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Human health applications of atmospheric remote sensing

Human health applications of atmospheric remote sensing. Simon Hales, Housing and Health Research Programme, University of Otago, Wellington, New Zealand simon.hales@otago.ac.nz. Outline. Air pollution as a global public health issue 3 examples:

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Human health applications of atmospheric remote sensing

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  1. Human health applications of atmospheric remote sensing Simon Hales, Housing and Health Research Programme, University of Otago, Wellington, New Zealand simon.hales@otago.ac.nz

  2. 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

  3. 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.

  4. 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.

  5. SCIAMACHY 1:USA • 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).

  6. SCIAMCHY 2:Australia • Modelling relations between emissions and surface concentration • Prediction of public health (mortality) implications of hypothetICAL transport policy

  7. 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

  8. Spatial averaging of model predictions by small area (statistical local area, SLA):

  9. 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

  10. 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)

  11. 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

  12. 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

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