An urban air quality assessment based on a meteorological perspective.

An integrated approach to understanding all measured pollutants with multi-discipline in different time scales and understanding the mechanisms hidden under low air quality (AQ) conditions is essential for tackling potential air pollution issues. In this study, the air pollution of Sivas province was analyzed with meteorological and PM2.5 data over six years to assess the city's AQ in terms of PM2.5 pollution and analyze the effect of meteorological factors on it. It was found that the winter period (January-February-November-December) of every year except 2019-which has missing data-is the period with the highest air pollution in the province. In addition, the days exceeding the daily PM2.5 limit values in 2016, 2017, 2020, and 2021 were also seen in the spring and summer months, which inclined the study to focus on additional pollutant sources such as long-range dust transport and road vehicles. The year 2017 has the highest values and was analyzed in detail. Pollution periods with the most increased episodes in 2018 were analyzed with the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) and Dust Regional Atmospheric Model (DREAM) models. As a result of the study, the average PM2.5 values in 2017 were 31.66 ± 19.2 µg/m3 and a correlation of -0.49 between temperature and PM2.5. As a result of model outputs, it was found that the inversion is intensely observed in the province, which is associated with an increase of PM2.5 during the episodes. Dust transport from northwestern Iraq and northeastern Syria is observed, especially on days with daily average PM2.5 values above 100 µg/m3. Additionally, planetary boundary layer (PBL) data analysis with PM pollution revealed a significant negative correlation (r = -0.61). Air pollutants, particularly PM2.5, were found to be higher during lower PBL levels.

Analysing of atmospheric conditions and their effects on air quality in Istanbul using SODAR and CEILOMETER.

In this study, first, air pollution that is caused by the air pollutants' concentration exceeding the limit value in Istanbul between 2017 and 2020 were analysed. In addition to this analysis, the effects of meteorological parameters on pollution were also examined within the same period of time. Second, for a 14-day period during which the concentration values of the air pollutants were calculated higher than the standards, therefore, were selected as an episode. In that respect, measurements of both pollutant and meteorological parameters were obtained from air quality monitoring stations. The Weather Research and Forecasting (WRF) model was used to examine the changes of meteorological parameters in the surface and upper atmospheric levels. The cross-correlation function (CCF) was performed together with both air quality monitoring station and the WRF model output data to examine the effects of temporal changes in meteorological parameters on air pollutant concentrations on a temporal scale. In addition, some meteorological parameters were obtained from remote sensing systems (SODAR and Ceilometer). Finally, with the help of the trajectory analysis model, it was determined whether the pollutant parameters were transported or not. Consequently, within a 3-year period, the most critical parameters in terms of pollution throughout the city were assessed as NO2 and PM10. Moreover, low wind speeds and high pressure values during the episode prevented the dispersion of pollutants and caused air pollution in Istanbul.

Effects of air pollution on respiratory hospital admissions in Istanbul, Turkey, 2013 to 2015.

We examined the associations between the daily variations of air pollutants and hospital admissions for respiratory diseases in Istanbul, the largest city of Turkey. A time series analysis of counts of daily hospital admissions and outdoor air pollutants was performed using single-pollutant Poisson generalized linear model (GLM) while controlling for time trends and meteorological factors over a 3-year period (2013-2015) at different time lags (0-9 days). Effects of the pollutants (Excess Risk, ER) on current-day (lag 0) hospital admissions to the first ten days (lag 9) were determined. Data on hospital admissions, daily mean concentrations of air pollutants of PM10, PM2.5 and NO2 and daily mean concentrations of temperature and humidity of Istanbul were used in the study. The analysis was conducted among people of all ages, but also focused on different sexes and different age groups including children (0-14 years), adults (35-44 years) and elderly (≥65 years). We found significant associations between air pollution and respiratory related hospital admissions in the city. Our findings showed that the relative magnitude of risks for an association of the pollutants with the total respiratory hospital admissions was in the order of: PM2.5, NO2, and PM10. The highest association of each pollutant with total hospital admission was observed with PM2.5 at lag 4 (ER = 1.50; 95% CI = 1.09-1.99), NO2 at lag 4 (ER = 1.27; 95% CI = 1.02-1.53) and PM10 at lag 0 (ER = 0.61; 95% CI = 0.33-0.89) for an increase of 10 µg/m3 in concentrations of the pollutants. In conclusion, our study showed that short-term exposure to air pollution was positively associated with increased respiratory hospital admissions in Istanbul during 2013-2015. As the first air pollution hospital admission study using GLM in Istanbul, these findings may have implications for local environmental and social policies.