Correlating MODIS aerosol optical thickness data with ground-based PM2.5 observations across Texas for use in a real-time air quality prediction system

Hutchison, Keith D.; Smith, Solar S; Faruqui, Shazia J.

doi:10.1016/j.atmosenv.2005.08.036

Cited by 76 publications

(37 citation statements)

References 14 publications

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“…1) centered on NBHM are evaluated to verify the assumptions used in Ichoku et al (2002). Table 2 provides all nine criteria used to obtain MODIS AOT.…”

Section: Methodsmentioning

confidence: 99%

“…Even if the MODIS pixel was small enough, it does not represent the same viewing conditions due to differences between observation areas, varying path lengths through the atmosphere, and sensor sensitivity to aerosol properties. Ichoku et al (2002) averaged level 2 MODIS AOT pixels using a 5×5 pixel box over the surface measurement locations and 15-min observations over one hour to represent a similar air mass as observed by MODIS. This was justified by examining the normal speed of aerosol transport (50 km/h) using animation of the Total Ozone Mapping Spectrometer (TOMS) imagery over the Atlantic Ocean.…”

Section: Methodsmentioning

confidence: 99%

“…Decreasing box size, decreases the number of pixels used to derive MODIS AOT values for NBHM, and may affect the overall mean AOT and these can change PM 2.5 estimation from satellites due to change in spatial sampling of aerosols (Hutchison et al, 2005). Larger box sizes generally indicates sampling of more heterogeneous type of aerosols, which can over or under estimate AOT values compared to point measurements from ground stations.…”

Section: Varying Box Size Around Ground Stationmentioning

confidence: 99%

“…Table 1 lists the parameters that are used in the current study. We also apply various quality (Ichoku et al, 2002) and analysis methods (Wang and Christopher, 2003) that do not use quality flags. While obtaining coincident MODIS pixels with the PM 2.5 measurements, nine different criteria are applied to evaluate AOT product for PM 2.5 air quality applications (Table 2).…”

Section: Modismentioning

confidence: 99%

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Seven year particulate matter air quality assessment from surface and satellite measurements

2008

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Abstract. Using seven years of the Moderate ResolutionImaging Spectroradiometer (MODIS) aerosol optical thickness (AOT) data and ground measurements of particulate matter mass over one site in the Southeastern United States (33.55 • N, 86.82 • W) we present a comprehensive analysis of various aspects of particulate matter air quality. Monthly, seasonal and inter-annual relationships are examined with emphasis on sampling biases, quality indicators in the AOT product and various cloud clearing criteria. Our results indicate that PM 2.5 mass concentration over Northern Birmingham has decreased by about 23% in year 2006 when compared to year 2002 and air quality during summer months was poor when compared to winter months. MODIS-Terra AOT data was available only about 50% of the time due to cloud cover and unfavorable surface conditions. However, the mean difference in monthly mean PM 2.5 was less than 2.2 µgm −3 derived using all the data and from only those days when satellite AOT was available indicating that satellite data does not have sampling issues. The correlation between PM 2.5 and MODIS AOT increased from 0.52 to 0.62 when hourly PM 2.5 data were used instead of daily mean PM 2.5 data. Changing box size for satellite data around the ground station during comparisons produced less than ±0.03 difference in mean AOT values for 90% of observations. Application of AOT quality flags reduced the sample size but does not affect AOT-PM 2.5 relationship significantly. We recommend using AOT quality flags for daily analysis, whereas long time scale analysis can be performed by using all AOT retrievals to obtain better sampling. Our analysis indicates that satellite data is a useful tool for monitoring particulate matter air quality especially in regions where ground measurements are not available. While these resultsCorrespondence to: P. Gupta (gupta@nsstc.uah.edu) are representative for the region of interest, it is difficult to extrapolate these results to a larger context without detailed analysis using ancillary data sets.

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“…1) centered on NBHM are evaluated to verify the assumptions used in Ichoku et al (2002). Table 2 provides all nine criteria used to obtain MODIS AOT.…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Varying Box Size Around Ground Stationmentioning

confidence: 99%

Section: Modismentioning

confidence: 99%

See 2 more Smart Citations

Seven year particulate matter air quality assessment from surface and satellite measurements

2008

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“…The benefits of a combination of ground based measurements and modeled fields and satellite data have been illustrated by and Hutchison et al (2003Hutchison et al ( , 2004Hutchison et al ( , 2005 for the mapping of NO 2 levels in the Netherlands and air quality prediction in Texas, respectively.…”

Section: Introductionmentioning

confidence: 99%

Statistical mapping of PM10 concentrations over Western Europe using secondary information from dispersion modeling and MODIS satellite observations

Kassteele

Koelemeijer

Dekkers

et al. 2006

Stoch Environ Res Ris Assess

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This paper illustrates the use of statistical techniques to standardize ground based measurements of particulate matter (PM10). Concentrations are interpolated over Western Europe using uncertain secondary information from a chemical transport model and of aerosol optical thickness from MODIS satellite observations. A consistent overview of PM10 concentrations over Europe based solely on ground based measurements is complicated by differences between countries. Different monitoring methods are used and calibrations are applied. There also is an inherent limitation to the spatial representativeness of ground based measurements. Validation showed that adding secondary information from either the chemical transport model or the satellite observations improved the PM10 mapping. The URMSE decreased from 5.14 to 4.26 and 4.58, respectively. A combination of both sources of secondary information gave the most accurate and precise predictions, with an URMSE of 3.62. This means that both external sources contain additional information on the spatial distribution of PM10 concentrations and should therefore be preferred.

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Merging Satellite Measurement with Ground-Based Air Quality Monitoring Data to Assess Health Effects of Fine Particulate Matter Pollution

Liebens

Rao

2011

Geospatial Analysis of Environmental Health

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Correlating MODIS aerosol optical thickness data with ground-based PM2.5 observations across Texas for use in a real-time air quality prediction system

Cited by 76 publications

References 14 publications

Seven year particulate matter air quality assessment from surface and satellite measurements

Seven year particulate matter air quality assessment from surface and satellite measurements

Statistical mapping of PM10 concentrations over Western Europe using secondary information from dispersion modeling and MODIS satellite observations

Merging Satellite Measurement with Ground-Based Air Quality Monitoring Data to Assess Health Effects of Fine Particulate Matter Pollution

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