Understanding the Adsorption Behavior of Organic Contaminants on Conventional and Emerging Sorbents in Water by Developing New Partitioning Models

Abstract

Emerging contaminants are released into the water bodies from various industrial and agricultural, trade and commerce activities and from use of personal care products in our daily lives. The discharge of these chemicals in the natural water bodies being in low concentration ranging from (ng/L to µg/L) make them escape from the conventional wastewater treatment plants (CWWTP). Therefore, advanced treatment strategies such as the use of adsorbents are employed to remove the contaminants of emerging concern from municipal and industrial water systems. In this research, partitioning models were developed to understand what properties of chemicals govern the adsorption behavior and to predict the adsorption potential of widely used adsorbents. Conventional and emerging adsorbents were evaluated individually using diverse set of chemicals with using the multivariant linear regression (MLR) algorithm and cross-validation tests. The model was developed for 165 neutral organic compounds in which adsorption capacity was estimated on four different types of adsorbents including carbon nanotubes (CNTs), granular activated carbon (GAC), biochar, and resins. A relationship was developed between various parameters like the surface area of the adsorbent (BET), total pore volume (V t ), the equilibrium concentration of adsorbate (Ce), octanol-water coefficient (K ow ), and air-water coefficient (K aw ). The four- partition models of CNTs, GAC, biochar, and resins were stable, robust, and accurate with statistical indices such as (R 2 =0.79 ̵ 0.69) and Root Mean Square Error (RMSE) ranging from (0.323 ̵ 0.5064). The correlation coefficient for the adsorbent resin is small among all other adsorbents because of considering total pore volume (V t ) instead of the macropore volume (V m ) and micropore volume (V mi ) to maintain uniformity in parameters for all models. The sensitivity analysis inferred that the interaction of the parameters with each other greatly affects the adsorption phenomena, and each model has its own influential parameter for that specific adsorbent. The four models do not only offer a theoretical method for predicting adsorption capacity of adsorbents but also have the prediction power of adsorption behavior of all organic pollutants in an aqueous environment reducing the need for further experiments. These models for sorbents can be further improved by adding more authentic experimental data and can be utilized in the future for estimating the adsorption behavior of ionized emerging contaminants in real water.