Methodology for Identifying Optimal Locations of Water Quality Sensors in River Systems

A method to optimally site river water quality sensors is expanded and applied to a case study river to explore the application of mathematical siting methods to the design of river sampling networks. Fecal coliform contamination due to flooded swine waste lagoons was modeled as it moves downstream, and optimal sensor locations are located by minimizing the objective function of the optimization problem. The results of the simulations are analyzed by varying the number of allowed sampling locations and the simulated contamination event. For the case study application, the model suggests three sampling locations along the modeled river section. These three suggested sensing points did not greatly vary in location for different river flows and contamination events, indicating the robustness of the model results for this specific case study. Generally, the application of mathematical contaminant modeling is a useful and systematic approach to aid the design of river water quality monitoring networks.     

A real-time monitoring system for Kerava river quality

The hygienic quality of the water of the Kerava river, southern Finland, deteriorates occasionally. The purpose of the study was to design a real-time monitoring system that would inform the public using the river for recreational purposes about the changes in water quality. The system was constrained to consist of on-line sensing of water quality and quantity, and adjacent forecasting models. Four different system alternatives were analyzed and compared. The first alternative observes river flow in real-time; the second alternative also monitors water temperature, turbidity, pH, conductivity and dissolved oxygen. The data collected in this way are used to forecast Streptococcus and E. coli concentrations, using canonical correlation and regression analysis. The third configuration is a two-step procedure, where river flow is first predicted by an ARMAX model and the hygienic state is then based on the flow estimate, as in the first assemblage. The most expensive monitoring system, which at present is the least well-known, is to apply the Lidar system, where the hygienic status of the river quality is observed directly using laser technology, placing less emphasis on modeling. In this paper, the alternatives are formulated and a preliminary comparison is made, using the criteria of operational feasibility, prediction uncertainty, investment and maintenance costs, and suitability for in-situ monitoring.     

Spatial–temporal variation and comparative assessment of water qualities of urban river system: a case study of the river Bagmati (Nepal)

The study presents the assessment of variation of water qualities, classification of monitoring networks and detection of pollution sources along the Bagmati River and its tributaries in the Kathmandu valley of Nepal. Seventeen stations, monitored for 23 physical and chemical parameters in pre-monsoon, monsoon, post-monsoon and winter seasons, during the period 1999-2003, were selected for the purpose of this study. The study revealed that the upstream river water qualities in the rural areas were increasingly affected from human sewage and chemical fertilizers. In downstream urban areas, the river was heavily polluted with untreated municipal sewage. The contribution of industries to pollute the river was minimal. The higher ratio of COD to BOD (3.74 in the rural and 2.06 in the urban) confirmed the increased industrial activities in the rural areas. An increasing trend of nitrate was found in the rural areas. In the urban areas, increasing trend of phosphorus was detected. The water quality measurement in the study period showed that DO was below 4 mg/l and BOD, COD, TIN, TP and TSS above 39.1, 59.2, 10.1, 0.84 and 199 mg/l, respectively, in the urban areas. In the rural areas, DO was above 6.2 mg/l and BOD, COD, TIN, TP and TSS below 15.9, 31, 5.24, 0.41 and 134.5 mg/l, respectively. The analysis for data from 1988 to 2003 at a key station in the river revealed that BOD was increasing at a rate of 1.8 mg/l in the Bagmati River. A comparative study for the water quality variables in the urban areas showed that the main river and its tributaries were equally polluted. The other comparison showed the urban water qualities were significantly poor as compared with rural. The cluster analysis detected three distinct monitoring groups: (1) low water pollution region, (2) medium water pollution region, (3) heavy water pollution region. For rapid assessment of water qualities using the representative sites could serve to optimize cost and time without loosing any significance of the outcome. The factor analysis revealed distinct groups of sources and pollutions (organics, nutrients, solutes and physicochemical).     

乌鲁木齐河流域浅层地下水防污性能评价

地下水防污性能的评价是确定地下水资源保护措施是否可行的关键。本文根据乌鲁木齐河流域浅层地下水水文地质状况，采用DRASTIC模型，选取包气带岩性、厚度与结构、地形作为评价因子，在乌鲁木齐河流域地下水污染现状评价的基础上，用模糊综合评判法评价了地下水防污性能，从而为乌鲁木齐河流域地下水污染防治提供了依据。     

Forecasting of daily total atmospheric ozone in Isfahan

A neural network combined to an artificial neural network model is used to forecast daily total atmospheric ozone over Isfahan city in Iran. In this work, in order to forecast the total column ozone over Isfahan, we have examined several neural networks algorithms with different meteorological predictors based on the ozone-meteorological relationships with previous day's ozone value. The meteorological predictors consist of temperatures (dry and dew point) and geopotential heights at standard levels of 100, 50, 30, 20 and 10 hPa with their wind speed and direction. These data together with previous day total ozone forms the input matrix of the neural model that is based on the back propagation algorithm (BPA) structure. The output matrix is the daily total atmospheric ozone. The model was build based on daily data from 1997 to 2004 obtained from Isfahan ozonometric station data. After modeling these data we used 3 year (from 2001 to 2003) of daily total ozone for testing the accuracy of model. In this experiment, with the final neural network, the total ozone are fairly well predicted, with an Agreement Index 76%.     

Evaluation of the impacts of mine drainage from a coal waste pile on the surrounding environment at Smolnica, southern Poland

Mine drainage impacts from a coal waste pile at Smolnica, Poland have been monitored. Groundwater in an unconfined aquifer downgradient from the pile has near-neutral pH, but high concentrations of sulfate (up to 3,827 mg/l), chloride (up to 903 mg/l), and sodium (up to 2,606 mg/l). Concentrations of iron and manganese are elevated only locally, and concentrations of other metals are low. The behavior of sulfate seems to be conservative in the downgradient aquifer, and gypsum may only be precipitating locally. Concentrations of iron and manganese seem to be controlled by the precipitation of ferric oxide and hydroxides and rhodochrosite, respectively. Complete neutralization of mine drainage by carbonates is consistent with high concentrations of calcium (up to 470 mg/l) and magnesium (up to 563 mg/l) and also with high strontium concentrations of up to 3.08 mg/l, observed in groundwater downgradient from the pile. Hydraulic head profiles at two sites within the river bottom sediments indicate upward flow toward the river with large local differences in groundwater recharge. Water chemistry profiles in the river bottom sediments and geochemical modeling suggest conservative behavior of Na, Cl, and SO₄ and precipitation of Fe and Mn at the groundwater/river water interface. Mine drainage enters the Bierawka River and causes increasing sulfate concentrations. In contrast, concentrations of sodium and chloride in the Bierawka River decrease downgradient from the pile because water in the river upgradient from the pile is already highly contaminated by these species from the discharge of mining waters. Concentrations of Fe and Mn in the river water are low, as a consequence of the precipitation of Fe and Mn oxide and hydroxides. Direct geochemical modeling was able to reproduce measured concentrations of conservative species (e.g., Na, Cl, and SO₄), but errors for metals and Ba were relatively large. In addition, calculated P_CO2 values in the river water are very high, suggesting that equilibrium with atmospheric P_CO2 and P_O2 has not been reached, and at least some reactions should be modeled as kinetic processes. High concentrations of Na, Cl, and SO₄ contribute to the contamination of the Odra River, which is joined by the Bierawka River farther downgradient, thus limiting the use of river water for recreation and other purposes.     

A General Equilibrium Assessment of Climate Change Impacts on Swiss Winter Tourism with Adaptation

Winter alpine tourism has been repeatedly identified as one of the economic sectors most at risk from climate change in Switzerland. However, all of the costs that have been estimated so far for the Swiss tourism sector are, to some extent, misleading as they do not, or only partially, incorporate adaptation possibilities and general equilibrium effects. We attempt to fill this gap using a computable general equilibrium model that is specifically designed for the purposes of this research. Our modeling efforts first consist in creating a tourism sector with a part of it being dependent on snow. We also carefully model the snowmaking technology. Using climate change scenarios on future snow cover, we analyze their impacts on the Swiss ski industry. We find welfare effects for the Swiss economy ranging from − 23 to 113 million CHF in 2050. This range arises from the use of various assumptions concerning adaptation possibilities. We also show that geographical substitutions between international ski destinations have large positive effects for Switzerland. From a more general perspective, our results exemplify the risks of estimating the consequences of climate change based only on domestic impacts of climate change with no adaptation being implemented.

     

Influence of changes in hydrodynamic conditions on cadmium transport in tidal river network of the Pearl River Delta, China

With rapid economic development, the Pearl River Delta (PRD) of China has experienced a series of serious heavy metal pollution events. Considering complex hydrodynamic and pollutants transport process, one-dimensional hydrodynamic model and heavy metal transport model were developed for tidal river network of the PRD. Then, several pollution emergency scenarios were designed by combining with the upper inflow, water quality and the lower tide level boundary conditions. Using this set of models, the temporal and spatial change process of cadmium (Cd) concentration was simulated. The influence of change in hydrodynamic conditions on Cd transport in tidal river network was assessed, and its transport laws were summarized. The result showed the following: Flow changes in the tidal river network were influenced remarkably by tidal backwater action, which further influenced the transport process of heavy metals; Cd concentrations in most sections while encountering high tide were far greater than those while encountering middle or low tides; and increased inflows from upper reaches could intensify water pollution in the West River (while encountering high tide) or the North River (while encountering middle or low tides).     

平原感潮河网地区非点源污染负荷匡算

平原感潮河网地区非点源污染严重,由于具有交叉污染等特性,造成了整个平原河网存在严重的环境污染和环境安全问题。构建了大尺度非点源污染负荷匡算模型,对南通平原河网地区非点源污染负荷进行了匡算。根据不同的产污方式,将非点源污染分为城镇降雨径流污染、畜禽养殖污染、农田降雨径流污染、农村生活污染和水产养殖污染等5种类型,分别计算其流失过程。     

Ecological assessments of surface water bodies at the river basin level: a case study from England

Ecological assessments of surface water bodies are essential in order to evaluate the level of degradation in freshwater ecosystems and to address the subsequent decline in services they provide. These assessments cover multiple aspects of the aquatic environment, particularly biological elements due to their ability to respond to all pressures within an ecosystem. Such assessments can enable the identification of the multiple pressures which threaten water bodies, facilitating sustainable decisions regarding their management to be identified. Here, the design requirements of the networks which facilitate ecological assessments are presented. A river basin district in England is used as a case study to investigate the number of elements monitored, the number of failing elements and the relationship between failing elements. Findings demonstrate the value of ensuring that monitoring networks are risk based and appropriately designed to meet their objectives. This therefore requires that monitoring is not only for the communicating of compliance but also for use iteratively so that the design of monitoring networks and ultimately management can be continually improved.     

Reactivity, interactions and transport of trace elements, organic carbon and particulate material in a mountain range river system (Adour River, France)

The background levels, variability, partitioning and transport of eleven trace elements-Ag, Al, As, Cd, Co, Cr, Cu, Mn, Pb, Zn and U-were investigated in a mountain range river system (Adour River, France). This particular river system displayed a turbulent hydrodynamic regime, characterized by flash-transient discharge conditions leading to fast shifts in suspended particulate matter (SPM) concentrations as high as two orders of magnitude (12 to 600 mg l(-1)). The distribution of SPM was accurately predicted with a "hysteresis" transport model, indicating that about 75% of the annual solids load was exported within 20 to 40 days. Dissolved and particulate concentrations of most trace elements were low compared to their concentrations in other reference river systems expect for Pb and Cr, associated with historical anthropogenic activities. Although dissolved and particulate metal concentrations were steady for most elements during low and average discharge conditions, significant changes were observed with increasing river discharge. The changes in trace element concentrations in the two compartments was found to induce a partitioning anomaly referred to as the particulate concentration effect. This anomaly was significant for Cr, Mn, Pb, Zn, Cu and organic carbon (p < 0.03). The processes driving this anomaly were possibly linked to the modification and/or increase of colloidal organic and inorganic vectors, suggested by the significant increase of DOC (p < 0.001) and dissolved Al concentrations (p < 0.05) during flood conditions. A complementary process linked to the influence of coarse particles of low complexation capacity and transported mainly during high discharge may also effect trace element concentrations. Annual metal fluxes transported by this river system were estimated using the hysteresis SPM model with consideration of these fate processes. Metals in the Adour River system are primarily exported into the Bay of Biscay (Atlantic Ocean).     

Nutrient mitigation in a temporary river basin

We estimate the nutrient budget in a temporary Mediterranean river basin. We use field monitoring and modelling tools to estimate nutrient sources and transfer in both high and low flow conditions. Inverse modelling by the help of PHREEQC model validated the hypothesis of a losing stream during the dry period. Soil and Water Assessment Tool model captured the water quality of the basin. The ‘total daily maximum load’ approach is used to estimate the nutrient flux status by flow class, indicating that almost 60 % of the river network fails to meet nitrogen criteria and 50 % phosphate criteria. We recommend that existing well-documented remediation measures such as reforestation of the riparian area or composting of food process biosolids should be implemented to achieve load reduction in close conjunction with social needs.     

Testing the MAGIC acid rain model in highly organic,low-conductivity waters using multiple calibrations

Accurate predictions of acid precipitation effects on water resources are important in order to allow a better understanding of various pollution control strategy outcomes. Dynamic geochemical models have been developed to address this need, but have to be tested under a variety of environmental conditions to provide confidence in their predictions. The most commonly used aquatic acidification model in North America and Europe is the model of acidification of groundwater in catchments (MAGIC). Though extensively used, MAGIC has never been tested in catchments with extremely low ionic strength water and high in natural organic acids (NOAs) from wetlands, two conditions which are common in large parts of Canada. We calibrated the model for two catchments located in Nova Scotia, Canada, which had some of the most dilute freshwaters reported in the literature and very high NOA. We also evaluated the variability inherent in calibration data sets by using five separate data sets collected over a 15-year period at the same sites. We show good model simulations for the main cations and anions in catchment waters. However, modeling pH is more difficult in the highly organic waters and requires modification to the acid dissociation constants. Calculated acid neutralization capacity can also be more difficult to model due to the low ion content making small errors more important. In theory, multiple calibrations of a model at a same site should produce identical hindcasts and predictions. In reality, the multiple calibrations produced a series of similar, but not identical outcomes which give a probable range of past values and future outcomes. We feel that this practical approach to validation is a useful addition to the arsenal of model testing tools.     

Occurrence and behaviour of nonylphenol and octylphenol in Nanming River, Guiyang City, China

Occurrence, variation and behaviour of nonylphenol (NP) and octylphenol (OP) were studied in surface water and groundwater in Guiyang, Guizhou Province, southwestern China. Discharge of wastewater from Guiyang City was the main source of alkylphenols (APs) entering the aquatic environment. The concentrations of NP and OP in river water ranged from 40 to 1582 ng L(-1) and from below the lowest limit of detection (LOD) to 67 ng L(-1), respectively. NP and OP were also detected in groundwater. Both NP and OP exhibited spatial and temporal variations in river water and groundwater. It was found that concentrations of NP and OP in river water was low upstream and dramatically increased downstream, and higher concentration of NP was found in winter compared to that in summer. Proportions of NP and OP were trapped by suspended particulate matter (SPM), which accounted for 7.6-50.0% and 3.4-25.6% of their total concentration in the river water system, respectively. Seasonal changes in water flow were responsible for the temporal variations of APs. To determine the behaviour of APs along the river, a mass balance equation based on chloride was used. The results showed that a mixing process was the predominant factor to determine upstream APs concentrations; while the discharge of wastewater controlled the concentrations of APs downstream. Considering the adverse effect of APs on organisms, combined effect modeling was used to assess the toxicity to fish. It was found that the predicted mixture effect for APs in river water on fish vitellogenin induction was low upstream and medium downstream, respectively.     

A Tool for Classification and Regression Using Random Forest Methodology: Applications to Landslide Susceptibility Mapping and Soil Thickness Modeling

Classification and regression problems are a central issue in geosciences. In this paper, we present Classification and Regression Treebagger (ClaReT), a tool for classification and regression based on the random forest (RF) technique. ClaReT is developed in Matlab and has a simple graphic user interface (GUI) that simplifies the model implementation process, allows the standardization of the method, and makes the classification and regression process reproducible. This tool performs automatically the feature selection based on a quantitative criterion and allows testing a large number of explanatory variables. First, it ranks and displays the parameter importance; then, it selects the optimal configuration of explanatory variables; finally, it performs the classification or regression for an entire dataset. It can also provide an evaluation of the results in terms of misclassification error or root mean squared error. We tested the applicability of ClaReT in two case studies. In the first one, we used ClaReT in classification mode to identify the better subset of landslide conditioning variables (LCVs) and to obtain a landslide susceptibility map (LSM) of the Arno river basin (Italy). In the second case study, we used ClaReT in regression mode to produce a soil thickness map of the Terzona catchment, a small sub-basin of the Arno river basin. In both cases, we performed a validation of the results and a comparison with other state-of-the-art techniques. We found that ClaReT produced better results, with a more straightforward and easy application and could be used as a valuable tool to assess the importance of the variables involved in the modeling.     

A Budget Model to Scale Nutrient Biogeochemical Cycles in Two Semienclosed Gulfs

The nutrient dynamics of the Strymonikos and Ierissos Gulfs, two semienclosed coastal water bodies, are studied using a simple steady-state budget model, according to the Land–Ocean Interaction in the Coastal Zone modeling guidelines. Strymon river plume dynamics prevailed in the area of the Inner and Outer Srymonikos Gulfs, comprising two layers, while the Ierissos Gulf was defined as a one-box system. Seasonal and mean annual model input data for river discharge, precipitation, evaporation, and concentrations of salt, phosphorus, and nitrogen were obtained during four field campaigns. Results from the nitrogen and phosphorus cycling revealed the importance of river discharge in the horizontal and vertical transport of these substances within the system. Furthermore, it occurred that the major biogeochemical transformation of nitrogen and phosphorus takes place in the immediate nearshore zone (Inner Strymonikos Gulf), while the outer system sustains its nutrient dependence on oceanic exchanges. Therefore, under the summer low flow conditions, the river-influenced inner system acts as a net source of nitrogen and phosphorus, while under increased Strymon River discharge, phosphorus is transferred to the biological material (and the sediments), and the system moves to an autotrophic state. The outer system showed an opposite behavior being autotrophic throughout the year and heterotrophic in February. The Ierissos Gulf, a system not directly influenced by significant river discharge, experienced a seasonally independent behavior with net heterotrophic and denitrification processes prevailing. Model scenarios demonstrated that phosphate concentration increases, even under low river flow conditions and stimulates primary production in excess of respiration, resulting in nitrogen fixation prevalence in the Inner Strymonikos Gulf.     

Aerial and terrestrial-based monitoring of channel erosion,headcutting, and sinuosity

Headcuts are points of accelerated channel erosion that frequently have ecological consequences. A particularly large and dynamic headcut in southwest Wyoming has affected natural and anthropogenic resources for decades. To better understand and address this issue, we undertook a review of the headcut’s upstream retreat, followed by photogrammetric monitoring of the present condition for erosion monitoring. Aerial photography shows the Bitter Creek headcut retreated >?200 m upstream in 68 years (1948–2016) at ~?1.4 m year^?1. Following installation of a concrete slab structure in the mid-1970s, headcut retreat slowed to ~?0.5 m year^?1. Channel sinuosity downstream of the headcut is greater than upstream, which we attribute to the presence of the headcut, given that there are no major changes in valley geometry, geology, or soils through this reach. Both aerial and terrestrial-based image platforms were used to collect stereo imagery and create 3D photogrammetric models of the headcut in 2016. From these two models, we measured soil loss downstream of the headcut at ~?126 m³ m^?1 valley length. Since 1954, soil loss within the channel has been ~?98 m³ year^?1 or ~871 t ha^?1 year^?1since then. Models created from aerial- and terrestrial-based images differed in volumetric estimates by 2%, indicating that either method could be used for this type of monitoring. The ground-based imagery model showed more detail, especially on vertical and overhanging surfaces, while the aerial imagery model produced a more realistic orthomosaic and efficiently covered a larger area. Ground-based image acquisition took longer and was more costly per unit area, but is an efficient method for small project areas, or areas where aerial imagery cannot be safely or practically acquired. Historical imagery and photogrammetric modeling proved very useful in elucidating stream dynamics associated with this large, dynamic headcut.     

Modelling geosmin concentrations in three sources of raw water in Quebec, Canada

The presence of off-flavour compounds such as geosmin, often found in raw water, significantly reduces the organoleptic quality of distributed water and diverts the consumer from its use. To adapt water treatment processes to eliminate these compounds, it is necessary to be able to identify them quickly. Routine analysis could be considered a solution, but it is expensive and delays associated with obtaining the results of analysis are often important, thereby constituting a serious disadvantage. The development of decision-making tools such as predictive models seems to be an economic and feasible solution to counterbalance the limitations of analytical methods. Among these tools, multi-linear regression and principal component regression are easy to implement. However, due to certain disadvantages inherent in these methods (multicollinearity or non-linearity of the processes), the use of emergent models involving artificial neurons networks such as multi-layer perceptron could prove to be an interesting alternative. In a previous paper (Parinet et al., Water Res 44: 5847-5856, 2010), the possible parameters that affect the variability of taste and odour compounds were investigated using principal component analysis. In the present study, we expand the research by comparing the performance of three tools using different modelling scenarios (multi-linear regression, principal component regression and multi-layer perceptron) to model geosmin in drinking water sources using 38 microbiological and physicochemical parameters. Three very different sources of water, in terms of quality, were selected for the study. These sources supply drinking water to the Québec City area (Canada) and its vicinity, and were monitored three times per month over a 1-year period. Seven different modelling methods were tested for predicting geosmin in these sources. The comparison of the seven different models showed that simple models based on multi-linear regression provide sufficient predictive capacity with performance levels comparable to those obtained with artificial neural networks. The multi-linear regression model (R ²?=?0.657, <0.001) used only four variables (phaeophytin, sum of green algae, chlorophyll-a and potential Redox) in comparison with ten variables (potassium, heterotrophic bacteria, organic nitrogen, total nitrogen, phaeophytin, total organic carbon, sum of green algae, potential Redox, UV absorbance at 254 nm and atypical bacteria) for the best model obtained with artificial neural networks (R ²?=?0.843).     

DO-BOD modeling of River Yamuna for national capital territory, India using STREAM II, a 2D water quality model

The study illustrates the utility of STREAM II as a modeling package to determine the pollution load due to organic matter in the River Yamuna during its course through the National Capital Territory that is Delhi, India. The study was done for a period from 1995–2005. Model simulates the dissolved oxygen and biochemical oxygen demand parameters in a two-dimensional fashion by performing the numerical solution to a set of differential equations representing aquatic life with the help of Crank–Nicholson finite difference method. The model was simulated and calibrated through the field water-quality primary data and the secondary data which were taken from Central Pollution Control Board. The main reasons for the high river pollution is increasing population of Delhi and other states, leading to generation of huge amounts of domestic sewage into the river Yamuna. The model gave a good agreement between calibrated and observed data, thus, actualizing the validity of the model. However, discrepancies noticed during model calibrations were attributed to the assumptions adopted in the model formulation and to lack of field data.