The Application of Remote Sensing in Sustainable Air Quality Monitoring

Dear Colleagues,

According to the World Health Organization (WHO), air pollution has become a global environmental burden, with 92% of the world’s population currently living in areas where the air quality level exceeds the WHO guideline level of 10μg m⁻³; about 3 million annual deaths worldwide are related to outdoor air pollution. Outdoor air pollution also contributes to cardiovascular and respiratory deaths, especially in densely populated regions (Yan et al., 2019). In order to prevent further worsening of air pollution, protect public health, and reduce economic losses, many countries have taken significant measures to improve their air quality. Ground-based monitoring sites have been monitored around the world, but the spatial coverage is lacking and inhomogeneous. To overcome this issue, space-borne remote sensing has been widely used to obtain spatially continuous air quality information (Cao et al., 2018). However, many of these studies focus on data fusion or reconstruction rather than monitoring, which is vital for providing timely and detailed air quality information.

Recently, a new aerosol component approach has been proposed and developed for globally monitoring size-resolved aerosol composition species (such as, black carbon, brown carbon, mineral dust, fine- and coarse-mode non-absorbing soluble and insoluble, etc.) from multi-angle polarization satellite measurements (POLDER/PARASOL, DPC/GF-5) (Li et al., 2019, 2020, 2021). Multi-angle polarization satellite measurements provide the potential to derive extensive aerosol chemical composition and optical properties on a global/regional scale.

The past years have seen an increased interest in improving the overall accuracy and accelerating the computational process for air quality monitoring. Advanced techniques such as machine learning aim at extracting complex nonlinear relationships between variables and have been intensively used in air quality monitoring (Yan et al., 2021). Joint efforts from both physical-based methods and machine learning-based methods are also required to optimize air quality monitoring.

Therefore, this Special Issue is organized to develop and advance air quality monitoring at an urban, national, or global scale through remote sensing. The submissions are encouraged from interdisciplinary fields (environment science, atmospheric science, engineering, health, and other scientific areas) focused on improving the accuracy, practicability, and innovative approaches to air quality monitoring.

Reference:

Cao, S., Zhao, W., Guan, H., Hu, D., Mo, Y., Zhao, W., and Li, S. (2018). Comparison of remotely sensed PM_2.5 concentrations between developed and developing countries: results from the US, Europe, China, and India. Journal of Cleaner Production, 182, 672-681.

Li, L., Che H., X. Zhang, C. Chen, X. Chen, K. Gui, Y. Liang, F. Wang, Y. Derimian, D. Fuertes, O. Dubovik, Y. Zheng, L. Zhang, B. Guo, Y. Wang, X.  Zhang, (2021). A satellite-measured view of aerosol component content and optical property in a haze-polluted case over North China Plain, Atmospheric Research, 105958, doi:10.1016/j.atmosres.2021.105958

Li, L., Che, H., Derimian, Y., Dubovik, O., Schuster, G.L, Chen, C., Li, Q., Wang, Y., Guo, B., Zhang, X., (2020). Retrievals of fine mode light-absorbing carbonaceous aerosols from POLDER/PARASOL observations over East and South Asia. Remote Sensing of Environment, 247, 111913, doi:10.1016/j.rse.2020.111913

Li, L., Dubovik, O., Derimian, Y., Schuster, G.L., Lapyonok, T., Litvinov, P., Ducos, F., Fuertes, D., Chen, C., Li, Z., Lopatin, A., Torres, B., Che, H., (2019). Retrieval of aerosol components directly from the satellite and ground-based measurements. Atmos. Chem. Phys. 19, 13409–13443, doi:10.5194/acp-19-13409-2019

Yan, M., Wilson, A., Bell, M.L., Peng, R.D., Sun, Q., Pu, W., Yin, X., Li, T. and Anderson, G.B. (2019). The shape of the concentration-response association between fine particulate matter pollution and human mortality in Beijing, China, and its implications for health impact assessment. Environmental Health Perspectives, 127(6), p.067007.

Yan, X., Zang, Z., Jiang, Y., Shi, W., Guo, Y., Li, D., Zhao, C., & Husi, L. (2021). A Spatial-Temporal Interpretable Deep Learning Model for improving interpretability and predictive accuracy of satellite-based PM2.5. Environmental Pollution, 273, 116459.

Dr. Xing Yan
Dr. Lei Li
Dr. Shisong Cao
Dr. Meilin Yan
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• real-time air quality monitoring
• remote sensing
• machine learning
• air pollution at urban, national, or global scales
• applications of air quality monitoring data
• air pollution exposure assessment
• environmental health research
• multi-angle polarization satellite measurements
• aerosol component retrieval