SOSE Awards - Extended Abstract

7/31/2019 SOSE Awards - Extended Abstract

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Utilization of Aerosol Optical Depth Derived From the Multiangle Imaging

Spectroradiometer (MISR) for Air Quality Measurement in the Manila Observatory:

Observations and Analysis

1 Background of the Study

Aerosols are liquid and solid particles that are suspended in the atmosphere. The termparticulate matter (PM) refers to aerosols and their health impact. PM2.5 means particulate matter

with size less than 2.5m. Moreover, from the climate perspective, studies have shown that theunderstanding of aerosols is a key factor in enhancing the predictive power of climate models

because of the aerosols reflective and absorptive tendencies that affect the earths radiationbudget [2].

Around the world, particulate matter account for much of urban and even rural pollution,motivating studies to quantify the gravity of their adverse effects on the health of humans. One

particular study reports that there is 1-8% increase in mortality for every 50g/m3 increase in PM

concentration [1, 4]. One stark example that emphasizes the adverse effects of PM can be found

in the Tata Energy Research Institute in India data that recorded an estimated 18,600 premature

deaths associated with poor air quality in the Delhi region per year It is in light of these that themonitoring of the health effects of particulate matter have been undertaken and more stringent

regulations in the allowed levels of PM have been employed by environmental agencies around

the world such as the United States Environment Protection Agency. Amidst all these, satelliteremote sensing proposes an alternative for detecting PM by producing regular high resolution

data that is available to the public.

The Multiangle Imaging SpectroRadiometer (MISR), an instrument aboard NASAs

Earth Observing Systems Terra satellite, aids in this goal of a more global and regular picture of

aerosols. MISR is in sun-synchronous polar orbit that has a local pass over time of around

10:30am. It reports its data in four spectral bands and nine cameras. MISR Paths 115, 116 and

117 cross the Manila Observatory. The aerosol optical depth (AOD) is reported by MISR. The

AOD is a measure of the extinction of light that passes through the atmosphere and, similar toPM concentration, is also an indication of the amount of aerosols present in the atmosphere. [3,

5, 6]The goal of the study is three-fold. The first is to assess the capacity of MISR data as a

surrogate for ground measured data by determining the correlation between the two data sets.

The second goal, given that a correlation exists, would be the determination of the model thatbest captures this relationship. Finally given all these, the final goal is to explore further the

capacity of the MISR data set.

3 MethodologyIn 2004 and 2005, PM2.5 and PM10 data were measured every fifteen minutes from a -

ray sampler stationed at the Climate Studies Division of Manila Observatory. In this study,

10:30a.m. PM data were used since the Terra satellite overpass in the Philippines occurred every

10:30, each day. Local sources of PM around the Manila Observatory region include vehicular

traffic, sulfate, nitrate, biomass burning, sea salt, and soil (http://www.observatory.ph/).MISR_AM1_AEROSOL files, which is the file format used to denote a MISR Level 2

Aerosol products, were acquired from the Langley Research Center data pool. Paths 115-117 for

2004 and 2005, since these are the only years for which PM concentrations are available, wereselected as they are known to pass over MO. The paths were identified using the MISR Browser

online tool that allowed for the matching of latitude and longitude location to the appropriate
http://www.observatory.ph/http://www.observatory.ph/http://www.observatory.ph/http://www.observatory.ph/


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path. The raw data set included 120 co-incident measurements from the ground and with MISR.

To remove outlying data points, the Cooks Distance was computed and the data set was prunedto 109 available points for analysis. The files were then analyzed using the HDF View

application software that was designed to read, visualize and for some cases edit the contents of

the HDF file. Among the numerous MISR data fields, two data sets were chosen. The first was

RegBestEstimateSpectralOptDepth, which represents the best estimate of the AOD within theMISR 1.1km x 1.1km study area while the second was the Fractional AOD data, which indicates

the per component contribution of several MISR aerosol models.

The PM2.5 concentrations were correlated with the MISR aerosol optical depth product.The equation that was generated from this correlation was used to verify the relationship between

the PM and MISR AOD data. In addition to this, a binning analysis was used to emphasize the

correlative nature of the two data sets. Ten bins in 10g/m3

intervals were used for PM2.5.Four different computational approaches were explored. The first approach used a simple

linear regression. MISR AOD and PM data were correlated using a linear regression. Predicted

PM values were acquired by substituting the MISR AOD value into the resulting regression

equation. The next two models used artificial neural networks (ANN). The first used MISR AOD

as its input data while the second used MISR AOD and relative humidity. The last approach usedmultivariate regression with MISR Fractional AOD values as predictors. MatLab was used in the

implementation of the artificial neural network with the default learning algorithm and transferfunction in the softwares Neural Networks Toolbox. The resulting models were compared using

theR2values of the correlation and and their average error percentage. All data sets other than

MISR AOD and MISR fractional AOD were acquired from the MO.For each aerosol model used in the MISR algorithm, the percentage of their contribution

to the total aerosol optical depth was recorded both for the overall and the daily averages. After

which, the models were categorized based on their speciation and percentage contributions were

computed for the particular speciations.

4 Results and DiscussionThis study showed that there exists a good correlation between MISR-derived AOD and

PM2.5 concentration readings from the Manila Observatory. Through all four year studied. As a

whole, as shown in figure 1a-b below, the overall correlation for all years was 0.7037 and toemphasize this correlation, a binning analysis resulting to a correlation of 0.9844 was done.

Figure 1. Shown are the (a) overall scatterplot for 2004, 2005 and 2011 and the (b) binning analysis.

Four different computational approaches were identified based on a survey of literature,

which were of similar objectives to this study. In the analysis, it was shown that the fractionalAOD approach that utilized multivariable regression had the lowest average error percentage at

17.4%. This error percentage is significantly lower than those reported in similar studies, which

is usually in the range of 25-30%. The error values are shown in figures 2a-d.

y = 0.007x + 0.0355

R = 0.7037

0

0.2

0.4

0.6

0.8

0 10 20 30 40 50 60 70 80 90

MISRAOD

PM2.5 Column Density (g/m3)

y = 0.0072x + 0.032

R = 0.9844

0.00

0.20

0.40

0.60

0.80

0 10 20 30 40 50 60 70 80 90

MISRAOD

PM2.5 Column Density (g/m3)

(a) (b)


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Figure 2. Scatter plots of actual and predicted PM values for (a) Linear Regression (b) ANN(AOD) (c) ANN(AOD

and RH) and (d) Multivariate Regression of Fractional AOD.

Besides the relative accuracy of the MISR fractional AOD data set in the computation of

ground-based PM data, another interesting aspect of it is its ability to lend insight into the

microphysical characteristics of the aerosols present as well. The MISR Fractional AOD data setreports 72 aerosol types that are appropriated to various characterizations. In the location being

studied, only 7 aerosol types manifested. These aerosol types were AOD1, AOD2, AOD3,

AOD6, AOD8, AOD14 and AOD21. Each aerosol type as reported by MISR is attributed to aspecific kind of aerosol. It is known that AOD1, AOD3 and AOD14 are all sulfate and nitrate

based aeorosols, AOD2, AOD6 and AOD8 are black carbon based aerosols and AOD21 is a dust

based aerosol. In using these categorization, it was found out that the most prevalent kind of

aerosol present in the atmosphere was the black carbon kind with a percentage contribution as

high as 65.6%.

5 Conclusions

In general, the study shows the utility of using data from MISR as a surrogate for ground-

measured PM concentration data. Specifically, this study has shown that1. There exists a good relationship between MISR AOD data and PM2.5 ground data.2. Out of four various statistical methods used to relate MISR data with PM 2.5 ground data,

the lowest error came with the use of the multivariate regression approach with MISRfractional AOD data.

3. Using the MISR fractional AOD data, one can glean other information about the aerosolspresent in the atmosphere.

y = 1.0031xR = 0.7214

ERROR: 20.5%0

20

40

60

80

100

120

0 20 40 60 80 100

ComputedPM2.5

Value

Actual PM2.5 Value

y = 1.0058x

R = 0.7261

ERROR: 18.3%0

20

40

60

80

100

120

0 20 40 60 80 100

ComputedPM2.5

Value

Actual PM2.5 Value

y = 0.9791x

R = 0.7099

ERROR: 18.6%0

20

40

60

80

100

120

0 20 40 60 80 100ComputedPM2.5

Value

Actual PM2.5 Value

y = 0.9576x

R = 0.7315

ERROR: 17.4%0

20

40

60

80

100

120

0 20 40 60 80 100ComputedPM2.5

Value

Actual PM2.5 Value

AOD1

AOD2

AOD3

AOD6

AOD8

AOD14

AOD21

66%13%

21%Carbon

Sulfates/Nitrates

Dust

(a)

(b)

(c)

(d)


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SOURCES

1. Gupta, P. and Christopher, S. A., Seven year particulate matter air quality assessmentfrom surface and satellite measurements,Atmos. Chem. Phys. 8, 3311-3324, 2008.

2. Kaufman, Yoram J., Direct wind measurements of Saharan dust events from Terra andAqua satellites, Geophysical Research Letters, 31, L06122, doi:10.1029/2003GL019338,

2004.3. Khan, R., et al., Multiangle Imaging Spectroradiometer global aerosol optical depthvalidationbased on two years of coincident Aerosol Robotic Network observations,Journal

of Geophysical Research, 110, pp 1-16, 2005.4. Krewski, D., Burnett, R. T., Goldberg, M. S., Hoover, K., Siemiatycki, J., Jerrett, M.,

Abrahamowicz, A. and White,W. H., Reanalysis of the Harvard Six Cities Study and the

American Cancer Society Study of Particulate Air Pollution and Mortality, A Special Report

of the Institutes Particle Epidemiology Reanalysis Project, Health Effects Institute,Cambridge MA, 97 , 2000.

5. National Aeronautics and Space Administration: Jet Proulsion Laboratory. (2010).Multiangle Imaging SpectroRadiometer. Retrieved September 21, 2010, from

http://www.nrel.gov/learning/re_biofuels.html.6. Yang L., et al., Estimating Fine Particulate Matter Component Concentrations and SizeDistributions Using Satellite-Retrieved Fractional Aerosol Optical Depth: Part 1Method

Development,Air and Waste Management, 57. Pp1351, 1360.

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SOSE Awards - Extended Abstract