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Sources of particulate-matter air pollution and its oxidative potential in Europe

Abstract

Particulate matter is a component of ambient air pollution that has been linked to millions of annual premature deaths globally1,2,3. Assessments of the chronic and acute effects of particulate matter on human health tend to be based on mass concentration, with particle size and composition also thought to play a part4. Oxidative potential has been suggested to be one of the many possible drivers of the acute health effects of particulate matter, but the link remains uncertain5,6,7,8. Studies investigating the particulate-matter components that manifest an oxidative activity have yielded conflicting results7. In consequence, there is still much to be learned about the sources of particulate matter that may control the oxidative potential concentration7. Here we use field observations and air-quality modelling to quantify the major primary and secondary sources of particulate matter and of oxidative potential in Europe. We find that secondary inorganic components, crustal material and secondary biogenic organic aerosols control the mass concentration of particulate matter. By contrast, oxidative potential concentration is associated mostly with anthropogenic sources, in particular with fine-mode secondary organic aerosols largely from residential biomass burning and coarse-mode metals from vehicular non-exhaust emissions. Our results suggest that mitigation strategies aimed at reducing the mass concentrations of particulate matter alone may not reduce the oxidative potential concentration. If the oxidative potential can be linked to major health impacts, it may be more effective to control specific sources of particulate matter rather than overall particulate mass.

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Fig. 1: PM and OPv sources at rural and urban sites.
Fig. 2: Levels and sources of PM10 and DTTvPM10 in Europe.
Fig. 3: Source-segregated exposures to PM10 and OPvPM10, their dependence on population density, and historical and projected emissions.

Data availability

The full dataset shown in the figures and tables is publicly available at https://doi.org/10.5281/zenodo.4048589Source data are provided with this paper.

Code availability

The standard CAMx model (version 6.3) is an open source model and free to download at http://www.camx.com/. The modified module with split OA sources (PSI-VBS) is available at https://doi.org/10.5281/zenodo.3540826.

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Acknowledgements

We thank the Swiss Federal Office of Environment; Liechtenstein; Ostluft; and the Swiss cantons Basel, Graubünden and Thurgau. We also thank AWEL Zurich for providing us with samples collected in Islisbergtunnel. For the air-quality modelling, we thank the TNO for providing anthropogenic emissions, the European Centre for Medium-Range Weather Forecasts (ECMWF) for access to the meteorological data, National Center for Atmospheric Research (NCAR) for the initial and boundary conditions, the European Environmental Agency (EEA) for the air-quality data and the Swiss National Supercomputing Centre (CSCS). The support by Ramboll for the CAMx model is gratefully acknowledged. We acknowledge the Swiss Federal Laboratories for Materials Science and Technology (Empa) and the National Air Pollution Monitoring Network (NABEL) for providing air-quality data. We acknowledge the use of country borders from https://thematicmapping.org/downloads/world_borders.php shared under a Creative Commons Attribution-Share Alike license. We thank N. Marchand for scientific discussions. K.R.D. acknowledges support by VULCAIN and Swiss National Science Foundation mobility grant P2EZP2_181599. M.G. and J.D. acknowledge financial support by the Swiss National Science Foundation grant CR32I3_166325. A.A. and O.F. acknowledge financial support by the French Ministry of Environment. G.U. and J.L.J. thank the programmes LEFE CHAT (grant 863353), LABEX OSUG@2020 (ANR-10-LABX-56), ANR-19-CE34-0002-01, and the " Investissements d’avenir” programme (ANR-15-IDEX-02) for supporting this work and funding analytical instruments.

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Authors

Contributions

K.R.D., I.E.H. and A.S.H.P. designed the research. G.U. performed the OP measurements. K.R.D., G.S. and A.V. performed the offline aerosol mass spectrometer measurements. J.-L.J. measured the organic markers. A.A. measured the polyaromatic hydrocarbons and oxy-polyaromatic hydrocarbons (quinones). Z.L., L.-E.C. and M.G. performed the toxicological experiments. J.J., S.A., K.R.D. and I.E.H. performed the air-quality and OP modelling. A.S. and M.S. performed the TNO model runs. J.J.P.K. and M.S. provided emission data. S.W., O.F. and G.U. provided OP data for model validation. F.C. provided analytical software for source apportionment. K.R.D., J.J. and I.E.H. performed the data analysis. K.R.D., G.U., J.J., L.-E.C., A.V., G.S., F.C., A.S., M.S., A.A., S.A., J.D., U.B., I.E.H., J.-L.J. and A.S.H.P. interpreted the results and wrote the manuscript.

Corresponding authors

Correspondence to Jianhui Jiang, Imad El Haddad or André S. H. Prévôt.

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The authors declare no competing interests.

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Peer review information Nature thanks Flemming Cassee, Ally Lewis and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Fig. 1 Seasonal variability of SOA types, their markers and quinones.

Concentration time series (every fourth day) of bioSOA and the sum of 3-MBTCA and pinic acid (both oxidation products of terpenes), aSOA and phthalic acid (an oxidation product of naphthalene and methyl-naphthalenes), as well as time series (bimonthly) of the mass fraction of polyaromatic quinones of OA in Frauenfeld..

Source data.

Extended Data Fig. 2 Cellular response to exposure to PM with varying OP activity.

Comparison between IL-6 release (fold change to field blank) in re-differentiated human bronchial epithelia exposed to filter extracts and DTT activity of the deposited PM per cell surface. The error bars represent the standard error of replicate experiments. The relative errors for DTT per cell surface are in comparison to IL-6 release five to ten times smaller and are therefore not displayed. A linear regression (IL-6 release = (1.17 ± 0.14) × DTT per cell surface + (0.28 ± 0.07)) is displayed (grey line) with 95% confidence interval (grey-shaded area). More detailed comparisons between the cellular responses to deposited PM are presented in Leni et al.103..

Source data.

Extended Data Fig. 3 DCFHv sources at rural and urban measurement sites.

Contributions of metal (crustal, vehicular wear, residential heating) and OA (SCOA, HOA, COA, BBOA, aSOA, bioSOA) sources and other PM components to \({{\rm{DCFH}}}_{{\rm{PM}}10}^{{\rm{v}}}\) and \({{\rm{DCFH}}}_{{\rm{PM}}2.5}^{{\rm{v}}}\) at five sites with different emission characteristics (109 composite samples): a and f, urban roadside (ber, Bern Bollwerk); b and g, urban background (zue, Zurich Kaserne); c and h, rural background (pay, Payerne MeteoSuisse); d, rural alpine valley (mag, Magadino-Cadenazzo); e, wintertime pollution episode in alpine valley (vi, S. Vittore Center); i, DCHFm of contributing metal and OA sources; and j, comparison between modelled (mod.) and measured DCHFv..

Source data.

Extended Data Fig. 4 AAv sources at rural and urban measurement sites.

Contributions of metal (crustal, vehicular wear, residential heating) and OA (SCOA, HOA, COA, BBOA, aSOA, bioSOA) sources and other PM components to \({{\rm{AA}}}_{{\rm{PM}}10}^{{\rm{v}}}\) and \({{\rm{AA}}}_{{\rm{PM}}2.5}^{{\rm{v}}}\) at five sites with different emission characteristics (109 composite samples): a and f, urban roadside; b and g, urban background; c and h, rural background; d, rural alpine valley; e, wintertime pollution episode in alpine valley; i, AAv of contributing metal and OA sources; and j, comparison between modelled and measured AAv..

Source data.

Extended Data Fig. 5 Source contributions to different OPv assays.

The modelled total \({{\rm{DTT}}}_{{\rm{PM}}10}^{{\rm{v}}},{\,{\rm{DCFH}}}_{{\rm{PM}}10}^{{\rm{v}}}{\,{\rm{and}}\,{\rm{AA}}}_{{\rm{PM}}10}^{{\rm{v}}}\) (top row) and the contributions of the relevant sources (lower rows) (chosen in the multiple linear regression model): traffic POA (HOA), biomass-burning POA (BBOA), biogenic SOA (bioSOA), anthropogenic SOA (aSOA), coarse organic vehicular emissions (SCOA), and coarse inorganic/metal vehicular emissions (vehicular wear)..

Source data.

Extended Data Fig. 6 Validation of OPv modelling results.

Detailed comparison between measured and modelled OP at Lens for the three essays investigated here (DTT, DCFH and AA). a, location of Lens along with the dominating DTT source; b, modelled contributions of the different sources to OP; and c, comparison between modelled and measured OP..

Source data.

Extended Data Fig. 7 Largest contributing sources to OPv and PM mass concentrations in Europe.

Largest contributors to OPv in PM10 (DTTv, DCFHv, AAv) in each grid cell over land surface in the modelled area for PM10 and PM2.5..

Source data.

Extended Data Fig. 8 Source contributions to PM and OP exposure, and their dependence on population density for PM2.5 and OPvPM2.5.

a, Contributions of aerosol sources and components to the total PM, DTT, DCFH and AA exposure for both PM10 and PM2.5 in Europe (relative contributions to the respective exposure and absolute exposure, copied from Fig. 3a). Exposures are computed as population integrated amount of OPv or PM in inhaled ambient air accumulated over a full year. Error bars depict the range between the 25% and 75% quartiles obtained from the Monte Carlo analysis propagating the uncertainty of OPm of the single sources from the multiple linear regression model. b, \({{\rm{DTT}}}_{{\rm{PM}}2.5}^{{\rm{v}}}/{{\rm{PM}}}_{2.5}\) and population (top), PM2.5 concentrations and relative source contributions to PM2.5 (middle), and relative source contributions to \({{\rm{DTT}}}_{{\rm{PM}}2.5}^{{\rm{v}}}\) and \({{\rm{DTT}}}_{{\rm{PM}}2.5}^{{\rm{v}}}\) (bottom) in comparison to the population density in the modelled domain. We note that data for PM2.5 are shown here, while Fig. 3b shows data for PM10.

Source data.

Supplementary information

Supplementary Information

This file contains Supplementary Tables 1-3 and Supplementary Figures 1-30.

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Daellenbach, K.R., Uzu, G., Jiang, J. et al. Sources of particulate-matter air pollution and its oxidative potential in Europe. Nature 587, 414–419 (2020). https://doi.org/10.1038/s41586-020-2902-8

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