High resolution air quality modelling and prediction

Napier, Yoana Borisova (2022) High resolution air quality modelling and prediction. PhD thesis, University of Glasgow.

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Air pollution is one of the leading world problems. Across the world, many organizations are in charge of researching safe levels of air pollution, which do not affect people's health. This research has resulted in regulations, which in Scotland are set by the Scottish government. However, monitoring air pollution is very expensive which leads to sparsity in the data. This thesis aims to address this issue by investigating the miniature automated sensor (MAS) networks and the emulation of air quality models data. MAS are a cheaper alternative to the current air quality monitoring stations. Therefore, the quality of the measurements from MAS (in a realistic for citizen science application) are assessed using Bland-Altman analysis and compared to the air quality monitoring stations' recordings using linear regression models. It is found that the MAS do not have the required level of accuracy, although their recordings are significantly capturing the pollutants' concentrations' fluctuations.

Alternatively, in order to assess the effect of unobserved meteorological conditions on pollutants' concentrations, simulated data from ADMS-Urban for Scotland is used. Based on single station and multiple station Gaussian Process (GP) models, emulators for the NO2 annual average are produced and used to identify the meteorological conditions for which the regulations will be breached. Therefore, a variety of measures can be set in motion when such conditions occur to prevent a breach of the regulation. A quasi-Poisson generalised linear model (GLM) is used to emulate the number of NO2 hourly exceedances in a year over the regulatory limit of 200 �g m[sup]3, thus identifying the meteorological conditions for which the regulations will be breached and for measures preventing the breaches to be placed. To emulate the yearly time series for NO2 hourly concentrations, a hyperspatial-temporal emulator with a block-design matrix is proposed. In order to improve the computational speed, the emulator is produced for overlapping blocks of data for periods of interest. The results from the emulator identified periods of possible high NO2 hourly pollutant concentrations and allowed to identify the emissions levels and meteorological conditions, which lead to high hourly NO2 concentrations. Overall, all proposed emulators have very good out-of-sample performance in predicting the simulated data.

Item Type: Thesis (PhD)
Qualification Level: Doctoral
Subjects: H Social Sciences > HA Statistics
T Technology > T Technology (General)
Colleges/Schools: College of Science and Engineering > School of Mathematics and Statistics > Statistics
Supervisor's Name: Scott, Professor Marian and Lee, Professor Duncan
Date of Award: 2022
Depositing User: Theses Team
Unique ID: glathesis:2022-82815
Copyright: Copyright of this thesis is held by the author.
Date Deposited: 20 Apr 2022 07:59
Last Modified: 20 Apr 2022 08:02
Thesis DOI: 10.5525/gla.thesis.82815
URI: http://theses.gla.ac.uk/id/eprint/82815

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