Particulate matter from wood burning emissions (Cwood) was quantified at five locations in the United Kingdom (UK), comprising three rural and two urban sites between 2009 and 2021. The aethalometer method was used. Mean winter Cwood concentrations ranged from 0.26 μg m-3 (in rural Scotland) to 1.30 μg m-3 (London), which represented on average 4% (in rural environments) and 5% (urban) of PM10 concentrations; and 8% of PM2.5. Concentrations were greatest in the evenings in winter months, with larger evening concentrations in the weekends at the urban sites. Random-forest (RF) machine learning regression models were used to reconstruct Cwood concentrations using both meteorological and temporal explanatory variables at each site. The partial dependency plots indicated that temperature and wind speed were the meteorological variables explaining the greatest variability in Cwood, with larger concentrations during cold and calm conditions. Peaks of Cwood concentrations took place during and after events that are celebrated with bonfires. These were Guy Fawkes events in the urban areas and on New Year's Day at the rural sites; the later probably related to long-range transport. Time series were built using the RF. Having removed weather influences, long-term trends of Cwood were estimated using the Theil Sen method. Trends for 2015-2021 were downward at three of the locations (London, Glasgow and rural Scotland), with rates ranging from -5.5% year-1 to -2.5% year-1. The replacement of old fireplaces with lower emission wood stoves might explain the decrease in Cwood especially at the urban sites The two rural sites in England observed positive trends for the same period but this was not statistically significant.
Keywords: Aethalometer; Biomass; Long-term trends; Random forest modelling; Wood heating; de-weathering algorithm.
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