Rajneesh Gautam

I am a postdoctoral researcher at SLU, developing and optimising innovative in-situ remediation technologies to address PFAS contamination in soil and groundwater. My current research integrates air sparging and foam fractionation with LC-MS/MS analysis and numerical modelling to enhance removal efficiency and understand subsurface transport.

Previously, I completed my PhD at Victoria University (Australia) focusing on membrane-based anaerobic treatment for simultaneous wastewater treatment and biomethane recovery. With a multidisciplinary background across chemical and environmental engineering, I aim to contribute robust, scalable solutions for complex environmental problems.

• Developing and calibrating numerical models to predict PFAS desorption dynamics, transport behaviours, and long-term remediation performance, supporting data-driven optimisation of field applications.
• Applying high-resolution LC-MS/MS and advanced data analysis to characterise PFAS speciation and quantify remediation efficiency.
• Investigating key process parameters, operational controls, and system design considerations to maximise the scalability and sustainability of PFAS remediation solutions.
• Previously engineered submerged anaerobic membrane bioreactor (AnMBR) systems for the simultaneous treatment of high-strength industrial wastewater and the recovery of biomethane.
• Designed predictive models of membrane fouling incorporating EPS accumulation and operational stressors, leading to optimised cleaning and sparging strategies.
• Experienced in bridging experimental studies, process design, and computational modelling to address complex environmental engineering challenges.

• Analysis of PFAS in soil, groundwater, and process samples using advanced LC-MS/MS techniques
• Development of dynamic Python models for contaminant transport, desorption, and remediation scenario assessments
• Conducted meta-analyses and systematic reviews on PFAS occurrence, fate, and associated environmental risks
• Developed a computational framework that integrates machine learning tools and statistical methods to analyze high-throughput LC-HRMS data for the identification of unknown chemicals
• Optimised experimental systems and applied statistical and process engineering approaches to evaluate contaminant removal strategies

 
Postdoctoral Researcher at Division of Environmental Organic Chemistry and Ecotoxicology, SLU, Uppsala, Sweden

PhD in Chemical Engineering, Victoria University, Melbourne, Australia (2019–2024)

M.Tech in Energy and Environment, Babasaheb Bhimrao Ambedkar University, Lucknow, India (2013–2015)

B.Tech in Civil Engineering, BBDNITM, Lucknow, India (2009-2013)