Human health applications of atmospheric remote sensing Simon Hales, Housing and Health Research Programme, University of Otago, Wellington, New Zealand

Outline Air pollution as a global public health issue 3 examples: – Global burden of disease from ambient PM estimated using MODIS aerosol – Calibrating SCIAMACHY NO 2 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

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 (PM 10 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 PM 10 levels in 3000 cities with populations greater than 100,000.

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.

SCIAMACHY 1:USA Annual (2003) average NO 2 and PM2.5 data for monitoring stations in the USA The annual average NO 2 data were derived from hourly averages (up to 24 measurements per day, or 8760 measurements per year).

rN NO 2satellite vs NO 2station NO 2satellite vs PM NO 2station vs PM

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

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

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

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

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)

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

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