Calculation of pseudo PM2.5 annual mean concentrations in Europe based on annual mean PM10 concentrations and other supplementary data. ETC/ACC Technical Paper 2010/9.

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00020699%3A_____%2F11%3A%230000591" target="_blank" >RIV/00020699:_____/11:#0000591 - isvavai.cz</a>

Výsledek na webu

<a href="http://acm.eionet.europa.eu/reports/ETCACC_TP_2010_9_pseudo_PM2.5_stations" target="_blank" >http://acm.eionet.europa.eu/reports/ETCACC_TP_2010_9_pseudo_PM2.5_stations</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Calculation of pseudo PM2.5 annual mean concentrations in Europe based on annual mean PM10 concentrations and other supplementary data. ETC/ACC Technical Paper 2010/9.

Popis výsledku v původním jazyce

ETC/ACC has developed methods to spatially assess a number of air pollutants for all of Europe. Currently maps are produced for PM10 but not PM2.5 due to a lack of monitoring data for PM2.5, reported in AirBase. This makes spatial assessment of PM2.5 very uncertain and therefore PM2.5 maps are currently not operationally produced. To try to improve this situation two approaches for producing "pseudo" PM2.5 measurements are investigated for European wide mapping of PM2.5. These pseudo measurements are based on the measured PM10 concentrations but with addition of supplementary geographical, population and meteorological data. The two approaches are "Empirical Ensemble-based Virtual Sensing" (EEVS), which is based on Artificial Neural Networks, and multiple linear regression (MLR), a standard variational technique. They give similar results, with the EEVS approach showing slightly lower RMSE than MLR. For all other metrics assessed there was no significant difference. Neither approach wa

Název v anglickém jazyce

Calculation of pseudo PM2.5 annual mean concentrations in Europe based on annual mean PM10 concentrations and other supplementary data. ETC/ACC Technical Paper 2010/9.

Popis výsledku anglicky

ETC/ACC has developed methods to spatially assess a number of air pollutants for all of Europe. Currently maps are produced for PM10 but not PM2.5 due to a lack of monitoring data for PM2.5, reported in AirBase. This makes spatial assessment of PM2.5 very uncertain and therefore PM2.5 maps are currently not operationally produced. To try to improve this situation two approaches for producing "pseudo" PM2.5 measurements are investigated for European wide mapping of PM2.5. These pseudo measurements are based on the measured PM10 concentrations but with addition of supplementary geographical, population and meteorological data. The two approaches are "Empirical Ensemble-based Virtual Sensing" (EEVS), which is based on Artificial Neural Networks, and multiple linear regression (MLR), a standard variational technique. They give similar results, with the EEVS approach showing slightly lower RMSE than MLR. For all other metrics assessed there was no significant difference. Neither approach wa

Druh

O - Ostatní výsledky

CEP obor

DI - Znečištění a kontrola vzduchu

OECD FORD obor

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Projekt

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Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2011

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů