Case study II: application of the divalent cation bridging theory to improve biofloc properties and industrial activated sludge system performance-using alternatives to sodium-based chemicals.

The objective of this study was to investigate the application of the divalent cation bridging theory (DCBT) as a tool in the chemical selection process at an activated sludge plant to improve settling, dewatering, and effluent quality. According to the DCBT, to achieve improvements, the goal of chemical selection should be to reduce the ratio of monovalent-to-divalent (M/D) cations. A study was conducted to determine the effect of using magnesium hydroxide [Mg(OH)2] as an alternative to sodium hydroxide (NaOH) at a full-scale industrial wastewater treatment plant. Floc properties and treatment plant performance were measured for approximately one year during two periods of NaOH addition and Mg(OH)2 addition. A cost analysis of plant operation during NaOH and Mg(OH)2 use was also performed. During NaOH addition, the M/D ratio was 48, while, during Mg(OH)2 addition, this ratio was reduced to an average of approximately 0.1. During the Mg(OH)2 addition period, the sludge volume index, effluent total suspended solids, and effluent chemical oxygen demand were reduced by approximately 63, 31, and 50%, respectively, compared to the NaOH addition period. The alum and polymer dose used for clarification was reduced by approximately 50 and 60%, respectively, during Mg(OH)2 addition. The dewatering properties of the activated sludge improved dewatering as measured by decreased capillary suction time and specific resistance to filtration (SRF), along with an increase in cake solids from the SRF test. This corresponded to a reduction in the volume of solids thickened by centrifuges at the treatment plant, which reduced the disposal costs of solids. Considering the costs for chemicals and solids disposal, the annual cost of using Mg(OH)2 was approximately 30,000 dollars to 115,000 dollars less than using NaOH, depending on the pricing of NaOH. The results of this study confirm that the DCBT is a useful tool for assessing chemical-addition strategies and their potential effect on activated sludge performance.     

An investigation of distributed lag models in the context of air pollution and mortality time series analysis

In particulate air pollution mortality time series studies, the particulate air pollution exposure measure used is typically the current day's or the previous day's air pollution concentration or a multi-day moving average air pollution concentration. Distributed lag models (DLMs) that allow for differential air pollution effects that are spread over multiple days are seen as an improvement over using a single- or multi-day moving average air pollution exposure measure. However, at the current time, the statistical properties of DLMs as a measure of air pollution exposure have not been investigated. In this paper, a simulation study is used to investigate the performance of DLMs as a measure of air pollution exposure in comparison with single- and multi-day moving average air pollution exposure measures under various forms for the true effect of air pollution on mortality. The simulation study shows that DLMs offer a more robust measure of the effect of air pollution on mortality and avoid the potential for a large negative bias compared with single- or multi-day moving average air pollution exposure measures. This is important information. In many U.S. cities, particulate air pollution concentrations are observed only once every six days, meaning it is often only possible to use single-day particulate air pollution exposure measures. The results from this paper will help quantify the magnitude of the negative bias that can result from using single-day exposure measures. The implications of this work for future air pollution mortality time series studies are discussed. The data used in this paper are concurrent daily time series of mortality, weather, and particulate air pollution from Cook County, IL, for the period 1987-1994.     

ESTIMATING DRAINAGE AREA ON STEEP UNIFORM SLOPES1

ABSTRACT: A study was conducted to derive a simple procedure for estimating the tributary drainage area that may potentially develop at the base of a uniformly graded, steep slope. Data were extracted from a nil development flume study in which 112 rills and their tributary drainage areas were documented. The tributary drainage area for each rill was correlated to the slope length and the slope gradient. The findings are considered applicable to planar, convex, and divergent slopes with lengths of up to 130 m. Field verification is needed to extend the applicability of the relation.     

MODELING STUDIES FOR PLANNING: THE GREEN BAY PROJECT1

ABSTRACT: A major contaminant monitoring and modeling study is underway for Green Bay, Lake Michigan. Monitoring programs in support of contaminant modeling of large waterbodies, such as for Green Bay, are expensive and their extent is often limited by budget limitations, laboratory capacity, and logistic constraints. Therefore, it is imperative that available resources be used in the most efficient manner possible. This use, or allocation of resources, may be aided through the application of readily available models in the planning stages of projects. To aid in the planning effort for the Green Bay project, a workshop was held and studies designed to aid in the allocation of resources for a year-long intensive field study. Physical/chemical and food chain models were applied using historical data to aid in project planning by identifying processes having the greatest impact on the predictive capability of mass balance models. Studies were also conducted to estimate errors in computed tributary loadings and in-Bay concentrations and contaminant mass associated with different sampling strategies. The studies contributed to the overall project design, which was a collaborative effort with many participants involved in budgeting, field data collection, analysis, processing of data, quality assurance, data management and modeling activities.     

Some experiences in hazard identification and risk shortlisting

‘Rapid ranking’ has been used for many years as a method of determining the priority which should be given to formal investigation of the wide range of hazards and risks present on major process industry sites. Such methods tend to evolve to meet the needs of specific applications. Some historical and recent applications are outlined, and the nature of the developments which they led to are discussed. Particular topics discussed include: development of the method such that it is usable by operating managers rather than specialists; use to raise risk consciousness; adaptation for use as an aid to hazard identification; use to define both which risks should be studied and which risks, even if low, should be incorporated in routine monitoring and periodic auditing programmes; use as a basis for comparison of the relative risks presented by a variety of different industrial installations; use for a range of different types of risk; and extension beyond ranking to include ‘shortlisting’ i.e. deciding which risks to include in a study programme and which to leave out. Philosophical difficulties with the approach are discussed, including the danger of omitting from detailed study a risk which is erroneously shown as low, and the uncertainty of the absolute level of the ranked risks. Approaches to minimizing these problems are discussed.     

INCREASING REGULATORS' CONFIDENCE IN POINT-NONPOINT POLLUTANT TRADING SCHEMES1

ABSTRACT: One of the principal stumbling blocks to regulatory agencies' adopting pollutant trading schemes is the complex of uncertainties surrounding any change in institutions. This is especially true if nonpoint pollution sources are to be involved along with point sources. Regulators are understandably reluctant to switch from tried-and-true point source permit systems, even if trading schemes can be shown (on paper, at least) to result in lower public expenditures. We propose a set of practical criteria for point-nonpoint pollutant trading systems that promise to increase regulators' confidence that the new system will be equally effective in controlling pollution and at the same time more likely to capture efficiencies in pollution reduction practices.     

USING A GIS FOR ESTIMATING INPUT PARAMETERS IN URBAN STORMWATER QUALITY MODELING1

ABSTRACT: Geographic Information Systems (GIS) are being used increasingly as a method of preparing, analyzing, and displaying data for watershed analysis and modeling. Although GIS technology is a powerful tool for integrating and analyzing watershed characteristics, the initial preparation of the necessary database is often a time consuming and costly endeavor. This demonstration project assesses the viability of creating a cost-effective spatial database for urban stormwater modeling from existing digital and hard-copy data sources. The GIS was used to provide input parameters to the Source Loading and Management Model (SLANM), an empirical urban stormwater quality model. Land use characteristics, drainage boundaries, and soils information were geocoded and referenced to a base data layer consisting of transportation features. GIS overlay and data manipulation capabilities were utilized to preprocess the input data for the model. Model output was analyzed through postprocessing by GIS, and results were compared to a similar recent modeling study of the same watershed. The project, undertaken for a small urban watershed located in Plymouth, Minnesota, successfully demonstrates that the use of GIS in stormwater management can allow even small communities to reap the benefits of stormwater quality modeling.