微波消解-分光光度法测定COD的研究

通过试验研究,提出了测定COD的新方法——微波消解-分光光度法。该方法准确度、精密度符合分析要求,与国标中回流法的测定结果没有显著差异。而且采用该方法测定COD可克服回流法、微波消解-滴定法及HACH-COD测定法的操作复杂、耗时长等缺点。     

金竹山土朱煤矿开采地表沉降规律与灰色预测模型研究

针对金竹山矿业公司土朱煤矿煤层赋存条件,依据采动理论的裂缝垂直分带模型,分析地表沉降和塌陷的机理;提出在采煤活动阶段应进行地表实际位移观测,经数据处理后得到地面沉降曲线,以确保地面人类活动的安全;在采煤活动后阶段则实施灰色预测地面沉降,即通过采煤活动阶段的地表实际观测数据为历史原始数据序列,建立灰色Logistic模型;并对采煤活动后阶段的地面沉降进行预测。精度检验表明:灰色Logistic模型预测精度高,利用该模型预测地面沉降可减少地面沉降监测经费和实时提供预警信息,以确保开采区域内人们生命财产安全。     

江苏省空气质量预报准确度及改进方法分析

介绍了江苏省重污染天气监测预报预警系统以及大气重污染预警会商流程,将2015年13个地级市的模式预报、人工预报结果分别与实际观测值进行比较。结果表明:人工预报更准确,PM_(2.5)日均值、臭氧日最大8 h平均值、AQI 3个指标人工预报和实况的相关性分别比模式预报高出12.8%、0.3%、11.4%,平均标准误差(MNE)分别低20.7%、3.1%、23.1%。依据国家空气质量预报技术指南评分办法,对各市2015年全年空气质量级别为"良"时进行评分。通过开展07∶00预报更新,使2015年上半年空气质量预报级别得分平均提高了0.9分,全年级别得分平均提高了2.6分;通过改进模式预报参数,使PM_(2.5)日均预报值、臭氧日最大8 h平均预报值、AQI预报值和实际观测值的相关性比上年同期分别提高26.0%、5.0%、33.9%,MNE分别降低3.6%、31.3%、7.6%。     

便携式气相色谱质谱测定水中挥发性有机物再现性和准确度研究

建立了便携式顶空-气相色谱-质谱（HS-GC-MS）测定水中挥发性有机物的方法。以水中苯系物、卤代烃和氯代苯等VOCs为研究对象,在同一实验室内,使用3台便携式HS-GC-MS模拟现场应急监测环境开展检测活动,研究测定结果的再现性和准确度。实验结果表明,检测结果的相对标准偏差为0.7%~13%;绝大多数目标化合物测定结果的回收率为80%~120%。综合来看,方法的精密度和准确度基本满意,能够满足应急监测工作的需要。     

基于改进的非线性GM(1,1)模型的职业病预测研究

为了更准确地预测职业病,在传统GM(1,1)模型的基础上,提出改进的非线性GM(1,1)模型,在传统的GM(1,1)模型中引入弱化算子,将紧邻均值与原始数据之间的线性假设改为非线性假设,提高曲线的拟合度。以2005—2014年的全国职业病例数为研究对象,进行数据拟合和预测分析,其中以2014年职业病例数作为验证数据,并利用后验差比值和小误差概率2个参数,检验该改进模型的预测精度。由应用实例的分析结果可知:在职业病发病趋势的预测方面,改进的非线性GM(1,1)模型的预测精度提高到一级,曲线拟合度较高,预测得到2015年的职业病例数为34 900例。     

CLUE-S模型对村镇土地利用变化的模拟与精度评价

利用航片、IKONOS和Quickbird影像得到研究区3期（1991、2001和2009年）土地利用历史数据,运用CLUE S模型,基于1991、2001年的土地利用数据对2009年土地利用格局进行模拟预测,并将模拟结果与2009年真实土地利用数据进行比较。类型水平上,选择ROC曲线统计和偏离度指数分别对各地类的Logistic回归拟合精度和CLUE S模型模拟精度进行评价;景观水平上,采用景观指数和Kappa指数系列方法,从综合预测能力、景观格局、数量和空间位置等方面对CLUE S模型的模拟精度进行全面评估。结果表明:①CLUE S模型对各土地利用类型的模拟精度均较高,各地类的Logistic回归的拟合精度随着模拟分辨率的提高而逐渐增加;②随着模拟时间的缩短,CLUE S模型对整体景观格局的模拟精度提高;③该模型对土地利用数量的模拟精度明显优于其对空间位置的模拟精度。总体而言,CLUE S模型在村镇尺度的模拟效果良好,预测精度较高,但在空间位置和景观格局精度方面还有待提高     

Prediction of effluent concentration in a wastewater treatment plant using machine learning models

Of growing amount of food waste, the integrated food waste and waste water treatment was regarded as one of the efficient modeling method. However, the load of food waste to the conventional waste treatment process might lead to the high concentration of total nitrogen(T-N) impact on the effluent water quality. The objective of this study is to establish two machine learning models—artificial neural networks(ANNs) and support vector machines(SVMs), in order to predict 1-day interval T-N concentration of effluent from a wastewater treatment plant in Ulsan, Korea. Daily water quality data and meteorological data were used and the performance of both models was evaluated in terms of the coefficient of determination(R~2), Nash–Sutcliff efficiency(NSE), relative efficiency criteria(d rel). Additionally, Latin-Hypercube one-factor-at-a-time(LH-OAT) and a pattern search algorithm were applied to sensitivity analysis and model parameter optimization, respectively. Results showed that both models could be effectively applied to the 1-day interval prediction of T-N concentration of effluent. SVM model showed a higher prediction accuracy in the training stage and similar result in the validation stage.However, the sensitivity analysis demonstrated that the ANN model was a superior model for 1-day interval T-N concentration prediction in terms of the cause-and-effect relationship between T-N concentration and modeling input values to integrated food waste and waste water treatment. This study suggested the efficient and robust nonlinear time-series modeling method for an early prediction of the water quality of integrated food waste and waste water treatment process.     

Development of a model to simulate soil heavy metals lateral migration quantity based on SWAT in Huanjiang watershed, China

Lateral transportation of soil heavy metals in rainfall events could significantly increase the scope of pollution. Therefore, it is necessary to develop a model with high accuracy to simulate the migration quantity of heavy metals. A model for heavy metal migration simulation was developed based on the SWAT(Soil and Water Assessment Tool) model. This model took into consideration the influence of soil p H value, soil particle size, runoff volume, sediment amount,concentration of water-soluble heavy metals dissolved in runoff and insoluble absorbed to the soil particles. This model was reasonable in Huanjiang watershed, Guangxi Zhuang Autonomous Region, south China, covering an area of 273 km~2. The optimal drainage area threshold was determined by analyzing the effects of watershed subdivision on the simulation results to ensure the simulation accuracy. The main conclusions of this paper were:(1) watershed subdivision could affect simulation migration quantity of heavy metals;(2) the quantity of heavy metals transported by sediment accounted for 97%–99% of the total migration quantity in the study watershed. Therefore, sediment played the most important role in heavy metal migration;(3) the optimal drainage area threshold percentage to ensure high simulation accuracy was determined to be 2.01% of the total watershed;(4) with the optimal threshold percentage, this model could simulate the migration quantity of As, Pb and Cd accurately at the total watershed and subwatershed level. The results of this paper were useful for identifying the key regions with heavy metal migration.     

基于改进灰色模型的管壁蜡沉积厚度预测研究

为对含蜡原油管道中的蜡沉积厚度进行准确预测,在函数cot(x2)变换的基础上,结合平移变换思想,利用cot(x2+c)变换建立新的改进GM(1,1)模型。以现场管道结蜡数据和室内环道结蜡数据为例,对比改进GM(1,1)模型、基于函数cot(x2)变换建立的GM(1,1)模型及传统GM(1,1)模型之间的预测精度,并分析平移量c对改进GM(1,1)模型预测精度的影响。结果表明:改进GM(1,1)模型的预测精度最高,其次是基于函数cot(x2)变换建立的GM(1,1)模型,而传统GM(1,1)模型的预测精度最低;随着平移量的增大,改进GM(1,1)模型的平均相对预测误差呈现出先减小后增大的趋势,因此合理的平移量有助于模型精度的提高。应用改进GM(1,1)模型来预测管道蜡沉积厚度是可行的,该方法可为含蜡原油管道蜡沉积厚度的准确预测提供参考和借鉴。