First order reversal curves (FORCs) as indicators of magnetic PM sources

Real-time monitoring of particulate matter (PM) is performed by traditional air quality monitors using size-fractionated light-scattering laser photometers or gravimetric analysis. However, these quick methods do not fully characterise the particulates, in particular, the more toxic metals such as Fe. Here, we propose the use of first order reversal curves (FORCs) as a method to discriminate different sources of anthropogenic magnetic particles. FORCs is a useful magnetic characterisation technique that is sensitive to particle size, mineralogy and domain state. By measuring known sources of particulates, we hope to come up with a ‘magnetic’ proxy in a heterogenous source of PM in the air using principal component analysis (PCA). FORC fingerprints for exhaust, non-exhaust vehicular sources, resuspended dust and train-related PM are distinct, providing a cost-effective way to monitor relative proportions in the ambient air. The first set of specimens involved measuring FORCs on known, size-fractionated brake abrasion dust on an accelerating gradient magnetometer. Processed FORCs for brake dust residue specimens show a distinct combination of narrow central ridge, extending from 0 to up to 200 mT, and a low-coercivity, vertically spread multi-domain signal. Similarly, known exhaust-pipe residue samples were measured, displaying a more conventional ‘magnetite-like’ signal comprising a lower coercivity central ridge (0-80 mT) and a tri-lobate signal attributed to vortex state and/or magnetostatic interactions. Tyre and road wear samples are generally low-coercivity and vertically spread, hinting at mostly coarser, resuspended dust that has settled down. Third set of data involved measuring gravimetric specimens from London Underground. The source of magnetic particulates in fundamentally the same but different ventilation rates on platforms meant the ‘fingerprints’ were different. Principally, FORC fingerprints are sensitive to grain size, mineralogy and total magnetic content, demonstrating a low-cost approach for identifying different proportions of magnetic particulates. These data will be complemented by measurements with high-resolution microscopy such as TEM and EELS to characterise the magnetic state of particulates and add value to conventional air quality monitoring systems.