The research focuses on the analysis of PM and PM concentrations variability at 11 stations in selected urbanized areas of Poland (Tricity, Poznań, Łódź, Kraków). Methods comprised: the analysis of basic statistical characteristics in yearly/monthly/daily/hourly scale and threshold exceedance frequencies. Also, correlations between PM and meteorological variables were investigated. GEV distribution analysis allowed the estimation of the return levels of monthly maxima of PM and PM. Results show that in Tricity there are fewer than 5 % of days with PM and PM threshold exceedance. In Kraków, the standards are only met during summer and the frequency of daily PM limit exceedance in winter was around 65-90 %. GEV analysis indicates that 10y return level of PM monthly maximum daily average do not usually exceed 250 μg/m at most of the stations (Kraków agglomeration is an exception here). In winter, the meteorological conditions unfavourable to the pollutant's dispersion comprise: high-pressure systems, stable equilibrium in the atmosphere and limited turbulence occur quite often together with low wind speed and reduced height of the planetary boundary layer.
Air pollution continues to have a significant impact on Europeans living in urban areas. Each year, elevated concentration episodes of PMx are responsible for a large number of premature deaths (mostly due to heart diseases and strokes). Poland is one of the most polluted countries in Europe according to annual EEA reports. A high winter PMx concentration is mostly the result of high emission and unfavourable weather conditions combined with environmental features. It is crucial to create the most accurate PMx concentration forecast so as to be able to alert society on time along with the needed municipal mitigation schemes. The research is aimed at assessing the possibility of short-term forecast of PMx concentrations by means of machine learning tools with the subsequent identification of primary meteorological covariates. The data comprises 10 years of winter hourly PM10 and PM2.5 concentrations in 4 large Polish agglomerations: Poznań, Kraków, Łódź, and Gdańsk. The research covered a total of 11 urban air quality monitoring stations, including background, traffic, and industrial types. The selected cities cover areas of high population density and quite a diverse environment stretching from the Baltic Sea coast (Tricity), through lowlands (Łódź, Poznań) to highlands (Kraków). We applied four ML models: stepwise regression (AIC-based), two tree-based algorithms (Random Forest and XGBoost), and a neural network model. The analysis and the application of the crossvalidation scheme provided a clear assessment of the optimal algorithm. The presented study confirms the high applicability of ML tools for short-term air quality prediction with the perfect prog approach. Among the used algorithms, there is a clear ranking, with the worst results achieved by linear methods and gradual enhancement through Neural Networks, Random Forest, and finally, XGBoost algorithm providing the best results. This is apparent in the regression approach and binary forecasts for threshold exceedance.
Optofluidics is increasingly gaining impact in a number of different fields of research, namely biology and medicine, environmental monitoring and green energy. However, the market for optofluidic products is still in the early development phase. In this manuscript, we discuss modular platforms as a potential concept to facilitate the transfer of optofluidic sensing systems to an industrial implementation. We present microfluidic and optical networks as a basis for the interconnection of optofluidic sensor modules. Finally, we show the potential for entire optofluidic networks.
Snow cover (SC) is a great indicator of climate change. It is highly related to temperature. Since the Polish climate faces warmer conditions, changes in the wintertime precipitation phase are observed. More frequent rainfall instead of snowfall is noticed at the end of the 20th and beginning of the 21st centuries. This work presents the spatial distribution, its changes, and variabilities of selected parameters that described snow cover in Poland from 1966/1967 to 2020/2021. The snow characteristics used in the study comprise of SC duration (SCD), the first and last day with SC in the season (SCbeg and SCend), the potential duration of SC season (PSCD), SC stability (SCS), average, maximum and accumulated SC depth (HS, HSmax and AHS). The changes in snow cover waves were analysed. Generally, the Polish climate is mild in the west, where the snow cover is finer and occurs relatively rarely and becomes more continental toward the north‐east, where the snow cover has a better condition to accumulate and preserve. The southern parts of the country are covered by mountain ranges of the Carpathians and Sudetes, where the snow cover remains the longest. The spatial distribution of the coefficient of variability is reversely proportional to SC characteristics—the highest in the north‐east (especially for the HS, HSmax and AHS). The most significant changes in SC are related to a decrease in SCD (5–7 days/decade), HSmax (1–2 cm/decade) and AHS (30–60 cm/decade). At the same time, the snow cover season becomes considerably shorter (especially in the western and central parts—about 10 days/decade). The trends in SCS are not yet significant in most of the country. This study reveals that the snow cover in Poland is under constant change, and the negative trends, which were hardly or not visible at the end of the 20th century, in the last decades have become statistically significant in a greater number of Polish stations.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.