Verifying emission factors and national POPs emission inventories for the UK using measurements and modelling at two rural locations

Different approaches are used to verify the adequacy of emission factors (EFs) and their use in emission inventories of persistent organic pollutants (POPs). The applicability of EFs was tested using atmospheric dispersion modelling to predict atmospheric concentrations of polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), dibenzo-p-dioxins (PCDDs) and dibenzofurans (PCDFs) and resulting toxic equivalents (SigmaTEQ) and particulate matter <10 microm (PM(10)) in two rural locations in northern England (UK). The modelling was based on general assumptions of fuel composition, consumption and heating needs to simulate emissions of POPs from the domestic burning of coal and wood where ambient measurements were made in the winter of 1998. The model was used to derive the local contribution to ambient air concentrations, which were estimated independently based on comparative air measurements. The results support the hypothesis that in both villages, the majority of PAHs and the lower chlorinated PCDFs were locally released. The situation for PCBs and polychlorinated naphthalenes (PCNs) was different. While the EFs show the release of both compound groups from the domestic burning of coal and wood, the ambient levels of these "legacy POPs" in the villages were still clearly dominated by other sources. Rural areas relying mainly on fossil fuels can exceed the proposed UK ambient air quality standard for benzo[a]pyrene during winter. The measured EFs were then used to estimate the importance of the domestic burning of coal and wood to national emission inventories for these compound classes. Extrapolations to the UK suggest that the domestic burning of pure wood and coal were minor emitters for chlorinated POPs but contributed strongly to PAH and PM(10) levels in 2000. Finally, the UK's national POPs emission inventories based on source inventories and EF, as used here, were compared to estimates derived using the increase in atmospheric concentration of selected POPs.     

Inspection of non-CO2 greenhouse gases from emission sources and in ambient air by Fourier-transform-infrared-spectrometry: Measurements with FTIS-MAPS

Infrared spectrometry is a versatile basis to analyse greenhouse gases in the atmosphere. A multicomponent air pollution software (MAPS) was developed for retrieval of gas concentrations from radiation emission as well as absorption measurements. Concentrations of CO, CH₄, N₂O, and H₂O as well as CO₂, NO, NO₂, NH₃, SO₂, HCl, HCHO, and the temperature of warm gases are determined on-line. The analyses of greenhouse gases in gaseous emission sources and in ambient air are performed by a mobile remote sensing system using the double-pendulum interferometer K300 of the Munich company Kayser-Threde. Passive radiation measurements are performed to retrieve CO, N₂O, and H₂O as well as CO₂, NO, SO₂, and HCl concentrations in smoke stack effluents of thermal power plants and municipal incinerators and CO and H₂O as well as CO₂ and NO in exhausts of aircraft engines. Open-path radiation measurements are used to determine greenhouse gas concentrations at different ambient air conditions and greenhouse gas emission rates of diffusive sources as garbage deposits, open coal mining, stock farming together with additional compounds (e.g. NH₃), and from road traffic together with HCHO. Some results of measurements are shown. A future task is the verification of emission cadastres by these inspection measurements.     

A Bayesian approach for incorporating uncertainty and data worth in environmental projects

A data worth model is presented for the analysis of alternative sampling schemes in a special project where decisions have to be made under uncertainty. This model is part of a comprehensive risk analysis algorthm with the acronym BUDA. The statistical framework in BUDA is Bayesian in nature and incorporates both parameter uncertainty and natural variability. In BUDA a project iterates among the analyst, the decision maker, and the field work. As part of the analysis, a data worth model calculates the value of a data campaign before the actual field work, thereby allowing the identification of an optimum data collection scheme. A goal function which depicts the objectives of a project is used to discriminate among different alternatives. A Latin hypercube sampling scheme is used to propagate parameter uncertainties to the goal function. In our example the uncertain parameters are the parameters which describe the geostatistical properties of saturated hydraulic conductivity in a Molasse environment. Our results indicated that failing to account for parameter uncertainty produces unrealistically optimistic results, while ignoring the spatial structure can lead to an inefficient use of the existing data.