环境监测数据线性回归分析中回归模型的比较

应用偏重于优化因变量拟合效果的最小二乘回归分析法对环境监测随机数据样本进行数值评估原则上是不可取的。文章以降水无机阴阳离子电荷平衡分析为例,对最小二乘法、压轴回归法和最小正交平方和法的回归分析结果进行了比较。结果表明,压轴回归分析法适合于处理随机数据样本,得到的回归系数b值较大,变量之间的数值变化关系较为密切。     

现代控制理论在河流水质系统状态估计中的应用

本文基于导出的河流水质系统随机状态空间模型，提出了以能够即时测定的ＤＯ浓度实时估计不易测定的ＢＯＤ浓度的塔穆勒滤波方法。通过仿真实例，证明了该滤波方法能在较大范围内适用于河流水质系统的状态估计，用ＤＯ估计ＢＯＤ是可行的。     

水泥窑窑头余热锅炉热水器振动原因分析

本文对一台水泥窑窑头余热锅炉热水器的振动问题进行了分析,发现其成因为管屏内发生了脉动现象。根据锅炉运行参数与结构形式,提出了针对性的处理及预防措施,可为同类余热锅炉的运行操作与设计优化提供参考。     

The Relative Efficiency of Ranked Set Sampling in Ordinary Least Squares Regression

Ranked set sampling was developed for situations where measurement cost is expensive compared with unit acquisition. This paper presents results of simulations and theory examining the impact of balanced ranked set sampling on the relative efficiencies of the slope and intercept estimators of an ordinary least squares regression. Perfect ranking of either the independent or the dependent variable is assumed throughout. In contradistinction to most of the published ranked set sampling work, it is demonstrated that balanced ranked set sampling offers at most little improvement in the relative efficiencies of the slope estimator at any sample size.     

Predicting non-carcinogenic hazard quotients of heavy metals in pepper (Capsicum annum L.) utilizing electromagnetic waves

• There was significant absorption of heavy metals by the pepper in contaminated soils. • The target hazard quotient (THQ) indices followed the order of Pb>Zn>>Cd » Ni. • Relationships exist between contaminated plants and electromagnetic wave. • PCA and random search can select the main spectra and predict THQ for each element. Given the tendency of heavy metals to accumulate in soil and plants, the purpose of this study was to determine the contamination levels of Cd, Ni, Pb, and Zn on peppers (leaves and fruit) grown in contaminated soils in industrial centers. For this purpose, we measured the uptake of the four heavy metals by peppers grown in the heavy metal contaminated soils throughout the four growth stages: two-leaf, growth, flowering, and fruiting, and calculated various vegetation indices to evaluate the heavy metal contamination potentials. Electromagnetic waves were also applied for analyzing the responses of the target plants to various heavy metals. Based on the relevant spectral bands identified by principal component analysis (PCA) and random search methods, a regression method was then employed to determine the most optimal spectral bands for estimating the target hazard quotient (THQ). The THQ was found to be the highest in the plants contaminated by Pb (THQ= 62) and Zn (THQ= 5.07). The results of PCA and random search indicated that the spectra at the bands of b₅₇₀, b₆₅₀, and b₇₆₀ for Pb, b₄₀₀ and b₁₀₃₀ for Ni, b₄₀₀ and b₈₈₀ for Cd, and b₅₆₀, b₉₁₀, and b₁₀₅₀ for Zn were the most optimal spectra for assessing THQ. Therefore, in future studies, instead of examining the amount of heavy metals in plants by chemical analysis in the laboratory, the responses of the plants to the electromagnetic waves in the identified bands can be readily investigated in the field based on the established correlations.     

Addressing onsite sampling in recreation site choice models

Independent experts and politicians have criticized statistical analyses of recreation behavior, which rely upon onsite samples due to their potential for biased inference. The use of onsite sampling usually reflects data or budgetary constraints, but can lead to two primary forms of bias in site choice models. First, the strategy entails sampling site choices rather than sampling individuals—a form of bias called endogenous stratification. Under these conditions, sample choices may not reflect the site choices of the true population. Second, exogenous attributes of the individuals sampled onsite may differ from the attributes of individuals in the population—the most common form in recreation demand is avidity bias. We propose addressing these biases by combining two the existing methods: Weighted Exogenous Stratification Maximum Likelihood estimation and propensity score estimation. We use the National Marine Fisheries Service's Marine Recreational Fishing Statistics Survey to illustrate methods of bias reduction, employing both simulated and empirical applications. We find that propensity score based weights can significantly reduce bias in estimation. Our results indicate that failure to account for these biases can overstate anglers' willingness to pay for improvements in fishing catch, but weighted models exhibit higher variance of parameter estimates and willingness to pay.     

Application of a Random Forest algorithm to predict spatial distribution of the potential yield of Ruditapes philippinarum in the Venice lagoon, Italy

We present a modelling framework that combines machine learning techniques and Geographic Information Systems to support the management of an important aquaculture species, Manila clam (Ruditapes philippinarum). We use the Venice lagoon (Italy), the first site in Europe for the production of R. philippinarum, to illustrate the potential of this modelling approach. To investigate the relationship between the yield of R. philippinarum and a set of environmental factors, we used a Random Forest (RF) algorithm. The RF model was tuned with a large data set (n = 1698) and validated by an independent data set (n = 841). Overall, the model provided good predictions of site-specific yields and the analysis of marginal effect of predictors showed substantial agreement among the modelled responses and available ecological knowledge for R. philippinarum. The most influent environmental factors for yield estimation were percentage of sand in the sediment, salinity, and water depth. Our results agree with findings from other North Adriatic lagoons. The application of the fitted RF model to continuous maps of all the environmental variables allowed estimates of the potential yield for the whole basin. Such a spatial representation enabled site-specific estimates of yield in different farming areas within the lagoon. We present a possible management application of our model by estimating the potential yield under the current farming distribution and comparing it to a proposed re-organization of the farming areas. Our analysis suggests a reduction of total yield is likely to result from the proposed re-organization.     

Forecasting turbine problems by means of the state space framework

A forecasting system is set up to improve the diagnosis in a Condition Monitoring Programme of a critical turbine placed at an industrial plant. The system is based on a statistical model in a State Space framework, such that the local mean level of the vibration state of the equipment is estimated directly from the data, based on a continuous-time set up. This model is combined with a cost model in Conditioned Monitoring, by which the time of preventive replacement is produced when the minimum of the expected cost per unit of time is reached into the future. Such measure is a combination of the costs of failure, the costs of a preventive replacement and the probabilities of reaching the alarm levels fixed by some criteria. The system is estimated by Maximum Likelihood and thoroughly tested on the equipment. The main tests relate to statistical properties of the model residuals and a comprehensive comparison with an alternative system, namely a linear trend regression model in continuous time. The system produced a reasonable forecasting performance and sensible time of preventive replacement prediction and outperformed the alternative forecasting system.     

基于随机森林的开封市重型柴油车NOx排放总量估算模型

近年来,在相关法律文件的推动下,监控平台技术得到发展与应用,有助于准确、实时获取海量实车行驶和排放数据,为建立机动车排放清单提供了重要数据基础.基于此背景,本文依托河南开封重型柴油车远程监控平台排放大数据,考虑了中观尺度上的活动因子分布特征及活动因子的影响权重,利用随机森林回归模型对重型柴油车污染物排放量预测方法进行研究.结果表明：①开封市重型柴油车行驶工况呈Ω型分布,而且随着发动机排量的增大,工况范围有逐渐缩小的趋势,且重型柴油车有20%的时间行驶在怠速区域和至少40%的时间行驶在中低转速区域.②在样本车中,在活动因子影响程度占比排序方面,有超过80%的柴油车将车速排在首位,其次分别是SCR床温、废气流量、发动机转速、燃料流量.③NO_x排放总量估算模型计算的6种典型排量下的重型柴油车NO_x排放总量与实际排放总量相比,误差不超过25%,能够满足实际需求.     

Measuring eddy covariance fluxes of ozone with a slow-response analyser

Ozone (O₃) fluxes above a temperate mountain grassland were measured by means of the eddy covariance (EC) method using a slow-response O₃ analyser. The resultant flux loss was corrected for by a series of transfer functions which model the various sources of high- and, in particular, low-pass filtering. The resulting correction factors varied on average between 1.7 and 3.5 during night and daytime, respectively. A cospectral analysis confirmed the accuracy of this approach. O₃ fluxes were characterised by a comparatively large random uncertainty, which during daytime typically amounted to 60%. EC O₃ fluxes were compared against O₃ flux measurements made concurrently with the flux-gradient (FG) method. The two methods generally agreed well, except for a period between sunrise and early afternoon, when the FG method was suspected of being affected by the presence of photochemical sources/sinks. O₃ flux magnitudes and deposition velocities determined with the EC method compared nicely with the available literature from grassland studies. We conclude that our understanding of the causes and consequences of various sources of flux loss (associated with any EC system) has sufficiently matured so that also less-than-ideal instrumentation may be used in EC flux applications, albeit at the cost of relatively large empirical corrections.