Development of quantitative structure-property relationship model for predicting the field sampling rate (Rs) of Chemcatcher passive sampler.

Abstract

Passive sampling technology has been considered as a promising tool to measure the concentration of environmental contaminants. With this technology, sampling rate (Rs) is an important parameter. However, as experimental methods employed to obtain the Rs value of a given compound were time-consuming, laborious, and expensive. A cost-effective method for deriving Rs is urgent. In addition, considering the great dependence of Rs value on water matrix properties, the laboratory measured Rs may not be a good alternative for field Rs. Thus, obtaining the field Rs is very necessary. In this study, a multiparameter quantitative structure-property relationship (QSPR) model was constructed for predicting the field Rs of 91 polar to semi-polar organic compounds. The determination coefficient (R2Train), leave-one-out cross-validated coefficient (Q2LOO), bootstrap coefficient (Q2BOOT), and root mean square error (RMSETrain) of the training set were 0.772, 0.706, 0.769, and 0.230, respectively, while the external validation coefficient (Q2EXT) and RMSEEXT of the validation set were 0.641 and 0.253, respectively. According to the acceptable criteria (Q2 > 0.600, R2 > 0.700), the model had good robustness, goodness-of-fit, and predictive performances. Therefore, we could use the model to fill the data gap for substances within the applicability domain on their missing Rs value.