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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., Competing Interests: Competing interestsThe authors declare no competing interests., (© The Author(s), under exclusive licence to Springer Nature B.V. 2022.)

This study discusses the concentration characteristics of PM 2.5 and PM 10 , as well as pollution meteorology in large-scale and long-term heavy pollution in the Beijing-Tianjin-Hebei region and its surrounding areas from November 23 to December 4, 2018, where the primary pollutants are comprised of PM 2.5 and PM 10 . The monitoring results obtained from ground-based and vehicle-mounted lidars, as well as the HYSPLIT-4 backward trajectory combined with meteorological factors analysis are discussed. The accuracy and uncertainty of the air quality forecast model of NAQPMS, CMAQ, and CAMx during heavy air pollution were analyzed retrospectively. The results show that PM 2.5 and sand dust in most cities in the south-central region contribute to severe pollution levels. The hourly peak concentrations of PM 10 in Zhangjiakou, Beijing, Shijiazhuang, Handan, and Zhengzhou were 1589, 864, 794, 738, and 766 μg·m -3 , respectively. The respective hourly peak concentrations of PM 2.5 were 239, 319, 387, 321, and 380 μg·m -3 . Ground static pressure field, high humidity, inversion, and other static and stable conditions, as well as sand dust transmitted from the northwest, were important pollution meteorological and weather factors. The monitoring data of ground-based lidar and vehicle-mounted lidar combined with the HYSPLIT-4 backward trajectory analysis showed that the air pollutant transmitted from the Southwest and Southeast during the heavy pollution period was primarily PM 2.5 . The air pollutant transmitted from the Northwest during the two sand dust processes. Moreover, the model of NAQPMS, CMAQ, and CAMx performed well in forecasting the heavy pollution process in the Beijing-Tianjin-Hebei region and its surrounding areas. However there are slight deviations for some individual cities, related to uncertainty in the meteorological model prediction, atmospheric chemical reaction mechanism, and pollution source list. Furthermore, the reduction in pollution source emissions caused by pollution emergency measures was also one of the main reasons for the overestimation.

Hourly data for sulfur dioxide (SO 2 ), nitrogen oxides (NO x ), and inhalable particulate matter (PM 10 ) over a 33-month period from a network of air quality monitoring stations across Qingdao, a major coastal city in eastern China, along with surface and upper-air meteorological data, are used to characterize the spatiotemporal variability of these pollutants in the region and the role of meteorological conditions play in pollution episodes. Large differences in the concentrations of all three pollutants are found between densely populated or industrial areas and suburban commercial or residential or coastal tourist areas, but the differences are relatively small between older and newer parts of the residential-commercial areas and between old and newly developed industrial areas. Wavelet analyses revealed a strong seasonal cycle for all three pollutants, introseasonal variability with a periodicity depending on pollutant and location, and diurnal and a semi-diurnal variability with season-dependent amplitude and phase. Low wind speed is found to be the leading factor for pollution buildup in the region. These results may prove useful for urban planning and development and implementation of effective air pollution control strategies for other coastal regions with economic development similar to Qingdao.