Air pollution detection using MODIS data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F11%3A33119528" target="_blank" >RIV/61989592:15310/11:33119528 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1117/12.898107" target="_blank" >http://dx.doi.org/10.1117/12.898107</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1117/12.898107" target="_blank" >10.1117/12.898107</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Air pollution detection using MODIS data

Popis výsledku v původním jazyce

The quality of the environment has a great impact on public health while air quality is a major factor that is especially relevant for respiratory diseases. PM10 (particulate matter below 10 mý) particles are among the most dangerous pollutants, which enter the lower respiratory tract and cause serious health problems. Obtaining reliable air pollution data is limited to a number of ground measuring stations and their spatial location. We used an alternative approach and created statistical models that employed remotely sensed imageries. To establish empirical relationships, we used multi-temporal (2006-2009) MODIS aerosol optical thickness data (product MOD04, Level 2) and the PM10 ground mass concentrations. The north-western part of the Czech Republic (namely the Karlovarský and the Ustecký regions) was chosen as a test site, as all the different types of cultural landscape (forest-economical, agricultural, mining, and urban) can be found within one MODIS scene. This study was focuse

Název v anglickém jazyce

Air pollution detection using MODIS data

Popis výsledku anglicky

The quality of the environment has a great impact on public health while air quality is a major factor that is especially relevant for respiratory diseases. PM10 (particulate matter below 10 mý) particles are among the most dangerous pollutants, which enter the lower respiratory tract and cause serious health problems. Obtaining reliable air pollution data is limited to a number of ground measuring stations and their spatial location. We used an alternative approach and created statistical models that employed remotely sensed imageries. To establish empirical relationships, we used multi-temporal (2006-2009) MODIS aerosol optical thickness data (product MOD04, Level 2) and the PM10 ground mass concentrations. The north-western part of the Czech Republic (namely the Karlovarský and the Ustecký regions) was chosen as a test site, as all the different types of cultural landscape (forest-economical, agricultural, mining, and urban) can be found within one MODIS scene. This study was focuse

Druh

D - Stať ve sborníku

CEP obor

DE - Zemský magnetismus, geodesie, geografie

OECD FORD obor

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Projekt

<a href="/cs/project/GA205%2F09%2F1079" target="_blank" >GA205/09/1079: Metody umělé inteligence v GIS</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2011

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings - Earth Resources and Environmental Remote Sensing/GIS Applications II

ISBN

978-0-8194-8808-4

ISSN

0277-786X

e-ISSN

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Počet stran výsledku

15

Strana od-do

1-14

Název nakladatele

SPIE

Místo vydání

Bellingham, USA

Místo konání akce

Praha

Datum konání akce

20. 9. 2011

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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