影响工业废水中BOD_5测定因素的分析与研究

采用稀释与接种法测定工业废水中的BOD5,并结合CODcr的测定结果,分析影响BOD5测定的因素如稀释倍数、接种液配比等条件进行试验后,得出了最佳试验条件,从而达到提高测定BOD5准确度的目的。     

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.     

基于增强回归树的城市PM2.5日均值变化分析:以常州为例

利用2014年12月至2015年11月常州市区6个国控监测站空气污染物浓度逐时数据,分析了PM_(2.5)浓度季节变化特征,采用增强回归树模拟分析了PM10、4种气态污染物和7个气象因子对ρ(PM_(2.5))日变化的贡献.结果表明,常州市区PM_(2.5)污染季节差异明显,冬季污染严重且持续时间长,夏季污染较轻.四季ρ(PM_(2.5))空间分布特征存在一定差异,但各季内不同监测站差异较小.增强回归树对ρ(PM_(2.5))日均值进行模拟和验证得到,训练数据的相关性为0.981,交叉验证的相关性为0.957.此外,模拟值与实测值的标准化平均偏差为1.80%,标准化平均误差为10.41%,可见模型拟合效果较好.PM10、气态污染物、气象因子和区域输送及扩散这4种影响类型对全年ρ(PM_(2.5))日均值差异的贡献率分别为23.4%、28%、36.2%和12.6%,表明在对ρ(PM_(2.5))日均值差异的影响上,气象因子二次形成一次源区域输送及扩散.在对ρ(PM_(2.5))日均值差异贡献率大于5%的因子中,ρ(PM_(2.5))日均值与PM10、相对湿度、CO和O3正相关,与温度、SO2和混合层高度负相关,与大气压和NO2关系较复杂.区域输送及扩散方面,东南风向、偏西风向和偏北风向等上风向周边城市的污染物输送对常州市区PM_(2.5)污染存在较大的负面影响.     

基于距离相关系数和支持向量机回归的PM2.5浓度滚动统计预报方案

针对目前空气质量统计预报方法存在的主要缺陷,本文提出了距离相关系数和支持向量机回归相结合的统计预报方案DC-SVR.利用淮安市2013年1—12月PM_(2.5)观测资料和常规气象观测资料,首先在选入预报当日气象要素的基础上,增加选取前期污染物和气象要素作为预报因子,再采用距离相关系数分季节从预报因子中筛选出重要预报因子,最后采用支持向量机回归对PM_(2.5)浓度值进行逐日滚动统计预报.研究发现,淮安地区气温和气压对PM_(2.5)的距离相关性要高于其他气象要素,夏秋季PM_(2.5)与气象要素的距离相关性较春冬季好.基于距离相关系数和支持向量机回归建立DC-SVR模型,PM_(2.5)的试预报值和实测值的全年相关系数高达0.76,平均偏差仅为1.13μg·m~(-3),平均绝对误差为23.47μg·m~(-3).通过与支持向量机回归、人工神经网络的统计预报效果对比,DC-SVR模型有效降低预报因子维数且能自适应选取最佳参数,预报精度显著优于其他3种统计预报方案,可为业务化预报提供参考.     

多点激励下圆柱面巨型网格结构地震响应分析

空间相关性对大跨度空间结构的影响不容忽视。本文采用动力时程分析方法对圆柱面巨型网格结构进行了多点激励下的地震响应分析,并与一致激励下的结构地震响应进行对比,探讨了主体结构单独承载和子结构参与协同承载这2种情况下,不同行波激励对结构关键节点和杆件响应的影响及其变化特点;通过比较分析结构在多点激励和一致激励下的响应差异,得出圆柱面巨型网格结构在地震输入时需考虑多点激励的结论,可供该类结构的抗震设计参考。     

Humic substances

GOAL, SCOPE AND BACKGROUND: Freshwater bodies which chemistry is dominated by dissolved humic substances (HS) seem to be the major type on Earth, due to huge non-calcareous geological formations in the Northern Hemisphere and in the tropics. Based on the paradigm of the inertness of being organic, direct interactions of dissolved HS with freshwater organisms are mostly neglected. However, dissolved organic carbon, the majority of which being HS, are natural environmental chemicals and should therefore directly interact with organisms. Major results that widened our perspective on humic substance ecology come from experiments with the compost nematode, Caenorhabditis elegans, which behaved contradictorily to textbook knowledge and provoked an in-depth re-consideration of some paradigms. APPROACH: To overcome old paradigms on HS and their potential interactions with organisms, we reviewed recent international literature, as well as 'grey' literature. We also include results from own ongoing studies. RESULTS: This review focuses on direct interactions of dissolved HS with freshwater organisms and disregards indirect effects, such as under-water light quenching. Instead we show with some macrophyte and algal species that HS adversely interfere with photosynthesis and growth, whereby closely related algal species show different response patterns. In addition to this, HS suppress cyanobacteria more than eukaryotic algae. Quinones in the HS appear to be the effective structure. Furthermore, HS can modulate the offspring numbers in the nematode C. elegans and cause feminization of fish and amphibians--they possess hormone-like properties. The ecological consequences of this potential remain obscure at present. HS also have the potential to act as chemical attractants as shown with C. elegans and exert a mild chemical stress upon aquatic organisms in many ways: induction of molecular chaperons (stress proteins), induction and modulation of biotransformation and anti-oxidant enzymes. Furthermore, they produce an oxidative stress with lipidperoxidation as one clear symptom or even stress defense strategy. Stronger chemical stresses by HS may even lead to teratogenic effects as shown with fish embryos; all physiological responses to HS-mediated stress require energy, which were compensated on the expense of yolk as shown with zebra fish embryos. One Finnish field survey supports the view of a strong chemical stress, as the weight yield in fish species decreases with increasing HS content in the lakes. DISCUSSION: HS exert a variety of stress symptoms in aquatic and compost organisms. According to current paradigms of ecotoxicology, these symptoms have to be considered adverse, because their compensation consumes energy which is deducted from the main metabolism. However, the nematode C. elegans looks actively for such stressful environments, and this behavior is only understandable in the light of new paradigms of aging mechanisms, particularly the Green Theory of Aging. In this respect, we discuss the mild HS-mediated stress to aquatic and compost organisms. New empirical findings with HS themselves and HS building blocks appear to be consistent with this emerging paradigm and show that the individual lifespan may be expanded. At present the ecological consequences of these findings remain obscure. However, a multiple-stress resistance may be acquired which improves the individual fitness in a fluctuating environment. CONCLUSIONS: It appears that dissolved HS have to be considered abiotic ecological driving forces, somewhat less obvious than temperature, nutrients, or light. PERSPECTIVES: The understanding of the ecological control by dissolved humic substances is still fragmentary and needs to be studied in more details.     

The application of generalized linear mixed models to multi-level sampling for insect population monitoring

Iwao's quadratic regression or Taylor's Power Law (TPL) are commonly used to model the variance as a function of the mean for sample counts of insect populations which exhibit spatial aggregation. The modeled variance and distribution of the mean are typically used in pest management programs to decide if the population is above the action threshold in any management unit (MU) (e.g., orchard, forest compartment). For nested or multi-level sampling the usual two-stage modeling procedure first obtains the sample variance for each MU and sampling level using ANOVA and then fits a regression of variance on the mean for each level using either Iwao or TPL variance models. Here this approach is compared to the single-stage procedure of fitting a generalized linear mixed model (GLMM) directly to the count data with both approaches demonstrated using 2-level sampling. GLMMs and additive GLMMs (AGLMMs) with conditional Poisson variance function as well as the extension to the negative binomial are described. Generalization to more than two sampling levels is outlined. Formulae for calculating optimal relative sample sizes (ORSS) and the operating characteristic curve for the control decision are given for each model. The ORSS are independent of the mean in the case of the AGLMMs. The application described is estimation of the variance of the mean number of leaves per shoot occupied by immature stages of a defoliator of eucalypts, the Tasmanian Eucalyptus leaf beetle, based on a sample of trees within plots from each forest compartment. Historical population monitoring data were fitted using the above approaches.     

‘A powerful alternative to Williams’ test with application to toxicological dose-response relationships of normally distributed data

The comparison of increasing doses of a treatment to a negative control is frequently part of toxicological studies. For normally distributed data Williams (1971, 1972) introduced a maximum likelihood test under total order restriction. But until now there seems to have been no solution for the arbitrary unbalanced case. According to the idea proposed by Robertson et al. (1988) we will apply in this article the basic concept of Williams on the class of multiple contrast tests for the general unbalanced parametric set-up. Simulation results for size and power and two examples for estimating the minimal toxic dose (MTD) are given.     

A measurement of community disaster resilience in Korea

Building a community that is resilient to disasters has become one of the main goals of disaster management. Communities that are more disaster resilient often experience less impact from the disaster and reduced recovery periods afterwards. This study develops a methodology for constructing a set of indicators measuring Community Disaster Resilience Index (CDRI) in terms of human, social, economic, environmental, and institutional factors. In this study, the degree of community resilience to natural disasters was measured for 229 local municipalities in Korea, followed by an examination of the relationship between the aggregated CDRI and disaster losses, using an ordinary least squares (OLS) regression method and a geographically weighted regression (GWR) method. Identifying the extent of community resilience to natural disasters would provide emergency managers and decision-makers with strategic directions for improving local communities' resilience to natural disasters while reducing the negative impacts of disasters.     

Boosted Regression Tree Models to Explain Watershed Nutrient Concentrations and Biological Condition

Boosted regression tree (BRT) models were developed to quantify the nonlinear relationships between landscape variables and nutrient concentrations in a mesoscale mixed land cover watershed during base‐flow conditions. Factors that affect instream biological components, based on the Index of Biotic Integrity (IBI), were also analyzed. Seasonal BRT models at two spatial scales (watershed and riparian buffered area [RBA]) for nitrite‐nitrate (NO₂‐NO₃), total Kjeldahl nitrogen, and total phosphorus (TP) and annual models for the IBI score were developed. Two primary factors — location within the watershed (i.e., geographic position, stream order, and distance to a downstream confluence) and percentage of urban land cover (both scales) — emerged as important predictor variables. Latitude and longitude interacted with other factors to explain the variability in summer NO₂‐NO₃ concentrations and IBI scores. BRT results also suggested that location might be associated with indicators of sources (e.g., land cover), runoff potential (e.g., soil and topographic factors), and processes not easily represented by spatial data indicators. Runoff indicators (e.g., Hydrological Soil Group D and Topographic Wetness Indices) explained a substantial portion of the variability in nutrient concentrations as did point sources for TP in the summer months. The results from our BRT approach can help prioritize areas for nutrient management in mixed‐use and heavily impacted watersheds.