Burner Alert

Welcome to the UK’s first real time sensor-based alert system for wood burning stoves.

Find the burner alert status in your area

Fine particles emitted from log burners pose a risk to public health. Children, the elderly and those with existing health conditions are especially vulnerable. Stove users can use the Burner Alert to check whether lighting a stove will contribute to already unhealthy levels of particulate matter air pollution.
How do I use it?
The three levels of burner alert align with the World Health Organisation’s 2022 guideline values for fine particulate matter (PM2.5) air pollution over a 24-hour period.
A burn alert is triggered when air pollution goes above the 15 ug/m3.
Burner Alert Guideline levels:
‘No Alert’ (Green)particle pollution in this area is well below guideline levels. Air quality is not currently unhealthy, although stove use may increase your ward’s levels.
‘Advisory’ (Amber) particle pollution in this area is approaching guideline levels. Please consider not lighting your stove, particularly if you have an alternative source of heating.
‘Burner Alert’ (Red)particle pollution in this area is already above guideline levels. Avoid lighting your stove unless you do not have an alternative source of heating.

This burner alert system has been created by Rohit Chakraborty (University of Sheffield), Vibhuti Patel (University of Sheffield) and James Heydon (University of Nottingham) on the basis of recommendations made in their research.
Ground-based ambient air pollutant observations from governmental sites, sensor.community's Sheffield network and  along with tomorrow.io’s high-resolution state-of-the-science Environment Model (CHEM) based on global weather and chemistry models, including
  • Cutting-edge trace gas and weather observations from recently-launched satellite sun-synchronous orbiting instruments, such as Differential Optical Absorption Spectroscopy (DOAS) measurement and,
  • Traffic data covering highways and local roads are used to generate real-time pollution alerts in the local wards.
This data, together with a Neural Network based machine learning algorithm to capture the spatiotemporal uncertainties, ensure the best real-time air quality measurements are provided.

The system is jointly funded by the University of Sheffield and the University of Nottingham.  We also acknowledge the input and collaboration of our industrial partner, AirRated in creating this tool. Lastly, we would like to thank Graham Turnbull for his work in setting up the Sensor.Community sensor network in Sheffield.
For further queries please contact: rohit.chakraborty@sheffield.ac.uk

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