Estimating ground-level particulate matter concentrations using satellite-based data: a review |
| |
| Authors: | Minso Shin Yoojin Kang Seohui Park Cheolhee Yoo Lindi J Quackenbush |
| |
| Institution: | 1. School of Urban &2. Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea https://orcid.org/0000-0003-3051-9746;3. Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of Koreahttps://orcid.org/0000-0003-3006-6994;4. Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea;5. Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of Koreahttps://orcid.org/0000-0002-3922-2300;6. Environmental Resources Engineering, College of Environmental Science and Forestry, State University of New York, Syracuse, NY, USA |
| |
| Abstract: | ABSTRACTParticulate matter (PM) is a widely used indicator of air quality. Satellite-derived aerosol products such as aerosol optical depth (AOD) have been a useful source of data for ground-level PM monitoring. However, satellite-based approaches for PM monitoring have limitations such as impacts of cloud cover. Recently, many studies have documented advances in modeling for monitoring PM over the globe. This review examines recent papers on ground-level PM monitoring for the past 10 years focusing on modeling techniques, sensor types, and areas. Satellite-based retrievals of AOD and commonly used approaches for estimating PM concentrations are also briefly reviewed. Research trends and challenges are discussed based on the review of 130 papers. The limitations and challenges include spatiotemporal scale issues, missing values in satellite-based variables, sparse distribution of ground stations for calibration and validation, unbalanced distribution of PM concentrations, and difficulty in the operational use of satellite-based PM estimation models. The literature review suggests there is room for further investigating: 1) the spatial extension of PM monitoring to global scale; 2) the synergistic use of satellite-derived products and numerical model output to improve PM estimation accuracy, gap-filling, and operational monitoring; 3) the use of more advanced modeling techniques including data assimilations; 4) the improvement of emission data quality; and 5) short-term (hours to days) PM forecasts through combining satellite data and numerical forecast model results. |
| |
| Keywords: | Particulate matter aerosol air quality monitoring |
|
|
![ORCID Icon]({"type":"image","src":"/templates/jsp/images/orcid.png"} "ORCID Icon")
https://orcid.org/0000-0003-3051-9746;3. Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea