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Borhani F, Shafiepour Motlagh M, Ehsani AH, Rashidi Y, Ghahremanloo M, Amani M, Moghimi A. Current Status and Future Forecast of Short-lived Climate-Forced Ozone in Tehran, Iran, derived from Ground-Based and Satellite Observations. WATER, AIR, AND SOIL POLLUTION 2023; 234:134. [PMID: 36819757 PMCID: PMC9930078 DOI: 10.1007/s11270-023-06138-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 01/27/2023] [Indexed: 06/18/2023]
Abstract
In this study, the distribution and alterations of ozone concentrations in Tehran, Iran, in 2021 were investigated. The impacts of precursors (i.e., CO, NO2, and NO) on ozone were examined using the data collected over 12 months (i.e., January 2021 to December 2021) from 21 stations of the Air Quality Control Company (AQCC). The results of monthly heat mapping of tropospheric ozone concentrations indicated the lowest value in December and the highest value in July. The lowest and highest seasonal concentrations were in winter and summer, respectively. Moreover, there was a negative correlation between ozone and its precursors. The Inverse Distance Weighting (IDW) method was then implemented to obtain air pollution zoning maps. Then, ozone concentration modeled by the IDW method was compared with the average monthly change of total column density of ozone derived from Sentinel-5 satellite data in the Google Earth Engine (GEE) cloud platform. A good agreement was discovered despite the harsh circumstances that both ground-based and satellite measurements were subjected to. The results obtained from both datasets showed that the west of the city of Tehran had the highest averaged O3 concentration. In this study, the status of the concentration of ozone precursors and tropospheric ozone in 2022 was also predicted. For this purpose, the Box-Jenkins Seasonal Autoregressive Integrated Moving Average (SARIMA) approach was implemented to predict the monthly air quality parameters. Overall, it was observed that the SARIMA approach was an efficient tool for forecasting air quality. Finally, the results showed that the trends of ozone obtained from terrestrial and satellite observations throughout 2021 were slightly different due to the contribution of the tropospheric ozone precursor concentration and meteorology conditions.
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Affiliation(s)
- Faezeh Borhani
- School of Environment, College of Engineering, University of Tehran, P.O. Box, Tehran, 14155-6135 Iran
| | - Majid Shafiepour Motlagh
- School of Environment, College of Engineering, University of Tehran, P.O. Box, Tehran, 14155-6135 Iran
| | - Amir Houshang Ehsani
- School of Environment, College of Engineering, University of Tehran, P.O. Box, Tehran, 14155-6135 Iran
| | - Yousef Rashidi
- Environmental Sciences Research Institute, Shahid Beheshti University, Tehran, Iran
| | - Masoud Ghahremanloo
- Department of Earth and Atmospheric Sciences, University of Houston, Houston, TX 77004 USA
| | - Meisam Amani
- Wood Environment and Infrastructure Solutions, Ottawa, ON K2E 7L5 Canada
| | - Armin Moghimi
- Department of Remote Sensing and Photogrammetry, Faculty of Geodesy and Geomatics Engineering, Toosi University of Technology, Tehran, K. N Iran
- Institute of Photogrammetry and GeoInformation, Leibniz Universitat Hannover, Hannover, Germany
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Mohammadi A, Pishgar E, Fatima M, Lotfata A, Fanni Z, Bergquist R, Kiani B. The COVID-19 Mortality Rate Is Associated with Illiteracy, Age, and Air Pollution in Urban Neighborhoods: A Spatiotemporal Cross-Sectional Analysis. Trop Med Infect Dis 2023; 8:85. [PMID: 36828501 PMCID: PMC9962969 DOI: 10.3390/tropicalmed8020085] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 01/23/2023] [Accepted: 01/24/2023] [Indexed: 01/28/2023] Open
Abstract
There are different area-based factors affecting the COVID-19 mortality rate in urban areas. This research aims to examine COVID-19 mortality rates and their geographical association with various socioeconomic and ecological determinants in 350 of Tehran's neighborhoods as a big city. All deaths related to COVID-19 are included from December 2019 to July 2021. Spatial techniques, such as Kulldorff's SatScan, geographically weighted regression (GWR), and multi-scale GWR (MGWR), were used to investigate the spatially varying correlations between COVID-19 mortality rates and predictors, including air pollutant factors, socioeconomic status, built environment factors, and public transportation infrastructure. The city's downtown and northern areas were found to be significantly clustered in terms of spatial and temporal high-risk areas for COVID-19 mortality. The MGWR regression model outperformed the OLS and GWR regression models with an adjusted R2 of 0.67. Furthermore, the mortality rate was found to be associated with air quality (e.g., NO2, PM10, and O3); as air pollution increased, so did mortality. Additionally, the aging and illiteracy rates of urban neighborhoods were positively associated with COVID-19 mortality rates. Our approach in this study could be implemented to study potential associations of area-based factors with other emerging infectious diseases worldwide.
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Affiliation(s)
- Alireza Mohammadi
- Department of Geography and Urban Planning, Faculty of Social Sciences, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran
| | - Elahe Pishgar
- Department of Human Geography, Faculty of Earth Sciences, Shahid Beheshti University, Tehran 19839-69411, Iran
| | - Munazza Fatima
- Department of Geography, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan
- Department of Geography, University of Zurich, CH-8006 Zurich, Switzerland
| | - Aynaz Lotfata
- Geography Department, Chicago State University, Chicago, IL 60628-1598, USA
| | - Zohreh Fanni
- Department of Human Geography, Faculty of Earth Sciences, Shahid Beheshti University, Tehran 19839-69411, Iran
| | | | - Behzad Kiani
- Centre de Recherche en Santé Publique, Université de Montréal, 7101, Avenue du Parc, Montreal, QC H3N 1X9, Canada
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Borhani F, Shafiepour Motlagh M, Ehsani AH, Rashidi Y, Maddah S, Mousavi SM. On the predictability of short-lived particulate matter around a cement plant in Kerman, Iran: machine learning analysis. INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY : IJEST 2022; 20:1513-1526. [PMID: 36405244 PMCID: PMC9643923 DOI: 10.1007/s13762-022-04645-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Revised: 10/17/2022] [Accepted: 10/29/2022] [Indexed: 06/16/2023]
Abstract
UNLABELLED One of the greatest environmental risks in the cement industry is particulate matter emission (i.e., PM2.5 and PM10). This paper aims to develop descriptive-analytical solutions for increasing the accuracy of predicting particulate matter emissions using resample data of Kerman cement plant. Photometer instruments DUST TRAK and BS-EN-12341 method were used to determine concentration of PM2.5 and PM10. Sampling was performed on 4 environmental stations of Kerman cement plant in the four seasons. In order to accurate assessment of particulate matter concentration, a new model was proposed to resample cement plant time series data using Pandas in Python. The effect of meteorological parameters including wind speed, relative humidity, air temperature and rainfall on the particulate matter concentration was investigated through statistical analysis. The results indicated that the maximum annual average of 24-h of PM2.5 belonged to the east side (opposite the clinker depot) in 2019 (31.50 μg m-3) and west side (in front of the mine) in 2020 (31.00 μg m-3). Also, maximum annual average of 24-h of PM10 belonged to the west side (in front of the mine) in 2020 (121.00 μg m-3) and east side (opposite the clinker depot) in 2020 (120.75 μg m-3). The PM2.5 and PM10 concentrations are more than the allowable limit. The results demonstrate that particulate matter concentration increases with increasing relative humidity and rainfall. Finally, the SARIMA model was used to predict the particulate matter concentration. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1007/s13762-022-04645-3.
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Affiliation(s)
- F. Borhani
- School of Environment, College of Engineering, University of Tehran, Tehran, Iran
| | | | - A. H. Ehsani
- School of Environment, College of Engineering, University of Tehran, Tehran, Iran
| | - Y. Rashidi
- Environmental Sciences Research Institute, Shahid Beheshti University, Tehran, Iran
| | - S. Maddah
- School of Environment, College of Engineering, University of Tehran, Tehran, Iran
| | - S. M. Mousavi
- Department of Environmental Planning and Design, Shahid Beheshti University, 1983969411 Tehran, Iran
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Borhani F, Shafiepour Motlagh M, Rashidi Y, Ehsani AH. Estimation of short-lived climate forced sulfur dioxide in Tehran, Iran, using machine learning analysis. STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT : RESEARCH JOURNAL 2022; 36:2847-2860. [PMID: 35035281 PMCID: PMC8741550 DOI: 10.1007/s00477-021-02167-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 12/24/2021] [Indexed: 05/31/2023]
Abstract
This paper presents a time-series analysis of SO2 air concentration and the effects of particulates (either PM2.5 and PM10) concentrations and meteorological conditions (relative humidity and wind speed) on SO2 trend in Tehran for the period from 2011 to 2020. The source data were obtained from 21 monitoring stations of Air Quality Control Company and meteorological stations in Tehran. To predict the status of future concentration of SO2, PM2.5 and PM10, a Box-Jenkins ARIMA approach was used to model the monthly time series. Considering the whole period of ten years, a somewhat downward trend was noted for SO2 air concentration, even though a slight rising trend was observed in 2020 year. Monthly sulfur dioxide concentrations showed the lowest value in June and the highest value in January. Seasonal concentrations were lowest in spring and highest in winter. Then, in the ArcGIS software, the IDW method was used to obtain air pollution zoning maps. As a result, the highest average concentration of SO2 occurred in the north and southwest of Tehran. In the last step, Relations between the SO2 concentration and particulate matters and relative humidity and wind speed were calculated statistically using the daily average data. We finally concluded that the combined effect of particulate matters and relative humidity with the increasing role of Sulfur dioxide overcomes the decreasing role of wind speed. This study can contribute to a better understanding of the SO2 air pollution in Tehran affected by meteorological conditions and the rapid urbanization and industrialization, followed by the possible combustion of fuel oil in power plants and health problems.
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Affiliation(s)
- Faezeh Borhani
- School of Environment, College of Engineering, University of Tehran, P.O. Box, 14155-6135 Tehran, Iran
| | - Majid Shafiepour Motlagh
- School of Environment, College of Engineering, University of Tehran, P.O. Box, 14155-6135 Tehran, Iran
| | - Yousef Rashidi
- Environmental Sciences Research Institute, Shahid Beheshti University, Tehran, Iran
| | - Amir Houshang Ehsani
- School of Environment, College of Engineering, University of Tehran, P.O. Box, 14155-6135 Tehran, Iran
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Borhani F, Shafiepour Motlagh M, Ehsani AH, Rashidi Y. Evaluation of short-lived atmospheric fine particles in Tehran, Iran. ARABIAN JOURNAL OF GEOSCIENCES 2022; 15:1398. [PMCID: PMC9373883 DOI: 10.1007/s12517-022-10667-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 08/02/2022] [Indexed: 06/16/2023]
Abstract
Fine particles (PM2.5) have adverse impacts and risks on air quality and human health. The present research focuses on the concentrations of PM2.5, air quality index (AQI), and assessment of hospital admissions due to COPD attributed to PM2.5 particle levels in Tehran during the last 10 years from 2011 to 2020. The effects of meteorological parameters (i.e., wind speed, humidity, and temperature) and AQI on PM2.5 concentrations were examined using data from 21 active monitoring stations of the Air Quality Control Company (AQCC) and Mehrabad Meteorological Station. The health impact assessment of PM2.5 in terms of hospital admissions due to chronic obstructive pulmonary disease (COPD) was obtained by the AirQ2.2.3 model. Based on the results, the annual average PM2.5 concentrations decreased from 2011 through 2020. The results also show a significant effect of meteorological data on the changes in PM2.5 particle concentration. We also noticed that reduction of annual PM2.5 concentration from 38.55 (AQI = 104.08) in 2011 to 28.59 μg m−3 (AQI = 83.87) in 2020 could prevent 779 (by about 70%) premature deaths, and the estimated number of excess cases human respiratory system attributed to PM2.5 at central relative risk (RR) during the last decade was 6158 persons. Also, air quality got from unhealthy for sensitive groups of people to moderate air quality. Finally, any reduction in concentrations of PM2.5 in Tehran can reduce the number of hospital admissions due to COPD significantly. The results of investigations on PM2.5 particles have shown the need for the national clean air program policies and the necessity of urgent actions to improve the air quality to human health in Tehran.
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Affiliation(s)
- Faezeh Borhani
- School of Environment, College of Engineering, University of Tehran, P.O. Box, Tehran, 14155-6135 Iran
| | - Majid Shafiepour Motlagh
- School of Environment, College of Engineering, University of Tehran, P.O. Box, Tehran, 14155-6135 Iran
| | - Amir Houshang Ehsani
- School of Environment, College of Engineering, University of Tehran, P.O. Box, Tehran, 14155-6135 Iran
| | - Yousef Rashidi
- Environmental Sciences Research Institute, Shahid Beheshti University, Tehran, Iran
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