Study provides first global view of the unequal spatiotemporal distribution of PM2.5 exposure


“Short-term exposure to ambient PM2.5 is a leading contributor to the global burden of diseases and mortality. However, few studies have provided the global spatiotemporal variations of daily PM2.5 concentrations over recent decades.”


"In this modelling study, we implemented deep ensemble machine learning (DEML) to estimate global daily ambient PM2.5 concentrations at 0.1° × 0.1° spatial resolution between Jan 1, 2000, and Dec 31, 2019.

In the DEML framework, ground-based PM2.5 measurements from 5446 monitoring stations in 65 countries worldwide were combined with GEOS-Chem chemical transport model simulations of PM2.5 concentration, meteorological data, and geographical features.

At the global and regional levels, we investigated annual population-weighted PM2.5 concentrations and annual population-weighted exposed days to PM2.5 concentrations higher than 15 μg/m3 (2021 WHO daily limit) to assess spatiotemporal exposure in 2000, 2010, and 2019.

Land area and population exposures to PM2.5 above 5 μg/m3 (2021 WHO annual limit) were also assessed for the year 2019. PM2.5 concentrations for each calendar month were averaged across the 20-year period to investigate global seasonal patterns."

:point_right: Read full research study


Thank you very much for sharing this article Sebastian.
It was interesting to read.
Also, important to see that the authors underlined the problems with possible data assessment gaps due to: “(…) sparse ground station distribution in most regions.” (p. 217).
The unequal side of air quality on Earth is multifold.


re:: “(…) sparse ground station distribution in most regions”

If citizens themselves install data points via their own sensors on their houses, the network of data points can be more finely meshed and meaningful, provided that the comparability of the data quality is guaranteed.

Both :link: Fab Lab Barcelona as well as :link: Lichen Social Innovation recently shared with us proof that this is possible.

Why don’t municipalities simply roll out the allocation of sensors calibrated according to the EU / municipal / legislative requirements? - What is your estimate and/or experience @Stakeholders @CitizensTestingGroup ?