Speaker
Description
Aerosol pollution in urban areas poses a serious threat to human health (Brunekreef and Holgate 2002). In the last decades, the possibility of estimating the aerosol mass concentration at ground level (Particulate Matter, PM) from satellite observations of optical properties (Aerosol Optical Depth, AOD) was thoroughly investigated (eg. Hoff and Christopher 2009; Sorek-Hamer, Chatfield, and Liu 2020) with the aim of enhancing the monitoring of aerosol levels and assist health studies about the effects of aerosol exposure. A variety of methodologies have been validated and are available, such as statistical methods (Ma et al. 2022) and machine learning (Unik et al. 2023). These approaches are useful to resolve issues such as missing satellite retrievals and different spatio-temporal resolution between ground-based and satellite observations, but require large datasets to be trained and the physical interpretation of the results is usually not straightforward.
A physical approach stems from the theoretical relationship between the aerosol mass concentration and the aerosol optical properties and is able to take into account specific factors such as the aerosol vertical profile over urban areas, their average chemical composition and relative optical properties. In particular, aerosols are characterized by different hygroscopic properties (i.e., particle growth due to ambient humidity) depending on their chemical composition.
The first part of this study shows experimental evidences that linearly correlate satellite AOD at high spatial resolution and ground-based PM2.5 measurements, by using a semi-empirical methodology. PM measurements were carried out in the city of Bologna.
The Po Valley, where Bologna is located, is recognized as one of the most polluted areas in Europe due to the high pollutant emissions combined with morphological and local orographic characteristics that favour atmospheric stability, and has already been identified as a target of interest for this type of studies (e.g. Arvani et al. 2016; Ferrero et al. 2019). In the second part of this study, we attempt to assess the sensitivity of the theoretical equations to specific variables, such as the Planetary Boundary Layer, the Relative Humidity at ground-level, the Effective Radius of the aerosols, and the aerosol optical properties.
References:
Arvani, Barbara, R. Bradley Pierce, Alexei I. Lyapustin, Yujie Wang, Grazia Ghermandi, and Sergio Teggi. 2016. ‘Seasonal Monitoring and Estimation of Regional Aerosol Distribution over Po Valley, Northern Italy, Using a High-Resolution MAIAC Product’. Atmospheric Environment 141 (September):106–21. https://doi.org/10.1016/j.atmosenv.2016.06.037.
Brunekreef, Bert, and Stephen T. Holgate. 2002. ‘Air Pollution and Health’. The Lancet 360 (9341): 1233–42. https://doi.org/10.1016/S0140-6736(02)11274-8.
Ferrero, L., A. Riccio, B. S. Ferrini, L. D’Angelo, G. Rovelli, M. Casati, F. Angelini, et al. 2019. ‘Satellite AOD Conversion into Ground PM10, PM2.5 and PM1 over the Po Valley (Milan, Italy) Exploiting Information on Aerosol Vertical Profiles, Chemistry, Hygroscopicity and Meteorology’. Atmospheric Pollution Research 10 (6): 1895–1912. https://doi.org/10.1016/j.apr.2019.08.003.
Hoff, Raymond M., and Sundar A. Christopher. 2009. ‘Remote Sensing of Particulate Pollution from Space: Have We Reached the Promised Land?’ Journal of the Air & Waste Management Association 59 (6): 645–75. https://doi.org/10.3155/1047-3289.59.6.645.
Ma, Zongwei, Sagnik Dey, Sundar Christopher, Riyang Liu, Jun Bi, Palak Balyan, and Yang Liu. 2022. ‘A Review of Statistical Methods Used for Developing Large-Scale and Long-Term PM2.5 Models from Satellite Data’. Remote Sensing of Environment 269 (February):112827. https://doi.org/10.1016/j.rse.2021.112827.
Sorek-Hamer, Meytar, Robert Chatfield, and Yang Liu. 2020. ‘Review: Strategies for Using Satellite-Based Products in Modeling PM2.5 and Short-Term Pollution Episodes’. Environment International 144 (November):106057. https://doi.org/10.1016/j.envint.2020.106057.
Unik, Mitra, Imas Sitanggang, Lailan Syaufina, and I Nengah Jaya. 2023. ‘PM2.5 Estimation Using Machine Learning Models and Satellite Data: A Literature Review’. International Journal of Computer Science and Applications 14 (June):2023.
https://doi.org/10.14569/IJACSA.2023.0140538.
