Supervisors: Rod Jones (Chemistry) and Alex Archibald (Chemistry)
Importance of the area of research:
Poor air quality poses one of the greatest threats to life (WHO, 2014). Every year more than 6 million people die as a result of poor outdoor air quality and as a result billions of dollars have been spent to reduce the primary emissions of air pollutants. However, air pollution is largely a secondary problem and problems with air pollution persist. Nowhere are these problems greater than in the developing world. High quality measurements of air pollutants and their precursors are needed over a range of spatial (regional, urban, personal) and temporal (high frequency, seasonal, multi annual) scales. These data provide our best evidence to develop an understanding of the sources and transformations of air pollutants. Access to new platforms (the 300m IOP tower in central Beijing) and an unparalleled density of measurements will provide a unique opportunity to provide new insight into the problems of air quality in urban areas.
Quantification of the sources and mechanisms of transformation of primary air pollutants is crucial to helping provide solutions to improve air quality. This project will involve the in situ collection and interpretation of air pollutant measurements using new low cost sensor networks (Heimann et al., 2015). Statistical techniques will be applied to these data to understand the sources and transformations of the species measured. These data will be used to derive new estimates for emissions that will be tested using state of the art air quality models. New scenarios will be developed to assess the impacts of mitigating emissions on air quality.
What the student will do:
The student will evaluate a new generation of low cost high quality air quality monitoring instruments and use these instruments to produce a highly vertically resolved data set of air pollutants from the IOP tower in central Beijing. The student will combine the sensor instrument data with a variety of mathematical inversion techniques to determine the source of air pollutants, both in space and time. Where appropriate, numerical model simulations will be used to help interpret the observational data.
Please contact the lead supervisor directly for further information relating to what the successful applicant will be expected to do, training to be provided, and any specific educational background requirements.
World Heath Organisation 2014, www.who.int.
Heimann I., et al., Source attribution of air pollution by spatial scale separation using high spatial density networks of low cost air quality sensors. Atmos. Environ., 113, 2015.
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