Aerosol optical depth (AOD) (ground and satellite based) regime over Megacities and possible links with their
population changes.
Trainer: Stelios Kazadzis
Introduction: About 55% of the world’s population resides in urban areas and this number is projected to increase
to 70% by 2050 (UN, 2019). This population growth in cities raises urgent and critical environmental issues, such as
air quality (WHO, 2021) and its degradation, which is known to be related to increased morbidity and mortality rates.
In particular, air pollution in cities constituted the 4th leading risk factor for early death on a global scale (HEI, 2020)
in 2019. The worst pollutant affecting megacities is suspended particulate matter or aerosols, which can be
quantified in optical terms using AOD. The latter is the most comprehensive variable for assessing the aerosol load
of the atmospheric column. In a recent study by Papachristopoulou et al. (2022) the state of urban aerosols in 81
cities with a population over 5 million was investigated. This was based on AOD from satellite measurements, with
a fine spatial and temporal resolution (0.1°, daily), and over an 18-year period (2003 – 2020). An AOD decrease was
found for US/Canadian, European, and East Asian cities. For Chinese cities although they were found to have the
highest aerosol loads, they also have the highest AOD decrease, in response to the rigorous emission control
measures implemented in the country, especially after 2010. The highest AOD increase was found in Indian cities,
reflecting the increasing urbanization and industrialization of the country.
Idea: The idea is to use ground based (e.g. AERONET) or/and spaceborne (e.g. MODIS) measurements of AOD for
different megacities of the world and to investigate possible links between changes in cities’ aerosol loads and their
population and emissions.
Datasets ready to use:
• MODIS Aqua AOD combined product, and Dust optical depth (DOD) product, the MIDAS dataset (Gkikas et
al., 2021), daily values for the period 2003-2020, global coverage at high spatial resolution (0.1°).
• For 27 out of the 81 cities AERONET level 2, version 3 daily mean product of AOD at 500 nm for stations with
at least 8 years of data within the study period and Ångström exponent at 440–870 nm.
• From UN collection of datasets (https://population.un.org/wup/), for the 81 cities their population estimates
and projections in thousands of inhabitants in an annual base for a long time period (1950-2035) (UN 2018).
• The Gridded Population of the World, Version 4 (GPWv4) (CIESIN 2018) dataset. Provides for the years 2000,
2005, 2010, 2015, and 2020 gridded estimates of human population counts (number of persons per pixel) at
a global scale consistent with national censuses and population registers with respect to relative spatial
distribution (aggregated to 2.5 arc-minute (~5km)).
Suggested Tasks:
Temporal variability:
1. Calculate the MODIS AOD trends (for megacities or Cities with the greatest population growth). Try to link
the results for selected cases with information about emissions for the selected city.
2. Calculate the AERONET AOD trends for cities with available time series for at least 8 years.
3. Calculate the population changes for the same period.
4. Try to find links between AOD and population trends.
5. Investigate the dust contribution using the MIDAS DOD product and filter the dust affected cities.
6. Explore possible emission time series from the literature.
Spatial variability:
1. Calculate the geographical distribution (for cities with adequate satellite data availability at 0.1o pixel) of
changes gridded estimates of human population counts for the years 2000, 2005, 2010, 2015, and 2020
(possible spatial extend).
2. Find the corresponding changes in AOD to investigate if the AOD changes are related spatially with the city’s
expansion.
Extra: find cities with more than 1 available AOD ground based stations and relate the spatial variability with those
from high spatial resolution satellite data and gridded population data. Of course any other interesting ideas from the people who will work on the topic are welcome.
References:
Gkikas, A., Proestakis, E., Amiridis, V., Kazadzis, S., Di Tomaso, E., Tsekeri, A., Marinou, E., Hatzianastassiou,
N., and Pérez García-Pando, C.: ModIs Dust AeroSol (MIDAS): a global fine-resolution dust optical depth data
set, Atmos. Meas. Tech., 14, 309–334, https://doi.org/10.5194/amt-14-309-2021 , 2021.
Health Effects Institute (HEI): State of Global Air 2020: 2020, Special Report, Boston, MA, Health Effects
Institute, Boston, ISSN 2578-6873.
Papachristopoulou, K., et al.: 2022, Aerosol optical depth regime over megacities of the world, Atmos. Chem.
Phys., 22, 15703-15727, https://doi.org/10.5194/acp-22-15703-2022
United Nations, Department of Economic and Social Affairs, P. D.: World Urbanization Prospects: The 2018
Revision, Online Edition, https://population.un.org/wup/Download/, 2018b.
United Nations. (2019). World Urbanization Prospects 2018: The 2018 Revision. In (ST/ESA/SER.A/420).
Department of Economic and Social Affairs, Population Division.
https://population.un.org/wup/Publications/Files/WUP2018-Report.pdf
World Health Organization (WHO): 2021, WHO global air quality guidelines: particulate matter (PM2.5 and
PM10), ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide, WHO,
https://apps.who.int/iris/handle/10665/345329