Utility of stormwater monitoring.

Stormwater runoff is now a major contributor to the pollution of coastal waters in the United States. Public agencies are responding by requiring stormwater monitoring to satisfy the National Pollutant Discharge Elimination System stormwater permit. However, studies to understand the utility of the current programs or to improve their usefulness have not yet been performed. In this paper, we evaluate the land-use-based program, the industrial stormwater permit program, and beach water-quality monitoring in the County of Los Angeles, California, to determine if the results will be helpful to planners and regulators in abating stormwater pollution. The utility of the program has been assessed based on the programs' ability to accurately estimate the emissions for different classes of land use. The land-use program appears successful, while the industrial monitoring program does not. Beach water-quality monitoring suffers from a lack of real-time monitoring techniques. We also provide suggested improvements, such as sampling method and time, and parameter selection.     

Effect of nickel on some aspects of protein metabolism in the gill and kidney of the freshwater fish, Cyprinus carpio L

Nickel has an adverse effect on some aspects of protein metabolism of the freshwater fish, Cyprinus carpio. The main changes observed were: (a) Decrease in soluble, structural and total proteins, AlAT and AAT activities with an increase in the levels of free amino acids, protease and GDH activities and ammonia in the gill and kidney at 1, 2, 3, and 4 days of exposure to a lethal concentration, 40 mg litre(-1) of nickel; (b) Increase in soluble, structural and total proteins, free amino acids and the activities of protease, AlAT, AAT and GDH with a decrease in ammonia and urea in these organs at 1, 5, 10 and 15 days of exposure to sublethal concentration, 8 mg litre(-1); (c) The magnitude in these changes increased over time with both concentrations of the metal, and was more marked in gill than in kidney.     

Modelling episodic acidification of surface waters: the state of science

Field studies of chemical changes in surface waters associated with rainfall and snowmelt events have provided evidence of episodic acidification of lakes and streams in Europe and North America. Modelling these chemical changes is particularly challenging because of the variability associated with hydrological transport and chemical transformation processes in catchments. This paper provides a review of mathematical models that have been applied to the problem of episodic acidification. Several empirical approaches, including regression models, mixing models and time series models, support a strong hydrological interpretation of episodic acidification. Regional application of several models has suggested that acidic episodes (in which the acid neutralizing capacity becomes negative) are relatively common in surface waters in several regions of the US that receive acid deposition. Results from physically based models have suggested a lack of understanding of hydrological flowpaths, hydraulic residence times and biogeochemical reactions, particularly those involving aluminum. The ability to better predict episodic chemical responses of surface waters is thus dependent upon elucidation of these and other physical and chemical processes.     

Effects of clearfelling on stream and soil water aluminium chemistry in three UK forests

Data are presented demonstrating how clearfelling has changed soil and stream water aluminium chemistry. For soil waters, a strong empirical relationship was observed between inorganic aluminium (Al(inorg)) and total inorganic anion (TIA) concentrations. Before felling, chloride and sulphate accounted for the largest proportion of the TIA concentration. After felling, in soils where nitrification was active, nitrate became increasingly important. Where this led to an increase in TIA, Al(inorg) concentrations increased. Over five years, nitrate concentrations have fallen, along with TIA, resulting in a sympathetic decline in Al(inorg). Streams draining clearfelled areas initially became more acid, although chloride and sulphate concentrations decreased. Stream water nitrate concentrations increased soon after felling and remained higher than controls for up to four years. While nitrate concentrations were high, Al(inorg) remained unchanged. Subsequently, as nitrate and TIA decreased, Al(inorg) also declined to concentrations below those in the control stream. Clearfelling upland forests will not necessarily result in immediate improvements in water quality, although long-term benefits may be seen before canopy-closure of the next crop.     

A Variance Estimator for Constrained Estimates of Change in Relative Categorical Frequencies

Consistent estimators of change and state becomes an issue when sample data come from a mix of permanent and temporary observation units. A joint maximum likelihood estimator of state and change creates estimates of state that depend on antecedent viz. posterior survey results and may differ from estimates of state derived from a single-date analysis of the sample data. A constrained estimator of change in relative categorical frequencies that eliminates this potential inconsistency is proposed and a model based estimator of their sampling variance is developed. The performance of the constrained estimator is quantified against six criteria and a joint maximum likelihood estimator in simulated sampling from 15 populations with three combinations of permanent and temporary samples, four to six categorical class attributes, and constant size between sampling dates. Bias of the constrained estimators was negligible but larger than for joint maximum likelihood estimators. Mean absolute deviations and variances of constrained estimators were generally at par with the joint estimators. Constrained estimators of root mean square errors and achieved coverage of nominal confidence intervals of constrained estimators were occasionally better. A generalized variance function for the constrained estimates of change is provided as a computational shortcut.     

Development of monitoring technology for airborne particulate matter

Particulate matter suspended in the air has adverse effects onhuman health. Its level of concentration is an important parameter in evaluating the degree of hazard it poses to the atmosphere. Conventional methods used in measuring particulatematter are often filter-based, which indicates some disadvantagesbecause such a base requires labor and time. In this study, to achieve real-time measurements, a new electrical method was developed for measuring PM10 and PM2.5 concentrations. The basicprinciple is to electrically charge particles passing through thePM inlet using a corona charger and measure the currents createdby charged particles to obtain the number concentration of particulate matter. A new type inlet based on the particle cupimpactor configuration was designed and its performance was evaluated. A unipolar diffusion charger was developed and thecharger's efficiency was determined experimentally in terms ofPn, which represents the penetration through the charger,P, times the average charge number acquired by a particle,n, for different particle sizes. The correlation was constructed between the PM10 (or the PM2.5) mass concentrationsand the electrical currents due to particles, which were chargedby the diffusion charger.     

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.     

Multi-objective, decision-based assessment of a water quality monitoring network in a river system

Water quality monitoring network design has historically tended to use experience, intuition and subjective judgement in locating monitoring stations. Better design procedures to optimize monitoring systems need to simultaneously identify significant planning objectives and consider a number of social, economic and environmental constraints. The consideration of multiple objectives may require further decision analysis to determine the preference weights associated with the objectives to aid in the decision-making process. This may require the application of an optimization study to extract such information from decision makers or experts and to evaluate the overall effectiveness of locating strategies. This paper assesses the optimal expansion and relocation strategies of a water quality monitoring network using a two-stage analysis. The first stage focuses on the information retrieval of preference weights with respect to the designated planning objectives. With the aid of a pre-emptive goal programming model, data analysis is applied to obtain the essential information from the questionnaire outputs. The second stage then utilizes a weighted multi-objective optimization approach to search for the optimal locating strategies of the monitoring stations in the river basin. Practical implementation is illustrated by a case study in the Kao-Ping River Basin, south Taiwan.     

Optimal and suboptimal control of anaerobic digesters

Anaerobic digester failure due to entry of inhibitors or sudden changes in the feed substrate concentration may be encompassed beneficially by applying optimal control theory. An almost proportional relationship between the dilution rate and the methane production rate leads to a simple suboptimal control law with only minor loss in performance, after the occurrence of the above mentioned events.