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Development of Data Analytic Approaches for Air Quality Data

Grange, Stuart (2019) Development of Data Analytic Approaches for Air Quality Data. PhD thesis, University of York.

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Abstract

Continuous air quality monitoring networks were commissioned in the mid-twentieth century throughout the developed world to underpin the understanding of air pollution. These monitoring networks have produced a vast observational record which continues to grow. However, these data are generally used for simple tasks such as checking for compliance to legal standards or guidelines and the additional information contained in the data sets is not well leveraged to aid scientific understanding and inform policy makers. This thesis addresses this issue and has the goal of extracting additional information from "routine"' air quality monitoring data using new, and novel data analyses with a focus on the impact of transportation activities across Europe. Specifically, this thesis outlines the development of bivariate polar plots with pair-wise statistics to aid source apportionment, the development of a European air quality database which much of this thesis's work is based on, a European-wide analysis of roadside nitrogen dioxide (NO2), and the development of a framework and software to robustly detect and quantify changes in pollutant concentrations. The additional functionality of bivariate polar plots was useful for isolating the natural and anthropogenic sources of pollutants and is now included in the open source openair R package. The NO2 analysis revealed that directly emitted NO2 from road vehicles is decreasing across Europe and assumed emissions are too high resulting in pessimistic projections of future compliance. This conclusion is very important for policy makers to consider in their planning of disruptive interventions, most relevant of which are low emission zones because the observations suggest that the outlook is better than traditionally thought. For those analysing trends, a new technique has been developed that is highly effective at robustly characterising and quantifying the effects of interventions and the tools developed are available in the form of the open source rmweather R package.

Item Type: Thesis (PhD)
Related URLs:
Keywords: Air quality, Data science, Machine learning, R, York, Chemistry
Academic Units: The University of York > Chemistry (York)
Depositing User: Mr Stuart Grange
Date Deposited: 25 Mar 2019 12:25
Last Modified: 25 Mar 2019 12:25
URI: http://etheses.whiterose.ac.uk/id/eprint/23306

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