AURORA

from Air pollUtion souRces tO moRtAlity

Started
July 1, 2021
Status
In Progress
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Abstract

Atmospheric aerosols (particulate matter, PM) are liquid or solid particles suspended in the air with diameters ranging from a few nanometers to tens of micrometers. Poor air quality associated with high levels of PM is a major public health problem, and is one of the five leading causes of premature deaths worldwide, alongside high blood pressure, smoking, diabetes, and obesity.

Human exposure to PM caused ~8.9 million deaths, or ~10% of the total global mortality burden in 2015, more than car accidents, HIV, and malaria combined. Without any action, these numbers are expected to double by 2050.

PM health effects can be acute and chronic and have been associated with cardiovascular diseases, respiratory symptoms, cancer, diabetes, sudden infant mortality, and neurodegenerative diseases (upon penetrating the blood-brain barrier). The magnitude of the association between PM exposure and the probability of death is based on the total PM mass, while PM’s health effects are strongly driven by its chemical composition and size, and hence its origin.

PM originates from natural (e.g. volcanos, pollen) or anthropogenic (e.g. combustion) sources, and can be primary from direct emissions (e.g. metals from vehicular wear) or secondary, formed in the atmosphere through complex oxidation mechanisms of gaseous precursors (e.g. from trees, car/industrial exhaust, residential heating).

Our ability to identify the major PM sources responsible for health outcomes is a two-fold challenge that requires (1) a fundamental understanding of PM emissions and formation processes and (2) the consideration of the high diversity and spatial heterogeneity of PM emissions, especially in urban settings where most of the population resides.

AURORA unifies the expertise from distinct fields of science, including analytical & atmospheric chemistry, numerical modeling, epidemiology, and medical science, to propose an innovative modeling framework, which integrates data science, geo-statistics, and process-based simulations to achieve a unique combination of source specificity, spatial and temporal coverage and resolution required for human exposure assessments. Model outputs will be combined with invaluable records of acute and chronic diseases developed and maintained over the course of 30 years to derive the pathogenicity of PM sources and their contribution to different health outcomes on a European scale.

People

Collaborators

SDSC Team:
Daniel Trejo Banos
Ekaterina Krymova
Guillaume Obozinski
Yun Cheng

PI | Partners:

PSI, Laboratory of Atmospheric Chemistry:

  • Dr. Imad el Haddad
  • Dr. Kaspar Rudolf Daellenbach
  • Dr. Petros Vasiliakos
  • Dr. Upadhyay Abhishek Kumar

More info

Swiss Tropical and Public Health Institute:

  • Prof. Nicole Probst-Hensch
  • Dr. Kees de Hoogh
  • Dr. Danielle Vienneau

More info

description

Problem:

AURORA will link for the first time single PM emission sources and formation processes to health effects. A data-science-based ensemble model will be developed combining both geostatistical data and CTM outputs to produce source-specific, fine-resolution, PM concentration fields for large scales and long-terms, suitable for assessing human exposure to source-specific PM.

Impact:

Aurora has tangible impacts on air quality, public health, and the economy. The identification of the sources of harmful components in PM air pollution is the Holy Grail for atmospheric scientists, air quality modelers, and epidemiologists. Aurora will enhance the fundamental understanding of key emissions and processes controlling PM concentrations, chemical composition and noxiousness, and their sensitivity to changes in energy and land use that cities are currently experiencing.

Proposed approach:

None of the existing approaches fulfills the requirements to relate PM sources and complex formation processes to its health effects. This is an opportune moment for atmospheric chemists, numerical modelers, data scientists, exposure scientists, and epidemiologists to work together to develop such an approach.

The goal is set to go beyond linear dimension reduction techniques currently used in the field to better represent the chemical complexity of the atmospheric system and the interdependence between sources.

We will implement new techniques to efficiently explore the parameter space of our numerical model and optimize them using field observations. The developed machine learning techniques will help to:

  1. generate fine-resolution PM concentration fields from different sources and processes using coarse resolution CTM outputs and geostatistical data, and
  2. infer relationships between source-specific PM concentrations and mortality or diseases.

If the model performance is satisfactory, we could predict the PM sources that contribute most to lung diseases and mortality in Europe.

Gallery

Figure 1: Levels and sources of PM10 and DTTvPM10 in Europe.
Figure 2: from Daellenbach et al, Nature, 2020.

Annexe

Additional resources

Bibliography

  1. Daellenbach, Kaspar R., et al. “Sources of particulate-matter air pollution and its oxidative potential in Europe.” Nature587.7834 (2020): 414-419.
  2. Chen, Gang, et al. “European Aerosol Phenomenology-8: Harmonised Source Apportionment of Organic Aerosol using 22 Year-long ACSM/AMS Datasets.” Environment International (2022): 107325.
  3. Daellenbach, K. R.; Bozzetti, C.; Křepelová, A.; Canonaco, F.; Wolf, R.; Zotter, P.; Fermo, P.; Crippa, M.; Slowik, J. G.; Sosedova, Y.; Zhang, Y.; Huang, R. J.; Poulain, L.; Szidat, S.; Baltensperger, U.; El Haddad, I.; Prévôt, A. S. H., “Characterization and source apportionment of organic aerosol using offline aerosol mass spectrometry”. Atmos. Meas. Tech. 2016, 9 (1), 23-39.

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