Establishment of turbidity forecasting model and early-warning system for source water turbidity management using back-propagation artificial neural network algorithm and probability analysis

The purpose of this study is to establish a turbidity forecasting model as well as an early-warning system for turbidity management using rainfall records as the input variables. The Taipei Water Source Domain was employed as the study area, and ANOVA analysis showed that the accumulative rainfall records of 1-day Ping-lin, 2-day Ping-lin, 2-day Fei-tsui, 2-day Shi-san-gu, 2-day Tai-pin and 2-day Tong-hou were the six most significant parameters for downstream turbidity development. The artificial neural network model was developed and proven capable of predicting the turbidity concentration in the investigated catchment downstream area. The observed and model-calculated turbidity data were applied to developing the turbidity early-warning system. Using a previously determined turbidity as the threshold, the rainfall criterion, above which the downstream turbidity would possibly exceed this respective threshold turbidity, for the investigated rain gauge stations was determined. An exemplary illustration demonstrated the effectiveness of the proposed turbidity early-warning system as a precautionary alarm of possible significant increase of downstream turbidity. This study is the first report of the establishment of the turbidity early-warning system. Hopefully, this system can be applied to source water turbidity forecasting during storm events and provide a useful reference for subsequent adjustment of drinking water treatment operation.     

Spatial�Ctemporal assessment and redesign of groundwater quality monitoring network: a case study

Assessment of groundwater quality monitoring networks requires methods to determine the potential efficiency and cost-effectiveness of the current monitoring programs. To this end, the concept of entropy has been considered as a promising method in previous studies since it quantitatively measures the information produced by a network. In this study, the measure of transinformation in the discrete entropy theory and the transinformation?Cdistance (T?CD) curves, which are used frequently by other researchers, are used to quantify the efficiency of a monitoring network. This paper introduces a new approach to decrease dispersion in results by performing cluster analysis that uses fuzzy equivalence relations. As a result, the sampling (temporal) frequency determination method also recommends the future sampling frequencies for each location based on certain criteria such as direction, magnitude, correlation with neighboring stations, and uncertainty of the concentration trend derived from representative historical concentration data. The proposed methodology is applied to groundwater resources in the Tehran?CKaradj aquifer, Tehran, Iran.     

Documenting measurement sensitivity and bias of field-measured parameters in water quality monitoring programs

Measurement sensitivity and bias quality control metrics are commonly reported for water-quality parameters measured in the laboratory. Less commonly recognized is that they should also be reported for field-measured parameters. Periodic evaluation helps document data quality and can help serve as early warning if there are problems with methods or techniques that could negatively affect ability to interpret threshold values and trends over time. This study focuses on traditional assessment of bias and introduces a new method for estimating measurement sensitivity of water-quality parameters measured monthly in the field. Alternative measurement sensitivity is a new data quality indicator used to demonstrate how quantifying sensitivity at the measurement level can improve understanding the uncertainty affecting each reported data value. That, in turn, can help interpret the meaning of results from many separate data points measured in the field. In this 30-month study, pH and specific conductance consistently met, and dissolved oxygen did not always meet NPS and USGS quality control standards for bias. Evaluation of dissolved oxygen bias and sensitivity during the study provided impetus to improve calibration techniques that resulted in data that later met quality goals.     

Quantitative assessment of eutrophication: A scoring system for characterising water quality in coastal marine ecosystems

A scoring system based on nutrient concentrations was developed to assess coastal water quality according to the trophic level. Three nutrient data sets from eutrophic, mesotrophic and oligotrophic waters were used as the reference information for setting up a semi quantitative water quality scale (from 0 to 5) to express different nutrient loadings. The validity and sensitivity of the method was applied to a number of stations spaced out along the coastal area of Rhodes (Greece). A score for each nutrient/sampling site was calculated and the scorecard formed, was the data matrix used for numerical classification of the stations. The results showed (a) good discrimination between cutrophic, mesotrophic and oligotrophic waters (b) nitrate among the nutrients showed the maximum sensitivity in characterising pollution levels. The reference data sets used for assessing eutrophication levels ensured the objectivity of the method. The proposed method is described step-by-step and it is suggested that the method can be further adapted to describe other forms of pollution becoming a useful quantitative technique in coastal management practices.     

Model-driven development of covariances for spatiotemporal environmental health assessment

Known conceptual and technical limitations of mainstream environmental health data analysis have directed research to new avenues. The goal is to deal more efficiently with the inherent uncertainty and composite space-time heterogeneity of key attributes, account for multi-sourced knowledge bases (health models, survey data, empirical relationships etc.), and generate more accurate predictions across space-time. Based on a versatile, knowledge synthesis methodological framework, we introduce new space-time covariance functions built by integrating epidemic propagation models and we apply them in the analysis of existing flu datasets. Within the knowledge synthesis framework, the Bayesian maximum entropy theory is our method of choice for the spatiotemporal prediction of the ratio of new infectives (RNI) for a case study of flu in France. The space-time analysis is based on observations during a period of 15 weeks in 1998–1999. We present general features of the proposed covariance functions, and use these functions to explore the composite space-time RNI dependency. We then implement the findings to generate sufficiently detailed and informative maps of the RNI patterns across space and time. The predicted distributions of RNI suggest substantive relationships in accordance with the typical physiographic and climatologic features of the country.     

Water quality of potential reference lakes in the Arkansas Valley and Ouachita Mountain ecoregions,Arkansas

This report describes a study to identify reference lakes in two lake classifications common to parts of two level III ecoregions in western Arkansas—the Arkansas Valley and Ouachita Mountains. Fifty-two lakes were considered. A screening process that relied on land-use data was followed by reconnaissance water-quality sampling, and two lakes from each ecoregion were selected for intensive water-quality sampling. Our data suggest that Spring Lake is a suitable reference lake for the Arkansas Valley and that Hot Springs Lake is a suitable reference lake for the Ouachita Mountains. Concentrations for five nutrient constituents—orthophosphorus, total phosphorus, total kjeldahl nitrogen, total nitrogen, and total organic carbon—were lower at Spring Lake on all nine sampling occasions and transparency measurements at Spring Lake were significantly deeper than measurements at Cove Lake. For the Ouachita Mountains ecoregion, water quality at Hot Springs Lake slightly exceeded that of Lake Winona. The most apparent water-quality differences for the two lakes were related to transparency and total organic carbon concentrations, which were deeper and lower at Hot Springs Lake, respectively. Our results indicate that when nutrient concentrations are low, transparency may be more valuable for differentiating between lake water quality than chemical constituents that have been useful for distinguishing between water-quality conditions in mesotrophic and eutrophic settings. For example, in this oligotrophic setting, concentrations for chlorophyll a can be less than 5 μg/L and diurnal variability that is typically associated with dissolved oxygen in more productive settings was not evident.     

Optimization of pressure gauge locations for water distribution systems using entropy theory

It is essential to select the optimal pressure gauge location for effective management and maintenance of water distribution systems. This study proposes an objective and quantified standard for selecting the optimal pressure gauge location by defining the pressure change at other nodes as a result of demand change at a specific node using entropy theory. Two cases are considered in terms of demand change: that in which demand at all nodes shows peak load by using a peak factor and that comprising the demand change of the normal distribution whose average is the base demand. The actual pressure change pattern is determined by using the emitter function of EPANET to reflect the pressure that changes practically at each node. The optimal pressure gauge location is determined by prioritizing the node that processes the largest amount of information it gives to (giving entropy) and receives from (receiving entropy) the whole system according to the entropy standard. The suggested model is applied to one virtual and one real pipe network, and the optimal pressure gauge location combination is calculated by implementing the sensitivity analysis based on the study results. These analysis results support the following two conclusions. Firstly, the installation priority of the pressure gauge in water distribution networks can be determined with a more objective standard through the entropy theory. Secondly, the model can be used as an efficient decision-making guide for gauge installation in water distribution systems.     

Mapping Turbidity in the Charles River, Boston Using a High-resolution Satellite

The usability of high-resolution satellite imagery for estimating spatial water quality patterns in urban water bodies is evaluated using turbidity in the lower Charles River, Boston as a case study. Water turbidity was surveyed using a boat-mounted optical sensor (YSI) at 5 m spatial resolution, resulting in about 4,000 data points. The ground data were collected coincidently with a satellite imagery acquisition (IKONOS), which consists of multispectral (R, G, B) reflectance at 1 m resolution. The original correlation between the raw ground and satellite data was poor (R2 = 0.05). Ground data were processed by removing points affected by contamination (e.g., sensor encounters a particle floc), which were identified visually. Also, the ground data were corrected for the memory effect introduced by the sensor's protective casing using an analytical model. Satellite data were processed to remove pixels affected by permanent non-water features (e.g., shoreline). In addition, water pixels within a certain buffer distance from permanent non-water features were removed due to contamination by the adjacency effect. To determine the appropriate buffer distance, a procedure that explicitly considers the distance of pixels to the permanent non-water features was applied. Two automatic methods for removing the effect of temporary non-water features (e.g., boats) were investigated, including (1) creating a water-only mask based on an unsupervised classification and (2) removing (filling) all local maxima in reflectance. After the various processing steps, the correlation between the ground and satellite data was significantly better (R2 = 0.70). The correlation was applied to the satellite image to develop a map of turbidity in the lower Charles River, which reveals large-scale patterns in water clarity. However, the adjacency effect prevented the application of this method to near-shore areas, where high-resolution patterns were expected (e.g., outfall plumes).     

Estimation of chlorophyll a content in inland turbidity waters using WorldView-2 imagery: a case study of the Guanting Reservoir,Beijing, China

Complex optical properties, such as non-pigment suspension and colored dissolved organic matter (CDOM), make it difficult to achieve accurate estimations of remotely sensed chlorophyll a (Chla) content of inland turbidity. Recent attempts have been made to estimate Chla based on red and near-infrared regions where non-pigment suspension and CDOM have little effect on water reflectance. The objective of this study is to validate the applicability of WV-2 imagery with existing effective estimation methods from MERIS when estimating Chla content in inland turbidity waters. The correlation analysis of measured Chla content and WV-2 imagery bands shows that the Chla sensitive bands of WV-2 are red edge, NIR 1, and NIR 2. The coastal band is designed for seawater Chla detection. However, the high correlation with turbidity data and low correlation with Chla made coastal band unsuitable for estimating Chla in inland waters. The high-resolution water body images were extracted by combining the spectral products (NDWI) with the spatial morphological products (sobel edge detection). The estimation results show that the accuracy of the single band and NDCI is not as good as the two-band method, three-band method, stepwise regression algorithm (SRA) and support vector machines (SVM). The SVM estimation accuracy was the highest with an R², RMSE, and URMSE of 0.8387, 0.4714, and 19.11%, respectively. This study demonstrates that the two-band and three-band methods are effective for estimating Chla in inland water for WV-2 imagery. As a high-precision estimation method, SVM has great potential for inland turbidity water Chla estimation.     

Sediment fluxes and the littoral drift along northeast Andhra Pradesh Coast, India: estimation by remote sensing

The littoral drift regime along the northeastern coast of India was investigated by analyzing coastal drift indicators and shoreline changes based on multitemporal satellite images. The study of offshore turbidity patterns and quantitative estimation of suspended sediments was undertaken to understand the magnitude and direction of movement of sediment fluxes. The study revealed that: (1) the character of coastal landforms and sedimentation processes indicate that the sediment transport is bidirectional and monsoon dependent; (2) multidate, multitemporal analysis of satellite images helps to show the nature of sediment transport along the coast. The dominant net sediment transport is in a NE direction along the eastern coast of India. Finally, this assessment demonstrates the potential of remote sensing technology in understanding the coastal morphometric changes, long-term sediment transport, shoreline changes, and offshore turbidity distribution pattern and the implications for the transport of sediment-associated pollutants.     

Assessing possible visitor-use impacts on water quality in Yosemite National Park, California

There is concern that visitor-use associated activities, such as bathing, dish washing, wastewater production, and stock animal use near lakes and streams, could cause degradation of water quality in Yosemite National Park. A study was conducted during 2004-2007 to assess patterns in nutrient and Escherichia coli (E. coli) concentrations in the Merced and Tuolumne Rivers and characterize natural background concentrations of nutrients in the park. Results indicated that nutrient and E. coli concentrations were low, even compared to other undeveloped sites in the United States. A multiple linear regression approach was used to model natural background concentrations of nutrients, with basin characteristics as explanatory variables. Modeled nitrogen concentrations increased with elevation, and modeled phosphorus concentrations increased with basin size. Observed concentrations (±uncertainty) were compared to modeled concentrations (±uncertainty) to identify sites that might be impacted by point sources of nutrients, as indicated by large model residuals. Statistically significant differences in observed and modeled concentrations were observed at only a few locations, indicating that most sites were representative of natural background conditions. The empirical modeling approach used in this study can be used to estimate natural background conditions at any point along a study reach in areas minimally impacted by development, and may be useful for setting water-quality standards in many national parks.     

苏州河近20年水质状况研究

通过对近20年来上海市苏州河沿程13个断面的常规水质监测教据进行整理分析,总结各常规监测指标项目的变化趋势.并通过分析沿程水质变化和几项重要水质指标的比值变化并探讨了变化的原因,从而得出上海市苏州河水质得到明显改善的结论.     

未确知测度模型在城市环境空气质量评价中的应用

基于环境空气的信息不确定性特点,运用未确知数学理论,建立了城市环境空气质量评价的未确知测度模型。根据未确知测度的大小,确定样本所属的质量级别及样本间的优劣排序。以唐山市为例,运用所建模型对其环境空气质量状况进行了评价。研究表明,未确知测度模型对于多指标的城市空气质量评价,理论上是可行的,结果是可信的。     

Seasonal variation in physicochemical properties of coastal waters of Kalpakkam, east coast of India with special emphasis on nutrients

A study pertaining to the seasonal variation in physicochemical properties of the coastal waters was carried out at Kalpakkam coast for a period of 1 year (February 2006 to January 2007). It revealed that the coastal water was significantly influenced by freshwater input during North East (NE) monsoon and post-monsoon periods. Concentration of all the nutrients and dissolved oxygen (DO) was relatively high during the NE monsoon, whereas, salinity and chlorophyll-a (chl-a) were at their minimum level during this period. Phytoplankton production peak was observed in summer during which a typical marine condition prevailed. The present observed values of nitrate, phosphate, silicate, and turbidity are significantly high (five to ten times) compared to that of the pre-Tsunami period from this coast. Relatively low DO and chl-a concentration was noticed during the post-Tsunami period. A notable feature of this study is that though nutrient concentration in the coastal waters during post-Tsunami period has increased significantly, turbidity, the most single dominating factor, was found to adversely affect the phytoplankton production during post-Tsunami period as reflected by relatively low chl-a concentration. Thus, the post-Tsunami period may result in a change in coastal biodiversity pattern concomitant with change in coastal water quality.     

Development of MODIS data-based algorithm for retrieving sea surface temperature in coastal waters

A new algorithm was developed for retrieving sea surface temperature (SST) in coastal waters using satellite remote sensing data from Moderate Resolution Imaging Spectroradiometer (MODIS) aboard Aqua platform. The new SST algorithm was trained using the Artificial Neural Network (ANN) method and tested using 8 years of remote sensing data from MODIS Aqua sensor and in situ sensing data from the US coastal waters in Louisiana, Texas, Florida, California, and New Jersey. The ANN algorithm could be utilized to map SST in both deep offshore and particularly shallow nearshore waters at the high spatial resolution of 1 km, greatly expanding the coverage of remote sensing-based SST data from offshore waters to nearshore waters. Applications of the ANN algorithm require only the remotely sensed reflectance values from the two MODIS Aqua thermal bands 31 and 32 as input data. Application results indicated that the ANN algorithm was able to explaining 82–90% variations in observed SST in US coastal waters. While the algorithm is generally applicable to the retrieval of SST, it works best for nearshore waters where important coastal resources are located and existing algorithms are either not applicable or do not work well, making the new ANN-based SST algorithm unique and particularly useful to coastal resource management.     

Assessing the value of information for water quality management: a watershed perspective from China

To tackle China’s pervasive water pollution, tremendous efforts are needed to achieve more and better information. However, resources for information collection (e.g., water quality monitoring, field experiments, etc.) are very limited for large watersheds with significant nonpoint source pollution. Thus, it is crucial to identify the priority of information acquisition. Based on the theory of value of information (VOI), a stochastic optimization approach was developed in this study to evaluate the importance of information. The approach was applied to several key polluted water bodies in China (e.g., Lake Taihu, Lake Chaohu, and Lake Dianchi). The major findings include: (1) because of the severe pollution and large uncertainty, the VOI for the targeted water bodies is substantial; (2) when the uncertainty is significant, a stricter regulation would result in a higher VOI, and therefore provide more incentives for data collection; (3) due to the interaction among different information sources, collecting multiple types of information simultaneously could be more valuable than collecting one after another; and (4) the importance of a specific type of information could vary significantly across watersheds. The proposed approach can be readily extended to more complex models and more sophisticated watershed cases. It could effectively support watershed management in China, as well as in other countries.     

Sediment grain size estimation using airborne remote sensing, field sampling, and robust statistic

Remote sensing has been used since the 1980s to study parameters in relation with coastal zones. It was not until the beginning of the twenty-first century that it started to acquire imagery with good temporal and spectral resolution. This has encouraged the development of reliable imagery acquisition systems that consider remote sensing as a water management tool. Nevertheless, the spatial resolution that it provides is not adapted to carry out coastal studies. This article introduces a new methodology for estimating the most fundamental physical property of intertidal sediment, the grain size, in coastal zones. The study combines hyperspectral information (CASI-2 flight), robust statistic, and simultaneous field work (chemical and radiometric sampling), performed over Santander Bay, Spain. Field data acquisition was used to build a spectral library in order to study different atmospheric correction algorithms for CASI-2 data and to develop algorithms to estimate grain size in an estuary. Two robust estimation techniques (MVE and MCD multivariate M-estimators of location and scale) were applied to CASI-2 imagery, and the results showed that robust adjustments give acceptable and meaningful algorithms. These adjustments have given the following R(2) estimated results: 0.93 in the case of sandy loam contribution, 0.94 for the silty loam, and 0.67 for clay loam. The robust statistic is a powerful tool for large dataset.     

Application of a three-tier framework to assess ecological condition of Gulf of Mexico coastal wetlands

A multi-level coastal wetland assessment strategy was applied to wetlands in the northern Gulf of Mexico (GOM) to evaluate the feasibility of this approach for a broad national scale wetland condition assessment (US Environmental Protection Agency’s National Wetlands Condition Assessment). Landscape-scale assessment indicators (tier 1) were developed and applied at the sub-watershed (12-digit hydrologic unit code (HUC)) level within the GOM coastal wetland sample frame with scores calculated using land-use maps and geographic information system. Rapid assessment protocols (tier 2), using a combination of data analysis and field work, evaluated metrics associated with landscape context, hydrology, physical structure, and biological structure. Intensive site monitoring (tier 3) included measures of soil chemistry and composition, water column and pore-water chemistry, and dominant macrophyte community composition and tissue chemistry. Relationships within and among assessment levels were evaluated using multivariate analyses with few significant correlations found. More detailed measures of hydrology, soils, and macrophyte species composition from sites across a known condition gradient, in conjunction with validation of standardized rapid assessment method, may be necessary to fully characterize coastal wetlands across the region.     

A game theoretic approach for interbasin water resources allocation considering the water quality issues

In this paper, a new game theoretic methodology is developed for interbasin water transfer management with regard to economic, equity, and environmental criteria. The main objective is to supply the competing users in a fair way, while the efficiency and environmental sustainability criteria are satisfied and the utilities of water users are incorporated. Firstly, an optimization model is developed to proportionally allocate water to the competing users in water donor and receiving basins based on their water demands. Secondly, for different coalitions of water users, the water shares of the coalitions are determined using an optimization model with economic objectives regarding the physical and environmental constraints of the system. In order to satisfy water-quality requirements, the impacts of decreasing the instream flow in donor basin are estimated using a water-quality simulation model, and the required treatment levels for effluents discharged into the river, downstream of the water transfer point are determined. Finally, to achieve equity and to provide sufficient incentives for water users to participate in the cooperation, some cooperative game theoretic approaches are utilized for reallocation of net benefits to water users. This model is applied to a large-scale interbasin water allocation problem including two different basins struggling with water scarcity in Iran. The results show that this model can be utilized as an effective tool for optimal interbasin water allocation management involving stakeholders with conflicting objectives subject to physical and environmental constraints.     

滨海湿地环境评价方法研究

为研究我国典型滨海湿地环境评价方法,利用卫星遥感技术,结合地面生态调查,构建了以自然湿地面积比例、人工岸线比例等8项因子共同组成的指标体系,通过层次分析法和专家打分法进行权重赋值后,阐述了数据处理和计算的主要流程,提出了滨海湿地环境指数CWEI的概念和表征意义。应用上述方法对江苏盐城湿地珍禽国家级自然保护区进行了典型示范评价,结果表明该方法能够较好地反映滨海湿地环境状况与变化趋势。