Department of Chemical Engineering, University of Johannesburg, P.O. Box 17011, Doornfontein, Johannesburg, South Africa.
Environ Sci Pollut Res Int. 2011 Mar;18(3):479-84. doi: 10.1007/s11356-010-0395-y. Epub 2010 Sep 19.
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.
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.
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.
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".
直接向城市污水管网排放的工业企业排放污水的监测是城市进行的主要水污染控制活动之一。对于主要是工业化的城市，如果采样计划设计不当，这项任务可能非常昂贵且效果不佳。在大多数情况下，样本是随机采集的，而对各种工业排放模式没有适当的了解。因此，所获得的信息不能很好地反映正在排放的污水的质量。
通过采用统计学方法来设计采样计划，可以解决这些问题。这种方法可用于确定采样频率、估计目标污染指标参数的平均浓度所需的样本数量以及所涉及的不确定性的大小。
本文通过案例研究展示了这种方法的好处和应用。结果发现，通过使用系统采样而不是随机采样，可以大大减少样本数量和样本分析成本。
统计学方法极大地提高了污染指标参数的月度平均值的估计值，并且当与“污染者付费原则”结合使用时，是一种有效的污染控制方法。
