title: Designing of sampling programmes for industrial effluent monitoring.
abstract: BACKGROUND
  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.
  
  
  METHODS
  These problems can be resolved by adapting a statistical approach to the design of sampling programmes. This approach is useful in determining the frequency of sampling, the number of samples needed to estimate the average concentration of target pollution indicator parameters and the magnitude of the uncertainty involved.
  
  
  RESULTS
  The benefits and applications of this approach are demonstrated by a case study presented in this paper. It was found that the number of samples and cost of sample analysis can be greatly reduced by the use of systematic instead of random sampling.
  
  
  CONCLUSIONS
  The statistical approach greatly improves the estimate of monthly means of pollution indicator parameters and is an effective approach for pollution control when coupled with the "polluter pays principle".
authors.affiliation: Department of Chemical Engineering, University of Johannesburg, P.O. Box 17011, Doornfontein, Johannesburg, South Africa. fntuli@uj.ac.za
authors.forename: Freeman
authors.initials: F
authors.lastname: Ntuli
authors.forename: Pardon K
authors.initials: PK
authors.lastname: Kuipa
authors.forename: Edison
authors.initials: E
authors.lastname: Muzenda
mesh_terms: D004784:Environmental Monitoring / Q000191:economics / Q000379:methods*; D007220:Industrial Waste / Q000032:analysis*; D007221:Industry / Q000706:statistics & numerical data; D014874:Water Pollutants, Chemical / Q000032:analysis*; D014877:Water Pollution, Chemical / Q000331:legislation & jurisprudence / Q000517:prevention & control / Q000706:statistics & numerical data*
chemical_list: D007220:Industrial Waste; D014874:Water Pollutants, Chemical