Statistics for Environmental Engineers

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Experiments are performed to (1) screen a set of factors (independent variables) and learn which produce an effect, (2) estimate the magnitude of effects produced by changing the experimental factors, (3) develop an empirical model, and (4) develop a mechanistic model. Factorial experimental designs are efficient tools for meeting the first two objectives. Many times, they are also excellent for objective three and, at times, they can provide a useful strategy for building mechanistic models.


Factorial designs allow a large number of variables to be investigated in few experimental runs. They have the additional advantage that no complicated calculations are needed to analyze the data produced. In fact, important effects are sometimes apparent without any calculations. The efficiency stems from using settings of the independent variables that are completely uncorrelated with each other. In mathematical terms, the experimental designs are orthogonal. The consequence of the orthogonal design is that the main effect of each experimental factor, and also the interactions between factors, can be estimated independent of the other effects.

Case Study: Compaction of Fly Ash


There was a proposal to use pozzolanic fly ash from a large coal-fired electric generating plant to build impermeable liners for storage lagoons and landfills. Pozzolanic fly ash reacts with water and sets into a rock-like material. With proper compaction this material can be made very impermeable. A typical criterion is that the liner must have a permeability of no more than 10_7 cm/sec. This is easily achieved using small quantities of fly ash in the laboratory, but in the field there are difficulties because the rapid pozzolanic chemical reaction can start to set the fly ash mixture before it is properly compacted. If this

Скачать в pdf «Statistics for Environmental Engineers»