Regions of pollution with particulate matter in Poland

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Poor urban air quality due to high concentrations of particulate matter (PM) remains a major public health problem worldwide. Therefore, research efforts are being made to forecast ambient PM concentrations. In this study, artificial neural networks (ANNs) were employed to generate models forecasting hourly PM 10 concentrations 1-6 h ahead, involving 3 measurement locations in the Tricity Agglomeration, Poland. In Poland, the majority of high PM concentration cases occurs in winter due to coal combustion being the main energy carrier. For this reason, the present study covers only the periods of the winter calendar (December, January, February) in the period 2002/2003-2016/2017. Inputs to the models were the values of hourly PM 10 concentrations and meteorological factors such as air temperature, relative humidity, air pressure, and wind speed. The results of the neural network models were satisfactory and the values of the coefficient of determination (R 2) for the independent test set for three sites ranged from 0.452 to 0.848. The values of the index of agreement (IA) were from 0.693 to 0.957, the fractional mean bias (FB) values were 0 or close to 0 and the root mean square error (RMSE) values varied from 8.80 to 23.56. It is concluded that ANNs have been proven to be effective in the prediction of air pollution levels based on the measured air monitoring data.

Section: General Description Of Pm 10 and Meteorology Variablessupporting

confidence: 72%

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

Nidzgorska-Lencewicz

2018

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“…The division of Poland according to the frequency of smog episodes is shown in Figure 2. In 2018, Rawicki et al [19] showed that due to meteorological conditions and the occurrence of smog episodes, Poland could be divided into three areas: I-southern, in which smog episodes last the longest and occur most often, II-central with moderate frequency of smog episodes and III-northern, where smog episodes occur sporadically. This is due to the fact that a constant balance dominates in the southern part of Poland, and a much larger share of unstable balance occurs in the northern part [20,21].…”

Section: Introductionmentioning

confidence: 99%

Smog Episodes in Poland

Wielgosiński

Czerwińska

2020

The phenomenon of above-average air pollution, i.e., smog, in urban areas is known. Two types of smog have been described in the literature: London and Los Angeles smog. They differ in the conditions of formation and areas of occurrence. In recent years, the phenomenon of smog has also been observed in Poland, where the main reason for poor air quality is exceeding the permissible PM10 concentrations. The main source of particulate matter emissions in Poland is the so-called “low emission”, i.e., released by emitters up to 40 m high, mainly from domestic boilers and traffic. Based on the data from the environmental protection inspection, an analysis was carried out of the impact of atmospheric factors, such as atmospheric pressure and air temperature, on air pollution caused by particulate matter in Poland. Next, data concerning the chemical composition of PM10 particulate matter in Poland was analyzed. In the next stage, tests were carried out on ammonia emissions from biomass and coal combustion processes to determine the source of ammonium ions as a component of particulate matter. The results of analyzes and research allowed us to formulate the thesis about the existence of a specific type of smog called “Polish smog” and determine the conditions for its formation.

“…The very high share of particulate matter fraction < 2.5 µg in pollution with particulate matter is evidenced by the results from a series of previous winter seasons (2009/10-2014/15) recorded all over the country. In some regions of Poland, the hourly particulate matter concentrations, which generally fell between 0 and 75 µg•m −3 , were recorded more frequently with respect to PM 2.5 than PM 10 , in a longer, i.e., 10 year-long, series of winter seasons (2005/06-2014/15) [34].…”

Section: Particulate Matter Concentrationsmentioning

confidence: 97%

Thermal Inversion and Particulate Matter Concentration in Wrocław in Winter Season

Nidzgorska-Lencewicz

Czarnecka

2020

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Studies on air quality frequently adopt clustering, in particular the k-means technique, owing to its simplicity, ease of implementation and efficiency. The aim of the present paper was the assessment of air quality in a winter season (December–February) in the conditions of temperature inversion using the k-means method, representing a non-hierarchical algorithm of cluster analysis. The air quality was assessed on the basis of the concentrations of particulate matter (PM10, PM2.5). The studies were conducted in four winter seasons (2015/16, 2016/17, 2017/18, 2019/20) in Wrocław (Poland). As a result of the application of the v-fold cross test, six clusters for each fraction of PM were identified. Even though the analysis covers only four winter seasons, the applied method has unequivocally revealed that the characteristics of surface-based (SBI) and elevated inversions (ELI) affect the concentration level of both fractions of particulate matter. In the case of PM10, the average lowest daily concentration (15.5 µg·m−3) was recorded in the conditions of approx. 205 m in thickness, 0.5 °C intensity of the SBI and at the height of the base of the ELI at approx. 1700 m a.g.l., a thickness of 148 m and an intensity of 1.2 °C. In turn, the average highest concentration (136 µg·m−3) was recorded at a thickness of SBI of approx. 400 m and an intensity of 1.4 °C. Such high concentration occurred when the lowest location of ELI formed at 764 m a.g.l. with a thickness of 308 m and an intensity of 0.96 °C. A marked role of the thickness of the SBI and ELI as well as the height of the base of the lowest location of ELI was also manifested with respect to PM2.5 concentrations.