| Literature DB >> 24243170 |
R W Crabtree1, C P Crockett, J C Ellis.
Abstract
The control of continuous polluting discharges in the U.K. was placed in a framework of statistically based legislation nearly 10 years ago. Both discharge standards and desired river quality class objectives are assessed within a probabilistic system of pollution control whereby a minimum level of 95 percent compliance with standards has to be achieved. The use of such a statistical framework permits occasional infringements of what would otherwise be fixed standards. This enables the Water Industry to manage river quality without undue risk of prosecution or unnecessary capital expenditure on effluent treatment.The change to a statistically based system has been a slow process carried out in stages: from the introduction of a river quality classification; setting long term river quality objectives; then setting discharge consents to achieve these objectives; and finally monitoring compliance with the consents and objectives. Each of these stages has required the development of the necessary statistical tools for river quality planning and management. Due to the decentralised nature of the currently catchment-based Water Authorities, several different statistical approaches have been adopted. However, if, as is planned for the near future, river quality management is carried out by a national regulatory body, then some rationalisation of current methodologies will have to be undertaken.This paper introduces and examines current U.K. approaches to river quality management and pollution control. Particular emphasis is placed on the statistical modelling techniques used for consent setting and compliance testing. Some of the commonly used techniques are compared and evaluated. A description is presented of work that is underway to develop a framework for the establishment and assessment of intermittent pollution control criteria.Year: 1989 PMID: 24243170 DOI: 10.1007/BF00394227
Source DB: PubMed Journal: Environ Monit Assess ISSN: 0167-6369 Impact factor: 2.513