Illinois redesigns its ambient water quality monitoring network

Illinois has been operating an ambient water quality network of almost 600 stations for several years. In 1977 changes in program emphasis toward intensive monitoring, the need for improved procedures and quality control in monitoring operations, and the desire to create a single data base of all Illinois State monitoring data, resulted in a redesign of the ambient monitoring program.A unique cooperative program between the Illinois Environmental Protection Agency and the US Geological Survey provides for their monitoring a portion of the network. The Survey provides flow data at most network stations as well as extensive manpower training, equipment, data processing, and program quality control. Informal agreements with other agencies have permitted a great reduction in the monitoring effort required by the Illinois Environmental Protection Agency.     

Modelling of the location of vehicle recycling facilities: A case study in Poland

The technical progress causes that increasing number of used devices presents a threat for environment, particularly in the rural areas. It can be prevented by organizing a proper system of waste disposal. Currently, the most important problem to solve is recycling of vehicles. The key element for the improvement of the functioning of the recycling network in Poland is to redesign the system so that it will allow for a reduction of the total cost related to the vehicle recycling. This paper presents a modelling approach that could be used to establish one important part of the reverse logistics (RL) network for end-of-life vehicles (ELVs) by defining the optimum locations for dismantling facilities. The proposed modelling approach is illustrated using Mazovia province in Poland as an example. The optimization criteria for the location of the elements of the recycling network are the components of the total cost of the ELV's recycling. Due to high complexity of the model a genetic algorithm has been adapted for solving the model and getting a good solution in a reasonable run time. The criteria of optimization was cost of the following processes: transportation, storage, and dismantling of ELVs. The results of simulation proved that the transportation costs of parts and materials may amount to about 70%, and that the cost of dismantling may exceed 25% of the total cost of recycling. The obtained results confirmed that genetic algorithm method can be used effectively to location the ELV's dismantling facilities. The effect of changing the location of processing facilities on the location of dismantling stations was also studied. The developed model is universal and may be used to determine the locations of different kinds of facilities organized in a reverse recycling network.     

Passive Samplers for NOx Monitoring: A Critical Review

Nitrogen oxide is an important gaseous air pollutant. It plays a major role in atmospheric chemistry, particularly in the formation of secondary air pollutants such as ozone, peroxyacyl nitrate (PAN), nitrate aerosols, and contributes to environmental acidification. A comprehensive assessment of NO₂ levels in the atmosphere is required for developing effective strategies for control of air pollution and improvement of air quality. Consequently, spatial NO₂ monitoring has become an important aspect of air quality assessment. Present NO₂ monitoring networks are mostly confined to urban areas, having a limited number of monitoring sites. Wide spatial NO₂ monitoring in India is constrained because of the limited availability of the monitoring equipment, such as continuous chemiluminesence monitors, high volume and handy samplers, which are expensive and need to be imported. Moreover, they require elaborate infrastructure, scientific personnel, technical support and uninterrupted power supply. In an effort to overcome the above shortcomings, passive samplers have been successfully used for large scale monitoring of NO₂ in the ambient environment.In recent years, passive samplers have been gaining increasing attention because they are simple, lightweight and cheap devices, which operate without any power source. Passive samplers have been found to be efficient, cost effective and free from the need for elaborate calibration and maintenance. Hence, they are ideally suited for developing a wide spatial network for NO₂ monitoring. This paper presents a comprehensive review including, historical development and critical assessment of validation studies along with comparison of both badge and tube type passive samplers. An attempt has been also made to highlight advantages and limitations, as well as discussion on the factors affecting the efficiency of passive samplers under field conditions.     

Predicting Carbon Monoxide Concentrations in the Air of Pardis City,Iran, Using an Artificial Neural Network

To date, several methods have been proposed to explain the complex process of air pollution prediction. One of these methods uses neural networks. Artificial neural networks (ANN) are a branch of artificial intelligence, and because of their nonlinear mathematical structures and ability to provide acceptable forecasts, they have gained popularity among researchers. The goal of our study as documented in this article was to compare the abilities of two different ANNs, the multilayer perceptron (MLP) and radial basis function (RBF) neural networks, to predict carbon monoxide (CO) concentrations in the air of Pardis City, Iran. For the study, we used data collected hourly on temperature, wind speed, and humidity as inputs to train the networks. The MLP neural network had two hidden layers that contained 13 neurons in the first layer and 25 neurons in the second layer and reached a mean bias error (MBE) of 0.06. The coefficient of determination (R²), index of agreement (IA), and the Nash–Scutcliffe efficiency (E) between the observed and predicted data using the MLP neural network were 0.96, 0.9057, and 0.957, respectively. The RBF neural network with a hidden layer containing 130 neurons reached an MBE of 0.04. The R², IA, and E between the observed and predicted data using the RBF neural network were 0.981, 0.954, and 0.979, respectively. The results provided by the RBF neural network had greater acceptable accuracy than was the case with the MLP neural network. Finally, the results of a sensitivity analysis using the MLP neural network indicated that temperature is the primary factor in the prediction of CO concentrations and that wind speed and humidity are factors of second and third importance when forecasting CO levels.     

The Euler Number as an Index of Spatial Integrity of Landscapes: Evaluation and Proposed Improvement

The spatial integrity of a habitat or landscape is determined by the occurrence of habitat fragments and of perforations inside them. A landscape is said to have less spatial integrity with increasing numbers of fragments and perforations. The Euler number (ε) is a numerical measure of spatial integrity, based upon the difference |n _f −n _p | between the number of fragments (n _f ) and the number of perforations (n _p ). In this contribution, ε is evaluated, and an improvement is presented as a new index ε*, which is a combination of two metrics (ε _i , ε _d ) based on n _f and n _p . The term ε _i quantifies the intensity of perforation and/or fragmentation. The term ε _d measures the extent to which fragmentation predominates perforation, and vice versa. The intensity and dominance measures are combined into an Euclidean distance measure, generating the new ensemble value ε*, calculated as ε*= (n _f +n _p )⁻¹√[1 +n _f ²]. Use, sensitivity, and application of ε*, ε _i , and ε _d are illustrated using percolation maps. Application of the new metrics by environmental scientists is encouraged because (1) no negative values can be generated with ε*, ε _i , and ε _d ; (2) the range of ε*, ε _i , and ε _d is fixed; (3) process dominance and intensity are both assessed; (4) ε*, ε _i , and ε _d are easy to calculate and to interpret; and (5) ε* is not only based upon |n _f −n _p |, as ε is. Guidelines for practical use by means of a biplot of ε _i and ε _d are given.     

Study of the performance of a coolant mixed with an Al2O3/SiC nanomaterial in an engine radiator

One of the important components of a car to control the temperature of a car's engine is the radiator. To increase the heat absorption capacity of the coolant/fluid used in the radiator with minimum pumping power, innovative fluids called nanofluids have become the main area of research these days. Therefore, with the development of new technologies in the field of “nano-materials” and “nano-fluids,” the physical and chemical properties of coolant/fluid can be improved which in turn improves the radiator and engine efficiency, and reduces radiator weight and size. In this article, the heat transfer by forced convection in nanofluids based on Al₂O₃ and SiC was studied experimentally and compared to that of base fluid in an automotive radiator. The nanofluid is mixed with ethylene glycol and the fluid is prepared by the sonication method. The nanofluids were prepared by varying the nanomaterials and the amounts of nanomaterials in the base fluid and their heat transfer performance in the radiator was analyzed using ANSYS FLUENT software. Approximately 15% and 12% increase in radiator efficiency by using Al₂O₃ mixed nanofluid and SiC mixed nanofluid, respectively.     

PREDICTION OF TWO-YEAR PEAK STREAM-DISCHARGES USING NEURAL NETWORKS1

ABSTRACT: The cascade correlation neural network was used to predict the two-year peak discharge (Q₂) for major regional river basins of the continental United States (US). Watersheds ranged in size by four orders of magnitude. Results of the neural network predictions ranged from correlations of 0.73 for 104 test data in the Souris-Red Rainy river basin to 0.95 for 141 test data in California. These results are improvements over previous multilinear regressions involving more variables that showed correlations ranging from 0.26 to 0.94. Results are presented for neural networks trained and tested on drainage area, average annual precipitation, and mean basin elevation. A neural network trained on regional scale data in the Texas Gulf was comparable to previous estimates of Q₂ by regression. Our research shows Q₂ was difficult to predict for the Souris-Red Rainy, Missouri, and Rio Grande river basins compared to the rest of the US, and acceptable predictions could be made using only mean basin elevation and drainage areas of watersheds.     

Techno-Economic Study of CO2 Capture from an Existing Cement Plant Using MEA Scrubbing

Carbon dioxide is the major greenhouse gas responsible for global warming. Man-made CO₂ emissions contribute approximately 63% of greenhouse gases and the cement industry is responsible for approximately 5% of CO₂ emissions emitting nearly 900 kg of CO₂ per 1000 kg of cement. CO₂ from a cement plant was captured and purified to 98% using the monoethanolamine (MEA) based absorption process. The capture cost was $51 per tonne of CO₂ captured, representing approximately 90% of total cost. Steam was the main operating cost representing 39% of the total capture cost. Switching from coal to natural gas reduces CO₂ emissions by about 18%. At normal load, about 36 MW of waste heat is available for recovery to satisfy the parasitic heat requirements of MEA process; however, it is very difficult to recover.     

Detection and mapping of water pollution variation in the Nile Delta using multivariate clustering and GIS techniques

The limited water resources of Egypt lead to widespread water-stress. Consequently, the use of marginal water sources, such as agricultural drainage waters, provides one of the national feasible solutions to the problem. However, the marginal quality of the drainage waters may restrict their use.The objective of this research is to develop a tool for planning and managing the reuse of agricultural drainage water for irrigation in the Nile Delta. This is achieved by classifying the pollution levels of drainage water into several categories using a statistical clustering approach that may ensure simple but accurate information about the pollution levels and water characteristics at any point within the drainage system.The derived clusters are then visualized by using a Geographical Information System (GIS) to draw thematic maps based on the entire Nile Delta, thus making GIS as a decision support system. The obtained maps may assist the decision makers in managing and controlling pollution in the Nile Delta regions. The clustering process also provides an effective overview of those spots in the Nile Delta where intensified monitoring activities are required. Consequently, the obtained results make a major contribution to the assessment and redesign of the Egyptian national water quality monitoring network.     

Urban contractual agreements as an adaptive governance strategy: under what conditions do they work in multi-level cooperation?

ABSTRACT

The Norwegian urban growth agreement (UGA) is a governance platform combining transport-infrastructure development with land-use and transport policy. It is a policy package of measures involving network cooperation between national, regional and local government levels established to coordinate transport and land-use development. Shared responsibility for goal achievement, autonomy and learning and adaptation as new knowledge and experience arise are clear prerequisites for the UGAs. This makes it relevant to investigate the conditions for the UGAs to work as an adaptive governance strategy because their central features are in line with the attributes of adaptive governance. Further, adaptive governance is an approach to handle complex problems like transport development issues. The study shows that UGAs have several strengths in terms of autonomy and learning. However, the multi-level cooperation in the UGAs is framed by complex underlying structures of roles and powers, which challenge the working and legitimacy of the governance structures. Multi-level adaptive governance processes like the UGAs require attention to issues of power and legitimacy. Securing transparency and democratic anchorage is paramount in bringing such processes in line with the intended benefits of adaptive governance.     

Ecosystem Services Provided by Agroecosystems: A Qualitative and Quantitative Assessment of this Relationship in the Pampa Region,Argentina

The development of an analytical framework relating agricultural conditions and ecosystem services (ES) provision could be very useful for developing land-use systems which sustain natural resources for future use. According to this, a conceptual network was developed, based on literature review and expert knowledge, about the functional relationships between agricultural management and ES provision in the Pampa region (Argentina). We selected eight ES to develop this conceptual network: (1) carbon (C) balance, (2) nitrogen (N) balance, (3) groundwater contamination control, (4) soil water balance, (5) soil structural maintenance, (6) N₂O emission control, (7) regulation of biotic adversities, and (8) biodiversity maintenance. This conceptual network revealed a high degree of interdependence among ES provided by Pampean agroecosystems, finding two trade-offs, and two synergies among them. Then, we analyzed the conceptual network structure, and found that both environmental and management variables influenced ES provision. Finally, we selected four ES to parameterize and quantify along 10 growing seasons (2000/2001–2009/2010) through a probabilistic methodology called Bayesian Networks. Only N balance was negatively impacted by agricultural management; while C balance, groundwater contamination control, and N₂O emission control were not. Outcomes of our work emphasize the idea that qualitative and quantitative methodologies should be implemented together to assess ES provision in Pampean agroecosystems, as well as in other agricultural systems.     

Public Participation and Local Environmental Planning: The collective action problem and the potential of social capital

Expanding the opportunities for public participation in environmental planning is not always the best option. Starting from an institutional public choice analysis of public participation in terms of the collective action problem, this paper emphasises the roots of participatory activities in the incentive structures facing potential participants. It then goes on to consider the strategies that may be adopted for encouraging greater public involvement and looks particularly to the social capital literature for suggestions of how institutional redesign may alter these incentive structures. The paper concludes by distinguishing three different modes of environmental planning, in terms of the rationale for participation, the severity of the collective action problem and the associated participatory strategy that can be adopted.     

Environmental impacts of different food waste resource technologies and the effects of energy mix

The environmental impacts of food waste management strategies and the effects of energy mix were evaluated using a life cycle assessment model, EASEWASTE. Three different strategies involving landfill, composting and combined digestion and composting as core technologies were investigated. The results indicate that the landfilling of food waste has an obvious impact on global warming, although the power recovery from landfill gas counteracts some of this. Food waste composting causes serious acidification (68.0 PE) and nutrient enrichment (76.9 PE) because of NH₃ and SO₂ emissions during decomposition. Using compost on farmland, which can marginally reduce global warming (−1.7 PE), acidification (−0.8 PE), and ecotoxicity and human toxicity through fertilizer substitution, also leads to nutrient enrichment as neutralization of emissions from N loss (27.6 PE) and substitution (−12.8 PE). A combined digestion and composting technology lessens the effects of acidification (−12.2 PE), nutrient enrichment (−5.7 PE), and global warming (−7.9 PE) mainly because energy is recovered efficiently, which decreases emissions including SO₂, Hg, NO_x, and fossil CO₂ during normal energy production. The change of energy mix by introducing more clean energy, which has marginal effects on the performance of composting strategy, results in apparently more loading to acidification and nutrient enrichment in the other two strategies. These are mainly because the recovered energy can avoid fewer emissions than before due to the lower background values in power generation. These results provide quantitative evidence for technical selection and pollution control in food waste management.     

Impact of an Invasive Exotic Species on Stream Nitrogen Levels in Southern Illinois1

Abstract: Autumn‐olive (Elaeagnus umbellata Thunb.) is an invasive, exotic shrub that has become naturalized in the eastern United States. Autumn‐olive fixes nitrogen (N) via a symbiotic relationship with the actinomycete Frankia. At the plot scale, the presence of autumn‐olive has been related to elevated soil water nitrate‐N (NO₃^?‐N) concentrations. This study examined the relationship between autumn‐olive cover in a watershed and stream water quality. Stream water nitrate‐N (NO₃^?‐N) and ammonium‐N (NH₄⁺‐N) concentrations were measured in 12 first order ephemeral streams draining watersheds with mixed forest cover and a range of 0‐35% autumn‐olive cover. Percent autumn‐olive cover was positively correlated with mean stream NO₃^?‐N concentrations, but was not correlated with mean stream NH₄⁺‐N concentrations. While other studies have demonstrated a significant relationship between native N‐fixers and stream NO₃^?‐N, this is the first study to document a relationship for an invasive, exotic N‐fixing species. Results suggest that this exotic species can be an additional source of NO₃^? in local and regional water bodies and demonstrates an additional negative ecosystem consequence of invasion beyond losses in biodiversity.     

Desorption of CO2 from activated carbon fibre–phenolic resin composite by electrothermal effect

CO₂ capture by electrothermal swing adsorption is considered superior over conventional adsorption approaches: temperature swing adsorption and pressure swing adsorption. In this work, the effects of electricity, preheating and flow rate were studied. An increase in energy input by electricity has been found able to improve desorption performance more significantly than an increase in current level. However, higher current level is recommended because it can minimise energy loss while passing electricity. Higher flow rate can also be beneficial due to the improved desorption rate and reduced desorption time. However, there is a drop in CO₂ concentration in the effluent gas. When desorption takes place at a high current level, preheating is not required as it extends desorption duration with no obvious improvement in desorption rate. CO₂ capture by electrothermal swing adsorption has also been tested with different concentrations of CO₂. It is found that electrothermal swing adsorption can be more energy efficient while dealing with higher concentration CO₂.     

The consequences of failure should be considered in siting geologic carbon sequestration projects

Geologic carbon sequestration is the injection of anthropogenic CO₂ into deep geologic formations where the CO₂ is intended to remain indefinitely. If successfully implemented, geologic carbon sequestration will have little or no impact on terrestrial ecosystems aside from the mitigation of climate change. However, failure of a geologic carbon sequestration site, such as large-scale leakage of CO₂ into a potable groundwater aquifer, could cause impacts that would require costly remediation measures. Governments are attempting to develop regulations for permitting geologic carbon sequestration sites to ensure their safety and effectiveness. At present, these regulations focus largely on decreasing the probability of failure. In this paper we propose that regulations for the siting of early geologic carbon sequestration projects should emphasize limiting the consequences of failure because consequences are easier to quantify than failure probability.     

CO2 storage capacity estimation: Methodology and gaps

Implementation of CO₂ capture and geological storage (CCGS) technology at the scale needed to achieve a significant and meaningful reduction in CO₂ emissions requires knowledge of the available CO₂ storage capacity. CO₂ storage capacity assessments may be conducted at various scales—in decreasing order of size and increasing order of resolution: country, basin, regional, local and site-specific. Estimation of the CO₂ storage capacity in depleted oil and gas reservoirs is straightforward and is based on recoverable reserves, reservoir properties and in situ CO₂ characteristics. In the case of CO₂-EOR, the CO₂ storage capacity can be roughly evaluated on the basis of worldwide field experience or more accurately through numerical simulations. Determination of the theoretical CO₂ storage capacity in coal beds is based on coal thickness and CO₂ adsorption isotherms, and recovery and completion factors. Evaluation of the CO₂ storage capacity in deep saline aquifers is very complex because four trapping mechanisms that act at different rates are involved and, at times, all mechanisms may be operating simultaneously. The level of detail and resolution required in the data make reliable and accurate estimation of CO₂ storage capacity in deep saline aquifers practical only at the local and site-specific scales. This paper follows a previous one on issues and development of standards for CO₂ storage capacity estimation, and provides a clear set of definitions and methodologies for the assessment of CO₂ storage capacity in geological media. Notwithstanding the defined methodologies suggested for estimating CO₂ storage capacity, major challenges lie ahead because of lack of data, particularly for coal beds and deep saline aquifers, lack of knowledge about the coefficients that reduce storage capacity from theoretical to effective and to practical, and lack of knowledge about the interplay between various trapping mechanisms at work in deep saline aquifers.     

Optimal heat rejection pressure for transcritical CO2 refrigeration cycle with an expander

Heat rejection pressure plays an important role in designing a transcritical CO₂ refrigeration system, and it has an optimal value to maximize the system’s coefficient of performance (COP). With a thermodynamic simulation model, the optimal heat rejection pressure is studied in the paper for an expander cycle, as well as conventional throttle valve cycle. The effects of compressor efficiency, expander efficiency, gas cooler outlet temperature, and evaporation temperature on the optimal heat rejection pressure are analyzed. It is the first time for a transcritical CO₂ expander cycle that the optimal heat rejection pressure is correlated with the gas cooler outlet temperature and the evaporation temperature at given compressor efficiency and expander efficiency. The average deviation from the correlation to simulation results is less than 1.0%. The correlation provides a guideline to system development and performance optimization of a transcritical CO₂ expander cycle.     

Application of Multiattribute Decision-Making Methods for the Determination of Relative Significance Factor of Impact Categories

A relative significance factor (f _i ) of an impact category is the external weight of the impact category. The objective of this study is to propose a systematic and easy-to-use method for the determination of f _i . Multiattribute decision-making (MADM) methods including the analytical hierarchy process (AHP), the rank-order centroid method, and the fuzzy method were evaluated for this purpose. The results and practical aspects of using the three methods are compared. Each method shows the same trend, with minor differences in the value of f _i . Thus, all three methods can be applied to the determination of f _i . The rank order centroid method reduces the number of pairwise comparisons by placing the alternatives in order, although it has inherent weakness over the fuzzy method in expressing the degree of vagueness associated with assigning weights to criteria and alternatives. The rank order centroid method is considered a practical method for the determination of f _i because it is easier and simpler to use compared to the AHP and the fuzzy method.     

A basic neural traffic noise prediction model for Tehran’s roads

We present an artificial neural network model to predict hourly A-weighted equivalent sound pressure levels (L_Aeq,1h) for roads in Tehran at distances less than 4 m from the nearside carriageway edge. Our model uses the UK Calculation of Road Traffic Noise (CORTN) approach. Data were obtained from 50 sampling locations near five roads in Tehran at nearside carriageway edge distances of less than 4 m. The data were randomly assigned to training, testing, and holdout subsets. Model training was carried out using the training and testing subsets and comprised 60% and 20% of the data, respectively. Model validation was performed using the remaining 20% of data as a holdout subset. We examine the overall model efficiency using non-parametric tests, such as the Wilcoxon matched-pairs signed-rank test for the training step and the Kolmogorov–Smirnov test for two independent samples for the validation step. Our results indicate that a neural network approach can be applied for traffic noise prediction in Tehran in a statistically sound manner. The Wilcoxon matched-pairs signed-ranks test detects no significant difference between the absolute testing set errors of the developed neural network and a calibrated version of the CORTN model.