A note on data augmentation and iterative simulation for survival rate estimation

In this paper we examine the use of data augmentation techniques for simplifying iterative simulation in the context of both Bayesian and classical statistical inference for survival rate estimation. We examine two distinct model families common in population ecology to illustrate our ideas, ring-recovery models and capture–recapture models, and we present the computational advantage of this approach. We discuss also the fact that problems associated with identifiability in the classical framework can be overcome using data augmentation, but highlight the dangers in doing so under both inferential paradigms.

Estimation of crop residue in China based on a Monte Carlo analysis

Accurate crop residue resource estimation is important for bioenergy development.This is done by the ratio of residue to grain(R/G),which is usually regarded constant and is widely used for crop residue estimation though uncertainty is inevitable in practice.In this study,a Monte Carlo algorithm was applied to estimate national crop residue by R/G taken from published reports in China.The estimated result was further mapped in pixels by geographic information system.In2009,the amount of crop residue was found to be 802.32 million tons(Mt),with 679.36 and 947.28 Mt as the lower and upper limits for 95%confidence limits.Chinese crop residue was dominated by rice,wheat,and corn,accounting for74.57%(598.29 Mt).From 1949 to 2009,the amount of crop residue increased by four times,accompanied by component change.The spatial distribution of crop residue in China is markedly heterogeneous.Compared to the shortage of crop residue in northwest China,there is an abundant crop residue of about 334 Mt in eastern China,attracting 90%of the country's electricity or heat generation plants.     

基于蒙特卡罗模拟的煤矸山周边农用地土壤重金属健康风险评估

以重庆市煤矸山周边农用地土壤为研究对象,运用内梅罗指数、地累积指数和潜在生态风险指数分析土壤重金属污染水平,并采用蒙特卡罗模拟的健康风险模型,探析研究区土壤重金属对人体的健康风险.结果表明,研究区土壤Cd、Hg、As、Pb、Cr、Cu、Ni和Zn含量均值超重庆市土壤背景值,地累积指数（I_geo）表现为：Cd>Hg>Cu>As>Ni>Zn>Cr>Pb;内梅罗指数表明土壤以轻污染（1<P_N≤2）为主,各重金属污染指数（P_i）表现为：Cd>Cu>Ni>Cr>Zn>As>Pb>Hg;潜在生态风险指数表明土壤以中等生态风险（150≤RI<300）为主,各重金属生态风险指数（E_rⁱ）表现为：Cd>Hg>As>Cu>Ni>Pb>Cr>Zn.土壤健康风险表明,儿童和成人非致癌健康风险可忽略,但存在可耐受致癌健康风险,综合致癌风险指数（TCR）均值分别为1.04E-5和3.94E-6,主要致癌因子为As和Cd.敏感性分析表明非致癌健康风险土壤颗粒摄入速率（R_ingest）敏感性最高,其次是As含量,致癌健康风险土壤颗粒摄入速率As含量敏感性最高,其次是土壤颗粒摄入速率（R_ingest）.整体上儿童非致癌和致癌健康风险高于成人.     

突发型大气污染源位置识别反演问题的数值模拟

在突发型大气污染事件中,能否根据临时监测数据对污染源的位置进行快速识别,对于城市大气污染源的控制管理以及改善城市空气质量意义重大.为了构建突发型大气污染源位置识别的空间反演算法,本文通过分析大气应急污染监测的临时采样数据,结合污染物浓度扩散模型,随机生成污染源和计算污染物浓度的空间分布,对突发型大气污染源进行定位并与实际测量结果进行对比分析,采用蒙特卡洛模拟(Monte Carlo simulation)对相关参数进行讨论,最终构建能对突发型大气污染源进行快速估计定位的空间反演算法.研究结果表明,本文构建的空间反演算法输出的污染源坐标与实际情况相符.因此,该算法可用于突发型大气污染源位置的快速识别.     

中国稻田土壤镉污染及务农性暴露概率风险

为了解中国稻田土壤镉污染时空变异特征及其务农性接触的健康风险,对1991~2017年发表的205篇有关稻田土壤镉调查文献进行系统回顾,并采用USEPA推荐的健康风险评估概率模型对健康风险进行评估.结果表明,全国稻田土壤镉平均浓度[（0.54±1.38）mg/kg]显著高于全国表层土壤镉背景值（0.097mg/kg）.虽未见显著的时间变异（β=-0.002,95%置信区间：-0.025~0.022）,但不同地区和不同人为扰动下的稻田土壤镉浓度存在较大异质性：华南[（0.88±1.59）mg/kg]>西南[（0.46±0.85）mg/kg]>华东[（0.44±1.45）mg/kg]>华北[（0.36±1.05）mg/kg]>东北[（0.35±0.83）mg/kg]>西北[（0.01±0.05）mg/kg]和矿冶[（1.77±2.18）mg/kg]>其他工业活动[（0.64±1.22）mg/kg]>电子垃圾拆解[（0.59±0.87）mg/kg]>污灌[（0.57±1.26）mg/kg]>交通[（0.56±0.86）mg/kg]>常规稻田[（0.25±0.34）mg/kg].除西北地区外,各地区稻田土壤镉浓度均出现不同程度的超标现象,尤其华南地区34%条土壤镉浓度数据超过农用地土壤镉污染风险管制值（1.5mg/kg）.土壤镉务农性接触的非致癌风险（HQ<<1）处于可接受水平,其中皮肤接触是主要途径,女性是务农性土壤镉暴露的高风险人群,华南和西南地区是务农性土壤镉暴露的高风险地区.     

考虑参数随机性的焊点热疲劳失效分析

目的研究电子产品焊点几何参数的随机性对焊点热疲劳寿命的影响。方法基于Coffin-Masson的修正式Engelmaier模型,建立考虑多参数随机性的引线型封装的焊点热疲劳寿命评估模型,并利用蒙特卡罗法对该模型的精确度进行验证。以SMT鸥翼型(gull-wing)TSOP062封装为例,将焊点几何参数的随机性对热疲劳寿命的影响进行量化分析。结果影响焊点寿命的几何参数离散系数越大,焊点疲劳寿命越小,随机变量越多,焊点热疲劳寿命下降的越明显,焊点高度是影响元器件热疲劳寿命的敏感参数。结论该方法在已知小批次产品的参数波动信息的情况下,能预测整批次产品的热疲劳寿命,极大地减少试验时间和成本,提高电子产品及装备的可靠性。     

Terraced fields and Mediterranean landscape structure: An analytical case study from Antikythera, Greece

Terraces are important capital investments in a range of agricultural landscapes worldwide, typically enduring well beyond any single farming cycle and over many human generations. This paper begins by emphasising that, while human population growth may often be loosely linked to terrace construction efforts, the association is by no means a straightforward one. We then argue that the choice of which parts of the landscape to terrace is driven by a range of cultural and environmental priorities that are most usefully explored by a combination of global, local and auto-correlative modelling, as well as via simulation-based methods. The results demonstrate that surficial geology, terrain slope, pre-existing terraces and pre-existing patterns of human habitation are all important structuring features. We also consider terraces as method for soil conservation, question the uncritical use of meso-scale erosion models and argue that patterns of catastrophic soil loss are often overstated in Mediterranean contexts. However, erosion modelling can, if deployed cautiously and comparatively, nonetheless be used to explore ways in which terraces do indeed manage localised soil movement in agriculturally favoured parts of the landscape, with our results suggesting that a substantial proportion of the erosion in this regions is indeed ameliorated by such measures.     

城市总体规划环境影响评价方法

开发不同类型规划的环境影响评价技术和方法是当前国内的热点研究领域之一.围绕城市总体规划特点,在分析识别规划经济增长和用地布局方案中存在的多种不确定性的基础上,以产业和用地为基本评估单元,综合运用蒙特卡罗随机采样技术和HSY算法,并将其与地理信息系统进行整合,建立了基于结构与空间不确定性分析的城市规划环评方法和系统评估模型.     

基于改进蒙特卡罗方法的再生水回用健康风险评价

传统的健康风险评价主要是进行单值点计算的确定性评价,为了解决再生水回用对人体健康风险影响的不确定性,利用Logistic混沌系统方法来产生可靠、简单、高效的随机数,进而改进传统蒙特卡罗方法中利用线性同余产生随机数的方法,以提高随机数输入的可靠性程度,最终提出了一种基于改进蒙特卡罗算法的再生水回用健康风险评价方法.最后,以西安市某污水处理厂再生水中的风险指标(As、Cd、Cr、Mn、Pb和Zn)为研究对象进行蒙特卡罗模拟计算.结果表明,各指标满足终生可接受风险水平的概率分别为97.25%、93.48%、97.86%、98.69%、97.13%和96.38%,影响各指标进入人体的单位体重日均暴露剂量和终生风险水平的主要因素为污染物浓度和胃肠吸收因子.研究结果可为再生水回用的不确定性健康风险评价提供一定的依据.     

Bayesian calibration of the nitrous oxide emission module of an agro-ecosystem model

Nitrous oxide (N₂O) is the main biogenic greenhouse gas contributing to the global warming potential (GWP) of agro-ecosystems. Evaluating the impact of agriculture on climate therefore requires a capacity to predict N₂O emissions in relation to environmental conditions and crop management. Biophysical models simulating the dynamics of carbon and nitrogen in agro-ecosystems have a unique potential to explore these relationships, but are fraught with high uncertainties in their parameters due to their variations over time and space. Here, we used a Bayesian approach to calibrate the parameters of the N₂O submodel of the agro-ecosystem model CERES-EGC. The submodel simulates N₂O emissions from the nitrification and denitrification processes, which are modelled as the product of a potential rate with three dimensionless factors related to soil water content, nitrogen content and temperature. These equations involve a total set of 15 parameters, four of which are site-specific and should be measured on site, while the other 11 are considered global, i.e. invariant over time and space. We first gathered prior information on the model parameters based on the literature review, and assigned them uniform probability distributions. A Bayesian method based on the Metropolis–Hastings algorithm was subsequently developed to update the parameter distributions against a database of seven different field-sites in France. Three parallel Markov chains were run to ensure a convergence of the algorithm. This site-specific calibration significantly reduced the spread in parameter distribution, and the uncertainty in the N₂O simulations. The model’s root mean square error (RMSE) was also abated by 73% across the field sites compared to the prior parameterization. The Bayesian calibration was subsequently applied simultaneously to all data sets, to obtain better global estimates for the parameters initially deemed universal. This made it possible to reduce the RMSE by 33% on average, compared to the uncalibrated model. These global parameter values may be used to obtain more realistic estimates of N₂O emissions from arable soils at regional or continental scales.