Discovering meaningful information from large amounts of environment and health data to reduce uncertainties in formulating environmental policies

This study uses knowledge discovery concepts to analyze large amounts of data step by step for the purpose of assisting in the formulation of environmental policy. We performed data cleansing and extracting from existing nation-wide databases, and used regression and classification techniques to analyze the data. The current water hardness in Kaohsiung, Taiwan contributes to the prevention of cardiovascular disease (CVD) but exacerbates the development of renal stones (RS). However, to focus on water hardness alone to control RS would not be cost effective at all, because the existing database parameters do not adequately allow for a clear understanding of RS. Analysis of huge amounts of data can most often turn up the most reliable and convincing results and the use of existing databases can be cost-effective.     

Decision making under uncertainty in case of soil remediation

Decisions on soil remediation are one of the most difficult management issues of municipal and state agencies. The assessment of contamination is uncertain, the costs of remediation are high, and the impacts on the environment are multiple. This paper presents a general, transparent, and consistent method for decision making among the remediation alternatives. Soil washing, phytoremediation, and no remediation are exemplarily considered. Multi-criteria utility functions including (a) the cost of remediation (b) the impact on human health and agricultural productivity, and (c) the economic gain after remediation are constructed using probability density functions representing contamination for all site coordinates. Herewith, the probability of different types of (i) correct decisions such as a hit or a true rejection and (ii) erroneous decisions such as a false alarm or miss are examined. The decision theoretic model is applied to a case study on heavy metal contaminated soil. This case study reveals the non-linear structure of multi-criteria-decision making. The case study shows that the geostatistical uncertainties of the log-normal distributed soil contamination must be taken into account: When uncertainties are not considered and the utilities are assessed according to the estimated value for a spatial unit, only few (N=26) spatial units result where the utility score of the alternative soil washing are higher than the utility score to the no remediation alternative. However, when taking into account geostatistical uncertainties of the log-normal soil distribution this number is about ten times greater (N=237). Furthermore, the use of 'maximizing expected utility' as decision rule is critical in that it may lead to a high probability of misses.     

气候政策研究中的数学模型评述

按照成本分析和综合分析2个层次，分别介绍了投入产出模型、可计算一般均衡模型、宏观计量经济模型、工程经济模型、动态能源优化模型、能源系统模拟模型、综合评估模型等不同模型方法的特点及其在气候政策分析中的应用。指出可计算一般均衡模型是应用得最为广泛的方法之一。概括了气候政策模型研究的4个发展趋势，包括加强综合评估模型的应用、扩展成本与效益的内涵、注重不同模型之间的比较、强调不确定性分析。结果中国研究现状，分析了当前面昨的问题，认为国内气候政策模型研究与中国在国际气候变化领域的地位仍不相称，强度要进一步加强研究队伍建设、扩展研究领域、跟踪国际研究前沿问题，以便为国家的气候谈判政策提供更有效的科学依据和决策支持。     

Sustainable pattern analysis of a publicly owned Material Recovery Facility in a fast-growing urban setting under uncertainty

Sustainable development goals are achievable through the installation of Material Recovery Facilities (MRFs) in certain solid waste management systems, especially those in rapidly expanding multi-district urban areas. MRFs are a cost-effective alternative when curbside recycling does not demonstrate long-term success. Previous capacity planning uses mixed integer programming optimization for the urban center of the city of San Antonio, Texas to establish that a publicly owned material recovery facility is preferable to a privatized facility. As a companion study, this analysis demonstrates that a MRF alleviates economic, political, and social pressures facing solid waste management under uncertainty. It explores the impact of uncertainty in decision alternatives in an urban environmental system. From this unique angle, waste generation, incidence of recyclables in the waste stream, routing distances, recycling participation, and other planning components are taken as intervals to expand upon previous deterministic integer-programming models. The information incorporated into the optimization objectives includes economic impacts for recycling income and cost components in waste management. The constraint set consists of mass balance, capacity limitation, recycling limitation, scale economy, conditionality, and relevant screening restrictions. Due to the fragmented data set, a grey integer programming modeling approach quantifies the consequences of inexact information as it propagates through the final solutions in the optimization process. The grey algorithm screens optimal shipping patterns and an ideal MRF location and capacity. Two case settings compare MRF selection policies where optimal solutions exemplify the value of grey programming in the context of integrated solid waste management.     

Negotiating environmental agreements under ratification constraints

We analyze the impact of ratification constraints on the optimal terms of international environmental agreements (IEAs). For this, we combine the literature on IEAs with the public choice literature on two-level games. By also incorporating uncertain preferences of the pivotal voter in the ratification stage, we make several important contributions: (i) we contribute to a more realistic modeling of the temporal structure of international agreements, (ii) we show a surprising, yet intuitive non-monotonic relationship between the optimal commitment level and the variance of ratification decisions, (iii) we identify reasons to expect a larger number of countries to join international negotiations than predicted by most of the coalition formation literature based on a representative agent model. Ratification constraints thereby can improve the welfare gains from stable international agreements.     

Projecting Climate Change Effects on Forest Net Primary Productivity in Subtropical Louisiana,USA

This study projected responses of forest net primary productivity (NPP) to three climate change scenarios at a resolution of 5 km × 5 km across the state of Louisiana, USA. In addition, we assessed uncertainties associated with the NPP projection at the grid and state levels. Climate data of the scenarios were derived from Community Climate System Model outputs. Changes in annual NPP between 2000 and 2050 were projected with the forest ecosystem model PnET-II. Results showed that forest productivity would increase under climate change scenarios A1B and A2, but with scenario B1, it would peak during 2011–2020 and then decline. The projected average NPP under B1 over the years from 2000 to 2050 was significantly different from those under A1B and A2. Forest NPP appeared to be primarily a function of temperature, not precipitation. Uncertainties of the NPP projection were due to large spatial resolution of the climate variables. Overall, this study suggested that in order to project effects of climate change on forest ecosystem at regional level, modeling uncertainties could be reduced by increasing the spatial resolution of the climate projections.     

原子吸收法测定海水中铁含量的不确定度评定

为了保证海水中铁元素含量的检测质量,更好地深入了解铁在整个海水体系的生物地球化学中扮演的角色,需科学地评定检测结果的分散性。文章依据《测量不确定度的评定与表示》(JJF1059-1999)的理论,以浙江近海海水为例,评定原子吸收法测定海水中铁含量的不确定度。测得浙江近海样品中铁的浓度为5.2μg/L,扩展不确定度U=0.8μg/L(k=2)。通过对各不确定度分量进行评定发现,利用该方法测定海水中铁含量时,对其合成标准不确定度的主要贡献来自于样品制备过程,尤其是萃取过程。     

气相分子吸收光谱法测定水中硫化物浓度不确定度评定

采用气相分子吸收光谱仪法测定水样中硫化物的浓度,对其不确定度来源进行分析、评估。结果表明,当水样浓度为3．42mg/L时,考虑测定过程的标准溶液的配制、曲线拟合、仪器测量重复性等因素对测定结果造成影响,测得硫化物的相对合成标准不确定0．13mg/L,其中最主要的分量是由硫化物标准溶液引起的测量不确定度。     

A highly resolved temporal and spatial air pollutant emission inventory for the Pearl River Delta region, China and its uncertainty assessment

A highly resolved temporal and spatial Pearl River Delta (PRD) regional emission inventory for the year 2006 was developed with the use of best available domestic emission factors and activity data. The inventory covers major emission sources in the region and a bottom–up approach was adopted to compile the inventory for those sources where possible. The results show that the estimates for SO₂, NO_x, CO, PM₁₀, PM_2.5 and VOC emissions in the PRD region for the year 2006 are 711.4 kt, 891.9 kt, 3840.6 kt, 418.4 kt, 204.6 kt, and 1180.1 kt, respectively. About 91.4% of SO₂ emissions were from power plant and industrial sources, and 87.2% of NO_x emissions were from power plant and mobile sources. The industrial, mobile and power plant sources are major contributors to PM₁₀ and PM_2.5 emissions, accounting for 97.7% of the total PM₁₀ and 97.2% of PM_2.5 emissions, respectively. Mobile, biogenic and VOC product-related sources are responsible for 90.5% of the total VOC emissions. The emissions are spatially allocated onto grid cells with a resolution of 3 km × 3 km, showing that anthropogenic air pollutant emissions are mainly distributed over PRD central-southern city cluster areas. The preliminary temporal profiles were established for the power plant, industrial and on-road mobile sources. There is relatively low uncertainty in SO₂ emission estimates with a range of −16% to +21% from power plant sources, medium to high uncertainty for the NO_x emissions, and high uncertainties in the VOC, PM_2.5, PM₁₀ and CO emissions.     

Info-gap theory and robust design of surveillance for invasive species: The case study of Barrow Island

Surveillance for invasive non-indigenous species (NIS) is an integral part of a quarantine system. Estimating the efficiency of a surveillance strategy relies on many uncertain parameters estimated by experts, such as the efficiency of its components in face of the specific NIS, the ability of the NIS to inhabit different environments, and so on. Due to the importance of detecting an invasive NIS within a critical period of time, it is crucial that these uncertainties be accounted for in the design of the surveillance system. We formulate a detection model that takes into account, in addition to structured sampling for incursive NIS, incidental detection by untrained workers. We use info-gap theory for satisficing (not minimizing) the probability of detection, while at the same time maximizing the robustness to uncertainty. We demonstrate the trade-off between robustness to uncertainty, and an increase in the required probability of detection. An empirical example based on the detection of Pheidole megacephala on Barrow Island demonstrates the use of info-gap analysis to select a surveillance strategy.