Modelling particle number size distribution: a continuous approach
Abstract Particulate matter (PM) is well known to be detrimental to health, and it is crucial to apportion PM into the underlying sources to target policies. Particle number size distribution (PNSD) is the most accessible data to identify these sources, which provides information on the PM sizes. Here, we propose a new functional factor model for PNSD, which allows to disentangle PM into sources and contributions while considering the complex dependencies of the data across different sizes and periods. Through a simulation study, we show that this method is able to identify sources correctly, and we use it to analyse hourly PNSD data collected in London for 7 years, finding 6 well-defined sources. Our proposed methodology is fast, accurate, and reproducible.
Authors
- Monica Pirani (ORCID: https://orcid.org/0000-0002-6418-3278)
- Wesley S. Burr (ORCID: https://orcid.org/0000-0002-2058-1899)
- Marta Blangiardo (ORCID: https://orcid.org/0000-0002-1621-704X)
- Carolina Euán (ORCID: https://orcid.org/0000-0003-3408-2887)
- Israel Martínez‐Hernández (ORCID: https://orcid.org/0000-0002-4122-2529)
- Melanie Meis (ORCID: https://orcid.org/0000-0002-8753-4528)
Institutions
- Consejo Nacional de Investigaciones Científicas y Técnicas (AR)
- Universidad de Buenos Aires (AR)
- MRC Centre for Environment and Health
- Imperial College London (GB)
- Fundación Ciencias Exactas y Naturales (AR)
- Trent University (CA)
- Lancaster University (GB)
Publication Details
- Journal
- Journal of the Royal Statistical Society Series C (Applied Statistics)
- Published
- 2024-10-14
- DOI
- https://doi.org/10.1093/jrsssc/qlae053
- Citations
- 3
- Primary Topic
- Air Quality and Health Impacts
- Type
- article
- Field-Weighted Citation Impact
- 0.94
Funders
- Lancaster University
- Medical Research Council