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.

Tropospheric fine particulate matter (PM) poses serious health risks and has a significant impact on global climate change. The measurement of various aspects of PM is challenging due to its complex chemical nature. This dissertation addresses various aspects of PM, including composition, measurement, and visibility. The U.S. Environmental Protection Agency (EPA) proposed a new secondary standard based on visibility in urban areas using 24-h averaged measurements of either light scatter or PM concentration. However shorter averaging times may better represent human perception of visibility. Data from two studies conducted in Lindon, UT, 2012, and Rubidoux, CA, 2003, were used to compare different techniques to estimate visibility, particularly the effect of relative humidity on visibility estimations. Particle composition was measured in Salt Lake City during January-February of 2009. One-hour averaged concentrations of several gas phase and particle phase inorganic species were measured. The results indicate ammonium nitrate averages 40% of the total PM2.5 mass in the absence of inversions and up to 69% during strong inversions. Also, the formation of ammonium nitrate is nitric acid limited, while the formation of ozone appears to be oxidant and volatile organic carbon (VOC) limited. Reduction of NOx will reduce ammonium nitrate secondary particle formation, however, a decrease in NOx may increase ozone concentration.Due to the complexity of PM it is poorly characterized. A large fraction of PM is composed of organic compounds, but these compounds are not regularly monitored due to limitations in current sampling techniques. The GC-MS Organic Aerosol Monitor (OAM) combines a collection device with thermal desorption, gas chromatography and mass spectrometry to quantitatively measure the carbonaceous components of PM on an hourly averaged basis. A compact GC and simple pre-concentrator were developed for the system to decouple separation from manual injection and enhance separation of environmentally-relevant polar organic compounds, such as levoglucosan. The GC-MS OAM is fully automated and has been successfully deployed in the field. It uses a chemically deactivated filter for collection followed by thermal desorption and GC-MS analysis. Laboratory tests show that detection limits range from 0.2 to 3 ng for many atmospherically relevant compounds. The GC-MS OAM was deployed in the field for semi-continuous measurement of the organic markers, levoglucosan, dehydroabietic acid, and several polycyclic aromatic hydrocarbons (PAHs) during winter (January to March), 2015 and 2016. Results illustrate the significance of this monitoring technique to more fully characterize the organic components of PM and identify sources of pollution.

Co-firing is a combustion process in which more than one type of fuel is used. In many cases, co-firing reduces fuel costs and/or reduces the environmental impact. The objective of this research was to test the hypothesis that adding biogas to be co-fired with biomass in a traditional cookstove reduces indoor air pollution and increases the combustion efficiency. The impact of co-firing on indoor air pollution is assessed by comparing the concentrations of carbon monoxide and particulate matter in the exhaust stream of a co-fired cookstove to a cookstove fueled with biomass alone. The concentrations of each of these pollutants were measured using a portable emissions monitoring system. Combustion efficiency is defined as the ratio of energy released by combustion to energy in the fuel. Instead of combustion efficiency, the impact of co-firing was assessed on the modified combustion efficiency, which is defined as CO2/(CO2+CO) on a molar basis. This is because CO and CO2 concentrations can be measured. In addition, the impact of cofiring on other parameters such as thermal efficiency, specific fuel consumption rate, and specific emission of CO, CO2, and PM were assessed. Previous investigation of biomass combustion in traditional cookstoves indicates that power harvested using a thermoelectric generator can be used to drive a fan and increase the amount of air flowing into the combustion zone. The impact of using a fan on indoor air pollution and combustion efficiency was also assessed. It was found that co-firing biomass with optimum amount of biogas reduced the emission of CO by 32 % and PM by 33 % and increased the modified combustion efficiency by 1.3 %. It was found that using a fan reduced the emission of CO by 35 % and PM by 39 % and increased the modified combustion efficiency by 1.1 %. Finally, the combination of co-firing and use of a fan reduced the emission of CO by 58 % and PM by 71 % and increased the modified combustion efficiency by 2.8 %.

<p>Indoor air quality in office buildings can impact the health, well-being, and productivity of occupants. In most buildings, occupants exhaled breath is the primary source of carbon dioxide (CO₂). Concentrations of indoor CO₂ are also strongly associated with the operational mode of the mechanical ventilation system. While CO2 is routinely monitored in indoor environments, there are few spatially-resolved real-time measurements of CO₂ throughout mechanical ventilation systems. Such measurements can provide insight into indoor- and outdoor-generated CO₂ dispersion throughout a building and between the building and the outdoor atmosphere. This thesis aims to investigate spatiotemporal variations in CO₂ concentrations and mass fluxes throughout a mechanical ventilation system of a living laboratory office in a LEED-certified building. The impact of human occupancy patterns and ventilation conditions of CO₂ concentrations and fluxes was evaluated. </p> <p>A four-month measurement campaign was conducted in one of the four living laboratory offices at the Ray W. Herrick Laboratories. The living laboratory offices feature precise control and monitoring of the mechanical ventilation system via an advanced building automation system. Various mechanical ventilation modes were implemented, such as variable outdoor air exchange rates (AERs) and recirculation ratios. A novel multi-location sampling manifold was used to measure CO₂ at eight locations throughout the ventilation system, such as across the outdoor, supply, and return air ducts. Office occupancy was measured via a chair-based temperature sensor array. Volumetric airflow rate data and CO₂ concentration data were used to estimate CO₂ mass fluxes through the ventilation system. The CO₂ mass flux for the outdoor and exhaust air was used to evaluate the net CO₂ transport from the office to the outdoor atmosphere. </p> <p>The measurements demonstrate that there exist significant spatiotemporal variations in CO₂ concentrations across the outdoor, supply, and return air ducts. CO₂ concentrations varied with human occupancy in the office and the outdoor AER of the mechanical ventilation system. Due to human-associated CO₂ emissions, the net CO₂ mass flux from the office to the outdoor environment was approximately 700 kg of CO₂ per year. Thus, occupied offices may represent an important, yet unrecognized, source of CO₂ to the urban atmosphere.</p>

Many applied statistical applications face the potential problem of model contamination and measurement error. The form and degree of contamination as well as the measurement error are usually unknown and sample-specific, which brings additional challenges for researchers. In this thesis, we have proposed several Bayesian inference models to address these issues, with the application to one type of special data for allergen concentration measurement, which is called serial dilution data and is self-calibrated. In our first chapter, we address the problem of model contamination by using a multilevel model to simultaneously flag problematic observations and estimate unknown concentrations in serial dilution data, a problem where the current approach can lead to noisy estimates and difficulty in estimating very low or high concentrations. In our second chapter, we propose the Bayesian joint contamination model for modeling multiple measurement units at the same time while adjusting for differences between experiments using the idea of global calibration, and it could account for uncertainty in both predictors and response variables in Bayesian regression. We are able to get efficacy gain by analyzing multiple experiments together while maintaining robustness with the use of hierarchical models. In our third chapter, we develop a Bayesian two-step inference model to account for measurement uncertainty propagation in regression analysis when the joint inference model is infeasible. We aim to increase model inference reliability while providing flexibility to users by not restricting the type of inference model used in the first step. For each of the proposed methods, We also demonstrate how to integrate multiple model building blocks through the idea of Bayesian workflow. In extensive simulation studies, we show that our proposed methods outperform other commonly used approaches. For the data applications, we apply the proposed new methods to the New York City Neighborhood Asthma and Allergy Study (NYC NAAS) data to estimate indoor allergen concentrations more accurately as well as reveal the underlying associations between dust mite allergen concentrations and the exhaled nitric oxide (NO) measurement for asthmatic children. The methods and tools developed here have a wide range of applications and can be used to improve lab analyses, which are crucial for quantifying exposures to assess disease risk and evaluating interventions.

Air pollution affects the environment negatively, boosts climate change, and is the cause of millions of deaths per year, first and foremost affecting the people living in urban areas. Since the early 20th century, many cities have been planned around cars, which are the main contributors to the bad air quality. However, after the Covid-19 pandemic, cities have been reshaped to enhance active travel and to provide more space for greenery. In London, this reassessment of the urban areas has led to the Low-Traffic Neighbourhoods (LTNs). The LTNs origins from 2019, however, most of them were implemented during the pandemic because of the crucial times demanding social distance, while also enabling people to walk and cycle more in their local borough. The LTNs only allow residents, emergency vehicles and blue badge carriers to enter, if travelling by a motorised vehicle. The scheme further aims for more greenery to be implemented. The aim of this thesis is to study the impact from the LTNs on the air quality of the local area, specifically regarding PM10 and NOX, by using openly available data from the Imperial College London. Furthermore, the existing Green Infrastructure (GI) around each sensor, as well as the traffic, has been studied and compared to the air pollutant levels. This has been done to be able to analyse the air pollutants in relation to the surrounding GI and the level of traffic. The methodology further consists of mapping the air pollutants measured by the sensors; a statistical analysis; an interview with Sally Oldfield, the Nature Conservation Manager at Islington Ecology Centre; and field studies to the sensors used in the thesis, both the ones in LTNs and the ones in non-LTNs. The boroughs included in the study are the City of London, Islington, Wandsworth, and Westminster.  Previous research about the LTNs have focused on health and social issues, and the research about traffic schemes have focused on Low Emission Zones (LEZ) and Ultra Low Emission Zones (ULEZ). Studies on the air quality impact of the Covid-19 lockdowns have been done on New York, Madrid and Barcelona. The previous research on air pollutants in urban areas show a difficulty in mapping the movement of the pollutants hence the varied variables having an impact, such as wind, weather, the height and positions of the surrounding buildings. Research on the impact on the air quality from GI in general, has shown that the efficiency is dependent on the planning, type and size of the vegetation, as well as the distance to the emission source. However, the studies on GI are uncertain in how effective it is in terms of air quality improvement.  The result of this thesis shows a decline in NOX- and PM10-values after the implementation of the LTN by all sensors. The annual patterns further show that the yearly trends of the pollutants remained, however the magnitude is lower after the implementation of the LTNs. The daily patterns show varied results, where NOX has clear connections to the traffic, and the sources of PM10 are uncertain. Lastly, the statistical analysis showed that the data series came from different distributions, except the PM10-values by one of the sensors in Islington. Although a reduction was seen by all sensors, this might be because of, e.g., the Covid-19 pandemic. Furthermore, a correlation between GI and lower values of the pollutants could be seen by some sensors, however the results varied, making it difficult to distinguish any correlation. In conclusion, the absence of traffic can be seen to reduce the air pollutants NOX and PM10, where GI might have a positive impact. Suggesting to reshape urban areas to enable active travel, and reduce the possibilities to travel by car, with the exception of blue badge carriers and emergency vehicles. Although the impact on air improvement from GI is uncertain, it is suggested to be incorporated in the planning due to its other benefits such as recreation, well- being, and biodiversity.

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Current methods for the risk assessment of environmental exposures commonly involve questionnaires, stationary monitoring, and personal air sampling. However, as these approaches do not capture the body's internal response, they lend minimal understanding to the biologic consequence of exposure. In order to address the unmet need of connecting external exposure measurements with signatures of internal exposure, this thesis examines the overarching hypothesis that transcriptomic changes in the human airway epithelium can serve as indicators of physiologic responses to inhaled pollutants. This is an extension of previous work that has demonstrated an airway ''field of injury'' effect where cigarette smoke exposure alters gene-expression in epithelial cells lining the respiratory tract. Specifically, I examine transcriptomic changes and the biologic responses associated with exposure to the following pollutants: environmental tobacco smoke (Aim 1), household air pollution from smoky coal combustion (Aim 2), and electronic cigarette vapor (Aim 3). First, I performed whole-genome transcriptional profiling of the nasal epithelium in children and adults and detected gene-expression changes associated with exposure to environmental tobacco smoke. Next, I employed similar approaches to detect a signature of coal smoke exposure in the buccal epithelium of healthy, non-smoking females exposed to household air pollution Xuanwei, China. The findings from these studies suggest that upper airway gene-expression can reflect the host response to prolific sources of environmental exposures that are major risk factors for chronic lung disease. Lastly, I examine the cellular and physiologic consequences of electronic cigarette (ECIG) aerosol exposure by analyzing transcriptomic profiles of human bronchial epithelial cells that have either been (1) differentiated and exposed in vitro or (2) acquired via bronchoscopy from the airway epithelium of ECIG users. The studies detailed in this dissertation offer valuable insight that will accelerate the efforts to evaluate the health effects of both well-established and emerging types of inhaled exposures in large-scale population studies. Furthermore, the transcriptomic strategies woven throughout the following chapters push for a novel assessment paradigm that may enable the public health community to rapidly characterize the physiologic host response to inhalation exposures of different sources, and to evaluate the biologic consequences of exposure-reduction initiatives. / 2017-05-01T00:00:00Z

Yes / Air pollution has been integrated into global challenges over the last few years due to its negative impact on the health of human beings, increasing socio-economic risks and its contribution to climate change. This study attempts to evaluate the current status of Iran's air pollution with regard to the sources of emissions, control policies, as well as the health and climate consequences that have resulted through available data from monitoring stations reported in the literature, official documents and previous published papers. Many large cities in Iran surpass the permissible concentration of air pollutants, particularly particulate matter, sulfur dioxide, black carbon and ozone. Although regulations and policies are in place and enormous efforts are being made to address air pollution issues in the country, implementation and enforcement are not as effective as they could be. The significant challenges may be regarded as the inefficiency of regulation and supervision systems, the lack of air quality monitoring systems and technology, particularly in industrial cities rather than Tehran as well as the lack of continual feedback and investigations on the efficiency of regulation. Providing such an up-to-date report can bring opportunities for international collaboration, which is essential in addressing the air pollution worldwide. We suggest that a way forward could be more focused on conducting systematic reviews using scientometric methods to show an accurate picture and trend in air pollution and its association in Iran, implementing an integrated approach for both climate change and air pollution issues, collaborating with international counterparts to share knowledge, tools, and techniques.

[pt] O Réveillon em Copacabana, no Rio de Janeiro, na virada do dia 31/12 para o dia 01/01, ocorre com a queima de 17 a 25 t de fogos de artifícios com duração entre 12 a 17 minutos. Estudos realizados em outros países durante comemorações com queima de fogos mostram um aumento significativo de alguns poluentes no ar. Este estudo avaliou a qualidade do ar em Copacabana durante as comemorações do ano novo de 2015 a 2020, considerando as concentrações do MP10, SO2, CO e O3 amostrados na estação automática. Pelo teste de Shapiro-Wilk, uma distribuição assimétrica no banco de dados foi observada, e através do teste não paramétrico as concentrações diárias entre os dias 31 de dezembro e 01 de janeiro foram testadas. A composição química MP2,5 amostrado na estação semiautomática de Copacabana em 2019, foi realizada através das técnicas analíticas de cromatografia de íons (CI) e espectrometria de massa por plasma acoplado indutivamente (ICP-MS), traduzido do inglês, inductively coupled plasma mass spectrometry. Durante o ano de 2019, as concentrações diária e anual do MP(2,5) não ultrapassaram os padrões nacionais de qualidade do ar, mas segundo os padrões recomendados pela OMS, a qualidade do ar esteve inapropriada. As concentrações elementares e iônicas determinadas por ICP-MS e CI indicaram espécies características de fontes veiculares e naturais. Em 01 de janeiro de 2020 a concentração dos íons Cl(-) e Na(+), foi atribuída ao spray marinho, e a ausência dos ânions C2H3COO(-), CH2(COO)2(2-) CHOO(-) e C2O4(2-) foi atribuída à diminuição da circulação urbana. / [en] New Year s Eve in Copacabana, Rio de Janeiro, at the turn of 12/31 to 01/01, takes place with the burning of 17 to 25 t of fireworks lasting between 12 and 17 minutes. Studies carried out in other countries during celebrations with fireworks show a significant increase in some pollutants in the air. This study evaluated the air quality in Copacabana during the New Year celebrations from 2015 to 2020, considering the concentrations of PM10, SO2, CO and O3 sampled at the automatic station. By the Shapiro-Wilk test, an asymmetry distribution in the database was observed, and through the non-parametric test the daily concentrations between December 31st and January 1st were tested. The chemical composition PM2.5 sampled at the semi-automatic station in Copacabana in 2019 was performed using analytical techniques of ion chromatography (IC) and inductively coupled plasma mass spectrometry (ICP-MS). During the year 2019 concentrations of PM2.5 not exceeded to national air quality standards, but according to the standards recommended by the WHO, the air quality was inappropriate. The elemental and ionic concentrations determined by ICP-MS and CI indicated species characterized from vehicular and natural sources. On January 1, 2020, the concentration ions of Cl(-)and Na(+) was attributed to marine spray, and the absence of the anions C2H3COO(-), CH2(COO)2(2-) CHOO(-) e C2O4(2-) was attributed to the lowest urban circulation.