A simple and fast method to downscale chemistry transport model output fields from the regional to the urban/district scale

International audienceFor policy applications, the need to improve the resolution of environmental variables is crucial. Air pollution assessment indeed requires the use of air pollutant concentration fields at a high resolution, to better evaluate the exposure of citizens. In this paper, we propose a fast proxy-based downscaling strategy, to downscale air quality modelling results using the fraction of the pollutant concentration influenced by precursor emissions in a given cell. The approach combines in an additive way (i) a classically interpolated background pollutant fraction, with (ii) a proxy-based concentration derived from the emissions. The proxy-based pollutant fraction is spread over the high resolution mesh into the surrounding cells with a Gaussian approach to account for diffusion effects. The evaluation of our approach against observations shows its relevance to create reliable air pollution concentration fields at a higher resolution, starting from a coarse resolution modelling results

An Evaluation of the CHIMERE Chemistry Transport Model to Simulate Dust Outbreaks across the Northern Hemisphere in March 2014

Mineral dust is one of the most important aerosols over the world, affecting health and climate. These mineral particles are mainly emitted over arid areas but may be long-range transported, impacting the local budget of air quality in urban areas. While models were extensively used to study a single specific event, or make a global analysis at coarse resolution, the goal of our study is to simultaneously focus on several affected areas—Europe, North America, Central Asia, east China and the Caribbean area—for a one-month period, March 2014, avoiding any parameter fitting to better simulate a single dust outbreak. The simulation is performed for the first time with the hemispheric version of the CHIMERE model, with a high horizontal resolution (about 10 km). In this study, an overview of several simultaneous dust outbreaks over the Northern Hemisphere is proposed to assess the capability of such modeling tools to predict dust pollution events. A quantitative and qualitative evaluation of the most striking episodes is presented with comparisons to satellite data, ground based particulate matter and calcium measurements. Despite some overestimation of dust concentrations far from emission source areas, the model can simulate the timing of the arrival of dust outbreaks on observational sites. For instance, several spectacular dust storms in the US and China are rather well captured by the models. The high resolution provides a better description and understanding of the orographic effects and the long-range transport of dust plumes

Which relations between deterministic simulations and observations?

International audienceThe state of environment (air, rivers and groundwater, etc.) is described by observations on one hand and by deterministic simulations based on the physics and chemistry and/or on the biology of complex phenomenon on the other hand, with results that usually differ. Generally the deterministic simulation is less variable than observations, but the differences cannot be explained by the only differences of support between observations (spatial "point" values) and simulation results (representing rather averaged quantities on the grid). In order to enhance the simulation to match the observations, a "simple" bivariate model consists in splitting the studied variable Y as the sum of the deterministic simulation S and a correction term R which is supposed to be not correlated (spatially or temporally) with S: Y = S + R. The observations Z differ from Y by a measurement error term. Within this model, the estimation of Y from the observations Z can be reduced to the kriging of the residual R from the " innovations " Z - S at observation points. Joint exploratory analysis of observations and results of deterministic simulations shows that this bivariate model does not always suit to the data. Innovations appear to be correlated with the simulation S. In order to take such correlations into account, Chilès, Séguret et al. (2008) proposed an intrinsic correlation model between the variable Y and the deterministic simulation S. This intrinsic correlation model is generalized here to the linear model of coregionalization. Examples are presented in air and river quality modeling. Consequences for the estimation are examined

Spatio-temporal assessment of pregnant women exposure to chlorpyrifos at a regional scale

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