EU effect: Exporting emission standards for vehicles through the global market economy.

Emission data from EDGAR (Emissions Database for Global Atmospheric Research), rather than economic data, are used to estimate the effect of policies and of the global exports of policy-regulated goods, such as vehicles, on global emissions. The results clearly show that the adoption of emission standards for the road transport sector in the two main global markets (Europe and North America) has led to the global proliferation of emission-regulated vehicles through exports, regardless the domestic regulation in the country of destination. It is in fact more economically convenient for vehicle manufacturers to produce and sell a standard product to the widest possible market and in the greatest possible amounts. The EU effect (European Union effect) is introduced as a global counterpart to the California effect. The former is a direct consequence of the penetration of the EURO standards in the global markets by European and Japanese manufacturers, which effectively export the standard worldwide. We analyze the effect on PM2.5 emissions by comparing a scenario of non-EURO standards against the current estimates provided by EDGAR. We find that PM2.5 emissions were reduced by more than 60% since the 1990s worldwide. Similar investigations on other pollutants confirm the hypothesis that the combined effect of technological regulations and their diffusion through global markets can also produce a positive effect on the global environment. While we acknowledge the positive feedback, we also demonstrate that current efforts and standards will be totally insufficient should the passenger car fleets in emerging markets reach Western per capita figures. If emerging countries reach the per capita vehicle number of the USA and Europe under current technological conditions, then the world will suffer pre-1990 emission levels.

Space-time analysis of the Air Quality Model Evaluation International Initiative (AQMEII) Phase 1 air quality simulations.

UNLABELLED: This study presents an evaluation of summertime ozone concentrations over North America (NA) and Europe (EU) using the database generated from Phase 1 of the Air Quality Model Evaluation International Initiative (AQMEII). The analysis focuses on identifying temporal and spatial features that can be used to stratify operational model evaluation metrics and to test the extent to which the various modeling systems can replicate the features seen in the observations. Using a synoptic map typing approach, it is demonstrated that model performance varies with meteorological conditions associated with specific synoptic-scale flow patterns over both eastern NA and EU. For example, the root mean square error of simulated daily maximum 8-hr ozone was twice as high when cloud fractions were high compared with when cloud fractions were low over eastern NA. Furthermore, results show that over both NA and EU the regional models participating in AQMEII were able to better reproduce the observed variance in ambient ozone levels than the global model used to specify chemical boundary conditions, although the variance simulated by almost all regional models is still less that the observed variance on all spatiotemporal scales. In addition, all modeling systems showed poor correlations with observed fluctuations on the intraday time scale over both NA and EU. Furthermore, a methodology is introduced to distinguish between locally influenced and regionally representative sites for the purpose of model evaluation. Results reveal that all models have worse model performance at locally influenced sites. Overall, the analyses presented in this paper show how observed temporal and spatial information can be used to stratify operational model performance statistics and to test the modeling systems' ability to replicate observed temporal and spatial features, especially at scales the modeling systems are designed to capture. IMPLICATIONS: The analyses presented in this paper demonstrate how observed temporal and spatial information can be used to stratify operational model performance and to test the modeling systems' ability to replicate observed temporal and spatial features. Decisions for the improvement of regional air quality models should be based on the information derived from only regionally representative sites.

Third international challenge to model the medium- to long-range transport of radioxenon to four Comprehensive Nuclear-Test-Ban Treaty monitoring stations.

In 2015 and 2016, atmospheric transport modeling challenges were conducted in the context of the Comprehensive Nuclear-Test-Ban Treaty (CTBT) verification, however, with a more limited scope with respect to emission inventories, simulation period and number of relevant samples (i.e., those above the Minimum Detectable Concentration (MDC)) involved. Therefore, a more comprehensive atmospheric transport modeling challenge was organized in 2019. Stack release data of Xe-133 were provided by the Institut National des Radioéléments/IRE (Belgium) and the Canadian Nuclear Laboratories/CNL (Canada) and accounted for in the simulations over a three (mandatory) or six (optional) months period. Best estimate emissions of additional facilities (radiopharmaceutical production and nuclear research facilities, commercial reactors or relevant research reactors) of the Northern Hemisphere were included as well. Model results were compared with observed atmospheric activity concentrations at four International Monitoring System (IMS) stations located in Europe and North America with overall considerable influence of IRE and/or CNL emissions for evaluation of the participants' runs. Participants were prompted to work with controlled and harmonized model set-ups to make runs more comparable, but also to increase diversity. It was found that using the stack emissions of IRE and CNL with daily resolution does not lead to better results than disaggregating annual emissions of these two facilities taken from the literature if an overall score for all stations covering all valid observed samples is considered. A moderate benefit of roughly 10% is visible in statistical scores for samples influenced by IRE and/or CNL to at least 50% and there can be considerable benefit for individual samples. Effects of transport errors, not properly characterized remaining emitters and long IMS sampling times (12-24 h) undoubtedly are in contrast to and reduce the benefit of high-quality IRE and CNL stack data. Complementary best estimates for remaining emitters push the scores up by 18% compared to just considering IRE and CNL emissions alone. Despite the efforts undertaken the full multi-model ensemble built is highly redundant. An ensemble based on a few arbitrary runs is sufficient to model the Xe-133 background at the stations investigated. The effective ensemble size is below five. An optimized ensemble at each station has on average slightly higher skill compared to the full ensemble. However, the improvement (maximum of 20% and minimum of 3% in RMSE) in skill is likely being too small for being exploited for an independent period.

Real-time monitoring data for real-time multi-model validation: coupling ENSEMBLE and EURDEP.

The ENSEMBLE system has been considerably expanded and improved. The system that allows the real-time collection of atmospheric dispersion forecasts their real-time consultation and ensemble dispersion analysis has been coupled with the EUropean Radiological Data Exchange Platform (EURDEP) for the acquisition of real-time monitoring data on environmental (mainly in air) radiological measurements. This paper explains how the coupling has been realized and presents the potentials of this unique system that is presently in use in more than 25 countries around the world.

Influence of long-range atmospheric transport pathways and climate teleconnection patterns on the variability of surface 210Pb and 7Be concentrations in southwestern Europe.

The variability of the atmospheric concentration of the 7Be and 210Pb radionuclides is strongly linked to the origin of air masses, the strength of their sources and the processes of wet and dry deposition. It has been shown how these processes and their variability are strongly affected by climate change. Thus, a deeper knowledge of the relationship between the atmospheric radionuclides variability measured close to the ground and these atmospheric processes could help in the analysis of climate scenarios. In the present study, we analyze the atmospheric variability of a 14-year time series of 7Be and 210Pb in a Mediterranean coastal city using a synergy of different indicators and tools such as: the local meteorological conditions, global and regional climate indexes and a lagrangian atmospheric transport model. We particularly focus on the relationships between the main pathways of air masses and sun spots occurrence, the variability of the local relative humidity and temperature conditions, and the main modes of regional climate variability, such as the North Atlantic Oscillation (NAO) and the Western Mediterranean Oscillation (WeMO). The variability of the observed atmospheric concentrations of both 7Be and 210Pb radionuclides was found to be mainly positively associated to the local climate conditions of temperature and to the pathways of air masses arriving at the station. Measured radionuclide concentrations significantly increase when air masses travel at low tropospheric levels from central Europe and the western part of the Iberian Peninsula, while low concentrations are associated with westerly air masses. We found a significant negative correlation between the WeMO index and the atmospheric variability of both radionuclides and no significant association was observed for the NAO index.

The Fukushima-137Cs deposition case study: properties of the multi-model ensemble.

In this paper we analyse the properties of an eighteen-member ensemble generated by the combination of five atmospheric dispersion modelling systems and six meteorological data sets. The models have been applied to the total deposition of (137)Cs, following the nuclear accident of the Fukushima power plant in March 2011. Analysis is carried out with the scope of determining whether the ensemble is reliable, sufficiently diverse and if its accuracy and precision can be improved. Although ensemble practice is becoming more and more popular in many geophysical applications, good practice guidelines are missing as to how models should be combined for the ensembles to offer an improvement over single model realisations. We show that the ensemble of models share large portions of bias and variance and make use of several techniques to further show that subsets of models can explain the same amount of variance as the full ensemble mean with the advantage of being poorly correlated, allowing to save computational resources and reduce noise (and thus improving accuracy). We further propose and discuss two methods for selecting subsets of skilful and diverse members, and prove that, in the contingency of the present analysis, their mean outscores the full ensemble mean in terms of both accuracy (error) and precision (variance).

Forecasting the consequences of accidental releases of radionuclides in the atmosphere from ensemble dispersion modelling.

The RTMOD system is presented as a tool for the intercomparison of long-range dispersion models as well as a system for support of decision making. RTMOD is an internet-based procedure that collects the results of more than 20 models used around the world to predict the transport and deposition of radioactive releases in the atmosphere. It allows the real-time acquisition of model results and their intercomparison. Taking advantage of the availability of several model results, the system can also be used as a tool to support decision making in case of emergency. The new concept of ensemble dispersion modelling is introduced which is the basis for the decision-making application of RTMOD. New statistical parameters are presented that allow gathering the results of several models to produce a single dispersion forecast. The devised parameters are presented and tested on the results of RTMOD exercises.

Can the confidence in long range atmospheric transport models be increased? The pan-european experience of ensemble.

Is atmospheric dispersion forecasting an important asset of the early-phase nuclear emergency response management? Is there a 'perfect atmospheric dispersion model'? Is there a way to make the results of dispersion models more reliable and trustworthy? While seeking to answer these questions the multi-model ensemble dispersion forecast system ENSEMBLE will be presented.

[Rapid determination of total blood proteins and serum albumin using automatic instrumentation]. / Determinazione rapida delle proteine totali e della albumina del siero con strumenti automatici.

We describe the automation of serum total protein and albumin determination on two analyzers. Classical biuret and bromcresol green reactions have been slightly modified to meet the performance characteristics of the two analyzers (Multistat and Theia), in order to achieve and elevated productivity.