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A large number of epidemiological studies have identified air pollution as a major risk to human health. Short-term and long-term exposures to air pollutants such as PM2.5, NO2 and O3 cause cardiovascular and respiratory diseases, cancer and other adverse health effects. These lead to decreased quality of life, increased hospital visits and premature mortality. Due to a high spatial and temporal variability of air pollution exposure, exposure inequalities exist within the society. Published studies suggest that it is often the most deprived and susceptible who are disproportionately exposed to the highest concentrations of some of the most ubiquitous air pollutants. However, most epidemiological and exposure studies do not take into account the spatio-temporal variability of air pollutants and population mobility within their assessments which is likely to lead to exposure misclassification and, consequently, a bias in the associated health effects. Several modelling approaches have been developed to improve estimates of population exposure. Either statistical or deterministic models are now commonly used to predict air pollution concentrations. Deterministic models include atmospheric chemistry transport models (ACTMs), which tend to be used for larger study areas on a regional or higher scale, and Gaussian dispersion models, which on a local - urban - scale are able to predict concentrations at very high spatial resolutions. The aim of this thesis is to investigate how workplace-related population mobility and spatio-temporal variability of air pollution affect population exposure and its inequality in the UK. Firstly, the effect of exposure to ambient air pollution at the place of work or study on overall population exposure in the UK is examined using publicly available data from Census 2011. The analysis is conducted for the whole of the UK (England, Wales, Scotland and Northern Ireland), and separately for Scotland only. The residential population distribution and daytime population distribution data are combined with concentration fields of key air pollutants (PM2.5, NO2 and O3) generated by the EMEP4UK atmospheric chemistry transport model at relatively high spatial (approximately 1.5 km × 2 km) and temporal (hourly) resolutions to calculate population exposure of stay-at-home 'static' population and a 'dynamic' population which spends a proportion of time on weekdays at the place of work or study. The calculated exposures of static and dynamic populations are compared, and sensitivity studies of different working hours of the dynamic population are conducted. The highest difference between dynamic and static population exposures is observed for NO2 (0.28 µg m-3 or 2.0% increase in the UK, 0.29 µg m-3 or 23.1% increase in Scotland) for working hours between 08:00 and 18:00. The calculated differences for PM2.5 and O3 are much smaller. Whilst at the population level the exposure difference is small, a case study using virtual individuals suggests a potential large variation between individuals. Secondly, the exposure of dynamic population and population subgroups to air pollution is examined in a case study of the Central Belt of Scotland region. Additionally, the two largest and demographically contrasting urban areas within the region - Glasgow and Edinburgh - are considered separately. For the analysis, anonymised personal data of the participants of the Scottish Longitudinal Study (SLS), which is a representative sample of the Scottish population, are linked at the postcode unit level with air pollution concentrations generated by EMEP4UK (approximately 0.8 km × 1.4 km spatial resolution). The SLS participants are stratified by age, ethnicity and socio-economic status (SES) for the population subgroup exposure assessment. Exposures at residential address and the place of work or study are considered using three different work pattern scenarios and the results are compared with exposures of the 'static' population. Exposure gradients are observed across all demographic characteristics. Young people between 21 and 30 years of age tend to have the highest exposure to NO2 and PM2.5, and lowest to O3; however, those aged 31 to 50 tend to be most affected by inclusion of exposure at workplace. The patterns for SES and ethnicity are complex and study area specific; however, people in the two least deprived deciles consistently have the lowest residential and residential-workplace exposure to NO2 and PM2.5 but tend to see the highest increase in exposure due to workplace mobility. Overall, including exposure at place of work in exposure estimates tends to alleviate some of the exposure inequalities observed in the static population exposure assessments. Thirdly, the effect of using different air pollution models on 'dynamic' population exposure estimates and its inequalities is investigated. The city of Edinburgh is chosen as the study area. Two models are considered: EMEP4UK and a Gaussian plume dispersion model, ADMS Urban. Detailed traffic emission data for all major and some minor roads in the city, and gridded emissions from other sources, are used in the ADMS Urban modelling. The model output is verified against available monitoring data. Differences in modelled output are observed between the models which are subsequently translated into differences in population exposure estimates. The effect of workplace exposure on overall population exposure to NO2 is larger for the ADMS-Urban model than for the EMEP4UK model; however, the magnitude is still very small (≤ 1.6%). For O¬3 and PM2.5, the effects are smaller and largely comparable between the models. With some notable exceptions, both models show similar patterns in both exposure inequality and the influence of workplace mobility on it. Lastly, the overall findings and their implication for assessment of exposure and exposure inequality are discussed. This work suggests that inclusion of the place of work or study in exposure assessments makes only a small difference for population-scale burden assessment, particularly for those pollutants with secondary contributions such as O¬3 and PM2.5. This conclusion is not particularly sensitive to the atmospheric chemistry/dispersion model used.

The co-location of air pollution and socio-economic deprivation is increasingly well documented and studies have found that the socio-spatial distribution of health-related environmental characteristics, specifically air pollution, can be an important driver of geographical inequalities in health. The most deprived members of society face the highest exposures and the greatest risks due to a concept termed the triple jeopardy. Children face an increased susceptibility to air pollution exposure, and exposure can result in a range of health issues, such as asthma. Linking longitudinal data from the Millennium Cohort Study (MCS), air pollution data available from EMEP4UK, and area level deprivation data from the Index of Multiple Deprivation, this thesis aims to explore the relationship between air pollution exposure, and both individual and area level socio-economic status to understand how these exposures interact to impact respiratory health in children. Following data linkage, cross-sectional analysis, time series analysis and multilevel modelling are employed to examine the data. Multilevel modelling is used to appropriately attribute variations in spatial health outcomes to differences between places, differences between people within places and differences over time. The use of multilevel modelling is an innovative step in understanding the relationship between socio-economic factors, air pollution and health outcomes. Multilevel modelling found that 85% of the variation in asthma prevalence in children lies within MSOAs, whilst 14% of the variation was found to be over time. In comparison, 47% of the variation in wheezing was found to be due to differences over time. Two- and three-way interaction terms were included in the analysis to explore the impact of individual level socio-economic status, area level deprivation and air pollution exposure on asthma and wheezing prevalence in children, however no association was found. Moving forward, focussing interventions on improving both individual and area level socio-economic status, and implementing policies to lower pollution in the most deprived areas could help alleviate the health burden faced by the most deprived in society when exposed to air pollution.

Nitrogen dioxide (NO2) is an air pollutant identified as a public health concern. Exposure to NO3 is associated with a number of adverse respiratory health effects, and ultimately with premature mortality. It also contributes as a precursor to formation of tropospheric ozone (O3) and ammonium nitrate fine particulate matter (PM2.5, particles with aerodynamic diameters < 2.5 μm). Rapid economic growth, industrialization, and urbanization in China are leading to substantial adverse air quality issues, including high levels of annual mean NO2 concentrations. It is important to quantify human exposure to NO2 to evaluate its health impacts and to assess the effectiveness of mitigation approaches. Since 2013, the China National Environmental Monitoring Centre (CNEMC) has been implementing a nationwide monitoring network for the routine measurement of ambient air pollutant concentrations. Previous studies into population exposure used the monitor data as a proxy for human exposure. However, NO2 concentrations within cities have shown high spatial variations. The monitoring network only provides concentrations at a limited number of discrete points, which is inadequate to describe the spatial variability of urban air pollution. New methods need to be developed to tackle these challenges. The overall aim of this PhD project is to explore modelling approaches for better estimating intra-urban variability of NO2 for human exposure research in China, given the obstacles in data availability of monitored data, emission inventories, and other highly spatially resolved data in China. Guangzhou is chosen as an exemplar geographic domain. It is the third largest city in China, with a population of 14 million and an area of 7,433 km2, and does not currently meet the Chinese air quality standard (GB 3095-2012) for NO2, which is set as 40 μg m-3 as an annual average. The Guangzhou local government has an air quality compliance plan that aspires to annual average NO2 concentrations of 40 μg m−3 by 2020. Two modelling methods are widely used to simulate pollutant concentrations at relatively high spatial resolution within urban areas: dispersion modelling and land-use regression (LUR) modelling. Dispersion modelling aims to simulate the physical chemical processes that link the emissions of pollutants from sources and their transport and dispersion. Recently, urban dispersion models have been developed in Beijing, Shanghai, Chongqing, Hangzhou, Kunming, Hong Kong, Harbin, Lanzhou, Urumqi, Liaoning province, Jinan, Fushun, and Macao using ADMS and AERMOD. Substandard modelling results can arise due to insufficient monitor data and incomplete or inaccurate emission inventories. LUR relies on existing measurements to derive the statistical relationship between pollutant concentrations at a given location and predictor variables representing the emission and dispersion of air pollutants. An appropriately sized and designed monitoring network is an important component for the development of a robust LUR model. LUR models are now being applied to simulate pollutant concentrations with high spatial resolution in Chinese urban areas. Current challenges and future needs in employing LUR approaches were identified first in this PhD work. Details of twenty-four recent LUR models for NO2 and PM2.5/PM10 (particles with aerodynamic diameters < 10 μm) were reviewed. LUR modelling in China is currently constrained by a scarcity of input data, especially air pollution monitor data. There is an urgent need for accessible archives of qualityassured measurement data and for higher spatial resolution proxy data for urban emissions, particularly in respect of traffic-related variables. The rapidly evolving nature of the Chinese urban landscape makes maintaining up-to-date land-use and urban morphology datasets essential for LUR models. Given the limited number of monitoring sites in Guangzhou and the geographical scale of the domain, an integrated modelling approach combining dispersion modelling with ADMS-Urban and LUR has been developed in this PhD work. ADMS-Urban was applied in Guangzhou using input data including emissions from the Multi-resolution Emission Inventory for China (MEIC), road geometry from OpenStreetMap, and hourly meteorological data from the European Centre for Medium-Range Weather Forecasts (ECMWF). Concentrations of NO2 were simulated by ADMS-Urban at 83 ‘virtual’ monitoring sites spanning the six districts in Guangzhou and weighted according to population (since the overall focus is estimation of population NO2 exposure). The LUR model was validated against both the 83 virtual sites (adj R2: 0.96, RMSE: 5.48 μg m−3; LOOCV R2: 0.96, RMSE: 5.64 μg m−3) and, independently, against available observations (n = 11, R2: 0.63, RMSE: 18.0 μg m−3). The modelled population-weighted long-term average concentration of NO2 across Guangzhou in 2017 was 52.5 μg m−3, which contributes an estimated 7,270 (6,960−7,620) attributable deaths. This hybrid modelling approach is then applied to explore the scale of emissions reductions necessary within the Guangzhou domain to achieve compliance with a number of different interpretations of an NO2 concentration target of 40 μg m−3. (The Guangzhou Ambient Air Quality Compliance Plan does not explicitly state how to practically assess compliance.) The modelling results show that achieving compliance requires different levels of emission reductions, depending on how the concentration target was defined; for example, to reduce the average concentration at all monitoring sites below 40 μg m−3, requires a 60% reduction of emissions from all source sectors. In contrast, to attain ≤40 μg m−3 concentration across the whole of Guangzhou requires a 90% emissions reduction. The impacts of the emissions reductions on NO2-attributable premature mortality are also calculated and illustrate that use of a concentration value as a target does not fully convey the underlying health gains even when the target is not met. In the final part of this thesis, the findings and implications from the modelling studies are discussed in the context of current air quality management system in China. Whilst the results are based on detailed and consistent model results for the specific situation in Guangzhou, they are relevant for, and can provide evidence to, decision makers designing effective air pollution control policies in other fast-growing megacities in China and elsewhere globally. The challenges and limitations for the development of a highly spatial revolved model for human exposure are discussed.

Air pollution can lead to environmental impacts. Over the past decades there have been some success stories reducing pollutant emission, namely sulphur dioxide (SO2). However, impacts on ecosystems from atmospheric nitrogen (N) pollution are still seen as a major threat for European biodiversity. Across Europe over 70% of Natura 2000 sites are at risk for eutrophication with over 70% of the Natura 2000 area in Europe (EU28( exceeding critical loads for nutrient nitrogen deposition. Agricultural ammonia is a key contributor to the threat to these sites due to the close proximity of agricultural activities and protected sites. Source attribution modelling using an atmospheric transport model showed that agricultural livestock production in the UK is the dominant nitrogen source for N disposition across the UK Natura 2000 network. Nearly 90% of all sites had livestock as their dominant source, contributing 32% of the total nitrogen deposition across the whole network. 76% of all Special Areas of Conservation (SAC) sites exceeded their critical load for nutrient nitrogen, representing 74% of the entire SAC area. The extent of exceedance is also notable with many sites experiencing depositions of >50 kg N/ha/yr over the critical load. the situation for acidity critical load exceedance is less sever, by 51% of sites are still exeeded. Legislation to regulate pollutant emissions to air and protect biodiversity are often not integrated, and there has been no common European approach for determining the impacts of nitrogen deposition on individual Natura sites, or on conservation status. Off-site sources of air pollution present difficulties in assessing and attributing impacts, because deposition can result from local sources (1-2 km), or very far away sources (>1000 kms). Managing nitrogen losses on the farm and improving the efficient use of nitrogen are key components for overall reduction in NH3 emissions. Many nitrogen management options are available to abate ammonia from agricultural activities. On the one hand, technical and management measures include controlling emissions from manure storage and spreading, livestock feeding strategies, and improving housing systems. Trees, on the other hand, are effective scavengers of both gaseous and particulate pollutants from the atmosphere, making tree belts potentially effective landscape features to support ammonia abatement strategies. Using a coupled deposition and turbulence model the recapture efficiency of tree planting around ammonia sources was estimated. Using different canopy structure scenarios, tree depths and differing leaf area density (LAD) and leaf are index (LAI) were adjusted for a main canopy and a backstop canopy. Recapture efficiency for ammonia ranged from 27% (trees planted around housing systems), up to 60% (under-story livestock silvopastoral systems). Practical recapture potential was set at 20% and 40% for housing and silvopastoral systems respectively. Model results from scaling up to national level suggest that tree planting in hot spot areas of ammonia emissions would lead to reduced N deposition on nearby sensitive habitats. Scenarios for on-farm emission control through tree planting showed national reductions in nitrogen deposition to semi-natural areas of 0.14% (0.2 kt N-NHx) to 2.2% (3.15 kt N-NHx). Scenarios mitigating emissions from cattle and pig housing yielded the highest reductions. The afforestation strategy showed national-scale emission reductions of 6% (8.4 kt N-NHx) to 11% (15.7 kt N-NHx) for 25% and 50% afforestation scenarios respectively. Increased capture by the planted trees also generated an added benefit of reducing long-range transport effects, including a decrease in wet deposition of up to 3.7 kt N-NHx (4.6%) and a decrease in export from the UK of up to 8.3 kt N-NHx (6.8%). Agroforestry measures for ammonia abatement were shown to be cost-effective for both planting downwind of housing and in silvopastoral systems, when costs to society were taken into account. Planting trees was also cost-effective from a climate change perspective. Comparing the cost per kg of NH3 abated showed that planting trees is a method of ammonia emission mitigation comparable with other (technical) measures. The costs for planting trees downwind of housing were calculated at €2.6-7.3/kg NH3. Agroforestry for ammonia abatement offers multiple benefits for the farmer and synergistic effects for society as a whole including i) carbon sequestration. ii) visibility screening around housing units, iii) imporved animal welfare for silvopastoral systems, iv) reducing critical load exceedance on protected sites, v) price advantage of 'woodland chick' productions, vi) supporting the Industrial Emission Directive (IED) requirements for emission reduction, vii) supporting national afforestation policies. The results of this work support the notion that in the emerging discussion about the values of ecosystem services and the role of nature-based solution to tackle persistent environmental challenges, tree planting has a large potential in rural and urban environments.

Adverse health effects from exposure to air pollution, although at present only partly understood, are a global challenge and of widespread concern. Quantifying human exposure to air pollutants is challenging, as ambient concentrations of air pollutants at potentially harmful levels are ubiquitous and subject to high spatial and temporal variability. At the same time, individuals have their very own unique activity-patterns. Hence exposure results from intertwined relationships between environmental and human systems add complexity to the assessment process. It is essential to develop a deeper understanding of individual exposure pathways and situations occurring in people’s everyday lives. This is important especially with regard to exposure and health impact assessment which provide the basis for public health advice and policy development. This thesis describes the development and application of a personal monitoring method to assess exposure to fine particulate matter in a variety of microenvironments. Tools and methods applied are tested with respect to feasibility, intrusiveness, performance and potential for future applications. The development of the method focuses on the application in everyday environments and situations in an attempt to capture as much of the total exposure as possible, across a complete set of microenvironments. Seventeen volunteers took part in the pilot study, collected data and provided feedback on methodology and tools applied. The low-cost particle counter applied showed good agreement with reference instruments when studied in two different environments. Based on the assessment of the two instruments functions to derive particle mass concentration from the original particle number counts have been defined. The application of the devices and tools received positive feedback from the volunteers. Limitations are mainly related to the non-weatherproof design of the particle counter. The collection of time-activity patterns with GPS and time-activity diaries is challenging and requires careful processing. Resulting personal exposure profiles highlight the influence of individual activities and contextual factors. Highest concentrations were measured in indoor environments where people also spent the majority of time. Differences between transport modes as well as between urban and rural areas were identified.