Inclusion of land cover and traffic data in NO2 mapping methodology

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00020699%3A_____%2F17%3AN0000149" target="_blank" >RIV/00020699:_____/17:N0000149 - isvavai.cz</a>

Result on the web

<a href="http://acm.eionet.europa.eu/reports/ETCACM_TP_2016_12_LC_and_traffic_data_in_NO2_mapping" target="_blank" >http://acm.eionet.europa.eu/reports/ETCACM_TP_2016_12_LC_and_traffic_data_in_NO2_mapping</a>

DOI - Digital Object Identifier

—

Result language

angličtina

Original language name

Inclusion of land cover and traffic data in NO2 mapping methodology

Original language description

The ETC/ACM produces annually Europe-wide PM2.5, PM10 and Ozone maps of air quality using geostatistical techniques. Nitrogen dioxide (NO2) maps have been produced at irregular intervals. Now that the importance of NO2 mapping is growing due to new evidence on negative health impacts of NO2 there is a wish to produce NO2 maps on annual basis. However, NO2 maps produced so far have relative high uncertainty compared to the particulate matter and ozone maps. Furthermore, these maps reflect rural and urban background areas only, not accounting for local hot spots (traffic), although traffic is the most important source of NO2. To produce more advanced European-scale maps of annual average NO2 concentrations on a regular basis, this paper investigates the explanatory power of land use and traffic related data in the spatial regression modelling of NO2. The Corine Land Cover (CLC, 100m grids) and the Global Road inventory Project (GRIP, vector) data appear to be suitable data sources due to their high resolution and European-wide coverage. Next to this, mapping of traffic related air quality using measurement data from traffic stations and available supplementary data has been explored. Different options on how to include best such a traffic map layer in the background map and in the exposure estimates has been examined. All analyses have been based on 2013 data. As a result one concludes that inclusion of land cover and road data brings clear improvement of NO2 mapping methodology.

Czech name

—

Czech description

—

Type

O - Miscellaneous

CEP classification

—

OECD FORD branch

10509 - Meteorology and atmospheric sciences

Project

—

Continuities

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Publication year

2017

Confidentiality

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