Application of lichen-biomonitoring to assess spatial variability of urban air quality in Manchester, UK

Airborne pollutants are increasingly impacting on urban populations, contributing to acute and chronic human health issues, e.g. cardiovascular and lung diseases, leading to approximately 40,000 premature deaths within the UK. Within the City of Manchester, two automated monitoring stations record atmospheric pollutants (i.e. CO, SO2, NOx and PM), but do not record airborne metal and PAH concentrations and are restricted in number, thus only recording localised air quality. This necessitates the application of additional monitoring methods to assess spatial variability of air quality, i.e. using biomonitors and passive monitoring devices. Lichens are proven biomonitors for atmospheric pollution, due to their morphology, lacking roots, cuticle and stomata, and thus absorbing, adsorbing and accumulating nutrients and atmospheric pollutants within their biological tissue. The aim of the study was to document and assess spatial variability of air quality in the City of Manchester, by applying a high spatial resolution lichen biomonitoring approach. Xanthoria parietina and Physcia spp. lichens (N=94) were analysed for carbon, nitrogen and sulphur contents and their stable-isotope-ratio signatures (δ13C, δ15N and δ34S). Furthermore, a new method was developed to extract nitrate and ammonium from lichen material, to investigate relative importance of both compounds on bulk nitrogen and δ15N values. Airborne metal and polycyclic aromatic hydrocarbon concentrations were further analysed to investigate potential sources (i.e. vehicular emissions) and potential human health risks. Lichen chemical data, was in part, ground-truthed by NOx diffusion tube analysis for NO2 concentrations. Findings indicated the beneficial use of lichens to biomonitor air quality at a high spatial resolution. Elevated pollutant loadings in lichens illustrated deteriorated air quality in Manchester. However, a complex mixture of pollutants affecting air quality in Manchester were indicated, with regard to its urban layout (i.e. road network, traffic counts and building heights) and subsequent dispersion and distribution of pollutants. This work contributed to a better insight into the variability of urban air quality, which could then be applied to (comparable) urban environments. Moreover, a high spatial lichen biomonitoring approach can be used to investigate and identify areas of concern regarding human health risks

Road transport and emissions modelling in England and Wales: A machine learning modelling approach using spatial data

An expanding street network coupled with an increasing number of vehicles testifies to the significance and reliance on road transportation of modern economies. Unfortunately, the use of road transport comes with drawbacks such as its contribution to greenhouse gases (GHG) and air pollutant emissions, therefore becoming an obstacle to countries’ objectives to improve air quality and a barrier to the ambitious targets to reduce Greenhouse Gas emissions. Unsurprisingly, traffic forecasting, its environmental impacts and potential future configurations of road transport are some of the topics which have received a great deal of attention in the literature. However, traffic forecasting and the assessment of its determinants have been commonly restricted to specific, normally urban, areas while road transport emission studies do not take into account a large part of the road network, as they usually focus on major roads. This research aimed to contribute to the field of road transportation, by firstly developing a model to accurately estimate traffic across England and Wales at a granular (i.e., street segment) level, secondly by identifying the role of factors associated with road transportation and finally, by estimating CO2 and air pollutant emissions, known to be responsible for climate change as well as negative impacts on human health and ecosystems. The thesis identifies potential emissions abatement from the adoption of novel road vehicles technologies and policy measures. This is achieved by analysing transport scenarios to assess future impacts on air quality and CO2 emissions. The thesis concludes with a comparison of my estimates for road emissions with those from DfT modelling to assess the methodological robustness of machine learning algorithms applied in this research. The traffic modelling outputs reveal traffic patterns across urban and rural areas, while traffic estimation is achieved with high accuracy for all road classes. In addition, specific socioeconomic and roadway characteristics associated with traffic across all vehicle types and road classes are identified. Finally, CO2 and air pollution hot spots as well as the impact of open spaces on pollutants emissions and air quality are explored. Potential emission reduction with the employment of new vehicle technologies and policy implementation is also assessed, so as the results can support urban planning and inform policies related to transport congestion and environmental impacts mitigation. Considering the disaggregated approach, the methodology can be used to facilitate policy making for both local and national aggregated levels

Spatial dependence of body mass index and exposure to night-time noise in the Geneva urban area

In this study, we calculated the night-noise mean (SonBase 2014, compatible with the EU Environmental Noise Directive) for the 5 classes obtained after computation of Local Indicators of Spatial Association (LISA; Anselin et al 1995) on the BMI of the participants in the Bus Santé study, a cohort managed by the Geneva University Hospitals (N=15’544; Guessous et al 2014). We expected the mean of dBs to be significantly higher in the group showing spatial dependence of high BMI values (high-high class). We ran an ANOVA and multiple T-tests to compare the dB means between LISA clusters. The approach was applied to the participants of the whole State Geneva cohort, and to a reduced set of individuals living in the urban environment of the municipality of Geneva only

Using GeoSpatial Analysis to Evaluate Relationships Between Cancer Incidence and Social Factors in Brooklyn, NY

This study explored the spatial distribution of cancer incidence in Brooklyn, NY. Using publicly available data, the relationships between cancer incidence and factors linked to cancer were investigated. Furthermore, the study explored the value of using large amounts of data with GIS techniques to quickly analyze geographic trends for cancer

Public perception on the state of air quality in Malta

Public perception on the state of air quality is an unclear and hazy concept whereby every individual has different opinions and views. In fact, the state of air quality is a major concern in many countries, including in Malta. This problem arises from the emissions of a number of pollutants to the atmosphere from a host of processes such as the combustion of fuel to generate electricity and the internal combustion engines to manoeuvre cars. Amongst these pollutants, there are benzene and nitrogen dioxide, which are very hazardous since they cause problems related to human health and contribute in the formation of ground ozone. The levels of these pollutants are continuously being measured by the Malta Environmental and Planning Authority by means of diffusion tubes distributed in various localities. This competent authority was appointed by the 2008 Directive on Ambient Air Quality and Cleaner Air for Europe, which also set up annual limits for the atmospheric pollutants. This research study aims to analyse the actual diffusion tube readings of benzene and nitrogen dioxide levels in Malta and their distribution trends along the island throughout the previous decade. This would eventually be compared with the opinions perceived by the public on the state of air quality collected by questionnaires by using appropriate statistical tests

Assessing Health Vulnerability to Air Pollution in Seoul Using an Agent-Based Simulation

This study aims to investigate the exposure to air pollution in Seoul and the consequent health effects in Seoul South Korea, and suggest possible solutions using agent-based modelling (ABM). ABM is a useful technique that can simulate pollution generation and exposure, mobility patterns of unique individuals, and explore future scenarios. The first study compared Universal Kriging and Generalised Additive Models to spatially interpolate pollution station data over Seoul. A new method was discovered to enhance the accuracy of NO2 on roads. Next, ABM was used to evaluate potential health loss for a set of demographic groups after being cumulatively exposed to particulates (PM10), with a nominal heath impact threshold of 100µg/m3. Finally, a traffic simulation examined the coupled problem of non-exhaust emissions and behaviour and estimate exposure to PM10 for groups of drivers and pedestrians in central Seoul. Having tested the sensitivity to calibrated parameters, scenarios of traffic restriction and modification of pedestrian behaviour to avoid polluted areas was investigated. With less difference between interpolation methods, the result showed a remarkable contrast between roadside and background NO2 as well as a daily cycle, while PM10 had a small variance between hours but had greater seasonal oscillation. The first ABM study showed that disparities in health may arise as a result of differences in socioeconomic status, especially when the group was exposed over a long period, and road proximity caused additional health loss. In the traffic simulation study, extreme PM10 was found along roadways, but although drivers were exposed to extreme values, longer exposure for pedestrians led to higher health risks. Despite the absence of reliable data linking exposure to actual health effects, it is possible to make progress with ABM. In addition, pollution exposure can vary by commuting patterns and the urban development of one’s location. Scenarios can be advantageous for healthcare policy – to aid the most vulnerable groups and districts

Evaluation of visualisations of geographically weighted regression, with perceptual stability

Given the large volume of data that is regularly accumulated, the need to properly manage, efficiently display and correctly interpret, becomes more important. Complex analysis of data is best performed using statistical models and in particular those with a geographical element are best analysed using Spatial Statistical Methods, including local regression. Spatial Statistical Methods are employed in a wide range of disciplines to analyse and interpret data where it is necessary to detect significant spatial patterns or relationships. The topic of the research presented in this thesis is an exploration of the most effective methods of visualising results. A human being is capable of processing a vast amount of data as long as it is effectively displayed. However, the perceptual load will at some point exceed the cognitive processing ability and therefore the ability to comprehend data. Although increases in data scale did increase the cognitive load and reduce processing, prior knowledge of geographical information systems did not result in an overall processing advantage. The empirical work in the thesis is divided into two parts. The first part aims to gain insight into visualisations which would be effective for interpretation and analysis of Geographically Weighted Regression (GWR), a popular Spatial Statistical Method. Three different visualisation techniques; two dimensional, three dimensional and interactive, are evaluated through an experiment comprising two data set sizes. Interactive visualisations perform best overall, despite the apparent lack of researcher familiarity. The increase in data volume can present additional complexity for researchers. Although the evaluation of the first experiment augments understanding of effective visualisation display, the scale at which data can be adequately presented within these visualisations is unclear. Therefore, the second empirical investigation seeks to provide insight into data scalability, and human cognitive limitations associated with data comprehension. The general discussion concludes that there is a need to better inform researchers of the potential of interactive visualisations. People do need to be properly trained to use these systems, but the limits of human perceptual processing also need to be considered in order to permit more efficient and insightful analysis

L'analisi spaziale

The so-called data deluge, along with ever-increasing technological capabilities, tantalizes geographers with exciting opportunities for spatial data analysis. These opportunities come with challenges, because data and technology, alone, cannot address the pressing questions of our world. Spatial analysis, a.k.a. spatial statistics, is a lot more than colourful maps and attractive displays: still relatively underrepresented in the Italian geography, this discipline has grown from a strictly quantitative niche to part of a critical spatial science and continues to stimulate new developments in statistics because, as we know, spatial is special. This book, published in the series “New Geographies. Work Tools”, adds spatial analysis to the Italian geographer’s toolbox. Not a how-to manual, it presents some of the core analytical issues through the redundancy of narrative language and mathematical language. It traces the journey of spatial analysis from its roots in quantitative geography, GIS, and statistics, towards the definition of its own identity and the acceptance of its own relativity and limitations. It discusses the relationship of spatial analysis with GIScience and its efforts to embed critiques within its own discourse, emphasizing the role of theory, the importance of hypothesis testing, and acknowledging the ethics surrounding the use and analysis of data. A few examples illustrate practical implementations, showing the value added by spatial statistics in yielding reliable analyses that can support management decisions. It concludes with a brief outlook on the Italian geographic literature, where spatial analysis – like elsewhere – can play a role in competently accepting today’s opportunities and challenges, in a constructive dialogue within geography as a whole