Using surrogate passive sampler devices and predictive machine learning algorithms to replace invertebrate use in micropollutant bioconcentration test
Lead Research Organisation:
King's College London
Department Name: Analytical & Environmental Sciences
Abstract
Passive sampler devices (PSDs) are often used to monitor longer term occurrence of micropollutants, and mainly hydrophobic organic chemicals (HOCs), in the environment. PSDs are generally composed of a polymeric sorbent and collect solutes by passive diffusion. Recently, PSDs have been used as potential surrogates for fish/invertebrates in bioconcentration studies for HOCs (logKow 4-6)1.
More recently, PSDs have been tailored for polar organic chemicals (POCs). For example, pharmaceuticals and personal care products (PPCPs) have a logKow of -1 to 4 and have multiple ionisation states. This makes modelling for PPCPs significantly more challenging. Laboratory based risk assessment for 7,000 pharmaceuticals (excluding their metabolites and transformation products) is impractical and highly costly.
There exists an exciting opportunity in this project to develop and apply predictive approaches to prioritise laboratory testing or remove the need to use model organisms entirely. In 2016, we were the first to successfully model and predict POC uptake rate constants (Rs) onto PSDs1. We have also recently used machine learning by itself to predict PPCP bioconcentration in G. pulex with some limited success in a previously BBSRC funded CASE studentship (BB/K501177/1). Therefore, it is now timely for us to extend this knowledge to 3R-type predictive approaches for additional POC bioconcentration studies now incorporating PSDs to improve the machine learning approach and to mimic the actual bioavailability more realistically. This also represents an excellent way to prioritise risk assessment for selected emerging POCs to biota for which no knowledge or standard reference materials exist.
More recently, PSDs have been tailored for polar organic chemicals (POCs). For example, pharmaceuticals and personal care products (PPCPs) have a logKow of -1 to 4 and have multiple ionisation states. This makes modelling for PPCPs significantly more challenging. Laboratory based risk assessment for 7,000 pharmaceuticals (excluding their metabolites and transformation products) is impractical and highly costly.
There exists an exciting opportunity in this project to develop and apply predictive approaches to prioritise laboratory testing or remove the need to use model organisms entirely. In 2016, we were the first to successfully model and predict POC uptake rate constants (Rs) onto PSDs1. We have also recently used machine learning by itself to predict PPCP bioconcentration in G. pulex with some limited success in a previously BBSRC funded CASE studentship (BB/K501177/1). Therefore, it is now timely for us to extend this knowledge to 3R-type predictive approaches for additional POC bioconcentration studies now incorporating PSDs to improve the machine learning approach and to mimic the actual bioavailability more realistically. This also represents an excellent way to prioritise risk assessment for selected emerging POCs to biota for which no knowledge or standard reference materials exist.
Publications
Richardson AK
(2021)
Rapid direct analysis of river water and machine learning assisted suspect screening of emerging contaminants in passive sampler extracts.
in Analytical methods : advancing methods and applications
Richardson AK
(2022)
A miniaturized passive sampling-based workflow for monitoring chemicals of emerging concern in water.
in The Science of the total environment
Studentship Projects
Project Reference | Relationship | Related To | Start | End | Student Name |
---|---|---|---|---|---|
BB/M009513/1 | 30/09/2015 | 31/03/2024 | |||
2125200 | Studentship | BB/M009513/1 | 30/09/2018 | 29/03/2023 | Alexandra Richardson |
Description | The development of a novel, miniaturised 3D-printed passive sampler device that can act as a surrogate for invertebrates during pollution studies. |
Exploitation Route | The 3D-printed device can be carried forward by others for different applications (e.g. air quality and soil monitoring) and can be adapted to possess traits of other passive samplers. |
Sectors | Education Government Democracy and Justice Manufacturing including Industrial Biotechology |
Description | IMPART |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Public/other audiences |
Results and Impact | The IMPART (Imperial Monitoring using Passive samplers to Assess Rivers and Tributaries) project engaged 17 citizen scientists from three locations (London, Sheffield and Norwich) in a week-long monitoring program where they deployed novel, miniaturised 3D-printed passive samplers in their local water systems. The passive sampler devices were sent back to Imperial College London for analysis and the results were presented to the participants and other members of the public in November 2022. A public-facing report is forthcoming in April 2023. |
Year(s) Of Engagement Activity | 2022 |