The role of the spatial representation in the relation between chronic exposure to NO2 and respiratory health outcomes is studied through a spatial approach encompassing three conceptual components: the geography of susceptibility, the geography of exposure and the geography of risk. A spatially explicit methodology that defined natural neighbourhoods for the city of Ottawa is presented; it became the geography of analysis in this research. A LUR model for Ottawa is developed to study the geography of exposure. Model sensitivity to the spatial representation of population showed that dasymetric population mapping did not provide significant improvements to the LUR model over population at the dissemination block level. However, both the former were significantly better than population represented at the dissemination area. Spatial representation in the geography of exposure was also evaluated by comparing four kriging and cokriging interpolation models to the LUR. Geostatistically derived NO2 concentration maps were weakly correlated with LUR model results. The relationship between mean NO2 concentrations and respiratory health outcomes was assessed within the natural neighbourhoods. We find a statistically significant association between NO2 concentrations and respiratory health outcomes as measured by global bivariate Moran’s I. However, for regression model building, NO2 had to be forced into the model, demonstrating that NO2 is not one of the main contributing variables to respiratory health outcomes in Ottawa. The results point toward the importance of the socioeconomic status on the health condition of individuals. Finally, the role of spatial representation was assessed using three different spatial structures, which also permitted to better understand the role of the modifiable areal unit problem (MAUP) in the study of the relationship between exposure to NO2 and health. The results confirm that NO2 concentration is not a major contributing factor to the respiratory health in Ottawa but clearly demonstrate the implications that the use of opportunistic administrative boundaries can have on results of exposure studies. The effects of the MAUP, the scale effect and the zoning effect, were observed indicating that a spatial structure that embodies the scale of major social processes behind the health condition of individuals should be used when possible.

Air quality forecasts are of great value since several pollutants in our environment effect both human health, global climate, vegetation, crop yields, animals, materials and acidification of forests and lakes. Air-quality forecasts help to make people aware of the presence and the quantity of pollutants, and give them a chance to protect themselves, their business and the Earth. Many different air-quality models are in daily use all over the world, providing citizens with forecasts of air quality and warnings of unhealthy air quality if recommended highest concentrations are exceeded. This study adapts the WRF meteorological model (Weather research and Forecasting model) to be a driver of the CMAQ air-quality model (models-3 Community Multiscale Air Quality model). Forecasts of ozone and nitrogen dioxide concentrations from this coupled WRF/CMAQ modelling system are tested against observed data during a four-day period in May, 2006. The Lower Fraser Valley study area is a fertile valley surrounded by mountain chains in southwest British Columbia, Canada. The valley stretches from the Pacific coast eastwards towards the Rocky Mountains. This valley hosts more than 2 million people and it is west Canada’s fastest growing region. The Lower Fraser Valley holds a big city, Vancouver, several suburbs, numerous industries and a widespread agricultural production. During the analysed four-day period in May, a synoptic high-pressure built over the region, favoring high concentrations of pollutants as ozone and nitrogen dioxide. The created WRF/CMAQ model forecasted an acceptable magnitude of nitrogen dioxide but the daily variations are not recreated properly by the model. The WRF/CMAQ model forecasts the daily variation of ozone in a satisfying way, but the forecasted concentrations are overestimated by between 20 and 30 ppb throughout the study. Factors that could contribute to the elevated ozone concentrations were investigated, and it was found that the weather forecasting model WRF was not generating fully reliable meteorological values, which in turn hurt the air-quality forecasts. As the WRF model usually is a good weather forecasting model, the short spin-up time for the model could be a probable cause for its poor performance. / Prognoser över luftkvaliteten är mycket värdefulla, då flera luftföroreningar i vår närmiljö påverkar människans hälsa, det globala klimatet, vegetation, djur, material och bidrar till försurning av skog och vattendrag. Luftkvalitetsprognoser gör människan mer medveten om närvaron av luftföroreningar och i vilken mängd de finns. De ger människan en chans att vidta skyddsåtgärder för att skydda sig själv, sitt eventuella levebröd, och Jorden. Många olika luftkvalitetsmodeller används idag dagligdags över hela världen och förser invånare med prognoser för luftkvaliteten och varningar om koncentrationerna av föroreningar överstiger rekommenderade värden. I denna studie används väderprognosmodellen WRF (Weather Research and Forecasting model) för att driva luftkvalitetsmodellen CMAQ (models-3 Community Multiscale Air Quality model). Prognoser av ozon- och kvävedioxidhalterna i luften från den kopplade WRF/CMAQ modellen analyseras mot observerade data under en fyra dagars period i maj, 2006. Studieområdet Lower Fraser Valley är en bördig dalgång som är omgiven av bergskedjor i sydvästra British Columbia, Kanada. Dalen sträcker sig från Stilla havskusten och österut mot Klippiga bergen. I denna dalgång bor mer än 2 miljoner människor och det är västra Kanadas snabbast växande region. Lower Fraser Valley rymmer en storstad, Vancouver, flera förorter, många industrier och även stora jordbruksområden. Den fyra dagars period i maj som analyseras karaktäriseras av ett högtrycksbetonat synoptiskt väderläge med lokala variationer, vilka tillsammans är gynnsamma för att uppmäta höga koncentrationer av luftföroreningar som ozon och kvävedioxid. Den skapade WRF/CMAQ modellen prognostiserar godtagbar magnitud hos kvävedioxid men den dagliga variationen återskapas inte av modellen. Modellen prognostiserar den dagliga variationen av ozonkoncentration på ett tillfredsställande sätt, men storleksmässigt ligger koncentrationerna en faktor 20-30 ppb för högt rakt av under hela studien. Kringliggande faktorer som kan påverka koncentrationen ozon studeras närmare och det framkommer att den meteorologiska prognosmodellen WRF inte genererar fullt tillförlitliga värden för en rättvisande luftkvalitetsprognos. Då WRF modellen vanligtvis är en bra prognosmodell kan den korta initialiseringstiden för modellen vara en trolig orsak till dess otillräckliga prestation.

Die Veröffentlichung analysiert die Ursachen der NO2-Belastung in Sachsen und prognostiziert deren Entwicklung. Die aktualisierte Version des Handbuchs der Emissionsfaktoren des Straßenverkehrs (HBEFA 3.1) ist eine der Datengrundlagen. Hauptverursacher von NO2-Grenzwertüberschreitungen in sächsischen Städten ist der Kfz-Verkehr. Die sächsischen Daten von Verkehrsstationen im Zeitraum von 1995 bis 2009 bestätigen den bundesweit abnehmenden Trend. An hoch belasteten Straßen sind weitere dauerhafte und/oder temporäre Maßnahmen erforderlich, um den NO2-Grenzwert bis spätestens 2015 einzuhalten. Die Erneuerung der Kfz-Flotte reicht dafür nicht aus. Als sofort wirkende Maßnahme hat auch unter Berücksichtigung des neuen HBEFA 3.1 die Umweltzone ihre Berechtigung. Die Festlegung von wirksamen Maßnahmen erfordert eine individuelle Planung.

In today’s world, where air pollution has become a ubiquitous problem, city air is normally monitored. Such monitoring can produce large amounts of data, and this enables the development of statistical and machine learning techniques for modeling and forecasting air quality. However, the complex nature of air pollution makes such data a challenge to fully utilize. To this end, machine learning methods, especially deep neural networks, have in recent years emerged as a promising technology for more accurate predictions of air pollution levels, and the research problem in this work is; To capture and model the complex dynamics of air pollution with machine learning methods, with an emphasis on deep neural networks. Connected to the research problem is the research question; How can machine learning, in particular deep neural networks, be used to forecast air pollution levels and pollution peaks? An emphasis is put on pollution peaks, as these are the episodes when existing forecasting models tend to give the largest prediction errors. In this work, historical data from air monitoring sensors were utilized to train several neural network architectures, as well as a more straightforward multiple linear regression model, for forecasting background levels of nitrogen dioxide in the center of Stockholm. Several evaluation metrics showed that the neural network models outperformed the multiple linear regression model, however, none of the models had the desired structure of the forecast errors, and all models failed to successfully capture sudden pollution peaks. Nevertheless, the results point to an advantage for the more complex neural network models, and further advances in the field of machine learning, together with higher resolution data, have the potential to improve air quality forecasts even more and cross conventional forecasting limits.

Exposure to ground-level ozone (O₃), which forms secondarily in the atmosphere, intensifies the risk of respiratory and cardiovascular diseases. Effective mitigation strategies require understanding the spatiotemporal variability of O₃ precursors, including nitrogen oxides (NOx) and volatile organic compounds (VOCs), as well as O₃ formation photochemistry. This thesis examines the concentrations of trace gases closely related to O₃ production, specifically nitrogen dioxide (NO₂, the dominant component of NOx) and formaldehyde (HCHO, a proxy for VOC reactivity), as well as photochemical conditions. I investigate how these factors differ on high-O₃ days, change diurnally, and respond to the temporal resolution of anthropogenic emissions. The focus is on the summer of 2018 due to the availability of trace gas retrievals from the TROPOspheric Monitoring Instrument (TROPOMI) and in situ measurements from field campaigns. I first investigate New York City (NYC) and the Baltimore/Washington D.C. area, where high O₃ levels frequently occur in summer. On high-O₃ days (when the maximum daily 8-hour average (MDA8) O₃ exceeds 70 ppb), tropospheric vertical column densities (VCDTrop) of HCHO and NO₂ increase in urban centers. The HCHO/NO2 VCDTrop ratio, proposed as an indicator of local surface O₃ production sensitivity to its precursors, generally rises due to a more pronounced increase in HCHO VCDTrop. This suggests a shift toward a more NOx-sensitive O₃ production regime that could enhance the effectiveness of NOx controls on the highest O₃ days. As retrievals of tropospheric trace gases from Low Earth Orbit (LEO) satellites like TROPOMI are limited to one overpass per day (early afternoon), I then analyze spatial variability in HCHO and NO₂ concentration diurnal patterns and connect changes in column densities with surface concentrations. Diurnal HCHO patterns indicate the impact of temperature-dependent VOC emissions, while a bimodal surface NO₂ pattern reflects diurnal patterns of local anthropogenic NOx emissions and boundary layer dynamics. Column concentration peaks generally occur about four hours after surface concentration peaks (morning for NO2 and midday for HCHO), highlighting the challenge of relating column densities to health-related surface concentrations. I also explore how the temporal resolution of anthropogenic emissions influences air pollution levels and diurnal variations. Surface NOx and O3 concentrations show different spatial patterns of change when switching from daily mean to hourly varying nitric oxide emissions. In urban areas of both the western and eastern CONUS, adding hourly NO emissions increases daytime emissions, leading to O₃ decreases, indicating NOx-saturated O₃ chemistry. In the western CONUS, monthly mean surface NO₂ increases, while in the eastern CONUS, characterized by shorter NO₂ lifetimes, NO₂ decreases. These sensitivities highlight the importance of accounting for diurnal changes when inferring emissions from concentrations. This thesis advances our understanding of O₃-NOx-VOC air pollution by exploring variations in both surface and column conditions across urban-rural gradients. It integrates in situ measurements, space-based observations, and modeling techniques and assesses advanced modeling tools for future applications. These findings support the future applications of geostationary satellite retrievals for continuous trace gas observation throughout daylight hours, supplementing the once-a-day LEO satellite data used in this thesis, with implications such as aiding source attribution and targeting cost-effective control measures for O₃ mitigation.

Unexpectedly high concentrations of NO₂ have been noted in stack emissions from industrial gas turbines. NO₂ formation appears to occur through the so called "HO₂ mechanism II in which NO combines with HO₂ to produce NO₂ and OH. In this study, the formation of NO₂ was investigated through computer modeling and experimental testing. Computer modeling utilized the CHEMKIN chemical kinetics program and a subset of a previously published C-H-O-N system mechanism. Experimental work was conducted using a high pressure flow reactor designed and built in the course of the study. The effects of pressure, temperature, and the presence of a NO₂ promoting hydrocarbon, methane, were investigated. It was discovered that as pressure increased from 1 atm. to 8.5 atm., the rate and amount of NO converted to NO₂ also increased. There also appeared to be a temperature "window" between approximately 800 and 1000 K in which NO to NO₂ conversion readily occurred. The presence of methane was seen to enhance NO conversion to NO₂, and a ratio of [CH₄]/[NO] was found to be a useful parameter in predicting NO₂ formation. Significant NO conversion to NO₂ was noted for [CH₄]/[NO] > 1 at the hydrocarbon injection point. Experimental results validated those trends obtained from modeling with a modified C-H-O-N mechanism. / Master of Science

No description available.

Reactions of strongly oxidizing pollutants with unsaturated hydrocarbon surfaces are important to many areas of scientific interest. For example, reactions of unsaturated hydrocarbons on the surface of tropospheric aerosols could have a great effect on the oxidizing capacity of the troposphere while the reaction products could be involved in the formation of clouds and smog. These reactions are also important in understanding the toxic effect inhalation of these pollutants have on the pulmonary surfactant of the lung, the only amicable air-water interface of the body. The fatty acids of this surfactant are as much as 30% unsaturated, and exposure to oxidizing pollutant is known to alter both the composition and function of the surfactant. Understanding the reaction mechanism will further the knowledge of how this toxicity occurs. While the reactions of strongly oxidizing pollutants, such as ozone and nitrogen dioxide, with alkenes in the gas and solution phases are well known, the interfacial reaction mechanisms of these species is not fully understood. The goal of this study is to determine the reaction mechanism when an unsaturated hydrocarbon monolayer at the gas-surface interface is exposed to gas phase nitrogen dioxide. An olefin-terminated thiol was synthesized and a self-assembled monolayer on Au(111) made and characterized using Reflection-Absorption Infrared Spectroscopy (RAIRS). This unsaturated surface was then exposed to NO2 at a pressure of 1x10-4 mbar in a UHV (Ultrahigh Vacuum) chamber. Time-resolved RAIRS was preformed in situ to monitor the reaction during exposure. X-ray Photoelectron Spectroscopy and RAIRS determined the surface reaction product as an aldehyde. While the mechanism can not be precisely determined, two mechanisms involving either the hydrogen abstraction or radical addition of the NO2 to yield an aldehyde are proposed. / Master of Science

2024 / Nitrogen dioxide (NO2) is a criteria air pollutant that is deleterious to human health and the environment, but characterizing its distribution is challenging. This challenge arises from its abundant and heterogeneous sources, short lifetime, and the limited spatial extent of surface monitoring networks. In lieu of comprehensive surface monitoring, space-based retrievals of NO2 abundance may address gaps in our understanding of its spatiotemporal variability. Space-based observations of NO2, however, have coarse-resolution sensors, requiring well-constrained inputs, and until recently have only collected one observation per day (at most), limiting their utility for characterizing diurnal variability or intra-urban heterogeneity. Throughout this dissertation, I constrain the precision of ground- and space-based remote sensing instruments dedicated to retrieving NO2 abundance, as well as explaining the spatiotemporal variability of NO2 to provide new insights relevant to urban air quality. Chapter 1 of this dissertation explains the motivation for this dissertation in more detail. In Chapter 2 of this dissertation, I quantify previously unexamined aspects of the diurnal precision of ground-based spectroscopic column NO2 observations using a high spatiotemporal resolution model of the 2013 DISCOVER-AQ campaign domain around the Houston, TX area. Pandora is a ground-based instrument commonly used to observe NO2 columns in the atmosphere. Networks of these instruments are distributed throughout the world, and their precision and accuracy make the instrument favorable for observing the spatiotemporal variability of NO2 and validating satellite instrument NO2 observations. Pandora-derived NO2 observations are often considered implicitly precise relative to satellite observations, thus motivating this evaluation. With this model I developed an instrument viewing “operator” to simulate the Pandora instrument’s operation. This operator creates synthetic direct-sun (DS) differential optical absorption spectroscopy (DOAS) columns which, when compared with modeled overhead columns, reveal that urban heterogeneity results in late-day (4-6 pm, LT) observations being less precise than previously estimated. In Chapter 3 of this dissertation (Adams et al., 2023) long-term collocated surface and column NO2 observations at Boston University were used to understand drivers of total column NO2 variability in a coastal urban setting. I found that variations in column and surface NO2 abundance were governed by different processes. The temporal variability of NO2 column density was highly dependent upon meteorology, while concentrations of NO2 at the surface were more dependent upon surface emissions patterns and boundary layer entrainment. I found that the apparent equal mixing height of NO2 plumes within the boundary layer were not sensitive to prevailing meteorology or boundary layer stability. Additionally, I found that the sea breeze fostered uniquely large temporal variations in column NO2. I demonstrated that sea breeze conditions challenge the ability of satellite-derived column NO2 observations to accurately characterize day-to-day variation. In Chapter 4 of this dissertation, I use long-term measurements of Pandora-derived total column NO2 at Boston University, Blue Hill Observatory (Milton, MA) and Harvard University. This long-term record confirmed that variation in temporal gradients in column NO2 observed in chapter 3 correspond to spatial gradients. Differences in column NO2 between sites as a function of time of day allowed us to infer the scale and formation of spatial column NO2 gradients. Finally, I evaluated to what extent satellite-derived column NO2 retrievals are capable of interpreting emissions differences across time and space. Generally, the TROPOMI satellite instrument overpasses struggled to characterize changes in column NO2 gradients across the Boston and Harvard University measurement locations between 2020 and 2021 relative to Pandora. However, TROPOMI resolved differences in the distributions of NO2 across urban-suburban scales that were not as obvious in the Pandora measurements. My results suggest that this difference in strengths at various scales is a result of the Pandora’s sensitivity to near-field emissions perturbations, in contrast with TROPOMI’s satellite footprint method which averages across larger-scales. Chapter 5 of this dissertation summarizes the conclusions from Chapters 2, 3, and 4 and provides suggestions for future investigators.

The health impacts of urban air pollution are a growing concern in our rapidly urbanizing world. Urban air pollutants show high intra-urban spatial variability linked to urban land use and land cover (LULC). This correlation of air pollutants with LULC is widely recognized; LULC data is an integral input into a wide range of models, especially land use regression models developed by epidemiologists to study the impact of air pollution on human health. Given the demonstrated links between LULC and urban air pollution, and between urban air pollution and health, an interesting question arises: what is the potential of LULC modifications to mitigate the health impacts of urban air pollution? In this dissertation we assess the potential of LULC modifications to mitigate the health impacts of NO2, a respiratory irritant and strong marker for combustion-related air pollution, in the Portland-Vancouver metropolitan area in northwestern USA. We begin by measuring summer and winter NO2 in the area using a spatially dense network of passive NO2 samplers. We next develop an annual average model for NO2 based on the observational data, using random forest -- for the first time in the realm of urban air pollution -- to disentangle the effects of highly correlated LULC variables on ambient NO2 concentrations. We apply this random forest (LURF) model to a 200m spatial grid covering the study area, and use this 200m LURF model to quantify the effect of different urban land use categories on ambient concentrations of NO2. Using the changes in ambient NO2 concentrations resulting from land use modifications as input to BenMAP (a health benefits assessment tool form the US EPA), we assess the NO2-related health impact associated with each land use category and its modifications. We demonstrate how the LURF model can be used to assess the respiratory health benefits of competing land use modifications, including city-wide and local-scale mitigation strategies based on modifying tree canopy and vehicle miles traveled (VMT). Planting trees is a common land cover modification strategy undertaken by cities to reduce air pollution. Statistical models such as LUR and LURF demonstrate a correlation between tree cover and reduced air pollution, but they cannot demonstrate causation. Hence, we run the atmospheric chemistry and transport model CMAQ to examine to what extent the dry deposition mechanism can explain the reduction of NO2 which statistical models associate with tree canopy. Results from our research indicate that even though the Portland-Vancouver area is in compliance with the US EPA NO2 standards, ambient concentrations of NO2 still create an annual health burden of at least $40 million USD. Our model suggests that NO2 associated with high intensity development and VMT may be creating an annual health burden of $7 million and $3.3 million USD respectively. Existing tree canopy, on the other hand, is associated with an annual health benefit of $1.4 million USD. LULC modifications can mitigate some fraction of this health burden. A 2% increase in tree canopy across the study area may reduce incidence rates of asthma exacerbation by as much as 7%. We also find that increasing tree canopy is a more effective strategy than reducing VMT in terms of mitigating the health burden of NO2. CMAQ indicates that the amount of NO2 removed by dry deposition is an order of magnitude smaller than that predicted by our statistical model. About one-third of the difference can be explained by the lower NO2 values predicted by CMAQ, and one-third may be attributable to parameterization of stomatal uptake.