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Modelling hourly spatio-temporal PM2.5 concentration in wildfire scenarios using dynamic linear models

Author(s): Joseph Sánchez-Balseca, Agustí Pérez-Foguet
Year Published: 2020
Description:

Particulate matter with aerodynamic diameter < 2.5 μm (PM2.5) is one of the main pollutants generated in wildfire events with negative impacts on human health. In research involving wildfires and air quality, it is common to use emission models. However, the commonly used emission approach can generate errors and contradict the empirical data. This paper adopted a statistical approach based in evidence of ground level monitoring and satellite data. An hourly PM2.5 spatio-temporal model based on a dynamic linear modelling framework with Bayesian approach was proposed in a territorial context with a reduced number of monitoring stations for particulate matter. The model validation is complicated by the fact that all monitoring stations are used in the model calibration. The novel validation method proposed considered both the particulate matter with aerodynamic diameter < 10 μm (PM10) recorded as daily value from 24-h mean every six days as well as the PM2.5/PM10 ratio. Modelling was carried out to provide satisfactorily the exposure level of PM2.5 in a case study of wildfire event.

Citation: Sánchez-Balseca, Joseph; Pérez-Foguet, Agustí. 2020. Modelling hourly spatio-temporal PM2.5 concentration in wildfire scenarios using dynamic linear models. Atmospheric Research 242:104999. https://doi.org/10.1016/j.atmosres.2020.104999
Topic(s): Fire Behavior, Simulation Modeling, Fire Effects, Smoke & Air Quality, Smoke & Populations, Smoke Emissions
Ecosystem(s): None
Document Type: Book or Chapter or Journal Article
NRFSN number: 21989
FRAMES RCS number: 61066
Record updated: Sep 29, 2020