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Estimating the spatial distribution of PM2.5 concentration by integrating geographic data and field measurements
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| Zeitschriftentitel: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
|---|---|
| Personen und Körperschaften: | , , , |
| In: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-7/W4, 2015, S. 209-213 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
Copernicus GmbH
|
| Schlagwörter: |
| author_facet |
Zhai, L. Sang, H. Zhang, J. An, F. Zhai, L. Sang, H. Zhang, J. An, F. |
|---|---|
| author |
Zhai, L. Sang, H. Zhang, J. An, F. |
| spellingShingle |
Zhai, L. Sang, H. Zhang, J. An, F. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Estimating the spatial distribution of PM2.5 concentration by integrating geographic data and field measurements General Earth and Planetary Sciences General Environmental Science |
| author_sort |
zhai, l. |
| spelling |
Zhai, L. Sang, H. Zhang, J. An, F. 2194-9034 Copernicus GmbH General Earth and Planetary Sciences General Environmental Science http://dx.doi.org/10.5194/isprsarchives-xl-7-w4-209-2015 <jats:p>Abstract. Air quality directly affects the health and living of human beings, and it receives wide concern of public and attaches great important of governments at all levels. The estimation of the concentration distribution of PM2.5 and the analysis of its impacting factors is significant for understanding the spatial distribution regularity and further for decision supporting of governments. In this study, multiple sources of remote sensing and GIS data are utilized to estimate the spatial distribution of PM2.5 concentration in Shijiazhuang, China, by utilizing multivariate linear regression modelling, and integrating year average values of PM2.5 collected from local environment observing stations. Two major sources of PM2.5 are collected, including dust surfaces and industrial polluting sources. The area attribute of dust surfaces and point attribute of industrial polluting enterprises are extracted from high resolution remote sensing images and GIS data in 2013. 30m land cover products, annual average PM2.5 concentration values from the 8 environment monitoring stations, annual mean MODIS AOD data, traffic and DEM data are utilized in the study for regression modeling analysis. The multivariate regression analysis model is applied to estimate the spatial distribution of PM2.5 concentration. There is an upward trend of the spatial distribution of PM2.5 concentration gradually from west to east, of which the highest concentration appears in the municipal district and its surrounding areas. The spatial distribution pattern relatively fit the reality. </jats:p> Estimating the spatial distribution of PM<sub>2.5</sub> concentration by integrating geographic data and field measurements The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
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| title |
Estimating the spatial distribution of PM2.5 concentration by integrating geographic data and field measurements |
| title_unstemmed |
Estimating the spatial distribution of PM2.5 concentration by integrating geographic data and field measurements |
| title_full |
Estimating the spatial distribution of PM2.5 concentration by integrating geographic data and field measurements |
| title_fullStr |
Estimating the spatial distribution of PM2.5 concentration by integrating geographic data and field measurements |
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Estimating the spatial distribution of PM2.5 concentration by integrating geographic data and field measurements |
| title_short |
Estimating the spatial distribution of PM2.5 concentration by integrating geographic data and field measurements |
| title_sort |
estimating the spatial distribution of pm<sub>2.5</sub> concentration by integrating geographic data and field measurements |
| topic |
General Earth and Planetary Sciences General Environmental Science |
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http://dx.doi.org/10.5194/isprsarchives-xl-7-w4-209-2015 |
| publishDate |
2015 |
| physical |
209-213 |
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<jats:p>Abstract. Air quality directly affects the health and living of human beings, and it receives wide concern of public and attaches great important of governments at all levels. The estimation of the concentration distribution of PM2.5 and the analysis of its impacting factors is significant for understanding the spatial distribution regularity and further for decision supporting of governments. In this study, multiple sources of remote sensing and GIS data are utilized to estimate the spatial distribution of PM2.5 concentration in Shijiazhuang, China, by utilizing multivariate linear regression modelling, and integrating year average values of PM2.5 collected from local environment observing stations. Two major sources of PM2.5 are collected, including dust surfaces and industrial polluting sources. The area attribute of dust surfaces and point attribute of industrial polluting enterprises are extracted from high resolution remote sensing images and GIS data in 2013. 30m land cover products, annual average PM2.5 concentration values from the 8 environment monitoring stations, annual mean MODIS AOD data, traffic and DEM data are utilized in the study for regression modeling analysis. The multivariate regression analysis model is applied to estimate the spatial distribution of PM2.5 concentration. There is an upward trend of the spatial distribution of PM2.5 concentration gradually from west to east, of which the highest concentration appears in the municipal district and its surrounding areas. The spatial distribution pattern relatively fit the reality.
</jats:p> |
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| author | Zhai, L., Sang, H., Zhang, J., An, F. |
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| description | <jats:p>Abstract. Air quality directly affects the health and living of human beings, and it receives wide concern of public and attaches great important of governments at all levels. The estimation of the concentration distribution of PM2.5 and the analysis of its impacting factors is significant for understanding the spatial distribution regularity and further for decision supporting of governments. In this study, multiple sources of remote sensing and GIS data are utilized to estimate the spatial distribution of PM2.5 concentration in Shijiazhuang, China, by utilizing multivariate linear regression modelling, and integrating year average values of PM2.5 collected from local environment observing stations. Two major sources of PM2.5 are collected, including dust surfaces and industrial polluting sources. The area attribute of dust surfaces and point attribute of industrial polluting enterprises are extracted from high resolution remote sensing images and GIS data in 2013. 30m land cover products, annual average PM2.5 concentration values from the 8 environment monitoring stations, annual mean MODIS AOD data, traffic and DEM data are utilized in the study for regression modeling analysis. The multivariate regression analysis model is applied to estimate the spatial distribution of PM2.5 concentration. There is an upward trend of the spatial distribution of PM2.5 concentration gradually from west to east, of which the highest concentration appears in the municipal district and its surrounding areas. The spatial distribution pattern relatively fit the reality. </jats:p> |
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| spelling | Zhai, L. Sang, H. Zhang, J. An, F. 2194-9034 Copernicus GmbH General Earth and Planetary Sciences General Environmental Science http://dx.doi.org/10.5194/isprsarchives-xl-7-w4-209-2015 <jats:p>Abstract. Air quality directly affects the health and living of human beings, and it receives wide concern of public and attaches great important of governments at all levels. The estimation of the concentration distribution of PM2.5 and the analysis of its impacting factors is significant for understanding the spatial distribution regularity and further for decision supporting of governments. In this study, multiple sources of remote sensing and GIS data are utilized to estimate the spatial distribution of PM2.5 concentration in Shijiazhuang, China, by utilizing multivariate linear regression modelling, and integrating year average values of PM2.5 collected from local environment observing stations. Two major sources of PM2.5 are collected, including dust surfaces and industrial polluting sources. The area attribute of dust surfaces and point attribute of industrial polluting enterprises are extracted from high resolution remote sensing images and GIS data in 2013. 30m land cover products, annual average PM2.5 concentration values from the 8 environment monitoring stations, annual mean MODIS AOD data, traffic and DEM data are utilized in the study for regression modeling analysis. The multivariate regression analysis model is applied to estimate the spatial distribution of PM2.5 concentration. There is an upward trend of the spatial distribution of PM2.5 concentration gradually from west to east, of which the highest concentration appears in the municipal district and its surrounding areas. The spatial distribution pattern relatively fit the reality. </jats:p> Estimating the spatial distribution of PM<sub>2.5</sub> concentration by integrating geographic data and field measurements The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spellingShingle | Zhai, L., Sang, H., Zhang, J., An, F., The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Estimating the spatial distribution of PM2.5 concentration by integrating geographic data and field measurements, General Earth and Planetary Sciences, General Environmental Science |
| title | Estimating the spatial distribution of PM2.5 concentration by integrating geographic data and field measurements |
| title_full | Estimating the spatial distribution of PM2.5 concentration by integrating geographic data and field measurements |
| title_fullStr | Estimating the spatial distribution of PM2.5 concentration by integrating geographic data and field measurements |
| title_full_unstemmed | Estimating the spatial distribution of PM2.5 concentration by integrating geographic data and field measurements |
| title_short | Estimating the spatial distribution of PM2.5 concentration by integrating geographic data and field measurements |
| title_sort | estimating the spatial distribution of pm<sub>2.5</sub> concentration by integrating geographic data and field measurements |
| title_unstemmed | Estimating the spatial distribution of PM2.5 concentration by integrating geographic data and field measurements |
| topic | General Earth and Planetary Sciences, General Environmental Science |
| url | http://dx.doi.org/10.5194/isprsarchives-xl-7-w4-209-2015 |