Tropospheric Ozone in Tehran, Iran, during the last 20 years

Current Status and Future Forecast of Short-lived Climate-Forced Ozone in Tehran, Iran, derived from Ground-Based and Satellite Observations

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.

The COVID-19 Mortality Rate Is Associated with Illiteracy, Age, and Air Pollution in Urban Neighborhoods: A Spatiotemporal Cross-Sectional Analysis

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.

On the predictability of short-lived particulate matter around a cement plant in Kerman, Iran: machine learning analysis

One of the greatest environmental risks in the cement industry is particulate matter emission (i.e., PM_2.5 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 PM_2.5 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 PM_2.5 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 PM_2.5 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.

Estimation of short-lived climate forced sulfur dioxide in Tehran, Iran, using machine learning analysis

This paper presents a time-series analysis of SO₂ air concentration and the effects of particulates (either PM_2.5 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, PM_2.5 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.

Evaluation of short-lived atmospheric fine particles in Tehran, Iran

Fine particles (PM_2.5) have adverse impacts and risks on air quality and human health. The present research focuses on the concentrations of PM_2.5, air quality index (AQI), and assessment of hospital admissions due to COPD attributed to PM_2.5 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 PM_2.5 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 PM_2.5 in terms of hospital admissions due to chronic obstructive pulmonary disease (COPD) was obtained by the AirQ_2.2.3 model. Based on the results, the annual average PM_2.5 concentrations decreased from 2011 through 2020. The results also show a significant effect of meteorological data on the changes in PM_2.5 particle concentration. We also noticed that reduction of annual PM_2.5 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 PM_2.5 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 PM_2.5 in Tehran can reduce the number of hospital admissions due to COPD significantly. The results of investigations on PM_2.5 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.