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Assessing Health Effects of Particulate Matter Using MODIS Aerosol Data. Zhiyong Hu [email protected] 850-474-3494. Background. Tropospheric aerosols: liquid or solid particles suspended in the air

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Assessing health effects of particulate matter using modis aerosol data
Assessing Health Effects of Particulate Matter Using MODIS Aerosol Data

Zhiyong Hu

[email protected]

850-474-3494


Background
Background Aerosol Data

  • Tropospheric aerosols: liquid or solid particles suspended in the air

  • Natural sources: sea-spray uplift, soil-dust uplift, volcanic eruptions, natural biomass burning, plant material emissions, and meteoric debris.

  • Anthropogenic sources: fugitive dust emissions, biomass burning, fossil fuel combustion, and industrial sources.

  • Fine mode aerosol: <=2μm (homogeneous nucleation and emissions from combustion and biomass burning).

  • Course mode: > 2 μm (sea-spay, natural soil dust, and fugitive soil dust).


Aerosol optical depth
Aerosol Optical Depth Aerosol Data

Aerosol optical depth (AOD): or aerosol optical thickness, the optical depth due to extinction by the aerosol component of the atmosphere.

The total optical depth is comprised of molecular optical depth (due to scattering), gaseous optical depth (due to absorption), and cloud and aerosol optical depths (due to scattering and absorption).

Molecular optical depth depends only upon surface pressure and wavelength.

For a clear sky the optical depths due to gaseous absorption can be calculated for each wavelength allowing the aerosol optical depth to be separated by satellite remote sensing.

Fine AOD: the part of the total AOD contributed by fine aerosols.


Health effects of fine aerosol particles
Health Effects of Fine Aerosol Particles Aerosol Data

  • Although most regulations of air pollution focus on gases, aerosol particles cause more visibility degradation and possibly more health problems than do gases (Jacobson, 2002).

  • PM2.5 causes the most severe health problems, e.g., cardiopulmonary problems, respiratory illness, and premature death.

  • For the use of public health assessment, particulate matter ground monitoring data often lacks spatially complete coverage.

  • Some studies have found that AOD calculated from satellite remotely sensed imagery are positively correlated to ambient PM concentrations.


Moderate resolution imaging spectroradiometer modis
MODerate-resolution Imaging Spectroradiometer (MODIS) Aerosol Data

  • Onboard NASA Satellites Terra & Aqua

    • Launched 1999, 2002

    • 705 km polar orbits, descending (10:30 a.m.) & ascending (1:30 p.m.)

  • Sensor Characteristics

    • 36 spectral bands ranging from 0.41 to 14.385 µm

    • Cross-track scan mirror with 2330 km swath width

    • Spatial resolutions:

      • 250 m (bands 1 - 2)

      • 500 m (bands 3 - 7)

      • 1000 m (bands 8 - 36)

    • 2% reflectance calibration accuracy


Modis atmosphere products
MODIS Atmosphere Products Aerosol Data

Pixel-level (level-2) products

  • Cloud mask for distinguishing clear sky from clouds.

  • Cloud radiative and microphysical properties.

  • Cloud top pressure, temperature, and effective emissivity.

  • Cloud optical thickness, thermodynamic phase, and effective radius

  • Aerosol optical properties

    Optical depth over the land and ocean

    Size distribution (parameters) over the ocean

  • Atmospheric moisture and temperature gradients

  • Column water vapor amount

    Gridded time-averaged (level-3) atmosphere product.

    Daily, 8-day, and monthly products (1° ´1° equal angle grid).


Monitoring and forecasting of air quality aerosols
MONITORING AND FORECASTING OF AIR QUALITY: AEROSOLS Aerosol Data

Annual mean PM2.5 concentrations (2002)derived from MODIS AODs

van Donkelaar et al. [JGR 2007]


Objective and Methods of the Study Aerosol Data

  • Objective: use of aerosol data derived from satellite remote sensing as an air pollution indicator to assess the health effect of particulate matter

  • Methods

  • - Assess MODIS level 2 hourly AOD against EPA hourly PM2.5

  • monitoring data.

  • - Use MODIS Level 3 yearly mean fine AOD to explore relationship

  • b/w fine AOD and EPA PM2.5 annual summary data.

  • - Map comparison, and spatial statistical modeling.


Disease data
Disease Data Aerosol Data

  • Low birth weight (by county)

    - CDC WONDER Online Database.

    - Counts of all birth weights and low birth weights (< 2,500 gram).

    - Each birth record represents one living baby.

    - Year born: 2004.

    - Gestational age at birth: 37-39 weeks.

    - Counties with a total population less than 100,00 report births under “Unidentified counties” and thus, were excluded from analysis.

  • Stroke mortality 1999-2005 (by county) (ICD-10 code: I64)

    - CDC WONDER Online Database.

    - Total population, death count, age-adjusted rate (using the census 2000 standard population).

    - “Unreliable” data removed from the analysis.


Assess MODIS level 2 hourly AOD against EPA PM2.5 monitoring data

120 AOD images used, covering March 1 – October 31


Pm2.5 = 7.916+0.032AOD data

R square = 0.5960

Adjusted R square = 0.5957

P < 0.001


Modis level 3 fine aod
MODIS Level 3 Fine AOD data

  • Monthly mean AOD, 2003-2004.

  • Monthly mean fraction of AOD in the fine mode.

  • Fine mode AOD were calculated by multiplying monthly mean AOD by fine fraction.

  • Yearly mean fine AODs calculated by averaging the monthly mean. However, winter months (11, 12, 1, 2) data were not used due to unsuccessful retrieval of data for parts of northern regions covered by snow and ice (the land “deep blue” algorithm relies on dark targets).


PM data2.5 = 7.303+0.047 FAOD

R2 = 0.667

Adjusted R2 = 0.665

P < 0.001


PM2.5 = 7.303+0.047 FAOD data

RMSE = 2.76 ug/m3



Spatial autocorrelation
Spatial Autocorrelation data

Uni-variate Moran’s I:

mean rate in neighbors vs. low birth weight rate.

Bi-variate Moran’s I: Average Low birth weight in neighbors vs. fine AOD


Bivariate lisa cluster map
Bivariate LISA Cluster Map data

LISA - Local indicators of spatial autocorrelation


Statistical modeling of low birth weight rate and fine aod spatial lag model
Statistical Modeling of Low Birth dataWeight Rate and Fine AOD -Spatial Lag Model



Spatial autocorrelation1
Spatial Autocorrelation data

Age adjusted stroke mortality rate vs. mean rate in the neighbors.

Mean age-adjusted stroke mortality rate in the neighbors vs. fine AOD.




Conclusions
Conclusions Fine AOD - Spatial Lag Model

  • U.S. southeast-east regions have higher fine AOD values than west-northwest regions.

  • Significant positive relation between AOD and PM2.5 in Eastern US.

  • Significant positive relation b/w PM25 and Fine AOD.

  • Low birth weight rate and age adjusted stroke mortality rate show similar spatial pattern as fine AOD.

  • There are positive association between fine AOD and low birth weight as well as stroke.

  • Satellite measurement of AOD could directly be used as an air pollution indicator for public health effect assessment in the lack of ground monitoring data.


Acknowledgements
Acknowledgements Fine AOD - Spatial Lag Model

This study is a component of the "Assessment of Environmental Pollution and Community Health in Northwest Florida" supported by U.S. EPA Cooperative Agreement Award X-9745002 to the University of West Florida. The content of this report are solely the responsibility of the authors and do not necessarily represent the official views of the U.S. EPA.


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