Poor UK air quality has important social and economic impacts with ~29,000 premature deaths and costs to society of ~£20 billion, annually. It is important to understand the controlling factors and be able to forecast it. Therefore, the operational UK Met Office Air Quality in the Unified Model (AQUM), a short-range forecast model of atmospheric chemistry and aerosols, has been designed to predict hazardous air quality events. This study presents the first evaluation of AQUM using satellite observations of trace gases. These satellite observations, in comparison with AQUM, have also been used to investigate the influence of synoptic weather on UK air quality. Satellite data are prone to large random, systematic and smoothing errors. An algorithm has been developed to calculate and reduce the random error component of time-averaged Differential Optical Absorption Spectroscopy (DOAS) NO2 retrievals. It reduces the time-averaged tropospheric NO2 errors by 30-70% through the cancellation of random errors, which allows for a more critical evaluation of regional models. Comparisons with the processed observations of tropospheric column NO2 for 2006 show that AQUM overestimates column NO2 over northern England and Scotland in summer and across the domain in winter. Sensitivity experiments suggest that the model’s treatment of NOx point source (power station) emissions and missing N2O5 heterogeneous chemistry are the cause of these column NO2 overestimations. Satellite column NO2 composited under daily classifications of UK synoptic weather indicate that unstable (stable) cyclonic (anticyclonic) conditions lead to column NO2 transport (accumulation) away from (over) UK source regions. Wind direction influences column NO2 as source region leeward transport can be detected from space. AQUM, composited using the same methodology, successfully captures these air quality - synoptic weather relationships, giving confidence in its ability to forecast air pollution under different synoptic conditions.

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Current urban air pollution is a major environmental risk to population health. Much of the evidence on air pollution and its eects are based on studies focused on a single pollutant, where co-pollutants are treated as confounders or modifying factors. In reality, polluted air exists as a complex mixture of particles, gases and toxic substances and people experience a simultaneous exposure to multiple pollutants and sources. This thesis is concerned with statistical methods for characterising exposure metrics of airborne particulate matter (PM) and sources within a time series framework. Two original Bayesian modelling approaches are presented, with application to real-world data and inference based on Markov Chain Monte Carlo methods. A hierarchical modelling approach, which incorporates temporal and spatial statistical structures, was developed for estimating and predicting short-term concentrations of particles from dierent sources in an urban environment. Taking advantage of a varying coecient model, this approach modelled the long-range transport of the secondary PM and local primary components, combining observed concentrations from monitoring networks with output from a local-scale dispersion model, while accounting for factors with direct or indirect inuence on the particle distribution and formation. A semiparametric model, based on a Dirichlet process mixture model dened by a stick-breaking construction, was proposed for clustering time points with similar particle and health response proles. This model used a one-step procedure for dimension reduction and regression, while adjusting for aspects associated with time variation such as trend and seasonality through smooth functions. It also provided a tool to assess the changes in health eects from various policies to control ambient PM. These models are exible and reproducible in dierent environmental contexts, and were able to capture dependencies in real data and predict temporal and spatio-temporal responses with associated uncertainty.

Epidemiological time-series studies have demonstrated associations between short term exposure to nitrogen dioxide (N02) and adverse health outcomes. It is unclear whether these associations are independent of particulate matter (PM) or other traffic related pollutants. This has led to uncertainties about the public health importance of N02. This thesis presents a comprehensive, systematic review and meta-analysis of time-series studies of N02 and daily mortality and hospital admissions which aimed to determine if N02-associations are independent of PM; evaluate heterogeneity; and calculate coefficients for use in health impact assessments (HIA). Peer-reviewed time-series studies of N02 were assembled from databases and existing reviews. Descriptive data and estimates from single-and two-pollutant models along with data on effect-modifiers were extracted from the studies. Random-effects summary estimates were calculated globally and stratified by different geographic regions, and tests for heterogeneity and small study bias were conducted. 239 peer-reviewed time-series studies of N02 were identified. 76 cause- and age specific combinations of mortality or hospital admissions were meta-analysed. Two pollutant model study estimates generally showed that the N02-associations are independent of PM mass. For all-cause mortality, a 10 ug/m3 increase in 24 hour N02 was associated with a 0.78% (95% CI: 0.47, 1.09) increase in the risk of death, which reduced to 0.60% (0.33,0.87) after control for PM, and heterogeneity between geographical region-specific summary estimates was removed (I2 from 66.9% to 0%). Few studies assessed N02 together with traffic-related PM. None of the factors examined, including daily PM10 and the NO2/PM10 concentration ratio, accounted for the substantial heterogeneity between region-specific N02 estimates. This review provides evidence of associations between N02 and adverse health outcomes that are independent of PM mass. However, the limited evidence on adjustment of N02-associations for traffic-related PM means that some uncertainty remains regarding possible confounding and HIA exercises should reflect this.

Endocrine disrupting compounds (EDCs) can disrupt normal hormone signalling and production, and potentially contribute to a range of adverse effects on the environment and health. In vitro bioassay analysis was performed (mammalian reporter gene assays; RGAs) to assess the hormone receptor activity of common wastewater contaminants at environmentally relevant concentrations. The estrogenic enhancing capabilities of three parabens, 4-tert-octylphenol, carbamazepine and ketoconazole were identified; carbamazepine also disrupted glucocorticoid receptor (GR) transcriptional activity. In addition, RGAs were used, coupled with a high content analysis (HCA) assay, to investigate the potential for persistent organic pollutants (POPs) to induce stress responses via disruption of the GR. PFOS, PFDA, BDE-47 and p,p’-DDE altered mammalian stress responses. HCA was also used to investigate chemically defined POP mixture induced toxicity on a liver cell model and successfully detected early signs of cellular stress. While it is important to understand and classify the effects of contaminants, it is also crucial to gain information about their presence in the environment. To this end a UPLC MS/MS method was developed to monitor fifteen common aquatic contaminants. Screening of wastewater treatment work effluent revealed 10 of the 15 contaminants were still detectable after WWTW treatment. Furthermore anthropogenic pollution was evident in the rural catchment rivers. These catchments also have the phenomenon of unexplained static biological water recovery. The prevalence of EDC activity was investigated using an effect-directed bioassay analysis approach. Areas of concern were highlighted (androgen receptor and GR antagonism). Furthermore a statistically significant association between GR antagonism and average score per taxon (ASPT) values (a measure of biological water quality) was highlighted for one catchment. The use of novel techniques as HCA can help advance knowledge on mixture effects due to the ability to incorporate multiple parameters. The ability to detect pre-lethal signs of cellular stress is important for risk assessment. Furthermore, this thesis shows the importance of constant and updated screening methods of environmental waters. The inclusion of bioassays for screening water samples gives valuable information which targeted methods are unable to provide.

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The River Thames and associated waterways form Britain’s largest estuary and presents especial challenges in environmental health control. It is used on the disposal point at the same time supports substantial commercial fisheries. It is the major port of the UK handling imports in 2002 of over 55 million tonnes whilst providing a leisure resource to many of the six million inhabitants that live in its catchment. These potentially conflicting uses have considerable potential negative effects upon both the food chain and human health (Department of Health 1994). Current controls on this extremely complex environment are exercised by a variety of Government and non-Governmental agencies. These organisations have many overlapping responsibilities and there are areas of duplication and omission which need to be addressed. The study undertaken using modified soft systems methodology and a structured interview process examines all these organisations in depth and proposes a single model agency. This proposed agency would be stakeholder accountable, technically proficient, make better use of resources, and be a more effective lobbyist. It would also have the capability to effect the necessary improvements identified in this study.

Occupants of buildings are exposed to indoor pollution from cooking and smoking and infiltrated outdoor pollution. The fabric of a building (doors, windows, ventilation etc.) has an influence on the infiltration of outdoor pollution into the building. In some studies, personal exposure has been investigated within homes and different transport modes. However, there is a lack of knowledge about pollution level variations along congested, busy and quiet roads in urban areas and its infiltration into the buildings located some distance from or along the roads. Only a few studies have investigated dynamic and static indoor/outdoor monitoring simultaneously in the same urban area to establish relative levels of exposure in different microenvironments. The aim of this study was to investigate PM10 exposure to indoor and outdoor air pollution simultaneously as a function of activity patterns in urban streets/areas. This thesis describes the research carried out to investigate indoor and outdoor monitoring of PM10 exposure within and outside the air quality management area (AQMA), in Gosforth, Newcastle upon Tyne, UK. It examined the results of several days (at a sampling rate of one second or one minute) of monitoring of particulate matter (PM10) levels simultaneously indoors (static monitoring) and outdoors (static and dynamic monitoring). The static monitoring was conducted in a number of houses and commercial premises in Gosforth and Jesmond areas in Newcastle whilst dynamic monitoring was conducted along the High Street in Gosforth. For static monitoring, PM10 monitors were installed in the lounge and kitchen in houses and the reception areas of the commercial properties. The property owners were asked to record activity (such as cooking, vacuum cleaning, door opening etc.) in a diary for at least one day during the week and a day at weekends. For dynamic monitoring along the High Street Gosforth, the observer carried a portable PM10 monitor and a GPS monitor in a back pack and walked on the pavement alongside the street. The observer also noted the traffic condition, passing of HGV and buses, crossing of junctions and other activities, such as street cleaning, construction, cigarette smoking, all of which influence PM levels. Arc GIS software and statistical techniques were used to map spatial and temporal variations in PM10 levels recorded during several dynamic monitoring campaigns. Similarly, temporal variations in PM10 levels in houses were also plotted. Statistical techniques were used to fit distributions to the temporal variations in PM10 ii concentrations. Timestamps of traffic activities and events aligned with the time series for the dynamic monitoring have helped to identify their influence on PM10 levels. This research applied the basic theory of the statistical technique known as ‘decomposition’ to reveal features in the probability density functions (pdfs) derived from static measurements (indoor/outdoor) as well dynamic. The decomposition technique was used to characterise the influence of various sources and events on indoor and outdoor PM10 levels, to provide a richer understanding of whether exposure is influenced by the traffic flow regimes in the vicinity of properties. The decomposition technique was used to characterize pollution measured indoors disaggregating the contributions to the total pdfs of sources such as cleaning, cooking, sleeping as well as from outdoors with sources mainly traffic activity, street works. The dynamic second by second averaged to one minute PM10 levels were also decomposed to map onto sources associated with traffic condition. Component distributions fitted by the decomposition technique were lognormal for both static and dynamic monitoring. The results of the time series analysis have shown that monitored exposures vary substantially and are unique to the location and temporal variation of the measured microenvironment whether indoors in a kitchen or lounge, inside a commercial property or whether out of doors at the facade of a building or dynamically on a pavement alongside a road. The application of the decomposition technique was demonstrated to be promising. Static indoor and outdoor pdfs were mainly characterised by three or more log-normal distributions whilst the dynamically monitored data were fitted with three. Activities such as cooking, those associated with doors and windows opened or closed, use of extractor fan in the kitchen and vacuum cleaning were found to have a strong influence on indoor PM10 concentrations. Also, outdoor PM10 levels were governed more by the stop-start and idling characteristics of traffic rather than level of flow and traffic has little influence on temporal variations in indoor PM10 over time of the day. Instead it is the indoor activity that mainly governs the temporal variations in measured indoor concentrations of PM10. Multi-lognormal distributions explained typically 83% to 98% of the measured variance in the total pdfs. Finally, the author is not aware of any studies which have used the decomposition statistical technique to analyse dynamic and static indoor/outdoor monitoring in the same urban area to develop a fundamental understanding of the relative importance of the different sources of pollution in different microenvironments on personal exposure levels.

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