Microplastics and Nanoplastics: Detection, Characterization and Removal Methods

To equip participants with the scientific understanding and computational skills needed to interpret plastic-particle measurements, assess polymer-identification evidence and evaluate removal performance.

• Explain the sources, environmental pathways and size classifications of microplastics and nanoplastics.
• Introduce sampling, extraction and quality-control practices for reliable analysis.
• Demonstrate automated particle counting and morphological measurements using microscopy images.
• Introduce spectral preprocessing and reference matching for polymer identification.
• Compare characterization methods and their suitability for different particle sizes and sample types.
• Evaluate treatment performance while distinguishing capture, sludge transfer, fragmentation and degradation.

📅 Day 1: Microplastic Detection & Automated Particle Analysis

• Focus: Detecting particles and generating reliable quantitative measurements.
• Sources, environmental pathways and size classifications of microplastics and nanoplastics.
• Sampling and extraction: filtration, density separation and organic-matter digestion.
• Optical microscopy, fluorescence screening and Nile Red staining.
• Automated image segmentation, particle counting and morphological classification.
• Contamination control, procedural blanks, recovery testing and detection limits.
• Distinguishing suspected plastic particles from chemically confirmed plastics.

🛠️ Hands-on: Analyse a Prepared Microscopy-Image Set

• Analyse a prepared, calibrated microscopy-image set in Google Colab.
• Segment particles, calculate counts, equivalent diameters and aspect ratios, and plot size distributions.

🧰 Free Tools: Google Colab/Jupyter Notebook, scikit-image, OpenCV, pandas, matplotlib.

📌 Output: Annotated particle images, measurement table and size-distribution plot.

📅 Day 2: Polymer Identification & Nanoplastic Characterization

• Focus: Interpreting chemical fingerprints and selecting suitable analytical methods.
• Polymer identification using µ-FTIR and Raman spectroscopy.
• Spectral preprocessing: baseline correction, smoothing and normalization.
• Reference-library matching and confidence assessment.
• Weathering, biofouling and interference from natural materials.
• SEM/TEM for morphology; DLS and nanoparticle tracking analysis for particle sizing.
• Py-GC/MS for polymer-mass quantification; emerging SERS and AF4-based approaches.
• Nanoplastic identification limits: size measurements alone do not establish polymer identity.

🛠️ Hands-on: Process & Identify Polymer Spectra

• Process selected open FTIR spectra in Google Colab and match them against polymer references.
• Compare PE, PP, PS and PET fingerprints and flag uncertain identifications.

🧰 Free Tools: Google Colab/Jupyter Notebook, NumPy, SciPy, matplotlib; Open Specy for browser-based spectral comparison.

📌 Output: Processed spectra, tentative polymer assignments and match-score table.

📚 Suggested Data: Selected spectra from the open FLOPP/FLOPP-e libraries or the µFTIR test dataset for known synthetic and natural materials. These resources include reference materials and environmental or biofouled samples relevant to identification challenges.

📅 Day 3: Removal Technologies & Treatment Performance Evaluation

• Focus: Comparing treatment methods and interpreting what removal results demonstrate.
• Coagulation–flocculation, sedimentation and dissolved-air flotation.
• Membrane filtration: microfiltration, ultrafiltration and nanofiltration.
• Adsorbents, biochar and magnetic separation.
• Emerging electrocoagulation, photocatalytic and biological approaches.
• Effects of particle size, polymer type, surface charge and wastewater composition.
• Distinguishing capture, transfer to sludge, fragmentation and actual degradation.
• Treatment trade-offs: fouling, energy demand, residual management and scale-up.

🛠️ Hands-on: Analyse Treatment Performance

• Analyse a prepared influent–effluent–sludge dataset in Google Colab.
• Calculate size-specific removal efficiencies and evaluate particle or mass balances.
• Compare treatment options using performance and operating indicators.

🧰 Free Tools: Google Colab/Jupyter Notebook, pandas, NumPy, matplotlib.

📌 Output: Removal-efficiency plots, balance assessment and treatment-comparison table.

• Researchers, PhD scholars and academicians working in environmental science, analytical chemistry, polymer science, nanotechnology or water treatment.
• Postgraduate and advanced undergraduate students interested in plastic pollution and environmental monitoring.
• Laboratory analysts and technical staff involved in particle analysis, spectroscopy or water-quality testing.
• Industry professionals working in wastewater treatment, filtration, membranes, waste management and environmental consulting.
• Sustainability and pollution-control professionals seeking a practical understanding of plastic-particle measurement and treatment evidence.

By the end of the workshop, participants will be able to:

• Explain the distinction between suspected plastic particles and chemically identified polymers.
• Use a guided notebook to segment microscopy images and calculate particle counts, equivalent diameters and aspect ratios.
• Generate annotated images, measurement tables and particle-size distributions.
• Preprocess FTIR spectra and compare PE, PP, PS and PET fingerprints with reference spectra.
• Interpret match scores and flag uncertain polymer assignments.
• Explain why particle-sizing results alone cannot establish nanoplastic identity.
• Calculate size-specific removal efficiencies and assess particle or mass balances when suitable data are available.
• Compare treatment options using removal performance, energy demand, fouling and residual-management indicators.