La pollution atmosphérique est le produit de fortes émissions de polluants (et de leurs précurseurs) et de conditions météorologiques défavorables. Les particules fines (PM2.5) sont l'un des polluants les plus dangereux pour la santé publique. L'exposition répétée ou prolongée à ces particules entraîne chaque année des maladies respiratoires et cardio-vasculaires chez les personnes exposées ainsi que des morts prématurées. L'évolution du climat dans les années à venir aura un impact sur des variables météorologiques (température, vents, précipitations, ...). Ces variables influencent à leur tour divers facteurs, qui affectent la qualité de l'air (émissions, lessivage par les précipitations, équilibre gaz/particule, ...). Si de nombreuses études ont déjà projeté l'effet du changement climatique sur les concentrations d'ozone, peu se sont intéressées à son effet sur les concentrations de particules fines, en particulier à l'échelle du continent européen. C'est ce que cette thèse se propose d'étudier. La circulation atmosphérique de grande échelle est étroitement liée aux variables météorologiques de surface. Par conséquent, il est attendu qu'elle ait également un impact sur les concentrations de PM2.5. Nous utilisons dans cette thèse une approche statistique pour estimer les concentrations futures de PM2.5 à partir d'observations présentes de PM2.5, de quelques variables météorologiques pertinentes et d'outils permettant de représenter cette circulation atmosphérique (régimes et types de temps). Le faible nombre d'observations journalières de PM2.5 et de ses composants en Europe nous a conduit à créer un jeu de données pseudo-observées à l'aide du modèle de qualité de l'air Polyphemus/Polair3D, puis à l'évaluer de façons opérationnelle et dynamique, afin de s'assurer que l'influence des variables météorologiques sur les concentrations de PM2.5 est reproduite de manière satisfaisante par le modèle. Cette évaluation dynamique d'un modèle de qualité de l'air est, à notre connaissance, la première menée à ce jour.Les projections de PM2.5 sur les périodes futures montrent une augmentation systématique des concentrations de PM2.5 au Royaume-Uni, dans le nord de la France, au Benelux et dans les Balkans, et une diminution dans le nord, l'est et le sud-est de l'Europe, en Italie et en Pologne. L'évolution de la fréquence des types de temps ne suffit pas toujours à expliquer l'évolution de ces concentrations entre les périodes historique et futures, car les relations entre circulation atmosphérique de grande échelle et types de temps, entre types de temps et variables météorologiques, et entre variables météorologiques et concentrations de PM2.5 sont amenées à évoluer dans le futur et contribuent à l'évolution des concentrations de PM2.5. L'approche statistique développée dans cette thèse est nouvelle pour l'estimation de l'impact du climat et du changement climatique sur les concentrations de PM2.5 en Europe. Malgré les incertitudes qui y sont associées, cette approche est facilement adaptable à différents modèles et scénarios, ainsi qu'à d'autres régions du monde et d'autres polluants. En utilisant des observations pour définir la relation polluant-météorologie, cette approche serait d'autant plus robuste / Air pollution is the result of high emissions of pollutants (and pollutant precursors) and unfavorable meteorological conditions. Fine particulate matter (PM2.5) is one of the pollutants of great concern for human health. Every year, a repeated or continuous exposure to such particles is responsible for respiratory and cardiovascular diseases among the concerned populations and leads to premature deaths. Climate change is expected to impact meteorological variables (temperature, wind, precipitation,...). Those variables will influence numerous factors, which will affect air quality (emissions, precipitation scavenging, gas/particle equilibrium,...). A large body of studies have already investigated the effects of climate change on ozone, whereas only a few have addressed its effects on PM2.5 concentrations, especially over Europe. This is the subject we investigate in this thesis. Large-scale circulation is closely linked to surface meteorological variables. Therefore, it is expected that it will impact PM2.5 concentrations too. In this thesis, we develop a statistical algorithm to estimate future PM2.5 concentrations from present PM2.5 observations, selected meteorological variables and tools to represent this circulation (weather regimes and weather types). The lack of daily observations of PM2.5 and its components over Europe prevents us to used observations. Consequently, we have created a pseudo-observed PM2.5 data set, by using the Polyphemus/Polair3D air quality Chemical-Transport Model. Both operational and dynamic evaluations were conducted against EMEP measurements, to ensure that the influence of meteorological variables on PM2.5 concentrations is correctly reproduced by the model. As far as we know, this dynamic evaluation of an air quality model with respect to meteorology is the first conducted to date.Future PM2.5 concentrations display an increase over the U.K., northern France, Benelux, and in the Balkans, and a decrease over northern, eastern, and southeastern Europe, Italy, and Poland compared to the historical period. The evolution of weather type frequencies is not sufficient to explain the PM2.5 changes. The relationships between the large-scale circulation and the weather types, between the weather types and meteorological variables, and between meteorological variables and PM2.5 concentrations evolve with future meteorological conditions and also contribute to PM2.5 changes. The statistical method developed in this thesis is a new approach to estimate the impact of climate and climate change on PM2.5 concentrations over Europe. Despite some uncertainties, this approach is easily applicable to different models and scenarios, as well as other geographical regions and other pollutants. Using observations to establish the pollutant-meteorology relationship would make this approach more robust

La connaissance des principales sources de l’aérosol permet d’améliorer, d’adapter et de cibler les mesures prises pour réduire les concentrations de particules fines. Ainsi, l’identification et la hiérarchisation des sources de particules fines sont des étapes essentielles à la mise en place d'une politique efficace d'amélioration de la qualité de l'air. Le travail mené durant cette thèse s’inscrit dans cette démarche puisqu'il avait pour objectif de quantifier les sources de PM2.5 en milieu industriel. Afin de répondre à cet objectif, deux campagnes de prélèvements ont été réalisés dont une sous les vents des principales activités industrielles afin de caractériser leurs émissions (profils) et une en zones urbaines caractéristiques de l’exposition de la population aux particules fines. Les résultats ont permis d'obtenir des empreintes représentatives des principales activités industrielles de la zone d'étude. L’analyse ME-2 menée a permis, avec la combinaison d’analyses radiocarbones, de déterminer que la source de combustion de biomasse est la source majoritaire pendant l’automne et l’hiver où les épisodes de PM2.5 ont été observés. La source industrielle est la source majoritaire des PM2.5 au printemps et en été mais ne constitue pas un driver fort de la concentration des PM2.5. Toutefois, cette étude a montré que les sources industrielles impactent significativement la population de particules (taille, composition, etc.) dans la zone d’étude. / In order to limit the impact of air quality on human health, public authorities need reliable and accurate information on the sources contribution. So, the identification of the main sources of PM2.5 is the first step to adopt efficient mitigation policies. This work carry out in this thesis take place in this issue and was to determine the main sources of PM2.5 inside an industrial area. To determinate the main sources of PM2.5, two campaigns were lead to collect daily PM2.5 to: 1/ determine the enrichment of atmospheric pollutants downwind from the main industrial activities and 2/ collect PM2.5 in urban areas characteristic of the population exposition. Results allowed to obtain very representative profiles for the main industrial activities implanted inside the studied area. ME-2 analysis, combined to radiocarbon measurements, allowed to highlight the very high impact of Biomass Burning sources for all the PM2.5 pollution events recorded from early autumn to March. This study showed that industrial sources, even if they are the major sources during spring and summer, are not the major PM2.5 driver. However, this study highlights that industrial sources impact significantly the aerosol population (size, composition, etc.) in the studied area.

Air pollution cause approximately 5000 premature deaths in Sweden each year. Residential wood combustion of solid biomass (RWC) is responsible for at least 1000 based on a relative risk coefficient of 17 % per 10 μg/m3 exposure. The carcinogenic properties of RWC emissions is linked to their content of particulates and polycyclic aromatic hydrocarbons (PAH’s). The ambition of this study is to answer whether cancer risk may be used as indicator for out dated heating technology with high emissions of carcinogenic air pollutants, and which socioeconomic costs that can be linked to such a scenario. The efficacy of different instruments that are discussed in Swedish environmental policy is also discussed. A transdisciplinary approach, constituting of a literature review, statistical analysis, gap analysis and multi criteria analysis was applied as study design. A literature review resulted in a mapping of the state of the art concerning RWC particulates and their impact on cancer in Sweden together with its related socioeconomic costs. The study is focused on PM2,5 and B(a)P emissions. A statistical analysis examined the potential relationship between short-lived micro nuclei (MN) in Swedish 12-year old school children, and their exposure to the carcinogenic PAH Benzo(a)pyrene (B(a)P) from RWC in Sweden. The results suggest that higher rates of lung cancer incidence, and socioeconomic costs may be found in areas burdened with high rates of RWC emissions from outdated heating technology. The MCA suggest that a combination of instruments is most suitable to achieve the targeted specification for B(a)P in the Clean Air objective, as found in previous CBA’s, and that other instrument may lack efficacy. / Varje år orsakar luftföroreningar omkring 5000 prematura dödsfall i Sverige. Småskalig vedeldning (RWC) ansvarar för åtminstone 1000 av dessa, baserat på den relativa risk-koefficienten 17 % per 10 μg/m3 exponering. De cancerogena egenskaperna hos vedeldningsutsläpp beror bland annat på dess partiklar som bland annat innehåller polycykliska aromatiska kolväten (PAH:er). Ambitionen med denna studie är att besvara hur framtida cancerrisk kan användas som indikator för luftföroreningar från omodern uppvärmningsteknik och vilka samhällsekonomiska följder som kan länkas till en sådan. Dessutom undersöktes styrkraften hos de styrmedel som diskuteras i svensk miljöpolicy. Med hjälp av ett tvärvetenskapligt tillvägagångssätt, bestående av litteraturgenomgång, statistisk analys, gapanalys och multikriterieanalys besvarades frågorna. Litteraturgenomgången resulterade i en kartläggning av det nuvarande kunskapsläget om vedpartiklars påverkan på cancer i Sverige och dess relaterade samhällskostnader. Studien är fokuserad på PM2,5 och B(a)P emissioner. En statistisk analys undersökte korrelationen mellan en biomarkor för framtida cancerrisk, kortlivade mikrokärnor (MN), hos svenska 12-åriga skolelever, och deras vedröksexponering. Resultaten indikerar svagt att större risk för lungcancer kan spås i områden med relativt högre exponering för vedrökskomponenten B(a)P, alltså områden med omodern uppvärmningsutrustning. Multikriterieanalysen visar, liksom i tidigare kostnad-effektivitetsanalyser, att en kombination av olika styrmedel har störst potential att uppnå specifikationerna för PM2,5 och B(a)P i det svenska miljökvalitetsmålet Ren luft. Andra styrmedel kan sakna styrkraft.

Air quality has long been a major health concern for citizens around the world, and increased levels of exposure to fine particulate matter (PM2:5) has been definitively linked to serious health effects such as cardiovascular disease, respiratory illness, and increased mortality. PM2:5 is one of six attainment criteria pollutants used by the EPA, and is similarly regulated by many other governments worldwide. Unfortunately, the high cost and complexity of most current PM2:5 monitors results in a lack of detailed spatial and temporal resolution, which means that concerned individuals have little insight into their personal exposure levels. This is especially true regarding hyper-local variations and short-term pollution events associated with industrial activity, heavy fossil fuel use, or indoor activity such as cooking. Advances in sensor miniaturization, decreased fabrication costs, and rapidly expanding data connectivity have encouraged the development of small, inexpensive devices capable of estimating PM2:5 concentrations. This new class of sensors opens up new possibilities for personal exposure monitoring. It also creates new challenges related to calibrating and characterizing inexpensively manufactured sensors to provide the level of precision and accuracy needed to yield actionable information without significantly increasing device cost. This thesis addresses the following two primary questions: 1. Can an inexpensive air quality monitor based on mass-manufactured dust sensors be calibrated efficiently in order to achieve inter-device agreement in addition to agreement with professional and federally-endorsed particle monitors? 2. Can an inexpensive air quality monitor increase the confidence and capacity of individuals to understand and control their indoor air quality? In the following thesis, we describe the development of the Speck fine particulate monitor. The Speck processes data from a low-cost dust sensor using a Kalman filter with a piecewise sensing model. We have optimized the parameters for the algorithm through short-term co-location tests with professional HHPC-6 particle counters, and verified typical correlations between the Speck and HHPC-6 units of r2 > 0:90. To account for variations in sensitivity, we have developed a calibration procedure whereby fine particles are aerosolized within an open room or closed calibration chamber. This allows us to produce Specks for commercial distribution as well as the experiments presented herein. Drawing from previous pilot studies, we have distributed low-cost monitors through local library systems and community groups. Pre-deployment and post-deployment surveys characterize user perception of personal exposure and the effect of a low-cost fine particulate monitor on empowerment.

This thesis analyses the formation of haze by taking the case of severer haze in Beijing in the winter of 2015, which was caused by the collective effect of human activities, topography and meteorological. Among these causes, anthropogenic emissions contributed most, such as coal-fired emissions and vehicle emissions. The haze not only brings direct harm to health, but also slowly changes the way people live in the haze. Beijing has issued the Clean Air Action Plan to mitigate haze. Additionally, a series of stringent control measures were adopted during Beijing Olympics and APEC summit. These measures, such as vehicle emissions reduction and coal-free programme effectively reduced the PM concentration but failed to reduce GHG emissions. Hence, the causes for the lack of sustainability of air pollution control measures are included in thesis.

This thesis presents the use of data assimilation with optimal interpolation (OI) to develop atmospheric aerosol concentration estimates for the United States at high spatial and temporal resolutions. Concentration estimates are highly desirable for a wide range of applications, including visibility, climate, and human health. OI is a viable data assimilation method that can be used to improve Community Multiscale Air Quality (CMAQ) model fine particulate matter (PM2.5) estimates. PM2.5 is the mass of solid and liquid particles with diameters less than or equal to 2.5 μm suspended in the gas phase. OI was employed by combining model estimates with satellite and surface measurements. The satellite data assimilation combined 36 x 36 km aerosol concentrations from CMAQ with aerosol optical depth (AOD) measured by MODIS and AERONET over the continental United States for 2002. Posterior model concentrations generated by the OI algorithm were compared with surface PM2.5 measurements to evaluate a number of possible data assimilation parameters, including model error, observation error, and temporal averaging assumptions. Evaluation was conducted separately for six geographic U.S. regions in 2002. Variability in model error and MODIS biases limited the effectiveness of a single data assimilation system for the entire continental domain. The best combinations of four settings and three averaging schemes led to a domain-averaged improvement in fractional error from 1.2 to 0.97 and from 0.99 to 0.89 at respective IMPROVE and STN monitoring sites. For 38% of OI results, MODIS OI degraded the forward model skill due to biases and outliers in MODIS AOD. Surface data assimilation combined 36 × 36 km aerosol concentrations from the CMAQ model with surface PM2.5 measurements over the continental United States for 2002. The model error covariance matrix was constructed by using the observational method. The observation error covariance matrix included site representation that scaled the observation error by land use (i.e. urban or rural locations). In theory, urban locations should have less effect on surrounding areas than rural sites, which can be controlled using site representation error. The annual evaluations showed substantial improvements in model performance with increases in the correlation coefficient from 0.36 (prior) to 0.76 (posterior), and decreases in the fractional error from 0.43 (prior) to 0.15 (posterior). In addition, the normalized mean error decreased from 0.36 (prior) to 0.13 (posterior), and the RMSE decreased from 5.39 μg m-3 (prior) to 2.32 μg m-3 (posterior). OI decreased model bias for both large spatial areas and point locations, and could be extended to more advanced data assimilation methods. The current work will be applied to a five year (2000-2004) CMAQ simulation aimed at improving aerosol model estimates. The posterior model concentrations will be used to inform exposure studies over the U.S. that relate aerosol exposure to mortality and morbidity rates. Future improvements for the OI techniques used in the current study will include combining both surface and satellite data to improve posterior model estimates. Satellite data have high spatial and temporal resolutions in comparison to surface measurements, which are scarce but more accurate than model estimates. The satellite data are subject to noise affected by location and season of retrieval. The implementation of OI to combine satellite and surface data sets has the potential to improve posterior model estimates for locations that have no direct measurements.

Excessive PM2.5 emissions in China threaten peoples’ health and cause massive economic burdens to society. Under pressure from the public, and the international community, China published PM2.5 standards for the first time in March 2012. Following the introduction of standards, several pilot cities began to build PM2.5 monitoring networks. This paper is designed to explore whether PM2.5 monitoring can be effectively undertaken and implemented in China and whether monitoring results can offer a technical basis to facilitate a significant reduction in actual PM2.5 emissions and protect public health. PM2.5 monitoring is essential in helping the government and public monitor pollution levels and supervise local compliance with PM2.5 standards. Key aspects to facilitate an effective monitoring process are discussed in the analysis. In addition, a case study – Lanzhou’s PM2.5 monitoring network – is provided to analyze and improve current PM2.5 monitoring practices at local levels, as well as suggest credible technical support to local authorities so as to cut PM2.5 emissions levels. Based on detailed analysis, the results suggest that PM2.5 monitoring can be successfully implemented in China by following several key principles – designing a representative PM2.5 monitoring network, applying QA/QC to ensure data quality, interpreting the data scientifically to understand real pollution levels, etc. In addition, this paper recommends three measures critical to realizing PM2.5 reduction goals: (1) emissions source control, (2) public participation to add input to the decision-making process and supervise local compliance with PM2.5 standards, and (3) non-governmental organization/international cooperation to improve local government and environmental agencies’ capacity with regards to environmental protection. Lessons derived from the case study can help improve PM2.5 monitoring performance not just in Lanzhou, but in cities that share similar monitoring issues across China. Scientific monitoring, together with the application of the above three measures, can more effectively curb PM2.5 emissions, improve air quality, and mitigate negative health effects associated with air pollution. / acase@tulane.edu

For several years, residents of Cache County, Utah have suffered from the recurrence of what has come to be known as the winter-inversion, or “red-air-day” season. Each year during this season – which occurs primarily in the months of December, January, and February – particulate matter concentrations measuring two and half micrometers or less (commonly known as PM2.5) rise and languish (for periods of days or even weeks) above federally mandated standards, causing extensive harm to community health and confounding what have thus far been the relatively tepid control efforts undertaken by local and state policymakers. Through time-series regression modeling, we establish a statistical relationship between PM2.5 concentrations and vehicle use in Cache County, and further calculate a gas-price elasticity for the region. Next, we analyze the benefits and costs associated with a potential seasonal gas tax which, if set appropriately and enforced effectively, could decrease vehicle use and thereby lower health costs through concomitant decreases in PM2.5 concentrations. Specifically, we find a relatively strong positive relationship between percentage of vehicle trips reduced and associated reductions in PM2.5concentrations, and a gas price elasticity of approximately -0.31 in what we call a “high price variability environment.” Based upon these results, benefit-cost analysis suggests a potentially positive social net benefit for Cache County associated with imposing a seasonal gas tax to reduce PM2.5 concentrations during the winter-inversion season. Our benefit-cost analysis, which uses quantitative estimation techniques on both sides of the ledger, yields a first-of-its-kind social net benefit estimate for controlling elevated PM2.5 concentrations in Cache County through the imposition of a seasonal gas tax.

Exposure to fine particulate matter (PM2.5) in the ambient air is associated with various health effects. There is increasing evidence which implicates the central role played by specific chemical components such as heavy metals of PM2.5. Given the fact that humans are exposed to complex mixtures of environmental pollutants such as PM2.5, research efforts are intensifying to study the mixtures composition and the emission sources of ambient PM, and the exposure-related health effects. Factor analysis as well source apportionment models are statistical tools potentially useful for characterizing mixtures in PM2.5. However, classic factor analysis is designed to analyze samples of independent data. To handle (spatio-)temporally correlated PM2.5 data, a Bayesian approach is developed and using source apportionment, a latent factor is converted to a mixture by utilizing loadings to compute mixture coefficients. Additionally there have been intensified efforts in studying the metal composition and variation in ambient PM as well as its association with health outcomes. We use non parametric smoothing methods to study the spatio-temporal patterns and variation of common PM metals and their mixtures. Lastly the risk of low birth weight following exposure to metal mixtures during pregnancy is being investigated.

Cette thèse présente la caractérisation géochimique du matérielle particulaire de l'air respirable (PM10 et PM2.5) du Santiago du Chili. L'objective principal de l'étude, est l'identification des sources polluantes à travers des traçages d'éléments chimiques afin d'identifier leur origine et les processus de génération principaux. Ceci passe par l'analyse des concentrations en éléments majeurs et traces, leurs variations entre sites, saisonnières et interannuelles. Il s'agit d'un pré-requis pour aborder l'impact de ces polluants en terme de santé publique, et fournir des outils pour faire évoluer les politiques publiques. Ce présent travail, a permis aussi, de quantifier les niveaux et variations des concentrations en éléments chimiques potentiellement toxiques qui peuvent impacter sur le taux de mortalité liées à pathologies cardiaques ou respiratoires.