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Characterization and Transport Pathways of High PM2.5 Pollution Episodes During 2015–2021 in Tehran, Iran
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Abstract
Purpose
High PM2.5 pollution episodes can affect entire regions around the world and have substantial impacts on climate, visibility, and human health. Understanding the main characteristics, origin, and transport pathways of PM2.5 episodes is crucial for determining the episodes causes, alleviating episodic pollution, and regional joint emission control strategies. We used daily average PM2.5 data as well as related meteorological data.
Methods
The STL (Seasonal-Trend decomposition based on Loess) method was utilized for the identification of PM2.5 episodes during January to December (2015-2021) in Tehran, Iran. The trends in the duration, frequency, and number of PM2.5 episodes were assessed and the PM2.5 baseline trends compared with these trends. Additionally, the characteristics of episodes were investigated using three categories.
Results
The results showed an all-site annual severity of PM2.5 pollution episodes durations as being more than 50% higher than the seasonal trend. The PM2.5 episode frequency trends indicated a reverse relationship between the episode frequency and their duration. The PM2.5 episodes with longer duration showed higher PM2.5 magnitude and concentration. Greater unfavorable meteorological conditions produced longer duration PM2.5 pollution episodes. There was only one high diel PM2.5 peak around midnight (23:00 pm–2:00 am) for most of the monitoring sites during episodes. Air parcel transport pathways during PM2.5 episodes indicated that the air trajectories having the highest polluted average PM2.5 values varied based on the episode duration. The trajectories from intermediate-distance transmission arriving in Tehran during PM2.5 pollution episodes showed the highest average PM2.5 values for all durations and total duration.
Conclusions
These findings provide insights into the potential for accurate approaches for forecasting episodes and development of effective emergency mitigation strategies for the anthropogenic emissions during meteorological conditions that drive episode formation.
Graphical Abstract
Springer Science and Business Media LLC
Title: Characterization and Transport Pathways of High PM2.5 Pollution Episodes During 2015–2021 in Tehran, Iran
Description:
Abstract
Purpose
High PM2.
5 pollution episodes can affect entire regions around the world and have substantial impacts on climate, visibility, and human health.
Understanding the main characteristics, origin, and transport pathways of PM2.
5 episodes is crucial for determining the episodes causes, alleviating episodic pollution, and regional joint emission control strategies.
We used daily average PM2.
5 data as well as related meteorological data.
Methods
The STL (Seasonal-Trend decomposition based on Loess) method was utilized for the identification of PM2.
5 episodes during January to December (2015-2021) in Tehran, Iran.
The trends in the duration, frequency, and number of PM2.
5 episodes were assessed and the PM2.
5 baseline trends compared with these trends.
Additionally, the characteristics of episodes were investigated using three categories.
Results
The results showed an all-site annual severity of PM2.
5 pollution episodes durations as being more than 50% higher than the seasonal trend.
The PM2.
5 episode frequency trends indicated a reverse relationship between the episode frequency and their duration.
The PM2.
5 episodes with longer duration showed higher PM2.
5 magnitude and concentration.
Greater unfavorable meteorological conditions produced longer duration PM2.
5 pollution episodes.
There was only one high diel PM2.
5 peak around midnight (23:00 pm–2:00 am) for most of the monitoring sites during episodes.
Air parcel transport pathways during PM2.
5 episodes indicated that the air trajectories having the highest polluted average PM2.
5 values varied based on the episode duration.
The trajectories from intermediate-distance transmission arriving in Tehran during PM2.
5 pollution episodes showed the highest average PM2.
5 values for all durations and total duration.
Conclusions
These findings provide insights into the potential for accurate approaches for forecasting episodes and development of effective emergency mitigation strategies for the anthropogenic emissions during meteorological conditions that drive episode formation.
Graphical Abstract.
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