Step-up multiple regression model to compute Chlorophyll a in the coastal waters off Cochin, southwest coast of India

The interaction effects of abiotic processes in the production of phytoplankton in a coastal marine region off Cochin are evaluated using multiple regression models. The study shows that chlorophyll production is not limited by nutrients, but their physiological regulations (responses to nutrients, pH, temperature and salinity) are mainly responsible for the increased biological production. The model explaining 77% of variability for chlorophyll a production is indicative of preconditioning of the coastal waters. The phytoplankton production is found to be sensitive to the environment, which varies seasonally. Further, the study suggests that supply of organic matter and grazing of zooplankton (not included) would improve the model efficiency. Despite this, the good agreement in the computed and measured chlorophyll a values shows that step-up multiple regression model is a useful tool to understand the influence of environmental variables on the production of phytoplankton in these coastal waters.     

Modelling vegetation greenness responses to climate variability in a Mediterranean terrestrial ecosystem

Accurate knowledge of the quality and environmental impact of the highway runoff in Pear River Delta, South China is required to assess this important non-point pollution source. This paper presents the quality characterization and environmental impact assessment of rainfall runoff from highways in urban and rural area of Guangzhou, the largest city of Pear River Delta over 1 year’s investigation. Multiple regression and Pearson correlation analysis were used to determine influence of the rainfall characteristics on water quality and correlations among the constituents in highway runoff. The results and analysis indicates that the runoff water is nearly neutral with low biodegradability. Oil and grease (O&G), suspended solids (SS) and heavy metals are the dominant pollutants in contrast to the low level of nutrient constituents in runoff. Quality of highway runoff at rural site is better than that of at urban site for most constituents. Depth and antecedent dry period are the main rainfall factors influencing quality of highway runoff. The correlation patterns among constituents in highway runoff at urban site are consistent with their dominant phases in water. Strong correlations (r ≥ 0.80) are found among chemical oxygen demand (COD), total phosphorus, Cu and Zn as well as conductivity, nitrate nitrogen and total nitrogen. O&G, COD, SS and Pb in highway runoff at urban site substantially exceed their concentrations in receiving water of Pear River. The soil directly discharged by highway runoff at rural site has contaminated seriously by heavy metals in surface layer accompanying with pH conversion from original acidic to alkaline at present.     

PSO和SVM混合算法确定太湖入湖河流水质主要影响因子

以影响太湖入湖河流水质的24个因子值为研究对象,将PSO算法与SVM算法相结合。PSO算法用于优化SVM算法的参数c和g,以利于快速、高效地确定c和g的全局最优值;SVM算法基于最优的c和g,分别以24,21,18,15,12,9和6个因子作为特征向量预测水质的污染程度。结果表明,当特征向量为9个影响因子时预测率最高。其参数c=18.56,g=1.35,对应的预测率为：全局预测率92.59%,重度污染水质预测率88.89%,轻度污染水质预测率94.45%。因此,通过PSO和SVM混合算法,可以确定影响太湖入湖河流水质的主要因子,利用这些主要因子对水质进行预测预警,不但可以节省时间,而且可以得到精确的结果。     

基于PSO-SVM算法的环境监测数据异常检测和缺失补全

针对环境监测数据异常和数据缺失问题,提出了基于支持向量机的粒子群优化数据异常检测和缺失补全算法。利用粒子群优化算法选取较优的支持向量机训练参数组合,以此建立非线性的支持向量机模型,并利用结果模型对测得的真实数据拟合预测。以宁夏回族自治区某污水处理厂的污染物测量数据作为实验数据,结果表明,利用该算法预测数据的准确率可达97.977%,检测异常数据准确度高,缺失数据补全正确。     

Optimal Expansion Strategy for a Sewer System under Uncertainty

Because of fast urban sprawl, land use competition, and the gap in available funds and needed funds, municipal decision makers and planners are looking for more cost-effective and sustainable ways to improve their sewer infrastructure systems. The dominant approaches have turned to planning the sanitary sewer systems within a regional context, while the decentralized and on-site/cluster wastewater systems have not overcome the application barriers. But regionalization policy confers uncertainties and risks upon cities while planning for future events. Following the philosophy of smart growth, this paper presents several optimal expansion schemes for a fast-growing city in the US/Mexico borderlands—the city of Pharr in Texas under uncertainty. The waste stream generated in Pharr is divided into three distinct sewer sheds within the city limit, including south region, central region, and north region. The options available include routing the wastewater to a neighboring municipality (i.e., McAllen) for treatment and reuse, expanding the existing wastewater treatment plant (WWTP) in the south sewer shed, and constructing a new WWTP in the north sewer shed. Traditional deterministic least-cost optimization applied in the first stage can provide a cost-effective and technology-based decision without respect to associated uncertainties system wide. As the model is primarily driven by the fees charged for wastewater transfer, sensitivity analysis was emphasized by the inclusion of varying flat-rate fees for adjustable transfer schemes before contracting process that may support the assessment of fiscal benefits to all parties involved. Yet uncertainties might arise from wastewater generation, wastewater reuse, and cost increase in constructing and operating the new wastewater treatment plant simultaneously. When dealing with multiple sources of uncertainty, the grey mixed integer programming (GIP) model, formulated in the second stage, can further allow all sources of uncertainties to propagate throughout the optimization context, simultaneously leading to determine a wealth of optimal decisions within a reasonable range. Both models ran for three 5-year periods beginning in 2005 and ending in 2020. The dynamic outputs of this analysis reflect the systematic concerns about integrative uncertainties within this decision analysis, which enable decision makers and stakeholders to make all-inclusive decisions for sanitary sewer system expansion in an economically growing region.     

Design and evaluation of a local analytic-deliberative process for climate adaptation planning

In the midst of rapidly proliferating engagement efforts around climate adaptation, attention to the design and evaluation of decision support processes and products is warranted. We report on the development and evaluation of a process framework called the Vulnerability, Consequences, and Adaptation Planning Scenarios (VCAPS) process. VCAPS is a systematic approach to integrate local knowledge with scientific understanding by providing opportunities for facilitated, deliberative learning-based activities with local decision makers about climate change vulnerability and adaptation. We introduce the conceptual basis of the process in analytic-deliberation, hazard management, and vulnerability. Our evaluations from eight coastal communities where the approach was applied point to four assets of VCAPS: it promotes synthesis of local and scientific knowledge; it stimulates systems thinking and learning; it facilitates governance by producing action plans with transparent justifications; and it accommodates participant time constraints and preferences.     

Theoretical and Empirical Analysis on Influence Factors of Modern Agricultural Equipment Level in the Processof Agricultural Sustainable Development

To promote modern agricultural equipment level is one characteristic of constructing and developing modern agriculture in China.This paper makes up stepwise linear regression analysis model of influence factors of modern agricultural equipment level,and chooses rural labor,per capita income of rural residents,rural investment,proportion of people at secondary education level and at higher level in per hundred rural labor force and arable land area as independent variables,and total power of machine as induced variable.The major results show that the relativity of modern agricultural equipment level,rural investment and education level of peasants is remarkable,and they are the major influence factors of modern agricultural equipment level.Raising investment level of rural infrastructure construction as well as and research and development and promotion of advanced and applicable modern agricultural equipment,improving quality and education level of peasants can accelerate the development of China’s modern agricultural equipment effectively in the process of agricultural sustainable development.     

Use of an Action‐Selection Framework for Human‐Carnivore Conflict in the Bangladesh Sundarbans

Abstract: Human–carnivore conflict is manifested in the death of humans, livestock, and carnivores. The resulting negative local attitudes and retribution killings imperil the future of many endangered carnivores. We tailored existing management tools to create a framework to facilitate the selection of actions to alleviate human–carnivore conflict and applied the framework to the human–tiger conflict in the Bangladesh Sundarbans. We identified potential actions that consider previous management efforts, local knowledge, cost‐effectiveness, fieldwork experience of authors and project staff, previous research on tiger ecology by the authors, and recommendations from human–carnivore conflict studies in other countries. Our framework includes creation of a profile to improve understanding of the nature of the conflict and its underlying causality. Identified actions include deterrents, education, direct tiger management, and response teams. We ranked actions by their potential to reduce conflict and the monetary cost of their implementation. We ranked tiger‐response teams and monitoring problem tigers as the two best actions because both had relatively high impact and cost‐effectiveness. We believe this framework could be used under a wide range of human–wildlife conflict situations because it provides a structured approach to selection of mitigating actions.     

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.