降水对农田排水沟渠中氮磷流失的影响

以浙江嘉兴双桥农场为研究对象,分析降水对农田沟渠中氮、磷流失的影响,揭示氮、磷在降水径流中流失的一般规律. 在单晚稻生长期间,同步监测了施肥后的2次降水量和沟渠径流量,并对2次降水径流的全过程进行污染物含量的测定,对农田排水沟渠径流量与污染物含量随2次降水变化过程进行监测.结果表明:污染物含量变化呈现出大致相同的趋势,且污染物含量峰值比流量峰值提前2～3 h;在降水初期,污染物含量随径流量的增大而升高;随着流量的继续增大,含量呈下降趋势. 施肥后4 d内降水的农田沟渠中氮、磷随径流的流失量比4 d后大. 影响氮、磷流失的因素主要有降水量、降水时间间隔、径流量、施肥的种类和数量、土壤前期含水量以及沟渠中的沉积物等. 氮流失的主要形态为氨氮和硝氮;磷流失的主要形态为磷酸盐. 通过回归分析发现,径流量与氮、磷排放负荷间符合多项式关系.      

运用回归分析与人工神经网络预测含硫芳香族化合物好氧生物降解速率常数

为了研究人工神经网络预测有机化合物生物降解的性能,运用多元线性回归方法和误差反向传递人工神经网络模型以基团代码作为结构描述符,分别拟合、预测了一批含硫芳香族化合物的一级好氧生物降解速率常数．结果表明,由于神经网络自动考虑了基团间的交互作用,它对生物降解这类复杂问题有极高的求解能力,预测的均方误差为０．００１０２,远低于线性回归模型的预测误差０．０１５９１     

对金州沿海地区地下水中氯离子与硬度相关关系的研究

根据1991～2002年大连市金州沿海地区地下水中硬度与氯离子的检测结果,对硬度 氯离子浓度进行一元线性回归,回归方程为y=2.1975x-3.1072,经相关系数和回归线的显著性检验,此方程有99%的可信度。     

多元逐步回归对苯胺类化合物结构与毒性模型研究

采用Chemoffice6．0中MOPAC-AMl量子化学法计算了24种苯胺类化合物的6种量子化学结构参数．其中取17个化合物作为样本集对-lgEC50进行多元逐步回归分析．得到最佳方程．经自由度校正的回归系数R=0．985。应用所建立的QSAR模型验证了苯胺类化合物的EC50值．并通过“Jackknife”中的逐一抽取法进行模型检验,得出该模型具有很好的稳定性．平均残差仅为0．05个对数单位．小于文献值。经过7个预测样本对该模型进行验证．结果表明．该模型具有很好的预测能力。同时分析了苯胺类化合物的毒性机理。     

绿色防控技术对稻农经济收益的影响及其作用机制

中国农业正在向绿色发展转变,绿色防控技术在确保粮食安全和保护生态环境方面发挥着重要作用。文章基于四川省623户水稻种植户的微观调查数据,采用倾向得分匹配法消除了样本选择性偏差,分析了绿色防控技术对农户经济收益的影响及其作用机制,并对研究结果进行了一系列的稳健性检验。研究结果表明:①绿色防控技术的采纳能够显著改善稻农的经济收益。与未采纳绿色防控技术的状态相比,采纳绿色防控技术能够使农户每亩水稻的毛收入增加104.96%,每亩水稻的净利润增加40.45%。②从作用机制上看,采纳绿色防控技术使稻农获得了更高的市场溢价,同时有利于激励稻农扩大水稻种植规模,并增加对农地的投入,包括提高有机肥和种子的投入费用。③尽管采纳绿色防控技术带来了一定的消极影响,即提高了病虫害损失率,导致一定程度的减产,但整体而言,采纳绿色防控技术所带来的价格溢价弥补了因病虫害引致的产量损失,改善了稻农的经济收益。④绿色防控技术对稻农经济收益的影响会由于教育水平的差异以及互联网的应用而存在明显的异质性。最后,研究从加大农村人力资本投入、完善农村互联网设施、强化绿色防控技术政策支持以及加强对农用化学投入品的监管等四个方面提出对策建议。     

Redundancy analysis for characterizing the groundwater quality in coastal industrial areas

Groundwater quality in coastal area has been an issue of interest because of excessive groundwater extraction for human use, for example, industrialization, irrigation, which can lead to saltwater intrusion. The study develops an integrated data analysis procedure based on multivariate statistics principal component analysis (PCA), hierarchical cluster analysis (HCA) and redundancy analysis (RDA), to determine the effects of key environmental conditions on the formulation of groundwater pollutants. This proposed method was demonstrated by analyzing groundwater quality monitoring data collected between 2011 and 2014 from four coastal industrial areas in Changhua county of Taiwan, namely Chuansing, Xianxi, Lukang and Fangyuan industrial parks. First, different environmental conditions in each industrial region were explored by PCA. The spatial hierarchy and spatial distribution of pollutant categories were then identified using HCA with the kriging method. Finally, the effect of environmental conditions on constitutive pollutants were identified with RDA. The three environmental patterns identified from the analytical results in Chuansing, Lukang and Xianxi were the salination factor (including conductivity and general hardness (GH)), water level and redox condition (including dissolved oxygen and oxidation–reduction potential). Fangyuan industrial park had only two patterns, namely salination (including conductivity and GH) and oxygen content (including DO and depth). The pollutant category indicated high concentrations of all pollutants in Chuansing and Fangyuan, and higher concentration of SO₄^2?, TDS, Cl^? in Xianxi, and of NH₃-N, Mn, Fe and TOC in Lukang. According to RDA results, salination caused the high concentrations of NH₃N, Cl^?, TDS in Chuansing, and of Cl^?, TDS and SO₄^2? in Xianxi and Lukang. Additionally, salination caused high concentrations of Fe in both Lukang and Fangyuan industrial parks in combination with those three pollutants. The redox condition was linked to high content of NO₃^? in Chuansing and Lukang, and of TOC in Xianxi. In Fangyuan industrial park, NO₃^? was also linked to high oxygen concentration. In summary, the combination of PCA, HCA and RDA enables the analysis of monitoring data to support policy decision-making.     

Hydrogen explosion incident mitigation in steam reforming units through enhanced inspection and forecasting corrosion tools implementation

Hydrogen (H₂) explosion effects recently examined, are confirming the devastating loss scenarios to humans, environment, assets, and associated business interruption. H₂ production is a core process in refineries used in further process steps. Steam reforming of natural gas or a mix with naphtha or LPG is a common hydrogen production technique, where the latest technologies have adopted enhanced metallurgies to minimize explosion risk and the associated maintenance cost following plant degradation owing to corrosion effects. However, corrosion rates are still high in specific areas of piping and process equipment. The aim of this paper is to present a methodology based on semi-quantitative RBI modeling according to regulations by API and recent EN standards, adopting a family of linear regression forecasting models that depict the yearly corrosion rate (per corrosion loop) of a hydrogen production steam reforming unit; this is done under different operating conditions (e.g., temperature, pressure, and fluid speed), metallurgy and other related physicochemical variables. The model is based on the examination of both ultrasonic wall thinning measurements and the examination of quantitative crosslinking total corrosion effects along with the physicochemical properties prevailing in different plant corrosion loops. The outcome of the regression analysis is an expansive family of multivariable equations describing, with a defined accuracy, the yearly corrosion rate and associated lifespan forecast per corrosion loop, and per examined part. These equations were further utilized in a custom-made database that can be used as an additional loss prevention tool by the hydrogen production unit management team. Evaluation results regarding the tool efficiency are presented in the following of this paper.     

Adoption and economic impacts of laser land leveling in the irrigated rice-wheat system in Haryana,India using endogenous switching regression

This study assesses the factors affecting the adoption of laser land leveling (LLL) and its impact on crop yields and net returns. It uses household survey data collected from 621 randomly selected farmers in Karnal District of Haryana, India, and applies endogenous switching regression models. Unbiased model results show that the adoption of LLL has significant positive impacts on yields (rice +549 kg ha⁻¹; wheat +471 kg ha⁻¹) and net returns (an aggregate increase of US$230/ha) in the rice-wheat production system, thereby raising farmers' income substantially. Our results show that LLL adoption at the farm level is influenced by land size and quality, tenure system, availability of farm machinery (tractor), access to finance and farm cooperatives, gender of household head, level of education and training and access to extension services. Therefore, LLL scaling strategies need to consider these bio-physical and socio-economic parameters to reach adoption at scale and generate large social, economic, and environmental benefits.     

基于多元线性回归的煤矿驾驶员情绪预测模型*

为研究煤矿人车驾驶员情绪状态不良引发的煤矿驾驶事故问题,设计眼动实验并建立基于多元线性回归的驾驶员情绪状况预测模型对驾驶员情绪状况进行预测;通过眼动仪采集煤矿驾驶员良好情绪与不良情绪状态下的各项眼动指标,记录其主观情绪状态;使用多元线性回归方程对数据进行分析与建模,采用平均相对误差对预测模型进行评估。结果表明:每秒注视点个数、平均扫视速度、反应时间、危险源辨识个数在情绪变化前后存在显著差异,且与情绪状态呈较强相关;基于多元线性回归的驾驶员情绪状况预测模型预测精度较高,平均相对误差为8.16%。模型适用于煤矿人车驾驶员的情绪监测,可为煤矿驾驶员安全行驶提供保障。