John Henry Nightingale
Northallerton,North Yorkshire
Summary
Computational chemical scientist specialising in predictive modelling, chemical data science, and high-resolution mass spectrometry workflows. Experienced in integrating Python-based computational pipelines, cheminformatics-assisted approaches, mechanistic modelling, and machine learning methodologies to predict chemical behaviour, prioritise molecular risk, and analyse complex chemical datasets across environmental and biological systems.
Overview
7
7
years of professional experience
Work History
Scientific Project Lead
the University of Leeds
01.2022 - Current
- ‘The fate and effects of human pharmaceuticals on soil & plant health’ – Supervised by Prof. Laura Carter
- Developed scalable Python workflows integrating HRMS and predictive modelling for chemical identification and environmental exposure assessment.
- Developed, evaluated and validated environmental fate models (FOCUS PEARL, SWASH, SimpleTreat, and Python) to predict transport, transformation, and plant uptake of contaminants for risk assessment.
- Led laboratory, mesocosm, and chemical fate field studies, including a 22-farm UK monitoring programme and international wastewater irrigation trials.
- Developed & validated LC–MS/MS and HRMS workflows, including suspect screening, non-target analysis, and automated Python-based data pipelines.
- Designed and validated analytical methods for complex matrices (soil, water, plants, biosolids, honey), including SPE and multi-residue approaches.
- Analysed large-scale water and soil datasets to link contaminant occurrence with environmental processes and risk.
- Conducted ecotoxicological and exposure assessments, integrating fate, transformation products, and toxicity endpoints.
- Delivered MSc-level teaching in environmental data science, contaminant fate, and risk assessment, including field and laboratory training.
- Supervised PhD, MSc, and undergraduate students.
Study Director – Environmental Fate
Fera Science Ltd – Centre for Chemical Safety & Stewardship
01.2021 - 01.2022
- Maintaining GLP experiments
- Data integrity – GLP
- Regulatory-ready reports for competent authorities
- Contribution to EU registration dossiers – veterinary medicines
- Regulatory science
PhD
Geography University of Leeds and Fera Science Ltd
01.2019 - 01.2021
- ‘Managing the release of emerging contaminants into the environment’
- Evaluating the role of heterogenic manure properties on veterinary medicine degradation - https://doi.org/10.1016/j.chemosphere.2021.133191
- Exploring the influence of manure application techniques on veterinary medicine losses to water - https://doi.org/10.1016/j.jenvman.2023.117361
- Environmental monitoring of veterinary medicines at field scale - https://doi.org/10.1016/j.scitotenv.2025.180842
- Analytical chemistry (LC-MS, HR-MS)
Education
PhD - Geography
University of Leeds
01.2021
BSc -MSc - Environmental science research
University of Leeds
01.2018
Skills
- Python
- Pandas
- NumPy
- Scikit-learn
- RDKit
- R
- AI
- SQL
- Automated workflows
- Data pipelines
- Classical machine learning workflows
- Predictive chemical modelling
- QSAR
- Regression modelling
- Statistical analyses
- Computational Chemistry
- Modelling
- ADME concepts
- Environmental fate modelling
- Chemical descriptor analysis
- Exposure modelling
- Metabolite identification
- HRMS suspect/non-target screening
- Instrumentation
- Analytics
- LC-MS/MS
- HPLC
- ICP-OES/MS
- Project management
- Environmental monitoring
- Data analysis
- Regulatory compliance
- Risk assessment
- Python programming
- Analytical chemistry
- Predictive modeling
- Laboratory management
- Method development
- Team leadership
- Teaching and training
- Project planning and development
- Data review and analysis
- Workflow optimisation
Technical
Python (pandas, NumPy, scikit-learn), RDKit, R, AI, SQL familiarity, Automated workflows and data pipelines, Classical machine learning workflows for predictive chemical modelling and prioritisation, QSAR & regression modelling, Statistical analyses, ADME concepts, Environmental fate modelling & Chemical descriptor analysis, Exposure modelling, Metabolite identification, HRMS suspect/non-target screening, LC-MS/MS, HRMS - identified previously unreported contaminants - developed workflows for novel chemical prioritisation, integrated HRMS with computational screening approaches, HPLC, ICP-OES/MS
Publications
58.4 Research Interest Score | 67 Citations | 4 h-index | 18 Recommendations
ORCID - 0000-0002-8690-0303
Publications of relevance
- Nightingale et al. (2025) Sci. Total Environ. 1005, 180842. Farm-scale occurrence of veterinary medicines and groundwater modelling evaluation.
- Nightingale et al. (2025) J. Hazard. Mater. 493, 138297. Framework for pharmaceutical accumulation in crops under wastewater irrigation.
- Nightingale et al. (2022) Chemosphere 290, 133191. Influence of pig slurry pH on antibiotic degradation.
References
Prof. Paul Kay; Water Chemistry; Email. P.Kay@Leeds.ac.uk; No. 07813092279
Prof. Laura Carter; Environmental Chemistry; Email. L.J.Carter@Leeds.ac.uk; No. 07402213752
Dr Chris Sinclair; Chemical Safety Lead; Email. Chris.Sinclair@Bayer.com; No. 07554660209
Timeline
Scientific Project Lead
the University of Leeds
01.2022 - Current
Study Director – Environmental Fate
Fera Science Ltd – Centre for Chemical Safety & Stewardship
01.2021 - 01.2022
PhD
Geography University of Leeds and Fera Science Ltd
01.2019 - 01.2021
PhD - Geography
University of Leeds
BSc -MSc - Environmental science research
University of Leeds