Spatial variance in multimedia mass balance models: comparison of LOTOS-EUROS and SimpleBox for PCB-153

The aim of this study was to determine whether nested generic box models can be used to predict spatial variance. An inter-comparison study was performed for the nested box model SimpleBox, and the spatially resolved model LOTOS-EUROS, using PCB-153 emissions in Europe as an example. We compared the two models concerning (1) average environmental concentrations, (2) spatial concentration variances, (3) spatial concentration patterns (maps), and (4) agreement with measured concentrations for the air and soil compartments. In SimpleBox, the spatial concentration variances and patterns were calculated subsequently for each separate grid cell surrounded by a regional and a continental shell with homogeneous, averaged circumstances. Average European PCB-153 concentrations calculated by LOTOS-EUROS and SimpleBox for the period 1981-2000 agree well for the air and soil compartments. Moreover, the predicted concentrations of both models are in line with the measured PCB-153 concentrations in Europe during that period. For PCB-153, the prediction of spatial concentration variances with the nested multimedia fate model SimpleBox performs adequately in most cases, except for the lower concentration boundary in the air compartment. It is concluded that SimpleBox can be used to predict the spatial maximum and average concentrations of PCB-153 in the air and soil compartments. The proposed method has to be tested systematically for different types of compounds, emission scenarios, environmental compartments and spatial scales in order to allow conclusions about the general applicability of the method.     

Comparison of different stomatal conductance algorithms for ozone flux modelling

A multiplicative and a semi-mechanistic, BWB-type [Ball, J.T., Woodrow, I.E., Berry, J.A., 1987. A model predicting stomatal conductance and its contribution to the control of photosynthesis under different environmental conditions. In: Biggens, J. (Ed.), Progress in Photosynthesis Research, vol. IV. Martinus Nijhoff, Dordrecht, pp. 221-224.] algorithm for calculating stomatal conductance (g(s)) at the leaf level have been parameterised for two crop and two tree species to test their use in regional scale ozone deposition modelling. The algorithms were tested against measured, site-specific data for durum wheat, grapevine, beech and birch of different European provenances. A direct comparison of both algorithms showed a similar performance in predicting hourly means and daily time-courses of g(s), whereas the multiplicative algorithm outperformed the BWB-type algorithm in modelling seasonal time-courses due to the inclusion of a phenology function. The re-parameterisation of the algorithms for local conditions in order to validate ozone deposition modelling on a European scale reveals the higher input requirements of the BWB-type algorithm as compared to the multiplicative algorithm because of the need of the former to model net photosynthesis (A(n)).     

Validation of predicted exponential concentration profiles of chemicals in soils

Multimedia mass balance models assume well-mixed homogeneous compartments. Particularly for soils, this does not correspond to reality, which results in potentially large uncertainties in estimates of transport fluxes from soils. A theoretically expected exponential decrease model of chemical concentrations with depth has been proposed, but hardly tested against empirical data. In this paper, we explored the correspondence between theoretically predicted soil concentration profiles and 84 field measured profiles. In most cases, chemical concentrations in soils appear to decline exponentially with depth, and values for the chemical specific soil penetration depth (d(p)) are predicted within one order of magnitude. Over all, the reliability of multimedia models will improve when they account for depth-dependent soil concentrations, so we recommend to take into account the described theoretical exponential decrease model of chemical concentrations with depth in chemical fate studies. In this model the d(p)-values should estimated be either based on local conditions or on a fixed d(p)-value, which we recommend to be 10cm for chemicals with a log K(ow)>3.     

Element fluxes through European forest ecosystems and their relationships with stand and site characteristics

This paper describes a European wide assessment of element budgets, using available data on deposition, meteorology and soil solution chemistry at 121 Intensive Monitoring plots. Input fluxes from the atmosphere were derived from fortnightly or monthly measurements of bulk deposition and throughfall, corrected for canopy uptake. Element outputs from the forest ecosystem were derived by multiplying fortnightly or monthly measurements of the soil solution composition at the bottom of the root zone with simulated unsaturated soil water fluxes. Despite the uncertainties in the calculated budgets, the results indicate that: (i) SO4 is still the dominant source of actual soil acidification despite the generally lower input of S than N, due to the different behaviour of S (near tracer) and N (strong retention); (ii) base cation removal due to man-induced soil acidification is limited; and (iii) Al release is high in areas with high S inputs and low base status.     

ARX modeling approach to leak detection and diagnosis

This work presents a time series strategy for detection, location and quantification of leaks in large pipeline systems. The technology has two active components, which operate sequentially: the Detector and the Localizer. The Detector continuously screens real-time data, searching for any anomalies such as leaks, which are detected (or not) depending on their size and position. The Detector is based on auto-regressive multi-input/multi-output (MIMO) ARX predictors with one input filter. Subsequent to successful leak detection, the Localizer is launched to diagnose the leak via estimation of its parameters – diameter and location – using recorded data on a Search Time Window that includes information in the neighborhood of the instant of detection. The Localizer is also an ARX predictor, but with two input processors, the first is a filter for dynamic plant inputs and the second filter processes “parameter signals” of active leaks. The Localizer is developed beforehand via model identification with plant data under the action of known, artificially simulated, leaks. It is, therefore, able to recognize an active pattern of leak parameters, by maximizing the adherence of its predictions to data in the Search Time Window. The proposed detection and location methods were successfully tested in simulated leak scenarios for an industrial naphtha pipeline.