Multivariate characterization of elements accumulated in Wolfiporia extensa mushroom from Yunnan province of China

ABSTRACT

Dried sclerotia of Wolfiporia extensa have been used as medicine in Asia from Eastern Han Dynasty, and also used as traditional snack called “fulingjiabing” in Beijing, China. In this paper, 18 macro and trace elements (Ag, As, Ba, Cd, Co, Cr, Cs, Cu, Fe, Li, Mn, Ni, Pb, Rb, Se, Sr, V, and Zn) in both flesh and peel of Wolfiporia extensa from seven sites of Yunnan province in China were determined by inductively coupled plasma mass spectrometer. The average recovery rates of certified reference materials for GBW10015 (spinach leaves) ranged from 90.5 to 113%, for GBW10028 (citrus leaves) from 92.8 to 106%, and for GBW07603 (bush branch and leaves) from 83.3 to 114.6%. Generally speaking, the concentration of all elements determined was at common level. The results of this survey indicate that mineral compositions in peel were higher than in flesh. In peel, the contents of investigated trace metals in mushroom samples were found to be in the range of 1,660–13,400 µg·g^?1 dry matter (dm) for Fe and 29.6–710 µg·g^?1 dm for Mn. The mean contents of Cr, Cu, Rb, V, and Zn in peel were between 10 and 20 µg·g^?1 dm, followed by As, Co, Li, Ni, Pb, Se, and Sr with mean contents between 1 and 10 µg·g^?1 dm, while Ag, Cd, and Cs had mean contents of <1 µg·g^?1 dm. In flesh, the concentration of Fe was in the range of 54–900 µg·g^?1 dm, and it was 1.5–49 µg·g^?1 dm for Mn, followed by Ba, Cu, Rb, and Zn in the range of 1 to 10 µg·g^?1 dm, while for Ag, As, Cd, Co, Cr, Cs, Li, Ni, Pb, Se, Sr, and V it was <1 µg·g^?1 dm. The concentration of toxic elements, such as As, Cd, and Pb, in both flesh and peel was below the permissible limits of World Health Organization. However, As and Pb contents in peel were higher than the limits permitted in the Chinese Pharmacopoeia. The results of principal component analysis showed that the flesh of Wolfiporia extensa from all the seven sites of the Yunnan province tend to cluster together, most probably because the origin of mineral elements in both flesh and peel is wood substrate (old and dead pine trees).     

基于GM-SVR的小样本条件下化工设备可靠性预测

为了准确预测化工设备可靠性趋势，针对化工设备失效寿命数据为小样本的情形，基于灰色估计法与支持向量回归机在小样本数据处理中的优势，建立了失效寿命时间服从三参数威布尔分布的化工设备可靠性模型；结合GM（1，1）和SVR对模型进行参数估计，在压缩机可靠性分析中进行了实例应用，对比分析了最小二乘法、灰色估计法和GM-SVR的估计效果。研究结果表明:GM-SVR对威布尔分布参数的估计精度明显优于最小二乘法和灰色估计法，可以有效地应用于化工设备失效数据为小样本时的可靠性预测。     

Targeted Application of Seasonal Load Duration Curves Using Multivariate Analysis in Two Watersheds Flowing Into Lake Houston1

Teague, Aarin, Philip B. Bedient, and Birnur Guven, 2011. Targeted Application of Seasonal Load Duration Curves Using Multivariate Analysis in Two Watersheds Flowing Into Lake Houston. Journal of the American Water Resources Association (JAWRA) 47(3):620‐634. DOI: 10.1111/j.1752‐1688.2011.00529.x Abstract: Water quality is a problem in Lake Houston, the primary source of drinking water for the City of Houston, Texas, due to pollutant loads coming from the influent watersheds, including Spring Creek and Cypress Creek. Statistical analysis of the historic water quality data was developed to understand the source characterization and seasonality of the watershed. Multivariate analysis including principal component, cluster, and discriminant analysis provided a custom seasonal assessment of the watersheds so that loading curves may be targeted for season specific pollutant source characterization. The load duration curves have been analyzed using data collected by the U.S. Geologic Survey with corresponding City of Houston water quality data at the sites to characterize the behavior of the pollutant sources and watersheds. Custom seasons were determined for Spring Creek and Cypress Creek watersheds and pollutant source characterization compared between the seasons and watersheds.     

基于移动监测和土地利用回归模型的上海市近地面黑碳浓度空间模拟

黑碳(BC)是大气污染物的重要组成部分,对空气质量与人类生活健康产生重要的影响.本研究采用移动样带手段开展上海市近地面BC浓度监测,分析其基本统计特征和空间分异性.在此基础上,利用土地利用回归模型(LUR),探讨人口密度、经济产值和交通道路网密度等因素对上海市近地面BC浓度空间分异的影响.结果表明上海市近地面BC平均浓度为(9.86±8.68)μg·m~(-3),空间差异明显,郊区[(10.47±2.04)μg·m~(-3)]比市中心地区[(7.93±2.79)μg·m~(-3)]高32.03%(2.54μg·m~(-3)).气象要素(风速和相对湿度)和交通道路变量(路网长度、省道距离、高速距离等)显著影响上海市近地面BC浓度(r为0.5~0.7,P0.01).基于气象和交通道路变量的LUR模型能较好模拟上海近地面BC浓度(调整后R2为0.62~0.75,交叉验证R2为0.54~0.69,RMSE为0.15~0.20μg·m~(-3)),其中100 m和5 km缓冲距离的LUR模型相对较优,在一定程度上表明上海市近地面BC浓度主要受气象要素和交通源的影响.本研究有利于加深对上海市BC浓度空间分布格局及其影响因素的客观认识,可为模拟和预测BC对人类活动和自然环境的响应机制提供科学依据和理论支撑.     

供水管网铁释放的影响因素相对重要性分析

供水管网的铁释放问题近年来日益受到关注.影响供水管网铁释放的因素众多,本研究选取了pH、溶解氧(DO)、碱度(Alk)、硬度、氯离子(Cl~-)、硫酸根(SO_4~(2-))、温度(T)和水力停留时间(HRT)这8个影响因素,利用逐步回归和偏相关分析,建立了铁释放的非线性幂指数模型,并用标准化回归系数和偏相关系数比较了这些因素的相对重要性.结果表明,铁释放模型拟合效果良好;用标准化回归系数衡量影响因素相对重要性时,SO_4~(2-)、HRT、Cl~-和T是相对重要的因素,相对重要性排序为SO_4~(2-)≈HRTCl~-T;用偏相关系数衡量时,HRT、SO_4~(2-)、Alk、Cl~-和T是相对重要的因素,排序为HRTSO_4~(2-)AlkCl~-T.综合两种系数的分析结果,HRT、SO_4~(2-)和Cl~-是本研究条件下相对重要的因素,T和Alk的相对重要性视实际情况变动.     

基于神经网络的碳排放预测及影响因素分析

基于新疆1995—2014年农业生产碳排放源,建立碳排放关系数据库。应用广义神经网络(generalized regression neural network,GRNN)构建了排放量预测模型,结合平均影响值(mean impact value,MIV)方法对碳排放影响因素进行量化。结果表明:1)GRNN模型预测碳排放的平均绝对百分误差和拟合优度分别为2.7860%和0.8720;2)新疆人口、人均GDP、农业贡献值、农机总动力和农户固定资产投资等因素对农业生产碳排放的影响程度分别为0.6210、0.2377、0.3698、0.8500和0.1000。该成果可为新疆碳排放总量分析和影响因素量化方面提供参考。     

北京市潮白河再生水补水河段水质时空变异

城市河道是城市再生水利用的主要载体,而人工湿地是城市再生水河道补水前主要的水质净化方式.为了解再生水补水与人工湿地对再生水补水段水质的影响,选取北京市潮白河再生水补水河段作为研究对象,利用聚类分析、判别分析及因子分析等方法对不同季节水体的水质情况进行分析.结果表明:研究区内水体氮磷污染严重,其中TN污染表现为NO₃--N、NO₂--N和NH₄+-N的混合型污染.聚类分析结果表明,水质在季节尺度上表现为丰水期（6-9月）和枯水期（2月、3月、5月和12月）两大类;在空间尺度上受再生水补水和湿地净化的影响表现为显著的空间差异性.判别分析结果表明,再生水补给对河道水质的影响无显著季节差异,湿地净化功能在丰水期和枯水期差异较大且丰水期湿地的净化效果最为明显.因子分析结果表明,再生水作为城市河道的补充水源,一方面对河道中的F-、Chl-a等起到稀释作用,另一方面使得水体中的N、P及离子含量增加;丰水期湿地的净化作用使水体中氮磷等有机营养物质含量及ρ（TDS）等显著降低.      

基于GIS和LUR模型的西安市PM2.5浓度空间分布模拟研究

基于地理信息系统GIS和土地利用回归LUR模型,模拟西安市PM2.5浓度空间动态分布,结果表明:与PM2.5浓度相关性最高的分别为缓冲区为2 km的水域面积、人口密度和距离水域距离,R 2分别为0.501,0.393和0.280;与PM2.5浓度相关性最低的分别为缓冲区为4 km的水域面积、未利用地面积和耕地面积,R 2分别为0.039、0.021和0.017.未考虑风速建立的LUR模型多元回归的相关系数为0.856,R 2为0.733,考虑风速的相关系数为0.892,R 2为0.796,表明风速对于污染物的分布影响较大,LUR模型模拟效果较好.模拟的PM2.5年均浓度高风险区分布于中部,中风险区分布于中西部,低风险区分布于东南部和西部.     

ABC算法优化SVR的磨损故障预测模型

目的为了提高故障预测的精度,针对支持向量回归SVR(Support vector machine for regression,SVR)参数选择困难的问题,提出一种采用人工蜂群(artificial bee colony,ABC)算法优化支持向量回归(SVR)的故障预测模型(ABC-SVR)。方法该模型先对样本数据进行重构,然后将故障预测误差(适应度)作为优化目标,通过ABC算法寻优找到最优的SVR参数,建立故障预测模型。最后通过实例仿真验证模型的优越性。结果采用ABC算法优化的SVR故障预测模型进行时间序列预测,能够较好地跟踪发动机滑油金属元素浓度的变化过程,并且能够提前2个取样时间预测异常情况的出现。结论 ABC-SVR模型有效解决了SVR参数选择难题,能够更加准确地表现故障变化规律,提高了故障预测精度。     

川东北低山丘陵区细颗粒物主要相关因素和季节变化分析——以南充主城区为例

基于中国空气质量在线监测分析平台和全球天气精准预报网的大气质量和气象数据,以四川盆地东北低山丘陵区典型城市南充市主城区为例,检验了细颗粒物(PM_(2.5))浓度的概率密度分布,发现其接近对数正态分布,由相关分析确定了PM_(2.5)浓度的主要相关因素为CO、NO_2(相关系数r分别为0.76、0.55,P0.01),再通过对2014年1月—2016年6月的日数据的逐步回归筛选出最优的回归指标和模拟方程(决定系数R_(adj)~2为0.68,P0.05),2016年7月—2017年6月的数据验证表明模拟效果较好(拟合优度为0.64,相对误差15.48%);最后根据时序插值、浓度和IAQI(PM_(2.5))的时段均值发现PM_(2.5)浓度在年际上有降低趋势;在季节上由高到低依次为冬季、春季、秋季、夏季;PM_(2.5)浓度在1月和6月分别呈现出年内的峰值和谷值,5、10月出现了阶段性峰值,尤其是5月;IAQI(PM_(2.5))的季节变化与浓度变化规律相似;且PM_(2.5)与PM_(10)比值的均值为0.67,表明现阶段南充市主城区大气污染物中细颗粒物占有较大比重。