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.     

污染场地健康风险评价中多介质模型的优选研究

综合考虑了参数不确定性、模型不确定性对风险评价结果可信度的影响差异和国内实际污染场地健康风险评价中对已有模型的不当选择可能带来的负面效应,提出相对于参数不确定性的控制,针对目标情景的适当模型选择应成为提高风险评价结果可信度的更重要环节.本研究选取了模型理论架构上符合案例情景的美国环境保护署研发的3MRA、MMSOILS和美国劳伦斯伯克力国家实验室研发的CalTOX 3个被广泛使用的污染场地健康风险评价多介质模型,并与蒙特卡洛模拟相结合,载入污染厂址案例,并对特定受体进行了风险评价,由此定量分析了参数不确定性和模型选择不确定对评价结果可信度的影响程度,并通过对比3个模型的内在架构分析了不确定性的可能来源,最后总结提出了“6步综合评价模式”,该模式能有效提高污染场地健康风险评价的可信度及经济可行性.     

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%.      

Performance of a Bayesian state-space model of semelparous species for stock-recruitment data subject to measurement error

Measurement errors in spawner abundance create problems for fish stock assessment scientists. To deal with measurement error, we develop a Bayesian state-space model for stock-recruitment data that contain measurement error in spawner abundance, process error in recruitment, and time series bias. Through extensive simulations across numerous scenarios, we compare the statistical performance of the Bayesian state-space model with that of standard regression for a traditional stock-recruitment model that only considers process error. Performance varies depending on the information content in data, as determined by stock productivity, types of harvest situations, and amount of measurement error. Overall, in terms of estimating optimal spawner abundance S_MSY, the Ricker density-dependence parameter β, and optimal harvest rate h_MSY, the Bayesian state-space model works best for informative data from low and variable harvest rate situations for high-productivity salmon stocks. The traditional stock-recruitment model (TSR) may be used for estimating α and h_MSY for low-productivity stocks from variable and high harvest rate situations. However, TSR can severely overestimate S_MSY when spawner abundance is measured with large error in low and variable harvest rate situations. We also found that there is substantial merit in using h_MSY (or benchmarks derived from it) instead of S_MSY as a management target.     

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.      

Comparing model averaging with other model selection strategies for benchmark dose estimation

Model averaging (MA) has been proposed as a method of accommodating model uncertainty when estimating risk. Although the use of MA is inherently appealing, little is known about its performance using general modeling conditions. We investigate the use of MA for estimating excess risk using a Monte Carlo simulation. Dichotomous response data are simulated under various assumed underlying dose–response curves, and nine dose–response models (from the USEPA Benchmark dose model suite) are fit to obtain both model specific and MA risk estimates. The benchmark dose estimates (BMDs) from the MA method, as well as estimates from other commonly selected models, e.g., best fitting model or the model resulting in the smallest BMD, are compared to the true benchmark dose value to better understand both bias and coverage behavior in the estimation procedure. The MA method has a small bias when estimating the BMD that is similar to the bias of BMD estimates derived from the assumed model. Further, when a broader range of models are included in the family of models considered in the MA process, the lower bound estimate provided coverage close to the nominal level, which is superior to the other strategies considered. This approach provides an alternative method for risk managers to estimate risk while incorporating model uncertainty.

武汉汤逊湖未来水环境演变趋势的模拟

以流域为基本单元进行水质水量耦合,构建武汉汤逊湖水环境演变模型(TXHwater);借助Monte Carlo分析方法,对未来汤逊湖的水环境演变趋势进行了分析.结果表明,来水不确定条件下,COD模拟结果的不确定性较大,NH3-N和TN的不确定性中等,TP的不确定性最小.来水不确定性对流域汇流、巡司河入流过程影响较大,主要体现在汛期的5~9月份.如按现有规划,不考虑对污染物指标的削减,到2010年和2020年该湖未来水环境形势严峻,水质将演变为劣V类,主要控制指标为TN和TP,汛期6~8月浓度达到峰值.     

环境风险评价中的不确定性研究

在环境风险评价中经常会遇见不确定性问题,以无水氢氟酸储罐泄漏为例,分别研究了在计算事故发生概率、确定泄漏源、应用扩散模式、评价事故损伤结果等工作中遇到的不确定性问题。并以此分析了造成这些不确定性因素的原因,同时提出相应的解决方法,最后介绍了环境风险评价中不确定性问题的研究现状和方向。     

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.     

珠三角地区人为热排放演变趋势及不确定性分析

人类生产生活中产生的大量废热对城市环境中的温度、城市边界层结构、空气质量及人类健康都有着重要的影响.然而目前人为热排放清单的计算方法存在所需基础资料种类繁多、计算过程复杂,空间分布和排放量有较大偏差等不足.本研究采用统计回归法基于CO和NO_x污染源清单计算了2007—2015年珠江三角洲地区的人为热排放清单,对其排放趋势、热排放来源类型变化以及空间格局的演变进行了梳理.结果表明：2007—2015年珠三角地区年平均人为热均大于9 W·m^-2,人为热排放峰值出现在2010年,趋势为先增加后减少,这与燃料消耗总量的变化趋势基本一致,而工业源和道路移动源是影响人为热排放总量变化的最大因素：2007—2015年珠三角地区大部分区域人为热排放处于0~20 W·m^-2,高值区（>20 W·m^-2）主要分布于珠三角地区中心地带的城市群,高值区缩减的面积和速度在2010—2012年期间达到最大.进一步对2012年人为热清单的不确定性分析表明：总人为热的95%置信区间的不确定性为-16%~49%,其中电厂类别排放估算中的不确定性最小,为-13%~16%,而工业源的不确定性最高,为-46%~73%.