Water Quality Evaluation and Trend Analysis in Selected Watersheds of the Atlantic Region of Canada

Water quality indices (WQIs) have been developed to assess the suitability of water for a variety of uses. These indices reflect the status of water quality in lakes, streams, rivers, and reservoirs. The concept of WQIs is based on a comparison of the concentration of contaminants with the respective environmental standards. The number, frequency, and magnitude by which the environmental standards for specific variables are not met in a given time period are reflected in WQIs. Further, the water quality trend analysis predicts the behavior of the water quality parameters and overall water quality in the time domain. In this paper, the concept of WQI was applied to three selected watersheds of Atlantic region: the Mersey River, the Point Wolfe River, and the Dunk River sites. To have robust study, two different water quality indices are used: Canadian Water Quality Index (CWQI), and British Columbia Water Quality Index (BWQI). The complete study was conducted in two steps. The first step was to organize and process the data into a format compatible with WQI analysis. After processing the input data, the WQI was calculated. The second step outlined in the paper discusses detailed trend analysis using linear and quadratic models for all the three sites. As per the 25 years trend analysis, overall water quality for agriculture use observed an improving trend at all the three sites studied. Water quality for raw water used for drinking (prior to treatment) and aquatic uses has shown improving trend at Point Wolfe River. It is further observed that pH, SO₄, and NO₃ concentrations are improving at Dunk River, Mersey River, and Point Wolfe River sites. To ascertain the reliability and significance of the trend analysis, a detailed error analysis and parametric significance tests were also conducted It was observed that for most of the sites and water uses quadratic trend models were a better fit than the linear models.     

A pilot project to detect and forecast harmful algal blooms in the northern Gulf of Mexico

More timely access to data and information on the initiation, evolution and effects of harmful algal blooms can reduce adverse impacts on valued natural resources and human health. To achieve this in the northern Gulf of Mexico, a pilot project was initiated to develop a user-driven, end-to-end (measurements to applications) observing system. A key strategy of the project is to coordinate existing state, federal and academic programs at an unprecedented level of collaboration and partnership. Resource managers charged with protection of public health and aquatic resources require immediate notice of algal events and a forecast of when, where and what adverse effects will likely occur. Further, managers require integrated analyses and interpretations, rather than raw data, to make effective decisions. Consequently, a functional observing system must collect and transform diverse measurements into usable forecasts. Data needed to support development of forecasts will include such properties as sea surface temperature, winds, currents and waves; precipitation and freshwater flows with related discharges of sediment and nutrients; salinity, dissolved oxygen, and chlorophyll concentrations (in vivo fluorescence); and remotely-sensed spatial images of sea surface chlorophyll concentrations. These data will be provided via a mixture of discrete and autonomous in situ sensing with near real-time data telemetry, and remote sensing from space (SeaWiFS), aircraft (hyperspectral imagery) or land (high-frequency radar). With calibration across these platforms, the project will ultimately provide a 4-dimensional visualization of harmful algae events in a time frame suitable to resource managers.     

Using improved neural network model to analyze RSP,NOx and NO2 levels in urban air in Mong Kok,Hong Kong

As the health impact of air pollutants existing in ambient addresses much attention in recent years, forecasting of airpollutant parameters becomes an important and popular topic inenvironmental science. Airborne pollution is a serious, and willbe a major problem in Hong Kong within the next few years. InHong Kong, Respirable Suspended Particulate (RSP) and NitrogenOxides NO_x and NO₂ are major air pollutants due to thedominant diesel fuel usage by public transportation and heavyvehicles. Hence, the investigation and prediction of the influence and the tendency of these pollutants are ofsignificance to public and the city image. The multi-layerperceptron (MLP) neural network is regarded as a reliable andcost-effective method to achieve such tasks. The works presentedhere involve developing an improved neural network model, whichcombines the principal component analysis (PCA) technique and theradial basis function (RBF) network, and forecasting thepollutant levels and tendencies based in the recorded data. Inthe study, the PCA is firstly used to reduce and orthogonalizethe original input variables (data), these treated variables arethen used as new input vectors in RBF neural network modelestablished for forecasting the pollutant tendencies. Comparingwith the general neural network models, the proposed modelpossesses simpler network architecture, faster training speed,and more satisfactory predicting performance. This improvedmodel is evaluated by using hourly time series of RSP, NO_x and NO₂ concentrations collected at Mong Kok Roadside Gaseous Monitory Station in Hong Kong during the year 2000. By comparing the predicted RSP, NO_x and NO₂ concentrationswith the actual data of these pollutants recorded at the monitorystation, the effectiveness of the proposed model has been proven.Therefore, in authors' opinion, the model presented in the paper is a potential tool in forecasting air quality parameters and hasadvantages over the traditional neural network methods.     

Benchmarking for best practice environmental management

Benchmarking of environmental performance to demonstrate theachievement of best practice environmental management is acomponent of a new form of licensing of industrialdischarges in Western Australia. The paper describes theapproaches to benchmarking for the critical environmentalissues for an alumina refinery and wastewater treatmentplant. It also describes the lessons learnt from thebenchmarking process on appropriate methods, the benefitsand difficulties in the benchmarking process, and changesthat would assist benchmarking for best practiceenvironmental management.     

Optimal Timing to Adopt an Environmental Policy in a Strategic Framework

In this paper, the problem of optimal timing, when to adopt an environmental policy in a strategic framework is considered. Using real options theory and some basic tools of game theory, we show that, under certain assumptions, a country behaving strategically should wait longer before adopting such a policy than if it behaves unstrategically or within a larger entity. Such a postponed decision is sub-optimal as regards to the environment protection.     

对室内装饰装修材料检测中甲醛分析方法的研究

目前室内装饰装修材料中甲醛含量的检测有六个类别,分析方法不统一,且没有一定的质量控制措施.文章对分析方法进行了改进,并且研究用水中的甲醛标样来对检测过程进行质量控制,结果较好.     

一次监测值用于水质评价的研究

以2003年长江和淮河水系为例,通过计算各个评价项目的单月监测值分别与季均值和年均值的相对误差及其分布,以及水质类别相同的断面比例,认为对于旬报、月报短期水质评价,采用一次监测的数据评价水质是可靠的;对于季报和年报中、长期水质评价,采用一次监测数据评价的可靠程度较差,应采用2次或2次以上的监测数据平均值进行评价。     

环境监测业务管理系统的设计与开发

以郑州市环境监测中心站监测业务流程为例,从技术路线、系统结构设计、系统功能分析和技术实现等方面分析并介绍了环境监测业务管理系统的特点和开发时需解决的技术问题。探讨了建立基于B/S结构、J2EE技术架构、创新的组件装配方式、Oracle9i数据库、中间件和多层体系结构等多项先进技术进行系统实现的设计方案。     

地表水环境质量定性评价

针对目前缺乏统一的地表水环境质量定性评价准则的现状,根据我国主要水体的污染程度,提出了断面、河流、水系水质定性评价方法。     

Determination of Heavy Metals in Honey in Kahramanmaraş City, Turkey

Heavy metals in honey are of interest not only for quality control, but also for determination environmental contamination. The objective of this work was to determine distribution of the levels of selected heavy metals. Levels of copper (Cu), cadmium (Cd), manganese (Mn), Iron (Fe), magnesium (Mg) and nickel (Ni) in honey samples (21) in Kahramanmaraş region were determined by atomic absorption spectrometer (AAS). The mean values for Cu, Cd, Mn, Fe, and Mg were 0.01, 0.32, 0.03, 0.36 and 10.45 ppm, respectively. Nickel was not detected in honey samples. Kahramanmaraş honey samples were not free of heavy metals but the levels of them were found well below the permitted levels. The results suggested that honey may be useful for assessing the presence of environmental contaminants.