An Improved Method for Estimating Surface Fine Particle Concentrations Using Seasonally Adjusted Satellite Aerosol Optical Depth

Weber, Stephanie; Engel-Cox, Jill; Hoff, R. M.; Prados, A. I.; Zhang, Hai

doi:10.3155/1047-3289.60.5.574

Cited by 44 publications

(30 citation statements)

References 21 publications

(32 reference statements)

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“…EngelCox et al, 2004a;Zhang et al, , 2011Hoff and Christopher, 2009;Weber et al, 2010). Studies have shown that the relationship between PM 2.5 and AOD is not always suitable for simple regression models.…”

Section: Introductionmentioning

confidence: 99%

“…They observed the poorest correlation for the southwestern USA, with EPA region number 9 having a correlation coefficient of approximately 0.2. Weber et al (2010) extended the work by In a notable study, van Donkelaar et al (2006) took the alternative approach of using remote sensing and a global transport model to present a global estimate of the long-term average PM 2.5 concentrations between the years 2001 and 2006 using satellite observations of AOD from MODIS to estimate η = PM 2.5 /AOD. The three-dimensional (3D) chemical transport model used was GEOS-Chem (http://acmg.seas.harvard.edu/geos/), and the authors found a significant spatial agreement with their estimates; the correlation coefficient for North American PM 2.5 measurements was 0.77 and elsewhere 0.83.…”

Section: Introductionmentioning

confidence: 99%

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Estimating the global abundance of ground level presence of particulate matter (PM2.5)

et al. 2014

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Abstract. With the increasing awareness of the health impacts of particulate matter, there is a growing need to comprehend the spatial and temporal variations of the global abundance of ground level airborne particulate matter with a diameter of 2.5 microns or less (PM 2.5). Here we use a suite of remote sensing and meteorological data products together with groundbased observations of particulate matter from 8,329 measurement sites in 55 countries taken 1997-2014 to train a machinelearning algorithm to estimate the daily distributions of PM 2.5 from 1997 to the present. In this first paper of a series, we present the methodology and global average results from this period and demonstrate that the new PM 2.5 data product can reliably represent global observations of PM 2.5 for epidemiological studies.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimating the global abundance of ground level presence of particulate matter (PM2.5)

et al. 2014

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“…AOD-PM 2.5 regression as was pointed by past studies 8,12 and highlights problems in determining a single regression line for accurately computing PM 2.5 from AOD data. To avoid problems with the AOD-PM 2.5 regression, another approach is proposed for utilizing the multiyear AOD data in air quality applications.…”

Section: Pm 25 and Aod Analysismentioning

confidence: 80%

“…The reported correlations showed a high degree of agreement, yet there was still interstate and intrastate variation. Research from Weber et al 12 recommends that seasonality and different platforms should be included in analysis of the PM 2.5 -AOD relationship.…”

Section: Technical Papermentioning

confidence: 99%

Investigation into the Use of Satellite Data in Aiding Characterization of Particulate Air Quality in the Atlanta, Georgia Metropolitan Area

Alston

Sokolik

Doddridge

2011

Journal of the Air & Waste Management Association

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Poor air quality episodes occur often in metropolitan Atlanta, GA. The primary focus of this research is to assess the capability of satellites as a tool in characterizing air quality in Atlanta. Results indicate that intracity PM 2.5 (particulate matter Յ 2.5 m in aerodynamic diameter) concentrations show similar patterns as other U.S. urban areas, with the highest concentrations occurring within the city. PM 2.5 and MODIS (Moderate Resolution Imaging Spectroradiometer) aerosol optical depth (AOD) have higher values in the summer than spring, yet MODIS AOD doubles in the summer unlike PM 2.5 . Most (80%) of the Ozone Monitoring Instrument aerosol index (AI) is below 0.5 with little differences between spring and summer. Using this value as a constraint of the carbonaceous aerosol signal in the urban area, aerosol transport events such as wildfire smoke associated with higher positive AI values can be identified. The results indicate that MODIS AOD is well correlated with PM 2.5 on a yearly and seasonal basis with correlation coefficients as high as 0.8 for Terra and 0.7 for Aqua. A possible alternative view of the PM 2.5 and AOD relationship is seen through the use of AOD thresholds. These probabilistic thresholds provide a means to describe the air quality index (AQI) through the use of multiyear AOD records for a specific area. The National Ambient Air Quality Standards (NAAQS) are used to classify the AOD into different AQI codes and probabilistically determine thresholds of AOD that represent most of a specific AQI category. For example, 80% of cases of moderate AQI days have AOD values between 0.5 and 0.6. The development of AOD thresholds provides a useful tool for evaluating air quality from the use of satellites in regions where there are sparse ground-based measurements of PM 2.5 .

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“…Satellite observations of AOD to estimate PM exposures provide more complete spatial and temporal coverage than fixed monitoring sites [20][21][22][23][24][25], but the existing methods using AOD are still limited to the resolution of the instrument's aerosol product retrieval algorithm. Fortunately, the spatial resolutions of satellite aerosol products have improved dramatically since their inception.…”

Section: Introductionmentioning

confidence: 99%

Characterization of Subgrid-Scale Variability in Particulate Matter with Respect to Satellite Aerosol Observations

et al. 2018

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Recent use of satellite observations of aerosol optical depth (AOD) to characterize surface concentrations of particulate matter (PM) air pollution has proven extremely valuable in estimating exposures for health effects studies. While the spatial resolutions of satellite data provide far better coverage than existing fixed site surface monitoring stations, they are not able to capture atmospheric processes such as dilution of primary pollutants that vary at small spatial scales. As a result, small-scale variability due to highly localized sources such as traffic may be poorly represented, which in turn may lead to exposure measurement error in epidemiological analyses. Using a fixed spatial grid representing 4.4 km Multiangle Imaging SpectroRadiometer (MISR) aerosol observations, we examined the spatial variability in fine and coarse mode PM (PM 2.5 and PM 2.5-10 respectively) measured at ground monitors from a unique spatially-dense sampling campaign in Southern California. We found that while the variance in measured PM 2.5 differed seasonally (warm 6.82 µg 2 /m 6 and cool 24.5 µg 2 /m 6 ) across the study region, the average subgrid (<4.4 km) variance did not (warm 2.03 µg 2 /m 6 and cool 2.43 µg 2 /m 6 ) and was significantly smaller. On the other hand, ground monitor PM 2.5-10 concentrations showed large variance in warm (18.6 µg 2 /m 6 ) and cool (20.6 µg 2 /m 6 ) seasons, as well as seasonal differences in subgrid variance (warm 8.90 µg 2 /m 6 and cool 3.28 µg 2 /m 6 ). Geostatistical analysis of the semivariance as a function of distance indicated that variability in measured PM 2.5 and PM 2.5-10 concentrations was relatively constant for spatial scales of one to five kilometers, but there was evidence of small-scale (~500 m) variability in PM 2.5-10 concentrations in the cool season. The lack of small-scale spatial variability in the warm season was likely due to large photochemical contributions to regional PM 2.5 , and greater regional contributions to PM 2.5-10 from windblown dust. In contrast, in the cool season there tends to be greater localized concentrations from primary traffic sources due to stronger nocturnal inversions and delayed morning winds reducing dilution that contribute to greater spatial heterogeneity. Overall, these results suggest that regional contributions tend to dominate PM 2.5 , and spatial resolutions of satellite observations including the 4.4 km MISR and 3 km MODIS aerosol products aptly capture relevant spatial variability. Coarse PM 2.5-10 can have seasonally dependent localized contributions, leading to small-scale variability below current satellite aerosol product resolutions.

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An Improved Method for Estimating Surface Fine Particle Concentrations Using Seasonally Adjusted Satellite Aerosol Optical Depth

Cited by 44 publications

References 21 publications

Estimating the global abundance of ground level presence of particulate matter (PM2.5)

Estimating the global abundance of ground level presence of particulate matter (PM2.5)

Investigation into the Use of Satellite Data in Aiding Characterization of Particulate Air Quality in the Atlanta, Georgia Metropolitan Area

Characterization of Subgrid-Scale Variability in Particulate Matter with Respect to Satellite Aerosol Observations

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