Comparing the aggregated health risk calculated from different Earth observation resources

Abstract

Ambient air quality (AQ) is a recurrent issue in cities, exacerbated in low- and middle-income countries. Global AQ impacts on health can be assessed using an aggregated health risk indicator (ƩRIs), derived from in-situ stations, chemical transport models (CTMs) or satellite data. AQ monitoring is well covered in the city of Munich (Germany): in-situ stations, POLYPHEMUS CTM with three domains, CAMS-Reanalysis of the regional model ensemble and satellite data from MODIS. From these data sets, the ƩRIs was calculated considering four major air pollutants (NO2, PM10, PM2.5, O3), using their respective relative risk for the mortality-all causes health end-point. Then, the ƩRIs from the models and the satellite data were compared to the ƩRIs in-situ by means of basic statistics, time series and violin plots and the mean relative difference (MRD). Using the ƩRIs allows to further observe the contribution of individual pollutants to the index. For the mortality all causes health end point between 2017 and 2018, ground observations and CTMs show an increase of ca. 12-13% when exposed to ambient air pollution. The difference between traffic and background stations can be observed: ƩRIs in situ mean is higher at the traffic station than at the background stations. This order is however reversed when considering ƩRIs mean from the models. The four CTMs simulate the ƩRIs well and its seasonality is also represented. Most of the data are spread around the mean and the median for all data sets and stations with an overall distribution skewed towards high values. With 0.5<r2<0.6, POLYPHEMUS/DLR yields medium correlation, regardless the domain, while CAMS-Reanalysisreturns high correlation (r2 ≈ 0.8) for all the studied stations. The MRD indicates an underestimation of the ƩRIs by CAMS-Reanalysis, while POLYPHEMUS tends to overestimate it for the larger domains (positive MRD). The difference in the r2 between the two CTMs is due to their singularities: POLYPHEMUS/DLR uses free runs while CAMS-Reg uses a data assimilation process with station measurements (among them the two studied background stations). The overestimation of Johanneskirchen by POLYPHEMUS/DLR comes from its location nearby a power plant and the wind direction. Finally, the very high values in early 2017 can be explained by fireworks, which are not reproduced by models. It is show in this study that estimating a global health risk from air pollution is possible using in-situ measurements, models and satellite. Finally, satellite data can be helpful to assess the ƩRIs worldwide