巢湖塘西河河口湿地重金属污染风险不确定性评价

基于污染风险评价系统多种不确定性共存的特点,采用盲数描述和表征重金属污染风险评价模型的各项参数,在对采自巢湖塘西河河口湿地24个表层沉积物柱状样中Cu,Zn,Pb,Cd和Cr等指标分析测试的基础上,采用Matlab工具软件编制的潜在生态风险指数盲数评价模型程序,对巢湖塘西河河口湿地重金属污染风险进行定量评估. 结果表明:巢湖塘西河河口湿地处于中等污染至较高污染状态,其相应的可信度分别为0.589和0.411;潜在生态风险等级属较低至中等水平,其相应可信度分别为0.755和0.245. 5种重金属污染程度排序依次为Cu＞Cd＞Zn＞Pb＞Cr;潜在生态风险水平排序依次为Cd＞Cu＞Pb＞Cr＞Zn.      

基于LHS-MC青岛市工业源VOCs排放清单及不确定性

采用自下而上法,逐一采集企业活动水平,抽样调查企业获取治理措施变量,使用优化后的排放系数法建立青岛市工业源VOCs排放清单,同时将MC和LHS方法联合,模拟单变量和多变量对VOCs清单不确定性影响.结果表明,2019年青岛市工业源VOCs排放总量为4.47万t （未优化排放系数法：3.11万t）,排放贡献较大行业依次为橡胶与塑料制品业、金属制品业、石油/煤炭及其他燃料加工业,占总量40.26%.多变量模拟的不确定性高于单变量,工艺过程源（-9.72%~230.51%）和溶剂使用源（-14.14%~122.77%） VOCs清单的不确定性明显高于燃烧源（-15.62%~36.41%）.影响VOCs清单不确定性的行业和主要因素为：化工、造纸和纺织（排放因子）;金属制品、汽车制造和化工（去除率和设施运行率）;石油加工和黑色金属冶炼（样本数少）.VOCs排放主要集中在：西海岸新区东部、大珠山北部、即墨区南部、城阳区北部、胶州市东北部、平度市建成区和莱西市东南部.     

基于气象灾害的青岛市域生态风险评价

根据美国环保局（USEPA）的生态风险评价框架和相对风险模型（Relative Risk Model,RRM）,对青岛市域县级行政区的干旱、洪涝、大风和冰雹4种气象灾害进行了生态风险评价。评价结果表明,研究区内干旱生态风险最为严重,洪涝其次;耕地承受的风险最高,城镇和居民地其次;胶州的生态风险最大,青岛市区其次。而且胶州的干旱灾害、市区的大风灾害、平度的冰雹灾害以及胶南的草地成灾比较突出,成为风险分区的依据。最后,不确定性分析表明RRM不确定性的模拟值与计算值接近,验证了RRM在县级尺度上的适用性;敏感性分析说明相对风险值对干旱等级4最为敏感,且对生境分级敏感程度高于风险源分级。此外,利用生态系统生态服务功能的损失率确定暴露系数,降低了确定暴露系数带来的随机性。同时,生境变化需要进一步考虑以明确生态风险在时间尺度上的变化特点。     

北京地区二噁英类的环境多介质迁移和归趋模拟

以二噁英类为研究对象,利用LevelⅢ多介质逸度模型模拟计算了稳态假设下二噁英类在北京地区空气相、水相、土壤相和沉积物相中的浓度分布及各相间的迁移通量.结果表明:二噁英类在空气和土壤相中的模拟输出浓度(以I-TEQ计,全文同)范围分别为0.32～0.34 pg/m3和0.33～1.14 ng/kg,而相应环境相中的实测值分别为0.33 pg/m3和1.48 ng/kg,二者吻合较好,验证了该模型在北京地区的适用性;北京地区二噁英类的主要迁移过程为空气相—土相沉降和空气相—水相沉降.土壤和大气降解是二噁英类的主要消亡途径,二者占总降解量的99.1%.土壤相是二噁英类的最大储库,其占总量的97.8%.      

Scaling up of biomass simulation for Eucalyptus plantations based on landsenses ecology

ABSTRACT

Sustainable forest management on a regional scale requires accurate biomass estimation. At present, technologically comprehensive forecasting estimates are generated using process-based ecological models. However, isolation of the ecological factors that cause uncertainty in model behavior is difficult. To solve this problem, this study aimed to construct a meliorization model evaluation framework to explain uncertainty in model behavior with respect to both the mechanisms and algorithms involved in ecological forecasting based on the principle of landsenses ecology. We introduce a complicated ecological driving mechanism to the process-based ecological model using analytical software and algorithms. Subsequently, as a case study, we apply the meliorization model evaluation framework to detect Eucalyptus biomass forest patches at a regional scale (196,158 ha) using the 3PG2 (Physiological Principles in Predicting Growth) model. Our results show that this technique improves the accuracy of ecological simulation for ecological forecasting and prevents new uncertainties from being produced by adding a new driving mechanism to the original model structure. This result was supported by our Eucalyptus biomass simulation using the 3PG2 model, in which ecological factors caused 21.83% and 9.05% uncertainty in model behavior temporal and spatial forecasting, respectively. In conclusion, the systematic meliorization model evaluation framework reported here provides a new method that could be applied to research requiring comprehensive ecological forecasting. Sustainable forest management on regional scales contributes to accurate forest biomass simulation through the principle of landsenses ecology, in which mix-marching data and a meliorization model are combined.     

Modeling the impact of uncertainty in emissions trading markets with bankable permits

The various forms of uncertainty that firms may face in bankable emission permit trading markets will affect firms’ decision making as well as their market performance. This research explores the effect of increased uncertainty over future input costs and output prices on the temporal distribution of emission. In a dynamic programming setting, the permit price is a convex function of stochastic prices of coal and electricity. Increased uncertainty about future market conditions increases the expected permit price and causes a risk neutral firm to reduce ex ante emissions in order to smooth out marginal abatement costs over time. Finally, safety valves, both low-side and high-side, are suggested to reduce the impact of uncertainty in bankable emission trading markets.     

An uncertain energy planning model under carbon taxes

In this study, an interval fuzzy mixed-integer energy planning model (IFMI-EPM) is developed under considering the carbon tax policy. The developed IFMIEPM incorporates techniques of interval-parameter programming, fuzzy planning and mixed-integer programming within a general energy planning model. The IFMIEPM can not only be used for quantitatively analyzing a variety of policy scenarios that are associated with different levels of carbon tax policy, but also tackle uncertainties expressed as discrete intervals and fuzzy sets in energy and environment systems. Considering low, medium and high carbon tax rates, the model is applied to an ideal energy and environment system. The results indicate that reasonable solutions have been generated. They can be used for generating decision alternatives and thus help decision makers identify desired carbon tax policy.     

A density projection approach for non-trivial information dynamics: Adaptive management of stochastic natural resources

We demonstrate a density projection approximation method for solving resource management problems with imperfect state information. The method expands the set of partially-observed Markov decision process (POMDP) problems that can be solved with standard dynamic programming tools by addressing dimensionality problems in the decision maker's belief state. Density projection is suitable for uncertainty over both physical states (e.g. resource stock) and process structure (e.g. biophysical parameters). We apply the method to an adaptive management problem under structural uncertainty in which a fishery manager's harvest policy affects both the stock of fish and the belief state about the process governing reproduction. We solve for the optimal endogenous learning policy—the active adaptive management approach—and compare it to passive learning and non-learning strategies. We demonstrate how learning improves efficiency but typically follows a period of costly short-run investment.     

Climate change implications for disaster risk management in Japan: A case study on perceptions of risk management personnel and communities in Saijo city

This paper reviews climate change impacts and the existing disaster risk management system in Japan and offers the results of a structured questionnaire survey of the community leaders and disaster risk management personnel of Saijo city of Japan that assesses their perceptions about dealing with the extreme disasters by the existing disaster risk management systems. This study was inspired by the record number of typhoon landfall that has surprised the local government and communities in 2004. While unearthing the hidden vulnerabilities in cities like Saijo, this event has loosened the confidence of local communities on the disaster risk management systems. From the study, we conclude that the existing disaster risk management systems need further fillip and that the proactive community involvement in disaster risk management is still in nascent stages. Associating with the scientific community, involving the local communities (including the elderly), enhancing the redundancy in disaster risk management systems, inculcating strategic thinking and micro-level planning, conducting vulnerability assessments by considering the special circumstances including resource constraints of small cities and better policy coordination across the administrative hierarchy are some important considerations for dealing with the uncertainty brought by the extreme events.     

广州地区典型多溴联苯醚迁移和归趋行为模拟

运用多介质逸度模型对典型PBDEs(BDE47、BDE99、BDE209)在广州地区大气、水体、土壤、沉积物中的浓度分布与多介质间的迁移、归趋进行了模拟研究并分析了3种化合物在研究区域环境多介质间的迁移通量,确定其在环境中的主要迁移过程;结合实际监测数据,对模型的可靠性进行验证;以BDE47和BDE209为例,对模型的输入参数进行灵敏度分析;并以BDE209为例,对模型进行不确定性分析.通过模拟浓度与实测浓度的对比,表明模型在该地区具有很好的适用性.结果表明,环境系统达到平衡时,BDE47、BDE99和BDE209在土壤和沉积物中的含量分别占其在环境系统总含量的17.73%和82.26%,14.65%和85.35%,4.81%和95.19%;PBDEs从环境系统中的消失途径主要为大气平流输出和土壤降解;logKow和大气平流输入是影响化合物在环境相中浓度分布的最主要因素;不确定性分析指出BDE209在土壤相中浓度的变异系数最大.