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Abstract: The review of major 3-D global and regional real-time air quality forecasting (RT-AQF) models in Part I identifies several areas of improvement in meteorological forecasts, chemical inputs, and model treatments of atmospheric physical, dynamic, and chemical processes. Part II highlights several recent scientific advances in some of these areas that can be incorporated into RT-AQF models to address model deficiencies and improve forecast accuracies. Current major numerical, statistical, and computational techniques to improve forecasting skills are assessed. These include bias adjustment techniques to correct biases in forecast products, chemical data assimilation techniques for improving chemical initial and boundary conditions as well as emissions, and ensemble forecasting approaches to quantify the uncertainties of the forecasts. Several case applications of current 3-D RT-AQF models with the state-of-the-science model treatments, a detailed urban process module, and an advanced combined ensemble/data assimilation technique are presented to illustrate current model skills and capabilities. Major technical challenges and research priorities are provided. A new generation of comprehensive RT-AQF model systems, to emerge in the coming decades, will be based on state-of-the-science 3-D RT-AQF models, supplemented with efficient data assimilation techniques and sophisticated statistical models, and supported with modern numerical/computational technologies and a suite of real-time observational data from all platforms. [Copyright &y& Elsevier]

Abstract: Real-time air quality forecasting (RT-AQF), a new discipline of the atmospheric sciences, represents one of the most far-reaching development and practical applications of science and engineering, poses unprecedented scientific, technical, and computational challenges, and generates significant opportunities for science dissemination and community participations. This two-part review provides a comprehensive assessment of the history, current status, major research and outreach challenges, and future directions of RT-AQF, with a focus on the application and improvement of three-dimensional (3-D) deterministic RT-AQF models. In Part I, major milestones in the history of RT-AQF are reviewed. The fundamentals of RT-AQF are introduced. Various RT-AQF techniques with varying degrees of sophistication and skills are described comparatively. Among all techniques, 3-D RT-AQF models with online-coupled meteorology–chemistry and their transitions from mesoscale to unified model systems across scales represent a significant advancement and would greatly enhance understanding of the underlying complex interplay of meteorology, emission, and chemistry from global to urban scales in the real atmosphere. Current major 3-D global and regional RT-AQF models in the world are reviewed in terms of model systems, component models, application scales, model inputs, forecast products, horizontal grid resolutions, and model treatments of chemistry and aerosol processes. An important trend of such models is their coupling with an urban model or a computational fluid dynamic model for urban/local scale applications at 1 km or less and with an exposure model to provide real-time public health assessment and exposure predictions. Evaluation protocols are described along with examinations of current forecasting skills and areas with large biases of major RT-AQF models. [Copyright &y& Elsevier]

A four-dimensional variational data assimilation system (4D-Var) is developed to retrieve carbon monoxide (CO) fluxes at regional scale, using an air quality network. The air quality stations that monitor CO are proximity stations located close to industrial, urban or traffic sources. The mismatch between the coarsely discretised Eulerian transport model and the observations, inferred to be mainly due to representativeness errors in this context, lead to a bias (average simulated concentrations minus observed concentrations) of the same order of magnitude as the concentrations. 4D-Var leads to a mild improvement in the bias because it does not adequately handle the representativeness issue. For this reason, a simple statistical subgrid model is introduced and is coupled to 4D-Var. In addition to CO fluxes, the optimisation seeks to jointly retrieve influence coefficients, which quantify each station's representativeness. The method leads to a much better representation of the CO concentration variability, with a significant improvement of statistical indicators. The resulting increase in the total inventory estimate is close to the one obtained from remote sensing data assimilation. This methodology and experiments suggest that information useful at coarse scales can be better extracted from atmospheric constituent observations strongly impacted by representativeness errors. [ABSTRACT FROM AUTHOR]

Abstract: Ozone is a harmful air pollutant at ground level, and its concentrations are routinely measured with monitoring networks. The network design problem aims at determining the optimal positioning of the monitoring stations. In this study, the background stations of the French routine pollution monitoring network (BDQA) are partially redistributed over France under a set of design objectives. These background stations report ozone variations at large spatial scale comparable with that of a chemistry-transport model (CTM). The design criterion needs to be defined on a regular grid that covers France, where in general no ozone observations are available for validation. Geostatistical ozone estimation methods are used to extrapolate concentrations to these grid nodes. The geostatistical criteria are introduced to minimize the theoretical error of those geostatistical extrapolations. A physical criterion is also introduced to measure the ability of a network to represent a physical ozone field retrieved from CTM simulations using geostatistical extrapolation methods. A third type of criteria of geometrical nature, e.g. a maximal coverage of the design domain, are based uniquely on the distance between the network stations. To complete the network design methodology, a stochastic optimization method, simulated annealing, is employed in the algorithm to select optimally the stations. Significant improvement with all the proposed criteria has been found for the optimally redistributed network against the original background BDQA network. For instance, the relative improvements in the physical criterion value range from 21% to 32% compared to randomly relocated networks. Different design criteria lead to different optimally relocated networks. The optimal networks under physical criteria are the most heterogeneously distributed. More background stations are displaced to the coast, frontiers, and large urban agglomerations, e.g. Paris and Marseilles. The ozone heterogeneous fields are not as well reconstructed from optimal networks under geostatistical or geometrical criteria as from the optimal network obtained with the physical criterion. The values of the physical criterion for the geostatistically and geometrically optimal networks show deteriorations of about 8% and 17% respectively compared to that of the physically optimal network. [Copyright &y& Elsevier]

Abstract: Ozone is a harmful air pollutant at ground level, and its concentrations are measured with routine monitoring networks. Due to the heterogeneous nature of ozone fields, the spatial distribution of the ozone concentration measurements is very important. Therefore, the evaluation of distributed monitoring networks is of both theoretical and practical interests. In this study, we assess the efficiency of the ozone monitoring network over France (BDQA) by investigating a network reduction problem. We examine how well a subset of the BDQA network can represent the full network. The performance of a subnetwork is taken to be the root mean square error (rmse) of the hourly ozone mean concentration estimations over the whole network given the observations from that subnetwork. Spatial interpolations are conducted for the ozone estimation taking into account the spatial correlations. Several interpolation methods, namely ordinary kriging, simple kriging, kriging about the means, and consistent kriging about the means, are compared for a reliable estimation. Exponential models are employed for the spatial correlations. It is found that the statistical information about the means improves significantly the kriging results, and that it is necessary to consider the correlation model to be hourly-varying and daily stationary. The network reduction problem is solved using a simulated annealing algorithm. Significant improvements can be obtained through these optimizations. For instance, removing optimally half the stations leads to an estimation error of the order of the standard observational error (10 μg m−3). The resulting optimal subnetworks are dense in urban agglomerations around Paris (Île-de-France) and Nice (Côte d’Azur), where high ozone concentrations and strong heterogeneity are observed. The optimal subnetworks are probably dense near frontiers because beyond these frontiers there is no observation to reduce the uncertainty of the ozone field. For large rural regions, the stations are uniformly distributed. The fractions between urban, suburban and rural stations are rather constant for optimal subnetworks of larger size (beyond 100 stations). By contrast, for smaller subnetworks, the urban stations dominate. [Copyright &y& Elsevier]

Abstract: The objective of this article is to investigate the validity of a modeling system developed for forecasting atmospheric dispersion, the Polyphemus platform, with a special focus on radionuclides. The platform is briefly described and model-to-data comparisons are reported for three cases: the ETEX campaign, the Chernobyl accident and the Algeciras release. The results are similar to those usually given in the literature by state-of-the-art models. Some preliminary sensitivity analysis indicates the main sources for uncertainties. [Copyright &y& Elsevier]