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dc.contributor.authorKrause, Anika
dc.date.accessioned2021-02-02T15:45:03Z
dc.date.available2021-02-02T15:45:03Z
dc.date.submitted2020-09-01
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/317011
dc.description.abstractPoor air quality is a severe issue for society, affecting the health and well-being of huge parts of the population worldwide. To efficiently reduce the risk of premature death associated with air pollution, a deeper understanding of the causal links between air pollution exposure and human health is needed. However, conventional health studies are restricted by methodological limitations such as miss-estimations of personal exposure and the interdependence between different pollutant species when using traditional outdoor exposure metrics. Taking advantage of recent advancements in sensor technologies and computational techniques, this dissertation presents a novel methodological approach to improve air pollution exposure and dose estimates for epidemiological research. The novel methodology combines personal air quality monitors (PAMs) measuring nitrogen oxides (NOx), carbon monoxide (CO), ozone (O₃) and particulate matter (PM), with a time-location-activity model to generate accurate personal air pollution exposure estimations under field conditions. The monitors were comprehensively characterised and deployed in different exposure studies in the UK, China, Germany, and Kenya, supporting wider studies of air pollution and human health. The PAM measurements showed excellent agreement with standard instrumentation in indoor, outdoor, and commuting environments. Field deployments involving hundreds of participants revealed the substantial exposure misclassification introduced when using ambient measurements as metrics of exposure. The correlation between individual pollutants usually observed at air quality monitoring stations was found to substantially decrease using the high spatial resolution of the portable sensors, allowing more refined estimates of the health effects of different pollutants. The deployments showed that local emission sources had often a far more important impact on personal exposure than regional sources, and the air pollution composition changed distinctively between local microenvironments. The home environment was identified as an important exposure site, particularly in areas where populations rely on biomass burning for domestic energy and cooking. In industrialised countries, peak exposure events were recorded during commuting, although they frequently represented a minor component of the overall dose. By separating regional from local air pollution and classifying exposure by microenvironment, this work has made first steps towards assigning personal exposure to individual emission sources. The findings of this dissertation should lead to a paradigm shift in quantifying air pollution exposure in epidemiological studies and drive evidence-based policy to reduce the global burden of disease.
dc.description.sponsorshipThanks to Jesus College Cambridge for the Sheldrick Scholarship covering tuition fees and maintenance.
dc.rightsAll Rights Reserved
dc.rights.urihttps://www.rioxx.net/licenses/all-rights-reserved/
dc.subjectair pollution
dc.subjectenvironmental monitoring
dc.subjectpersonal exposure
dc.subjectepidemiology
dc.subjectsensors
dc.subjectportable air quality monitor
dc.subjectminiatured sensor technologies
dc.subjectnitrogen oxides (NOx)
dc.subjectcarbon monoxide (CO)
dc.subjectparticulate matter (PM)
dc.titleUsing novel portable air quality monitors to improve personal exposure and dose estimations for health studies
dc.typeThesis
dc.type.qualificationlevelDoctoral
dc.type.qualificationnameDoctor of Philosophy (PhD)
dc.publisher.institutionUniversity of Cambridge
dc.identifier.doi10.17863/CAM.64122
rioxxterms.licenseref.urihttps://www.rioxx.net/licenses/all-rights-reserved/
dc.contributor.orcidKrause, Anika [0000-0002-2212-0252]
rioxxterms.typeThesis
dc.publisher.collegeJesus
dc.type.qualificationtitlePhD in Atmospheric Science
cam.supervisorJones, Roderic L
cam.supervisorChatzidiakou, Evangelia
cam.supervisor.orcidJones, Roderic [0000-0002-6761-3966]
cam.supervisor.orcidChatzidiakou, Evangelia [0000-0002-8753-1386]


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