollutant concentration readings over time. The dataset included information such as date, time, pollutant concentrations and AQI was further preprocessed and divided into training and testing sets. The CNN was configured to utilize the normalized latitude and longitude grid, while the ARIMA model concurrently processed the pollutant concentrations. The model was trained on the training dataset, and a 6 hour forecast is generated for each test instance. The outcomes demonstrate the TBS model's ability to accurately predict AQI values. The integration of CNNs and time series model allowed for an clearer and deeper understanding of geographical and pollutant concentration factors that contribute to air quality variations.
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Affiliation(s)
| | - Nathezhtha T
- Vellore Institute of Technology Chennai, Chennai, India.
| | - Abirami S
- Vellore Institute of Technology Chennai, Chennai, India
| | - Sakthivel G
- Vellore Institute of Technology Chennai, Chennai, India
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4
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Madukpe VN, Zulkepli NFS, Noorani MSM, Gobithaasan RU. Comparative analysis of Ball Mapper and conventional Mapper in investigating air pollutants' behavior. ENVIRONMENTAL MONITORING AND ASSESSMENT 2025; 197:136. [PMID: 39760901 DOI: 10.1007/s10661-024-13477-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2024] [Accepted: 11/26/2024] [Indexed: 01/07/2025]
Abstract
This study investigates the effectiveness and efficiency of two topological data analysis (TDA) techniques, the conventional Mapper (CM) and its variant version, the Ball Mapper (BM), in analyzing the behavior of six major air pollutants (NO2, PM10, PM2.5, O3, CO, and SO2) across 60 air quality monitoring stations in Malaysia. Topological graphs produced by CM and BM reveal redundant monitoring stations and geographical relationships corresponding to air pollutant behavior, providing better visualization than traditional hierarchical clustering. Additionally, a comparative analysis of topological graph structures was conducted using node degree distribution, topological graph indices, and Dynamic Time Warping (DTW) to evaluate the sensitivity and performance of these TDA techniques. Both approaches yielded valuable insights in representing the air quality monitoring stations network; however, the complexity of CM, which requires multiple parameters, poses a challenge in graph construction. In contrast, the simplicity of BM, requiring only a single parameter, is preferable for representing air pollutant behavior. The findings suggest an alternative approach for assessing and identifying redundancies in air quality monitoring stations, which could contribute to improved air quality monitoring management and more effective regulatory policies.
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Affiliation(s)
- Vine Nwabuisi Madukpe
- School of Mathematical Sciences, Universiti Sains Malaysia, 11800 USM, Penang, Malaysia
| | | | - Mohd Salmi Md Noorani
- Department of Mathematical Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia
| | - R U Gobithaasan
- School of Mathematical Sciences, Universiti Sains Malaysia, 11800 USM, Penang, Malaysia
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5
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Shi Y, Wang S, Yu X. A novel hybrid optimization model for evaluating and forecasting air quality grades. ENVIRONMENTAL MONITORING AND ASSESSMENT 2024; 196:800. [PMID: 39120666 DOI: 10.1007/s10661-024-12939-x] [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: 03/18/2024] [Accepted: 07/24/2024] [Indexed: 08/10/2024]
Abstract
Air pollution has a significant global impact on natural resources and public health. Accurate prediction of air pollution is crucial for effective prevention and control measures. However, due to regional variations, different cities may have varying primary pollutants, posing new challenges for accurate prediction. In this paper, we propose a novel method called FP-RF, which integrates clustering algorithms to categorize multiple cities according to their air quality index values. Subsequently, we apply functional principal component analysis to extract the primary components of air pollution within each cluster. Furthermore, an enhanced random forest algorithm is utilized to predict air quality grades for each city. We conduct experimental evaluations using authentic historical data from Anhui Province spanning from 2018 to 2023. The results unequivocally establish the effectiveness of our model, with an average accuracy rate of 98.6% in forecasting six pollution grades and 96.04% accuracy in predicting 16 prefecture-level cities, surpassing performance compared to other baseline models.
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Affiliation(s)
- Yumei Shi
- School of Mathematics and Finance, Chuzhou University, 1 HuifengRoad, Chuzhou, 239000, Anhui, China
| | - Sheng Wang
- School of Mathematics and Finance, Chuzhou University, 1 HuifengRoad, Chuzhou, 239000, Anhui, China.
| | - Xiaomei Yu
- School of Mathematics and Finance, Chuzhou University, 1 HuifengRoad, Chuzhou, 239000, Anhui, China
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6
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Chen Q, Ding R, Mo X, Li H, Xie L, Yang J. An adaptive adjacency matrix-based graph convolutional recurrent network for air quality prediction. Sci Rep 2024; 14:4408. [PMID: 38388632 PMCID: PMC10883962 DOI: 10.1038/s41598-024-55060-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 02/20/2024] [Indexed: 02/24/2024] Open
Abstract
In recent years, air pollution has become increasingly serious and poses a great threat to human health. Timely and accurate air quality prediction is crucial for air pollution early warning and control. Although data-driven air quality prediction methods are promising, there are still challenges in studying spatial-temporal correlations of air pollutants to design effective predictors. To address this issue, a novel model called adaptive adjacency matrix-based graph convolutional recurrent network (AAMGCRN) is proposed in this study. The model inputs Point of Interest (POI) data and meteorological data into a fully connected neural network to learn the weights of the adjacency matrix thereby constructing the self-ringing adjacency matrix and passes the pollutant data with this matrix as input to the Graph Convolutional Network (GCN) unit. Then, the GCN unit is embedded into LSTM units to learn spatio-temporal dependencies. Furthermore, temporal features are extracted using Long Short-Term Memory network (LSTM). Finally, the outputs of these two components are merged and air quality predictions are generated through a hidden layer. To evaluate the performance of the model, we conducted multi-step predictions for the hourly concentration of PM2.5, PM10 and O3 at Fangshan, Tiantan and Dongsi monitoring stations in Beijing. The experimental results show that our method achieves better predicted effects compared with other baseline models based on deep learning. In general, we designed a novel air quality prediction method and effectively addressed the shortcomings of existing studies in learning the spatio-temporal correlations of air pollutants. This method can provide more accurate air quality predictions and is expected to provide support for public health protection and government environmental decision-making.
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Affiliation(s)
- Quanchao Chen
- School of Cyberspace Security/School of Cryptology, Hainan University, Haikou, China
| | - Ruyan Ding
- School of Cyberspace Security/School of Cryptology, Hainan University, Haikou, China
| | - Xinyue Mo
- School of Cyberspace Security/School of Cryptology, Hainan University, Haikou, China.
| | - Huan Li
- School of Cyberspace Security/School of Cryptology, Hainan University, Haikou, China.
| | - Linxuan Xie
- School of Cyberspace Security/School of Cryptology, Hainan University, Haikou, China
| | - Jiayu Yang
- School of Cyberspace Security/School of Cryptology, Hainan University, Haikou, China
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7
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Li X, Abdullah LC, Sobri S, Syazarudin Md Said M, Aslina Hussain S, Poh Aun T, Hu J. Long-term spatiotemporal evolution and coordinated control of air pollutants in a typical mega-mountain city of Cheng-Yu region under the "dual carbon" goal. JOURNAL OF THE AIR & WASTE MANAGEMENT ASSOCIATION (1995) 2023; 73:649-678. [PMID: 37449903 DOI: 10.1080/10962247.2023.2232744] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 05/31/2023] [Accepted: 06/20/2023] [Indexed: 07/18/2023]
Abstract
Clarifying the spatiotemporal distribution and impact mechanism of pollution is the prerequisite for megacities to formulate relevant air pollution prevention and control measures and achieve carbon neutrality goals. Chongqing is one of the dual-core key megacities in Cheng-Yu region and as a typical mountain-city in China, environmental problems are complex and sensitive. This research aims to investigate the exceeding standard levels and spatio-temporal evolution of criteria pollutants between 2014 and 2020. The results indicated that PM10, PM2.5, CO and SO2 were decreased significantly by 45.91%, 52.86%, 38.89% and 66.67%, respectively. Conversely, the concentration of pollutant O3 present a fluctuating growth and found a "seesaw" phenomenon between it and PM. Furthermore, PM and O3 are highest in winter and summer, respectively. SO2, NO2, CO, and PM showed a "U-shaped", and O3 showed an inverted "U-shaped" seasonal variation. PM and O3 concentrations are still far behind the WHO, 2021AQGs standards. Significant spatial heterogeneity was observed in air pollution distribution. These results are of great significance for Chongqing to achieve "double control and double reduction" of PM2.5 and O3 pollution, and formulate a regional carbon peaking roadmap under climate coordination. Besides, it can provide an important platform for exploring air pollution in typical terrain around the world and provide references for related epidemiological research.Implications: Chongqing is one of the dual-core key megacities in Cheng-Yu region and as a typical mountain city, environmental problems are complex and sensitive. Under the background of the "14th Five-Year Plan", the construction of the "Cheng-Yu Dual-City Economic Circle" and the "Dual-Carbon" goal, this article comprehensively discussed the annual and seasonal excess levels and spatiotemporal evolution of pollutants under the multiple policy and the newest international standards (WHO,2021AQG) backgrounds from 2014 to 2020 in Chongqing. Furthermore, suggestions and measures related to the collaborative management of pollutants were discussed. Finally, limitations and recommendations were also put forward.Clarifying the spatiotemporal distribution and impact mechanism of pollution is the prerequisite for cities to formulate relevant air pollution control measures and achieve carbon neutrality goals. This study is of great significance for Chongqing to achieve "double control and double reduction" of PM2.5 and O3 pollution, study and formulate a regional carbon peaking roadmap under climate coordination and an action plan for sustained improvement of air quality.In addition, this research can advanced our understanding of air pollution in complex terrain. Furthermore, it also promote the construction of the China national strategic Cheng-Yu economic circle and build a beautiful west. Moreover, it provides scientific insights for local policymakers to guide smart urban planning, industrial layout, energy structure, and transportation planning to improve air quality throughout the Cheng-Yu region. Finally, this is also conducive to future scientific research in other regions of China, and even megacities with complex terrain in the world.
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Affiliation(s)
- Xiaoju Li
- Department of Chemical and Environmental Engineering, Faculty of Engineering, University Putra Malaysia, Serdang, Malaysia
- Department of Resource and Environment, Xichang University, Xichang City, Sichuan Province, China
| | - Luqman Chuah Abdullah
- Department of Chemical and Environmental Engineering, Faculty of Engineering, University Putra Malaysia, Serdang, Malaysia
| | - Shafreeza Sobri
- Department of Chemical and Environmental Engineering, Faculty of Engineering, University Putra Malaysia, Serdang, Malaysia
| | - Mohamad Syazarudin Md Said
- Department of Chemical and Environmental Engineering, Faculty of Engineering, University Putra Malaysia, Serdang, Malaysia
| | - Siti Aslina Hussain
- Department of Chemical and Environmental Engineering, Faculty of Engineering, University Putra Malaysia, Serdang, Malaysia
| | - Tan Poh Aun
- SOx NOx Asia Sdn Bhd, Subang Jaya, Selangor, Malaysia
| | - Jinzhao Hu
- Department of Resource and Environment, Xichang University, Xichang City, Sichuan Province, China
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Wongnakae P, Chitchum P, Sripramong R, Phosri A. Application of satellite remote sensing data and random forest approach to estimate ground-level PM 2.5 concentration in Northern region of Thailand. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:88905-88917. [PMID: 37442931 DOI: 10.1007/s11356-023-28698-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 07/05/2023] [Indexed: 07/15/2023]
Abstract
Numerous epidemiological studies have shown that particulate matter with aerodynamic diameter up to 2.5 μm (PM2.5) is associated with many health consequences, where PM2.5 concentration obtained from the monitoring station was normally applied as the exposure level, so that the concentration of PM2.5 in unmonitored areas has not been captured. The satellite-derived aerosol optical depth (AOD) product is then used to spatially predict ground truth of PM2.5 concentration that covers the locations with no air quality monitoring station, but this method has seldom been developed in Thailand. This study aimed at estimating ground-level PM2.5 concentration at 3 km × 3 km spatial resolution over Northern region of Thailand in 2021 using the random forest model integrating the Moderate Resolution Imaging Spectroradiometer (MODIS) AOD products from Terra and Aqua satellites, meteorological factors, and land use data. A random forest model contained 100 decision trees was utilized to train the model, and 10-fold cross-validation approach was implemented to validate the model performance. The good consistency between actual (observed) and predicted concentrations of PM2.5 in Northern region of Thailand was observed, where a coefficient of determination (R2) and root mean square error (RMSE) of the model fitting were 0.803 and 14.30 μg/m3, respectively, and those of 10-fold cross-validation approach were 0.796 and 14.64 μg/m3, respectively. The three most important predictors for estimating the ground-level concentrations of PM2.5 in this study were normalized difference vegetation index (NDVI), relative humidity, and number of fire hotspot, respectively. Findings from this study revealed that integrating the MODIS AOD, meteorological variables, and land use data into the random forest model precisely and accurately estimated ground-level PM2.5 concentration over Northern region of Thailand that can be further used to investigate the effects of PM2.5 exposure on health consequences, even in unmonitored locations, in epidemiological studies.
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Affiliation(s)
- Pimchanok Wongnakae
- Department of Environmental Health Sciences, Faculty of Public Health, Mahidol University, 4th Floor, 2nd Building, Rajvithi Road, Bangkok, 10400, Thailand
| | - Pakkapong Chitchum
- Department of Environmental Health Sciences, Faculty of Public Health, Mahidol University, 4th Floor, 2nd Building, Rajvithi Road, Bangkok, 10400, Thailand
| | - Rungduen Sripramong
- Department of Environmental Health Sciences, Faculty of Public Health, Mahidol University, 4th Floor, 2nd Building, Rajvithi Road, Bangkok, 10400, Thailand
| | - Arthit Phosri
- Department of Environmental Health Sciences, Faculty of Public Health, Mahidol University, 4th Floor, 2nd Building, Rajvithi Road, Bangkok, 10400, Thailand.
- Center of Excellence on Environmental Health and Toxicology (EHT), OPS, Ministry of Higher Education, Research, Science and Innovation, Bangkok, Thailand.
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9
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Halder B, Ahmadianfar I, Heddam S, Mussa ZH, Goliatt L, Tan ML, Sa'adi Z, Al-Khafaji Z, Al-Ansari N, Jawad AH, Yaseen ZM. Machine learning-based country-level annual air pollutants exploration using Sentinel-5P and Google Earth Engine. Sci Rep 2023; 13:7968. [PMID: 37198391 DOI: 10.1038/s41598-023-34774-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 05/08/2023] [Indexed: 05/19/2023] Open
Abstract
Climatic condition is triggering human health emergencies and earth's surface changes. Anthropogenic activities, such as built-up expansion, transportation development, industrial works, and some extreme phases, are the main reason for climate change and global warming. Air pollutants are increased gradually due to anthropogenic activities and triggering the earth's health. Nitrogen Dioxide (NO2), Carbon Monoxide (CO), and Aerosol Optical Depth (AOD) are truthfully important for air quality measurement because those air pollutants are more harmful to the environment and human's health. Earth observational Sentinel-5P is applied for monitoring the air pollutant and chemical conditions in the atmosphere from 2018 to 2021. The cloud computing-based Google Earth Engine (GEE) platform is applied for monitoring those air pollutants and chemical components in the atmosphere. The NO2 variation indicates high during the time because of the anthropogenic activities. Carbon Monoxide (CO) is also located high between two 1-month different maps. The 2020 and 2021 results indicate AQI change is high where 2018 and 2019 indicates low AQI throughout the year. The Kolkata have seven AQI monitoring station where high nitrogen dioxide recorded 102 (2018), 48 (2019), 26 (2020) and 98 (2021), where Delhi AQI stations recorded 99 (2018), 49 (2019), 37 (2020), and 107 (2021). Delhi, Kolkata, Mumbai, Pune, and Chennai recorded huge fluctuations of air pollutants during the study periods, where ~ 50-60% NO2 was recorded as high in the recent time. The AOD was noticed high in Uttar Pradesh in 2020. These results indicate that air pollutant investigation is much necessary for future planning and management otherwise; our planet earth is mostly affected by the anthropogenic and climatic conditions where maybe life does not exist.
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Affiliation(s)
- Bijay Halder
- Department of Remote Sensing and GIS, Vidyasagar University, Midnapore, 721102, India
- New Era and Development in Civil Engineering Research Group, Scientific Research Center, Al-Ayen University, Nasiriyah, Thi-Qar, 64001, Iraq
| | - Iman Ahmadianfar
- Department of Civil Engineering, Behbahan Khatam Alanbia University of Technology, Behbahan, Iran
| | - Salim Heddam
- Agronomy Department, Faculty of Science, University, 20 Août 1955 Skikda, Route El Hadaik, BP 26, Skikda, Algeria
| | | | - Leonardo Goliatt
- Computational Modeling Program, Federal University of Juiz de Fora, Juiz de Fora, MG, Brazil
| | - Mou Leong Tan
- GeoInformatic Unit, Geography Section, School of Humanities, Universiti Sains Malaysia, 11800, Penang, Malaysia
- School of Geographical Sciences, Nanjing Normal University, Nanjing, 210023, China
| | - Zulfaqar Sa'adi
- Centre for Environmental Sustainability and Water Security, Research Institute for Sustainable Environment, Universiti Teknologi Malaysia (UTM), 81310, Sekudai, Johor, Malaysia
| | - Zainab Al-Khafaji
- Department of Building and Construction Technologies Engineering, AL-Mustaqbal University College, Hillah, 51001, Iraq
| | - Nadhir Al-Ansari
- Civil, Environmental and Natural Resources Engineering, Lulea University of Technology, 97187, Lulea, Sweden.
| | - Ali H Jawad
- Faculty of Applied Sciences, Universiti Teknologi MARA, 40450, Shah Alam, Selangor, Malaysia
| | - Zaher Mundher Yaseen
- Civil and Environmental Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran, 31261, Saudi Arabia.
- Interdisciplinary Research Center for Membranes and Water Security, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi Arabia.
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