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Ayinde BO, Musa MR, Ayinde AAO. Application of machine learning models and landsat 8 data for estimating seasonal pm 2.5 concentrations. Environ Anal Health Toxicol 2024; 39:e2024011-0. [PMID: 38631403 PMCID: PMC11079408 DOI: 10.5620/eaht.2024011] [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: 12/16/2023] [Accepted: 03/12/2024] [Indexed: 04/19/2024] Open
Abstract
Air pollution is a significant global challenge that affects many cities. In Europe, Bosnia and Herzegovina (BiH) are among the most highly polluted and are mainly affected by air pollution. In this study, we integrate open-source landsat 8 remote sensing products, topographical data, and the limited ground truth PM2.5 data to spatially predict the air quality level across different seasons in Tuzla Canton, BiH by adopting three pre-existing machine learning models, namely XGBoost, K-Nearest Neighbour (KNN) and Naive Bayes (NB). These classification models were implemented based on landsat 8 bands, environmental-derived indices, and topographical variables generated for the study area. Based on the predicted results, the XGBoost model exhibited the highest overall accuracy across all seasons. The predicted model results were used to generate spatial air quality maps. Based on the classification maps, the PM2.5 air quality level predicted for Tuzla Canton in the Winter Season is very unhealthy. The findings conclude that the PM2.5 air quality concentration in Tuzla Canton is relatively unsatisfactory and requires urgent intervention by the government to prevent further deterioration of air quality in Tuzla and other affected cantons in BiH.
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Rodriguez-Villamizar LA, Rojas Y, Grisales S, Mangones SC, Cáceres JJ, Agudelo-Castañeda DM, Herrera V, Marín D, Jiménez JGP, Belalcázar-Ceron LC, Rojas-Sánchez OA, Ochoa Villegas J, López L, Rojas OM, Vicini MC, Salas W, Orrego AZ, Castillo M, Sáenz H, Hernández LÁ, Weichenthal S, Baumgartner J, Rojas NY. Intra-urban variability of long-term exposure to PM 2.5 and NO 2 in five cities in Colombia. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:3207-3221. [PMID: 38087152 DOI: 10.1007/s11356-023-31306-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 11/26/2023] [Indexed: 01/18/2024]
Abstract
Rapidly urbanizing cities in Latin America experience high levels of air pollution which are known risk factors for population health. However, the estimates of long-term exposure to air pollution are scarce in the region. We developed intraurban land use regression (LUR) models to map long-term exposure to fine particulate matter (PM2.5) and nitrogen dioxide (NO2) in the five largest cities in Colombia. We conducted air pollution measurement campaigns using gravimetric PM2.5 and passive NO2 sensors for 2 weeks during both the dry and rainy seasons in 2021 in the cities of Barranquilla, Bucaramanga, Bogotá, Cali, and Medellín, and combined these data with geospatial and meteorological variables. Annual models were developed using multivariable spatial regression models. The city annual PM2.5 mean concentrations measured ranged between 12.32 and 15.99 µg/m3 while NO2 concentrations ranged between 24.92 and 49.15 µg/m3. The PM2.5 annual models explained 82% of the variance (R2) in Medellín, 77% in Bucaramanga, 73% in Barranquilla, 70% in Cali, and 44% in Bogotá. The NO2 models explained 65% of the variance in Bucaramanga, 57% in Medellín, 44% in Cali, 40% in Bogotá, and 30% in Barranquilla. Most of the predictor variables included in the models were a combination of specific land use characteristics and roadway variables. Cross-validation suggests that PM2.5 outperformed NO2 models. The developed models can be used as exposure estimate in epidemiological studies, as input in hybrid models to improve personal exposure assessment, and for policy evaluation.
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Affiliation(s)
| | - Yurley Rojas
- Escuela de Ingeniería Civil, Industrial de Santander, Carrera 27 Calle 9 Ciudad Universitaria, Bucaramanga, Colombia
| | - Sara Grisales
- Facultad Nacional de Salud Pública, Universidad de Antioquia, Calle 62 52-59, Medellín, Colombia
| | - Sonia C Mangones
- Facultad de Ingeniería, Universidad Nacional de Colombia, Carrera 45 26-85 Edificio 401, Bogotá, Colombia
| | - Jhon J Cáceres
- Escuela de Ingeniería Civil, Industrial de Santander, Carrera 27 Calle 9 Ciudad Universitaria, Bucaramanga, Colombia
| | - Dayana M Agudelo-Castañeda
- Departamento de Ingeniería Civil y Ambiental, Universidad del Norte, Km 5 Vía Puerto Colombia, Barranquilla, Colombia
| | - Víctor Herrera
- Departamento de Salud Pública, Universidad Industrial de Santander, Carrera 32 29-31, Bucaramanga, Colombia
- Facultad de Ciencias de La Salud, Universidad Autónoma de Bucaramanga, Calle 157 15-55 El Bosque, Floridablanca, Colombia
| | - Diana Marín
- Escuela de Medicina, Universidad Pontificia Bolivariana, Calle 78B 72ª-159, Medellín, Colombia
| | - Juan G Piñeros Jiménez
- Facultad Nacional de Salud Pública, Universidad de Antioquia, Calle 62 52-59, Medellín, Colombia
| | - Luis C Belalcázar-Ceron
- Facultad de Ingeniería, Universidad Nacional de Colombia, Carrera 45 26-85 Edificio 401, Bogotá, Colombia
| | - Oscar Alberto Rojas-Sánchez
- División de Investigación en Salud Pública, Instituto Nacional de Salud, Avenida Calle 26 51-20, Bogotá, Colombia
| | - Jonathan Ochoa Villegas
- Facultad de Ingenierías, Universidad San Buenaventura, Carrera 56C 51-110, Medellín, Colombia
| | - Leandro López
- Departamento de Salud Pública, Universidad Industrial de Santander, Carrera 32 29-31, Bucaramanga, Colombia
| | - Oscar Mauricio Rojas
- Área Metropolitana de Bucaramanga, Calle 89 Transveral Oriental Metropolitana, Bucaramanga, Colombia
| | - María C Vicini
- Corporación Para La Defensa de La Meseta de Bucaramanga, Carrera 23 37-63, Bucaramanga, Colombia
| | - Wilson Salas
- Departamento Administrativo de Gestión del Medio Ambiente, Alcaldía de Santiago de Cali, Avenida 5AN 20-08, Cali, Colombia
| | - Ana Zuleima Orrego
- Área Metropolitana del Valle de Aburrá, Carrera 53 40ª-31, Medellín, Colombia
| | | | - Hugo Sáenz
- Secretaría Distrital de Ambiente, Alcaldía de Bogotá, Avenida Caracas 54-38, Bogotá, Colombia
| | - Luis Álvaro Hernández
- Secretaría Distrital de Ambiente, Alcaldía de Bogotá, Avenida Caracas 54-38, Bogotá, Colombia
| | - Scott Weichenthal
- Department of Epidemiology, Biostatistics & Occupational Health, McGill University, 2001 McGill College Avenue, Montreal, Canada
| | - Jill Baumgartner
- Department of Epidemiology, Biostatistics & Occupational Health, McGill University, 2001 McGill College Avenue, Montreal, Canada
| | - Néstor Y Rojas
- Facultad de Ingeniería, Universidad Nacional de Colombia, Carrera 45 26-85 Edificio 401, Bogotá, Colombia
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Singh A, Vishnoi AS, Banday AH, Bora P, Pandey P. Influence of stubble burning on air quality of Northern India: a case study of Indo-Gangetic plains of India. ENVIRONMENTAL MONITORING AND ASSESSMENT 2023; 195:487. [PMID: 36939944 DOI: 10.1007/s10661-023-11027-w] [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: 02/06/2022] [Accepted: 02/13/2023] [Indexed: 06/18/2023]
Abstract
Stubble burning is an emerging environmental issue in Northern India, which has severe implications for the air quality of the region. Although stubble burning occurs twice during a year, first during April-May and again in October-November due to paddy burning, the effects are severe during October-November months. This is exacerbated by the role of meteorological parameters and presence of inversion conditions in the atmosphere. The deterioration in the atmospheric quality can be attributed to the emissions from stubble burning which can be perceived from the changes observed in land use land cover (LULC) pattern, fire events, and sources of aerosol and gaseous pollutants. In addition, wind speed and wind direction also play a role in changing the concentration of pollutants and particulate matter over a specified area. The present study has been carried out for the states of Punjab, Haryana, Delhi, and western Uttar Pradesh to study the influence of stubble burning on the aerosol load of this region of Indo-Gangetic Plains (IGP). In this study, the aerosol level, smoke plume characteristics, long-range transport of pollutants, and affected areas during October-November from year 2016 to 2020 were examined over the Indo-Gangetic Plains (Northern India) region by the satellite observations. By MODIS-FIRMS (Moderate Resolution Imaging Spectroradiometer-Fire Information for Resource Management System) observations, it was revealed that there was an increase in stubble burning events with the highest number of events being observed during the year 2016 and then a decrease in the number of events in subsequent years from 2017 to 2020. MODIS observations revealed a strong AOD gradient from west to east. The prevailing north-westerly winds assist the spread of smoke plumes over Northern India during the peak burning season of October to November. The findings of this study might be used to expand on the atmospheric processes that occur over northern India during the post-monsoon season. The pollutant, smoke plume features, and impacted regions of biomass-burning aerosols in this region are critical for weather and climate research, especially given the rising trend in agricultural burning over the previous two decades.
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Affiliation(s)
- Abhijeet Singh
- Department of Environmental Science and Technology, School of Environment and Earth Sciences, Central University of Punjab, Bathinda, Punjab-151401, India
| | - Ashok Singh Vishnoi
- Department of Environmental Science and Technology, School of Environment and Earth Sciences, Central University of Punjab, Bathinda, Punjab-151401, India
| | - Anwar Hameed Banday
- Department of Environmental Science and Technology, School of Environment and Earth Sciences, Central University of Punjab, Bathinda, Punjab-151401, India
| | - Pratyashee Bora
- Department of Environmental Science and Technology, School of Environment and Earth Sciences, Central University of Punjab, Bathinda, Punjab-151401, India
| | - Puneeta Pandey
- Department of Environmental Science and Technology, School of Environment and Earth Sciences, Central University of Punjab, Bathinda, Punjab-151401, India.
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Towards a Circular Economy Development for Household Used Cooking Oil in Guayaquil: Quantification, Characterization, Modeling, and Geographical Mapping. SUSTAINABILITY 2022. [DOI: 10.3390/su14159565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
The objective of the present study was to quantify, geo-locate, model, and characterize domestic used cooking oil (dUCO) generation for the city of Guayaquil. For this reason, and as a prerequisite for the proper planning of municipal cooking oil waste management in the city, we carried out 14-day fieldwork involving 532 households from different parishes of Guayaquil, combined with a survey to acquire data on their demographic and socioeconomic statistics. The artisanal characterization was further executed to 40 subsamples of dUCO to determine the density, moisture, solids content, and the volatile-matter characteristics present. Additionally, the Geographic Information System (GIS) was used to map the used cooking oil generation hotspots for the city, adding the Geographical Position System (GPS) of each participating household during the data acquisition. Finally, a multiple-regression model was proposed to establish correlations between the dUCO generated and five independent variables, such as household size, socioeconomic group, tenure status, education level, and income. Results showed that the per capita daily dUCO-generation rate was found to be 4.30 g/day/c or 4.99 mL/day/c, with a density of 0.86 g/mL. Filterable solids represented 0.37% for the entire dUCO collected sample, while separable water and grease represented 1.58% and 0.014%, respectively. In addition, the percentage of the volatile matter was found to be 7.7% ± 2.1% of the filtered dUCO. Using GIS mapping, we found that the areas near tourism sites have a higher dUCO generation value, considering the household survey. Following the developed multiple-regression model developed, it was found that household size and the socioeconomic group have the maximum effect on generating used cooking oil.
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Machine Learning for Determining Interactions between Air Pollutants and Environmental Parameters in Three Cities of Iran. SUSTAINABILITY 2022. [DOI: 10.3390/su14138027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
Air pollution, as one of the most significant environmental challenges, has adversely affected the global economy, human health, and ecosystems. Consequently, comprehensive research is being conducted to provide solutions to air quality management. Recently, it has been demonstrated that environmental parameters, including temperature, relative humidity, wind speed, air pressure, and vegetation, interact with air pollutants, such as particulate matter (PM), NO2, SO2, O3, and CO, contributing to frameworks for forecasting air quality. The objective of the present study is to explore these interactions in three Iranian metropolises of Tehran, Tabriz, and Shiraz from 2015 to 2019 and develop a machine learning-based model to predict daily air pollution. Three distinct assessment criteria were used to assess the proposed XGBoost model, including R squared (R2), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). Preliminary results showed that although air pollutants were significantly associated with meteorological factors and vegetation, the formulated model had low accuracy in predicting (R2PM2.5 = 0.36, R2PM10 = 0.27, R2NO2 = 0.46, R2SO2 = 0.41, R2O3 = 0.52, and R2CO = 0.38). Accordingly, future studies should consider more variables, including emission data from manufactories and traffic, as well as sunlight and wind direction. It is also suggested that strategies be applied to minimize the lack of observational data by considering second-and third-order interactions between parameters, increasing the number of simultaneous air pollution and meteorological monitoring stations, as well as hybrid machine learning models based on proximal and satellite data.
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Environmental Pollution Analysis and Impact Study-A Case Study for the Salton Sea in California. ATMOSPHERE 2022. [DOI: 10.3390/atmos13060914] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
A natural experiment conducted on the shrinking Salton Sea, a saline lake in California, showed that each one foot drop in lake elevation resulted in a 2.6% average increase in PM2.5 concentrations. The shrinking has caused the asthma rate continues to increase among children, with one in five children being sent to the emergency department, which is related to asthma. In this paper, several data-driven machine learning (ML) models are developed for forecasting air quality and dust emission to study, evaluate and predict the impacts on human health due to the shrinkage of the sea, such as the Salton Sea. The paper presents an improved long short-term memory (LSTM) model to predict the hourly air quality (O3 and CO) based on air pollutants and weather data in the previous 5 h. According to our experiment results, the model generates a very good R2 score of 0.924 and 0.835 for O3 and CO, respectively. In addition, the paper proposes an ensemble model based on random forest (RF) and gradient boosting (GBoost) algorithms for forecasting hourly PM2.5 and PM10 using the air quality and weather data in the previous 5 h. Furthermore, the paper shares our research results for PM2.5 and PM10 prediction based on the proposed ensemble ML models using satellite remote sensing data. Daily PM2.5 and PM10 concentration maps in 2018 are created to display the regional air pollution density and severity. Finally, the paper reports Artificial Intelligence (AI) based research findings of measuring air pollution impact on asthma prevalence rate of local residents in the Salton Sea region. A stacked ensemble model based on support vector regression (SVR), elastic net regression (ENR), RF and GBoost is developed for asthma prediction with a good R2 score of 0.978.
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Tourre YM, Paulin M, Dhonneur G, Attias D, Pathak A. COVID-19, air quality and space monitoring. GEOSPATIAL HEALTH 2022; 17. [PMID: 35385928 DOI: 10.4081/gh.2022.1052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 01/12/2022] [Indexed: 06/14/2023]
Abstract
Due to the worldwide spread of the coronavirus disease 2019 (COVID-19), human mobility and economic activity have slowed down considerably since early 2020. A relatively high number of those infected develop serious pneumonia leading to progressive respiratory failure, system disease and often death. Apart from close human-to-human contact, the acceleration and global diffusion of this pandemic has been shown to be associated with changes in atmospheric chemistry and air pollution by microscopic particulate matter (PM). Breathing air with high concentrations of nitrogen dioxide and PM can result in over-expression of the angiotensin converting enzyme-2 (ACE-2) leading to stress of organs, such as heart and kidneys. Satellite monitoring can play a crucial role in spatio-temporal surveillance of the disease by producing data on pollution as proxy for industrial activity, transport and traffic circulation. Real-time monitoring of COVID-19 in air and chemical pollution of the atmospheric boundary layer available from Earth-observing satellites commuting with Health Information Systems (HIS) would be useful for decision makers involved with public health.
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Affiliation(s)
- Yves M Tourre
- Former senior scientist at Lamont Doherty Earth Observatory (LDEO of Columbia University, NYC), and Engineer (Meteo-France), Toulouse.
| | - Mireille Paulin
- Program Environment, Space and Public Health, CNES, Toulouse.
| | - Gilles Dhonneur
- Department of Anaesthesia and Intensive Care, Curie Institute, Paris.
| | - David Attias
- Department of Pneumology, Clinique Pasteur, Toulouse.
| | - Atul Pathak
- Department of Cardiology, Princess Grace Hospital.
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Shi Y, Lau AKH, Ng E, Ho HC, Bilal M. A Multiscale Land Use Regression Approach for Estimating Intraurban Spatial Variability of PM 2.5 Concentration by Integrating Multisource Datasets. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 19:321. [PMID: 35010580 PMCID: PMC8751171 DOI: 10.3390/ijerph19010321] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 12/24/2021] [Accepted: 12/28/2021] [Indexed: 06/14/2023]
Abstract
Poor air quality has been a major urban environmental issue in large high-density cities all over the world, and particularly in Asia, where the multiscale complex of pollution dispersal creates a high-level spatial variability of exposure level. Investigating such multiscale complexity and fine-scale spatial variability is challenging. In this study, we aim to tackle the challenge by focusing on PM2.5 (particulate matter with an aerodynamic diameter less than 2.5 µm,) which is one of the most concerning air pollutants. We use the widely adopted land use regression (LUR) modeling technique as the fundamental method to integrate air quality data, satellite data, meteorological data, and spatial data from multiple sources. Unlike most LUR and Aerosol Optical Depth (AOD)-PM2.5 studies, the modeling process was conducted independently at city and neighborhood scales. Correspondingly, predictor variables at the two scales were treated separately. At the city scale, the model developed in the present study obtains better prediction performance in the AOD-PM2.5 relationship when compared with previous studies (R2¯ from 0.72 to 0.80). At the neighborhood scale, point-based building morphological indices and road network centrality metrics were found to be fit-for-purpose indicators of PM2.5 spatial estimation. The resultant PM2.5 map was produced by combining the models from the two scales, which offers a geospatial estimation of small-scale intraurban variability.
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Affiliation(s)
- Yuan Shi
- Institute of Future Cities (IOFC), The Chinese University of Hong Kong, Hong Kong, China
| | - Alexis Kai-Hon Lau
- Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China;
- Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
- Institute for the Environment, The Hong Kong University of Science & Technology, Clear Water Bay, Kowloon, Hong Kong, China
| | - Edward Ng
- Institute of Future Cities (IOFC), The Chinese University of Hong Kong, Hong Kong, China
- School of Architecture, The Chinese University of Hong Kong, Hong Kong, China;
- Institute of Environment, Energy and Sustainability (IEES), The Chinese University of Hong Kong, Hong Kong, China
| | - Hung-Chak Ho
- Department of Urban Planning and Design, The University of Hong Kong, Hong Kong, China;
| | - Muhammad Bilal
- Lab of Environmental Remote Sensing (LERS), School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China;
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Shogrkhodaei SZ, Razavi-Termeh SV, Fathnia A. Spatio-temporal modeling of PM 2.5 risk mapping using three machine learning algorithms. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2021; 289:117859. [PMID: 34340183 DOI: 10.1016/j.envpol.2021.117859] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Revised: 06/29/2021] [Accepted: 07/26/2021] [Indexed: 06/13/2023]
Abstract
Urban air pollution is one of the most critical issues that affect the environment, community health, economy, and management of urban areas. From a public health perspective, PM2.5 is one of the primary air pollutants, especially in Tehran's metropolis. Owing to the different patterns of PM2.5 in different seasons, Spatio-temporal modeling and identification of high-risk areas to reduce its effects seems necessary. The purpose of this study was Spatio-temporal modeling and preparation of PM2.5 risk mapping using three machine learning algorithms (random forest (RF), AdaBoost, and stochastic gradient descent (SGD)) in the metropolis of Tehran, Iran. Therefore, in the first step, to prepare the dependent variable data, the PM2.5 average was used for the four seasons of spring, summer, autumn, and winter. Then, using remote sensing (RS) and a geographic information system (GIS), independent data such as temperature, maximum temperature, minimum temperature, wind speed, rainfall, humidity, normalized difference vegetation index (NDVI), population density, street density, and distance to industrial centers were prepared as a seasonal average. To Spatio-temporal modeling using machine learning algorithms, 70% of the data were used for training and 30% for validation. The frequency ratio (FR) model was used as input to machine learning algorithms to calculate the spatial relationship between PM2.5 and the effective parameters. Finally, Spatio-temporal modeling and PM2.5 risk mapping were performed using three machine learning algorithms. The receiver operating characteristic (ROC) area under the curve (AUC) results showed that the RF algorithm had the greatest modeling accuracy, with values of 0.926, 0.94, 0.949, and 0.949 for spring, summer, autumn, and winter, respectively. According to the RF model, the most important variable in spring and autumn was NDVI. Temperature and distance to industrial centers were the most important variables in the summer and winter, respectively. The results showed that autumn, winter, summer, and spring had the highest risk of PM2.5, respectively.
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Affiliation(s)
| | - Seyed Vahid Razavi-Termeh
- Geoinformation Tech. Center of Excellence, Faculty of Geodesy and Geomatics Engineering, K.N. Toosi University of Technology, Tehran, 19697, Iran.
| | - Amanollah Fathnia
- Department of Geography, Faculty of Literature and Humanities, Razi University, Kermanshah, Iran.
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AlThuwaynee OF, Kim SW, Najemaden MA, Aydda A, Balogun AL, Fayyadh MM, Park HJ. Demystifying uncertainty in PM10 susceptibility mapping using variable drop-off in extreme-gradient boosting (XGB) and random forest (RF) algorithms. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021; 28:43544-43566. [PMID: 33834339 DOI: 10.1007/s11356-021-13255-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Accepted: 03/01/2021] [Indexed: 06/12/2023]
Abstract
This study investigates uncertainty in machine learning that can occur when there is significant variance in the prediction importance level of the independent variables, especially when the ROC fails to reflect the unbalanced effect of prediction variables. A variable drop-off loop function, based on the concept of early termination for reduction of model capacity, regularization, and generalization control, was tested. A susceptibility index for airborne particulate matter of less than 10 μm diameter (PM10) was modeled using monthly maximum values and spectral bands and indices from Landsat 8 imagery, and Open Street Maps were used to prepare a range of independent variables. Probability and classification index maps were prepared using extreme-gradient boosting (XGBOOST) and random forest (RF) algorithms. These were assessed against utility criteria such as a confusion matrix of overall accuracy, quantity of variables, processing delay, degree of overfitting, importance distribution, and area under the receiver operating characteristic curve (ROC).
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Affiliation(s)
- Omar F AlThuwaynee
- Department of Energy and Mineral Resources Engineering, Sejong University, 209 Neudong-roGwangjin-gu, Seoul, 05006, Republic of Korea.
| | - Sang-Wan Kim
- Department of Energy and Mineral Resources Engineering, Sejong University, 209 Neudong-roGwangjin-gu, Seoul, 05006, Republic of Korea.
| | | | - Ali Aydda
- Department of Geology, Faculty of Sciences, Ibn Zohr University, B.P. 8106, 80000, Agadir, Morocco
| | - Abdul-Lateef Balogun
- Geospatial Analysis and Modelling (GAM) Research Laboratory, Department of Civil and Environmental Engineering, Universiti Teknologi PETRONAS (UTP), 32610, Seri Iskandar, Perak, Malaysia
| | - Moatasem M Fayyadh
- Engineering Services and Asset Management, John Holland Group, Sydney, NSW, 2150, Australia
| | - Hyuck-Jin Park
- Department of Energy and Mineral Resources Engineering, Sejong University, 209 Neudong-roGwangjin-gu, Seoul, 05006, Republic of Korea
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Sannigrahi S, Kumar P, Molter A, Zhang Q, Basu B, Basu AS, Pilla F. Examining the status of improved air quality in world cities due to COVID-19 led temporary reduction in anthropogenic emissions. ENVIRONMENTAL RESEARCH 2021; 196:110927. [PMID: 33675798 PMCID: PMC9749922 DOI: 10.1016/j.envres.2021.110927] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Revised: 02/07/2021] [Accepted: 02/19/2021] [Indexed: 05/09/2023]
Abstract
Clean air is a fundamental necessity for human health and well-being. Anthropogenic emissions that are harmful to human health have been reduced substantially under COVID-19 lockdown. Satellite remote sensing for air pollution assessments can be highly effective in public health research because of the possibility of estimating air pollution levels over large scales. In this study, we utilized both satellite and surface measurements to estimate air pollution levels in 20 cities across the world. Google Earth Engine (GEE) and Sentinel-5 Precursor TROPOspheric Monitoring Instrument (TROPOMI) application were used for both spatial and time-series assessment of tropospheric Nitrogen Dioxide (NO2) and Carbon Monoxide (CO) statuses during the study period (1 February to May 11, 2019 and the corresponding period in 2020). We also measured Population-Weighted Average Concentration (PWAC) of particulate matter (PM2.5 and PM10) and NO2 using gridded population data and in-situ air pollution estimates. We estimated the economic benefit of reduced anthropogenic emissions using two valuation approaches: (1) the median externality value coefficient approach, applied for satellite data, and (2) the public health burden approach, applied for in-situ data. Satellite data have shown that ~28 tons (sum of 20 cities) of NO2 and ~184 tons (sum of 20 cities) of CO have been reduced during the study period. PM2.5, PM10, and NO2 are reduced by ~37 (μg/m3), 62 (μg/m3), and 145 (μg/m3), respectively. A total of ~1310, ~401, and ~430 premature cause-specific deaths were estimated to be avoided with the reduction of NO2, PM2.5, and PM10. The total economic benefits (Billion US$) (sum of 20 cities) of the avoided mortality are measured as ~10, ~3.1, and ~3.3 for NO2, PM2.5, and PM10, respectively. In many cases, ground monitored data was found inadequate for detailed spatial assessment. This problem can be better addressed by incorporating satellite data into the evaluation if proper quality assurance is achieved, and the data processing burden can be alleviated or even removed. Both satellite and ground-based estimates suggest the positive effect of the limited human interference on the natural environments. Further research in this direction is needed to explore this synergistic association more explicitly.
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Affiliation(s)
- Srikanta Sannigrahi
- School of Architecture, Planning and Environmental Policy, University College Dublin Richview, Clonskeagh, Dublin, D14 E099, Ireland.
| | - Prashant Kumar
- Global Centre for Clean Air Research (GCARE), Department of Civil and Environmental Engineering, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, GU2 7XH, United Kingdom; Department of Civil, Structural & Environmental Engineering, Trinity College Dublin, Dublin, Ireland
| | - Anna Molter
- School of Architecture, Planning and Environmental Policy, University College Dublin Richview, Clonskeagh, Dublin, D14 E099, Ireland; Department of Geography, School of Environment, Education and Development, The University of Manchester, USA
| | - Qi Zhang
- Department of Earth and Environment, Boston University, Boston, MA, 02215, USA; Frederick S. Pardee Center for the Study of the Longer-Range Future, Frederick S. Pardee School of Global Studies, Boston University, Boston, MA, 02215, USA
| | - Bidroha Basu
- School of Architecture, Planning and Environmental Policy, University College Dublin Richview, Clonskeagh, Dublin, D14 E099, Ireland
| | - Arunima Sarkar Basu
- School of Architecture, Planning and Environmental Policy, University College Dublin Richview, Clonskeagh, Dublin, D14 E099, Ireland
| | - Francesco Pilla
- School of Architecture, Planning and Environmental Policy, University College Dublin Richview, Clonskeagh, Dublin, D14 E099, Ireland
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An Open Source GIS Application for Spatial Assessment of Health Care Quality Indicators. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 2021. [DOI: 10.3390/ijgi10040264] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Prevention quality indicators (PQIs) constitute a set of measures that can be combined with hospital inpatient data to identify the quality of care for ambulatory care sensitive conditions (ACSC). Geographical information system (GIS) web mapping and applications contribute to a better representation of PQI spatial distribution. Unlike many countries in the world, in Portugal, this type of application remains underdeveloped. The main objective of this work was to facilitate the assessment of geographical patterns and trends of health data in Portugal. Therefore, two innovative open source applications were developed. Leaflet Javascript Library, PostGIS, and GeoServer were used to create a web map application prototype. Python language was used to develop the GIS application. The geospatial assessment of geographical patterns of health data in Portugal can be obtained through a GIS application and a web map application. Both tools proposed allowed for an easy and intuitive assessment of geographical patterns and time trends of PQI values in Portugal, alongside other relevant health data, i.e., the location of health care facilities, which, in turn, showed some association between the location of facilities and quality of health care. However, in the future, more research is still required to map other relevant data, for more in-depth analyses.
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Gayen A, Haque SM, Mishra SV. COVID-19 induced lockdown and decreasing particulate matter (PM10): An empirical investigation of an Asian megacity. URBAN CLIMATE 2021; 36:100786. [PMID: 33552884 PMCID: PMC7846237 DOI: 10.1016/j.uclim.2021.100786] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2020] [Revised: 07/28/2020] [Accepted: 01/22/2021] [Indexed: 05/09/2023]
Abstract
The air quality in the cities of developing countries is deteriorating with the proliferation of anthropogenic activities that add pollutant matters in the lower part of the troposphere. Particulate matter with an aerodynamic diameter lower than 10 μm (PM10) is considered one of the direct indicators of air quality in an urban area as it brings health morbidities. The article empirically investigates the role COVID-19 related lockdown has played in bringing down pollution level (PM10) in the megacity of Kolkata. It does so by taking account of PM10 level in three stages - pre, presage and complete-lockdown timelines. The extracted results show a significant declining trend (about 77% vis-a-vis the pre-lockdown period) with 95% of the geographical area under 100 μm/m3 and a strong fit with the station-based records. The feasibility and robustness showed by the remotely sensed data along with other earth observatory information for larger-scale pollution prevalence make its adoption imperative. Simultaneously, it becomes urgent in times of lockdown when the physical mobility of maintenance and research staff to stations is significantly curtailed. The work contributes to study on PM10 by its ability to replicate in examining cities of both the global north and global south.
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Affiliation(s)
- Amiya Gayen
- Department of Geography, The University of Calcutta, 35 B. C. Road, Kolkata 700 019, India
| | - Sk Mafizul Haque
- Department of Geography, The University of Calcutta, 35 B. C. Road, Kolkata 700 019, India
| | - Swasti Vardhan Mishra
- Department of Geography, The University of Calcutta, 35 B. C. Road, Kolkata 700 019, India
- Department of Geography, Amity Institute of Social Sciences, Amity University Kolkata, Rajarhat, Newtown, Kolkata 700135, West Bengal, India
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Sorek-Hamer M, Chatfield R, Liu Y. Review: Strategies for using satellite-based products in modeling PM 2.5 and short-term pollution episodes. ENVIRONMENT INTERNATIONAL 2020; 144:106057. [PMID: 32889481 DOI: 10.1016/j.envint.2020.106057] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Revised: 08/06/2020] [Accepted: 08/10/2020] [Indexed: 06/11/2023]
Abstract
Short-term air pollution episodes motivate improved understanding of the association between air pollution and acute morbidity and mortality episodes, and triggers required mitigation plans. A variety of methods have been employed to estimate exposure to air pollution episodes, including GIS-based dispersion models, interpolation between sparse monitoring sites, land-use regression models, optimization models, line- or area-dispersion plume models, and models using information from imaging satellites, often including land-use and meteorological variables. There has been increasing use of satellite-borne aerosol products for assessing short-term air quality events. They provide better spatial coverage, but currently at the price of low temporal coverage and rather crude spatial resolution. This is a brief review on using satellite data for modeling short-term air quality and pollution events. The review can be pursued as a practical guide for modeling air quality with satellite-based products, as it includes important questions that should be considered in both the study design as well as the model development stages. Progress in this field is detailed and includes published models and their use in environmental and health studies. Both current and future satellite-borne capabilities are covered. It also provides links to access and download relevant datasets and some example R code for data processing and modeling.
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Affiliation(s)
- Meytar Sorek-Hamer
- NASA Ames Research Center, Moffett Field, CA, United States; Universities Space Research Association (USRA), Mountain View, CA, United States.
| | | | - Yang Liu
- Emory University, Rollins School of Public Health, Atlanta, GA, United States
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Mandal I, Pal S. COVID-19 pandemic persuaded lockdown effects on environment over stone quarrying and crushing areas. THE SCIENCE OF THE TOTAL ENVIRONMENT 2020; 732:139281. [PMID: 32417554 PMCID: PMC7211598 DOI: 10.1016/j.scitotenv.2020.139281] [Citation(s) in RCA: 89] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2020] [Revised: 04/30/2020] [Accepted: 05/06/2020] [Indexed: 04/14/2023]
Abstract
Stone quarrying and crushing spits huge stone dust to the environment and causes threats to ecosystem components as well as human health. Imposing emergency lockdown to stop infection of COVID 19 virus on 24.03.2020 in India has created economic crisis but it has facilitated environment to restore its quality. Global scale study has already proved the qualitative improvement of air quality but its possible impact at regional level is not investigated yet. Middle catchment of Dwarka river basin of Eastern India is well known for stone quarrying and crushing and therefore the region is highly polluted. The present study has attempted to explore the impact of forced lockdown on environmental components like Particulate matter (PM) 10, Land surface temperature (LST), river water quality, noise using image and field derived data in pre and during lockdown periods. Result clearly exhibits that Maximum PM10 concentration was 189 to 278 μg/m3 in pre lockdown period and it now ranges from 50 to 60 μg/m3 after 18 days of the commencement of lockdown in selected four stone crushing clusters. LST is reduced by 3-5 °C, noise level is dropped to <65dBA which was above 85dBA in stone crusher dominated areas in pre lockdown period. Adjacent river water is qualitatively improved due to stoppage of dust release to the river. For instance, total dissolve solid (TDS) level in river water adjacent to crushing unit is attenuated by almost two times. When entire world is worried about the appropriate policies for abating environmental pollution, this emergency lockdown shows an absolute way i.e. pollution source management may restore environment and ecosystem with very rapid rate.
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Affiliation(s)
- Indrajit Mandal
- Department of Geography, University of Gour Banga, Malda, India.
| | - Swades Pal
- Department of Geography, University of Gour Banga, Malda, India
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