A Field Study of Factors that Affect Acid-Volatile Sulfide Concentrations

Abstract

The spatial variability of acid-volatile sulfide (AVS) and its influential factors were studied through regression analysis to explain the spatial distribution of AVS and to predict the variability of metal availability under changing conditions simply and effectively. The AVS equation is used to derive oxidation-reduction potential (Eh), sulfate-reducing bacteria (SRB), organic carbon (OC), and total sulfur (TS). The relationships of these variables with AVS were then analyzed. Moreover, their effect on AVS was quantified through linear regression (LR) and principal component regression (PCR). These two regression equations were analyzed using a histogram of residual values and by comparing mean relative error (MRE) and root-mean-square error (RMSE) values. LR (Model 1) and PCR (Model 2) models were established as well. The MRE and RMSE values in the PCR model were 21.9 and 25.9%, respectively. In terms of these values, the PCR model is more accurate than the LR model. Furthermore, its predictive results were more reasonable. In conclusion, the PCR model can be used to predict the AVS concentrations based on the OC, Eh, SRB, and TS values. This model simplifies and facilitates the evaluation of metal toxicity under field conditions and can thus be used to manage sediments contaminated with metals.

Keywords: acid-volatile sulfide, spatial variability, influencing factors, principal component regression, linear regression