• Energy Research
• natural sciences close
• 13. Climate action close
• Technical University of Munich close

Bioplastics are alternatives of conventional petroleum-based plastics. Bioplastics are polymers processed from renewable sources and are biodegradable. This study aims at conducting an environmental impact assessment of the bioprocessing of agricultural wastes into bioplastics compared to petro-plastics using an LCA approach. Bioplastics were produced from potato peels in laboratory. In a biochemical reaction under heating, starch was extracted from peels and glycerin, vinegar and water were added with a range of different ratios, which resulted in producing different samples of bio-based plastics. Nevertheless, the environmental impact of the bioplastics production process was evaluated and compared to petro-plastics. A life cycle analysis of bioplastics produced in laboratory and petro-plastics was conducted. The results are presented in the form of global warming potential, and other environmental impacts including acidification potential, eutrophication potential, freshwater ecotoxicity potential, human toxicity potential, and ozone layer depletion of producing bioplastics are compared to petro-plastics. The results show that the greenhouse gases (GHG) emissions, through the different experiments to produce bioplastics, range between 0.354 and 0.623 kg CO2 eq. per kg bioplastic compared to 2.37 kg CO2 eq. per kg polypropylene as a petro-plastic. The results also showed that there are no significant potential effects for the bioplastics produced from potato peels on different environmental impacts in comparison with poly-β-hydroxybutyric acid and polypropylene. Thus, the bioplastics produced from agricultural wastes can be manufactured in industrial scale to reduce the dependence on petroleum-based plastics. This in turn will mitigate GHG emissions and reduce the negative environmental impacts on climate change.

Biological infestations in forests, e.g. the insect outbreaks, have been shown as favoured by future climate change trends. In Europe, the European spruce bark beetle (Ips typographus L.) is one of the main agents causing substantial economic disturbances in forests. Therefore, studies on spatio-temporal characterization of the area affected by bark beetle are of major importance for rapid post-attack management. We aimed at spatially detecting damage classes by combining multidate remote sensing data and a non-parametric classification. As study site served a part of the Bavarian Forest National Park (Germany). For the analysis, we used 10 geometrically rectified scenes of Landsat and SPOT sensors in the period between 2001 and 2011. The main objective was to explore the potential of medium-resolution data for classifying the attacked areas. A further aim was to explore if the temporally adjacent infested areas are able to be separated. The random forest (RF) model was applied using the reference data drawn from high-resolution aerial imagery. The results indicate that the sufficiently large patches of visually identifiable damage classes can be accurately separated from non-attacked areas. In contrast to those, the other mortality classes (current year, current year 1 and current year 2 infested classes) were mostly classified with higher commission or omission errors as well as higher classification biases. The available medium-resolution satellite images, combined with properly acquired reference data, are concluded to be adequate tools to map area-based infestations at advanced stages. However, the quality of reference data, the size of infested patches and the spectral resolution of remotely sensed data are the decisive factors in case of smaller areas. Further attempts using auxiliary height information and spatially enhanced data may refine such an approach.

Abstract Biofuels and the oleochemical industry are highly dependent on plant oils for the generation of renewable product lines. Consequently, production of plant lipids, such as palm and rapeseed oil, for industrial applications competes with agricultural activity and is associated with a negative environmental impact. Additionally, established chemical routes for upgrading bio-lipids to renewable products depend on metal-containing catalysts. Metal leaching during oil processing results in heavy metal contaminated process wastewater. This water is difficult to remediate and leads to the loss of precious metals. Therefore, the biofuels and chemical industry requires sustainable solutions for production and upgrading of bio-lipids. With regard to the former, a promising approach is the fermentative conversion of abundant, low-value biomass into microbial, particularly yeast-based lipids. This study describes the holistic, value-adding conversion of underexploited, macroalgae feedstocks into yeast oil, animal feed and biosorbents for metal-based detoxification of process wastewater. The initial step comprises a selective enzymatic liquefaction step that yields a supernatant containing 62.5% and 59.3% (w/dwbiomass) fermentable sugars from L. digitata and U. lactuca, respectively. By dispensing with chemical pretreatment constraints, we achieved a 95% (w/w) glucose recovery. Therefore, the supernatant was qualified as a cultivation media without any detoxification step or nutrition addition. Additionally, the hydrolysis step provided 27–33% (w/dwbiomass) of a solid residue, which was qualified as a metal biosorbent. Cultivation of the oleaginous yeast C. oleaginosus on the unprocessed hydrolysis supernatant provided 44.8 g L−1 yeast biomass containing 37.1% (w/dwbiomass) lipids. The remaining yeast biomass after lipid extraction is targeted as a performance animal feed additive. Selectivity and capacity of solid macroalgae residues as biosorbents were assessed for removal and recycling of rare and heavy metals, such as Ce+3, Pb+2, Cu+2 and Ni+2 from model wastewater. The biosorption capacity of the macroalgae residues (sorption capacity ∼ 0.7 mmol g−1) exceeds that of relevant commercially available adsorption resins and biosorbents. To facilitate the integration of our technology in existing chemical and biotechnological production environments, we have devised simple, rapid and cost-efficient methods for monitoring both lipogenesis and metal sorption processes. The application of the new optical monitoring tools is essential to determine yeast cell harvesting times and biosorption capacities respectively. For the first time we report on a waste-free bioprocess that combines sustainable, microbial lipid production from low value marine biomass with in-process precious metal recycling options. Our data allowed for a preliminary economic analysis, which indicated that each product could be cost competitive with current market equivalents. Hence, the synaptic nature of the technology platform provides for the economic and ecologic viability of the overall process chain.

Climate warming may threaten the germination strategies of many plants that are uniquely adapted to today’s climate. For instance, species that employ physical dormancy (PY) – the production of seeds that are impermeable to water until high temperatures break them, consequently synchronizing germination with favorable growing conditions – may find that their seeds germinate during unfavorable or potentially fatal periods if threshold temperatures are reached earlier in the year. To explore this, we subjected the seeds of five species with physical dormancy (from the genera Abrus, Bauhinia, Cassia, Albizia, and Acacia) to “mild” (+2°C) and “extreme” (+4°C) future warming scenarios and documented their germination over 2 years relative to a control treatment. Under current climatic conditions, a proportion of seeds from all five species remained dormant in the soil for 2 years. A mild warming of 2°C had little to no effect on the germination of four of the five study species. Contrastingly, an extreme warming of 4°C dramatically increased germination in all five species within the first year, indicating a reduction in their ability to persist in the soil long-term. Cassia fistula was particularly susceptible to warming, exhibiting a similar increase in germination under both mild and extreme warming relative to control. Our findings suggest that climate warming in the tropics may cause the seeds of species that rely on physical dormancy to stagger the risk of unsuccessful germination across years to leave soil seed banks prematurely – the long-term implications of which remain unknown.

Wood biomass is considered a renewable raw product, but the supply chain of wood biomass involves non-renewable energy inputs, and thus possibly entailing environmental impacts. The objective of this study was to analyze different environmental impacts (GHG emissions, without biogenic CO2; primary energy consumption, non-renewable; particulate matter) caused by the provision of forest biomass for the four main tree species in Bavaria using Life Cycle Assessment (LCA) techniques. Based on forest growth simulations, a set of realistic forest biomass supply chains for Bavarian forestry conditions were modeled for the raw wood product system from site preparation to forest road and to plant/farm gate, respectively, analyzing the four different process groups: [A1] site preparation, [A2] site tending, [A3] biomass harvesting and [T] transport of biomass to plant/farm gate. Total GHG emissions of about 383,000tCO2-eq∗year(-1) (18.95kgCO2-eq∗m(-3)) are estimated for the Bavarian forestry sector (from site preparation to plant/farm gate) in the reference year 2013 indicating a share of 0.41% in the total GHG emissions of Bavaria. 0.035MJ of non-renewable energy has to be invested in order to provide 1MJ of woody biomass to plant/farm gate (267MJ∗m(-3)). One hundred and sixty six tons of particulate matter emissions per year are calculated for the Bavarian forestry sector in 2013 (0.008kgPM2.5-eq∗m(-3)). Our LCA results reveal that there is no single GHG emission value for raw wood but a broad range of possible GHG emissions for the Bavarian forestry. Most decisive parameters are forest road maintenance, biomass harvesting, forwarding and biomass transport, and GHG emissions are also notably influenced by tree species, age class, wood assortment and site quality. We recommend that environmental impact calculations should be implemented, for example in wood certification procedures as they are important key indicators for sustainable forest management.

AbstractNatural gas is considered a bridging technology in the energy transition because it produces fewer carbon emissions than coal, for example. However, when leaks exist, methane is released into the atmosphere, leading to a dramatic increase in the carbon footprint of natural gas, as methane is a much stronger greenhouse gas than carbon dioxide. Therefore, we conducted a detailed study of methane emissions from gas‐powered end‐use appliances and then compared their climate impacts with those of electricity‐powered appliances. We used the Munich Oktoberfest as a case study and then extended the study to 25 major natural gas consuming countries. This showed that electricity has been the more climate‐friendly energy source at Oktoberfest since 2005, due to the extensive use of renewable electricity at the festival and the presence of methane emissions, particularly caused by the incomplete combustion and leakages of natural gas in cooking and heating appliances. By contrast, at the global level, our study shows that natural gas still produces lower carbon emissions for end‐user appliances than electricity in 18 of the 25 countries studied. However, as the share of renewable energy in the electricity mix steadily increases in most countries, the carbon footprint of electricity will be lower than that of natural gas in these countries in the near future. These findings from our comparison of the total carbon emissions of electric and gas‐powered end‐use appliances can help inform the debate on how to effectively address climate change.

Climate extremes have the potential to cause extreme responses of terrestrial ecosystem functioning. However, it is neither straightforward to quantify and predict extreme ecosystem responses, nor to attribute these responses to specific climate drivers. Here, we construct a factorial experiment based on a large ensemble of process-oriented ecosystem model simulations driven by a regional climate model (12 500 model years in 1985–2010) in six European regions. Our aims are to (1) attribute changes in the intensity and frequency of simulated ecosystem productivity extremes (EPEs) to recent changes in climate extremes, CO _2 concentration, and land use, and to (2) assess the effect of timing and seasonal interaction on the intensity of EPEs. Evaluating the ensemble simulations reveals that (1) recent trends in EPEs are seasonally contrasting: spring EPEs show consistent trends towards increased carbon uptake, while trends in summer EPEs are predominantly negative in net ecosystem productivity (i.e. higher net carbon release under drought and heat in summer) and close-to-neutral in gross productivity. While changes in climate and its extremes (mainly warming) and changes in CO _2 increase spring productivity, changes in climate extremes decrease summer productivity neutralizing positive effects of CO _2 . Furthermore, we find that (2) drought or heat wave induced carbon losses in summer (i.e. negative EPEs) can be partly compensated by a higher uptake in the preceding spring in temperate regions. Conversely, however, carry-over effects from spring to summer that arise from depleted soil moisture exacerbate the carbon losses caused by climate extremes in summer, and are thus undoing spring compensatory effects. While the spring-compensation effect is increasing over time, the carry-over effect shows no trend between 1985–2010. The ensemble ecosystem model simulations provide a process-based interpretation and generalization for spring-summer interacting carbon cycle effects caused by climate extremes (i.e. compensatory and carry-over effects). In summary, the ensemble ecosystem modelling approach presented in this paper offers a novel route to scrutinize ecosystem responses to changing climate extremes in a probabilistic framework, and to pinpoint the underlying eco-physiological mechanisms.