DATA REQUIREMENTS OF A WATER QUALITY MANAGEMENT PROGRAM1

ABSTRACT: Routine data collection currently consumes a large amount of the total resources devoted to water quality management. All too often data collection becomes an end in itself, with little thought given to the purpose of the data collection. The problem generally stems from a lack of proper routine surveillance system design and a failure on the part of the designers to initially identify the data needs of the management program. This study attempts, in a general way, to delineate the data needs of a water quality management program. This first required an identification of the activities involved in water quality management. The activities were then discussed in terms of the types of information needed to successfully complete their assigned tasks. Several detailed examples are given. The results of the discussion are summarized and several strategies are proposed to relate the results to surveillance system design.     

Investigation of a drum explosion of 30% aqueous KOCN

A 30% aqueous solution of KOCN placed in a 55 gallon HDPE drum at 50 °C began venting gas almost immediately. Although a vent was kept open the drum exploded within 1–2 h of being filled. This report reviews the steps taken after the accident to find its cause and to recommend safe operating conditions. The DIERS vent sizing package (VSP), used as a closed system adiabatic reactor, was able to simulate the incident under controlled laboratory conditions. Data were thereby collected for the first time on the runaway kinetics of the KOCN hydrolysis. Isothermal data were obtained in a highly sensitive microwatt heat flow calorimeter in an open system. It was demonstrated that even under isothermal conditions, the hydrolysis rates accelerated once underway, reaching maxima in 30 h at 25 °C and 6.7 h at 40 °C. There is satisfactory agreement of these results with other work on 0.5% KOCN solutions reported in earlier studies.     

Depletable externalities and pigouvian taxation

In their book Baumol and Oates [“The Theory of Environmental Policy: Externalities, Public Outlays, and the Quality of Life,” Prentice-Hall, Englewood Cliffs, N.J. (1975).] argue that whether an externality is depletable (private) or undepletable (public) is the key characteristic in determining the optimal pricing pattern. They argue that unlike the undepletable case a negative depletable externality requires not only a charge or tax on the generator of the externality but a payment or compensation to the victim in order to achieve Pareto optimality. It is shown that the key characteristic determining whether compensation of victims is required for efficiency is not the depletability of the externality but whether the victim can costlessly control or limit the amount of the damaging substance received.     

Forecasting air quality time series using deep learning

This paper presents one of the first applications of deep learning (DL) techniques to predict air pollution time series. Air quality management relies extensively on time series data captured at air monitoring stations as the basis of identifying population exposure to airborne pollutants and determining compliance with local ambient air standards. In this paper, 8 hr averaged surface ozone (O₃) concentrations were predicted using deep learning consisting of a recurrent neural network (RNN) with long short-term memory (LSTM). Hourly air quality and meteorological data were used to train and forecast values up to 72 hours with low error rates. The LSTM was able to forecast the duration of continuous O₃ exceedances as well. Prior to training the network, the dataset was reviewed for missing data and outliers. Missing data were imputed using a novel technique that averaged gaps less than eight time steps with incremental steps based on first-order differences of neighboring time periods. Data were then used to train decision trees to evaluate input feature importance over different time prediction horizons. The number of features used to train the LSTM model was reduced from 25 features to 5 features, resulting in improved accuracy as measured by Mean Absolute Error (MAE). Parameter sensitivity analysis identified look-back nodes associated with the RNN proved to be a significant source of error if not aligned with the prediction horizon. Overall, MAE's less than 2 were calculated for predictions out to 72 hours.

Implications: Novel deep learning techniques were used to train an 8-hour averaged ozone forecast model. Missing data and outliers within the captured data set were replaced using a new imputation method that generated calculated values closer to the expected value based on the time and season. Decision trees were used to identify input variables with the greatest importance. The methods presented in this paper allow air managers to forecast long range air pollution concentration while only monitoring key parameters and without transforming the data set in its entirety, thus allowing real time inputs and continuous prediction.     

A More Cost-Effective Emap Benthic Macrofaunal Sampling Protocol

Benthic macrofaunal sampling protocols in the U.S. Environmental Protection Agency's Environmental Monitoring and Assessment Program (EMAP) are to collect 30 to 50 random benthic macrofauna [defined as animals retained on a 0.5 mm (East and Gulf Coasts, USA) or a 1.0 mm mesh sieve (West Coast, USA)] samples per reporting unit using a 0.044 m² (East and Gulf Coasts) or 0.1 m² (West Coast) grab. Benthic macrofaunal community conditions in the reporting unit are characterized by cumulative distribution functions (CDFs) on end points of interest, such as number of species (S), abundance (A), and Shannon--Wiener diversity (H′). An EMAP and a companion field study were conducted concurrently in Tillamook Bay (Oregon, USA) to compare the cost effectiveness of benthic macrofauna samples collected using the EMAP West Coast (0.1 m² × ≥7 cm deep, 1.0 mm mesh), a 0.01 m² × 5 cm deep, 1.0 mm mesh, and a 0.01 m² × 5 cm deep, 0.5 mm mesh sampling protocol. Cost was estimated in relative laboratory sample-processing time. Sampling protocols were judged equally effective for EMAP purposes if, after linear transformation to adjust for scale changes in end point distributions, their S, A, and H′ CDFs were not significantly different. The 0.01 m² × 5 cm deep, 1.0 mm mesh sampling protocol was the most cost effective.     

Designing of sampling programmes for industrial effluent monitoring

Introduction  

Monitoring of effluent discharges from industrial establishments discharging directly into municipality sewers is one of the major water pollution control activities conducted by municipalities. For largely industrialised municipalities, the task can be quite expensive and not effective if sampling programmes are not properly designed. In most cases, samples are randomly collected without proper knowledge of the discharge patterns of various industries. As a result, the information obtained does not give a good reflection of the quality of effluent being discharged.     

“My garden is an expression of me”: Exploring householders' relationships with their gardens

Domestic gardens offer immense potential as sites for native biodiversity conservation. In urban areas they often comprise the largest land use, thus presenting an accessible and immediate way for urban dwellers to connect with nature and to support and enhance native biodiversity. This paper presents findings from a study of 55 domestic gardens undertaken in Dunedin, New Zealand, which explores householders' relationships with their gardens. The study data was derived from two interviews with householders, two photo exercises (approximately a year apart), together with a number of biological studies of the gardens. Gardens proved to be very important for our householders; for physical and mental health, as an expression of ownership and identity, as sites for social relationships, for connecting with nature and as site of domestic produce production. Householders' connections with nature were idiosyncratic, multifaceted and exhibited in ways that are more complex and varied than those usually considered by those working in the natural sciences and indeed biophilia supporters. We emphasize the importance of the people side of nature in seeking to build and support positive ecological change in the urban environment and the value of combining natural and social science approaches.     

Climatic, socio-economic, and health factors affecting human vulnerability to cholera in the Lake Victoria basin, East Africa

Cholera epidemics have a recorded history in the eastern Africa region dating to 1836. Cholera is now endemic in the Lake Victoria basin, a region with one of the poorest and fastest growing populations in the world. Analyses of precipitation, temperatures, and hydrological characteristics of selected stations in the Lake Victoria basin show that cholera epidemics are closely associated with El Ni?o years. Similarly, sustained temperatures high above normal (T(max)) in two consecutive seasons, followed by a slight cooling in the second season, trigger an outbreak of a cholera epidemic. The health and socioeconomic systems that the lake basin communities rely upon are not robust enough to cope with cholera outbreaks, thus rendering them vulnerable to the impact of climate variability and change. Collectively, this report argues that communities living around the Lake Victoria basin are vulnerable to climate-induced cholera that is aggravated by the low socioeconomic status and lack of an adequate health care system. In assessing the communities' adaptive capacity, the report concludes that persistent levels of poverty have made these communities vulnerable to cholera epidemics.