Characterisation of LA-ICP-MS and receptor modelling to measure, speciate and source apportion particulate matter (#1022)
Particulate matter (PM) has been observed to produce negative health impacts in humans1,2. However, these health impacts can vary depending on several factors, including their sources3,4. One approach to source apportioning PM is to determine its elemental composition, which can then be used as data inputs for receptor models that can characterise distinct source fingerprints. This project aims to identify a protocol for conducting elemental analysis of PM using Laser Ablation Inductively-Coupled Plasma Mass Spectrometry (LA-ICP-MS), which can serve as a high-throughput, low-cost alternative to analytical techniques employed for this purpose. Data from this analysis is then processed through a combination of the factor analysis receptor model Positive Matrix Factorisation (PMF) and back trajectories to estimate the contributions of different sources of PM.
PM samples were collected from Garden Island, Western Australia between April and May 2018 and Parkville, Victoria between December 2018 and April 2019, with the former being PM2.5 (as determined by Scanning Electron Microscope, SEM, analysis) and the latter being PM10. Several Garden Island samples were also analysed using Acid Digestion ICP-MS, a reference method for speciating PM5, to validate the accuracy of LA-ICP-MS results. SEM was also used to identify spatial distributions of particles on sample papers, which was used to determine the ideal proportion of sample to be scanned to representatively detect the particles present. It was found that LA-ICP-MS analysis was able to reflect atmospheric conditions of the sampling periods. For example, high concentrations of marker elements (e.g. K, S) for smoke during bushfire events as well as a near 1:1 ratio between Na and Cl that reflects the PM produced by sea spray. Source apportionment using PMF resolved elemental ratios that distinguished different source profiles, while back trajectories correlated calculated source contributions with actual atmospheric transport conditions.
- World Health Organisation. (2016). Ambient air pollution: A global assessment of exposure and burden of disease. Retrieved from http://apps.who.int/iris/bitstream/handle/10665/250141/9789241511353-eng.pdf?sequence=1
- Organisation for Economic Co-operation and Development. (2014). The cost of air pollution: Health impacts of road transport. Retrieved from: http://www.oecd.org/env/the-cost-of-air-pollution-9789264210448-en.htm
- Jalava, P. I., Salonen, R. O., Pennanen, A. S., Sillanpaa, M., Halinen, A. I., Happo, M. S., . . . Hirvonen, M. R. (2007). Heterogeneities in inflammatory and cytotoxic responses of RAW 264.7 macrophage cell line to urban air coarse, fine, and ultrafine particles from six European sampling campaigns. Inhalation Toxicology, 19(3), 213-225. doi:10.1080/08958370601067863
- Pun, V. C., Yu, I. T. S., Ho, K. F., Qiu, H., Sun, Z. W., & Tian, L. W. (2014). Differential Effects of Source-Specific Particulate Matter on Emergency Hospitalizations for Ischemic Heart Disease in Hong Kong. Environmental Health Perspectives, 122(4), 391-396. doi:10.1289/ehp.1307213
- Brown, R. J. C., Jarvis, K. E., Disch, B. A., Goddard, S. L., Adriaenssens, E., & Claeys, N. (2010). Comparison of ED-XRF and LA-ICP-MS with the European reference method of acid digestion-ICP-MS for the measurement of metals in ambient particulate matter. Accreditation and Quality Assurance: Journal for Quality, Comparability and Reliability in Chemical Measurement, 9, 493-502. doi:10.1007/s00769-010-0668-7