The use of Satellite data for environmental exposure assessment in epidemiological studies. F RANCESCA DE ’D ONATO

Satellite data In recent years the use of satellite data to define exposure in environmental epidemiology has become more widespread. Satellite data have the advantage of: providing greater spatial coverage compared to ground monitoring networks. Better spatial resolution/characterization of space Good temporal coverage Readily available, limited costs Standardized products in continuous evolution Need some technical know-how for data processing

Presentation layout Two case studies of the use of satellite data in environmental epidemiology at the DEP: 1.Urban heat island in Rome and the effect on mortality 2.Health effects of wild fire smoke in Europe (Eu-project PHASE)

1. Urban Heat Island Effect Objective Is there a spatial differentiation in the impact of temperature on mortality modified by location within the urban area of Rome due to UHI and socio-economic position? Health effects of high temperatures on mortality in urban areas are well known, however there is limited evidence of spatial differences in exposure within cities.

ESA ENVISAT AATSR (Advanced Along-Track Scanning Radiometer) IR dataPERIOD 4 YEARS summer ( )DATASET 277 summer (diurnal/noctural) orbits Image recurrence every 3 days/twice daily diurnal (9:30-10:30) nocturnal (18:00-21:00) SPATIAL RESOLUTION 1x1km 137 nocturnal orbits Sensor calibration n= Clear sky condition: TCC Ciampino monitoring site<=2octas Corresponding to Tb11μ ≥ 289K Orbits with >50% missing data n=32 POSTPROCESSINGsatellite data POSTPROCESSING satellite data 1. Satellite 1. Satellite dataset

The scope was to define the spatial variation of UHI in Rome we considered Land Surface Temperature (LST). A split window technique was used to define LST derived from brightness temperatures (Prata and Platt, 1991) A fixed grid (386 points) was defined in order to overlay satellite images The UHI spatial pattern was very similar in 47 daily maps, an average UHI map was generated Through a clustering technique the UHI indicator was set to 6 classes Each census block in Rome was attributed a mean UHI grid value on the basis of the maximum proportion in each grid. 1. Definition of UHI

Rome UHI daily average trend

Urban Heat Island for Rome

Differential Impact of Tappmax on mortality considering UHI Materials and Methods All subjects aged 65+ years, resident in Rome and deceased in summer (april-september) between within the G.R.R Case cross-over analysis to estimate % increase in mortality for every 1°C above 27°C Tappmax (lag 0-1) assuming a linear relationship. Model is adjusted for (PM10, sea level pressure, holidays, reduction in summer population Stratified by: age, gender, SEP (high, middle, low) (Cesaroni et al. 2006), UHI (1=low, 2=high)

Results Tappmax (lag 0-1)-mortality relationship for low and high levels of UHI, in the elderly (aged 65+) during summer in Rome ( ). LOW UHIHIGH UHI

Percent change in mortality for 1°C above the threshold of 27°C Tappmax (lag 0-1) UHI and SEP.

Specific objectives  Biomass buring emissions inventory for Europe from satellite data ( )  Database on wildfires episodes, air pollutants and health indicators  Estimates of health impact of wildfires in selected case studies (Italy, Greece, France, Finland) 2. Health effects of wild fire smoke in Europe

Two approaches used to estimate biomass burning emissions using satellite data Bottom-up: estimates of surface area/type burned by fires is converted into emitted gases and aerosols (NOAA AVHRR,SPOT VGT, MODIS, ATSR) Top-down: derive emissions from CO concentrations measured in the atmosphere. (inversion models) MOPITT-NASA Measurement of Pollution in the Atmosphere)

To derive biomass burning emission inventories…. M (quantity of gas emitted) = Burnt Biomass x Emission Factor(species) Satellite data maps of burnt biomass vegetation map (biomass density, combustion efficiency) emission factor values Burnt Biomass = Burnt areas x Biomass density x Burning efficiency EF=Emissions factors in g of species/kg of dry matter Species: BC, OC, CO, CO2, NOx, NMVOC, SO2

MODIS Products to define burnt areas Modis Fire products (2001- present) 1.Active fire – 1km, daily and 8 day summaries 2.Burnt areas – 500m global monthly 3.Fire radiative Power – 1km daily

SPOT/VGT Global Land Cover map (2000 – present) Assumptions in the model Resolution : 1km x 1km

Global Fire emissions database (GFED) Gridded monthly burned areas, fuel loads. Combustion completeness and fire emissions (C, Co2, CH4, NMHC, etc) Compiled using NASA-CASA biogeochemical model and satellite derived active fires and burned areas (Giglio et al. 2010)

Dispersion modelling to study regional transport of biomass burning emissions (aerosol concentrations and exposure, ) Method: Aerosol and gas modeling with regional models: Regional climatic model (RegCM4) Horizontal resolution : 10 km x 10km Vertical resolution : 18 vertical levels Species of special interest: BC, OC, PM2.5, NO2, O3, other gases RegCM3 BC concentrations JJA 2000, (Solmon et al., 2006)

Italian case study: Genova ( ). – Satellite data: fire episodes, emissions. – Ground data: forestry fire department data ( ), air pollution data, mortality and hospital admissions data (total, CVD, Resp) – Time series analysis to estimate the effect of forest fires and their emissions on health outcomes. Greek case study: AthensFinnish case study: Helsinki Health impact of long-range transport of PM from Russian forest fires PHASE Project. Wild fires emissions effect on health in Europe.

Conclusions Better spatial/temporal resolution provides more detailed exposure promoting spatio-temporal analysis Can be useful also for near-real time monitoring, forecasting in warning systems However, validation with ground data is needed important to consider the differences between satellite and ground indicators need to acquire expert know-how Satellite data pose an exciting methodological challenge