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The atmospheric concentrations of seventeen elements were measured in air filters at the Finnish Meteorological Institute station in Helsinki, Finland, during a period of 44 years (1962–2005). The mean annual concentrations were calculated and are presented from the lowest values to the highest ones Cr < Ni < Ti < Br < V < Mn < Cu < Zn < Cl < Al < Fe < K < Ca < Na < Pb < Si < S. Most of the elements (Fe, Si, Ti, K, Ca, Zn, Br, Pb, V, Ni, S, Cr, Na, Al, and Cl) present higher values during spring and winter season, while in summer the elements (Ti, Ca, S, and Na) are found in higher concentrationsdue to the weather conditions across seasons and the sources and emissions of air pollutants. There is a strong correlation between the elements (V-Ni, Si-Pb, Fe-Ca, V-Cr, Si-K, K-Ca, Fe-Ti, K-Na, Si-Ca, and V-S), indicating their common source. The identification of the sources of trace elements was performed based on positive matrix factorization analysis, using SoFi software. Four Suspended Particulate Matter (PM) sources were identified: road dust (due to usage of leaded fuel), heavy oil combustion/secondary sulfates, traffic emissions, and natural dust (soil). For the total of 44 years studied, significant decreases in concentrations were observed for all elements, most of which were over 50%: Na (−74%), Al (−86%), Si (−88%), S (−82%), K (−82%), Ca (−89%), Ti (−80%), V (−89%), Cr (−82%), Mn (−77%), Fe (−77%), Ni (−61%), Zn (−72%), and Pb (−95%). In general, a significant decline has been observed in the majority of the elemental concentrations since the end of the 1970s, underlying the effectiveness of different environmental policies that have been applied during the last few decades. [ABSTRACT FROM AUTHOR]

Due to the link between air pollutants and human health, reliable model estimates of hourly pollutant concentrations are of particular interest. Artificial neural networks (ANNs) are powerful modeling tools capable of reproducing the observed variations in pollutants with high accuracy. We present a simple ANN for the city of Hamburg that estimated the hourly NO2 concentration. The model was trained with a ten-year dataset (2007–2016), tested for the year 2017, and then applied to assess the efficiency of countermeasures against air pollution implemented since 2018. Using both meteorological data and describing the weekday dependent traffic variabilities as predictors, the model performed accurately and showed high consistency over the test data. This proved to be very efficient in detecting anomalies in the time series. The further the prediction was from the time of the training data, the more the modeled data deviated from the measured data. Using the model, we could detect changes in the time series that did not follow previous trends in the training data. The largest deviation occurred during the COVID-19 lockdown in 2020, when traffic volumes decreased significantly. Concluding our case study, the ANN based approach proved suitable for modeling the NO2 concentrations and allowed for the assessment of the efficiency of policy measures addressing air pollution.

We characterized the composition, diversity, and potential bacterial aerosol sources in Athens’ urban air by DNA barcoding (analysis of 16S rRNA genes) during three seasons in 2019. Air samples were collected using the recently developed Rutgers Electrostatic Passive Sampler (REPS). It is the first field application of REPS to study bacterial aerosol diversity. REPS samplers captured a sufficient amount of biological material to demonstrate the diversity of airborne bacteria and their variability over time. Overall, in the air of Athens, we detected 793 operational taxonomic units (OTUs), which were fully classified into the six distinct taxonomic categories (Phylum, Class, Order, etc.). These OTUs belonged to Phyla Actinobacteria, Firmicutes, Proteobacteria, Bacteroidetes, Cyanobacteria, and Fusobacteria. We found a complex community of bacterial aerosols with several opportunistic or potential pathogens in Athens’ urban air. Referring to the available literature, we discuss the likely sources of observed airborne bacteria, including soil, plants, animals, and humans. Our results on bacterial diversity are comparable to earlier studies, even though the sampling sites are different or geographically distant. However, the exact functional and ecological role of bioaerosols and, even more importantly, their impact on public health and the ecosystem requires further air monitoring and analysis.

We characterized the composition, diversity, and potential bacterial aerosol sources in Athens' urban air by DNA barcoding (analysis of 16S rRNA genes) during three seasons in 2019. Air samples were collected using the recently developed Rutgers Electrostatic Passive Sampler (REPS). It is the first field application of REPS to study bacterial aerosol diversity. REPS samplers captured a sufficient amount of biological material to demonstrate the diversity of airborne bacteria and their variability over time. Overall, in the air of Athens, we detected 793 operational taxonomic units (OTUs), which were fully classified into the six distinct taxonomic categories (Phylum, Class, Order, etc.). These OTUs belonged to Phyla Actinobacteria, Firmicutes, Proteobacteria, Bacteroidetes, Cyanobacteria, and Fusobacteria. We found a complex community of bacterial aerosols with several opportunistic or potential pathogens in Athens' urban air. Referring to the available literature, we discuss the likely sources of observed airborne bacteria, including soil, plants, animals, and humans. Our results on bacterial diversity are comparable to earlier studies, even though the sampling sites are different or geographically distant. However, the exact functional and ecological role of bioaerosols and, even more importantly, their impact on public health and the ecosystem requires further air monitoring and analysis. [ABSTRACT FROM AUTHOR]