The relationship between the character of atmospheric blocking and surface temperature has not been studied in depth for Turkey. Here, these relationships are investigated for the period 1977–2016. The seasonal mean temperature anomalies for all stations during blocked days varies between −2.1 and 0.8°C. There are four main patterns representing the mean seasonal temperature anomalies for all stations during blocked and non‐blocked days. The annual cycle for each group is nearly opposite, and this indicates the impact of blocking on observed temperature, as blocked days comprised 30% of the study period. When focusing on the spatial distribution of mean seasonal anomalies, the winter and fall seasons show that, almost all stations have negative temperature anomalies although anomalies are close to zero during warm seasons (spring and summer). The composite analysis shows that the western part of the country is strongly affected by cold air advection during upstream blocking events and the eastern part of the country is affected by warm temperature advection for downstream blocking events. There is a statistically significant (95% confidence level) negative correlation between blocking intensity and temperature anomalies in all seasons except spring. There is no relationship between both blocking duration and longitudinal extent and the seasonal mean temperature anomaly except during winter, which has a significant negative correlation. The temperature anomaly distribution stratified by season shows that strong positive anomalies are rarely observed in all seasons. Only winter and spring were associated with very strong positive anomalies and only at a few stations. Rex‐type atmospheric blocking events are observed during the period of not only the maximum temperature anomaly but also for minimum anomalies. However, the location of the blocking event differed from the typical situation above, with the cold and warm events being located downstream and upstream of Turkey, respectively.
Considering an integrated approach to assess all of the measured pollutants in a diurnal, monthly, seasonal, and annual time scales and understanding the mechanisms hidden under low air quality conditions are essential for tackling potential air pollution issues. Konya, located in central Anatolia, is the largest province of Turkey with a surface area of 40,838 km 2 and has different industrial activities. The lack of recent detailed studies limits our information on the underlying air pollution levels in Konya and obscuring policymakers to develop applicable mitigation measures. In this study, we used hourly monitored air quality data of CO, NO 2 , NO x , PM 10 , PM 2.5 , and SO 2 from five stations in Konya and investigated the temporal and spatial variabilities for the 2008–2018 period via statistical analysis. Upon analysis, particulate matter was found to be the dominant pollutant deteriorating the air quality of Konya. The highest 2008–2018 periodic mean value of PM 10 was found in Karatay Belediye as 70.5 µg/m 3 , followed by 67.4 µg/m 3 in Meram, 58.7 µg/m 3 in Selçuklu, and 43.7 µg/m 3 in Selçuklu Belediye. The 24-h limit value of PM 10 given as 50 µg/m 3 in the legislation was violated in all of the stations, mainly during winter and autumn. High positive correlations were found among the stations, and the highest correlation was obtained between Selçuklu Belediye and Karatay Belediye with a Pearson correlation coefficient of 0.77. Long-term data showed a decreasing trend in PM 10 concentrations. Diurnal variability is found to be more pronounced than weekly variability. For almost all of the pollutants, except for photochemical pollutants like O 3 , a prominent result was the nighttime and morning rush hours high-pollutant levels. A case study done for the January 29, 2018 to February 05, 2018 episode showed the importance of meteorology and topography on the high levels of pollution. Limitation of the pollutant transport and dilution by meteorological conditions and the location of Konya on a plain surrounded by high hills are believed to be the main reasons for having low air quality in the region.
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