Application of novel atmospheric measurement technology to wider environmental monitoring
Lead Research Organisation:
University of Hertfordshire
Department Name: School of Physics, Eng & Computer Scienc
Abstract
Next-generation aerosol detection for more accurate environment and health monitoring: Applying novel atmospheric measurement techniques to progress the state of the art in the characterisation of aerosols to better track pollution and airborne disease transmission.
Airborne particulates are emitted and transmitted by a wide range of sources and over all geographic scales, from indoors to globally. Our ability to identify the source of aerosols and model how they are transported is critical to both determining their likely impacts and implementing mitigation measures to protect human, animal and plant health. To achieve this, there is a need to better identify and characterise the types of particle across a range of environments: urban, agricultural, industrial, clinical, indoor. However, existing real-time monitoring techniques do not provide sufficient information on which to base effective action. While optical particle counters, used for air quality monitoring, can determine aerosol size, they are unable to differentiate between aerosol shape and therefore type, for example the difference between diesel droplets and brake dust, or pathogenic fungal spores and simply dust from a field. Nor can they identify source apportionment, for example whether the principal cause of an acute pollution episode is traffic, a factory or the weather.
Real-time detection technologies are limited to low-cost OPCs or expensive research-grade instruments that only identify a specific aerosol type. Our long-term vision is to engineer a novel monitoring system, by combining machine learning techniques with light-scattering technologies originally developed to classify atmospheric ice, that discriminates aerosols by not just size but, crucially, by shape and surface structure too. We believe this capability will allow the use of a single instrument to detect a broad range of airborne pollutants and pathogens, and identify the source of an influx of a particular aerosol, how far it may be transported and its environmental or health impact.
Airborne particulates are emitted and transmitted by a wide range of sources and over all geographic scales, from indoors to globally. Our ability to identify the source of aerosols and model how they are transported is critical to both determining their likely impacts and implementing mitigation measures to protect human, animal and plant health. To achieve this, there is a need to better identify and characterise the types of particle across a range of environments: urban, agricultural, industrial, clinical, indoor. However, existing real-time monitoring techniques do not provide sufficient information on which to base effective action. While optical particle counters, used for air quality monitoring, can determine aerosol size, they are unable to differentiate between aerosol shape and therefore type, for example the difference between diesel droplets and brake dust, or pathogenic fungal spores and simply dust from a field. Nor can they identify source apportionment, for example whether the principal cause of an acute pollution episode is traffic, a factory or the weather.
Real-time detection technologies are limited to low-cost OPCs or expensive research-grade instruments that only identify a specific aerosol type. Our long-term vision is to engineer a novel monitoring system, by combining machine learning techniques with light-scattering technologies originally developed to classify atmospheric ice, that discriminates aerosols by not just size but, crucially, by shape and surface structure too. We believe this capability will allow the use of a single instrument to detect a broad range of airborne pollutants and pathogens, and identify the source of an influx of a particular aerosol, how far it may be transported and its environmental or health impact.
