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Items: 1 to 20 of 144

1.

Use of satellite-based aerosol optical depth and spatial clustering to predict ambient PM2.5 concentrations.

Lee HJ, Coull BA, Bell ML, Koutrakis P.

Environ Res. 2012 Oct;118:8-15. doi: 10.1016/j.envres.2012.06.011. Epub 2012 Jul 28.

2.

Assessment and statistical modeling of the relationship between remotely sensed aerosol optical depth and PM2.5 in the eastern United States.

Paciorek CJ, Liu Y; HEI Health Review Committee.

Res Rep Health Eff Inst. 2012 May;(167):5-83; discussion 85-91.

PMID:
22838153
3.

Estimating regional spatial and temporal variability of PM(2.5) concentrations using satellite data, meteorology, and land use information.

Liu Y, Paciorek CJ, Koutrakis P.

Environ Health Perspect. 2009 Jun;117(6):886-92. doi: 10.1289/ehp.0800123. Epub 2009 Jan 28.

4.

Monthly analysis of PM ratio characteristics and its relation to AOD.

Sorek-Hamer M, Broday DM, Chatfield R, Esswein R, Stafoggia M, Lepeule J, Lyapustin A, Kloog I.

J Air Waste Manag Assoc. 2017 Jan;67(1):27-38. doi: 10.1080/10962247.2016.1208121.

PMID:
27589199
5.

Incorporating local land use regression and satellite aerosol optical depth in a hybrid model of spatiotemporal PM2.5 exposures in the Mid-Atlantic states.

Kloog I, Nordio F, Coull BA, Schwartz J.

Environ Sci Technol. 2012 Nov 6;46(21):11913-21. doi: 10.1021/es302673e. Epub 2012 Oct 11.

6.

High-resolution satellite-based analysis of ground-level PM2.5 for the city of Montreal.

Wang B, Chen Z.

Sci Total Environ. 2016 Jan 15;541:1059-1069. doi: 10.1016/j.scitotenv.2015.10.024. Epub 2015 Nov 11.

PMID:
26473708
7.

Calibrating MODIS aerosol optical depth for predicting daily PM2.5 concentrations via statistical downscaling.

Chang HH, Hu X, Liu Y.

J Expo Sci Environ Epidemiol. 2014 Jul;24(4):398-404. doi: 10.1038/jes.2013.90. Epub 2013 Dec 25.

8.

Limitations of remotely sensed aerosol as a spatial proxy for fine particulate matter.

Paciorek CJ, Liu Y.

Environ Health Perspect. 2009 Jun;117(6):904-9. doi: 10.1289/ehp.0800360. Epub 2009 Feb 21.

9.

Estimating ground-level PM2.5 in China using satellite remote sensing.

Ma Z, Hu X, Huang L, Bi J, Liu Y.

Environ Sci Technol. 2014 Jul 1;48(13):7436-44. doi: 10.1021/es5009399. Epub 2014 Jun 13.

PMID:
24901806
10.

Acute health impacts of airborne particles estimated from satellite remote sensing.

Wang Z, Liu Y, Hu M, Pan X, Shi J, Chen F, He K, Koutrakis P, Christiani DC.

Environ Int. 2013 Jan;51:150-9. doi: 10.1016/j.envint.2012.10.011. Epub 2012 Dec 7.

11.

Impacts of elevated-aerosol-layer and aerosol type on the correlation of AOD and particulate matter with ground-based and satellite measurements in Nanjing, southeast China.

Han Y, Wu Y, Wang T, Zhuang B, Li S, Zhao K.

Sci Total Environ. 2015 Nov 1;532:195-207. doi: 10.1016/j.scitotenv.2015.05.136. Epub 2015 Jun 10.

PMID:
26071961
12.

Estimating spatiotemporal distribution of PM1 concentrations in China with satellite remote sensing, meteorology, and land use information.

Chen G, Knibbs LD, Zhang W, Li S, Cao W, Guo J, Ren H, Wang B, Wang H, Williams G, Hamm NAS, Guo Y.

Environ Pollut. 2018 Feb;233:1086-1094. doi: 10.1016/j.envpol.2017.10.011. Epub 2017 Oct 13.

PMID:
29033176
13.

Spatial scales of pollution from variable resolution satellite imaging.

Chudnovsky AA, Kostinski A, Lyapustin A, Koutrakis P.

Environ Pollut. 2013 Jan;172:131-8. doi: 10.1016/j.envpol.2012.08.016. Epub 2012 Sep 29.

PMID:
23026774
14.

Estimating national-scale ground-level PM25 concentration in China using geographically weighted regression based on MODIS and MISR AOD.

You W, Zang Z, Zhang L, Li Y, Wang W.

Environ Sci Pollut Res Int. 2016 May;23(9):8327-38. doi: 10.1007/s11356-015-6027-9. Epub 2016 Jan 16.

PMID:
26780051
15.

Assessment of the relationship between satellite AOD and ground PM₁₀ measurement data considering synoptic meteorological patterns and Lidar data.

Zeeshan M, Kim Oanh NT.

Sci Total Environ. 2014 Mar 1;473-474:609-18. doi: 10.1016/j.scitotenv.2013.12.058. Epub 2014 Jan 4.

PMID:
24394370
16.

Assessment of primary and secondary ambient particle trends using satellite aerosol optical depth and ground speciation data in the New England region, United States.

Lee HJ, Kang CM, Coull BA, Bell ML, Koutrakis P.

Environ Res. 2014 Aug;133:103-10. doi: 10.1016/j.envres.2014.04.006. Epub 2014 Jun 4.

17.

Prediction of daily fine particulate matter concentrations using aerosol optical depth retrievals from the Geostationary Operational Environmental Satellite (GOES).

Chudnovsky AA, Lee HJ, Kostinski A, Kotlov T, Koutrakis P.

J Air Waste Manag Assoc. 2012 Sep;62(9):1022-31.

PMID:
23019816
18.

Spatial analysis of MODIS aerosol optical depth, PM2.5, and chronic coronary heart disease.

Hu Z.

Int J Health Geogr. 2009 May 12;8:27. doi: 10.1186/1476-072X-8-27.

19.

Estimating PM2.5 in Xi'an, China using aerosol optical depth: a comparison between the MODIS and MISR retrieval models.

You W, Zang Z, Pan X, Zhang L, Chen D.

Sci Total Environ. 2015 Feb 1;505:1156-65. doi: 10.1016/j.scitotenv.2014.11.024. Epub 2014 Nov 20.

PMID:
25466686
20.

Satellite remote sensing of particulate matter air quality: the cloud-cover problem.

Christopher SA, Gupta P.

J Air Waste Manag Assoc. 2010 May;60(5):596-602.

PMID:
20480859

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