Human health applications of atmospheric remote sensing

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Human health applications of atmospheric remote
sensing

• Simon Hales, Housing and Health Research
  Programme, University of Otago, Wellington, New
  Zealand simon.hales_at_otago.ac.nz

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Outline

• Air pollution as a global public health issue
• 3 examples
• Global burden of disease from ambient PM
  estimated using MODIS aerosol
• Calibrating SCIAMACHY NO2 with surface
  monitoring USA and Europe
• Assessing implications of climate/energy/transport
  policy on air pollution exposure and health
  impacts in Australia
• Next steps
• Estimating spatial distribution of exposure in
  New Zealand using OMI NOx
• UVB Vitamin D
• Conclusion

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Air pollution a global health issue

• Estimated 1.2 of deaths 0.5 PYLL (measures of
  global burden of disease, BoD)
• Data inputs for BoD from ambient air pollution
• Population exposed
• Long term average exposure (PM10 preferred)
• Dose-response from cohort studies deaths,
  hospital admissions
• Exposure uncertain, derived from sparse network
  of fixed (usually urban) monitoring sites, plus
  empirical modelling
• economic, weather, population data and available
  PM measurements in 304 cities used to estimate
  PM10 levels in 3000 cities with populations
  greater than 100,000.

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Remote sensing?

• More detailed exposure data would be preferable
• MODIS AOT calibrated using urban station data
• Result extrapolated to all land areas, population
  weighted and then aggregated at country scale
• Estimate of 20 global mortality (which is
  unfeasibly large)
• Probable over estimation of exposure, due to
  predominance of monitors in regions that are more
  polluted at the surface.

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SCIAMACHY 1USA

• Annual (2003) average NO2 and PM2.5 data for
  monitoring stations in the USA
• The annual average NO2 data were derived from
  hourly averages (up to 24 measurements per day,
  or 8760 measurements per year).

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r N
NO2satellite vs NO2station 0.70 457

NO2satellite vs PM2.5 0.64 130

NO2station vs PM2.5 0.57 130
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SCIAMCHY 2Australia

• Modelling relations between emissions and surface
  concentration
• Prediction of public health (mortality)
  implications of hypothetICAL transport policy

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Two step modelling approach

• Model A relationships between surface
  monitoring and average SCIAMACHY tropospheric
  retrievals for Sydney and Melbourne, Australia
• Found similar (linear) relationship for each city

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• Spatial averaging of model predictions by small
  area (statistical local area, SLA)

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Model B

• Use external data on point source and diffuse
  (vehicle) emissions
• Model relationship between average NOx and
  natural log of total emissions, by SLA
• Predict effect of changing emissions on exposure
  within SLAs

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• Potentially important input to climate/energy
  policy could help validate emission reductions?
• Can also estimate likely effects of
  energy/transport policy changes on human health
• In this example, the effect of 50 reduction in
  vehicle emissions is substantial (several hundred
  early deaths per year in each city)

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Next steps RS data and public health

• OMI data for NZ will be used as input to several
  epidemiological studies
• Estimates of spatial patterns of NOx for study of
  seasonal patterns of heart disease currently
  underway
• Applications of surface UVB estimates effect on
  Vitamin D synthesis in skin
• many public health implications emerging
• need to understand how much UVB exposure is
  optimal for different populations

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Conclusions

• Simple regression method using SCIAMACHY NOx data
  works quite well for USA, Australia but not
  Europe or New Zealand
• Possibly relates to
• Scale of satellite observations vs scale of
  spatial variation of NOx in different regions?
• Time of observations not representative?
• Regional differences in vertical profile
  (tropospheric column not representative of
  surface levels)?
• Cloud effects??
• Could be resolved by meteorological/transport
  modelling (for discussion)
• Thanks to Folkert Boersma, Ronald van der A for
  the invitation and travel funding
• SH is funded by the National Heart Foundation of
  NZ