Simultaneous determination of three herbicides by differential pulse voltammetry and chemometrics

A novel differential pulse voltammetry method (DPV) was researched and developed for the simultaneous determination of Pendimethalin, Dinoseb and sodium 5-nitroguaiacolate (5NG) with the aid of chemometrics. The voltammograms of these three compounds overlapped significantly, and to facilitate the simultaneous determination of the three analytes, chemometrics methods were applied. These included classical least squares (CLS), principal component regression (PCR), partial least squares (PLS) and radial basis function-artificial neural networks (RBF-ANN). A separately prepared verification data set was used to confirm the calibrations, which were built from the original and first derivative data matrices of the voltammograms. On the basis relative prediction errors and recoveries of the analytes, the RBF-ANN and the DPLS (D – first derivative spectra) models performed best and are particularly recommended for application. The DPLS calibration model was applied satisfactorily for the prediction of the three analytes from market vegetables and lake water samples.     

COMPARISON OF REGRESSION AND TIME-SERIES METHODS FOR SYNTHESIZING MISSING STREAMFLOW RECORDS1

ABSTRACT: Regression and time-series techniques have been used to synthesize and predict the stream flow at the Foresta Bridge gage from information at the upstream Pohono Bridge gage on the Merced River near Yosemite National Park. Using the available data from two time periods (calendar year 1979 and water year 1986), we evaluated the two techniques in their ability to model the variation in the observed flows and in their ability to predict stream flow at the Foresta Bridge gage for the 1979 time period with data from the 1986 time period. Both techniques produced reasonably good estimates and forecasts of the flow at the downstream gage. However, the regression model was found to have a significant amount of autocorrelation in the residuals, which the time-series model was able to eliminate. The time-series technique presented can be of great assistance in arriving at reasonable estimates of flow in data sets that have large missing portions of data.     

非时序加权灰色关联分析在环境质量综合评价中的应用

本文根据灰色关联分析的基本性质，提出一种环境质量综合评价的新方法。该方法用待评价样本各指标值组成参考数列，环境质量分级标准值组成被比较数列，通过计算参考数列与被比较数列的关联度，按最优关联识别原则综合评价环境质量等级。用４个实例与其它方法进行了对比，取得满意的效果。     

THE SENSITWITY OF NORTHERN SIERRA NEVADA STREAMFLOW TO CLIMATE CHANGE1

ABSTRACT: The sensitivity of streamflow to climate change was investigated in the American, Carson, and Truckee River Basins, California and Nevada. Nine gaging stations were used to represent streamflow in the basins. Annual models were developed by regressing 1961–1991 streamflow data on temperature and precipitation. Climate-change scenarios were used as inputs to the models to determine streamflow sensitivities. Climate-change scenarios were generated from historical time series by modifying mean temperatures by a range of +4°C to—4°C and total precipitation by a range of +25 percent to -25 percent. Results show that streamflow on the warmer, lower west side of the Sierra Nevada generally is more sensitive to temperature and precipitation changes than is streamflow on the colder, higher east side. A 2°C rise in temperature and a 25-percent decrease in precipitation results in stream-flow decreases of 56 percent on the American River and 25 percent on the Carson River. A 2°C decline in temperature and a 25-percent increase in precipitation results in streamflow increases of 102 percent on the American River and 22 percent on the Carson River.     

EMPIRICAL MODELS FOR TROPHIC STATE IN SOUTHEASTERN U.S. LAKES AND RESERVOIRS1

ABSTRACT: A cross-sectional data set of 80 lakes and reservoirs in nine southeastern states was examined to specify and parameterize trophic state relationships. The relationships fitted are based on measurements of several limnological variables taken over the course of a growing season or year in each of the lakes. The trophic state models relate phosphorus and nitrogen loading to inlake phosphorus and nitrogen concentrations, which in turn are related to maximum chlorophyll level, Secchi disk depth, dominant algal species, and hypolimnetic dissolved oxygen status. Due to the empirical nature of the study, causal conclusions are limited; rather, the models are most useful for prediction of average growing season conditions related to trophic state.     

成都大邑县西岭雪山风景名胜区环境美学评价研究

本文根据西岭雪山景区特色，将其分为三大景观区，从景观的美学价值出发，选择了１２个美学评价指标，运用层次分析法和征询评分加权法，对该风景区进行了环境美学综合评价。     

ANALYTICAL REGRESSION STAGE ANALYSIS FOR DEVILS HOLE,DEATH VALLEY NATIONAL PARK,NEVADA1

ABSTRACT: Devils Hole is a collapse depression connected to the regional carbonate aquifer of the Death Valley ground water flow system. Devils Hole pool is home to an endangered pupfish that was threatened when irrigation pumping in nearby Ash Meadows lowered the pool stage in the 1960s. Pumping at Ash Meadows ultimately ceased, and the stage recovered until 1988, when it began to decline, a trend that continued until at least 2004. Regional ground water pumping and changes in recharge are considered the principal potential stresses causing long term stage changes. A regression was found between pumpage and Devils Hole water levels. Though precipitation in distant mountain ranges is the source of recharge to the flow system, the stage of Devils Hole shows small change in stage from 1937 to 1963, a period during which ground water withdrawals were small and the major stress on stage would have been recharge. Multiple regression analyses, made by including the cumulative departure from normal precipitation with pumpage as independent variables, did not improve the regression. Drawdown at Devils Hole was calculated by the Theis Equation for nearby pumping centers to incorporate time delay and drawdown attenuation. The Theis drawdowns were used as surrogates for pumpage in multiple regression analyses. The model coefficient for the regression, R²= 0.982, indicated that changes in Devils Hole were largely due to effects of pumping at Ash Meadows, Amargosa Desert, and Army 1.     

DETERMINING SIGNIFICANCE AND PRECISION OF ESTIMATED PARAMETERS FOR RUNOFF FROM SEMIARID WATERSHEDS1

ABSTRACT Significant parameters for predicting thunderstorm runoff from small semiarid watersheds are determined using data from the Walnut Gulch watershed in southern Arizona. Based on these data, thunderstorm rainfall is dominant over watershed parameters for predicting runoff from multiple linear regression equations. In some cases antecedent moisture added significantly to the models. A technique is developed for estimating precision of predicted values from multiple linear regression equations. The technique involves matrix methods in estimating the variance of mean predicted values from a regression equation. The estimated variance of the mean predicted value is then used to estimate the variance of an individual predicted value. A computer program is developed to implement these matrix methods and to form confidence limits on predicted values based on both a normality assumption and the Chebyshev inequality.     

The hybrid model of empirical wavelet transform and relevance vector regression for monthly wind speed prediction

ABSTRACT

In order to improve the prediction ability for the monthly wind speed of RVR, the hybrid model of empirical wavelet transform and relevance vector regression (EWT-RVR) is proposed for monthly wind speed prediction in this study. Compared with empirical mode decomposition (EMD), empirical wavelet transform (EWT) can obtain a more consistent decomposition and have a mathematical theory. In order to testify the superiority of EWT-RVR, several traditional RVR models are used to compare with the proposed EWT-RVR method under the situation of the same embedding dimensions. The experimental results show that the proposed EWT-RVR method has a better prediction ability for monthly wind speed than RVR. It can be concluded that the proposed EWT-RVR method for monthly wind speed is effective.     

Predicting mass rapid transit noise levels on an elevated station

This study developed a noise prediction model for elevated mass rapid transit (MRT) platforms. Relevant physical and operational parameters (e.g. cruise speed, acceleration and deceleration rates for trains, building fa?ade setbacks and so on) were collected from the Bangkok mass transit system (BTS), the first elevated MRT system operated in Bangkok, Thailand. The equivalent continuous sound pressure levels (L(Aeq)) were collected from both sides of the MRT stations at the center of each platform. The relevant parameters were collected on both platforms and ground level, on both sides of MRT stations. These parameters were statistically tested to determine their correlation with MRT noise. The final model was built from highly correlated parameters using multiple regression analysis with a stepwise regression technique. Statistical evaluation showed a high degree of goodness-of-fit test for the model to the observed data. Therefore, it can be efficiently used for the projection of MRT noise in the affected areas.