Evaluation of accuracy of linear regression models in predicting urban stormwater discharge characteristics

Stormwater runoff has been identified as a source of pollution for the environment, especially for receiving waters. In order to quantify and manage the impacts of stormwater runoff on the environment, predictive models and mathematical models have been developed. Predictive tools such as regression models have been widely used to predict stormwater discharge characteristics. Storm event characteristics, such as antecedent dry days （ADD）, have been related to response variables, such as pollutant loads and concentrations. However it has been a controversial issue among many studies to consider ADD as an important variable in predicting stormwater discharge characteristics. In this study, we examined the accuracy of general linear regression models in predicting discharge characteristics of roadway runoff. A total of 17 storm events were monitored in two highway segments, located in Gwangju, Korea. Data from the monitoring were used to calibrate United States Environmental Protection Agency＇s Storm Water Management Model （SWMM）. The calibrated SWMM was simulated for 55 storm events, and the results of total suspended solid （TSS） discharge loads and event mean concentrations （EMC） were extracted. From these data, linear regression models were developed. R2 and p-values of the regression of ADD for both TSS loads and EMCs were investigated. Results showed that pollutant loads were better predicted than pollutant EMC in the multiple regression models. Regression may not provide the true effect of site-specific characteristics, due to uncertainty in the data.     

A time-compressed simulated geomagnetic storm influences the nest-exiting flight angles of the stingless bee Tetragonisca angustula

Insects have been used as models for understanding animal orientation. It is well accepted that social insects such as honeybees and ants use different natural cues in their orientation mechanism. A magnetic sensitivity was suggested for the stingless bee Schwarziana quadripunctata, based on the observation of a surprising effect of a geomagnetic storm on the nest-exiting flight angles. Stimulated by this result, in this paper, the effects of a time-compressed simulated geomagnetic storm (TC-SGS) on the nest-exiting flight angles of another stingless bee, Tetragonisca angustula, are presented. Under an applied SGS, either on the horizontal or vertical component of the geomagnetic field, both nest-exiting flight angles, dip and azimuth, are statistically different from those under geomagnetic conditions. The angular dependence of ferromagnetic resonance (FMR) spectra of whole stingless bees shows the presence of organized magnetic nanoparticles in their bodies, which indicates this material as a possible magnetic detector.     

Bi-PbO2电极电化学氧化去除模拟废水中氨氮的研究

本实验采用钛网作为基体,利用电沉积方法制备了纯PbO2电极和Bi-PbO2电极,通过SEM、XRD、XPS对电极的表面形态进行了表征,利用循环伏安法对Bi-PbO2电极电化学特性进行了研究.同时,以氨氮模拟废水作为研究对象,考察了Bi-PbO2电极的电催化活性,探讨了氨氮电化学氧化降解机理.结果表明,Bi-PbO2电极的形态表征、电催化活性明显高于纯PbO2电极,氨氮的去除效率随电流密度的增加而提高,碱性条件下氨氮的去除效果明显好于酸性条件,适量浓度的Cl-的引入在碱性条件下提高了氨氮的去除效果.当氨氮初始浓度为50 mg·L-1、电流密度为40 mA·cm-2、pH=12、Cl-浓度为600 mg·L-1时,电解120 min后,氨氮100%去除.氨氮的降解机理为:体系中无添加氯离子,酸性条件下氨氮主要是通过间接氧化去除,碱性条件下通过直接电氧化和间接氧化共同完成;体系中添加氯离子,氨氮的去除主要是通过溶液中生成的有效氯间接氧化去除.