Uncertainty assessment in European air quality mapping and exposure studies

Bruce Rolstad Denby,Jan Horálek2, Frank de Leeuw3, Peter de Smet3

1Norwegian Institute for Air Research (NILU), PO BOX 100, 2027 Kjeller, Norway2 Czech Hydrometeorological Institute (CHMI), Praha

3 The Netherlands Institute for Public Health and the Environment (RIVM)

EGU, Vienna, April 2012

Background

• Every year the European Environment Agency (EEA) publishes maps of air quality in Europe

• These maps are used for public information, to inform authorities and for trend analysis

• Uncertainty maps are also produced and provided alongside the air quality maps

• Population exposure for all of Europe is assessed in terms of average exposures per country and exposure above threshold

Uncertainty questions

• How can we best quantify the spatial uncertainty in the air quality maps?

• How can we best quantify the uncertainty in the exposure calculations?

• How can we best quantify the uncertainties in the trend analysis?

Example mapping methodology for PM2.5

• Select annual mean concentrations of monitored particulate matter (PM2.5/PM10 ) from AirBase (200/1200 stations)

• Acquire spatially distributed supplementary data (population, altitude, meteorology, CTM)

• Create ’Pseudo’ PM2.5 data from the PM10 data using linear regression with some of the supplementary data

• Linearly regress the log transformed PM2.5 data with the supplementary data to create a base map

• Krig the logarithmic residuals using ordinary kriging to 10 km grids and add to the base map

• The (sub)urban and rural stations are interpolated seperately and then combined based on a population weighting

Distribution of PM10 and PM2.5 stations

Creation of ’pseudo’ PM2.5 from PM10

• Linear regression at station sites using PM10 + latitude + longitude + sunshine duration + population

Both rural and

(sub)urban

Creation of base map for PM2.5

• Linear regression using spatially distributed CTM + altitude + population + wind speed data

rural (sub)urban

Residual kriging of the residual

• Fitted emperical semi-variograms

rural (sub)urban

Residual kriging of the residual

• Leave-one-out cross validation

rural (sub)urban

Urban and rural interpolations combined to make maps

Residual kriging variance used for uncertainty maps

Concentration map is combined with population map to estimate exposure

• Population map of Europe

Calculation of aggregated population weighted uncertainty

• Population weighted concentration (Cpw)

• Spatial correlation (ρ) determined from variogram model (ϒ) (c is sill)

• Covariance deconvolved to all grid points (Ci,Cj) using calculated kriging variance (σi,j)and spatial correlation (ρi,j)

• Population weighted (ai,j) aggregated variance (σw

2) is calculated per country

c

hch

)()(

jiijji CC ,cov

ji

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i

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jjiw CCaa ,cov

1 1

2

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ii

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pw CaP

PCC

Aggregated exposure per country• Population weighted concentration for 2007 and 2008

Cyprus NederlandGermany

• Based on the aggregated uncertainty per country, shifting the distribution by ± σw (bias)

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Concentration (ug/m3)

Fre

qu

en

cy (

nu

mb

er

of s

tatio

ns)

Calculation of threshold uncertainty

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Concentration (ug/m3)

Fre

qu

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-5 -5

0 10 20 30 40 50 600

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Concentration (ug/m3)

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+5 +5

• Population exposed above the limit value (25 ug/m3) for 2007 and 2008

Aggregated exposure above threshold

ItalyPoland

RomaniaSerbia

Greece

Calculation of threshold uncertainty

• This is not a satisfactory method but is intended to be indicative

• Requires a better, more formal approach– e.g. Monte Carlo simulations of the original

interpolations

• What other possibilities exist?

Summary

• European wide maps of air pollutants are made using linear regression and residual kriging

• Uncertainty of the maps is estimated using the residual kriging variance

• Aggregated uncertainty in population weighted concentrations is determined using the variogram and deconvolving

• Aggregated uncertainty in exposure thresholds is not satisfactoraly determined

Questions to the floor

• Is the residual kriging variance a sufficent uncertainty indicator for this application?– Does it account for monitoring, ’pseudo’, representativeness,

spatial regression and interpolation uncertainties?

• Is the method applied to determine aggregated population weighted concentation uncertainty adequate or even correct?

• How can we determine the uncertainty of the exposure thresholds?

Report available

• Mapping annual mean PM2.5 concentrations in Europe: application of pseudo PM2.5 station data. ETC/ACM Technical Paper 2011/5

• URL: http://acm.eionet.europa.eu/reports/ETCACM_TP_2011_5_spatialPM2.5mapping

• Google: ”Mapping annual mean PM2.5”