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Investigating Meteorological Factors Influencing Pollutant Concentrations and Copernicus Atmosphere Monitoring Service (CAMS) Model Forecasts in the Tehran Metropolis
by
Ghassabi, Zahra
, Karami, Sara
, Khoddam, Noushin
, Habibi, Maral
in
Air pollution
/ Airports
/ Atmosphere
/ atmospheric conditions
/ Atmospheric monitoring
/ cams model
/ Case studies
/ Correlation coefficient
/ Correlation coefficients
/ Cultural heritage
/ Energy consumption
/ Environmental aspects
/ Environmental monitoring
/ High pressure
/ Humidity
/ Industrial development
/ Low pressure
/ Mean sea level
/ Megacities
/ Methods
/ Nitrogen compounds
/ Nitrogen dioxide
/ Nitrogen oxides
/ Outdoor air quality
/ Particulate matter
/ pollutant concentrations
/ Pollutants
/ Pollution levels
/ Precipitation
/ Quality control
/ Relative humidity
/ Sea level pressure
/ Spatial distribution
/ Synoptic analysis
/ Tehran metropolis
/ Velocity
/ Warm seasons
/ Weather
/ Wind
/ Wind speed
2025
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Investigating Meteorological Factors Influencing Pollutant Concentrations and Copernicus Atmosphere Monitoring Service (CAMS) Model Forecasts in the Tehran Metropolis
by
Ghassabi, Zahra
, Karami, Sara
, Khoddam, Noushin
, Habibi, Maral
in
Air pollution
/ Airports
/ Atmosphere
/ atmospheric conditions
/ Atmospheric monitoring
/ cams model
/ Case studies
/ Correlation coefficient
/ Correlation coefficients
/ Cultural heritage
/ Energy consumption
/ Environmental aspects
/ Environmental monitoring
/ High pressure
/ Humidity
/ Industrial development
/ Low pressure
/ Mean sea level
/ Megacities
/ Methods
/ Nitrogen compounds
/ Nitrogen dioxide
/ Nitrogen oxides
/ Outdoor air quality
/ Particulate matter
/ pollutant concentrations
/ Pollutants
/ Pollution levels
/ Precipitation
/ Quality control
/ Relative humidity
/ Sea level pressure
/ Spatial distribution
/ Synoptic analysis
/ Tehran metropolis
/ Velocity
/ Warm seasons
/ Weather
/ Wind
/ Wind speed
2025
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Investigating Meteorological Factors Influencing Pollutant Concentrations and Copernicus Atmosphere Monitoring Service (CAMS) Model Forecasts in the Tehran Metropolis
by
Ghassabi, Zahra
, Karami, Sara
, Khoddam, Noushin
, Habibi, Maral
in
Air pollution
/ Airports
/ Atmosphere
/ atmospheric conditions
/ Atmospheric monitoring
/ cams model
/ Case studies
/ Correlation coefficient
/ Correlation coefficients
/ Cultural heritage
/ Energy consumption
/ Environmental aspects
/ Environmental monitoring
/ High pressure
/ Humidity
/ Industrial development
/ Low pressure
/ Mean sea level
/ Megacities
/ Methods
/ Nitrogen compounds
/ Nitrogen dioxide
/ Nitrogen oxides
/ Outdoor air quality
/ Particulate matter
/ pollutant concentrations
/ Pollutants
/ Pollution levels
/ Precipitation
/ Quality control
/ Relative humidity
/ Sea level pressure
/ Spatial distribution
/ Synoptic analysis
/ Tehran metropolis
/ Velocity
/ Warm seasons
/ Weather
/ Wind
/ Wind speed
2025
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Investigating Meteorological Factors Influencing Pollutant Concentrations and Copernicus Atmosphere Monitoring Service (CAMS) Model Forecasts in the Tehran Metropolis
Journal Article
Investigating Meteorological Factors Influencing Pollutant Concentrations and Copernicus Atmosphere Monitoring Service (CAMS) Model Forecasts in the Tehran Metropolis
2025
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Overview
In recent years, air pollution has become a significant issue for megacities. This study analyzed the air pollution levels in Tehran and the relationship between pollutant concentrations and atmospheric quantities during 2023. The correlation coefficients between wind speed, temperature, mean sea level pressure (MSLP), and relative humidity (RH) were calculated against the concentrations of NO2, NOx, PM10, and PM2.5. Additionally, one case study was conducted for each pollutant. Approximately 72% of haze phenomena in Tehran were recorded in November, December, and January. The monthly pattern of PM10 concentration indicated higher levels in the southern and western parts of Tehran. For PM2.5, in addition to these areas, significant concentrations were also observed in the central and eastern parts. NO2 concentrations were found to be higher in the northeast and northern areas. An inverse relationship was found between wind speed and temperature with pollutant concentrations. Positive correlations between MSLP and pollutant concentrations suggested that the pollutant levels also increased as air pressure rose. RH showed a significant direct relationship with PM2.5 and NOx. Synoptic analysis revealed that PM10 case studies often occurred during the warm season, with a thermal low pressure situated over the Iranian plateau. During PM2.5 and NO2 pollution events, Tehran was influenced by high pressure, and 10 m wind speeds were weak. Finally, verification of the 24 h forecast of the CAMS model showed that, while the model accurately predicted the spatial distribution of pollutants in most cases, it consistently underestimated the concentration levels.
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