Eurodelta multi-model simulated and observed particulate matter trends in Europe in the period of 1990–2010
<p>The Eurodelta-Trends (EDT) multi-model experiment, aimed at assessing the efficiency of emission mitigation measures in improving air quality in Europe during 1990–2010, was designed to answer a series of questions regarding European pollution trends; i.e. were there significant trends dete...
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Copernicus Publications
2022-06-01
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| Series: | Atmospheric Chemistry and Physics |
| Online Access: | https://acp.copernicus.org/articles/22/7207/2022/acp-22-7207-2022.pdf |
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| author | S. Tsyro W. Aas A. Colette C. Andersson B. Bessagnet B. Bessagnet G. Ciarelli F. Couvidat K. Cuvelier K. Cuvelier A. Manders K. Mar M. Mircea N. Otero N. Otero M.-T. Pay V. Raffort Y. Roustan M. R. Theobald M. G. Vivanco H. Fagerli P. Wind P. Wind G. Briganti A. Cappelletti M. D'Isidoro M. Adani |
| author_facet | S. Tsyro W. Aas A. Colette C. Andersson B. Bessagnet B. Bessagnet G. Ciarelli F. Couvidat K. Cuvelier K. Cuvelier A. Manders K. Mar M. Mircea N. Otero N. Otero M.-T. Pay V. Raffort Y. Roustan M. R. Theobald M. G. Vivanco H. Fagerli P. Wind P. Wind G. Briganti A. Cappelletti M. D'Isidoro M. Adani |
| author_sort | S. Tsyro |
| collection | DOAJ |
| description | <p>The Eurodelta-Trends (EDT) multi-model experiment, aimed at assessing the efficiency of emission mitigation measures in improving air quality in Europe during 1990–2010, was designed to answer a series of questions regarding European pollution trends; i.e. were there significant trends detected by observations? Do the models manage to reproduce observed trends? How close is the agreement between the models and how large are the deviations from observations? In this paper, we address these issues with respect to particulate matter (PM) pollution. An in-depth trend analysis has been performed for PM<span class="inline-formula"><sub>10</sub></span> and PM<span class="inline-formula"><sub>2.5</sub></span> for the period of 2000–2010, based on results from six chemical transport models and observational data from the EMEP (Cooperative Programme for Monitoring and Evaluation of the Long-range Transmission of Air Pollutants in Europe) monitoring network. Given harmonization of set-up and main input data, the differences in model results should mainly result from differences in the process formulations within the models themselves, and the spread in the model-simulated trends could be regarded as an indicator for modelling uncertainty.</p>
<p>The model ensemble simulations indicate overall decreasing trends in PM<span class="inline-formula"><sub>10</sub></span> and PM<span class="inline-formula"><sub>2.5</sub></span> from 2000 to 2010, with the total reductions of annual mean concentrations by between 2 and 5 (7 for PM<span class="inline-formula"><sub>10</sub></span>) <span class="inline-formula">µ</span>g m<span class="inline-formula"><sup>−3</sup></span> (or between 10 % and 30 %) across most of Europe (by 0.5–2 <span class="inline-formula">µ</span>g m<span class="inline-formula"><sup>−3</sup></span> in Fennoscandia, the north-west of Russia and eastern Europe) during the studied period. Compared to PM<span class="inline-formula"><sub>2.5</sub></span>, relative PM<span class="inline-formula"><sub>10</sub></span> trends are weaker due to large inter-annual variability of natural coarse PM within the former. The changes in
the concentrations of PM individual components are in general consistent with emission reductions. There is reasonable agreement in PM trends estimated by the individual models, with the inter-model variability below 30 %–40 % over most of Europe, increasing to 50 %–60 % in the northern and eastern parts of the EDT domain.</p>
<p>Averaged over measurement sites (26 for PM<span class="inline-formula"><sub>10</sub></span> and 13 for PM<span class="inline-formula"><sub>2.5</sub></span>), the mean ensemble-simulated trends are <span class="inline-formula">−0.24</span> and <span class="inline-formula">−0.22</span> <span class="inline-formula">µ</span>g m<span class="inline-formula"><sup>−3</sup></span> yr<span class="inline-formula"><sup>−1</sup></span> for PM<span class="inline-formula"><sub>10</sub></span> and PM<span class="inline-formula"><sub>2.5</sub></span>, which are somewhat weaker than the observed trends of <span class="inline-formula">−0.35</span> and <span class="inline-formula">−0.40</span> <span class="inline-formula">µ</span>g m<span class="inline-formula"><sup>−3</sup></span> yr<span class="inline-formula"><sup>−1</sup></span> respectively, partly due to model underestimation of PM concentrations. The correspondence is better in relative PM<span class="inline-formula"><sub>10</sub></span> and PM<span class="inline-formula"><sub>2.5</sub></span> trends, which are <span class="inline-formula">−1.7</span> % yr<span class="inline-formula"><sup>−1</sup></span> and <span class="inline-formula">−2.0</span> % yr<span class="inline-formula"><sup>−1</sup></span> from the model ensemble and <span class="inline-formula">−2.1</span> % yr<span class="inline-formula"><sup>−1</sup></span> and <span class="inline-formula">−2.9</span> % yr<span class="inline-formula"><sup>−1</sup></span> from the observations respectively. The observations identify significant trends (at the 95 % confidence level) for PM<span class="inline-formula"><sub>10</sub></span> at 56 % of the sites and for PM<span class="inline-formula"><sub>2.5</sub></span> at 36 % of the sites, which is somewhat less that the fractions of significant modelled trends. Further, we find somewhat smaller spatial variability of modelled PM trends with respect to the observed ones across Europe and also within individual countries.</p>
<p>The strongest decreasing PM trends and the largest number of sites with significant trends are found for the summer season, according to both the model ensemble and observations. The winter PM trends are very weak and mostly insignificant. Important reasons for that are the very modest reductions and even increases in the emissions of primary PM from residential heating in winter. It should be kept in mind that all findings regarding modelled versus observed PM trends are limited to the regions where the sites are located.</p>
<p>The analysis reveals considerable variability of the role of the individual aerosols in PM<span class="inline-formula"><sub>10</sub></span> trends across European countries. The multi-model simulations, supported by available observations, point to decreases in <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M39" display="inline" overflow="scroll" dspmath="mathml"><mrow class="chem"><msubsup><mi mathvariant="normal">SO</mi><mn mathvariant="normal">4</mn><mrow><mo>-</mo><mn mathvariant="normal">2</mn></mrow></msubsup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="28pt" height="17pt" class="svg-formula" dspmath="mathimg" md5hash="78678370516f94756dd497806dcbf517"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-22-7207-2022-ie00001.svg" width="28pt" height="17pt" src="acp-22-7207-2022-ie00001.png"/></svg:svg></span></span> concentrations playing an overall dominant role. Also, we see relatively large contributions of the trends of <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M40" display="inline" overflow="scroll" dspmath="mathml"><mrow class="chem"><msubsup><mi mathvariant="normal">NH</mi><mn mathvariant="normal">4</mn><mo>+</mo></msubsup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="24pt" height="15pt" class="svg-formula" dspmath="mathimg" md5hash="d0b0f5cd174742dc798542982bfda883"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-22-7207-2022-ie00002.svg" width="24pt" height="15pt" src="acp-22-7207-2022-ie00002.png"/></svg:svg></span></span> and <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M41" display="inline" overflow="scroll" dspmath="mathml"><mrow class="chem"><msubsup><mi mathvariant="normal">NO</mi><mn mathvariant="normal">3</mn><mo>-</mo></msubsup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="25pt" height="16pt" class="svg-formula" dspmath="mathimg" md5hash="12193b9fdcecd5060489ac774923195d"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-22-7207-2022-ie00003.svg" width="25pt" height="16pt" src="acp-22-7207-2022-ie00003.png"/></svg:svg></span></span> to PM<span class="inline-formula"><sub>10</sub></span> decreasing trends in Germany, Denmark, Poland and the Po Valley, while the reductions of primary PM emissions appear to be a dominant factor in bringing down PM<span class="inline-formula"><sub>10</sub></span> in France, Norway, Portugal, Greece and parts of the UK and Russia. Further discussions are given with respect to emission uncertainties (including the implications of not accounting for forest fires and natural mineral dust by some of the models) and the effect of inter-annual meteorological variability on the trend analysis.</p> |
| first_indexed | 2024-04-12T14:49:10Z |
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| issn | 1680-7316 1680-7324 |
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| last_indexed | 2024-04-12T14:49:10Z |
| publishDate | 2022-06-01 |
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| spelling | doaj.art-a992cad674fb459db42c0ec52035055b2022-12-22T03:28:32ZengCopernicus PublicationsAtmospheric Chemistry and Physics1680-73161680-73242022-06-01227207725710.5194/acp-22-7207-2022Eurodelta multi-model simulated and observed particulate matter trends in Europe in the period of 1990–2010S. Tsyro0W. Aas1A. Colette2C. Andersson3B. Bessagnet4B. Bessagnet5G. Ciarelli6F. Couvidat7K. Cuvelier8K. Cuvelier9A. Manders10K. Mar11M. Mircea12N. Otero13N. Otero14M.-T. Pay15V. Raffort16Y. Roustan17M. R. Theobald18M. G. Vivanco19H. Fagerli20P. Wind21P. Wind22G. Briganti23A. Cappelletti24M. D'Isidoro25M. Adani26Norwegian Meteorological Institute, 0313 Oslo, NorwayNorwegian Institute for Air Research (NILU), P.O. Box 100, 2027 Kjeller, NorwayINERIS, National Institute for Industrial Environment and Risks, Parc Technologique ALATA, 60550, Verneuil-en-Halatte, FranceSwedish Meteorological and Hydrological Institute, 60176 Norrköping, SwedenINERIS, National Institute for Industrial Environment and Risks, Parc Technologique ALATA, 60550, Verneuil-en-Halatte, Francenow at: European Commission, Joint Research Centre (JRC), Ispra, ItalyInstitute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, Helsinki, FinlandINERIS, National Institute for Industrial Environment and Risks, Parc Technologique ALATA, 60550, Verneuil-en-Halatte, FranceEuropean Commission, Joint Research Centre (JRC), Ispra, Italyretired with Active Senior AgreementTNO, Dept. Climate, Air and Sustainability, P.O. Box 80015, 3508 TA Utrecht, the NetherlandsInstitute for Advanced Sustainability Studies, Potsdam, GermanyENEA, Italian National Agency for New Technologies, Energy and Sustainable Economic Development, Via Martiri di Monte Sole 4, 40129 Bologna, ItalyInstitute for Advanced Sustainability Studies, Potsdam, Germanynow at: Institute of Geography and Oeschger Centre for Climate Change Research, University of Bern, Bern, SwitzerlandBSC, Barcelona Supercomputing Center, Centro Nacional de Supercomputaciòn, Nexus II Building, Jordi Girona, 29, 08034 Barcelona, SpainCEREA, École des Ponts, EDF R & D, Île-de-France, FranceCEREA, École des Ponts, EDF R & D, Île-de-France, FranceCIEMAT, Atmospheric Modeling Unit, Avda. Complutense 40, 28040 Madrid, Spain CIEMAT, Atmospheric Modeling Unit, Avda. Complutense 40, 28040 Madrid, Spain Norwegian Meteorological Institute, 0313 Oslo, NorwayNorwegian Meteorological Institute, 0313 Oslo, NorwayFaculty of Science and Technology, University of Tromsø, Tromsø, NorwayENEA, Italian National Agency for New Technologies, Energy and Sustainable Economic Development, Via Martiri di Monte Sole 4, 40129 Bologna, ItalyENEA, Italian National Agency for New Technologies, Energy and Sustainable Economic Development, Via Martiri di Monte Sole 4, 40129 Bologna, ItalyENEA, Italian National Agency for New Technologies, Energy and Sustainable Economic Development, Via Martiri di Monte Sole 4, 40129 Bologna, ItalyENEA, Italian National Agency for New Technologies, Energy and Sustainable Economic Development, Via Martiri di Monte Sole 4, 40129 Bologna, Italy<p>The Eurodelta-Trends (EDT) multi-model experiment, aimed at assessing the efficiency of emission mitigation measures in improving air quality in Europe during 1990–2010, was designed to answer a series of questions regarding European pollution trends; i.e. were there significant trends detected by observations? Do the models manage to reproduce observed trends? How close is the agreement between the models and how large are the deviations from observations? In this paper, we address these issues with respect to particulate matter (PM) pollution. An in-depth trend analysis has been performed for PM<span class="inline-formula"><sub>10</sub></span> and PM<span class="inline-formula"><sub>2.5</sub></span> for the period of 2000–2010, based on results from six chemical transport models and observational data from the EMEP (Cooperative Programme for Monitoring and Evaluation of the Long-range Transmission of Air Pollutants in Europe) monitoring network. Given harmonization of set-up and main input data, the differences in model results should mainly result from differences in the process formulations within the models themselves, and the spread in the model-simulated trends could be regarded as an indicator for modelling uncertainty.</p> <p>The model ensemble simulations indicate overall decreasing trends in PM<span class="inline-formula"><sub>10</sub></span> and PM<span class="inline-formula"><sub>2.5</sub></span> from 2000 to 2010, with the total reductions of annual mean concentrations by between 2 and 5 (7 for PM<span class="inline-formula"><sub>10</sub></span>) <span class="inline-formula">µ</span>g m<span class="inline-formula"><sup>−3</sup></span> (or between 10 % and 30 %) across most of Europe (by 0.5–2 <span class="inline-formula">µ</span>g m<span class="inline-formula"><sup>−3</sup></span> in Fennoscandia, the north-west of Russia and eastern Europe) during the studied period. Compared to PM<span class="inline-formula"><sub>2.5</sub></span>, relative PM<span class="inline-formula"><sub>10</sub></span> trends are weaker due to large inter-annual variability of natural coarse PM within the former. The changes in the concentrations of PM individual components are in general consistent with emission reductions. There is reasonable agreement in PM trends estimated by the individual models, with the inter-model variability below 30 %–40 % over most of Europe, increasing to 50 %–60 % in the northern and eastern parts of the EDT domain.</p> <p>Averaged over measurement sites (26 for PM<span class="inline-formula"><sub>10</sub></span> and 13 for PM<span class="inline-formula"><sub>2.5</sub></span>), the mean ensemble-simulated trends are <span class="inline-formula">−0.24</span> and <span class="inline-formula">−0.22</span> <span class="inline-formula">µ</span>g m<span class="inline-formula"><sup>−3</sup></span> yr<span class="inline-formula"><sup>−1</sup></span> for PM<span class="inline-formula"><sub>10</sub></span> and PM<span class="inline-formula"><sub>2.5</sub></span>, which are somewhat weaker than the observed trends of <span class="inline-formula">−0.35</span> and <span class="inline-formula">−0.40</span> <span class="inline-formula">µ</span>g m<span class="inline-formula"><sup>−3</sup></span> yr<span class="inline-formula"><sup>−1</sup></span> respectively, partly due to model underestimation of PM concentrations. The correspondence is better in relative PM<span class="inline-formula"><sub>10</sub></span> and PM<span class="inline-formula"><sub>2.5</sub></span> trends, which are <span class="inline-formula">−1.7</span> % yr<span class="inline-formula"><sup>−1</sup></span> and <span class="inline-formula">−2.0</span> % yr<span class="inline-formula"><sup>−1</sup></span> from the model ensemble and <span class="inline-formula">−2.1</span> % yr<span class="inline-formula"><sup>−1</sup></span> and <span class="inline-formula">−2.9</span> % yr<span class="inline-formula"><sup>−1</sup></span> from the observations respectively. The observations identify significant trends (at the 95 % confidence level) for PM<span class="inline-formula"><sub>10</sub></span> at 56 % of the sites and for PM<span class="inline-formula"><sub>2.5</sub></span> at 36 % of the sites, which is somewhat less that the fractions of significant modelled trends. Further, we find somewhat smaller spatial variability of modelled PM trends with respect to the observed ones across Europe and also within individual countries.</p> <p>The strongest decreasing PM trends and the largest number of sites with significant trends are found for the summer season, according to both the model ensemble and observations. The winter PM trends are very weak and mostly insignificant. Important reasons for that are the very modest reductions and even increases in the emissions of primary PM from residential heating in winter. It should be kept in mind that all findings regarding modelled versus observed PM trends are limited to the regions where the sites are located.</p> <p>The analysis reveals considerable variability of the role of the individual aerosols in PM<span class="inline-formula"><sub>10</sub></span> trends across European countries. The multi-model simulations, supported by available observations, point to decreases in <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M39" display="inline" overflow="scroll" dspmath="mathml"><mrow class="chem"><msubsup><mi mathvariant="normal">SO</mi><mn mathvariant="normal">4</mn><mrow><mo>-</mo><mn mathvariant="normal">2</mn></mrow></msubsup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="28pt" height="17pt" class="svg-formula" dspmath="mathimg" md5hash="78678370516f94756dd497806dcbf517"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-22-7207-2022-ie00001.svg" width="28pt" height="17pt" src="acp-22-7207-2022-ie00001.png"/></svg:svg></span></span> concentrations playing an overall dominant role. Also, we see relatively large contributions of the trends of <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M40" display="inline" overflow="scroll" dspmath="mathml"><mrow class="chem"><msubsup><mi mathvariant="normal">NH</mi><mn mathvariant="normal">4</mn><mo>+</mo></msubsup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="24pt" height="15pt" class="svg-formula" dspmath="mathimg" md5hash="d0b0f5cd174742dc798542982bfda883"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-22-7207-2022-ie00002.svg" width="24pt" height="15pt" src="acp-22-7207-2022-ie00002.png"/></svg:svg></span></span> and <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M41" display="inline" overflow="scroll" dspmath="mathml"><mrow class="chem"><msubsup><mi mathvariant="normal">NO</mi><mn mathvariant="normal">3</mn><mo>-</mo></msubsup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="25pt" height="16pt" class="svg-formula" dspmath="mathimg" md5hash="12193b9fdcecd5060489ac774923195d"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-22-7207-2022-ie00003.svg" width="25pt" height="16pt" src="acp-22-7207-2022-ie00003.png"/></svg:svg></span></span> to PM<span class="inline-formula"><sub>10</sub></span> decreasing trends in Germany, Denmark, Poland and the Po Valley, while the reductions of primary PM emissions appear to be a dominant factor in bringing down PM<span class="inline-formula"><sub>10</sub></span> in France, Norway, Portugal, Greece and parts of the UK and Russia. Further discussions are given with respect to emission uncertainties (including the implications of not accounting for forest fires and natural mineral dust by some of the models) and the effect of inter-annual meteorological variability on the trend analysis.</p>https://acp.copernicus.org/articles/22/7207/2022/acp-22-7207-2022.pdf |
| spellingShingle | S. Tsyro W. Aas A. Colette C. Andersson B. Bessagnet B. Bessagnet G. Ciarelli F. Couvidat K. Cuvelier K. Cuvelier A. Manders K. Mar M. Mircea N. Otero N. Otero M.-T. Pay V. Raffort Y. Roustan M. R. Theobald M. G. Vivanco H. Fagerli P. Wind P. Wind G. Briganti A. Cappelletti M. D'Isidoro M. Adani Eurodelta multi-model simulated and observed particulate matter trends in Europe in the period of 1990–2010 Atmospheric Chemistry and Physics |
| title | Eurodelta multi-model simulated and observed particulate matter trends in Europe in the period of 1990–2010 |
| title_full | Eurodelta multi-model simulated and observed particulate matter trends in Europe in the period of 1990–2010 |
| title_fullStr | Eurodelta multi-model simulated and observed particulate matter trends in Europe in the period of 1990–2010 |
| title_full_unstemmed | Eurodelta multi-model simulated and observed particulate matter trends in Europe in the period of 1990–2010 |
| title_short | Eurodelta multi-model simulated and observed particulate matter trends in Europe in the period of 1990–2010 |
| title_sort | eurodelta multi model simulated and observed particulate matter trends in europe in the period of 1990 2010 |
| url | https://acp.copernicus.org/articles/22/7207/2022/acp-22-7207-2022.pdf |
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