An urban air quality assessment based on a meteorological perspective

Area of Science:

• Environmental Science
• Atmospheric Science
• Public Health

Background:

• Air quality (AQ) is a critical public health concern requiring integrated, multi-disciplinary analysis.
• Understanding the mechanisms behind poor AQ, especially concerning PM2.5, is essential for effective pollution control strategies.
• Sivas province's air quality, particularly PM2.5 levels and influencing factors, requires detailed investigation.

Purpose of the Study:

• To assess the air quality of Sivas province focusing on PM2.5 pollution over a six-year period.
• To analyze the impact of meteorological factors on PM2.5 concentrations in Sivas.
• To identify and investigate additional sources of PM2.5, including long-range dust transport and road vehicles.

Main Methods:

• Analysis of six years of meteorological and PM2.5 data for Sivas province.
• Utilized Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) and Dust Regional Atmospheric Model (DREAM) for episode analysis.
• Correlational analysis between PM2.5, temperature, and planetary boundary layer (PBL) height.

Main Results:

• Winter months consistently showed the highest PM2.5 pollution, with exceptions in 2019 due to data gaps.
• Elevated PM2.5 levels were observed in spring and summer, indicating contributions from dust transport and vehicles.
• A significant negative correlation (r = -0.61) was found between PBL height and PM2.5, with higher pollution during lower PBL levels.
• Inversions were frequently observed, correlating with increased PM2.5 episodes.
• Dust transport from Iraq and Syria was identified as a significant factor on high pollution days (PM2.5 > 100 µg/m³).
• The average PM2.5 in 2017 was 31.66 ± 19.2 µg/m³, with a -0.49 correlation to temperature.

Conclusions:

• Seasonal variations and meteorological conditions significantly influence PM2.5 levels in Sivas.
• Long-range dust transport and lower PBL heights are critical contributors to severe PM2.5 pollution episodes.
• Integrated monitoring and modeling are crucial for understanding and managing air quality challenges in the region.