Where monitoring meets modelling: application of a dispersion model in the design of a monitoring campaign

In this work a methodology based on a dispersion model is proposed, described and tested, to plan a monitoring air quality campaign nearby a waste incinerator. Aim of the campaign is to evaluate the impact of the incinerator smokestack on the air quality, in an area including many emission sources. The goal of the proposed methodology is to optimize yhe choice of the sampling sites and periods, in order to assess the impact of the incinerator. comparison to the context, and an incinerator-related tracer could be difficult to recognise. Given these constraints, a special effort is required in selecting the sampling sites and periods, in order to collect simultaneous samples so that the differences between them, in chemical and physical characteristics, can mainly be attributable to the incinerator. Therefore, a priori information on the expected impact of the confounding emissions and - separately - of the plant, i.e. its distribution in space and time, is needed. In this paper, how the dispersion model simulations have been used to get such information is described. METHODOLOGY The method has 3 sequential steps: 1. the choice of the sites for monitoring the heaviest impact of the plant; 2. the choice of the monitoring periods; 3. the choice of the control sites. 1. A simulation has been carried out with the ADMS-Urban gaussian modified dispersion model (Carruthers et al., 1994; CERC, 2006), considering the plant as the lonely emission source. The simulation covers one year. The meteorology has been selected in a way without strong anomalies in respect to the climate. Due to its low daily and seasonal variability, emissions are considered constant. Output concentrations are calculated over a regular grid. As result of this first simulation, the regions of the integration domain where the impact of the plant is higher are identified. Inside each of these "high impact areas" one site for monitoring the maximum impact of the plant is selected on the basis of feasible criteria. Also, a "negligible impact area" is highlighted. In this area the simulated concentrations are at least 90% lower than the concentration calculated in the "high impact areas". 2. Another simulation, with the same input data as the first one has been caried out. The hourly concentration calculated over the highest impact sites (concentration time series) were the output of this simulation. The aim fo this two simulations is to identify not only the points where the impact of the plants are the highest (first simulation), but also the periods when those impacts are highest, for every high impact sites considered (second simulation). In particular, the months that show the highest simulated concentrations has been chosen for the campaign. The type 1. simulation has been performed in different time periods to compare seasonal different behaviour of different high impact sites. Moreover, for each monitoring sites, a classification tree is calibrated to forecast the probability to exceed a given threshold fo the calculated hourly impact of the plant (therefore not directly measurable). For the calibration, the meteorological parameters (input of ADMS-Urban) are used as predictors and the concentration (output of ADMS-Urban) as predictand. These classification trees are a fast tool to provide real-time information during the campaign, e.g. to support the decision to eventually delay the end of the campaign. 3. At last we have to select the control sites. In order to have information about the impact of the plant, each high impact monitoring site should be associated to a control site. This control sites are chosen in such a way to maximize the differences attributable to the plant stack and minimize the differences between them attributable to the surrounding sources. To do that, we need to perform a new simulation. The emission sources taken into account for this simulation were the "confounding"