Design of fuzzy synthetic evaluation model for air quality assessment

Little attention was paid to growing air quality concerns until about a decade earlier in India. Indian Government started continuous monitoring of the urban air quality in Taj corridor area to protect the heritage monuments like Agra Fort, Fatehpur Sikri, the bird sanctuary at Bharatpur National Park and also the human health associated with air pollution. The aim of this study was to address air quality assessment using fuzzy synthetic evaluation model. The model was designed for four air pollutants (sulphur dioxide, nitrogen dioxide, suspended particulate matters and respirable suspended particulate matter). In the present paper, an approach is demonstrated for the determination of fuzzy air quality index by aggregating the four pollutants. The model also considers the weights of individual pollutants during aggregation. The weights of individual pollutants were determined using analytical hierarchical process. The model was applied for air quality assessment in four monitoring stations situated in Taj Trapezium Zone.     

Effect of reservoir flushing on downstream river water quality

The effect of short-term reservoir flushing on downstream water quality in the Geum River, Korea was studied using field experiments and computer simulations. The reservoir release was increased from 30 to 200 m(3)/s within 6 h for the purpose of this experiment. The flushing discharge decreased the concentrations of soluble nitrogen and phosphorus species considerably, but the experimental results revealed a negative impact on organic forms of nutrients and biochemical oxygen demand (BOD). A dynamic river water quality model was applied to simulate the river hydraulics and water quality variations during the event. The model showed very good performance in predicting the travel time of flushing flow and the variations of dissolved forms of nitrogen and phosphorus constituents. However, it revealed a limited capability in simulating organic forms of nutrients and BOD because it does not consider the re-suspension mechanism of these constituents from sediment during the wave front passage.     

A river water quality management model for optimising regional wastewater treatment using a genetic algorithm

To achieve water quality goals and wastewater treatment cost optimisation in a river basin, a water quality management model has been developed through the integration of a genetic algorithm (GA) and a mathematical water quality model. The developed model has been applied to the Youngsan River, where water quality has decreased due to heavy pollutant loads from Kwangju City and surrounding areas. Pollution source, land use, geographic features and measured water quality data of the river basin were incorporated into the Arc/View geographic information system database. With the database, the management model calculated treatment type and treatment cost for each wastewater treatment plant in the river basin. Until now, wastewater treatment policy for polluted rivers in Korea has been, first of all, to construct secondary treatment plants for untreated areas, and secondarily, to construct advanced treatment plants for the river sections whose water quality is impaired and for which the water quality goal of the Ministry of Environment is not met. Four scenarios that do not use the GA were proposed and they were compared with the results of the management model using the GA. It became clear that the results based on the GA were much better than those for the other four scenarios from the viewpoint of the achievement of water quality goals and cost optimisation.     

Optimal water quality monitoring network design for river systems

Typical tasks of a river monitoring network design include the selection of the water quality parameters, selection of sampling and measurement methods for these parameters, identification of the locations of sampling stations and determination of the sampling frequencies. These primary design considerations may require a variety of objectives, constraints and solutions. In this study we focus on the optimal river water quality monitoring network design aspect of the overall monitoring program and propose a novel methodology for the analysis of this problem. In the proposed analysis, the locations of sampling sites are determined such that the contaminant detection time is minimized for the river network while achieving maximum reliability for the monitoring system performance. Altamaha river system in the State of Georgia, USA is chosen as an example to demonstrate the proposed methodology. The results show that the proposed model can be effectively used for the optimal design of monitoring networks in river systems.     

A spatial-statistical approach for modeling the effect of non-point source pollution on different water quality parameters in the Velhas river watershed--Brazil

In this article, a methodology for evaluating the effect of land use/land cover on the quality of nearby stream water in a semiarid environment is described and tested on a large watershed in Southeastern Brazil. The approach aims at identifying the width of the riparian area having the strongest effect on different water quality parameters. The land use/land cover data were generated from remotely sensed data while water quality point data were supplied by a government agency. Testing was conducted for both the rainy and dry seasons in an effort to understand the direct effect of surface runoff. The approach combines cartographic modelling using a geographical information system (GIS) and statistics to establish the strength of the relationship between water quality, land use and the distance from the stream. Results suggest a strong relationship between land use/land cover and turbidity, nitrogen and fecal coliforms. They also suggest that each of these parameters has a unique behavior when distance from the stream is considered. Finally, although it was expected that the models would apply better during the wet season, some parameters had the opposite behavior and displayed a better fit during the dry season.     

Emergy-based fuzzy optimization approach for water reuse in an eco-industrial park

The establishment of an eco-industrial park (EIP) provides opportunity for individual plants to cooperate with each other in order to utilize resources efficiently and thus reduce waste. The goal of an EIP is to “close the loop” through recycling and reuse of material and energy streams. Studies show with current freshwater consumption trends there would be water stress aggravated by global warming in the near future. This paper presents a model to design an EIP water reuse network that considers overall system sustainability as measured with emergy, as well as cost saving desired by individual plants. Case studies from literature are then solved to illustrate the advantage of this method in decision making. The illustrative examples show how the model achieves a compromise among the potentially conflicting fuzzy goals of the various EIP stakeholders.     

Sources of water pollution and evolution of water quality in the Wuwei basin of Shiyang river, Northwest China

Based on surveys and chemical analyses, we performed a case study of the surface water and groundwater quality in the Wuwei basin, in order to understand the sources of water pollution and the evolution of water quality in Shiyang river. Concentrations of major chemical elements in the surface water were related to the distance downstream from the source of the river, with surface water in the upstream reaches of good quality, but the river from Wuwei city to the Hongya reservoir was seriously polluted, with a synthetic pollution index of 25. Groundwater quality was generally good in the piedmont with dominant bicarbonate and calcium ions, but salinity was high and nitrate pollution occurs in the northern part of the basin. Mineralization of the groundwater has changed rapidly during the past 20 years. There are 23 wastewater outlets that discharge a total of 22.4 x 10(6)m(3)y(-1) into the river from Wuwei city, which, combined with a reduction of inflow water, were found to be the major causes of water pollution. Development of fisheries in the Hongya reservoir since 2000 has also contributed to the pollution. The consumption of water must be decreased until it reaches the sustainable level permitted by the available resources in the whole basin, and discharge of wastes must also be drastically reduced.     

Water quality modelling of the river Yamuna (India) using QUAL2E-UNCAS

This paper describes the utility of QUAL2E as a modelling package in the evaluation of a water quality improvement programme. In this study, QUAL2E was applied to determine the pollution loads in the river Yamuna during its course through the national capital territory of Delhi, India. The study aimed at examining the influence of different scenarios on river water quality. Four different pollution scenarios were analysed besides the 'business as usual' situation. The study revealed that it was necessary to treat the discharge from drains to the river Yamuna and diversion of a substantial load to the Agra canal for further treatment was also essential. It was also established through this study that maintaining a flow rate of more than 10 m(3)/s in the river could also help preserve the river's water quality. To clearly display the model outcomes and demarcate polluted zones in the river stretch, model results were interfaced with a Geographical Information System (GIS) to produce cartographic outputs. In addition, uncertainty analysis in the form of first-order error analysis and Monte Carlo analysis was performed, to realise the effect of each model parameter on DO and BOD predictions. The uncertainty analysis gave satisfactory results on simulated data.     

Evaluating water quality investments using cost utility analysis

This study borrows concepts from healthcare economics and uses cost utility analysis (CUA) to select an optimum portfolio of water quality enhancement projects in Perth, Western Australia. In CUA, costs are handled via standard discounted cash flow analysis, but the benefits, being intangible, are measured with a utility score. Our novel methodology combines CUA with a binary combinatorial optimisation solver, known as a 'knapsack algorithm', to identify the optimum portfolio of projects. We show how water quality projects can be selected to maximise an aggregate utility score while not exceeding a budget constraint. Our CUA model applies compromise programming (CP) to measure utility over multiple attributes in different units. CUA is shown to provide a transparent and analytically robust method to maximise benefits from water quality remediation investments under a constrained budget.     

A hybrid approach using ISM and fuzzy TOPSIS for the selection of reverse logistics provider

Return of used products is becoming an important logistics activity due to government legislation and increasing awareness among the people to protect the environment and reduce waste. For industries, the management of return flow usually requires a specialized infrastructure with special information systems for tracking and dedicated equipment for the processing of returns. Therefore, industries are turning to third-party reverse logistics providers (3PRLPs). In this study, a multi-criteria group decision-making (MCGDM) model in fuzzy environment is developed to guide the selection process of best 3PRLP. The interactions among the criteria are also analyzed before arriving at a decision for the selection of 3PRLP from among 15 alternatives. The analysis is done through Interpretive Structural Modeling (ISM) and fuzzy technique for order preference by similarity to ideal solution (TOPSIS). Finally the effectiveness of the model is illustrated using a case study on battery manufacturing industry in India.     

Estimating the effects of urban residential development on water quality using microdata

In this study, we examine the impact on water quality of urbanization using disaggregate data from Wake County, North Carolina. We use a unique panel data set tracing the conversion of individual residentially zoned land parcels to relate the density of residential development and the change in residential land use to three measures of water quality. Using a spatial econometrics model, we relate spatially and temporally referenced monitoring station readings to our measures of residential land use while controlling for other factors affecting water quality. We find that both the density of residential land use and the rate of land conversion have a negative impact on water quality. The impacts of these non-point sources are found to be larger in magnitude than those from urban point sources.     

Cost allocation of river water quality management based on the Separable Cost Remaining Benefit (SCRB) method

This paper presents a cost allocation method that applies the cooperative game theory and the Separable Costs Remaining Benefit method to a project that involves two local governments in water quality management in South Korea. The total project cost was estimated by using a parametric estimation method for reduction loads in accordance with the Total Pollution Load Management system. As a result, the cost allocation ratios between the City of Gwangju and Jeonnam Province are suggested to be 69.85% and 30.15% of the total project cost. The final cost allocation confirms the benefits to both governments and illustrates the cooperative game theory.     

Monitoring of natural and synthetic hormones in a polluted river

Natural (estradiol, estrone, testosterone, estriol) and synthetic hormones (ethinylestradiol) are constantly excreted into the environment from human and animal sources but little is known of their transport. The purpose of this study was to determine how far along a 100 km river course that hormones could be detected after contamination with sewage effluent or fishpond effluent. Fourteen sites in the Lower Jordan River drainage were sampled (two sites above the sewage effluent contamination, eight sites below the contamination and four tributaries) before and after the dry season of 2002 (Spring and Fall). Samples were tested for testosterone, estrogen (estrone and estradiol combined), estriol, ethinylestradiol, ammonia and fecal coli. It was found that the fecal coli count dropped exponentially (from 250,000 to 60/100 ml3) and the ammonia dropped from 15 to less than 1mg/l over the initial 25 km stretch. Over the same stretch, the hormone values declined by half from their maximum values for testosterone (3.3 ng/l), estriol (8.8 ng/l), ethinylestradiol (6.1 ng/l), and estrogen (4.9 ng/l). From 67 to 100 km mark, testosterone (4.8 ng/l) and estrogen (2.4 ng/l) were still elevated while ethinylestradiol and estriol were >or=1.5 ng/l. The high level of testosterone and estrogen between 67 and 100 km marks was probably due to major discharge from fishponds between 23 and 27 km marks. Levels of ethinylestradiol above 1 ng/l, a level which can affect fish, was seen in 70% (12/16) of the samples tested. The data suggest that hormones in readily measured quantities can be transported considerable distances from the source of pollution.     

Comparing contingent valuation and contingent ranking: a case study considering the benefits of urban river water quality improvements

This paper contrasts applications of both the contingent valuation (CV) and contingent ranking (CR) methods as applied to a common issue, the valuation of improvements to the water quality of an urban river (the River Tame, running through the city of Birmingham, UK). Building upon earlier experimental work, the CV design used ensures that respondents are fully aware of all impending valuation tasks prior to undertaking any one of those tasks. Such an approach is directly comparable to the CR design for which full awareness of all options is a pre-requisite. Findings indicate that the CV responses exhibit strong internal consistency with expected relationships observed between values and theoretically expected parameters. External comparisons show that CR valuations are substantially larger than those elicited through CV (with protest votes excluded), and that the response rate for the CR survey is significantly higher than that for the CV survey.     

Improving saline-sodic coalbed natural gas water quality using natural zeolites

Management of saline-sodic water from the coalbed natural gas (CBNG) industry in the Powder River Basin (PRB) of Wyoming and Montana is a major environmental challenge. Clinoptilolie zeolites mined in Nevada, California, and New Mexico were evaluated for their potential to remove sodium (Na+) from CBNG waters. Based on the exchangeable cation composition, naturally occurring calcium (Ca2+)-rich zeolites from New Mexico were selected for further evaluation. Batch adsorption experiments were conducted to evaluate the potential of the Ca(2+)-rich natural clinoptilolites to remove Na+ from saline-sodic CBNG waters. Batch adsorption experiments indicated that Na+ adsorption capacity ofclinoptilolite ranged from 4.3 (4 x 6 mesh) to 7.98 g kg(-1) (14 x 40 mesh). Among the different adsorption isotherms investigated, the Freundlich Model fitted the data best for smaller-sized (6 x 8, 6 x 14, and 14 x 40 mesh) zeolites. Passing the CBNG water through Ca(2+)-rich zeolite columns reduced the salt content (electrical conductivity [EC]) by 72% with a concurrent reduction in sodium adsorption 10 mmol 1/2 L(-1/2). Zeolite technology appears to be an effective water treatment alternative to industrial membrane treatment for removing Na+ from poor-quality CBNG waters.     

Soils, water quality, and watershed size: interactions in the Maumee and Sandusky river basins of northwestern Ohio

Soil variability in watersheds accounts for the problem of partitioning downstream water quality data and evaluating sources of non-point pollution. This review of previous water quality studies was conducted to examine more closely the influence of soil properties on pollutant export. The approach used in this paper was to start with data from the two largest watersheds (Maumee and Sandusky) and then compare them on a unit area export basis with data from intermediate-size and smaller watersheds. General relationships between pollutant levels at the river mouth and upstream soil conditions are vague and seemingly contradictory at the large-watershed scale. With smaller watersheds, it can be determined that soil texture, slope, and internal drainage are controlling factors for pollutant export. Although Paulding (very-fine, illitic, nonacid, mesic Typic Epiaquept) and Roselms (very-fine, illitic, mesic Aeric Epiaqualf) soils occupy only 5% of the Maumee basin, they generate more than 10 times as much sediment per unit area as the tile-drained Hoytville (fine, illitic, mesic Mollic Epiaqualf) soils that occupy 16% of the Maumee basin. Tile drainage of very poorly drained soils that are formed from either glacial till or silty to sandy lake deposits reduces runoff and increases downward movement of soluble nutrients into tile drains. The assumption that sloping moraine areas are the primary source of pollutants should be reexamined based on this review.     

GIS-based spatial regression and prediction of water quality in river networks: A case study in Iowa

Nonpoint source pollution is the leading cause of the U.S.’s water quality problems. One important component of nonpoint source pollution control is an understanding of what and how watershed-scale conditions influence ambient water quality. This paper investigated the use of spatial regression to evaluate the impacts of watershed characteristics on stream NO₃NO₂-N concentration in the Cedar River Watershed, Iowa. An Arc Hydro geodatabase was constructed to organize various datasets on the watershed. Spatial regression models were developed to evaluate the impacts of watershed characteristics on stream NO₃NO₂-N concentration and predict NO₃NO₂-N concentration at unmonitored locations. Unlike the traditional ordinary least square (OLS) method, the spatial regression method incorporates the potential spatial correlation among the observations in its coefficient estimation. Study results show that NO₃NO₂-N observations in the Cedar River Watershed are spatially correlated, and by ignoring the spatial correlation, the OLS method tends to over-estimate the impacts of watershed characteristics on stream NO₃NO₂-N concentration. In conjunction with kriging, the spatial regression method not only makes better stream NO₃NO₂-N concentration predictions than the OLS method, but also gives estimates of the uncertainty of the predictions, which provides useful information for optimizing the design of stream monitoring network. It is a promising tool for better managing and controlling nonpoint source pollution.     

Understanding long-term baseflow water quality trends using a synoptic survey of the ground water-surface water interface, central Wisconsin

The relationship between stream water quality and landscape activities is difficult to evaluate where the principal source of stream flow is ground water seepage because the average travel time from ground water recharge areas to stream discharge positions can be on the order of decades. We tested the idea that past and future baseflow water quality can be predicted based on a synoptic survey of ground water recharge age-dates (based on chlorofluorocarbon [CFC] measurements) and water quality measurements obtained at the ground water-surface water interface. In this study we (i) characterize the discharge-weighted age distribution and water quality of ground water seepage into the Little Plover River (LPR); (ii) use this information to backcast and forecast baseflow NO(3)(-) concentrations; and (iii) evaluate NO(3)(-) backcasts against historical baseflow data (1960 to 2000). The discharge-weighted apparent CFC age of ground water seepage into the LPR was 23.7 (+/-7) yr. Baseflow backcasts matched the four decade rise of baseflow NO(3)(-) from 2 to 8 mg L(-1). Baseflow forecasts included three scenarios. Scenario A projects the historical rise of NO(3)(-) in the LPR basin's ground water recharge through 2050. Scenario B projects a leveling off of NO(3)(-) in ground water recharge in the year 2000. Scenario C projects a leveling off in the year 1985. Under Scenario A, LPR baseflow NO(3)(-) will increase steadily from 8 to 19 mg L(-1) between 2000 and 2050. Under scenarios B and C baseflow NO(3)(-) will plateau at 13 mg L(-1) in 2030 and at 10 mg L(-1) in 2010, respectively. The approach developed in this study can be used to (i) reconstruct historical baseflow water quality patterns in the absence of long-term monitoring data and (ii) project the effects of potential management decision on future water quality.     

Estimating the economic value of improvements in river ecology using choice experiments: an application to the water framework directive

The Water Framework Directive is a major regulatory reform of water resources management within the European Union. Integrated catchment management plans must be prepared for all river basins, in order to achieve 'good ecological status' in all EU waters. Ecological status is a broader measure of water quality than the chemical and biological measures that were previously dominant. The Directive calls for a consideration of the economic costs and benefits of improvements to ecological status. In this paper, we use the choice experiment method to estimate the value of improvements in three components of ecological status. Given the high resource cost of valuation studies, benefits transfer methods will be needed in implementing the Directive. We thus also test the ability of choice experiments for benefits transfer across two very similar rivers in the UK.     

Development of a water quality loading index based on water quality modeling

Water quality modeling is an ideal tool for simulating physical, chemical, and biological changes in aquatic systems. It has been utilized in a number of GIS-based water quality management and analysis applications. However, there is considerable need for a decision-making process to translate the modeling result into an understandable form and thereby help users to make relevant judgments and decisions. This paper introduces a water quality index termed QUAL2E water quality loading index (QWQLI). This new WQI is based on water quality modeling by QUAL2E, which is a popular steady-state model for the water quality of rivers and streams. An experiment applying the index to the Sapgyo River in Korea was implemented. Unlike other WQIs, the proposed index is specifically used for simulated water quality using QUAL2E to mainly reflect pollutant loading levels. Based on the index, an iterative modeling-judgment process was designed to make decisions to decrease input pollutants from pollutant sources. Furthermore, an indexing and decision analysis can be performed in a GIS framework, which can provide various spatial analyses. This can facilitate the decision-making process under various scenarios considering spatial variability. The result shows that the index can evaluate and classify the simulation results using QUAL2E and that it can effectively identify the elements that should be improved in the decision-making process. In addition, the results imply that further study should be carried out to automate algorithms and subsidiary programs supporting the decision-making process.