Estimating Greenhouse Gas Emission Reduction Potential: An Assessment of Models and Methods for the Power Sector

The success of the Clean Development Mechanism of the Kyoto Protocol to reduce global greenhouse gas emissions requires that there are scientifically sound and usable measurement methods for the emission baseline. Three different methods are investigated in the context of China’s electrical sector: simulation with dispatch analysis, decomposition analysis, and operating and build margin analysis. It is found that dispatch analysis is the best method available as it is able to consider important regional and temporal dimensions, while decomposition analysis can help quantify the potential effects of different energy and environment policy choices.     

Grey Box and Component Models to Forecast Ozone Episodes: A Comparison Study

For the purpose of short-term forecasting of high ozone concentration episodes stochastic models have been suggested and developed in the literature. The present paper compares the quality of forecasts produced by a grey box and a component time-series model. The summer ozone patterns for three European urban areas (two continental and one mediterranean) are processed. By means of forecast performance indices according to EC and WHO guidelines, the following features of the models could be found: The grey box model is highly adaptive and produces forecasts with low error variance that increases with the time horizon of forecast. The component model is more 'stiff' that results in a higher forecast-error variance and poorer adaption in detail. The forecast horizon, however, could be enlarged with this model. The accuracy of predicting threshold exceedance is similar for both models. This can be understood from the assumption of a cyclical time development of ozone that was made for both models.     

A Comparison Between Regression Models and Genetic Programming for Predictions of Chlorophyll-a Concentrations in Northern Lakes

Chlorophyll-a (chl-a) concentrations are often used as a proxy for water quality problems as well as phytoplankton blooms. Available chl-a models range from simple phosphorus loading models to complex regression and dynamic models. A comparison of multiple regression models was made with genetic programming (GP) techniques to predict chl-a concentrations over a large range of 104 Swedish lakes. Independent variables used were lake area, mean depth, iron, latitude, ammonium, nitrogen + nitrate, pH, phosphate, secchi depth, silicon, temperature, total phosphorus, total nitrogen and total organic carbon. GP is a method based on the Darwinian evolution theory. This implies that a program will be able to test different mathematical equations, iterating and improving each equation using fundamental ideas from evolution theory to increase the predictive power. A good correspondence was found between the multiple regression and the GP modelling approach. No significant improvement of the predictive power was found using GP, and it is therefore recommended that multiple regression methods should be preferred when predicting chl-a concentrations as these models tend to be less complex and the modelling approach is easier to use. Results from GP were in some cases more accurate compared to multiple regressions; however, the best model was created by multiple regressions which used concentrations of total phosphorus, total nitrogen and latitude as independent variables. These findings will be an important note for limnologists and modelling managers when developing future models of chl-a concentrations in lakes.     

Statistical Analysis of Spatial Pattern: A Comparison of Grid and Hierarchical Sampling Approaches

Previous studies have combined random-site hierarchical sampling designs with analysis of variance techniques, and grid sampling with spatial autocorrelation analysis. We illustrate that analysis techniques and sampling designs are interchangeable using densities of an infaunal bivalve from a study in Poverty Bay, New Zealand. Hierarchical designs allow the estimation of variances associated with each level, but high-level factors are imprecisely estimated, and they are inefficient for describing spatial pattern. Grid designs are efficient for describing spatial pattern, and are amenable to conventional analysis. Our example deals with a continuous spatial habitat, but our conclusions also apply in disjunct or patchy habitats. The influence of errors in positioning is also assessed. The advantages of systematic sampling are reviewed, and more efficient hierarchical approaches are identified. The distinction between biological and statisticalsignificance in all analyses is emphasised.     

Time-Frequency Analysis of the Relationship Between EUA and CER Carbon Markets

In this paper, interactions or co-movement between the CER and EUA futures prices are examined in order to shed light on the dependency between the European Union Emissions Trading Scheme (EU ETS) and the clean development mechanism (MDP). Our analysis uses the wavelet method to model the correlation between CER and EUA in the time-frequency domain. It highlights the impact of different investors (according to their investment horizons) on the co-movement between the CER and EUA prices, and therefore, the behavior of individual investors as speculators, arbitrageurs, and hedgers on European allowance and CDM credits cumulatively. In this vein, we analyze according to the frequency intervals, price convergence, identification of potential factors that could explain a difference in futures prices, and structural changes in the EUA and CER prices. The application is made using daily EUA’s and CER’s prices data.     

A Model for Simulating the Soil Organic Carbon Pool of Steppe Ecosystems

In this research, the improved Terrestrial Ecosystem Regional (TECO-R) model was adapted to steppe ecosystems and then utilized to simulate the soil organic carbon pool in the period from 1989 to 2011 (excluding 1994, 2002, 2009, and 2010) for a typical steppe in Xilingol League of Inner Mongolia in China. The improved TECO-R model is an ecological model in combination of remote sensing data, which allows the spatial scale for the analysis of soil organic carbon which is not limited to vegetation or soil type. The spatial and temporal resolution advantages of remote sensing image can be well utilized in this model. The results indicate that in addition to an accurate simulation of the soil carbon pool of a steppe ecosystem, the vegetation aboveground carbon pool, grazing intensity of herbivores, mowing coefficient, litter carbon pool, root carbon pools of different vegetation layers, root-shoot ratio, actual residence time of different carbon pools, and allocation coefficients of different carbon pools in corresponding years are also available from the TECO-R model. Some of the above data are difficult to obtain through macro-observation but can be simulated with the TECO-R model by combining the model with input data; this is very important for a correct understanding of the feedback relationships between the steppe ecosystem’s carbon cycle and climate change (e.g., global warming) and human activities such as grazing.     

A Comparison of Sediment and Monitoring Data: Implications for Paleomonitoring a Small Lake

Long-term limnological monitoring data (from 1971 to 2001) were compared with sediment core record in Lake Viitna Linajärv (hereafter L. Viitna), a small lake in northern Estonia. The monitoring data show that L. Viitna changed from mesotrophic in the 1970s to eutrophic in the 1990s. The trends of paleodata thatintegrate the changes in the biogeochemical matter cycling in the lake over 2–3 years have clear signals about changes in the state ofthe ecosystem in L. Viitna during the last decades. A gradual increase in organic productivity should reflect a greater oxygen demand in the hypolimnion. As a result the hypolimnion becameseasonally anoxic earlier and its pH level remained low for a longer time. These fundamental changes near the sediment-water interface were recorded in the sediment core. The greatest changes occurred at the beginning of the 1980s (layers at a depthof ca. 20 cm), when the meso(eu?)trophic conditions in L. Viitnastarted to become increasingly more eutrophic. The variations ofpaleorecords in the upper part of the sediment core coincide temporally with changes in the water level of the lake.     

Comparison of Numerical Street Dispersion Models with Results from Wind Tunnel and Field Measurements

Numerical dispersion models developed and validated in different European countries were applied to data sets from wind tunnel and field measurements. The comparison includes the Danish Operational Street Pollution Model (OSPM) and the microscale flow and dispersion model MISKAM. The latter is recommended for application in built-up areas in the draft of the new German guideline VDI 3782/8. In a first step the models were applied to simplified street configurations. Different parameters as length and height of adjacent buildings and the angle of the incoming flow were varied. The results were compared to recent wind tunnel measurements. In a second step the models were applied to two extensively investigated field data sets from Jagtvej, Copenhagen and G ttinger Straße, Hannover. Intensified and more transparent and accessible validation procedures would be helpful for the thorough user.     

低碳经济背景下克拉玛依市的发展现状分析

克拉玛依是一座典型的资源型城市,正处于工业化、城市化快速发展的阶段,能源消费和CO2排放持续增长将成为克拉玛依经济发展的硬约束。本文以2002—2013年克拉玛依市统计年鉴中的产业结构、人口、能源消费和经济数据为基础,分析了克拉玛依市的产业结构、能源消费、CO2排放情况,提出了发展低碳城市的思考。     

Spatial Validation of Land Use Change Models Using Multiple Assessment Techniques: A Case Study of Transition Potential Models

Using land use and cover change (LUCC) models for the urban growth planning, environmental assessment, and decision-making needs the establishment of an appropriate level of confidence in their performance. The objective of this research is to explore the importance of using multiple assessment techniques in order to fairly evaluate the performance of land use models. An application is conducted by using the Land Change Modeler for Ecological Sustainability (LCM) which is an empirical and transition potential model. LCM is applied to model the agricultural to developed areas transition in Rennes metropolitan area (France). The land demand is estimated using the Markov Chain model; whereas, the transition potential map is implemented using the Multi-Layer Perceptron Neural Network (MLP) method based on historical changes and driving variables. The model performance is assessed based on a variety of the most commonly used validation techniques. At the study area level, the correctness and disagreement analysis shows that LCM performs better at predicting the amount than the allocation of developed areas. Additionally, landscape metrics reveal that LCM tends to predict a fragmented urban form, which seems evident because of the large number of the individual urban patches. At the municipality level, the error budget analysis shows that the model performance, which varies highly between different subareas, needs to be improved. Moreover, the cross-tabulation between the transition potential map and both the observed and the predicted agricultural to developed areas transitions reveals that the order of the transition potential values does not perfectly fit the observed change; whereas, the predicted change is not solely limited to areas with high potential.     

The GTP of Methane: Modeling Analysis of Temperature Impacts of Methane and Carbon Dioxide Reductions

The Global Temperature Potential (GTP) has recently been proposed as an alternative to the Global Warming Potential (GWP). Using two different Earth Models of Intermediate Complexity, we show that the solution to the 100-year sustained GTP for methane is significantly larger than the equivalent GWP due to the inclusion of future changes in greenhouse gas concentrations in the reference scenario and different atmospheric chemistry assumptions. This result suggests that methane reductions may be undervalued when using GWPs, but the policy implications depend on how the objectives of greenhouse gas policy are defined.     

Models at the interface between science and society: impacts and options

Within the CLEAR project a new approach to integrated assessment modelling has been developed for the participatory integrated assessment of regional climate change involving citizens' focus groups. The climate change decision problem was structured by focusing separately on climate impacts and mitigation options. The attempt was made to link the different scales of the problem from the individual to the global level. The abstract topic of climate change was related to options on the level of a citizen's individual lifestyle. The option of a low energy society was emphasised in order to embed the climate change decision problem in a wider range of societal concerns. Special emphasis was given to the characterisation and communication of uncertainties. The chosen approach allows different kinds of uncertainties in one framework to be addressed. The paper concludes with a summary of the experience made, and recommendations for the use of models in participatory integrated assessments. This revised version was published online in July 2006 with corrections to the Cover Date.     

A Review of Public Domain Water Quality Models for Simulating Dissolved Oxygen in Rivers and Streams

The review discusses six major public domain water quality models currently available for rivers and streams. These major models, which differ greatly in terms of processes they represent, data inputs requirements, assumptions, modeling capability, their strengths and weaknesses, could yield useful results if appropriately selected for the desired purposes. The public domain models, which are most suitable for simulating dissolved oxygen along rivers and streams, chosen in this review are simulation catchment (SIMCAT), temporal overall model for catchments (TOMCAT), QUAL2Kw, QUAL2EU, water quality analysis simulation program (WASP7), and quality simulation along rivers (QUASAR). Each of these models is described based on a consistent set of criteria-conceptualization, processes, input data, model capability, limitations, model strengths, and its application. The results revealed that SIMCAT and TOMCAT are over-simplistic but useful to quickly assess impact of point sources. The QUAL2Kw has provision for conversion of algal death to carbonaceous biochemical oxygen demand (CBOD) and thus more appropriate than QUAL2EU, where macrophytes play an important interaction. The extensive requirement of data in WASP7 and QUASAR is difficult to justify the time and costs required to set up these complex models. Thus, a single model could not serve all wide range of functionalities required. The choice of a model depends upon availability of time, financial cost and a specific application. This review may help to choose appropriate model for a particular water quality problem.     

The Comparison of Lagrangian and Gaussian Models in Predicting of Air Pollution Emission Using Experimental Study,a Case Study: Ammonia Emission

Ammonia, as a colorless gas with a sharp odor, is considered as one of the created odors in the composting and landfill of solid waste. We used experimental data to study the robustness of AERMOD and the forward Lagrangian stochastic (FLS) in predicting ammonia emission in short range. The study area was Kahrizak landfill and composting plants, Tehran, Iran. The boundary layer parameters for the FLS were calculated on the basis of mean values of temperature, wind speed, and direction. While, the boundary layers of AERMOD were computed on the basis of exact meteorological data. The results depicted that AERMOD prediction at distances less than 1000 m from the sources and the locations inside the sources were poor. However, the results of FLS indicated more agreement with the field measurement, which the coefficient of determination was 0.83. Both models predicted, in the distance of 2000 m from the source, the ammonia concentration would be insignificant.     

Dynamic Cost-Effective Reduction Strategies for Acidification in Europe: An Application to Ireland and the United Kingdom

This paper describes the application of an optimisation model for calculating cost-effective abatement strategies for the reduction of acidification in Europe while taking into account the dynamic character of soil acidification in a number of countries. Environmental constraints are defined in terms of soil quality indicators, e.g., pH, base saturation or the aluminium ion concentration in the soil solution within an optimisation model for transboundary air pollution.We present a case study for Ireland and the United Kingdom. Our results indicate that reduction of sulphur dioxide emission is more cost-effective than that of nitrogen oxides or ammonia. The reduction percentages for sulphur dioxide are highest, for two reasons: (i) marginal sulphur dioxide reduction costs are relatively low compared to marginal reduction costs of nitrogen oxides and ammonia and (ii) sulphur dioxide reduction is more effective in reducing acidification in physical terms than nitrogen oxides or ammonia abatement. Our dynamic analysis shows that a (fast) improvement of soil quality requires high emission reduction levels. These reduction levels are often higher than reduction levels that are typically deduced from the static critical loads approach. Once soil quality targets are reached, in our model, less stringent emission reductions are required to maintain the soil quality at a constant and good target level. Static critical load approaches that ignore dynamic aspects therefore may underestimate the emission reductions needed to achieve predefined soil quality targets.     

Models for the adsorption of organic compounds at gas-water interfaces

The solvation parameter model is used to characterize interactions responsible for adsorption at the gas-water interface for bulk water at 15 and 25 degrees C, snow at -6.8 degrees C, mineral-supported water films (alumina, calcium carbonate and quartz) at 15 degrees C, and dry soil at 15 degrees C. The mineral-supported water films and dry soil adsorption data are modeled at different relative humidities in the range 40-99%. The models produce satisfactory results with standard errors of the estimate of 0.12 to 0.17 for regression of the model predicted adsorption equilibrium constants against the experimental values (range for equilibrium constants -2 to -7 log units). The water surface is polar with a significant capacity for dipole-type and hydrogen-bonding interactions. In addition, it is strongly electron lone pair repulsive. Dispersion interactions favor adsorption at the water surface. Mineral-supported water films at relative humidities greater than 40% demonstrate adsorption properties similar to bulk water. The adsorption characteristics, however, depend on the relative humidity and the nature of the support. In the case of dry soil the adsorption properties at different relative humidities cannot simply be explained by adsorption of a water film covering the soil surface and the changes in adsorption characteristics with relative humidity are more complex than the mineral-supported water films.     

Managing Uncertainty in Risk-Based Corrective Action Design: Global Sensitivity Analysis of Contaminant Fate and Exposure Models Used in the Dose Assessment

A variance-based global sensitivity analysis (GSA) was applied to the dose assessment model used in the risk-based corrective action methodology of environmental risk analysis to identify key sources of variability and uncertainty and quantify the relative contribution of these sources to the variance of estimated dose. GSA was performed applying extended Fourier amplitude sensitivity test technique. The soil-to-air contaminant transport pathway within an inhalation exposure scenario was addressed. Three persistent semi-volatile carcinogenic chemicals, including polychlorinated biphenyls, benzo(a)pyrene, and 2,3,7,8-tetrachlorodibenzo-p-dioxin, were chosen as contaminants of concern.     

The Impact of Income Inequality on Carbon Emissions in Asian Countries: Non-Parametric Panel Data Analysis

Environmental Modeling & Assessment - Widening income inequality and environmental degradation are two of the most important problems that threat the sustainable development. For that, this...