Next Article in Journal
Air Quality Index Prediction in Six Major Chinese Urban Agglomerations: A Comparative Study of Single Machine Learning Model, Ensemble Model, and Hybrid Model
Previous Article in Journal
Modelling the Impact of Adverse Weather on Airport Peak Service Rate with Machine Learning
Previous Article in Special Issue
Variability of the Chemical Composition of the Atmospheric Aerosol in the Coastal Zone of the Southern Basin of Lake Baikal (East Siberia, Russia)
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Editorial

Special Issue Editorial: Long-Term Research on the Quality of Air and the Trends of Its Variability

by
Liudmila P. Golobokova
Limnological Institute Siberian Branch of the Russian Academy of Sciences, 664033 Irkutsk, Russia
Atmosphere 2023, 14(10), 1477; https://doi.org/10.3390/atmos14101477
Submission received: 21 November 2022 / Revised: 31 July 2023 / Accepted: 21 September 2023 / Published: 24 September 2023
Long-term observations are integral to encouraging research of atmospheric composition, the climate, and human health, and thus, filling some gaps in scientific knowledge. These observations lead to the development of information products that are adapted to various relevant applications, such as identifying sources of pollutant emissions, preparing reliable air quality predictions and evaluating the effectiveness of emission reduction policies. Continuous measurements provide a long time series and a unique opportunity to analyse the state of the atmosphere. Continuous observations can provide valuable datasets for improving computer models to quantify climate change trends and identify major influences in atmospheric chemistry and physics. Progress in new areas of research will depend on sustained measurements. The reports presented in the Special Issue, “Long-Term Research on the Quality of Air and the Trends of Its Variability”, are the result of collaborative work between researchers in an effort to control and reduce atmospheric emissions.
According to the United Nations Environment Programme (UNEP), air pollution is mainly caused by five human activities: agriculture, transport, industry, waste, and households [1].
Municipal solid waste (MSW) is one of the predominant factors that contribute to climate change. Numerous data show that one of the main problems associated with landfill is the generation of biogas (biogas). In many countries around the world, landfills are seen as giant bioreactors, loaded with energy raw materials that are significantly more cost-effective than traditional energy materials. Biogas utilisation can provide a healthier environment and reduce the risk of fires. Biogas is increasingly being used as a fuel to generate electricity, heat, or steam.
Using the LandGEM mathematical mode the authors of [2] estimated the total volume of biogas, CH4, CO2, and other gases in Pichacay and Las Iguanas sanitary landfills, located in Ecuador. The biogas generated in landfills can be used as an energy source to produce electricity. How profitable it will be to build such a power plant in the future remains unknown. Current research clearly shows the level of pollutants that enter the atmosphere now, and those that will enter it in the future, if appropriate measures are not taken. Although this study is perhaps more suitable for journals on the topic of energy, one of its main focuses is on air quality. Whether pollutant gases will be used for the needs of the population remains unknown. However, it is certain that we must take necessary measures to eliminate the consequences of landfills.
Another interesting study area is presented in [3]. This study shows the results of analysing the ionic components of PM10 at five different sites in Makkah, Saudi Arabia, using data collected from 8 March 2020 to 9 March 2021. Makkah proves to be an interesting study area as PM10 levels are consistently exceeding the air quality standards set by the WHO and European commission [4]. A multivariate approach was chosen using a principal component analysis (PCA). PCA is a multivariate statistical analysis method that can identify the major directions of variation in a given dataset. These data are much needed in a region subject to so few studies on air quality.
Experimental aerosol measurements carried out at the INAIL’s building in central Rome and at the Italian National Institute of Health (ISS)’s building [5]. The article presents an interesting study. The authors suggest that the use of PM2.5 as an indicator of anthropogenic combustion emissions may not be appropriate in some cases. This study analyses and compares three case studies, involving desert dust advection, sea salt advection, and forest fire aerosols from a remote area. The discussed data show that PM1 clearly represents anthropogenic combustion sources, whereas PM2.5 may be greatly affected by natural sources.
Interesting research is devoted to the combination of in situ data (gravimetric measurements), elemental crustal composition, and reanalysis in a Russian urban area [6]. One paper describes some results of a comprehensive experiment conducted in Moscow to study the composition of near-surface atmospheric aerosols. The paper focuses on six episodes of high PM concentration, analysing large-scale backtrajectories, the elemental composition of the PM, and some statistical features of the PM concentration time series observed in different urban sites. The authors focus on specific episodes during the period of 2020–2021, analysing six specific events where the daily maximum permissible concentration value of PM10 was significantly exceeded (60 μg/m3, according to Russian standards).
Moscow is a large metropolis in Russia, and interest in this region is not merely scientific. One study focuses on the atmosphere surrounding Moscow [7], investigating seasonal, weekly, and diurnal black carbon under the impact of urban and regional sources in an urban site in Moscow. Although this type of reporting of BC data is now very routine in research on European cities and in other locations worldwide, the dataset may be of particular interest considering the high population of Moscow. The paper presents in detail the variability of black carbon concentrations on different time scales. The level of black carbon is comparable to that of European cities.
In [8], the results of multi-year measurements in the western part of the Siberian region (Russia) are presented. This study investigated long-term trends (more than 20 years) of changes in the concentrations of total protein (a universal marker of the biogenic component of atmospheric aerosol) and culturable microorganisms in the atmospheric aerosol of the south of Western Siberia. Biological components of atmospheric aerosol affect the quality of atmospheric air. The conducted studies not only revealed the main patterns of changes in the concentrations of the total number of proteins and the observed biodiversity of microorganisms in these aerosols, but also identified gaps in knowledge of ongoing processes. These research gaps are confirmed by other studies [9,10,11]. Answering these questions will allow researchers to understand the ongoing changes in the observed concentrations of bioaerosols in different regions.
Marinaite et al. [12] analysed polycyclic aromatic hydrocarbons (PAH) in the atmosphere of the eastern part of the Siberian region (Russia). Based on long-term studies (2015–2017; 2019–2021) at two reference stations exposed to different levels of technogenic air pollution, Irkutsk and Listvyanka (urban and rural areas, Southern Baikal region, Russia), the seasonal and interannual dynamics of PAH concentrations in the atmospheric air were determined. Pollution emission sources and their relationship with PAHs and the meteorological conditions of the study area were investigated. It was found that the proportion of PAH-containing anthropogenic aerosol transport from industrial sources in Southern Baikal Region towards Lake Baikal was 65 to 71%.
One study focused on the state of the atmosphere in background areas of the world is investigated. This original study [13] considers the results of a long-term study of the chemical composition of aerosols in the atmosphere of the southern coastal zone of Lake Baikal. The study analyses ionic composition according to observations made in 2020 and 2021 and compared with earlier long-term results (2002–2019). The value of the study lies in its specific experimental data on the variability of chemical composition of near-surface aerosols on the coast of a unique natural object—Lake Baikal.
The study by Sakerin et al. summarises a long series of data and a wide range of analyses conducted across the South Atlantic Ocean and Southern Ocean, using multiyear (2004–2021) measurements form Russian Antarctic expeditions [14]. In this study, the authors analyse temporal and spatial patterns in aerosol optical depth and elemental black carbon, providing insights into the ionic composition of the aerosols in this area. The measured characteristics decrease with decreasing aerosol concentrations closer to Antarctica. The topic of the paper is of general interest to both the scientific world and other developments.
It is important to note that the costs of air quality monitoring are much lower than the costs of reducing air pollution. Most sources of air pollution are structural and embedded in the economic processes that underpin modern society. For this reason, it is difficult to prevent air pollution at an individual level, and thus collaborative efforts from researchers are required effort. Therefore, this Special Issue ‘Long-Term Research on the Quality of Air and the Trends of Its Variability’ was jointly produced by the authors.

Funding

This research received no external funding.

Acknowledgments

The editors would like to thank the author for their contributions, the reviewers for their comments and the editorial office for the support in publishing this issue.

Conflicts of Interest

The author declares no conflict of interest.

References

  1. UNEP—UN Environment Programme. About UN Environment Programme. Available online: https://www.unep.org/about-un-environment (accessed on 19 November 2022).
  2. Poma, P.; Usca, M.; Polanco, M.; Toulkeridis, T.; Mestanza-Ramón, C. Estimation of Biogas Generated in Two Landfills in South-Central Ecuador. Atmosphere 2021, 12, 1365. [Google Scholar] [CrossRef]
  3. Habeebullah, T.M.; Munir, S.; Zeb, J.; Morsy, E.A. Analysis and Sources Identification of Atmospheric PM10 and Its Cation and Anion Contents in Makkah, Saudi Arabia. Atmosphere 2022, 13, 87. [Google Scholar] [CrossRef]
  4. Air Quality Standards. 2021. Available online: https://www.who.int/news-room/fact-sheets/detail/ambient(outdoor)-air-quality-and-health (accessed on 21 November 2022).
  5. Manigrasso, M.; Soggiu, M.E.; Settimo, G.; Inglessi, M.; Protano, C.; Vitali, M.; Avino, P. PM Dimensional Characterization in an Urban Mediterranean Area: Case Studies on the Separation between Fine and Coarse Atmospheric Aerosol. Atmosphere 2022, 13, 227. [Google Scholar] [CrossRef]
  6. Gubanova, D.P.; Vinogradova, A.A.; Iordanskii, M.A.; Skorokhod, A.I. Variability of Near-Surface Aerosol Composition in Moscow in 2020–2021: Episodes of Extreme Air Pollution of Different Genesis. Atmosphere 2022, 13, 574. [Google Scholar] [CrossRef]
  7. Popovicheva, O.; Chichaeva, M.; Kovach, R.; Zhdanova, E.; Kasimov, N. Seasonal, Weekly, and Diurnal Black Carbon in Moscow Megacity Background under Impact of Urban and Regional Sources. Atmosphere 2022, 13, 563. [Google Scholar] [CrossRef]
  8. Safatov, A.S.; Andreeva, I.S.; Buryak, G.A.; Olkin, S.E.; Reznikova, I.K.; Belan, B.D.; Panchenko, M.V.; Simonenkov, D.V. Long-Term Studies of Biological Components of Atmospheric Aerosol: Trends and Variability. Atmosphere 2022, 13, 651. [Google Scholar] [CrossRef]
  9. Van Leuken, J.P.G.; Swart, A.N.; Droogers, P.; Van Pul, A.; Heederik, D.; Havelaar, A.H. Climate change effects on airborne pathogenic bioaerosol concentrations: A scenario analysis. Aerobiologia 2016, 32, 607–617. [Google Scholar] [CrossRef] [PubMed]
  10. Cáliz, J.; Triadó-Margarit, X.; Camarero, L.; Casamayor, E.O. A long-term survey unveils strong seasonal patterns in the airborne microbiome coupled to general and regional atmospheric circulations. Proc. Natl. Acad. Sci. USA 2018, 115, 12229–12234. [Google Scholar] [CrossRef] [PubMed]
  11. Li, X.; Cheng, X.; Wu, W.; Wang, Q.; Tong, Z.; Zhang, X.; Deng, D.; Li, Y. Forecasting of bioaerosol concentration by a back propagation neural network model. Sci. Total Environ. 2020, 698, 134315. [Google Scholar] [CrossRef] [PubMed]
  12. Golobokova, L.P.; Khodzher, T.V.; Zhamsueva, G.S.; Zayakhanov, A.S.; Starikov, A.; Khuriganova, O.I. Variability of the Chemical Composition of the Atmospheric Aerosol in the Coastal Zone of the Southern Basin of Lake Baikal (East Siberia, Russia). Atmosphere 2022, 13, 1090. [Google Scholar] [CrossRef]
  13. Marinaite, I.; Penner, I.; Molozhnikova, E.; Shikhovtsev, M.; Khodzher, T. Polycyclic Aromatic Hydrocarbons in the Atmosphere of the Southern Baikal Region (Russia): Sources and Relationship with Meteorological Conditions. Atmosphere 2022, 13, 420. [Google Scholar] [CrossRef]
  14. Sakerin, S.M.; Golobokova, L.P.; Kabanov, D.M.; Khuriganowa, O.I.; Pol’kin, V.V.; Radionov, V.F.; Sidorova, O.R.; Turchinovich, Y.S. Spatial Distribution of Aerosol Characteristics over the South Atlantic and Southern Ocean Using Multiyear (2004–2021) Measurements from Russian Antarctic Expeditions. Atmosphere 2022, 13, 427. [Google Scholar] [CrossRef]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Golobokova, L.P. Special Issue Editorial: Long-Term Research on the Quality of Air and the Trends of Its Variability. Atmosphere 2023, 14, 1477. https://doi.org/10.3390/atmos14101477

AMA Style

Golobokova LP. Special Issue Editorial: Long-Term Research on the Quality of Air and the Trends of Its Variability. Atmosphere. 2023; 14(10):1477. https://doi.org/10.3390/atmos14101477

Chicago/Turabian Style

Golobokova, Liudmila P. 2023. "Special Issue Editorial: Long-Term Research on the Quality of Air and the Trends of Its Variability" Atmosphere 14, no. 10: 1477. https://doi.org/10.3390/atmos14101477

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop