Land cover and traffic data inclusion in PM mapping

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00020699%3A_____%2F19%3AN0000169" target="_blank" >RIV/00020699:_____/19:N0000169 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.eionet.europa.eu/etcs/etc-atni/products/etc-atni-reports/etc-acm-report-18-2018-land-cover-and-traffic-data-inclusion-in-pm-mapping" target="_blank" >https://www.eionet.europa.eu/etcs/etc-atni/products/etc-atni-reports/etc-acm-report-18-2018-land-cover-and-traffic-data-inclusion-in-pm-mapping</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Land cover and traffic data inclusion in PM mapping

Popis výsledku v původním jazyce

Annual European-wide air quality maps have been produced using geostatistical techniques for many years and is based primarily on air quality measurements. The mapping method follows in principle the sequence of regression – interpolation – merging. It combines monitoring data, chemical transport model outputs and other supplementary data (such as altitude and meteorology) using a linear regression model followed by kriging of its residuals (‘residual kriging’), applied separately for rural and urban background areas. The rural and urban background map layers are subsequently merged on basis of population densities into one final concentration map for Europe. Inclusion of land cover and road type data among the set of the supplementary data demonstrated to improve the quality of urban and rural background layers in the NO2 map and is currently routinely applied in the NO2 mapping. In addition, an urban traffic map layer based on the measurement data from traffic stations is constructed and takes art in the merging process with the rural and urban background map layers to reach a final NO2 map. This report examines now – due to its proved added value in the NO2 mapping – whether for PM10 and PM2.5 the similar method provides also sufficient added value to include it on a routinely basis in the production of the final concentration map and population exposure estimates.. It concerns the inclusion of land cover data and road type data in the background map layers, as well as the inclusion of the urban traffic layer based on traffic measurement stations. The analysis is done based on 2015 data, being the most recent year with all data needed available when this study started.

Název v anglickém jazyce

Land cover and traffic data inclusion in PM mapping

Popis výsledku anglicky

Annual European-wide air quality maps have been produced using geostatistical techniques for many years and is based primarily on air quality measurements. The mapping method follows in principle the sequence of regression – interpolation – merging. It combines monitoring data, chemical transport model outputs and other supplementary data (such as altitude and meteorology) using a linear regression model followed by kriging of its residuals (‘residual kriging’), applied separately for rural and urban background areas. The rural and urban background map layers are subsequently merged on basis of population densities into one final concentration map for Europe. Inclusion of land cover and road type data among the set of the supplementary data demonstrated to improve the quality of urban and rural background layers in the NO2 map and is currently routinely applied in the NO2 mapping. In addition, an urban traffic map layer based on the measurement data from traffic stations is constructed and takes art in the merging process with the rural and urban background map layers to reach a final NO2 map. This report examines now – due to its proved added value in the NO2 mapping – whether for PM10 and PM2.5 the similar method provides also sufficient added value to include it on a routinely basis in the production of the final concentration map and population exposure estimates.. It concerns the inclusion of land cover data and road type data in the background map layers, as well as the inclusion of the urban traffic layer based on traffic measurement stations. The analysis is done based on 2015 data, being the most recent year with all data needed available when this study started.

Druh

O - Ostatní výsledky

CEP obor

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OECD FORD obor

10511 - Environmental sciences (social aspects to be 5.7)

Projekt

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Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2019

Kód důvěrnosti údajů

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