Influence of the atmospheric conditions on PM10 concentrations in Poznań, Poland

Abstract

This study investigates atmospheric conditions' influence on the mean and extreme characteristics of PM 10 concentrations in Poznań during the period 2006-2013. A correlation analysis was carried out to identify the most important meteorological variables influencing the seasonal dynamics of PM 10 concentrations. The highest absolute correlation values were obtained for planetary boundary layer height (r = −0.57), thermal (daily minimum air temperature: r = −0.51), anemological (average daily wind speed: r = −0.37), and pluvial (precipitation occurrence: r = −0.36) conditions, however the highest correlations were observed for temporal autocorrelations (1 day lag: r = 0.70). As regulated by law, extreme events were identified on the basis of daily threshold value i.e. 50 μg m . On average, annually there are approximately 71.3 days anywhere in the city when the threshold value is exceeded, 46.6 % of those occur in winter. Additionally, 83.7 % of these cases have been found to be continuous episodes of a few days, with the longest one persisting for 22 days. The analysis of the macro-scale circulation patterns led to the identification of an easy-to-perceive seasonal relations between atmospheric fields that favour the occurrence of high PM 10 concentration, as well as synoptic situations contributing to the rapid air quality improvement. The highest PM 10 concentrations are a clear reaction to a decrease in air temperature by over 3°C, with simultaneous lowering of PBL height, mean wind speed (by around 1 m s −1 ) and changing dominant wind directions from western to eastern sectors. In most cases, such a situation is related to the expansion of a high pressure system over eastern Europe and weakening of the Icelandic Low. Usually, air quality conditions improve along with an intensification of westerlies associated with the occurrence of low pressure systems over J Atmos Chem (2017) 74:115-139 DOI 10.1007

Application of Artificial Neural Networks in the Prediction of PM10 Levels in the Winter Months: A Case Study in the Tricity Agglomeration, Poland

“…Apart from emission, the predictors used in most prognostic models of air quality are weather conditions. The dispersion of pollutants is mainly determined by thermal conditions, air movement dynamics, and the type of circulation or thermal stratification within the atmospheric boundary layer [20,[33][34][35][36][37][38][39][40].…”

Regions of pollution with particulate matter in Poland

“…Data provided by IOŚ-PIB [1] show that combustion of fuels for energy generation, mainly coal in stationary sources [2], accounts for approximately 75% of PM10 national emission and almost 100% of SO2 national emission. Aerosanitary situation is determined by pollution emission values as well as by weather conditions which, on the one hand, result in intensification of heating process and, on the other, may contribute to effective dispersion or accumulation of pollutants leading to smog situations [3][4][5][6][7][8][9].…”

Suitability analysis of new air quality monitoring stations in Krakow as related to assessment of spatial and temporal variability of PM10 concentrations

“…Increased air concentrations of particulate matter have been a serious problem in many Polish cities for many years [1][2][3][4][5]. The particles suspended in ambient air (especially their fine fraction) along with the substances bound to them (e.g., PAHs, PCDDs/PCDFs, and heavy metals) can be harmful to human health [6][7][8][9][10][11][12].…”

“…However, the negative impact of these emissions can be intensified by specific topography and meteorological factors [13,14]. Many studies suggest that particulate matter concentrations and atmospheric conditions are correlated [1,15,16]. Wind speed and direction as well as mixing-layer height (thermal inversion) are very important factors that affect air pollutant concentrations [17,18].…”