Air pollution seasons in urban moderate climate areas through big data analytics.

High particulate matter (PM) concentrations have a negative impact on the overall quality of life and health. The annual trends of PM can vary greatly depending on factors such as a country's energy mix, development level, and climatic zone. In this study, we aimed to understand the annual cycle of PM concentrations in a moderate climate zone using a dense grid of low-cost sensors located in central Europe (Krakow). Over one million unique records of PM, temperature, humidity, pressure and wind speed observations were analyzed to gain a detailed, high-resolution understanding of yearly fluctuations. The comprehensive big-data workflow was presented with the statistical analysis of the meteorological factors. A big data-driven approach revealed the existence of two main PM seasons (warm and cold) in Europe's moderate climate zone, which do not correspond directly with the traditional four main seasons (Autumn, Winter, Spring, and Summer) with two side periods (early spring and early winter). Our findings also highlighted the importance of high-resolution time and space data for sustainable spatial planning. The observations allowed for distinguishing whether the source of air pollution is related to coal burning for heating in cold period or to agricultural lands burning during the warm period.

The influence of meteorological factors and terrain on air pollution concentration and migration: a geostatistical case study from Krakow, Poland.

Despite the very restrictive laws, Krakow is known as the city with the highest level of air pollution in Europe. It has been proven that, due to its location, air pollutants are transported to this city from neighboring municipalities. In this study, a complex geostatistical approach for spatio-temporal analysis of particulate matter (PM) concentrations was applied. For background noise reduction, data were recorded during the COVID-19 lockdown using 100 low-cost sensors and were validated based on indications from reference stations. Standardized Geographically Weighted Regression, local Moran's I spatial autocorrelation analysis, and Getis-Ord Gi* statistic for hot-spot detection with Kernel Density Estimation maps were used. The results indicate the relation between the topography, meteorological variables, and PM concentrations. The main factors are wind speed (even if relatively low) and terrain elevation. The study of the PM2.5/PM10 ratio allowed for a detailed analysis of spatial pollution migration, including source differentiation. This research indicates that Krakow's unfavorable location makes it prone to accumulating pollutants from its neighborhood. The main source of air pollution in the investigated period is solid fuel heating outside the city. The study shows the importance and variability of the analyzed factors' influence on air pollution inflow and outflow from the city.

The Use of Public Data from Low-Cost Sensors for the Geospatial Analysis of Air Pollution from Solid Fuel Heating during the COVID-19 Pandemic Spring Period in Krakow, Poland.

In this paper, we present a detailed analysis of the public data provided by low-cost sensors (LCS), which were used for spatial and temporal studies of air quality in Krakow. A PM (particulate matter) dataset was obtained in spring in 2021, during which a fairly strict lockdown was in force as a result of COVID-19. Therefore, we were able to separate the effect of solid fuel heating from other sources of background pollution, mainly caused by urban transport. Moreover, we analyzed the historical data of PM2.5 from 2010 to 2019 to show the effect of grassroots efforts and pro-clean-air legislation changes in Krakow. We designed a unique workflow with a time-spatial analysis of PM1, PM2.5, and PM10, and temperature data from Airly(c) sensors located in Krakow and its surroundings. Using geostatistical methods, we showed that Krakow's neighboring cities are the main sources of air pollution from solid fuel heating in the city. Additionally, we showed that the changes in the law in Krakow significantly reduced the PM concentration as compared to neighboring municipalities without a fossil fuel prohibition law. Moreover, our research demonstrates that informative campaigns and education are important initiating factors in order to bring about cleaner air in the future.

Scots Pine (Pinus sylvestris L.) Growth Suppression and Adverse Effects on Human Health Due to Air Pollution in the Upper Silesian Industrial District (USID), Southern Poland.

Air pollution emissions were not continually monitored in the Upper Silesian Industrial District (USID), southern Poland, and data is only available for the last 20 years. Long-lasting and severe tree ring reductions in pines growing 5-20 km north of the USID area recorded particularly high levels of air pollution emissions in the period 1950-1990. Especially high amounts of reductions and many missing rings were found in the period 1964-1981. At the same time, pines growing 60 km west of the USID do not record deep ring reductions; this proves that the phenomenon is of a regional nature. Increases in infant mortality and lung, bronchial, and tracheal cancer morbidity rates among males were also recorded in the USID during periods of high air pollution. Infant mortality rates increased several years after the tree ring reductions. Therefore, it may be possible to use tree ring reductions as an early indicator of the occurrence of adverse effects on human health.