Real-time environmental assessment of electricity use: a tool for sustainable demand-side management programs

Purpose

Demand-side management is a promising way to increase the integration of renewable energy sources by adapting part of the demand to balance power systems. However, the main challenges of evaluating the environmental performances of such programs are the temporal variation of electricity generation and the distinction between generation and electricity use by including imports and exports in real-time.

Methods

In this paper, we assessed the environmental impacts of electricity use in France by developing consumption factors based on historical hourly data of imports, exports, and electricity generation of France, Germany, Great Britain, Italy, Belgium, and Spain. We applied a life cycle approach with four environmental indicators: climate change, human health, ecosystem quality, and resources. The developed dynamic consumption factors were used to assess the environmental performances of demand-side management programs through optimized changes in consumption patterns defined by the flexibility of 1 kWh every day in 2012–2014.

Results and discussion

Between 2012 and 2014, dynamic consumption factors in France were higher on average than generation factors by 21.8% for the climate change indicator. Moreover, the dynamic consideration of electricity generation of exporting countries is essential to avoid underestimating the impacts of electricity imports and therefore electricity use. The demand response programs showed a range of mitigation up to 38.5% for the climate change indicator. In addition, an environmental optimization cost 1.4 € per kg CO2 eq. for 12% mitigation of emissions as compared to an economic optimization. Finally, embedding the costs of some environmental impacts in the electricity price with a carbon price enhanced the efficiency of economic demand response strategies on the GHG emissions mitigation.

Conclusions

The main scientific contribution of this paper is the development of more accurate dynamic electricity consumption factors. The dynamic consumption factors are relevant in LCAs of industrial processes or operational building phases, especially when consumption varies over time and when the power system participates in a wide market with exports and imports such as in France. In the case of demand-side management programs, dynamic consumption factors could prevent an environmentally damaging energy from being imported, despite the economic interest of system operators. However, the approach used in this study was attributional and did not assess the local grid responses of load shifting programs. Therefore, a more comprehensive model could be created to assess the local short-term dynamic consequences of located prospective consumptions and the global long-term consequences of demand-side management programs.

     

Updated and harmonised greenhouse gas emissions for crop inventories

Purpose

The emission of greenhouse gases (GHG) is a key criterion in the environmental assessment of biofuels. Life cycle inventories taking into account the latest methodological developments are an essential prerequisite for this assessment. In the last years, substantial progresses in the modelling of nitrogen emissions relevant for the climate as well as in modelling the emissions from land use change (LUC) have been achieved. Therefore, the biomass production inventories in the ecoinvent database were revised to take into account these developments.

Methods

The IPCC method tier 1 has been used for the assessment of N₂O emissions. Induced emissions from NH₃ and NO₃ were included as well. Due to the importance of the latter emissions for N₂O formation, these emissions have also been updated and harmonised. The Agrammon model was used for the NH₃ emissions. The SALCA-NO₃ model has been applied in the European inventories to estimate nitrate leaching, whilst in non-European inventories the SQCB-NO₃ model has been used. The quantification of the land use change areas has been based on annualized, retrospective data of the last 20 years. All carbon pools (from aboveground biomass to soil organic carbon) were considered and differentiated on a regional level for all of the natural vegetation categories affected. Whenever possible, default values and methods from the IPCC 2006 were applied.

Results and discussion

The changes for ammonia emissions were generally very small (?5 % on average). The nitrate emissions increased on average by +13 %, but this slight trend is the result of important downward and upward changes, whilst the average N₂O emissions decreased by ?26 %. For the existing inventories of soybean, palm oil and sugarcane production, significant increases of GHG emissions resulted from LUC modelling. This was mainly due to the consistent inclusion of all carbon stocks according to the IPCC guidelines. The calculation method can also result in important C sequestration effects in certain cases like African Jatropha production.

Conclusions

The changes in greenhouse gas emissions due to the updated methodology were significant. This shows that life cycle assessment studies for biofuels using older methodological bases need to be revised and could lead to different conclusions. The implemented and cultivated superstructure for LUC modelling is modular and flexible and can be easily extended to other important crop activities. The new parameterisation functionality applied for the activities provides powerful means for the simple generation of site-specific activities.

     

Global change could amplify fire effects on soil greenhouse gas emissions

Background

Little is known about the combined impacts of global environmental changes and ecological disturbances on ecosystem functioning, even though such combined impacts might play critical roles in shaping ecosystem processes that can in turn feed back to climate change, such as soil emissions of greenhouse gases.

Methodology/Principal Findings

We took advantage of an accidental, low-severity wildfire that burned part of a long-term global change experiment to investigate the interactive effects of a fire disturbance and increases in CO₂ concentration, precipitation and nitrogen supply on soil nitrous oxide (N₂O) emissions in a grassland ecosystem. We examined the responses of soil N₂O emissions, as well as the responses of the two main microbial processes contributing to soil N₂O production – nitrification and denitrification – and of their main drivers. We show that the fire disturbance greatly increased soil N₂O emissions over a three-year period, and that elevated CO₂ and enhanced nitrogen supply amplified fire effects on soil N₂O emissions: emissions increased by a factor of two with fire alone and by a factor of six under the combined influence of fire, elevated CO₂ and nitrogen. We also provide evidence that this response was caused by increased microbial denitrification, resulting from increased soil moisture and soil carbon and nitrogen availability in the burned and fertilized plots.

Conclusions/Significance

Our results indicate that the combined effects of fire and global environmental changes can exceed their effects in isolation, thereby creating unexpected feedbacks to soil greenhouse gas emissions. These findings highlight the need to further explore the impacts of ecological disturbances on ecosystem functioning in the context of global change if we wish to be able to model future soil greenhouse gas emissions with greater confidence.     

On finite-horizon control of genetic regulatory networks with multiple hard-constraints

Background

Probabilistic Boolean Networks (PBNs) provide a convenient tool for studying genetic regulatory networks. There are three major approaches to develop intervention strategies: (1) resetting the state of the PBN to a desirable initial state and letting the network evolve from there, (2) changing the steady-state behavior of the genetic network by minimally altering the rule-based structure and (3) manipulating external control variables which alter the transition probabilities of the network and therefore desirably affects the dynamic evolution. Many literatures study various types of external control problems, with a common drawback of ignoring the number of times that external control(s) can be applied.

Results

This paper studies the intervention problem by manipulating multiple external controls in a finite time interval in a PBN. The maximum numbers of times that each control method can be applied are given. We treat the problem as an optimization problem with multi-constraints. Here we introduce an algorithm, the "Reserving Place Algorithm'', to find all optimal intervention strategies. Given a fixed number of times that a certain control method is applied, the algorithm can provide all the sub-optimal control policies. Theoretical analysis for the upper bound of the computational cost is also given. We also develop a heuristic algorithm based on Genetic Algorithm, to find the possible optimal intervention strategy for networks of large size.

Conclusions

Studying the finite-horizon control problem with multiple hard-constraints is meaningful. The problem proposed is NP-hard. The Reserving Place Algorithm can provide more than one optimal intervention strategies if there are. Moreover, the algorithm can find all the sub-optimal control strategies corresponding to the number of times that certain control method is conducted. To speed up the computational time, a heuristic algorithm based on Genetic Algorithm is proposed for genetic networks of large size.

     

Local adaptation of annual weed populations to habitats differing in disturbance regime

Plants have evolved several strategies to cope with disturbance, and one strategy is tolerance. In tolerance, plants store resources (meristems, carbohydrates) so that they can resprout after disturbance and thereby compensate to some degree for losses. Because tolerance is costly (it occurs at the expense of current growth), we can expect adaptation to the local disturbance regime. In this study, we determined whether populations of a common European annual weed, Euphorbia peplus, are adapted to the local disturbance regime. We hypothesized that the tolerance and hence compensation for losses in seed and biomass production after experimental damage are greater in plants from more severely disturbed than from less severely disturbed populations. We also hypothesized that transgenerational effects can alter adaptation. We found that compensation for biomass loss to damage was greater for plants from more severely disturbed habitats than for plants from less severely disturbed habitats. This, however, was not at the expense of growth before damage because plants from both disturbance regimes did not show differences when not damaged. Transgenerational effects played a positive role in adaptation to disturbance during germination and maturity. We conclude that local adaptation together with transgenerational effects have evolved in more severely disturbed populations but not in less severely disturbed populations of E. peplus.     

Emission payback periods for increased residential insulation using marginal electricity modeling: a life cycle approach

Purpose

Increases in residential insulation can reduce energy consumption and corresponding life cycle emissions, but with increased manufacturing and transportation of insulation and the associated impacts. In this study, we conducted life cycle analyses of residential insulation and estimated payback periods for carbon dioxide (CO₂), nitrogen oxides (NOx), and sulfur dioxide (SO₂) emissions, using modeling techniques that account for regional variability in climate, fuel utilization, and marginal power plant emissions.

Methods

We simulated the increased production of insulation and energy savings if all single-family homes in the USA increased insulation levels to the 2012 International Energy Conservation Code, using an energy simulation model (EnergyPlus) applied to a representative set of home templates. We estimated hourly marginal changes in electricity production and emissions using the Avoided Emissions and Generation Tool (AVERT), and we estimated emissions related to direct residential combustion. We determined changes in upstream emissions for both insulation and energy using openLCA and ecoinvent. Payback periods were estimated by pollutant and region. In sensitivity analyses, we considered the importance of marginal versus average power plant emissions, transportation emissions, emission factors for fiberglass insulation, and sensitivity of emission factors to the magnitude of electricity reduction.

Results and discussion

Combining the life cycle emissions associated with both increased insulation manufacturing and decreased energy consumption, the payback period for increased residential insulation is 1.9 years for CO₂ (regional range 1.4–2.9), 2.5 years for NOx (regional range 1.8–3.9), and 2.7 years for SO₂ (regional range 1.9–4.8). For insulation, transportation emissions are limited in comparison with manufacturing emissions. Emission benefits displayed strong regional patterns consistent with relative demands for heating versus cooling and the dominant fuels used. Payback periods were generally longer using average instead of marginal emissions and were insensitive to the magnitude of electricity savings, which reflects the structure of the intermediate complexity electricity dispatch model.

Conclusions

The life cycle benefits of increased residential insulation greatly exceed the adverse impacts related to increased production across all regions, given insulation lifetimes of multiple decades. The strong regionality in benefits and the influence of a marginal modeling approach reinforce the importance of site-specific attributes and time-dynamic modeling within LCA.

     

Assessing tillage disturbance on assemblages of ground beetles (Coleoptera: Carabidae) by using a range of ecological indices

Many ecological studies have used diversity indices to assess the impact of environmental disturbance. In particular, ground beetles have been advocated as a good group for assessing disturbance. Most studies on various organisms have used only one or two indices. For our study of the impact of tillage disturbance on carabid beetles in farm fields in southern Ontario, Canada, we used seven different diversity indices (richness, Shannon–Wiener, Berger–Parker, Q-statistic, Margalef, and evenness). Few studies have used deviation from diversity abundance models as a measure of disturbance; however, we use three that are applicable to our data (geometric, log-normal and log-series). The indices and models were used to test the null hypothesis that there is no change in diversity with increasing tillage disturbance, and that there is no difference in diversity with different crops or years. We were not able to reject the null hypothesis that there is any diversity difference among farms. We also found that there was no single diversity index or model that was better than any other at detecting disturbance. These results are supplemented by a meta-analysis of 45 published data sets for the same taxon but in different habitats. The meta-analysis supports the conclusions from our field research that diversity indices and models are not useful for detecting the possible effect of disturbance on assemblages of carabid beetles.     

Including GHG emissions from mangrove forests LULUC in LCA: a case study on shrimp farming in the Mekong Delta,Vietnam

Purpose

Mangrove forests have been recognized as important regulators of greenhouse gases (GHGs), yet the resulting land use and land-use change (LULUC) emissions have rarely been accounted for in life cycle assessment (LCA) studies. The present study therefore presents up-to-date estimates for GHG emissions from mangrove LULUC and applies them to a case study of shrimp farming in Vietnam.

Methods

To estimate the global warming impacts of mangrove LULUC, a combination of the International Panel for Climate Change (IPCC) guidelines, the Net Committed Emissions, and the Missed Potential Carbon Sink method were used. A literature review was then conducted to characterize the most critical parameters for calculating carbon losses, missed sequestration, methane fluxes, and dinitrogen monoxide emissions.

Results and discussion

Our estimated LUC emissions from mangrove deforestation resulted in 124 t CO₂ ha^?1 year^?1, assuming IPCC’s recommendations of 1 m of soil loss, and 96% carbon oxidation. In addition to this, 1.25 t of carbon would no longer be sequestered annually. Discounted over 20 years, this resulted in total LULUC emissions of 129 t CO₂ ha^?1 year^?1 (CV = 0.441, lognormal distribution (ln)). Shrimp farms in the Mekong Delta, however, can today operate for 50 years or more, but are 1.5 m deep (50% oxidation). In addition to this, Asian tiger shrimp farming in mixed mangrove concurrent farms (the only type of shrimp farm that resulted in mangrove deforestation since 2000 in our case study) resulted in 533 kg methane and 1.67 kg dinitrogen monoxide per hectare annually. Consequently, the LULUC GHG emissions resulted in 184 and 282 t CO₂-eq t^?1 live shrimp at farm gate, using mass and economic allocation, respectively. These GHG emissions are about an order of magnitude higher than from semi-intensive or intensive shrimp farming systems. Limitations in data quality and quantity also led us to quantify the uncertainties around our emission estimates, resulting in a CV of between 0.4 and 0.5.

Conclusions

Our results reinforce the urgency of conserving mangrove forests and the need to quantify uncertainties around LULUC emissions. It also questions mixed mangrove concurrent shrimp farming, where partial removal of mangrove forests is endorsed based upon the benefits of partial mangrove conservation and maintenance of certain ecosystem services. While we recognize that these activities limit the chances of complete removal, our estimates show that large GHG emissions from mangrove LULUC question the sustainability of this type of shrimp farming, especially since mixed mangrove farming only provide 5% of all farmed shrimp produced in Vietnam.

     

Estimating the global warming emissions of the LCAXVII conference: connecting flights matter

Purpose

Conferences are an important element of scientific activity but can also be a major cause of environmental burden. With this in mind, we analysed the global warming emissions of the 2017 annual conference of the American Center for Life Cycle Assessment (ACLCA), in order to estimate the carbon footprint and identify potential ways to reduce it.

Methods

We used survey data from participants as well as literature sources to complete an attributional assessment of the greenhouse gas emissions per participant. A method to calculate the ‘ideal’ location is proposed, which can be used to identify ‘unreasonably’ distant conference locations.

Results and discussion

The average emissions per participant were found to be 952 kg CO_2eq, but with a large variability due to differences in travelled distance. Connecting flights were found to increase emissions up to 32% compared to direct flights, due to the increased number of take-offs and landings.

Conclusions

Results indicate that future studies should use distance-dependent flight emissions to increase the accuracy of the assessment. Some measures, such as meat-free menus, had a relatively minor contribution to emission reductions, but could be important as scientists advocating for the reduction of environmental burden should lead by example.

     

Life-cycle greenhouse gas inventory analysis of household waste management and food waste reduction activities in Kyoto, Japan

Purpose

Source-separated collection of food waste has been reported to reduce the amount of household waste in several cities including Kyoto, Japan. Food waste can be reduced by various activities including preventing edible food loss, draining moisture, and home composting. These activities have different potentials for greenhouse gas (GHG) reduction. Therefore, we conducted a life-cycle inventory analysis of household waste management scenarios for Kyoto with a special emphasis on food waste reduction activities.

Methods

The primary functional unit of our study was ??annual management of household combustible waste in Kyoto, Japan.?? Although some life-cycle assessment scenarios included food waste reduction measures, all of the scenarios had an identical secondary functional unit, ??annual food ingestion (mass and composition) by the residents of Kyoto, Japan.?? We analyzed a typical incineration scenario (Inc) and two anaerobic digestion (dry thermophilic facilities) scenarios involving either source-separated collection (SepBio) or nonseparated collection followed by mechanical sorting (MecBio). We assumed that the biogas from anaerobic digestion was used for power generation. In addition, to evaluate the effects of waste reduction combined with separate collection, three food waste reduction cases were considered in the SepBio scenario: (1) preventing loss of edible food (PrevLoss); (2) draining moisture contents (ReducDrain); and (3) home composting (ReducHcom). In these three cases, we assumed that the household waste was reduced by 5%.

Results and discussion

The GHG emissions from the Inc, MecBio, and SepBio scenarios were 123.3, 119.5, and 118.6 Gg CO₂-eq/year, respectively. Compared with the SepBio scenario without food waste reduction, the PrevLoss and ReducDrain cases reduced the GHG emissions by 17.1 and 0.5 Gg CO₂-eq/year. In contrast, the ReducHcom case increased the GHG emissions by 2.1 Gg CO₂-eq/year. This is because the biogas power production decreased due to the reduction in food waste, while the electricity consumption increased in response to home composting. Sensitivity analyses revealed that a reduction of only 1% of the household waste by food loss prevention has the same GHG reduction effect as a 31-point increase (from 50% to 81%) in the food waste separation rate.

Conclusions

We found that prevention of food losses enhanced by separate collection led to a significant reduction in GHG emissions. These findings will be useful in future studies designed to develop strategies for further reductions in GHG emissions.     

Impacts of flexible pavement design and management decisions on life cycle energy consumption and carbon footprint

Purpose

The study aims to develop a methodological framework to estimate life cycle energy consumption and greenhouse gas (GHG) emissions related to pavement design and management decisions. Another objective is to apply the framework to the design and management of flexible highway pavement in Hong Kong. Traditionally, pavement design and management decisions are solely based on economic considerations. This study quantifies the relationships between such decisions and the environmental impacts, thereby helping highway agencies understand the environmental implications of their decisions and make more balanced decisions to improve highway sustainability.

Methods

(1) A methodological framework is developed by integrating the mechanistic-empirical pavement design guide (ME-PDG) and life cycle assessment (LCA) methods. (2) The calculation processes for the detailed components in the framework are proposed by synthesizing existing models, data, and tools. (3) In applying the framework to pavement design and management in Hong Kong, a large number of simulations are conducted to generate pavement performance data at different combinations of pavement thickness, roughness trigger value, and traffic levels. (4) GHG emissions and energy consumption are calculated for each simulation scenario, and the results are used to build statistical regression models. (5) The simulation and calculation results are also analyzed to gain additional insights on the environmental impacts of pavement design and management decisions.

Results and conclusions

(1) The developed framework that integrates ME-PDG and LCA methods is useful to assess pavement-related life cycle energy consumption and GHG emissions. (2) The developed regression models can well capture the trends of life cycle energy consumption and GHG emissions at different traffic levels, using asphalt concrete (AC) layer thickness and roughness trigger value as independent variables. (3) Material production, road use, and congestion due to road closure dominate pavement-related life cycle energy use and GHG emissions. (4) Optimum pavement thickness and international roughness index (IRI) trigger values exist, and they vary with traffic levels.

     

A Systematic Review of Criminal Recidivism Rates Worldwide: Current Difficulties and Recommendations for Best Practice

Objectives

To systematically review recidivism rates internationally, report whether they are comparable and, on the basis of this, develop best reporting guidelines for recidivism.

Methods

We searched MEDLINE, Google Web, and Google Scholar search engines for recidivism rates around the world, using both non-country-specific searches as well as targeted searches for the 20 countries with the largest total prison populations worldwide.

Results

We identified recidivism data for 18 countries. Of the 20 countries with the largest prison populations, only 2 reported repeat offending rates. The most commonly reported outcome was 2-year reconviction rates in prisoners. Sample selection and definitions of recidivism varied widely, and few countries were comparable.

Conclusions

Recidivism data are currently not valid for international comparisons. Justice Departments should consider using the reporting guidelines developed in this paper to report their data.     

Estimation of greenhouse gas emissions from sewer pipeline system

Purpose

The aim of this study was to estimate the total greenhouse gas (GHG) emissions generated from whole life cycle stages of a sewer pipeline system and suggest the strategies to mitigate GHG emissions from the system.

Methods

The process-based life cycle assessment (LCA) with a city-scale inventory database of a sewer pipeline system was conducted. The GHG emissions (direct, indirect, and embodied) generated from a sewer pipeline system in Daejeon Metropolitan City (DMC), South Korea, were estimated for a case study. The potential improvement actions which can mitigate GHG emissions were evaluated through a scenario analysis based on a sensitivity analysis.

Results and discussion

The amount of GHG emissions varied with the size (150, 300, 450, 700, and 900 mm) and materials (polyvinyl chloride (PVC), polyethylene (PE), concrete, and cast iron) of the pipeline. Pipes with smaller diameter emitted less GHG, and the concrete pipe generated lower amount of GHG than pipes made from other materials. The case study demonstrated that the operation (OP) stage (3.67 × 10⁴ t CO₂eq year^?1, 64.9%) is the most significant for total GHG emissions (5.65 × 10⁴ t CO₂eq year^?1) because a huge amount of CH₄ (3.51 × 10⁴ t CO₂eq year^?1) can be generated at the stage due to biofilm reaction in the inner surface of pipeline. Mitigation of CH₄ emissions by reducing hydraulic retention time (HRT), optimizing surface area-to-volume (A/V) ratio of pipes, and lowering biofilm reaction during the OP stage could be effective ways to reduce total GHG emissions from the sewer pipeline system. For the rehabilitation of sewer pipeline system in DMC, the use of small diameter pipe, combination of pipe materials, and periodic maintenance activities are suggested as suitable strategies that could mitigate GHG emissions.

Conclusions

This study demonstrated the usability and appropriateness of the process-based LCA providing effective GHG mitigation strategies at a city-scale sewer pipeline system. The results obtained from this study could be applied to the development of comprehensive models which can precisely estimate all GHG emissions generated from sewer pipeline and other urban environmental systems.

     

Life cycle assessment and cost analysis of residential buildings in south east of Turkey: part 1—review and methodology

Purpose

Buildings are responsible for more than 40 % of global energy used, and as much as 30 % of global greenhouse gas emissions. In order to quantify the energy and material inputs and environmental releases associated with each stage of construction sector, life cycle energy, greenhouse gas emissions, and cost analysis of contemporary residential buildings have been conducted within two parts.

Methods

This paper is the first part of the study which includes the literature review and methodology used for such a comprehensive analysis. It was determined that there are three basic methods used in life cycle analysis: process analysis, input–output (I–O) analysis, and hybrid analysis. In this study, Inventory of Carbon and Energy (ICE) is used for the calculation of primary energy requirements and greenhouse gas emissions. The second part of this study is about the application of the methodology which considers two actual buildings constructed in Gaziantep, Turkey.

Results and discussion

The proposed research focused on building construction, operating, and demolition phases. Energy efficiency, emission parameters, and costs are defined for the building per square meter basis. It is seen that the primary energy use and emissions of residential buildings around the world falls in the range of about 10 to 40 GJ/m² and 1–10 t CO₂/m² respectively.

Conclusions

The literature survey demonstrates that there are limited number of studies about life cycle cost assessment (LCCA) of residential buildings in the world. It was decided to use the ICE database as it is one of the most comprehensive databases for building materials, globally. The results of the study show that minimizing energy, material, and land use by considering potential impacts to the environment on a life cycle basis are the basic steps in designing an energy-efficient and environmental-friendly building.

     

Two statistical criteria to choose the method for dilution correction in metabolomic urine measurements

Introduction

Different normalization methods are available for urinary data. However, it is unclear which method performs best in minimizing error variance on a certain data-set as no generally applicable empirical criteria have been established so far.

Objectives

The main aim of this study was to develop an applicable and formally correct algorithm to decide on the normalization method without using phenotypic information.

Methods

We proved mathematically for two classical measurement error models that the optimal normalization method generates the highest correlation between the normalized urinary metabolite concentrations and its blood concentrations or, respectively, its raw urinary concentrations. We then applied the two criteria to the urinary ¹H-NMR measured metabolomic data from the Study of Health in Pomerania (SHIP-0; n?=?4068) under different normalization approaches and compared the results with in silico experiments to explore the effects of inflated error variance in the dilution estimation.

Results

In SHIP-0, we demonstrated consistently that probabilistic quotient normalization based on aligned spectra outperforms all other tested normalization methods. Creatinine normalization performed worst, while for unaligned data integral normalization seemed to most reasonable. The simulated and the actual data were in line with the theoretical modeling, underlining the general validity of the proposed criteria.

Conclusions

The problem of choosing the best normalization procedure for a certain data-set can be solved empirically. Thus, we recommend applying different normalization procedures to the data and comparing their performances via the statistical methodology explicated in this work. On the basis of classical measurement error models, the proposed algorithm will find the optimal normalization method.

     

Estimation of Cross-Species Introgression Rates Using Genomic Data Despite Model Unidentifiability

Full-likelihood implementations of the multispecies coalescent with introgression (MSci) model treat genealogical fluctuations across the genome as a major source of information to infer the history of species divergence and gene flow using multilocus sequence data. However, MSci models are known to have unidentifiability issues, whereby different models or parameters make the same predictions about the data and cannot be distinguished by the data. Previous studies of unidentifiability have focused on heuristic methods based on gene trees and do not make an efficient use of the information in the data. Here we study the unidentifiability of MSci models under the full-likelihood methods. We characterize the unidentifiability of the bidirectional introgression (BDI) model, which assumes that gene flow occurs in both directions. We derive simple rules for arbitrary BDI models, which create unidentifiability of the label-switching type. In general, an MSci model with k BDI events has 2k unidentifiable modes or towers in the posterior, with each BDI event between sister species creating within-model parameter unidentifiability and each BDI event between nonsister species creating between-model unidentifiability. We develop novel algorithms for processing Markov chain Monte Carlo samples to remove label-switching problems and implement them in the bpp program. We analyze real and synthetic data to illustrate the utility of the BDI models and the new algorithms. We discuss the unidentifiability of heuristic methods and provide guidelines for the use of MSci models to infer gene flow using genomic data.     

Reactive oxygen species in aerobic methane formation from vegetation

The first report of aerobic methane emissions from vegetation by an unknown mechanism¹ suggested that this potential new source may make a significant contribution to global methane emissions. We recently investigated possible mechanisms and reported^2,3 experiments in which UV-irradiation caused methane emissions from pectin, a major plant cell wall polysaccharide. Our findings also suggest that UV-generated reactive oxygen species (ROS) release methane from pectin. This has implications for all other, UV-independent processes which may generate ROS in or close to the plant cell wall and suggests a need to evaluate additional systems for ROS-generated methane emissions in leaves.Key words: methane, hydroxyl radicals, reactive oxygen species, UV, methyl esters, pectinUntil recently, the global methane budget was thought to be well understood, the only natural process for methane generation being an anaerobic microbial mechanism.⁴ However, observations by Keppler et al.¹ of aerobic methane emissions from vegetation caused controversy and called for a re-assessment of the natural sources of methane. While no mechanism was originally suggested, a putative source, the methyl ester groups of pectin, was proposed based on carbon isotope analyses.¹ We tested this hypothesis directly and reported that UV light could drive methane emissions from pectin in vitro under aerobic conditions.² While UV light was necessary for generation of methane from pectin, it is not tenable that UV was directly attacking pectic methyl ester groups since these do not absorb UV of the wavelengths used (280–400 nm). Instead, we proposed that the energy from the UV light was being absorbed by compounds such as phenolics, and that a reactive intermediary would be formed in the process. Importantly, our process had to be non-enzymic since no enzymes were present in either experimental system.^1,2 Following this hypothesis, we tested the effect of reactive oxygen species (ROS) on pectin in vitro and discovered that certain ROS cause production of methane: hydroxyl radicals (^•OH) and singlet oxygen were effective, but hydrogen peroxide and superoxide were not.³ Also, the addition of ROS-specific scavengers to pectin sheets stopped or severely reduced UV-induced methane emissions from pectin, suggesting that ROS are the intermediary in the mechanism of aerobic methane formation from pectin (Fig. 1). De-esterified pectin was produced by saponification and emitted only trace amounts of methane upon UV-irradiation, clearly establishing ester groups as the source of methane^2,3 and confirming findings of other research groups.^5,6 However, we also found that acetyl ester groups may contribute to methane emissions from pectin and should therefore be considered in future experiments attempting to identify methane sources. Interestingly, we also observed, for the first time, ethylene, ethane and CO₂ emissions from pectin upon UV-irradiation,² which corroborates the ROS hypothesis since ROS attack of methyl esters is likely to form methyl radicals, which can then either form methane or dimerise to form ethylene or ethane.Open in a separate windowFigure 1Proposed pathway for ^•OH-driven methane generation from pectin upon UV irradiation. The compound illustrated here, l-tryptophan, is merely an example of a possible photosensitiser. Hydroxyl radicals (^•OH) are shown to attack a methyl galacturonate residue of the homogalacturonan component of the pectin molecule since this is likely to be the most abundant source of methane, but the methyl esters found in xylogalacturonan domains and the acetyl esters found in homogalacturonan and rhamnogalacturonan domains are also possible methane sources. Note that only ∼70% of all galacturonic acid residues of the pectin backbone are methyl-esterified. Inset photograph shows experimental set-up during UV-irradiation of pectin.ROS are produced and destroyed constantly throughout the lifetime of plants. The generation of ROS in vivo can generally be linked to two sources: (i) a response to an external stimulus which may be perceived as a threat or (ii) a signaling process in the cell which may happen during growth, hormone action or programmed cell death.⁷ Our experiments showed that ROS could lead to methane formation from methyl ester groups; however, the origin of the ROS may not be important, only their nature. Indeed, hydrogen peroxide and superoxide, widely reported to be formed during an oxidative burst following a biotic stress,⁸ did not generate methane from pectin in vitro, and are therefore unlikely to do so in vivo. Only the hydroxyl radical (^•OH) and singlet oxygen generation led to methane formation, and therefore any process which generates them could also trigger UV-independent methane production. Abiotic stresses, such as drought, heat or salinity, which have been shown to lead to the production of ^•OH in vegetation,⁹ could therefore be processes leading to aerobic methane formation, as could exposure to elevated ozone concentrations.¹⁰ Indeed, physical injury (by cutting) of plant material has recently been demonstrated to cause methane emissions.¹¹The origin of the ROS may not be important, as long as their generation is in or close to the pectin of the plant cell wall, since ^•OH cannot travel far within a cell. Indeed, it is estimated that ^•OH typically reacts with organic matter within ∼1 nm of the site of radical production.¹² Processes such as growth^13,14 and calcium signaling,¹⁵ which both involve ROS production as an intermediary in the mechanism but are not necessarily due to external stress, may therefore have the potential to generate methane aerobically. Any process involved in the complicated pathways of ROS-regulation, for which 152 genes are responsible in Arabidopsis thaliana,¹⁶ could be involved in methane emission if the ROS generation is localised close to pectin or other potential substrates.In addition, hydrogen peroxide, which is generated in the cell walls of healthy plants,¹⁷ can be converted in the cell wall into ^•OH by processes such as the Fenton reaction,^18,19 especially in the presence of apoplastic ascorbate.^20,21 A complete analysis of the potential for ^•OH and singlet oxygen to be present in the plant cell wall is therefore necessary for a proper understanding of the different mechanisms that may drive aerobic methane generation. Further experiments into the effects of abiotic stresses other than UV on aerobic methane production from different types of vegetation are necessary in order that future in-vitro studies under simulated natural conditions can be carried out correctly. This type of study, in conjunction with direct in-vivo field studies and satellite observations, are essential to allow global estimates to be made accurately in the future and help us understand the significance of ROS-driven methane emission.     

Accounting for inter-annual <Emphasis Type="Italic">variability</Emphasis> of farm activity data for calculation of greenhouse gas emissions in dairy farming

Purpose

This study examines the inter-annual variability of production data in an organic dairy farm and its effect on the estimation of product-related greenhouse gas emissions (GHG) using a detailed material flow model. It is believed that the examination of only one production year may not adequately reflect temporal representativeness and may therefore lead to unreliable results. The current study also provides a method to deal with variability when temporal representativeness cannot be ensured.

Methods

All material flows related to milk production from six consecutive milk years in an organic dairy farm in northern Germany were analysed. The milk yield of the 75 to 91 cows varied between 5418 and 7102 kg energy corrected milk (ECM) per cow and year. GHG emissions were estimated using calculation guidelines from the International Dairy Federation (IDF) and the Intergovernmental Panel on Climate Change (IPCC). Emissions were calculated in the Flow Analysis and Resource Management (FARM) model ensuring mass balances for nitrogen and phosphorous in every subsection of the model. Based on the variability of crop yields, the number of years for representative average data was calculated as well as an uncertainty when only a limited number of years was available.

Results and discussion

Estimated GHG emissions varied between 0.88 and 1.09 kg CO₂-eq kg^?1 ECM^?1 (mean, standard deviation of the mean = 0.97 and 0.07 kg CO₂-eq kg^?1 ECM^?1). Emissions from ruminant digestion had the highest contribution (50.9 ± 2.3) percent in relation to overall product-related GHG emissions. Direct emissions from soil showed the highest coefficient of variation (36%) due to simultaneous changes in fertilization amount, crop yield and milk yield which showed no significant direct relationship. The number of years needed to be assessed for representative average yields was between 27 and 215 years for clover grass and maize silage, respectively. When performing a sensitivity analysis based on the variability of crop yields, the assessed farm showed reliable results with average data of at least 4 years.

Conclusions

Temporal representativeness should be dealt with explicitly in GHG assessments for dairy farming. If the representativeness of crop yields cannot be ensured, an uncertainty bandwidth of the results based on variability of yields can provide a basis for comparing different farms or farming systems. This approach could also be extended to other variabilities in dairy farming for more reliability of results.

     

The Worst Drug Rule Revisited: Mathematical Modeling of Cyclic Cancer Treatments

In drug treatments of cancer, cyclic treatment strategies are characterized by alternating applications of two (or more) different drugs, given one at a time. One of the main problems of drug treatment in cancer is associated with the generation of drug resistance by mutations of cancerous cells. We use mathematical methods to develop general guidelines on optimal cyclic treatment scheduling, with the aim of minimizing the resistance generation. We define a condition on the drugs’ potencies which allows for a relatively successful application of cyclic therapies. We find that the best strategy is to start with the stronger drug, but use longer cycle durations for the weaker drug. We further investigate the situation where a degree of cross-resistance is present, such that certain mutations cause cells to become resistant to both drugs simultaneously. We show that the general rule (best-drug-first, worst-drug-longer) is unchanged by the presence of cross-resistance. We design a systematic method to test all strategies and come up with the optimal timing and drug order. The role of various constraints in the optimal therapy design, and in particular, suboptimal treatment durations and drug toxicity, is considered. The connection with the “worst drug rule” of Day (Cancer Res. 46:3876, 1986b) is discussed.