Ian Crawford is a Research Fellow at the Centre for Atmospheric Science, University of Manchester. His research combines his background in atmospheric physics with machine learning and data science approaches to develop frameworks to elucidate the impacts of bioaerosol emissions.

What are you working on as part of the Hub?

Many bioaerosols are powerful aeroallergens and pathogens which can significantly impact human, plant, and animal health. There is an emerging critical need not only for high temporal resolution detection methods to tackle challenges surrounding bioaerosol impacts, but to also ensure that data outputs are appropriately packaged to support a wide range of end users with differing needs and goals. As part of the hub I seek to improve real-time data pipelines. 

What is exciting you the most about your current research? 

I hope that our research will bridge disparate and often siloed disciplines who are interested in bioaerosols to accelerate impacts to benefit society. My work with real-time bioaerosol detection has demonstrated new capabilities which can expand our understanding of emission, their drivers, and how they impact personal exposure. To maximise the impacts of new technologies it will be critical to collaborate with stakeholders and partners in government agencies, industry, and technology vendors; this is the aspect I find most exciting as not only do we get to showcase the power of new detection methods, but we hopefully get to realise real beneficial change to improve air quality and inform future policy.

What difference do you hope your research will make?

With asthma and respiratory diseases becoming more prevalent and impacting an ever-growing proportion of the global population, it is my hope that the outputs delivered from my research facilitate a wide range of end users to tackle grand challenges surrounding how bioaerosols impact air quality and health through the provision of high-quality datasets. Emerging high time resolution detection methods not only have the power to inform on policy to bring about meaningful change but can also proactively protect vulnerable members of society through improved nowcasting and forecasting to predict deleterious emission events by integrating real-time data, allowing protective precautions to be taken ahead of time.

What are you most proud of in your research career?

I am most proud that my background in atmospheric physics and machine learning have enabled me to forge links with partners in government agencies and industry to more deeply investigate bioaerosol impacts, promoting pathways to meaningful change and societal benefit.