Short-term effects of anthropogenic/natural activities on the Tehran criteria air pollutants: Source apportionment and spatiotemporal variation

Ghaffarpasand, Omid; Nadi, Saeed; Shalamzari, Zahra Davari

doi:10.1016/j.buildenv.2020.107298

Cited by 20 publications

(6 citation statements)

References 33 publications

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“…Ghaffarpasend et al [7] concluded that motor vehicle emissions accounted for an average of 45% of the air pollutants in Tehran, Iran. Industrial processes contributed 10.7% of particulate matter with aerodynamic diameter equal to or less than 2.5 µm (PM 2.5 ), and fossil fuel combustion contributed 15.8% of particulate matter with aerodynamic diameter equal to or less than 10 µm (PM 10 ) emissions in Shandong Province of China [3].…”

Section: Introductionmentioning

confidence: 99%

Quantifying Air Pollutant Variations during COVID-19 Lockdown in a Capital City in Northwest China

Feng

Wang

et al. 2021

Atmosphere

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In the context of the outbreak of coronavirus disease 2019 (COVID-19), strict lockdown policies were implemented to control nonessential human activities in Xi’an, northwest China, which greatly limited the spread of the pandemic and affected air quality. Compared with pre-lockdown, the air quality index and concentrations of PM2.5, PM10, SO2, and CO during the lockdown reduced, but the reductions were not very significant. NO2 levels exhibited the largest decrease (52%) during lockdown, owing to the remarkable decreased motor vehicle emissions. The highest K+ and lowest Ca2+ concentrations in PM2.5 samples could be attributed to the increase in household biomass fuel consumption in suburbs and rural areas around Xi’an and the decrease in human physical activities in Xi’an (e.g., human travel, vehicle emissions, construction activities), respectively, during the lockdown period. Secondary chemical reactions in the atmosphere increased in the lockdown period, as evidenced by the increased O3 level (increased by 160%) and OC/EC ratios in PM2.5 (increased by 26%), compared with pre-lockdown levels. The results, based on a natural experiment in this study, can be used as a reference for studying the formation and source of air pollution in Xi’an and provide evidence for establishing future long-term air pollution control policies.

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Section: Introductionmentioning

confidence: 99%

Quantifying Air Pollutant Variations during COVID-19 Lockdown in a Capital City in Northwest China

Feng

Wang

et al. 2021

Atmosphere

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“…Results showed that PM1.1-2.1 dominated by the dust particles, and particles originated by the fuel combustion have a significant contribution to the fraction PM0.4-0.7. In another research Ghaffarpasand, Nadi and Davarishalmazari (22) try to apply new approach to address the data missing issue of the detailed air monitoring dataset of Tehran 2018 (hourly data of over 15 monitoring sites) and to study the short-term effects of anthropogenic/natural activities on the criteria air pollutants (CAP) in the urban and rural areas. Results show that the vehicular emissions were the predominant contributors to the CAP concentrations, accounting for an average of 45%, and desert dust and industries around the city were ranked as the subsequent principal CAP sources for the summer season.…”

Section: Introductionmentioning

confidence: 99%

Evaluating Visibility Range on Air Pollution using NARX Neural Network

Irani

Amiri

Deyhim

2021

J. Environ. Treat. Tech.

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Evaluating air visibility range is considered as one of the apparent criteria of air quality. Haze air as a conclusion of air pollution causes unpleasant breathing, psychological effects, and visibility restriction. In this study, NARX neural network applied to determine air visibility restriction factors. Data of air quality control stations of Baghshomal, Rastebazar, and Abresan in Tabriz City, Iran used which include PM2.5, PM10, NO2, SO2, O3, and CO for the duration of four years from 2013 to 2017 that considered as independent variables. NARX neural network created to find each pollutant relation to visibility restriction and networks used for simulation to analysis network results in conspectuses condition. The results showed that PM10 pollutant has the most influence on-air visibility with R=0.9 in the train, R=0.728 in the test, and R=0.75 in validation process. Also error results of the PM10 obtained as MSE=0.054. Moreover, simulation results demonstrated the least area integral between curves according to ascending order for six pollutant factors and verified PM10 accuracy in NARX network simulation. The total result as study conclusion verified NARX neural network efficiency to evaluate air visibility range while using air pollutant parameters.

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“…This nding indicates that the residential sector, and in particular heating systems, may signi cantly worsen air quality during the colder months of the year. (Ghaffarpasand et al, 2020). Furthermore, the mountain ranges in the north of Tehran stop the ow of the humid wind and prevent the polluted air to leave the city.…”

Section: Pollutants Concentrationmentioning

confidence: 99%

Atmospheric Concentrations, Seasonal Variations and Health Risk Assessment of PM 2.5 , PM 10 , and SO 2 in Tehran Metropolis, Iran

Ahmadian,

Rajabi,

Azhdarpoor

2023

Preprint

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This study assessed seasonal and annual variations as well as the health risks associated with exposure to PM2.5, PM10, and SO2 in the ambient air of Tehran from 2019 to 2021. The findings revealed that the average annual concentration of PM2.5, PM10, and SO2 varied from 28.24 to 32.34 µg/m3, 69.57 to 82.22 µg/m3, and 14.94 to 17.98 µg/m3, respectively. The amounts of PM2.5 and SO2 were the greatest in the west and southwest, while PM10 was the most abundant in the east and northeast which were above WHO guidelines. In exposure duration scenarios of 8 and 12 hours, the mean hazard quotient (HQ) for PM2.5 and PM10 was >1, suggesting an unacceptable risk to human health. There was no risk to human health according to the mean HQ for SO2 at all exposure periods of 3, 8, and 12 hours. Further evidence that exposure time plays a significant part in health hazards was provided by the fact that the mean HQ values of exposure to PM2.5 and PM10 in exposure times of 3 hours were both <1. The Sobol sensitivity analysis revealed that the PM2.5 concentration in HQ was the most sensitive indicator of the populace.

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Short-term effects of anthropogenic/natural activities on the Tehran criteria air pollutants: Source apportionment and spatiotemporal variation

Cited by 20 publications

References 33 publications

Quantifying Air Pollutant Variations during COVID-19 Lockdown in a Capital City in Northwest China

Quantifying Air Pollutant Variations during COVID-19 Lockdown in a Capital City in Northwest China

Evaluating Visibility Range on Air Pollution using NARX Neural Network

Atmospheric Concentrations, Seasonal Variations and Health Risk Assessment of PM 2.5 , PM 10 , and SO 2 in Tehran Metropolis, Iran

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