Multimethod Platform Based on Dynamic Image Analysis and spICP-MS for Number-Based Quantification of Microplastics

Area of Science:

• Environmental Science
• Analytical Chemistry
• Materials Science

Background:

• Microplastic pollution is a growing environmental concern.
• Accurate quantification of small microplastics (1-10 μm) is challenging.
• Existing methods often lack the precision needed for reliable particle number concentration determination.

Purpose of the Study:

• To develop and validate a multimethod platform for reliable microplastic particle number concentration measurement.
• To assess the performance of dynamic image analysis (DIA) and single particle inductively coupled plasma mass spectrometry (spICP-MS) for microplastic quantification.
• To identify critical parameters influencing measurement accuracy and uncertainty.

Main Methods:

• Combined dynamic image analysis (DIA) and single particle inductively coupled plasma mass spectrometry (spICP-MS).
• Validated the platform using polystyrene (PS) microparticles (1-10 μm).
• Investigated instrumental parameters affecting accuracy, including detection threshold (DIA) and transport efficiency calibration (spICP-MS).

Main Results:

• The DIA/spICP-MS platform reliably detected and quantified microplastics.
• Optimal conditions yielded comparable number-concentration values for 5 μm PS-Latex microspheres.
• Measurement uncertainties were approximately 2.5% (DIA) and 10.2% (spICP-MS) under optimal conditions.
• Key uncertainty contributors identified: particle detection variability (DIA) and transport efficiency calibration (spICP-MS).

Conclusions:

• The developed DIA/spICP-MS platform offers a reliable method for determining small microplastic particle number concentration.
• Instrumental parameter optimization is crucial for accurate microplastic quantification.
• Understanding uncertainty contributions is essential for robust microplastic analysis.