Severe Wind Hazard Assessment using Monte Carlo Simulation

A Monte Carlo-based model to assess severe wind hazard is presented. Synthetic wind datasets for hazard analysis have been generated using Monte Carlo simulation of the physics of severe wind gust generation, to overcome the limitations of data-based statistical models. These statistical models consider extreme wind gust speeds to calculate the average probability of exceedance of a given wind speed in a single year (return period), and hence the return period is calculated using extreme value distributions. Monte Carlo wind hazard results are shown to be comparable to those produced by data-based statistical methods. They are similar to the results reported by Holmes (Australian Journal of Structural Engineering 4(1):29–40, 2002) and also to the prescribed wind gust speeds of the Australian/NZ standards for wind loading of structures in Region A (non-cyclonic regions) of Australia (AS/NZS 1170.2 2002).     

基于蒙特卡罗模拟的阿特拉津健康风险评价

为评估阿特拉津(ATR)对人体的健康风险,通过文献检索及追溯方式,收集了93篇文献中关于我国环境介质中ATR的检测数据,基于美国环保署健康风险评价方法,并运用蒙特卡罗模拟方法,评价了我国成年男性和女性ATR的健康风险,分析了各参数的敏感性和相关性。结果显示,我国成年男性和女性的非致癌健康风险熵值分别为4.53×10~(-2)和4.30×10~(-2),分别有89.8%的成年男性和89.9%的成年女性风险熵值低于0.10;饮用水中ATR的浓度对其健康风险的贡献(即敏感性)分别为男性88.0%和女性83.3%,与健康风险的关联性(R)分别为男性0.907和女性0.895。我国ATR的非致癌健康风险处于可接受水平,饮用水中ATR对其健康风险的贡献最大。该方法可为有毒有害物质的健康风险预警和精准控制提供方法学参考。     

Human Capital,Innovation, and Climate Policy: an Integrated Assessment

This paper looks at the interplay between human capital and innovation when climate and educational policies are implemented. Following recent empirical studies, human capital and general purpose research and development (R&D) are introduced in an integrated assessment model used to study the dynamics of climate change mitigation. Our results suggest that climate policy stimulates general purpose as well as clean R&D but reduces the incentive to invest in human capital formation. Both innovation and human capital have a scale effect, which increases pollution, as well as a technique effect, which saves emissions for each unit of output produced. While the energy-saving effect prevails when innovation increases, human capital is pollution-using, also because of the gross complementarity between the labor and energy input. When the role of human capital is the key input in the production of general purpose and energy knowledge is accounted for, the crowding-out of education induced by climate policy is mitigated, though not completely offset. By contrast, a policy mix that combines educational as well as climate objectives offsets the human capital crowding-out, at moderate and short-term costs. Over the long run, the policy mix leads to global welfare gains.     

Monte Carlo assessment of uncertainty in the simulated hydrological response to land use change

We quantified the uncertainty in hydrological response for a set of land use change scenarios by varying plant parameters within realistic uncertainty bounds in a Monte Carlo analysis. The results show that simulated hydrological fluxes significantly change after the introduction of outwintering suckler cow management, despite the presence of a significant amount of output uncertainty due to uncertainty in the plant parameterisation. The key to a proper uncertainty assessment was to consider the uncertainty in the difference between the scenarios instead of the absolute uncertainty of each single scenario. Additionally, a sensitivity analysis showed that changing soil properties in response to land use change does not result in significantly different results in the scenario analysis.     

Mitigation Costs in a Globalized World: Climate Policy Analysis with REMIND-R

Within this paper, we present the novel hybrid model REMIND-R and its application in a climate policy context based on the EU target to avoid a warming of the Earth’s atmosphere by more than 2°C compared to the pre-industrial level. This paper aims to identify necessary long-term changes in the energy system and the magnitude of costs to attain such a climate protection target under different designs of the post-2012 climate policy regime. The regional specification of mitigation costs is analyzed in the context of globalization where regions are linked by global markets for emission permits, goods, and several resources. From simulation experiments with REMIND-R, it turns out that quite different strategies of restructuring the energy system are pursued by the regions. Furthermore, it is demonstrated that the variance of mitigation costs is higher across regions than across policy regimes. First-order impacts, in particular, reduced rents from trade in fossil resources, prevail regardless of the design of the policy regime.     

Assessment of Uncertainties in Modelling Land Use Change with an Integrated Cellular Automata–Markov Chain Model

Environmental Modeling & Assessment - Uncertainty in future land use change modelling is crucial to study as it may result in varying spatial characteristics and features of the model. An...     

A Flexible Global Warming Index for Use in an Integrated Approach to Climate Change Assessment

Global Warming Potential (GWP) is an index used to measure the cumulative radiative forcing of a tonne of greenhouse house gas (GHG) relative to that of a ‘reference’ gas (CO₂). Under the Kyoto Protocol, GWP can be used as a fixed index to govern the trade-off between different GHGs in a multi-gas approach to GHGs abatement. The use of fixed GWPs has been criticized for not being very cost effective compared to the use of some flexible indices. To gain wider acceptance, however, a flexible index must also prove to be easy to use, and the economic gains from its adoption must be significant. In this paper, we develop a flexible index based on the concept of marginal rather than cumulative or average global warming potentials. These marginal global warming potentials (MGWPs) can be endogenously determined within a climate model given a particular climate objective based on radiative forcing level. The MGPWs are then linked to the marginal abatement costs of the GHGs, which are also endogenously determined within an economic model. When the two concepts are linked in this way, the result is a cost-effective way of achieving a particular climate change objective with multigas abatement. We show that the savings in costs when using this flexible MGPWs can be significant, and more importantly, they are not uniformly distributed across different regions.

A New Approach to Plant Diversity Assessment Combining HPLC Data, Simplex Mixture Design and Discriminant Analysis

The quantitative assessment of plant diversity and its monitoring with time represent a key environmental issue for management and conservation of natural resources. Assessment of plant diversity could be based on chemical analyses of secondary metabolites (e.g. flavonoids, terpenoids), because of the substantial quantitative and qualitative between-individual variability in such compounds. At a geographical scale, the plant populations become widely dispersed, and their monitoring from numerous routine individual analyses could become restricting. To overcome such constraint, this study develops a multivariate calibration model giving the relative frequency of a particular taxon from a simple high-performance liquid chromatography (HPLC) analysis of a plant mixture. The model was built from a complete set of mixtures combining different taxons, according to an experimental design (Scheffé’s matrix). For each mixture, a reference HPLC pattern was simulated by averaging the individual HPLC profiles of the constitutive taxons. The calibration models, based on Bayesian discriminant analysis (BDA), gave statistical relationships between the contributions of each taxon in mixtures and reference HPLC patterns of these mixtures. Finally, these models were validated on new mixtures by using outside plants. This new biodiversity survey approach is illustrated on four chemical taxons (four chemotypes) of Astragalus caprinus (Fabaceae). The more differentiated the taxon, the better predicted its contributions (in mixtures) were by BDA calibration model. This new approach could be very useful for a global routine survey of plant diversity.     

Statistical Modeling of Health Effects on Climate-Sensitive Variables and Assessment of Environmental Burden of Diseases Attributable to Climate Change in Nepal

An ecological time-series study is conducted to quantify health-effect coefficients associated with climate-sensitive variables namely temperature, rainfall, relative humidity, and wind speed and estimate environmental burden of diseases attributed to temperature as the main climatic variable together with climate change in Nepal. The study is based upon daily data of climate-sensitive variables and hospitalizations collected for 5 years between 2009 and 2014. Generalized linear model is used to estimate health-effect coefficients accounting distributed lag effects. Results show 3.08%, 10.14%, and 3.27% rise in water-borne, vector-borne, and renal disease hospitalizations, respectively, and 3.67% rise in water- and vector-borne disease deaths per 1 °C rise in average temperature. Similarly, 2.45% and 1.44% rise in heart disease hospitalization and all-cause mortality, respectively per 1 °C rise in absolute difference of average temperature with its overall average (20 °C). The computed attributable fractions are 0.3759, 0.6696, 0.2909, and 0.1024 for water-borne, vector-borne, renal, and heart disease hospitalizations, respectively, and 0.0607 and 0.4335 for all-cause mortality and disease-specific mortality of water- and vector-borne diseases, respectively. The percent change in attributable burdens due to climate change are found to be 4.32%, 4.64%, 7.20%, and ?2.29% for water-borne, vector-borne, renal, and heart disease hospitalizations, respectively, and ?1.39% and 6.55% for all-cause deaths and water-borne and vector-borne disease deaths, respectively. In conclusion, climate-sensitive variables have significant effects on many major health burdens in Nepal. In the context of changing climatic scenarios around the world including that of Nepal, such changes are bound to affect the health burden of Nepalese people.     

Assessment of Flood Losses with Household Responses: Agent-Based Simulation in an Urban Catchment Area

Densely populated coastal urban areas are often exposed to multiple hazards, in particular floods and storms. Flood defenses and other engineering measures contribute to the mitigation of flood hazards, but a holistic approach to flood risk management should consider other interventions from the human side, including warning information, adaptive behavior, people/property evacuation, and the multilateral relief in local communities. There are few simulation approaches to consider these factors, and these typically focus on collective human actions. This paper presents an agent-based model that simulates flood response preferences and actions taken within individual households to reduce flood losses. The model implements a human response framework in which agents assess different flood scenarios according to warning information and decide whether and how much they invest in response measures to reduce potential inundation damages. A case study has been carried out in the Ng Tung River basin, an urbanized watershed in northern Hong Kong. Adopting a digital elevation model (DEM) as the modeling environment and a building map of household locations in the case area, the model considers the characteristics of households and the flood response behavior of their occupants. We found that property value, warning information, and storm conditions all influence household losses, with downstream and high density areas being particularly vulnerable. Results further indicate (i) that a flood warning system, which provides timely, accurate, and broad coverage rainstorm warning, can reduce flood losses by 30–40%; and (ii) to reduce losses, it is more effective and cheaper to invest early in response measures than late actions. This dynamic agent-based modeling approach is an innovative attempt to quantify and model the role of human responses in flood loss assessments. The model is demonstrated being useful for analyzing household scale flood losses and responses and it has the potential to contribute to flood emergency planning resource allocation in pluvial flood incidents.     

Rabbit Population Landscape-Scale Simulation to Investigate the Relevance of Using Rabbits in Regulatory Environmental Risk Assessment

This paper describes the development and testing of the ALMaSS rabbit model and its baseline, and subsequently its application to the question of lagomorph population vulnerability in environmental risk assessment (ERA). Development and testing following a pattern-oriented modelling protocol resulted in a model able to replicate local and landscape-level rabbit population patterns. We then tested how robust rabbit populations are to an (imaginary) extreme toxic stressor at a landscape level in a variety of landscapes, and to what extremes key uncertain model parameters must be pushed to cause extinctions. This was contrasted with the same (imaginary) toxic stressor applied to the already existing ALMaSS hare model. For EU risk assessment of plant protection products, these results clearly indicate that if the protection goal is population-level impacts, either in abundance and/or distribution, then the hare is a much more vulnerable species than the rabbit under all the conditions tested. Rabbits would only be more vulnerable than hares if the entire population were to be exposed simultaneously, when lower body mass would then be a critical factor. This did not occur even though the toxicant and exposure scenarios tested here were extreme and, in fragmented landscapes at scales used here, will not occur in reality from the use of plant protection products on crop fields. As well as specifically answering the question on rabbit versus hare vulnerability, this study generally illustrates the potential application of models for setting focal species for risk assessments.     

Climate impact of surface albedo change in Life Cycle Assessment: Implications of site and time dependence

Land use affects the global climate through greenhouse gas and aerosol emissions, as well as through changes in biophysical properties of the surface. Anthropogenic land use change over time has caused substantial climate forcing related to albedo, i.e. the share of solar radiation reflected back off the ground. There is growing concern that albedo change may offset climate benefits provided by afforestation, bioenergy or other emission reduction measures that affect land cover. Conversely, land could be managed actively to increase albedo as a strategy to combat global warming.Albedo change can be directly linked to radiative forcing, which allows its climate impact to be compared with that of greenhouse gases in Life Cycle Assessment (LCA). However, the most common LCA methods are static and linear and thus fail to account for the spatial and temporal dependence of albedo change and its strength as a climate forcer. This study sought to develop analytical methods that better estimate radiative forcing from albedo change by accounting for spatial and temporal variations in albedo, solar irradiance and transmission through the atmosphere. Simplifications concerning the temporal resolution and aggregation procedures of input data were evaluated.The results highlight the importance of spatial and temporal variations in determining the climate impact of albedo change in LCA. Irradiance and atmospheric transmittance depend on season, latitude and climate zone, and they co-vary with instantaneous albedo. Ignoring these dependencies led to case-specific errors in radiative forcing. Extreme errors doubled the climate cooling of albedo change or resulted in warming rather than cooling in two Swedish cases considered. Further research is needed to understand how different land use strategies affect the climate due to albedo, and how this compares to the effect of greenhouse gases. Given that albedo change and greenhouse gases act on different time scales, LCAs can provide better information in relation to climate targets if the timing of flows is considered in life cycle inventory analysis and impact assessment.     

Variations in Environmental Tritium Dose Estimates Due to Meteorological Data Averaging and Uncertainties in Pathway Model Parameters

The large amounts of tritium produced at the Savannah River Site (SRS) coupled with the current dose reconstruction study at the facility emphasize the importance of ensuring accurate and efficient prediction of tritium doses to the public. Presently, dose estimates to the general population in the site vicinity are calculated annually using a five year meteorological database. Determining whether detailed monthly dose estimates are necessary or whether annual averaged data is sufficient offers the potential for more efficient dose prediction. In this study, off site collective committed doses and maximum individual doses due to atmospheric tritium releases were calculated according to the methods outlined in the U.S. Nuclear Regulatory Commission's Regulatory Guide 1.109 and compared using monthly versus five-year meteorological data and source terms. Site-specific variables not currently utilized at SRS for annual dose estimates also have been included. In addition, the range of predicted doses, based on the distribution in model parameters given in the literature, were estimated. Finally, a sensitivity analysis was performed in order to determine the influence of model inputs on dose estimates. Results corroborate previous studies by indicating that the primary contributor to infant tritium dose is the ingestion of milk, while for all other age groups, the most important pathway is the ingestion of vegetation. These relative pathway contributions remain constant throughout the year for infants; for children, teenagers, and adults, however, inhalation and absorption of tritium through the skin increases in relative importance in the months of June to September. It was found that the model utilized was most sensitive to dose factors, the ratio of the specific activity of tritium oxide in vegetation to the specific activity of atmospheric tritium oxide, and breathing rates. Most importantly, it was found that over a five-year period, the use of averaged meteorological data results in total individual doses that are only 2 to 6% higher than doses determined monthly, depending on the pathway of interest.     

Combining a road pollution dispersion model with GIS to determine carbon monoxide concentration in Tennessee

The purpose of this paper is to develop an air pollution model that is independent from pollution monitoring sites and highly accurate through space and time. Total carbon monoxide concentration is computed with the use of traffic flow data, vehicle speed and dimensions, emission rates, wind speed, and temperature. The data are interpolated using a geographic information system universal kriging technique, and the end results produce state level air pollution maps with high local accuracy. The model is validated against Environment Protection Agency (EPA) pollution data. Overall, the model has 71 % agreement with EPA, overestimating values of carbon monoxide for less than 1 ppm. The model has three advantages over already assessed air pollution models. First, it is completely independent of any air pollution monitoring stations; thus, possible temporary or permanent unreliability or lack of the data is avoided. Second, being based on a 5,710 traffic count network, the problem of remote places coverage is avoided. Third, it is based on a straightforward equation, where minimal preprocessing of traffic and climatic data is required.     

System Dynamics Modeling and Policy Simulation for Urban Traffic: a Case Study in Beijing

This paper constructs a system dynamics model for simulating the impact of different strategies on urban traffic’s energy consumption and carbon emissions. Based on a case study in Beijing, the model includes three subsystems: (1) urban traffic, (2) population and economy, and (3) energy consumption and carbon emissions. First, the model is used to decompose the impact of different vehicles on energy consumption and carbon emissions. Decomposition results show that private cars have the most significant impact on urban traffic’s energy consumption and carbon emissions; however, total vehicle kilometers traveled by private cars are the smallest among four trip modes. Then, the model is used to simulate different urban traffic policies. Policies are categorized as follows: (a) driving restrictions on vehicle registration numbers, (b) a scheme for vehicle registrations via a lottery system, and (c) development of public transportation infrastructures. Scenario simulation results show that all those measures can reduce energy consumption and carbon emissions. Though the last strategy (c) contains several delays, its effect is more stable and far-reaching. Finally, some recommendations about easing traffic pressure and reducing traffic emissions are given.     

The Costa Rican Experience with Market Instruments to Mitigate Climate Change and Conserve Biodiversity

Two decades of developing relevant legal and institutional regimes for the sustainable and nondestructive use of natural resources have framed Costa Rica's pioneer approach to mitigate climate change and conserve its rich biological diversity. This policy framework provides an appropriate context for the actual and proposed development of market instruments designed to attract capital investments for carbon sequestration and biodiversity conservation, and allows the establishment of mechanisms to use those funds to compensate owners for the environmental services provided by their land. As a developing economy. Costa Rica is striving to internalize the benefits from the environmental services it offers, as a cornerstone of its sustainable development strategy.     

Holling's Figure-Eight Model: A Technical Reevaluation in Relation to Climate Change and Biodiversity

Holling proposed a four-phase conceptual model of ecosystem dynamics that includes exploitation, conservation, and destructive and renewal components to explain the failure of many natural resource management schemes. The model is drawn as a sideways figure-eight i.e. . There are two dimensions in this model, connectivity (abscissa) and the amount of capital stored in the system (ordinate). This conceptual model has been suggested as a guide to thinking about the impact of climate change on biodiversity, but the two dimensions are insufficient and the alignment of the figure-eight model is problematic when compared with actual data. Kay has adjusted the dimensions of the figure-eight model and renamed the abscissa as exergy stored and the ordinate as exergy consumed. We realign the original figure-eight model, labeling the abscissa as carbon stored and the ordinate as nutrients, such that the relative values of both axes are in qualitative agreement with data from four different studies. This new alignment is then shown to fit relatively well with Holling's original labels. This revision of the figure-eight model brings Holling's model into agreement with observations and provides insight into the linkages between biodiversity and climate change.     

Adaptation Opportunities to Climate Variability and Change in the Exploitation and Utilisation of Marine Living Resources

Because they contribute little to climate change, fisheries, aquaculture and other uses of marine renewable resources and environment have limited means to mitigate climate impacts. Adaptation is, therefore, critical. Though likely effects on oceans and fisheries can be identified, few can be quantified and, thus, priorized. Consequently, adaptation strategies should aim at enhancing the resilience of marine renewable resources and their uses and the current capacity to respond to surprises. Already, these uses are characterized by massive over-capacities, excessive resource exploitation, and pervasive conflicts within and between uses. Two complementary adaptation strategies are available. The first consists in adjusting conventional management systems to the new conditions of resource scarcity. The second aims at reducing the current resource constraint by promoting the development of aquaculture and a better utilization of fishery and aquaculture harvests. In this respect, small-scale and large-scale production systems, and developing and developed countries, have different capabilities. In summary, climate change does not modify, but enhances, existing priorities of environment and fisheries management and aquaculture development.     

Environmental and Biological Assessment of Exposure to Benzene in Petroleum Workers

Occupational exposure to benzene was measured in two gasoline marketing terminals and five major refineries in Singapore. A total of 280 workers were monitored over two years. This assessment was carried out with two primary objectives: (1) To find out the extent of occupational exposure to benzene in the petroleum industry in Singapore, (2) To identify suitable biomarkers for monitoring of low levels of benzene exposure. The exposure was measured in five different categories of petroleum and petrochemical workers, i.e., truck drivers, despatch assistant, process operators, oil movements operators and laboratory technicians. The results revealed wide variations in exposure, from 0.01 to 13.6 ppm for personal time weighted average (TWA) exposure over the whole workshift. The exposure of truck drivers appeared to be the highest, with geometric mean (GM) of 1.98 ppm (ranged from 0.25 to 13.6 ppm). The average benzene exposure for process operators was relative low with a GM of 0.04 ppm. Lowest benzene exposure was found in the laboratory technicians, with a GM of 0.02 ppm. As cigarette smoking is known to affect metabolism of benzene, data analyses on the relationships with environmental exposure were conducted only on the 190 nonsmokers. The results showed that urinary trans, trans-muconic acid (ttMA), unmetabolized benzene in urine (UBZ) and benzene in blood (BBZ) were better biomarkers for low level benzene exposure as compared to urinary phenolic metabolites in urine, such as hydroquinone, phenol and catechol.     

Reconciling National and Global Priorities in Adaptation to Climate Change: With an Illustration from Uganda

Many developing countries, especially in Africa, contribute only very small amounts to the world total of greenhouse gas emissions. For them, the reduction of such emissions is not a priority, and the more important issue is to find ways to reduce their vulnerability to the projected climate change which is being imposed upon them largely as a result of emissions from developed countries. This priority does not accord with the ultimate objective of the United Nations Framework Convention on Climate Change, which is to achieve stabilization of greenhouse gas emissions. This paper reports upon studies in Uganda designed to help in the development of a national adaptation strategy, and addresses the need to reconcile such a strategy with the global priority accorded to mitigation and with national economic development priorities. Some features of a national climate change adaptation strategy are identified and questions are raised about the need for an international regime to facilitate and support adaptation.