湖泊水质时空变化特征识别的贝叶斯方差分析方法

精细识别湖泊水质的时空变化特征是确定流域污染调控措施的基础.贝叶斯方差分析具有灵活的模型结构,可直接表征变量的时空动态特征.本文据此提出了基于该方法的湖泊水质时空变化特征识别的方法框架,研究了异龙湖稳态转换条件下富营养化指标的变化特征和滇池外海特征污染物达标率的时空变化2个案例,验证了该方法在总体服从正态分布和二项分布时的适用性.针对参数可交换性假设被忽视的问题,本文提出了一种基于模型选择准则的判定方法,并将其应用于滇池案例中.结果表明:(1)相对于清水稳态,异龙湖在浊水稳态时3种富营养化指标浓度更高,且年际方差所占总方差比例减小;(2)滇池外海总氮浓度超标率由2007—2013年间的40%左右降到2014—2016年间的10%左右,且波动性降低.随着监测数据的积累和监测时空精度的增加,贝叶斯方差分析在湖泊水质时空变化特征识别中具有广阔的应用前景.     

当前城市空气污染预报方法存在的问题及新思路

论述了当前国内外城市空气污染预报的主要方法以及存在的问题,探讨了一个基于贝叶斯网络的城市空气污染预报的新思路,并对其技术路线和关键问题等进行了初步探讨。新方法可以在一定程度上克服了现有方法理论上的缺陷和应用上的困难,而且具有更大的灵活性、现实性和可操作性,对于我国中小城市开展空气污染预报具有较好的应用前景。     

From the ground up: holistic management and grassroots rural adaptation to bovine spongiform encephalopathy across western Canada

Bovine spongiform encephalopathy (BSE) has been documented in 28 countries and adversely affected farmers and rural communities around the world. Our study examines the impacts of and adaptive responses of producers to BSE in western Canada. Moreover, it explores the role that holistic management (HM), and its combined focus on environmental, social, and economic sustainability, might play in mitigating the effects of BSE. One survey was sent to 835 HM producers and another to 9,740 producers across Manitoba, Saskatchewan, and Alberta. The disease, and concomitant climate change and low commodity prices, had devastating impacts on both groups. Yet, HM producers were much more optimistic about their ability to adapt to BSE and the future of agriculture than their non-HM counterparts. Social networks, namely HM clubs and the larger HM community, enabled these producers to mitigate the impacts of BSE. Agronomic responses, especially those associated with rotational grazing and increases in on-farm biodiversity were also important. That HM has been such an effective adaptive response to BSE indicates the importance of this and other grassroots responses to rural crises, whether they be associated with zoonotic diseases or indeed environmental change as a whole.     

基于贝叶斯正则化BP神经网络的砂土地震液化研究

砂土地震液化的影响因素具有高度的非线性关系,而神经网络在处理非线性问题上具有其独特的优越性.本文在探讨输人层模式的选择以及砂土液化影响因素的基础上,采用改进的贝叶斯正则化方法和"提前停止"算法建立了砂土地震液化预测模型,通过实例计算和模型评价,表明本模型的计算结果与规范法、改进的Seed简化法以及基于传统BP网络算法的...     

含氟氯类危险废物焚烧灰渣的熔融特性研究

为了解决低熔点含氟氯类危险废物（简称＂危废＂）在回转窑焚烧炉内燃烧时,灰渣熔化导致窑内结圈的问题,实验采用氧化物分析纯（SiO2、Al2O3、CaO、Fe2O3、Al2O3）和NaCl、KCl、NaF分析纯来模拟危废炉渣组分,研究了危废焚烧炉灰渣的熔融特性,并采用神经网络预测危废灰渣的熔融温度.结果表明,SiO2、Al...     

石河子-昌吉地区地下水水质时空变化及污染源解析

为了探明新疆石河子-昌吉地区地下水水质时空变化并解析污染源,综合运用基于香农熵的贝叶斯水质评价模型、 Spearman秩相关系数、主成分分析和绝对主成分得分-多元线性回归受体模型(APCS-MLR)等方法,对23眼原位井2016～2021年逐年地下水水质数据进行分析.结果表明：(1)研究区多年地下水水质状况总体较好,潜水水质类别以Ⅰ类和Ⅱ类为主,承压水主要为Ⅰ类.(2)从时间看,2016年和2017年分别为潜水和承压水水质变化的关键时间节点,Ⅳ类和Ⅴ类水仅出现在节点之前,之后在Ⅰ～Ⅲ类水范围内波动.(3)从空间看,潜水水质优劣排序为：石河子市≈呼图壁县>玛纳斯县>昌吉市;承压水为：石河子市≈昌吉市>呼图壁县>玛纳斯县.(4)研究区多年地下水水质类别和主要水质指标在时空变化上基本对应,且异质性较强.(5)潜水水质主要受到溶滤作用(67.30%)、溶滤-迁移作用(10.89%)和农业-生活污染(9.44%)的影响,溶滤-富集作用(52.08%)、农业-生活污染(16.06%)和碱性环境的离子交换作用(12.64%)对承压水水质影响较大.尽管研究区多年水质状况整体呈改...     

A Hierarchical Model for Estimating Long‐Term Trend of Atrazine Concentration in the Surface Water of the Contiguous U.S.

Atrazine is a herbicide frequently detected in both surface and groundwater in the United States (U.S.), but its spatiotemporal distribution and concentration trends have only been analyzed recently at regional or local scales. We employed a Bayesian hierarchical modeling approach to assess spatial and seasonal variation in atrazine concentration trends between 1990 and 2010 for the contiguous U.S. A Markov chain Monte Carlo simulation algorithm was used to address the problem of left‐censored data (i.e., atrazine concentration values below method reporting levels). We observed opposing temporal trends in the northern (flat or decreasing) and southern (increasing) regions of the U.S. This spatial variation in temporal trends can be partially explained by the relative amount of cropland in the region. Flat or decreasing trends in the north are more likely in regions with high cropland coverage while positive trends in the south are more likely in regions with low cropland coverage.     

基于轻量级梯度提升机的南京大气臭氧浓度预测

采用南京地区2015年1月至2016年12月期间的空气质量数据和常规气象资料数据,分析了南京地区O₃浓度变化特征,建立基于轻量级梯度提升机(LightGBM)的O₃浓度预测模型,并将该模型与支持向量机、循环神经网络和随机森林等3种在空气质量预测方向上常用的机器学习方法进行了对比,验证模型的有效性和可行性.结果表明,南京地区O₃浓度变化具有显著的季节性差异,浓度变化受前期浓度、气象因子和其他空气污染物浓度的共同影响.LightGBM模型较为准确地预测了南京地区地面O₃浓度(R²=0.92),且该模型的预测精度和计算效率等性能优于其他模型.尤其是在容易出现臭氧污染的高温天气,该模型预测准确性明显高于其他模型,模型稳定性较好.LightGBM具有预测准确度高、稳定性好、有良好的泛化能力和运算时间短等特点,在O₃浓度预测方面具有显著的优势.     

A dynamic simulation model to support reduction in illegal trade within legal wildlife markets

Sustainable wildlife trade is critical for biodiversity conservation, livelihoods, and food security. Regulatory frameworks are needed to secure these diverse benefits of sustainable wildlife trade. However, regulations limiting trade can backfire, sparking illegal trade if demand is not met by legal trade alone. Assessing how regulations affect wildlife market participants’ incentives is key to controlling illegal trade. Although much research has assessed how incentives at both the harvester and consumer ends of markets are affected by regulations, little has been done to understand the incentives of traders (i.e., intermediaries). We built a dynamic simulation model to support reduction in illegal wildlife trade within legal markets by focusing on incentives traders face to trade legal or illegal products. We used an Approximate Bayesian Computation approach to infer illegal trading dynamics and parameters that might be unknown (e.g., price of illegal products). We showcased the utility of the approach with a small-scale fishery case study in Chile, where we disentangled within-year dynamics of legal and illegal trading and found that the majority (∼77%) of traded fish is illegal. We utilized the model to assess the effect of policy interventions to improve the fishery's sustainability and explore the trade-offs between ecological, economic, and social goals. Scenario simulations showed that even significant increases (over 200%) in parameters proxying for policy interventions enabled only moderate improvements in ecological and social sustainability of the fishery at substantial economic cost. These results expose how unbalanced trader incentives are toward trading illegal over legal products in this fishery. Our model provides a novel tool for promoting sustainable wildlife trade in data-limited settings, which explicitly considers traders as critical players in wildlife markets. Sustainable wildlife trade requires incentivizing legal over illegal wildlife trade and consideration of the social, ecological, and economic impacts of interventions.     

变电站操作不安全行为核心影响因素分析*

为分析变电站中操作不安全行为的核心影响因素,基于国家电网某省变电站员工安全心理自评数据,运用心理学模型确定9种主要心理维度,融合Bayesian Search和Peter Clark(PC)算法,并使用专家知识修正以训练贝叶斯网络模型结构,应用Expectation Maximization(EM)算法训练模型参数,并运用Mutual Information(MI)和预测推理2种方法分析核心影响因素。研究结果表明:训练模型的预测错误率为14.8%,AUC为0.945 8,变电站操作不安全行为的核心影响因素为情绪稳定性和心理承受力。