Quantification of air quality in space and time and its effects on health

The long-term adverse effects on health associated with air pollution exposure can be estimated using either cohort or spatio-temporal ecological designs. In a cohort study, the health status of a cohort of people are assessed periodically over a number of years, and then related to estimated ambient pollution concentrations in the cities in which they live. However, such cohort studies are expensive and time consuming to implement, due to the long-term follow up required for the cohort. Therefore, spatio-temporal ecological studies are also being used to estimate the long-term health effects of air pollution as they are easy to implement due to the routine availability of the required data. Spatio-temporal ecological studies estimate the health impact of air pollution by utilising geographical and temporal contrasts in air pollution and disease risk across $n$ contiguous small-areas, such as census tracts or electoral wards, for multiple time periods. The disease data are counts of the numbers of disease cases occurring in each areal unit and time period, and thus Poisson log-linear models are typically used for the analysis. The linear predictor includes pollutant concentrations and known confounders such as socio-economic deprivation. However, as the disease data typically contain residual spatial or spatio-temporal autocorrelation after the covariate effects have been accounted for, these known covariates are augmented by a set of random effects. One key problem in these studies is estimating spatially representative pollution concentrations in each areal which are typically estimated by applying Kriging to data from a sparse monitoring network, or by computing averages over modelled concentrations (grid level) from an atmospheric dispersion model. The aim of this thesis is to investigate the health effects of long-term exposure to Nitrogen Dioxide (NO2) and Particular matter (PM10) in mainland Scotland, UK. In order to have an initial impression about the air pollution health effects in mainland Scotland, chapter 3 presents a standard epidemiological study using a benchmark method. The remaining main chapters (4, 5, 6) cover the main methodological focus in this thesis which has been threefold: (i) how to better estimate pollution by developing a multivariate spatio-temporal fusion model that relates monitored and modelled pollution data over space, time and pollutant; (ii) how to simultaneously estimate the joint effects of multiple pollutants; and (iii) how to allow for the uncertainty in the estimated pollution concentrations when estimating their health effects. Specifically, chapters 4 and 5 are developed to achieve (i), while chapter 6 focuses on (ii) and (iii). In chapter 4, I propose an integrated model for estimating the long-term health effects of NO2, that fuses modelled and measured pollution data to provide improved predictions of areal level pollution concentrations and hence health effects. The air pollution fusion model proposed is a Bayesian space-time linear regression model for relating the measured concentrations to the modelled concentrations for a single pollutant, whilst allowing for additional covariate information such as site type (e.g. roadside, rural, etc) and temperature. However, it is known that some pollutants might be correlated because they may be generated by common processes or be driven by similar factors such as meteorology. The correlation between pollutants can help to predict one pollutant by borrowing strength from the others. Therefore, in chapter 5, I propose a multi-pollutant model which is a multivariate spatio-temporal fusion model that extends the single pollutant model in chapter 4, which relates monitored and modelled pollution data over space, time and pollutant to predict pollution across mainland Scotland. Considering that we are exposed to multiple pollutants simultaneously because the air we breathe contains a complex mixture of particle and gas phase pollutants, the health effects of exposure to multiple pollutants have been investigated in chapter 6. Therefore, this is a natural extension to the single pollutant health effects in chapter 4. Given NO2 and PM10 are highly correlated (multicollinearity issue) in my data, I first propose a temporally-varying linear model to regress one pollutant (e.g. NO2) against another (e.g. PM10) and then use the residuals in the disease model as well as PM10, thus investigating the health effects of exposure to both pollutants simultaneously. Another issue considered in chapter 6 is to allow for the uncertainty in the estimated pollution concentrations when estimating their health effects. There are in total four approaches being developed to adjust the exposure uncertainty. Finally, chapter 7 summarises the work contained within this thesis and discusses the implications for future research.

Tracing outsideness: young women's institutional journeys and the geographies of closed space

Understanding confinement and its complex workings between individuals and society has been the stated aim of carceral geography and wider studies on detention. This project contributes ethnographic insights from multiple sites of incarceration, working with an under-researched group within confined populations. Focussing on young female detainees in Scotland, this project seeks to understand their experiences of different types of ‘closed’ space. Secure care, prison and closed psychiatric facilities all impact on the complex geographies of these young women’s lives. The fluid but always situated relations of control and care provide the backdrop for their journeys in/out and beyond institutional spaces. Understanding institutional journeys with reference to age and gender allows an insight into the highly mobile, often precarious, and unfamiliar lives of these young women who live on the margins. This thesis employs a mixed-method qualitative approach and explores what Goffman calls the ‘tissue and fabric’ of detention as a complex multi-institutional practice. In order to be able to understand the young women’s gendered, emotional and often repetitive experiences of confinement, analysis of the constitution of ‘closed space’ represents a first step for inquiry. The underlying nature of inner regimes, rules and discipline in closed spaces, provide the background on which confinement is lived, perceived and processed. The second part of the analysis is the exploration of individual experiences ‘on the inside’, ranging from young women’s views on entering a closed institution, the ways in which they adapt or resist the regime, and how they cope with embodied aspects of detention. The third and final step considers the wider context of incarceration by recovering the young women’s journeys through different types of institutional spaces and beyond. The exploration of these journeys challenges and re-develops understandings of mobility and inertia by engaging the relative power of carceral archipelagos and the figure of femina sacra. This project sits comfortably within the field of carceral geography while also pushing at its boundaries. On a conceptual level, a re-engagement with Goffman’s micro-analysis challenges current carceral-geographic theory development. Perhaps more importantly, this project pushes for an engagement with different institutions under the umbrella of carceral geography, thus creating new dialogues on issues like ‘care’ and ‘control’. Finally, an engagement with young women addresses an under-represented population within carceral geography in ways that raise distinctly problematic concerns for academic research and penal policy. Overall, this project aims to show the value of fine grained micro-level research in institutional geographies for extending thinking and understanding about society’s responses to a group of people who live on the margins of social and legal norms.