Air Quality Assessment over Gulf Coast

8/10/2019 Air Quality Assessment over Gulf Coast

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Venkata B. Dodla, and colleagues

Trent Lott Geospatial Visualization Research Centre

College of Science, Engineering &Technology

Jackson State University, Jackson MS 39217, USA

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8thInternational Symposium on Recent Advances in Environmental Health Research

Jackson, MS, USA

September 20, 2011

Air Quality Assessment over Gulf Coast by Establishing

Relationship Between MODIS-Satellite Derived Aerosol

Optical Thickness (AOT) and Surface PM2.5 Observations


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AQI Forecasting /Ozone/PM 2.5

This presentation is on our studies AQI Forecasting for PM2.5using SatelliteRemote sensing Data (MODIS Data)

o To Provide year-round coverage

o EPA pilot effort focusing on 36 major cities across the U.S

o Currently developing PM2.5 forecast tools for State and Local Agencies MODIS/AIRNow data fusion would be one of many tools

1. Simulation of surface Ozone pollution in the Central Gulf Coastal region during

summer synoptic condition using WRF/Chem air quality model. 2011,

Atmospheric Pollution Research, DOI:10.5094/APR.2012.005

2. Air Quality Modeling for Urban Jackson, MS Region using High Resolution

WRF/Chem Model. 2011, Int. J. Environ. Res. Public Health, 8, 2470-2490;doi:10.3390/ijerph8062470.

3. Sensitivity Evaluation of the WRF/Chem to Different PBL and Land Surface Physics

for Air Quality Simulations in the Mississippi Gulf Coastal Region. 2010, Advances

in Meteorology, Volume 2010, Article ID 319138, 24 pages. doi:10.1155/2010/

319138, Hindawi Publishing Corporation, New York, USA.

Ozone and PM2.5 are high significant pollutants for Air Quality Index(AQI)

Our Group earlier published a number of papers on simulation of Ozone

levels in Gulf Coast


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1:18

Coordinated Observations with the A-Train

TES

T, P, H2O, O3, CH4, COMLSO3, H2O, CO

HIRDLST, O3, H2O, CO2, CH4

OMIO3, aerosol climatology

aerosols,

polarization

CloudSat3-D cloud

climatologyCALIPSO3-D aerosol

climatology

AIRST, P,H2O, CO2, CH4

MODIScloud,

aerosols, albedo

OCO- CO2O2A-band

ps, clouds,

aerosols


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Air Pollution

Airborne particles and gases occurring in

concentrations that endanger the health andwell-being of organisms or disrupt the orderly

functioning of the environment. Lutgens and

Tarbuck

Air pollutants are categorized into two

categories:

Primary PollutantsSecondary Pollutants


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Primary Pollutants

Anthropogenic: Combustion Processes

Chemical Processes

Nuclear or Atomic Processes

Roasting, Heating and RefiningProcesses

Mining, Quarrying and Farming

Processes

Natural:

Volcanoes Breaking Seas

Pollens and Terpenes

Fire

Blowing Dust

Bacteria and Viruses

Primary Pollutants are airborne particles that are emitted directly fromidentifiable sources. These tiny structures are known collectively as Particulatematter (PM). Once suspended in either air or water, the mixture of the two

becomes known as an Aerosol.


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Secondary Pollutants

Secondary Pollutants are not emitted directly into the air, but form in theatmosphere from reactions taking place between primary pollutants.

SMOG (Smoke + Fog) VOG (Volcanic + Smog)

Ground Level Ozone


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OzoneStratospheric Ozone: GOOD Ozone

Contains 90% of atmospheric ozone

Primary Shield for UV Radiation from Sun

Produced by UV rays interacting with Oxygen

Tropospheric Ozone: Bad Ozone

Other 10% of Atmospheric Ozone

Forms close to the ground when

Hydrocarbons and Nitrogen Oxides react

with sunlight.

Detrimental to human respiratory health

Slows and alters growth of many species

of plants


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How are these pollutants spread?

Wind

Dilution is the solution topollution

Wind causes bodies of polluted air

to spread out across the Earth.

When winds are high, these

bodies are spread out over larger

areas, leading to a lower

concentration of pollutants.

When winds are low, these bodies

sit and stagnate over a smaller

area leading to localized pocketsof more concentrated pollution.


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Health Impacts of Air Pollution


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OBJECTIVES Establishing the relationship between the ground-level concentration of

fine particulate matter (PM2.5) using space-based measurements from the

Moderate Resolution Imaging Spectroradiometer (MODIS)

Improve accuracy of PM2.5AQI forecast for Regional/Seasonal

Characteristics and concentrations of air pollution is required at all

locations for assessment and implementation of mitigation measures.

prevent health and environmental impacts. Since monitoring at all

locations is not possible, improving observation network is essential,

utilization of satellite data improves spatial observations.

PM2.5 Observations MODIS-AOT Satellite

Establishing relationship between

PM2.5

and AOT

Estimation of PM2.5from AOT


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Percent increase in monthly mortality per increase in 1 g/m3of PM2.5

concentrations (June, 2000)


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Source: US EPA, 2003

PM2.5Continuous Monitoring Sites Reporting to AQS & AIRNow7/7/03


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Average quarterly PM2.5concentrations based on AIRNow data from 2005-2007

PM2.5concentrations

vary by

season and

region

Seasonal Patterns-National/Regional


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Derivation of Aerosol Optical Thickness (AOT)

from MODIS satellite data

Satellite imagery,

Large spatial coverage and reliable repeated measurements,

Advantageous tool to monitor aerosols and their transport patterns compared to

ground measurements

Aerosol Optical Thickness (AOT) - parameter - air quality.

AOT is the degree to which aerosols prevent the

transmission of light.

.


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Launched on Terra 12/18/1999, on Aqua 3/24/2002

Terra crosses Equator ~10:30 am, Aqua ~1:30 pm (orbit time is ~98

min)

36 spectral bands with resolutions spatial resolutions

250 m (bands 1 - 2)

500 m (bands 3 - 7) 1000 m (bands 836)

Global coverage 1-2 days

Swath Width ~ 2330 km

Altitude - 705 km

Orbit - Near-polar, Sun Synchronous

MODIS instrument on Terra satellite collecting

swaths of data over the Earth

MODIS (Moderate Resolution Imaging Spectroradiometer)


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Terra collects data on

descending node

Aqua collects data on

ascending node

TERRA

AQUA


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o The MODIS Aerosol Product (MOD04 (Terra), MYD04 (Aqua)) monitors the

ambient aerosol optical thickness over the oceans globally and over the

continents.o The aerosol size distribution is derived over the oceans, and the aerosol type

is derived over the continents. Fineaerosols (anthropogenic/pollution) and

courseaerosols (natural particles; e.g., dust) are also derived.

o The aerosol product includes the deep-bluealgorithm recently developed to

get aerosol optical thickness over bright land areas.

o Aerosol Total Optical Thickness is available through Giovanni at 550nm fromMODIS.

o Daily Level 2 AOT data available

o Time duration : Five minute

o Coverage: Once in a day

o Resolution: 10km X 10km - 1km pixels at nadir

o 3 spectral bands used in retrieving AOT - 0.47, 0.66 and 2.1 m

o GSFC urban/industrial aerosol model over Eastern United States used for

Retrieval of MODISAOT

Description of MODIS - Aerosol Product


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PM2.5 Surface Observations

o US EPA Monitors all over the USA.

Study region covers MS, parts of LA.

o ~32 monitoring sites in study region.

o PM2.5 data has been collected for

the entire year 2007 data with an

hour interval.

http://www.epa.gov/ttn/airs/airsaqs/

detaildata/downloadaqsdata.htm

Data used in the present study

MODIS AOT Datasets

AOT Data has been collected from both TERRA and AQUA for the year 2007 once in

a day, and the product is

Optical_Depth_Land_And_Ocean Description: Aerosol Optical Thickness at 0.55

m for both Ocean (best) and Land (corrected) with best quality data (QA

Confidence Flag = 3)

Dimensions: (Cell_Along_Swath, Cell_Across_Swath); (203,135)

Valid Range: -0.05 to 3.0 (Collection 050)

http://ladsftp.nascom.nasa.gov/allData/5/MOD04_L2/---- terra

http://ladsftp.nascom.nasa.gov/allData/5/MOD04_L2/ -- aqua


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As MODIS AOT data is available at 10kmx10km horizontal resolution; and surface

PM2.5 observations are available at point locations, we adopted two

methodologies to estimate the AOT value corresponding to the PM2.5 observationlocation.

Two kinds of PM2.5 datasets were prepared from hourly observations as (1) Daily-

Averaged value (2) MODIS Swath hour observation.

(1) MODIS-AOT at the four grid points (Grid Box method) around PM2.5

observation location are used to compute a simple and a distance weightedaverage

(2) Collect MODIS-AOT data falling within a specified radius and using them to

compute a simple average and a distance-weighted average. In this study we

used three values for radius as (i) 20 km; (ii) 50 km and (iii) 100 km

Both these methodologies are constrained to have at lease 4 data points.

TLGVRC developed the required FORTRAN software to retrieve the AOT data as an

in-house product.

Methodology


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Relationship between MODIS AOT and surface PM2.5

AOT (aerosol optical thickness)

MODIS (AQUA/TERRA) Data product

AOT vs PM2.5 relationship

Regression analysis

Software development

(TLGVRC)PM 2.5

Surface observations

Retrieval of AOT

to station location

Satellite Data has following Limitations

(i) AOT can be derived only during day time hence only one observation available per day.

(ii) AOT only available under less cloud cover or no-cloud.

(iii) Both in Grid Box method and Radial Distance Method, must have the AOT data at least

4 points.


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Averaged AOT from 4 neighboring points around PM2.5 observation site location given as

Distance Weighted AOT from 4 neighboring data points around PM2.5 Station location given as

4

y4)(x4,MODIS

y3)(x3,MODIS

y2)(x2,MODIS

y1)(x1,MODIS

),(

yxAOT

W4W3W2W1

W4*y4)(x4,

MODISW3*y3)(x3,

MODISW2*y2)(x2,

MODISW1*y1)(x1,

MODIS

),(.

yxAOTW


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Averaged AOT computed within the defined radius of around the PM2.5 observation site

location given as

n

iyx

AOT1

ii

n

)y,(xMODIS),(

Distance Weighted Average AOT computed within the defined radius of around the PM2.5

observation site location given as

n

i

i

i

i

yx

W

W

AOTWeighted

1

n

1 ii

),(

*)y,(x

MODIS

_

n-total number of observationsavailable within the required radius

n-total number of observations available within the required radius

W-weight (here distance between observation site location and we consider as weight)


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PM2.5 VS AOT, obtained from MODIS-TERRA swath hour considering all

points within 100 km radial distance and for the year 2007 data.

No of observations


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Correlation is better with hourly values as compared to daily averages. There is

no significant difference between simple and weighted averages.

Time MethodTotal No. of

Data PointsCC MAE RMSE

Daily

Average

SA 149 0.13 5.48 9.42

WA 149 0.13 5.48 9.42

Swath

Hour

SA 161 0.31 6.15 7.45

WA 161 0.33 6.15 7.45

99.9% Statistical significance

SASimple Average; WA-Distance Weighted Average

CC-Correlation Coefficient; MAE-Mean Absolute Error

RMSE-Root Mean Square Error

Relationship between PM2.5 and AOT from TERRA SatelliteGrid Box Method


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Radius

(km)Time Method

Total No. of


100

DailyAverage

SA 969 0.29 4.78 6.64

WA 969 0.29 4.79 6.65

Swath

Hour

SA 2041 0.31 5.91 8.59

WA 2041 0.30 5.92 8.62

50

Daily

Average

SA 864 0.28 4.83 6.76

WA 864 0.27 4.83 6.77

Swath

Hour

SA 1660 0.32 5.92 8.79

WA 1660 0.30 5.94 8.84

20

Daily

Average

SA 173 0.20 5.05 6.55

WA 173 0.19 5.06 6.57

Swath

Hour

SA 251 0.46 6.10 8.72

WA 251 0.43 6.13 8.88


99% Statistical significance




Relationship between PM2.5 and AOT from TERRA SatelliteRadius Method


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Correlation is better with hourly values as compared to daily averages. There is

no significant difference between simple and weighted averages.

Time MethodTotal No. of


Daily

Average

SA 7 -0.13 3.09 3.82

WA 7 -0.13 3.09 3.82

Swath

Hour

SA 24 0.60 3.60 4.54

WA 24 0.60 3.60 4.54





Relationship between PM2.5 and AOT from AQUA SatelliteGrid Box Method


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Radius

(km)Time Method

Total No. of


100

DailyAverage

SA 743 0.23 5.28 7.34

WA 743 0.23 5.28 7.34

Swath

Hour

SA 2037 0.30 5.64 7.88

WA 2037 0.30 5.64 7.89

50

Daily

Average

SA 645 0.22 5.24 7.43

WA 645 0.20 5.26 7.45

Swath

Hour

SA 1677 0.29 5.56 7.78

WA 1677 0.28 5.58 7.81

20

Daily

Average

SA 95 0.31 4.50 5.55

WA 95 0.33 4.51 5.50

Swath

Hour

SA 253 0.37 5.45 6.84

WA 253 0.38 5.45 6.83


99% Statistical significance




Relationship between PM2.5 and AOT from AQUA Satellite


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Conclusions

MODIS AOT data has been collected over MS region and PM2.5 surface

observations from US EPA to establish relationship between them.

(i) A positive relation exists between PM2.5 and Aerosol Optical Thickness.

(ii) Correlations are high with the MODIS Pass time observations hour as

compared to the Daily averaged PM2.5 observations.

(iii) No significant differences found with Simple Averaged method and

Distance average method. Simple average method is showing little

higher correlation than Distance weighted average method.

(iv) AOT computed within 100 km radius is having higher correlation than

with lesser radial distances.

(v) AOT computed using 4 neighboring method has less (160) observations

as compared to the method 100 km radius distance (2040).

(vi) TERRA Satellite provide more number of observations than AQUA

satellites


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TLGVRC Algorithms for spatial estimation of PM2.5 using

MODIS Aerosol Optical Depth

Spatial Estimation of PM2.5

Derive AOT data at PM2.5

observation site using Grid box

method and Radial Distance Method

Establishing Relationship Between

PM2.5and AOT

Estimate Spatial PM2.5using AOT

PM2.5 Observations

MODIS - AOT

Estimated PM2.5 from AOT


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NASA AURA SATELLITE (launched July 2004)

AuraMLS

TES nadirOMI

HIRDLSDirection of motion

TES limb

Polar orbit; four passive instruments observing same air mass within 14 minutes

OMI: UV/Vis solar backscatterO3, NO2, SO2, HCHO, BrO columns

TES: high spectral resolution thermal IR emissionnadir Ozone, COlimb Ozone, CO, HNO3

MLS: microwave emissionlimb Ozone, CO (upper troposphere)

HIRDLS: high vertical resolution thermal IR emissionOzone in upper troposphere/lower stratosphere

Tropospheric measurement capabilities:


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Researches the composition, chemistry,and dynamics of the Earths atmosphere

as well as study the ozone, air quality,and climate.

HIRDLS: High Resolution Dynamics Limb SounderObserves global distributionof temperature and composition of the upper troposphere, stratosphere, and mesosphere

MLS: Microwave Limb SounderUses microwave emission to measure

stratospheric temperature and upper tropospheric constituents

OMI : Ozone Monitoring InstrumentDistinguishes between aerosol types, such as

smoke, dust, and sulfates. Measure cloud pressure and coverage, which provide data toderive tropospheric ozone.

TES: Tropospheric Emission SpectrometerHigh-resolution infrared-imaging

Fourier transform spectrometer that offers a line-width-limited discrimination of

essentially all radiatively active molecular species in the Earth's lower atmosphere.

Instruments

EOS Aura


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HIRDLS

multi-channel, microwave radiometer

radiated thermal emissions from the atmospheric limb

spectral intervals in the range (6 to 17) mm, chosen to correspond to

specific gases and atmospheric "windows".

global 3-D fields of atmospheric temperature, several minor

constituents, and geostrophic winds.

34


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Microwave Limb Sounder (MLS)

The EOS MLS measures thermal emission frombroad spectral bands centered near 118, 190, 240,640 and 2250 GHz

35


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Microwave Limb Sounder (MLS)

36


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OMI : Ozone Monitoring Instrument

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The OMI instrument can distinguish between aerosol types, such assmoke, dust, and sulfates, and measures cloud pressure and coverage,

which provide data to derive tropospheric ozone.


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TES: Tropospheric Emission Spectrometer

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Example of TES products


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Thank YOU

Anjaneyulu. Yerramilli, Ph.D.

Professor & Director

Email: [email protected]

Phone: 601979-3654

Jackson State University

Authors thank the support for Atmospheric

Dispersion Project (ADP) by National Oceanic

and Atmospheric Administration (NOAA) and

NWS through the U.S. Department of

Commerce (SilverSprings,MD); Contract

#NA06OAR4600192

Air Quality Assessment over Gulf Coast

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