Using Geographically Referenced Data on Environmental Exposures for Public Health Research: A Feasibility Study Based on the German Socio-Economic Panel Study (SOEP)

Background: In panel datasets information on environmental exposures is scarce. Thus, our goal was to probe the use of area-wide geographically referenced data for air pollution from an external data source in the analysis of physical health. Methods: The study population comprised SOEP respondents in 2004 merged with exposures for NO2, PM10 and O3 based on a multi-year reanalysis of the EURopean Air pollution Dispersion-Inverse Model (EURAD-IM). Apart from bivariate analyses with subjective air pollution we estimated cross-sectional multilevel regression models for physical health as assessed by the SF-12. Results: The variation of average exposure to NO2, PM10 and O3 was small with the interquartile range being less than 10µg/m3 for all pollutants. There was no correlation between subjective air pollution and average exposure to PM10 and O3, while there was a very small positive correlation between the first and NO2. Inclusion of objective air pollution in regression models did not improve the model fit. Conclusions: It is feasible to merge environmental exposures to a nationally representative panel study like the SOEP. However, in our study the spatial resolution of the specific air pollutants has been too little, yet.SOEP, Geographically Referenced Data, Feasibility Study, Air Pollution, EURAD-IM, Physical Health

Tropospheric Chemical State Estimation by Four-Dimensional Variational Data Assimilation on Nested Grids

The University of Cologne chemistry transport model EURAD and its four-dimensional variational data assimilation implementation is applied to a suite of measurement campaigns for analysing optimal chemical state evolution and flux estimates by inversion. In BERLIOZ and VERTIKO, interest is placed on atmospheric boundary layer processes, while for CONTRACE and SPURT upper troposphere and tropopause height levels are focussed. In order to achieve a high analysis skill, some new key features needed to be developed and added to the model setup. The spatial spreading of introduced observational information can now be conducted by means of a generalised background error covariance matrix. It has been made available as a flexible operator, allowing for anisotropic and inhomogeneous correlations. To estimate surface fluxes with high precision, the facility of emission rate optimisation using scaling factors is extended by a tailored error covariance matrix. Additionally, using these covariance matrices, a suitable preconditioning of the optimisation problem is made available. Furthermore, a module of adjoint nesting was developed and implemented, which enables the system to operate from the regional down to the local scale. The data flow from mother to daughter grid permits to accomplish nested simulations with both optimised boundary and initial values and emission rates. This facilitates to analyse constituents with strong spatial gradients, which have not been amenable to inversion yet. Finally, an observation operator is implemented to get to assimilate heterogeneous sources of information like ground-based measurements, airplane measuring data, Lidar and tethered balloon soundings, as well as retrieval products of satellite observations. In general, quality control of the assimilation procedure is obtained by comparison with independent observations. The case study analyses show considerable improvement of the forecast quality both by the joint optimisation of initial values and emission rates and by the increase of the horizontal resolutions by means of nesting. Moreover, simulation results for the two airplane campaigns exhibit outstanding characteristics of the assimilation system also in the middle and upper troposphere region

Inverse Modelling and Combined State-Source Estimation for Chemical Weather

Air quality data assimilation aims to find a best estimate of the control parameters (see theory chapter) for those processes of the atmosphere which govern the chemical evolution of biologically relevant height levels, typically located in the the lowermost atmosphere. As in data assimilation (see theory chapters), we have to resort to numerical models to complement usually sparse observation networks; these models serve as system constraints. Several research groups are developing data assimilation methods similar to those applied to meteorological applications. Techniques range from nudging to advanced spatio-temporal methods such as four-dimensional variational (4D-Var) data assimilation and various simplifications of the Kalman filter (KF)

Using geographically referenced data on environmental exposures for public health research: a feasibility study based on the German Socio-Economic Panel Study (SOEP)

Voigtländer S, Goebel J, Claßen T, et al. Using geographically referenced data on environmental exposures for public health research: a feasibility study based on the German Socio-Economic Panel Study (SOEP). SOEPpapers. Berlin: Deutsches Institut für Wirtschaftsforschung; 2011.Background: In panel datasets information on environmental exposures is scarce. Thus, our goal was to probe the use of area-wide geographically referenced data for air pollution from an external data source in the analysis of physical health. Methods: The study population comprised SOEP respondents in 2004 merged with exposures for NO2, PM10 and O3 based on a multi-year reanalysis of the EURopean Air pollution Dispersion-Inverse Model (EURAD-IM). Apart from bivariate analyses with subjective air pollution we estimated cross-sectional multilevel regression models for physical health as assessed by the SF-12. Results: The variation of average exposure to NO2, PM10 and O3 was small with the interquartile range being less than 10µg/m3 for all pollutants. There was no correlation between subjective air pollution and average exposure to PM10 and O3, while there was a very small positive correlation between the first and NO2. Inclusion of objective air pollution in regression models did not improve the model fit. Conclusions: It is feasible to merge environmental exposures to a nationally representative panel study like the SOEP. However, in our study the spatial resolution of the specific air pollutants has been too little, yet