Akademik Veri Yönetim Sistemi
WATER, AIR AND SOIL POLLUTION : AN INTERNATIONAL JOURNAL OF ENVIRONMENTAL POLLUTION, cilt.234, sa.4, ss.1-23, 2023 (SCI-Expanded)
Van, the most crowded province in the east of Turkey, is afflicted by intense air pollution especially in winter. Permanence and transport of air pollutants are closely associated with the region’s meteorological features. Hourly and annual variations in PM10 and SO2 air pollutants and temperature, wind, pressure, and humidity atmospheric variables were investigated in Van city center for 2015–2020. A multiple non-linear regression (MLNR) model was used to research the effect of meteorological parameters on air quality. Stepwise and best-subset statistical methods were applied to optimize estimators in the MNLR model. In the winter months, increases above limit values were observed for PM10 and SO2 linked to increases in low-quality fuel consumption due to reducing temperatures in the evenings. Spearman analysis showed there were moderate inverse correlations with temperature (R2 = -0.42) and wind speed (R2 = -0.42) and weak positive correlations with pressure (R2 = 0.35) and humidity (R2 = 0.22) for the air quality index. The MNLR model using minimum temperature (Tmin), average wind speed (Ws), the maximum pressure (Pmax), and average humidity (Havg) was the most successful (R = 0.53, RMSE = 0.24) air quality model. The reduction in air quality was associated with colder temperatures, lower wind speed, higher atmospheric pressure and higher humidity. In conclusion, policymakers and implementors should pay attention to local climate features to effectively minimize urban air pollution.
Van, the most crowded province in the east of Turkey, is afflicted by intense air pollution especially in winter. Permanence and transport of air pollutants are closely associated with the region’s meteorological features. Hourly and annual variations in PM10 and SO2 air pollutants and temperature, wind, pressure, and humidity atmospheric variables were investigated in Van city center for 2015–2020. A multiple non-linear regression (MLNR) model was used to research the effect of meteorological parameters on air quality. Stepwise and best-subset statistical methods were applied to optimize estimators in the MNLR model. In the winter months, increases above limit values were observed for PM10 and SO2 linked to increases in low-quality fuel consumption due to reducing temperatures in the evenings. Spearman analysis showed there were moderate inverse correlations with temperature (R2 = -0.42) and wind speed (R2 = -0.42) and weak positive correlations with pressure (R2 = 0.35) and humidity (R2 = 0.22) for the air quality index. The MNLR model using minimum temperature (Tmin), average wind speed (Ws), the maximum pressure (Pmax), and average humidity (Havg) was the most successful (R = 0.53, RMSE = 0.24) air quality model. The reduction in air quality was associated with colder temperatures, lower wind speed, higher atmospheric pressure and higher humidity. In conclusion, policymakers and implementors should pay attention to local climate features to effectively minimize urban air pollution.