Department of Climate, Energy and the Environment, Ireland

Residential emissions remain highly uncertain in Ireland due to limitations in available data and the coarse spatial resolution of the Irish emissions inventory, which distributes emissions uniformly over 1km × 1km grid cells.

In EMERALD a task was dedicated to assessing how the estimates of residential heating emissions in Ireland which are a major source of PM₂.₅ in Irish pollution hotspots (towns), could be improved for use as input in the air quality models.

Within this project, an exploratory study combining particulate matter (PM) measurements and modelling was carried out in two Irish towns where air quality is strongly influenced by emissions from residential heating. For each town, measurement data from several PM sensors were combined with data assimilation techniques to better estimate the residential heating emissions and produce higher resolution PM concentration maps.

The results demonstrated that the official national emission inventories can significantly underestimate the residential heating emissions, sometimes by a factor of two to three, particularly during periods when residential heating is expected to be most intensive.  

For the two towns studied, improved residential emissions data was prepared which included an updated temporal profile, better representing the emissions at each hour of the day, week and month.

In this subsequent project with DCEE, the Bayesian Reverse Dispersion Modelling (RDM) methodology (ATMO-Trace) was applied across seven towns (cities) but in this case, only relying on a single official telemetric monitoring station instead of a dense network of PM measurement data, as this will not always be available. One of the aims were to refine and validate the new ATMO-Trace approach firstly applied in EMERALD and to assess if it could be extended to other residential heating hotspots across Ireland. Which in turn would provide a better understanding of the contributions of residential heating in those towns to support future clean air policy. 

Although the results are more difficult to interpret because of the limited measurement data, it was possible to generate improved residential emissions data for those towns which were used to produce high-resolution PM₂.₅ concentration maps for each town.

Those maps are being used to identify the key hotspots for which high density PM measurement campaigns are being established. Those campaigns will be used to support clean air actions in those towns.   

The study also clearly confirmed the underestimation of residential emissions in the national residential emissions inventory that is used as input to air quality models and provided recommendations on the way forward to improve the data to support clean air policy.