Co-benefits of post-2012 global climate mitigation policies

This paper provides an analysis of co-benefits for traditional air pollutants made possible through global climate policies using the Greenhouse Gas and Air Pollution Interactions and Synergies (GAINS) model in the time horizon up to 2050. The impact analysis is based on projections of energy consumption provided by the Prospective Outlook for the Long term Energy System (POLES) model for a scenario without any global greenhouse gas mitigation efforts, and for a 2°C climate policy scenario which assumes internationally coordinated action to mitigate climate change. Outcomes of the analysis are reported globally and for key world regions: the European Union (EU), China, India and the United States. The assessment takes into account current air pollution control legislation in each country. Expenditures on air pollution control under the global climate mitigation regime are reduced in 2050 by 250 billion € when compared to the case without climate measures. Around one third of financial co-benefits estimated world-wide in this study by 2050 occur in China, while an annual cost saving of 35 billion (Euros) € is estimated for the EU if the current air pollution legislation and climate policies are adopted in parallel. Health impacts of air pollution are quantified in terms of loss of life expectancy related to the exposure from anthropogenic emissions of fine particles, as well as in terms of premature mortality due to ground-level ozone. For example in China, current ambient concentrations of particulate matter are responsible for about 40 months-losses in the average life expectancy. In 2050, the climate strategies reduce this indicator by 50 %. Decrease of ozone concentrations estimated for the climate scenario might save nearly 20,000 cases of premature death per year. Similarly significant are reductions of impacts on ecosystems due to acidification and eutrophication.     

High-molecular weight sulfur-containing aromatics refractory to weathering as determined by Fourier transform ion cyclotron resonance mass spectrometry

Biomarkers and low-molecular weight polyaromatic compounds have been extensively studied for their fate in the environment. They are used for oil spill source identification and monitoring of weathering and degradation processes. However, in some cases, the absence or presence of very low concentration of such components restricts the access of information to spill source. Here we followed the resistance of high-molecular weight sulfur-containing aromatics to the simulated weathering condition of North Sea crude oil by ultra high-resolution Fourier transform ion cyclotron resonance mass spectrometry. The sulfur aromatics in North Sea crude having double bond equivalents (DBE) from 6 to 14 with a mass range 188-674 Da were less influenced even after 6 months artificial weathering. Moreover, the ratio of dibenzothiophenes (DBE 9)/naphthenodibenzothiophenes (DBE 10) was 1.30 and 1.36 in crude oil and 6 months weathered sample, respectively reflecting its weathering stability. It also showed some differences within other oils. Hence, this ratio can be used as a marker of the studied crude and accordingly may be applied for spilled oil source identification in such instances where the light components have already been lost due to environmental influences.     

Monitoring the formation of structures and patterns during initial development of an artificial catchment

The objective of this paper is to present observations, results from monitoring measurements, and preliminary conclusions about the development of patterns and structures during the first 5 years of development of an artificial catchment starting from point zero. We discuss the high relevance of initial system traits and external events for the system development and draw conclusions for further research. These investigations as part of a Collaborative Research Center, aim to disentangle and understand the feedback mechanisms and interrelationships of processes and their co-development with spatial and temporal structures and patterns by studying an initial, probably less complex ecosystem. Therefore, intensive measurements were carried out in the catchment with regard to the development of surface structures, hydrological patterns, vegetation dynamics, water chemistry, and element budgets. During the first 5 years, considerable changes within the catchment were observed. Both internal and external factors could be identified as driving forces for the formation of structures and patterns in the artificial catchment. Initial structures formed by the construction process and initial substrate characteristics were decisive for the distribution and flow of water. External factors like episodic events triggered erosion and dissection during this initial phase, promoted by the low vegetation cover, and the unconsolidated sandy substrate. The transformation of the initial geosystem into areas with evolving terrestrial or aquatic characteristics and from a very episodic to a more permanent stream network and discharge, together with the observed vegetation dynamics increased site diversity and heterogeneity with respect to water and nutrient availability and transformation processes compared with the more homogenous conditions at point zero. The processes and feedback mechanisms in the initial development of a new landscape may deviate in rates, intensity, and dominance from those known from mature ecosystems. It is therefore crucial to understand these early phases of ecosystem development and to disentangle the increasingly complex interactions between the evolving terrestrial and aquatic, biotic, and abiotic compartments of the system. Long-term monitoring of initial ecosystems may provide important data and parameters on processes and the crucial role of spatial and temporal structures and patterns to solve these problems. Artificially created catchments could be a suitable tool to study these initial developments at the landscape scale under known, designed, and defined boundary conditions.     

Comparative study of the microstructural and magnetic properties of fly ashes obtained from different thermal power plants in West Bengal, India

This paper deals with the physical nature of the fly ashes obtained from two thermal power plants, situated in West Bengal, India. The fly ash samples are characterized by using comprehensive techniques with an emphasis on their ultrafine nature. The particle sizes of the samples are estimated using scanning electron microcopy (SEM) and found to lie within 0.18–5.90 μm. For morphology and compositional analysis, we also use SEM coupled with energy dispersive X-ray spectrometry. From X-ray study of the fly ashes the nature of conglomeration is seen to be crystalline, and the major components are mullite (Al₆Si₂O₁₃) and quartz (SiO₂). The magnetic measurement of the fly ash samples was carried out by SQUID magnetometer. ⁵⁷Fe Mössbauer spectra are obtained using a conventional constant-acceleration spectrometer with a ⁵⁷Co/Rh Mössbauer source. The hyperfine parameters obtained, in general, support the findings as made from XRD analysis and provide a quantitative measure of different iron ions present in the samples. The paper presents experimental data on the physical aspects of the fly ash samples of the thermal power plants which comprise coarse, fine, and ultrafine magnetic particulate materials and attempts to provide an exhaustive analysis.     

A comparison of emission calculations using different modeled indicators with 1-year online measurements

The overall measurement of farm level greenhouse gas (GHG) emissions in dairy production is not feasible, from either an engineering or administrative point of view. Instead, computational model systems are used to generate emission inventories, demanding a validation by measurement data. This paper tests the GHG calculation of the dairy farm-level optimization model DAIRYDYN, including methane (CH₄) from enteric fermentation and managed manure. The model involves four emission calculation procedures (indicators), differing in the aggregation level of relevant input variables. The corresponding emission factors used by the indicators range from default per cow (activity level) emissions up to emission factors based on feed intake, manure amount, and milk production intensity. For validation of the CH₄ accounting of the model, 1-year CH₄ measurements of an experimental free-stall dairy farm in Germany are compared to model simulation results. An advantage of this interdisciplinary study is given by the correspondence of the model parameterization and simulation horizon with the experimental farm’s characteristics and measurement period. The results clarify that modeled emission inventories (2,898, 4,637, 4,247, and 3,600 kg CO₂-eq. cow^?1 year^?1) lead to more or less good approximations of online measurements (average 3,845 kg CO₂-eq. cow^?1 year^?1 (±275 owing to manure management)) depending on the indicator utilized. The more farm-specific characteristics are used by the GHG indicator; the lower is the bias of the modeled emissions. Results underline that an accurate emission calculation procedure should capture differences in energy intake, owing to milk production intensity as well as manure storage time. Despite the differences between indicator estimates, the deviation of modeled GHGs using detailed indicators in DAIRYDYN from on-farm measurements is relatively low (between ?6.4 % and 10.5 %), compared with findings from the literature.