Air—A New Open Access Journal
- 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.
Conflicts of Interest
References
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- Todorov, V.; Dimov, I. Innovative digital stochastic methods for multidimensional sensitivity analysis in air pollution modelling. Mathematics 2022, 10, 2146. [Google Scholar] [CrossRef]
- 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]
- 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]
- 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]
- 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]
- 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
Cluster 1 | Cluster 2 | Cluster 3 | Cluster 4 | Cluster 5 |
---|---|---|---|---|
Performance | Impact | PM2.5 | model | exposure |
temperature | China | particulate matter | ozone | health |
indoor air quality | Impacts | emissions | COVID-19 | mortality |
Environment | climate | PM10 | trends | fine particulate matter |
energy | climate change | particles | prediction | risk |
simulation | cities | source apportionment | PM2.5 | association |
system | city | urban | machine learning | disease |
thermal comfort | climate change | aerosol | variability | long-term exposure |
ventilation | urbanization | transport | NO2 | children |
efficiency | growth | black carbon | remote sensing | oxidative stress |
volatile organic compounds | carbon | aerosols | models | inflammation |
design | vegetation | area | aerosol optical depth | asthma |
optimization | consumption | deposition | MODIS | |
indoor air quality | region | chemical-composition | validation | |
buildings | CO2 emissions | dust | deep learning | |
parameters | dynamics | emission | algorithm | |
systems | energy consumption | ultrafine particles | ||
water | management | polycyclic aromatic hydrocarbons | ||
removal | economic growth | heavy metals | ||
humidity | policy | identification | ||
CO2 | ||||
NOX | ||||
sustainability | ||||
flow | ||||
dispersion | ||||
combustion | ||||
behaviour | ||||
kinetics |
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© 2023 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Wong, L.T. Air—A New Open Access Journal. Air 2023, 1, 89-93. https://doi.org/10.3390/air1010007
Wong LT. Air—A New Open Access Journal. Air. 2023; 1(1):89-93. https://doi.org/10.3390/air1010007
Chicago/Turabian StyleWong, Ling Tim. 2023. "Air—A New Open Access Journal" Air 1, no. 1: 89-93. https://doi.org/10.3390/air1010007