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.

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.

Sustainable urban development in the era of energy and digital transformation is crucial from a societal perspective. Utilizing modern techniques for analyzing large datasets, including machine learning and artificial intelligence, enables a deeper understanding of historical data and the efficient prediction of future events based on data from IoT sensors. This study conducted a multidimensional historical analysis of air pollution to investigate the impacts of energy transformation and environmental policy and to determine the long-term environmental implications of certain actions. Additionally, machine learning (ML) techniques were employed for air pollution prediction, taking spatial factors into account. By utilizing multiple low-cost air sensors categorized as IoT devices, this study incorporated data from various locations and assessed the influence of neighboring sensors on predictions. Different ML approaches were analyzed, including regression models, deep neural networks, and ensemble learning. The possibility of implementing such predictions in publicly accessible IT mobile systems was explored. The research was conducted in Krakow, Poland, a UNESCO-listed city that has had long struggle with air pollution. Krakow is also at the forefront of implementing policies to prohibit the use of solid fuels for heating and establishing clean transport zones. The research showed that population growth within the city does not have a negative impact on PMx concentrations, and transitioning from coal-based to sustainable energy sources emerges as the primary factor in improving air quality, especially for PMx, while the impact of transportation remains less relevant. The best results for predicting rare smog events can be achieved using linear ML models. Implementing actions based on this research can significantly contribute to building a smart city that takes into account the impact of air pollution on quality of life.

Air pollution is an important problem for public health. The spatiotemporal analysis is a crucial step for understanding the complex characteristics of air pollution. Using many sensors and high-resolution time-step observations makes this task a big data challenge. In this study, unsupervised machine learning algorithms were applied to analyze spatiotemporal patterns of air pollution. The analysis was conducted using PM10 big data collected from almost 100 sensors located in Krakow, over a period of one year, with data being recorded at 1-h intervals. The analysis results using K-means and SKATER clustering revealed distinct differences between average and maximum values of pollutant concentrations. The study found that the K-means algorithm with Dynamic Time Warping (DTW) was more accurate in identifying yearly patterns and clustering in rapidly and spatially varying data, compared to the SKATER algorithm. Moreover, the clustering analysis of data after kriging greatly facilitated the interpretation of the results. These findings highlight the potential of machine learning techniques and big data analysis for identifying hot-spots, cold-spots, and patterns of air pollution and informing policy decisions related to urban planning, traffic management, and public health interventions.

Pharmaceutical drugs, including antibiotics and hormonal agents, pose a significant threat to environmental and public health due to their persistent presence in aquatic environments. Colistin (KOL), fluoxetine (FLUO), amoxicillin (AMO), and 17-alpha-ethinylestradiol (EST) are pharmaceuticals (PhCs) that frequently exceed regulatory limits in water and wastewater. Current removal methods are mainly ineffective, necessitating the development of more efficient techniques. This study investigates the use of synthetic zeolite (NaP1_FA) and zeolite-carbon composites (NaP1_C), both derived from fly ash (FA), for the removal of KOL, FLUO, AMO, and EST from aquatic environments. Batch adsorption experiments assessed the effects of contact time, adsorbent dosage, initial concentration, and pH on the removal efficiency of the pharmaceuticals. The results demonstrated that NaP1_FA and NaP1_C exhibited high removal efficiencies for all tested pharmaceuticals, achieving over 90% removal within 2 min of contact time. The Behnajady-Modirshahla-Ghanbary (BMG) kinetic model best described the adsorption processes. The most effective sorption was observed with a sorbent dose of 1–2 g L⁻¹. Regarding removal efficiency, the substances ranked in this order: EST was the highest, followed by AMO, KOL, and FLUO. Sorption efficiency was influenced by the initial pH of the solutions, with optimal performance observed at pH 2–2.5 for KOL and FLUO. The zeolite-carbon composite NaP1_C, due to its hydrophobic nature, showed superior sorption efficiency for hydrophobic pharmaceuticals like FLUO and EST. The spectral analysis reveals that the primary mechanism for immobilizing the tested PhCs on zeolite sorbents is mainly due to physical sorption. This study underscores the potential of utilizing inexpensive, fly ash-derived zeolites and zeolite-carbon composites to remove pharmaceuticals from water effectively. These findings contribute to developing advanced materials for decentralized wastewater treatment systems, directly addressing pollution sources in various facilities.