In using microscopic imaging techniques, unbiased selection of sampling areas is often critical when judgment has to be used to find regions of interest. A conditional random sampling was designed to survey hematite particles on a mica surface using tapping-mode atomic force microscopy, based on three adapted-systematic-sampling methods designed to exclude subjective bias by limiting the freedom of arbitrarily selecting sampling areas. The results of these surveying methods were compared with the average particle surface density modeled by Poisson distribution. It was found that the conditional random sampling could survey particles effectively and improve the data reliability significantly. Ten population-known images from the same mica sheet were used to evaluate these methods, and an average relative error of 12% (maximum 21%) was obtained using the conditional random method with six sampling areas. It was used to investigate the effects of common organic pollutants, benzene, toluene, ethylbenzene, and xylenes on the transport of soil colloids.
