Influence of a portable air treatment unit on health-related quality indicators of indoor air in a classroom

During periods of two weeks in February and June 2010 the performance of portable air treatment units (PATUs) was evaluated in a primary school classroom using indicators of indoor air quality. Air samples were collected in an undisturbed setting on weekend days and in an occupied setting during teaching hours. In the first week PATUs were turned off and in the second week they were turned on. On weekend days PATUs reduced indoor levels of PM-10 by 87% in February and by 70% in June compared to weekend days when PATUs were turned off. On schooldays, indoor PM-10 was increased by 6% in February and reduced by 42% in June. For PM-2.5 reductions on weekend days were 89% in February and 80% in June. On school days PM-2.5 was increased by 15% in February and reduced by 83% in June. Turning on the PATUs reduced total VOC by 80% on weekend days and by 57% on school days (but not in June). No influence on formaldehyde, NO(2), O(3) and molds was observed. PATUs appeared to be less effective in removal of air pollutants when used in an occupied classroom compared to an unoccupied setting. Our study suggests that such devices should be tested in real-life settings to evaluate their influence on indoor air quality.     

Evaluating Uncertain CO2 Abatement over the Very Long Term

Climate change research with the economic methodology of cost–benefit analysis is challenging because of valuation and ethical issues associated with the long delays between CO₂ emissions and much of their potential damages, typically of several centuries. The large uncertainties with which climate change impacts are known today and the possibly temporary nature of some envisaged CO₂ abatement options exacerbate this challenge. For example, potential leakage of CO₂ from geological reservoirs, after this greenhouse gas has been stored artificially underground for climate control reasons, requires an analysis in which the uncertain climatic consequences of leakage are valued over many centuries. We here present a discussion of some of the relevant questions in this context and provide calculations with the top–down energy-environment-economy model DEMETER. Given the long-term features of the climate change conundrum as well as of technologies that can contribute to its solution, we considered it necessary extending DEMETER to cover a period from today until the year?3000, a time span so far hardly investigated with integrated assessment models of climate change.     

The Impact of Uncertainty in Climate Targets and CO2 Storage Availability on Long-Term Emissions Abatement

A major characteristic of our global interactive climate-energy system is the large uncertainty that exists with respect to both future environmental requirements and the means available for fulfilling these. Potentially, a key technology for leading the transition from the current fossil fuel-dominated energy system to a more sustainable one is carbon dioxide capture and storage. Uncertainties exist, however, concerning the large-scale implementability of this technology, such as related to the regional availability of storage sites for the captured CO₂. We analyze these uncertainties from an integrated assessment perspective by using the bottom-up model TIAM-ECN and by studying a set of scenarios that cover a range of different climate targets and technology futures. Our study consists of two main approaches: (1) a sensitivity analysis through the investigation of a number of scenarios under perfect foresight decision making and (2) a stochastic programming exercise that allows for simultaneously considering a set of potential future states-of-the-world. We find that, if a stringent climate (forcing) target is a possibility, it dominates the solution: if deep CO₂ emission reductions are not started as soon as possible, the target may become unreachable. Attaining a stringent climate target comes in any case at a disproportionally high price, which indicates that adaptation measures or climate damages might be preferable to the high mitigation costs such a target implies.     

Multiple kinetic Langmuir modeling to predict the environmental behaviour of As(v) in soils

A soil with a relatively high Fe content (2.82% [w/w]) was loaded for up to one year with As(v) by equilibrating it with a solution containing 1000 mg l(-1) As(v) at a soil mass-to-solution ratio of 0.1 kg l(-1). The incorporation of As(v) into the soil and its distribution over the soil phases were monitored by sampling at strategic time intervals using an operationally defined five-step sequential extraction procedure (Wenzel et al., Anal. Chim. Acta, 2001, 436, 309) and subsequent As measurement. A multiple kinetic Langmuir model was developed to retrieve the dynamic parameters (adsorption and desorption rate constants, capacities and Langmuir equilibrium constants) for each of the soil phases by numerical fitting of the experimental adsorption data to the model. Under the equilibration conditions used the adsorption rate constants for all five operationally defined soil phases were very similar but the desorption rate constants decreased by a factor of ca. 150 from soil phase 1 (non-specifically sorbed As) to 5 (residual phases). This implies that As(v) incorporation "deeper" into the soil leads to stronger binding which is associated with the Langmuir equilibrium constants (adsorption rate constants/desorption rate constants). Equilibration of the soil with As(v) was complete in ca. 10 days with As(v) predominantly bound to soil phase 2 (specifically sorbed As) and soil phase 3 (amorphous and poorly crystalline hydrous oxides). X-Ray absorption spectroscopy techniques revealed that these binding characteristics may be related to adsorption of As(v) on Si- and/or Al-containing structures and natural hydrous iron oxide (HFO) surface sites, respectively. Since the model is independent of the initial As(v) concentration in the solution and the soil mass-to-solution ratio, the behaviour of the thus characterized soil-As(v) system can be predicted for a range of conditions. Simulations showed that in an accidental As(v) spill the soil studied would actively scavenge As(v) by instantaneous adsorption onto all soil phases followed by redistribution of As(v) from weaker binding sites to stronger ones over time.     

Fractionation of traffic-emitted Ce, La and Zr in road dusts

The introduction of catalytic converters has led to a new environmental problem since catalysts emit platinum group elements (PGEs) which are among the least distributed elements in nature. Along with PGEs the vehicle exhaust catalysts contain also a number of stabilizers, commonly oxides of rare earth elements and alkaline earth elements such as Ce, La and Zr. Since vehicular emission of these elements has received little attention so far this work attempts to offer insight into their distribution and fate in the environment by measuring their speciation in road dust samples collected along several highways in Germany and a city centre (Saarbrücken). Speciation of the elements (fractionation into associated mineralogical phases) was carried out via a conventional sequential extraction protocol and the complexing abilities of humic substances in the organic matter were investigated by selective extraction methods in combination with size segregation. For evaluation purposes soil samples spiked with catalytic converter material were analyzed, showing a much lower fraction of Ce, La and Zr mobilized in comparison to the road dust samples. It was found that the elements were effectively bound to humic substances in road dust with a preference for complexation with low molecular weight compounds (<1600 Da).     

Advanced REACH Tool (ART): calibration of the mechanistic model

The mechanistic model of the Advanced Reach Tool (ART) provides a relative ranking of exposure levels from different scenarios. The objectives of the calibration described in this paper are threefold: to study whether the mechanistic model scores are accurately ranked in relation to exposure measurements; to enable the mechanistic model to estimate actual exposure levels rather than relative scores; and to provide a method of quantifying model uncertainty. Stringent data quality guidelines were applied to the collated data. Linear mixed effects models were used to evaluate the association between relative ART model scores and measurements. A random scenario and company component of variance were introduced to reflect the model uncertainty. Stratified analyses were conducted for different forms of exposure (abrasive dust, dust, vapours and mists). In total more than 2000 good quality measurements were available for the calibration of the mechanistic model. The calibration showed that after calibration the mechanistic model of ART was able to estimate geometric mean (GM) exposure levels with 90% confidence for a given scenario to lie within a factor between two and six of the measured GM depending upon the form of exposure.     

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Determining Ecoregions for Environmental and GMO Monitoring Networks

A representative environmental monitoring network at the regional scale cannot use raster-based or random sampling designs, but requires a stratified sampling procedure integrating different information layers, and it has to occur in ecologically differing homogeneous regions (ecoregions). These we have determined using a set of spatial strata with ecological variables which we analysed with classification and regression trees (CART). We present a framework for environmental monitoring, that covers different scales, and we transfer the framework to a potential GMO (genetically modified organisms) monitoring network. We use ecoregion and other environmental strata together with existing environmental monitoring networks to determine GMO monitoring sites more precisely.     

Application of a checklist for quality assistance in environmental modelling to an energy model

Large, complex energy models present considerable challenges to develop and test. Uncertainty assessments of such models provide only partial guidance on the quality of the results. We have developed a model quality assistance checklist to aid in this purpose. The model checklist provides diagnostic output in the form of a set of pitfalls for the model application. The checklist is applied here to an energy model for the problem of assessing energy use and greenhouse gas emissions. Use of the checklist suggests that results on this issue are contingent on a number of assumptions that are highly value-laden. When these assumptions are held fixed, the model is deemed capable of producing moderately robust results of relevance to climate policy over the longer term. Checklist responses also indicate that a number of details critical to policy choices or outcomes on this issue are not captured in the model, and model results should therefore be supplemented with alternative analyses.