Compliance and Emissions Trading under the Kyoto Protocol: Rules for Uncertain Inventories

A solution is proposed for proving compliance with emission targets and for emissions trading in the event of uncertainties in reported emission inventories. The solution is based on the undershooting concept, from which the mathematical conditions for both proving compliance with a risk α and calculating effective emissions for trading are derived. Based on the reported emission units, the number of permits granted is reduced in proportion to the uncertainty in the inventory. A country whose inventory has higher uncertainty is thereby allotted fewer permits than a country with the same inventory but smaller uncertainty.     

Accumulation of iron and arsenic in the Chandina alluvium of the lower delta plain, Southeastern Bangladesh

Accumulations of iron, manganese, and arsenic occur in the Chandina alluvium of southeastern Bangladesh within 2.5 m of the ground surface. These distinctive orange-brown horizons are subhorizontal and consistently occur within 1 m of the contact of the aerated (yellow-brown) and water-saturated (gray) sediment. Ferric oxyhydroxide precipitates that define the horizons form by oxidation of reduced iron in pore waters near the top of the saturated zone when exposed to air in the unsaturated sediment. Hydrous Fe-oxide has a high specific surface area and thus a high adsorption capacity that absorbs the bulk of arsenic also present in the reduced pore water, resulting in accumulations containing as much as 280 ppm arsenic. The steep redox gradient that characterizes the transition of saturated and unsaturated sediment also favors accumulation of manganese oxides in the oxidized sediment. Anomalous concentrations of phosphate and molybdenum also detected in the ferric oxyhydroxide-enriched sediment are attributed to sorption processes.     

A new method to estimate air-quality levels using a synoptic-regression approach. Part I: Present-day O3 and PM10 analysis

In order to make projections for future air-quality levels, a robust methodology is needed that succeeds in reconstructing present-day air-quality levels. At present, climate projections for meteorological variables are available from Atmospheric-Ocean Coupled Global Climate Models (AOGCMs) but the temporal and spatial resolution is insufficient for air-quality assessment. Therefore, a variety of methods are tested in this paper in their ability to hindcast maximum 8 hourly levels of O₃ and daily mean PM₁₀ from observed meteorological data. The methods are based on a multiple linear regression technique combined with the automated Lamb weather classification. Moreover, we studied whether the above-mentioned multiple regression analysis still holds when driven by operational ECMWF (European Center for Medium-Range Weather Forecast) meteorological data. The main results show that a weather type classification prior to the regression analysis is superior to a simple linear regression approach. In contrast to PM₁₀ downscaling, seasonal characteristics should be taken into account during the downscaling of O₃ time series. Apart from a lower explained variance due to intrinsic limitations of the regression approach itself, a lower variability of the meteorological predictors (resolution effect) and model deficiencies, this synoptic-regression-based tool is generally able to reproduce the relevant statistical properties of the observed O₃ distributions important in terms of European air quality Directives and air quality mitigation strategies. For PM₁₀, the situation is different as the approach using only meteorology data was found to be insufficient to explain the observed PM₁₀ variability using the meteorological variables considered in this study.     

Project house water: a novel interdisciplinary framework to assess the environmental and socioeconomic consequences of flood-related impacts

Protecting our water resources in terms of quality and quantity is considered one of the big challenges of the twenty-first century, which requires global and multidisciplinary solutions. A specific threat to water resources, in particular, is the increased occurrence and frequency of flood events due to climate change which has significant environmental and socioeconomic impacts. In addition to climate change, flooding (or subsequent erosion and run-off) may be exacerbated by, or result from, land use activities, obstruction of waterways, or urbanization of floodplains, as well as mining and other anthropogenic activities that alter natural flow regimes. Climate change and other anthropogenic induced flood events threaten the quantity of water as well as the quality of ecosystems and associated aquatic life. The quality of water can be significantly reduced through the unintentional distribution of pollutants, damage of infrastructure, and distribution of sediments and suspended materials during flood events. To understand and predict how flood events and associated distribution of pollutants may impact ecosystem and human health, as well as infrastructure, large-scale interdisciplinary collaborative efforts are required, which involve ecotoxicologists, hydrologists, chemists, geoscientists, water engineers, and socioeconomists. The research network “project house water” consists of a number of experts from a wide range of disciplines and was established to improve our current understanding of flood events and associated societal and environmental impacts. The concept of project house and similar seed fund and boost fund projects was established by the RWTH Aachen University within the framework of the German excellence initiative with support of the German research foundation (DFG) to promote and fund interdisciplinary research projects and provide a platform for scientists to collaborate on innovative, challenging research. Project house water consists of six proof-of-concept studies in very diverse and interdisciplinary areas of research (ecotoxicology, water, and chemical process engineering, geography, sociology, economy). The goal is to promote and foster high-quality research in the areas of water research and flood-risk assessments that combine and build off-laboratory experiments with modeling, monitoring, and surveys, as well as the use of applied methods and techniques across a variety of disciplines.     

Effects of thermobarical pretreatment of cattle waste as feedstock for anaerobic digestion

Lab-scale experiments were conducted to assess the impact of thermobarical treatment of cattle waste on anaerobic digestion. Treatment was at temperatures of 140–220 °C in 20 K steps for a 5-min duration. Methane yields could be increased by up to 58% at a treatment temperature of 180 °C. At 220 °C the abundance of inhibitors and other non-digestible substances led to lower methane yields than those obtained from untreated material. In an extended analysis it could be demonstrated that there is a functional correlation between the methane yields after 30 days and the formation rate and methane yield in the acceleration phase. It could be proved in a regression of these correlation values that the optimum treatment temperature is 164 °C and that the minimum treatment temperature should be above 115 °C.     

Geoadditive regression modeling of stream biological condition

Indices of biotic integrity have become an established tool to quantify the condition of small non-tidal streams and their watersheds. To investigate the effects of watershed characteristics on stream biological condition, we present a new technique for regressing IBIs on watershed-specific explanatory variables. Since IBIs are typically evaluated on an ordinal scale, our method is based on the proportional odds model for ordinal outcomes. To avoid overfitting, we do not use classical maximum likelihood estimation but a component-wise functional gradient boosting approach. Because component-wise gradient boosting has an intrinsic mechanism for variable selection and model choice, determinants of biotic integrity can be identified. In addition, the method offers a relatively simple way to account for spatial correlation in ecological data. An analysis of the Maryland Biological Streams Survey shows that nonlinear effects of predictor variables on stream condition can be quantified while, in addition, accurate predictions of biological condition at unsurveyed locations are obtained.     

Determination of the optimum MM5 configuration for long term CMAQ simulations of aerosol bound pollutants in Europe

Realistic meteorological fields are a prerequisite for the determination of pollutant concentrations and depositions by means of a chemistry transport model. Different configurations of the 5th generation NCAR/Penn State University mesoscale meteorological model MM5 were tested to determine the optimum set up for long term hindcasts that cover several months up to years. Four dimensional data assimilation (FDDA) significantly enhances the spatio temporal representation of temperature, humidity and wind. Best agreement with radiosonde observations could be achieved when temperature, humidity and wind were grid nudged every 6 h. The quality of the resulting meteorological fields showed no significant systematic temporal or spatial variation over Europe in a model run of the year 2000. It was found that the hydrological cycle was not correctly reproduced by the model when no nudging was applied. The relevant model run showed too high relative humidity and too high rainfall when compared to observations. This led to considerably lower aerosol concentrations close to ground and a shift in the deposition patterns of particle bound pollutants like the carcinogenic benzo(a)pyrene (B(a)P). Guest Editor: Dr. S. T. Rao.     

Understanding the distribution of cattle production systems in the South American Chaco

ABSTRACT

Impacts of cattle production vary among different production systems, but data on their distribution is scarce for most world regions. In this work, we combine datasets on cattle vaccination locations and land cover in a regression framework to define and map major cattle production systems in the Argentinean Dry Chaco. We also explore how cattle occurrence relates to spatial determinants. Results indicate that the region harbors about 5.5 million heads. Cattle density was mainly described by the share of pasture (69.9%), cropland (28.1%) and aridity (23.8%). We identified 12-major cattle production systems: six cow-calf, three whole-cycle, and three fattening systems. Of these, four systems had high woodland cover (>85%). Data generated is available in a website. Understanding the distribution of cattle production systems is important to assess the environmental impacts of beef production at broad scales. Integrating vaccination data with land-cover information provides a promising avenue to identify livestock systems.     

Monitoring of genetically modified Escherichia coli in laboratory wastewater

Containment of genetically modified (GM) microorganisms such as Escherichia coli is a legal requirement to protect the environment from an unintended release and to avoid horizontal gene transfer (HGT) of recombinant DNA to native bacteria. In this study, we sampled the laboratory wastewater (LWW) at a large Swiss university from three sources over 2 years and cultured ampicillin-resistant, presumptive GM E. coli. From a total of 285 samples, 127 contained presumptive GM E. coli (45%) at a mean concentration of 2.8 × 10² CFU/ml. Plasmid DNA of 11 unique clones was partially or entirely sequenced. All consisted of cloning vectors harboring research-specific inserts. To estimate the chance of HGT between GM E. coli and native bacteria in LWW, we identified taxa representative for the bacterial community in LWW using 16S rRNA amplicon sequencing and measured conjugation frequencies of E. coli with five LWW isolates. At optimal conjugation conditions, frequencies were between 3.4 × 10⁻³ and 2.4 × 10⁻⁵. Given the absence of transferable broad-host range plasmids and suboptimal conjugation conditions in the LWW system, we conclude that the chance of HGT is relatively low. Still, this study shows that the implementation of robust containment measures is key to avoid the escape of GM microorganisms.