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BACKGROUND: A growing body of research has suggested that exposure to traffic-related emissions is associated with numerous adverse health effects including prevalence and severity of symptoms of asthma, hospitalizations for acute myocardial infarctions, and cardiovascular-related mortality. No previous studies have assessed the association between proximity to traffic and respiratory and cardiovascular outcomes across all age groups. OBJECTIVE: The purpose of this study was to assess the association between proximity to traffic emissions within the City of Atlanta and respiratory and cardiovascular 911 Emergency Management Service (EMS) calls and subsequent emergency department (ED) visits. METHODS: Case and control diagnostic groups were established for 5,450 EMS calls received between 2004 and 2008 from residents of the City of Atlanta based on ICD-9 codes assigned within the ED. Case diagnostic groups included asthma, cardiovascular outcomes, and stroke. Gastrointestinal diagnostic groups were selected as controls. Cumulative traffic within a 100 m buffer of the call origination location was used as an indicator of exposure to traffic emissions. Using a case-control study design, the associations between exposure to traffic emissions and the case diagnostic groups were evaluated using logistic regression, controlling for potential confounding factors including age, gender, ethnicity, and socio-economic status (SES). Subgroup analyses were performed to evaluate differences by select age categories, gender, and SES. P-values of <0.05 and 95% confidence intervals (CI) were used to determine statistical significance. RESULTS: Increased cumulative traffic near the call location was associated with an increase in the odds of an EMS call and ED visit for cardiovascular outcomes compared to the control diagnostic group even after adjustment for confounding factors (OR = 1.07; 95% CI ,1.01-1.12). The strongest effects were among men and individuals aged 40-75 years. Increased cumulative traffic was also associated with an increased odds of an EMS call and ED visit for stroke among individuals aged 18-39 years after adjusting for confounding (OR = 1.16; 95% CI, 1.01-1.34). No statistically significant associations were found between increased cumulative traffic and the odds of an EMS call and ED visit for asthma. CONCLUSION: These results provide additional evidence that proximity to traffic is associated with adverse cardiovascular outcomes and stroke in certain age groups.

148f. / Submitted by Ana Hilda Fonseca (anahilda@ufba.br) on 2013-03-22T17:35:21Z No. of bitstreams: 1 TESE VERSÃO FINAL_Rogete.pdf: 3403393 bytes, checksum: 8ad0a12697759237a2a80d8d5568a3c8 (MD5) / Approved for entry into archive by Ana Hilda Fonseca(anahilda@ufba.br) on 2013-04-19T14:20:42Z (GMT) No. of bitstreams: 1 TESE VERSÃO FINAL_Rogete.pdf: 3403393 bytes, checksum: 8ad0a12697759237a2a80d8d5568a3c8 (MD5) / Made available in DSpace on 2013-04-19T14:20:43Z (GMT). No. of bitstreams: 1 TESE VERSÃO FINAL_Rogete.pdf: 3403393 bytes, checksum: 8ad0a12697759237a2a80d8d5568a3c8 (MD5) Previous issue date: 2013 / CAPES / O material particulado atmosférico (MP) é um dos principais poluentes do ar, este pode ser emitido por fontes naturais e antrópicas. Em ambientes urbanos o tráfego de veículos é um dos grandes responsáveis por sua emissão. Em sua composição estão associadas inúmeras espécies químicas, dentre as quais se destacam metais e metalóides. A origem destas espécies através do tráfego está relacionada a várias fontes, tais como, produtos de combustão do combustível e óleo; produtos de desgaste de pneus, lonas de freio, rolamentos; materiais de construção das vias de tráfego; e ainda a ressuspensão da poeira do solo. As frações mais finas do MP são as mais nocivas e a exposição a estas partículas é responsável por vários efeitos à saúde e estão associadas ao aumento de riscos de doenças respiratórias e cardiovasculares e problemas reprodutivos. Especialmente os metais podem causar câncer, mutações e problemas neurológicos. Neste trabalho, as concentrações de partículas em suspensão foram determinadas por meio de técnica gravimétrica; e as concentrações atmosféricas dos elementos traço As, Ba, Ca, Cd, Co, Cu, Fe, Hg, Mg, Mn, Ni, Pb, Sb, Sn, V e Zn, nas frações MP10 e MP2,5 coletadas no Terminal Rodoviário da Lapa, foram determinadas usando as técnicas de ICP-MS e ICP OES. As amostras de MP10 e MP2,5 do ano de 2010, foram coletadas sobre filtros de microfibra de quartzo em amostradores de grande volume (Hi-Vol). As amostras de MP10 coletadas em 2005 em outro estudo, foram utilizadas neste trabalho com a finalidade principal de se investigar a composição elementar do MP no período de transição B0 (2005) para B5 (2010). Para a seleção do procedimento de extração, com vistas ao tratamento das amostras de MP, foram realizados alguns experimentos e dentre estes, a mistura de 2 mL HNO3, 1 mL de H2O2 e 5 mL H2O, se mostrou adequada produzindo respostas satisfatórias. A validação do método garantiu recuperações na faixa de 83% a 114%. Os limites de quantificação do método nas determinações realizadas por ICP-MS foram estabelecidos entre 0,02 e 1,82 ng m-3 e nas determinações por ICP OES entre 1,9 e 896 ng m-3. Os resultados da concentração de partículas em suspensão para MP10 e MP2,5, apresentaram valor médio, respectivamente, de 134 μg m-3 e 83,2 μg m-3. Entre os elementos traço minoritários, as maiores concentrações médias encontradas em 2010, foram para Hg, Ni, Pb, Sn e V cujos valores para MP10 foram, respectivamente, 4,52; 4,53; 7,33; 4,41 e 5,68 ng m-3; e para MP2,5 foram, respectivamente, 2,68; 3,01; 4,57; 1,32 e 2,96 ng m-3. Entre os elementos majoritários as maiores concentrações encontradas em 2010, foram para Ba, Ca, Fe e Mg, cujos valores médios foram, respectivamente, para MP10, 587; 5260; 5190 e 1907 ng m-3 e MP2,5, 165; 2759; 2101; 947 ng m-3. A avaliação das concentrações dos elementos traço por turno em 2010 mostrou que nos turnos da manhã e tarde em conjunto, a concentração dos elementos traço compreendeu aproximadamente entre 58% a 87% do total emitido na fração MP10; e entre 62% a 85% na fração MP2,5. Os cálculos do fator de enriquecimento revelaram que os elementos de origem antrópica no MP em suspensão, portanto considerados enriquecidos, foram As, Ba, Cd, Cu, Hg, Pb, Sb, Sn e Zn. A análise multivariada permitiu identificar os elementos que caracterizaram as emissões do MP e a influência do tráfego conforme os turnos de amostragem. E ainda indicou que as diferenças entre as frações MP10 de 2005 e 2010, correspondentes ao período de transição de B0 para B5, foram relacionadas às concentrações de Cd, Mn, Cu e Hg. / Salvador

Ce travail porte sur la quantification d'incertitude dans la modélisation des émissions de polluants atmosphériques dues au trafic routier d'une aire urbaine. Une chaîne de modélisations des émissions de polluants atmosphériques est construite, en couplant un modèle d’affectation dynamique du trafic (ADT) avec un modèle de facteurs d’émission. Cette chaîne est appliquée à l’agglomération de Clermont-Ferrand (France) à la résolution de la rue. Un métamodèle de l’ADT est construit pour réduire le temps d’évaluation du modèle. Une analyse de sensibilité globale est ensuite effectuée sur cette chaîne, afin d’identifier les entrées les plus influentes sur les sorties. Enfin, pour la quantification d’incertitude, deux ensembles sont construits avec l’approche de Monte Carlo, l’un pour l’ADT et l’autre pour les émissions. L’ensemble d’ADT est évalué et amélioré grâce à la comparaison avec les débits du trafic observés, afin de mieux échantillonner les incertitudes / This work focuses on the uncertainty quantification in the modeling of road traffic emissions in a metropolitan area. The first step is to estimate the time-dependent traffic flow at street-resolution for a full agglomeration area, using a dynamic traffic assignment (DTA) model. Then, a metamodel is built for the DTA model set up for the agglomeration, in order to reduce the computational cost of the DTA simulation. Then the road traffic emissions of atmospheric pollutants are estimated at street resolution, based on a modeling chain that couples the DTA metamodel with an emission factor model. This modeling chain is then used to conduct a global sensitivity analysis to identify the most influential inputs in computed traffic flows, speeds and emissions. At last, the uncertainty quantification is carried out based on ensemble simulations using Monte Carlo approach. The ensemble is evaluated with observations in order to check and optimize its reliability

Population exposure to traffic pollution is a rapidly developing, multi-disciplinary scientific field. While the link between long-term exposure and respiratory issues is well-established, there are probable links to a number of more serious health effects, which are still not fully understood. In the interests of protecting human health, it is prudent that we take a cautionary approach and actively seek to reduce exposure levels, especially in the home environment where people spend a significant portion of their time. In many large cities, a substantial number of homes are situated on land immediately adjacent to busy freeways and other heavily-trafficked roads. Characterising exposures of local residents is incredibly challenging but necessary for advancing epidemiological understandings. While existing studies are plentiful, the results are mixed and generally not transferable to other urban areas due to the localised nature of the built environment and meteorological influences. This thesis aimed to employ a variety of methods to develop a holistic understanding of the influence of traffic emissions on near-highway residents' exposure in two communities of South Auckland, New Zealand, where Annual Average Daily Traffic (AADT) is as high as 122,000 vehicles. First, ultrafine particles (UFPs), nitrogen oxides (NOx), carbon monoxide (CO) and particulate matter ≤ 10 μm (PM₁₀) were continuously monitored using a series of fixed stations at different distances from the highways, over several months during the winters of 2010 and 2011. Emissions modelling output (based on traffic composition), was used within a dispersion model to compare modelled concentrations with monitored levels. In addition, community census meshblock units were mapped by level of social deprivation in order to assess potential inequities in highway emissions exposure. The second layer of local air quality investigation involved using a bicycle platform to systematically measure concentrations of UFPs, CO and PM₁₀ using the entire street-grid network throughout each community. This was done forty times - five times at four times of day (07:00, 12:00, 17:00 and 22:00), for each study area, with the aim of mapping the diurnal fluctuation of microspatial variation in concentrations. Using global positioning system (GPS) data and geographical information system (GIS) software, spatially-resolved pollutant levels were pooled by time of day and the median values mapped, providing a visualisation of the spatial extent of the influence of emissions from the highways compared to minor roads. The third layer involved using data from multiple ambient monitors, both within the local areas and around the city, to simulate fifty-four residents' personal exposure for the month of June, 2010. This required collecting timeactivity information which was carried out by door-to-door surveying. The time-activity data were transformed into microenvironment and activity codes reflecting residents movements across a typical week, which were then run through the US-EPA's Air Pollution Exposure Model (APEX). APEX is a probabilistic population exposure model for which the user sets numerous microenvironmental parameters such as Air Exchange Rates (AERs) and infiltration factors, which are used in combination with air pollutant concentrations, meteorological, and geospatial data, to calculate individuals' exposures. Simulated exposure outputs were grouped by residents' occupations and their home addresses were artificially placed at varying distances from the highways. The effects of residential proximity to the highway, occupation, work destination and commute distance were explored using a Generalised Linear Model (GLM). Surveyed residents were also asked a series of Likert-type, ordered response questions relating to their perceptions and understandings of the potential impacts of living near a significant emissions source. Their response scores were explored as a function of proximity to the highway using multivariate linear regression. This formed the final layer of this investigation into air quality throughout these South Auckland communities of Otahuhu and Mangere Bridge. Results show that concentrations of primary traffic pollutants (UFPs, NOx, CO) are elevated by 41 - 64% within the roadside corridor compared to setback distances approximately 150 m away and that the spatial extent of UFPs can reach up to 650 m downwind early in the morning and late in the evening. Further, social deprivation mapping revealed that 100% of all census meshblocks within 150 m either side of both highways are at the extreme end of the deprivation index (NZDep levels 8 - 10). Simulations for residents dispersed across the community of Otahuhu estimated daily NOx and CO exposure would increase by 32 and 37% (p<0.001) if they lived immediately downwind of the highway. If they were to shift 100 m further downwind, daily exposure would decline by 56 - 70% (p<0.001). The difference in individuals' exposure levels by occupation varied across the same distance by a factor of eight (p<0.05), with unemployed or retired persons the most exposed due to having more free time to spend outdoors at home (recreation, gardening, etc.). Those working in ventilated offices were the least exposed, even though ambient concentrations - likely due to a strong urban street canyon effect - were higher than the nearest highway monitor (5 m downwind) by 25 - 30% for NOx and CO, respectively. Inverse linear relationships were identified for distance from highway and measures of concern for health impacts, as well as for noise (p<0.05). Positive linear relationships were identified for distance from highway and ratings of both outdoor and indoor air quality (p<0.05). Measures of level of income had no conclusive statistically significant effect on perceptions (p>0.05). The main findings within this thesis demonstrate that those living within the highway corridor are disproportionately exposed to elevated long-term average concentrations of toxic air pollutants which may impact on physical health. While the socioeconomic characteristics could also heighten susceptibility to potential health impacts in these areas, certain activity patterns can help mitigate exposure. This thesis has also shown that there may be quantifiable psychological benefits of a separation buffer of at least 100 m alongside major highways. These results enhance a very limited knowledge base on the impacts of near-roadway pollution in New Zealand. Furthermore, the results lend additional support to the international literature which is working to reduce residential exposures and population exposure disparities through better policies and improved environmental planning. Where possible, the placement of sensitive population groups within highway corridors, e.g. retirement homes, social housing complexes, schools and childcare centres, should be avoided.

Outdoor air pollution is a major contributor to adverse health effects of citizens, in particular those living in urban environments. Air quality monitoring networks are set up to measure air quality in different environments in compliance with national and European legislation. Generally, only a few fixed monitoring sites are located within a city and thus cannot represent air pollutant concentrations in urban areas accurately enough to allow for a detailed human exposure assessment. Other approaches to derive detailed urban air pollutant concentration estimates exist, such as dispersion models and land-use regression (LUR) models. Low-cost portable air quality monitors are also emerging, which have the potential to add value to existing monitoring networks by providing measurements at greater spatial resolution and also to provide individual-level exposure assessment. The aim of this thesis is to demonstrate how measurements and modelling in combination allow detailed investigations of the variability of air pollutants in space and time in urban area, and in turn improve on the current exposure assessment methods. Three types of low-cost portable monitors measuring NO2, O3 (Aeroqual monitors) and PM2.5 (microPEM monitor) were evaluated against their respective reference instruments. The Aeroqual O3 monitor showed very good correlation (r2 > 0.9) with the respective reference instruments, but biases in the slope and intercept coefficients indicated that calibration of Aeroqual O3 monitor was needed. The Aeroqual NO2 monitor was subject to cross-sensitivity from O3, which, as demonstrated, can be effectively corrected by making O3 and NO2 measurements in tandem. Correlation between the microPEM monitor and its reference instrument was poor (r2 < 0.1) when PM2.5 concentrations were low (< 10 μg m-3), but significantly improved (r2 > 0.69) during periods with elevated PM2.5 concentrations. Relative humidity was not found to affect the raw results of PM2.5 measurements in a consistent manner. All three types of monitors cannot be used as equivalent or indicative methods instead of reference methods in studies that require quantification of absolute pollutant concentrations. However, the generally good correlations with reference instruments reassure their application in studies of relative trends of air pollution. Concentrations of PM2.5, ultrafine particles (UFP) and black carbon (BC) were quantified using portable monitors through a combination of mobile and static measurements in the city of Edinburgh, UK. The spatial variability of UFP and BC was large, of similar magnitude and about 3 times higher than the spatial variability of PM2.5. Elevated concentrations of UFP and BC were observed along streets with high traffic volumes whereas PM2.5 showed less variation between streets and a footpath without road traffic. Both BC and UFP significantly correlated with traffic counts, while no significant correlation between PM2.5 and traffic counts was observed. The relationships between UFP, NO2 and inorganic components of PM2.5 were further investigated through long-term measurements at roadside, urban background and rural sites. UFP moderately correlated with NOx (NO2 + NO) and showed varying relationships with NOx depending on the particle size distribution. Principal component analysis and air-mass back trajectory analysis revealed that PM2.5 concentrations were dominated by long-range transport of secondary inorganic aerosols, whereas UFP were mainly related to varying local emissions and meteorological conditions. These findings imply the need for different policies for managing human exposure to these different particle components: control of much BC and UFP appears to be manageable at local scale by restricting traffic emissions; however, abatement of PM2.5 requires a more strategic approach, in cooperation with other regions and countries on emissions control to curb long-range transport of PM2.5 precursors. A dispersion model (ADMS-Urban) was used to simulate high resolution NO2 and O3 concentrations in Edinburgh. The effects of different emission and meteorological input datasets on the resulting modelled NO2 concentrations were investigated. The modelled NO2 and O3 concentrations using the optimal model setup were validated against reference instrument and diffusion tube measurements. Temporal variability of NO2 was predicted well at locations that were not heavily influenced by local effects, such as road junctions and bus stops. Temporal variability of O3 was predicted better than for NO2. Long-term spatial variability of NO2 was found to correlate well with diffusion tube measurements, while modelled spatial variability of O3 in ADMS-Urban compared poorly with diffusion tube measurements. However, it was found that the O3 diffusion tube measurements may be subject to some unidentified biases affecting their accuracy. Land-use regression (LUR) models are widely used to estimate exposure to air pollution in urban areas. An appropriately sized and designed monitoring network is an important component for the development of a robust LUR model. Concentrations of NO2 were simulated by ADMS-Urban at ‘virtual’ monitoring sites in 54 different network designs of varying numbers and types of site, using a 25 km2 area including much of the Edinburgh city area. Separate LUR models were developed for each network. These LUR models were then used to estimate ambient NO2 concentrations at all residential addresses, which were evaluated against the ADMS-Urban modelled concentration at these addresses. The improvement in predictive capability of the LUR models was insignificant above ~30 monitoring sites, although more sites tended to yield more precise LUR models. Monitoring networks containing sites located within highly populated areas better estimated NO2 concentrations across all residential locations. LUR models constructed from networks containing more roadside sites better characterised the high end of residential NO2 concentrations but had increased errors when considering the whole range of concentrations. No particular composition of monitoring network resulted in good estimation simultaneously across all residential NO2 concentration and of the highest NO2 levels implying a lack of spatial contrast in LUR-modelled pollution surface compared with the dispersion model. Finally, the results from the measurement and modelling studies presented in thesis are synthesised in the context of current exposure assessment studies. Low-cost air-quality monitors currently do not possess and are unlikely in the near future to provide the robustness and accuracy to replace the existing routine monitoring network. Development of the low-cost air-quality should be aiming at upgrading them as the indicative method as defined in the data quality objective in the EU directive. The monitoring sites used to build LUR models should capture well the population distribution in the study area as opposed to capturing the greatest pollution contrast. The traditional methods of evaluating LUR models are also ineffective in characterising the models’ capability at estimating pollutant concentration at residential address. Given that the dispersion models are also subject to the availability and uncertainties in the input data, future air quality model development should endeavour to incorporate both dispersion and land-use regression models, where the uncertainty in the input data can be reduced by using LUR models built on actual measurements, and the limitation in the statistical modelling can be replaced by adopting the deterministic approach used in the dispersion model.