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

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

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

Implications of alternative assumptions regarding future air pollution control in RCP-like scenarios

Estimation of future emissions of short-lived trace gases and aerosols from human activities is a main source of uncertainty in projections of future air quality and climate forcing. The Representative Concentration Pathways (RCPs), however, all assume that worldwide ambitious air pollution control policies will be implemented in the coming decades. In this study, we therefore explore the consequences of four alternative emission scenarios generated using the IMAGE integrated assessment model following the methods used to generate the RCPs. These scenarios combine low and high air pollution variants of the scenarios with radiative forcing targets in 2100 of 2.6 W/m2 and 6.0 W/m2 (the high air pollution variants assume no improvement in emission factors, representing a hypothetical upper end of emission levels). Analysis using the global atmospheric chemistry and transport model TM5 shows that climate mitigation and air pollution control policy variants studied here have similar large-scale effects on the concentrations of ozone and black carbon; the impact of climate policy, however, has a stronger impact on sulphate concentrations. Air pollution control measures could significantly reduce the warming by tropospheric ozone and black carbon and the cooling by sulphate already in 2020, and on the longer term contribute to enhanced warming by methane. These effects tend to cancel each other at the global scale. According to our estimates the effect of the worldwide implementation of air pollution control measures on the total global mean direct radiative forcing in 2050 is +0.09 W/m2 in the 6.0 W/m2 scenario and -0.16 W/m2 in the 2.6 W/m2 scenario

Journal of Spatial Science / Evaluation of modified Interferon alpha mRNA constructs for the treatment of non-melanoma skin cancer

Application of in vitro transcribed (IVT) messenger ribonucleic acid (mRNA) is an increasingly popular strategy to transiently produce proteins as therapeutics in a tissue or organ of choice. Here, we focused on the skin and aimed to test if whole human skin tissue explant technology can be used to evaluate the expression efficacy of different IVT Interferon alpha (IFN-) mRNA constructs in situ, after biolistic delivery. Skin explants were viable and intact for at least five days based on histologic analysis and TUNEL staining. Using GFP reporter mRNA formulations, we found mostly epidermal expression after biolistic delivery. Two out of five sequence-optimized IFN- mRNA variants resulted in significantly improved IFN- protein expression in human skin compared to native IFN- mRNA transfection. IFN- secretion analysis of the surrounding culture media confirmed these results. We provide a proof-of-concept that IFN- mRNA delivery into intact human full thickness skin explants can be utilized to test mRNA sequence modifications ex vivo. This approach could be used to develop novel mRNA-based treatments of common epidermal skin conditions including non-melanoma skin cancer, where IFN- protein therapy has previously shown a strong therapeutic effect.(VLID)286371

Bias Correction Techniques to Improve Air Quality Ensemble Predictions: Focus on O3 and PM Over Portugal

Five air quality models were applied over Portugal for July 2006 and used as ensemble members. Each model was used, with its original set up in terms of meteorology, parameterizations, boundary conditions and chemical mechanisms, but with the same emission data. The validation of the individual models and the ensemble of ozone (O3) and particulate matter (PM) is performed using monitoring data from 22 background sites. The ensemble approach, based on the mean and median of the five models, did not improve significantly the skill scores due to large deviations in each ensemble member. Different bias correction techniques, including a subtraction of the mean bias and a multiplicative ratio adjustment, were implemented and analysed. The obtained datasets were compared against the individual modelled outputs using the bias, the root mean square error (RMSE) and the correlation coefficient. The applied bias correction techniques also improved the skill of the individual models and work equally well over the entire range of observed O3 and PM values. The obtained results revealed that the best bias correction technique was the ratio adjustment with a 4-day training period, demonstrating significant improvements for both analysed pollutants. The increase in the ensemble skill found comprehends a bias reduction of 88 % for O3, and 92 % for PM10, and also a decrease in 23 % for O3 and 43 % for PM10 in what concerns the RMSE. In addition, a spatial bias correction approach was also examined with successful skills comparing to the uncorrected ensemble for both pollutants