Removal of Pharmaceutical Micropollutants with Integrated Biochar and Marine Microalgae.

Amin Mojiri,Maedeh Baharlooeian,Ziyang Lou,Hossein Farraji,Reza Andasht Kazeroon

doi:10.3390/microorganisms9010004

Abstract

Using microalgae to remove pharmaceuticals and personal care products (PPCPs) micropollutants (MPs) have attracted considerable interest. However, high concentrations of persistent PPCPs can reduce the performance of microalgae in remediating PPCPs. Three persistent PPCPs, namely, carbamazepine (CBZ), sulfamethazine (SMT) and tramadol (TRA), were treated with a combination of Chaetoceros muelleri and biochar in a photobioreactor during this study. Two reactors were run. The first reactor comprised Chaetoceros muelleri, as the control, and the second reactor comprised Chaetoceros muelleri and biochar. The second reactor showed a better performance in removing PPCPs. Through the response surface methodology, 68.9% (0.330 mg L−1) of CBZ, 64.8% (0.311 mg L−1) of SMT and 69.3% (0.332 mg L−1) of TRA were removed at the initial concentrations of MPs (0.48 mg L−1) and contact time of 8.1 days. An artificial neural network was used in optimising elimination efficiency for each MP. The rational mean squared errors and high R2 values showed that the removal of PPCPs was optimised. Moreover, the effects of PPCPs concentration (0–100 mg L−1) on Chaetoceros muelleri were studied. Low PPCP concentrations (<40 mg L−1) increased the amounts of chlorophyll and proteins in the microalgae. However, cell viability, chlorophyll and protein contents dramatically decreased with increasing PPCPs concentrations (>40 mg L−1).

Highlights

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.license.Industrial and agricultural activities are the main sources of water pollution around the world [1]
CBZ, SMT and TRA were removed with two photobioreactors (one containing Chaetoceros muelleri and another containing Chaetoceros muelleri + biochar)
The performance of each reactor was optimised with the response surface methodology (RSM) and artificial neural network (ANN)

Summary

Introduction

Industrial and agricultural activities are the main sources of water pollution around the world [1]. In the scientific literature and CAS registry, more than 150 million inorganic and organic pollutants have been recorded [2]. The continuous input of microcontaminants to water bodies is a growing environmental problem given that many of these microcontaminants are non-biodegradable, persistent and bioaccumulative [3]. Among the important categories of these pollutants are emerging micropollutants, which can be detected in the environment at trace concentrations. These contaminants comprise personal care products, pharmaceuticals, pesticides, metallic trace elements and industrial chemicals [4]. Vakili et al [5] demonstrated that conventional treatment techniques used by municipal wastewater treatment plants have failed to eliminate emerging micropollutants completely

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