An experimental investigation of explanations for inconsistencies in responses to second offers in double referenda

This paper demonstrates the potential for induced preference experiments to test previously unverified explanations of observed behavior in contingent valuation surveys. The NOAA Panel on Contingent Valuation called for experimental evidence on potential biases in the double referendum format. We test Carson, Groves, and Machina's (Incentives and informational properties of preference questions, Plenary address to the European Association of Resource and Environmental Economists, Oslo, Norway, June 1999) simple cost uncertainty and weighted averaging explanations of inconsistent responses to follow-up offers in such double referenda against a baseline of certainty and truthful preference revelation. The results find evidence to support the Weighted Average hypothesis. Results regarding the cost uncertainty hypothesis are more ambiguous and merit further investigation.     

Modeling Parent and Metabolite Fate and Transport in Subsurface Drained Fields With Directly Connected Macropores1

Abstract: Few studies exist that evaluate or apply pesticide transport models based on measured parent and metabolite concentrations in fields with subsurface drainage. Furthermore, recent research suggests pesticide transport through exceedingly efficient direct connections, which occur when macropores are hydrologically connected to subsurface drains, but this connectivity has been simulated at only one field site in Allen County, Indiana. This research evaluates the Root Zone Water Quality Model (RZWQM) in simulating the transport of a parent compound and its metabolite at two subsurface drained field sites. Previous research used one of the field sites to test the original modification of the RZWQM to simulate directly connected macropores for bromide and the parent compound, but not for the metabolite. This research will evaluate RZWQM for parent/metabolite transformation and transport at this first field site, along with evaluating the model at an additional field site to evaluate whether the parameters for direct connectivity are transferable and whether model performance is consistent for the two field sites with unique soil, hydrologic, and environmental conditions. Isoxaflutole, the active ingredient in BALANCE^® herbicide, was applied to both fields. Isoxaflutole rapidly degrades into a metabolite (RPA 202248). This research used calibrated RZWQM models for each field based on observed subsurface drain flow and/or edge of field conservative tracer concentrations in subsurface flow. The calibrated models for both field sites required a portion (approximately 2% but this fraction may require calibration) of the available water and chemical in macropore flow to be routed directly into the subsurface drains to simulate peak concentrations in edge of field subsurface drain flow shortly after chemical applications. Confirming the results from the first field site, the existing modification for directly connected macropores continually failed to predict pesticide concentrations on the recession limbs of drainage hydrographs, suggesting that the current strategy only partially accounts for direct connectivity. Thirty‐year distributions of annual mass (drainage) loss of parent and metabolite in terms of percent of isoxaflutole applied suggested annual simulated percent losses of parent and metabolite (3.04 and 1.31%) no greater in drainage than losses in runoff on nondrained fields as reported in the literature.     

Analysis of pollutant levels in central Hong Kong applying neural network method with particle swarm optimization

Air pollution has emerged as an imminent issue in modernsociety. Prediction of pollutant levels is an importantresearch topic in atmospheric environment today. For fulfillingsuch prediction, the use of neural network (NN), and inparticular the multi-layer perceptrons, has presented to be acost-effective technique superior to traditional statisticalmethods. But their training, usually with back-propagation (BP)algorithm or other gradient algorithms, is often with certaindrawbacks, such as: 1) very slow convergence, and 2) easilygetting stuck in a local minimum. In this paper, a newlydeveloped method, particle swarm optimization (PSO) model, isadopted to train perceptrons, to predict pollutant levels, andas a result, a PSO-based neural network approach is presented. The approach is demonstrated to be feasible and effective bypredicting some real air-quality problems.     

Reduction of SO2 emissions by ammonia gas during unstaged combustion

The reduction of SO₂ by the addition of ammonia gas has been studied in a 2 m high fluidized bed combustor having a 30 cm static bed height and a freeboard height of 170 cm. Ammonia gas was injected at 52 cm above the distributor where the temperature is ca. 700° C by an uncooled stainless steel tube injector. Experiments were carried out to investigate the effects of amminia gas injection on sulphur dioxide emissions at unstaged conditions of: (i) excess air level, (ii) NH₃:SO₂ molar ratio, (iii) fluidizing velocity and (iv) bed height.A maximum reduction of 75% in SO₂ emissions was found at 40% excess air, at an NH₃:SO₂ molar ratio of 5.4. The onset of SO₂ reduction occurred at an NH₃:SO₂ ratio of 1.5 However, the most effective ratio was found to be between 3 and 5. Fluidizing velocity and bed height were also found to have significant influence on SO₂ reduction.It is difficult to determine how the SO₂ reduction varied with operating conditions. When ammonia is added in the main combustor zone, the temperature is much higher than that required for the occurrence of sulphur dioxide-ammonia and sulphur trioxide-ammonia reactions. However, this paper points out the significance of ammonia addition in the reduction of sulphur dioxide.     

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

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

a study of the temporal variability of atrazine in private well water. part ii: analysis of data

In 1988, the Iowa Department of Natural Resources, along withthe University of Iowa, conducted the Statewide Rural WellWater Survey, commonly known as SWRL. A total of 686private rural drinking water wells was selected by use of aprobability sample and tested for pesticides and nitrate. A subsetof these wells, the 10% repeat wells, were additionally sampledin October, 1990 and June, 1991. Starting in November, 1991,the University of Iowa, with sponsorship from the United StatesEnvironmental Protection Agency, revisited the 10% repeat wellsto begin a study of the temporal variability of atrazine and nitratein wells. Other wells, which had originally tested positive foratrazine in SWRL but were not in the 10% population, wereadded to the study population. Temporal sampling for a year-long period began in February of 1992 and concluded in Januaryof 1993. All wells were sampled monthly, a subset was sampledweekly, and a second subset was sampled for 14 day consecutiveperiods. Of the 67 wells in the 10% population tested monthly,7 (10.4%) tested positive for atrazine at least once during theyear, and 3 (4%) were positive each of the 12 months. Theaverage concentration in the 7 wells was 0.10 µg/L. Fornitrate, 15 (22%) wells in the 10% repeat population monthlysampling were above the Maximum Contaminant Level of 10 mg/L at least once. This paper, the second of two papers on thisstudy, describes the analysis of data from the survey. The firstpaper (Lorber et al., 1997) reviews the study design, theanalytical methodologies, and development of the data base.     

Modeling rhizofiltration: heavy-metal uptake by plant roots

The discovery of phytoaccumulation potential of plant species has led to its application for remediation of heavy-metal-contaminated soil and wastewater, which is termed as phytoextraction/rhizofiltration. For prediction, analysis, planning and cost-effective design of such systems, mathematical models not only are used as a screening tool but also provide optimal parameters like harvesting time, irrigation schedule, etc. Several laboratory and field scale studies have been carried out in the past, and mathematical expressions have been developed by various researchers for different phenomena like metal adsorption in soil, plant root growth with time, moisture and metal uptake by plant root, moisture movement in unsaturated zone, soil moisture relationship, etc. The complete design of any such phytoremediation program would require the knowledge of behavior of heavy-metal movement in soil, water and plant root system. In this paper, a model for simulating heavy-metal dynamics in soil, water and plant root system is developed and discussed. The governing non-linear partial differential equation is solved numerically by implicit finite difference method using Picard's iterative technique, and the formulation has been illustrated using a characteristic example. The source code is written in MATLAB.     

Chemical monitoring and evaluation of the water quality of the Douro River at the Crestuma-Lever dam

Water taken from the Crestuma-Lever dam on the Douro River was evaluated considering the Portuguese Decree-law no. 236/98 of 1st August, 1998. This paper presents the metrological characteristics, validation of analytical methodologies and the data obtained from seasonal samplings. The test parameters selected are: temperature, pH, chloride, conductivity, carbon dioxide, alkalinity, hardness, oxidability, dissolved oxygen, BOD5, total suspended solids, sulfide, phosphate, silica, ammonium, nitrite, nitrate, sulfate, sodium, potassium, calcium, magnesium, iron, manganese, copper and zinc. From data analysis we can conclude that the water presented the following characteristics: a pH situated in the neutral zone; a median mineralization; a medium hardness; well oxygenated water; and a carbonate hardness exclusively due to bicarbonates. The temperature, pH, dissolved oxygen, ammonia, chlorides, sulfates, copper, zinc and BOD5 were compared with the legal limits presented in annexes XXI and I of Decree-law 236/98. The results relating to total hardness, nitrites, calcium, magnesium, sodium, potassium, aluminium, sulfides and oxidability were compared with the legal values in annex VI as there are no limits relating to these parameters in annexes XXI and I.     

Soil properties and crop productivity as influenced by flyash incorporation in soil

Field experiments were carried out during 1996–97at Gulawathi, Muthiani and Salarpur Villages, IARI Farm, NewDelhi and NCPP Campus, Dadri to evaluate changes in soilcharacteristics and growth of wheat (Triticum aestivum L.),mustard (Brassica juncea L.), lentil (Lence esculentaMoench.), rice (Oryza sativa L.) and maize (Zea mays L.) byvarying amounts of flyash addition (up to 50t ha^-1) in soils atsowing/transplanting time of crops. Flyash addition in areasadjoining NCPP Thermal Power Plant, Dadri, Ghaziabad, U.P.ranged from 5–12 t ha^-1 yr^-1 in 1995–96. Shoot and root growthand yield of test crops at different locations after flyashincorporation resulted in beneficial effects of flyashaddition in most cases. The silt dominant texture of flyashimproved loamy sand to sandy loam textures of the surfacesoils at the farmers' fields. The increased growth in yield ofcrops with flyash incorporation was possibly due tomodifications in soil moisture retention and transmissioncharacteristics, bulk density, physico-chemical characterssuch as pH and EC and organic carbon content. The response offlyash addition in the soil on soil health and cropproductivity needs to be evaluated on long-term sustainableaspects.     

Flue gas treatability studies: a tool for techno-economic control of industrial air pollution

Air pollution problems in developing countries have gained larger fraction in the last decade especially due to non functioning and non implementation of effective air pollutioncontrol devices in industries. In industrial wastewater management, adequate treatability studies are conducted to arrive at a techno-economic treatment option. However no suchstudies were done for reducing air pollution or emission fromindustries until now in India. Little information was availableabout such studies in other countries. This article provides information about a novel technique known asflue gas treatability studies and to undertake such studies, a pilot scale system is installed in Air Pollution Control Divisionof M/s National Environmental Engineering research Institute, NEERI, Nagpur-20, India. This study is a tool for techno-economicselection of air pollution control systems specially for small/medium scale industrial emissions.