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Air—A New Open Access Journal

Department of Building Environment and Energy Engineering, The Hong Kong Polytechnic University, Hong Kong, China
Air 2023, 1(1), 89-93;
Submission received: 20 February 2023 / Accepted: 1 March 2023 / Published: 9 March 2023
Air (ISSN 2813-4168) is a new peer-reviewed, international, open access online academic journal for scientists in different disciplines related to air’s composition and impacts. It focuses on topics such as emissions, physical effects, chemical processes, and engineering control measures for gaseous, microbes, and particulate compounds in the regional, urban, indoor, and micro-environment, and their associated impacts on human comfort, health, air quality, and ecosystems.
This journal provides a platform for original research results in the broad areas of air science, technology, management, and governance. Papers published in Air will advance the international scientific community’s understanding of air that will influence regulations, management, and protection of air resources, ecosystems, and human health with sustainable urban development. It will drive future scientific research and sustainable technology development efforts with air science.
Air covers an extensive and ever-expanding research area. Global issues such as new airborne viruses, novel disinfection agents, chemicals, and air purification technology require further investigation, which will lead to an increase in the publication volume. Figure 1 indicates a significant increasing trend of 11% per year in the number of published journal articles indexed by the Web of Science core collection in 2000–2022 on 12 selected Air-related subjects. MDPI was ranked the third publisher in the period (based on the number of publications). The top five hot topics in this research area are air monitoring (25%), air quality model (14%), air policy (10%), air quality control (10%), and air quality and health (10%). Figure 2 depicts the relative growth of articles in the 12 subjects compared to those published in 2000. The average growth of publications was about ninefold. The topmost growing areas are air quality and sustainability (46 times), air quality and climate (20 times), air quality and comfort (16 times), air and human performance (15 times), and air quality and health (14 times), respectively.
The 2022 Web of Science Core Collection database lists approximately 20,000 journal articles on ‘air quality’, ‘air monitoring’, or ‘air pollution’. Based on the paper counts, MDPI is the second-largest publisher, with about 3500 papers, including 13 highly cited (top 1% of an academic field) articles [1,2,3,4,5,6,7,8,9,10,11,12,13]. Among 17,000 keywords involved in the 3500 published papers, 96 co-occurring keywords at a threshold of 35 times or more (i.e., ≥1% publications) were identified. This excluded similar keywords used in the search, i.e., air, air quality, air monitoring, air pollutant, ambient air, pollutant, pollution, quality, and monitoring. Figure 3 shows the co-keyword network of the keywords visualized using the bibliometric analysis software VOSviewer [14]. The size of a keyword node represents the keyword occurrence frequency. A link between two nodes represents a co-occurrence relationship, with the thickness indicating the length strength.
The keywords were presented as five clusters, as listed in Table 1.
Cluster 1 is defined by 28 keywords which are mainly attributed to the built environment, content, and impacts. The five contributing keywords (performance, temperature, indoor air quality, environment, and energy) contribute one-third of the total link strength of this cluster. The keyword ‘building’ and the directly linked nodes, i.e., thermal comfort, flow, simulation, energy, system, performance, indoor air quality, and environment, contributed to half of the cluster’s total length strength. Issues related to indoor air quality, sick building syndrome, and approaches for healthy buildings are the typical research focuses [1,2].
Cluster 2 is attributed to policy, management, and impact on air pollution and quality issues, e.g., carbon reduction and sustainable development. Air quality/policy as a driving force and its effects on various sustainable developments (urban planning, green finance, and air emission standard) are reported [3,4,5,6,7]. Keywords including ‘impact (impacts)’ contributed 28% and ‘climate’ contributed 16% of the total link strength—26% related to geographic locations.
Cluster 3 relates to pollutants and source apportionment. This cluster presents the most vital link strength related to air research. The top 5 (out of 20) contributing keywords account for 60% of the cluster link strength. Three of the five keywords (PM2.5, particulate matter, emissions, PM10, and particles), are particle related, indicating the parameter typically adopted in many studies in source apportionment, e.g., [7]. The emission inventories are developed and could be further refined with sensing and data technologies [8].
Cluster 4 appears with model development and applications, with advanced measurement and monitoring techniques. Applications of advanced techniques, such as machine learning, deep learning, satellite remote sensing, and the Moderate Resolution Imaging Spectroradiometer (MODIS), are included in this cluster. The top 5 (out of 16) contributing keywords are model, ozone, COVID-19, trends, and prediction, comprising 50% of the total link strength of this cluster. Air pollution and quality modelling of various digital techniques are highly cited topics (e.g., assimilation, Bayesian, deep learning, stochastic approaches, and neural networks) [8,9,10,11].
Cluster 5 addresses health and related issues. Air pollution, associated diseases, and their impacts on health systems are specific topics [12,13]. The top 5 (out of 12) keywords identified by the total link strength are exposure, health, mortality, fine particulate matter, and risk. They contribute 70% of the total link strength of the cluster.
In light of the above, Air publishes papers reflecting the broad categories of interest in these fields:
  • Air quality for health and comfort.
  • Air pollution and source characterisation.
  • Air purification and control techniques.
  • Air management, policy control, monitoring, and modelling.
  • Air–human interaction and sustainable development.
As a multidisciplinary journal, Air reflects the broad categories of interest in aerosols and bioaerosols, air pollution physics and chemistry, air pollutant sources, emissions, sensing, exposure control, health impact and risk assessment, air pollutant transport, transformation and fate, air quality, monitoring and modelling, impacts on climate, eco-systems, urban environment, indoor environment and microenvironment, and human response and public health in relation to exposure to air pollutants, air purification, ventilation, and other environmental control techniques. The research results present the basic and essential information to allow governors, policymakers, scientists, engineers, designers, building managers, owners, and operators to provide a sustainable, safe, healthy, and comfortable urban and built environment for human habitation.
The first issue of Air (Volume 1, Issue 1) covers a range of relevant topics, including (but not limited to) air pollution, monitoring and managing chemical and particulate emissions from various machinery sources, and activities for sustainable cities. As an editor, I would like to see healthy competition from researchers and practitioners representing diverse academic, research and development, governance, and control fields to shape the character of Air, starting with great potential by filling a unique niche.

Conflicts of Interest

The author declares no conflict of interest.


  1. Navaratnam, S.; Nguyen, K.; Selvaranjan, K.; Zhang, G.; Mendis, P.; Aye, L. Designing post COVID-19 buildings: Approaches for achieving healthy buildings. Buildings 2022, 12, 74. [Google Scholar] [CrossRef]
  2. Wang, M.; Li, L.; Hou, C.; Guo, X.; Fu, H. Building and health: Mapping the knowledge development of sick building syndrome. Buildings 2022, 12, 287. [Google Scholar] [CrossRef]
  3. Chen, Y.; Miao, Q.; Zhou, Q. Spatiotemporal differentiation and driving force analysis of the high-quality development of urban agglomerations along the Yellow River Basin. Int. J. Environ. Res. Public Health 2022, 19, 2484. [Google Scholar] [CrossRef] [PubMed]
  4. Rokicki, T.; Bórawski, P.; Bełdycka-Bórawska, A.; Żak, A.; Koszela, G. Development of electromobility in european union countries under covid-19 conditions. Energies 2021, 15, 9. [Google Scholar] [CrossRef]
  5. Yao, L.; Li, X.; Zheng, R.; Zhang, Y. The impact of air pollution perception on urban settlement intentions of young talent in China. Int. J. Environ. Res. Public Health 2022, 19, 1080. [Google Scholar] [CrossRef] [PubMed]
  6. Zeng, Y.; Wang, F.; Wu, J. The impact of green finance on urban haze pollution in China: A technological innovation perspective. Energies 2022, 15, 801. [Google Scholar] [CrossRef]
  7. Giechaskiel, B.; Melas, A.; Martini, G.; Dilara, P.; Ntziachristos, L. Revisiting total particle number measurements for vehicle exhaust regulations. Atmosphere 2022, 13, 155. [Google Scholar] [CrossRef]
  8. Hu, Y.; Zang, Z.; Chen, D.; Ma, X.; Liang, Y.; You, W.; Pan, X.; Wang, L.; Wang, D.; Zhang, Z. Optimization and evaluation of SO2 emissions based on WRF-Chem and 3DVAR data assimilation. Remote Sens. 2022, 14, 220. [Google Scholar] [CrossRef]
  9. Todorov, V.; Dimov, I. Innovative digital stochastic methods for multidimensional sensitivity analysis in air pollution modelling. Mathematics 2022, 10, 2146. [Google Scholar] [CrossRef]
  10. Yin, L.; Wang, L.; Huang, W.; Tian, J.; Liu, S.; Yang, B.; Zheng, W. Haze grading using the convolutional neural networks. Atmosphere 2022, 13, 522. [Google Scholar] [CrossRef]
  11. Jin, X.B.; Gong, W.T.; Kong, J.L.; Bai, Y.T.; Su, T.L. A variational Bayesian deep network with data self-screening layer for massive time-series data forecasting. Entropy 2022, 24, 335. [Google Scholar] [CrossRef] [PubMed]
  12. Nazar, W. Niedoszytko, MAir pollution in Poland: A 2022 narrative review with focus on respiratory diseases. Int. J. Environ. Res. Public Health 2022, 19, 895. [Google Scholar] [CrossRef] [PubMed]
  13. Xu, X.; Yang, H.; Li, C. Theoretical model and actual characteristics of air pollution affecting health cost: A review. Int. J. Environ. Res. Public Health 2022, 19, 3532. [Google Scholar] [CrossRef] [PubMed]
  14. Van Eck, N.; Waltman, L. Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics 2010, 84, 523–538. [Google Scholar] [CrossRef] [PubMed] [Green Version]

Short Biography of Author

Dr. Ling Tim Wong is an associate head and associate professor of the Department of Building Environment and Energy Engineering at The Hong Kong Polytechnic University. He was appointed as the Hong Kong Baptist University’s court member in 2016–2021 and council member in 2022–2023. Dr. Wong earned his bachelor’s degree and Ph.D. from The Hong Kong Polytechnic University in 1992 and 1997, respectively. He was the programme leader of BEng(Hons) in Building Services Engineering (with a specialism in Fire Engineering) in 1997–2002 and BEng (Hons) in Building Sciences and Engineering since 2020, and MSc in Fire and Safety Engineering in 2003–2008. He received the Hans B. Thorelli Award at the 2006 Awards for excellence and, in 2011, the highly commended award winner at the Literati Network Awards for Excellence by Emerald Literati Network. He was ranked in the world’s top 2% of scientists released by Stanford University in 2021 and 2022. His research interests include environmental quality and water and safety systems for the built environment.
Figure 1. Publication volume in 2000–2022 (Web of Science Core Collection).
Figure 1. Publication volume in 2000–2022 (Web of Science Core Collection).
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Figure 2. Publication growth ratio in 2000–2022 (Web of Science Core Collection).
Figure 2. Publication growth ratio in 2000–2022 (Web of Science Core Collection).
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Figure 3. Co-keyword network visualization based on ‘air quality’, ‘air monitoring’, or ‘air pollution’ occurrences.
Figure 3. Co-keyword network visualization based on ‘air quality’, ‘air monitoring’, or ‘air pollution’ occurrences.
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Table 1. Co-occurrence keywords.
Table 1. Co-occurrence keywords.
Cluster 1Cluster 2Cluster 3Cluster 4Cluster 5
temperatureChinaparticulate matterozonehealth
indoor air qualityImpactsemissionsCOVID-19mortality
EnvironmentclimatePM10trendsfine particulate matter
energyclimate changeparticlespredictionrisk
simulationcitiessource apportionmentPM2.5association
systemcityurbanmachine learningdisease
thermal comfortclimate changeaerosolvariabilitylong-term exposure
efficiencygrowthblack carbonremote sensingoxidative stress
volatile organic compoundscarbonaerosolsmodelsinflammation
designvegetationareaaerosol optical depthasthma
indoor air qualityregionchemical-compositionvalidation
buildingsCO2 emissionsdustdeep learning
systemsenergy consumptionultrafine particles
watermanagementpolycyclic aromatic hydrocarbons
removaleconomic growthheavy metals
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Wong, L.T. Air—A New Open Access Journal. Air 2023, 1, 89-93.

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Wong LT. Air—A New Open Access Journal. Air. 2023; 1(1):89-93.

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Wong, Ling Tim. 2023. "Air—A New Open Access Journal" Air 1, no. 1: 89-93.

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