Carbon dioxide (CO2) is the leading contributor to global warming and climate change. The increases in fossil fuel emissions, deforestation, and changes of land use have resulted in increased CO2 levels in the atmosphere from 280 ppm in 1765 to 390 ppm in 2010. Carbon mitigation policies for managing the biosphere to increase net CO2 uptake are dependent upon accurate knowledge of the biosphere fluxes. However, Northern Hemisphere bottom-up and top-down biosphere flux estimates show significant discrepancies, especially in North America. In this study, we design an analysis framework that integrates observations with models with the goal of reducing some of the key uncertainties in estimating CO2 fluxes and concentrations in the Midwest, USA. In this research, the biosphere model, WRF-VPRM model (Ahmadov et al., 2007) is used to simulate CO2 biosphere fluxes and atmospheric CO2 concentrations in the Midwest, USA at high spatial resolution. Reducing uncertainties in the predictions is accomplished by improving the model transport configurations (i.e. the WRF planetary boundary layer (PBL) scheme, the number of vertical layers and the horizontal resolution), utilizing a more detailed land cover map, optimizing VPRM photosynthesis and respiratory parameters for major crops (i.e. corn and soybean) against flux towers, and integrating CO2 tall tower observations and model through a top-down data assimilation method to improve the VPRM model parameters and in turn improving the flux and concentration estimates. The WRF-VPRM model configuration with the YonSei University PBL scheme produced the most accurate CO2 concentration predictions at the WBI tower at all three tower levels with the maximum error reduction of 17.1%. Increasing the number of vertical layers improved the CO2 estimates during nighttime and early morning, especially at 30 m, where the error was reduced by a maximum of ~ 20%. The differences in the monthly average net fluxes over the State of Iowa between the high resolution WRF-VPRM model and coarse resolution Carbon Tracker were significant, 71%, 18%, and 62% in June, July, and August, respectively. The fluxes calculated by the VPRM model are primarily dependent on 4 model parameters, half saturation value of photosynthesis (PAR0), light use efficiency (ë), and respiration parameters (á and â). These parameters are specific to vegetation types, regions, and time period. The default settings do not distinguish between corn and soybean, which are major crops in the Midwest and have significant different photosynthesis rates. When corn and soybean are explicitly included in the model, the flux estimate changed by 31.3% at 12 pm and 24.5% at 12 am. Two different methods were used to optimize for the VPRM model parameters which are optimization against Ameriflux NEE and using a top-down variational method. The simulation using optimized parameters from the variational method reduced the error during the daytime from 11.6 ppm to 7.8 ppm. The average fluxes optimized using the variational method changed by 17% and 38.6% at 12 pm and 12 am, respectively. The more accurate VPRM parameters lead to the more accurate biosphere fluxes, which will ease the evaluation of benefits of different carbon mitigation policies.

Der Bericht gibt Auskunft über die Luftqualität im Freistaat Sachsen im Jahr 2010. Das 29 Messstellen umfassende stationäre Luftmessnetz misst die Konzentration der Luftschadstoffe Schwefeldioxid, Stickstoffdioxid, Feinstaub-Partikel, Blei, Benzol und Ozon. Die Stickstoffdioxid-Konzentrationen sind an stark befahrenden Straßen weiterhin kritisch. Der Jahresgrenzwert konnte auch 2010 an verkehrsnahen Messstellen in Leipzig, Dresden und Chemnitz nicht eingehalten werden. Der 24-Stunden-Grenzwert der Partikelkonzentration (PM10) wurde an sieben Messstellen überschritten. Betroffen sind verkehrsnahe Messstellen, aber auch Messstellen an der Grenze zu Polen. Keine Probleme gab es dagegen bei der Einhaltung der Jahresgrenzwerte der Partikelkonzentrationen PM10 und PM2,5. In ländlichen Gebieten gab es, wie auch in den Vorjahren, Überschreitungen der Ozon-Zielwerte zum Schutz der menschlichen Gesundheit und der Vegetation. Die Konzentrationen von Schwefeldioxid, Benzol und Blei lagen auf dem Niveau der Vorjahre und damit weit unter den gesetzlichen Grenzwerten.

Low-cost sensors have the potential to create dense air monitoring networks that help enhance our understanding of pollution exposure and variability at the individual and neighborhood-level; however, sensors can be easily influenced by environmental conditions, resulting in performance inconsistencies across monitoring settings. During summer 2020, 20 low-cost particulate sensors were deployed with a reference PM2.5 monitor in Denton, Texas in preparation for calibration. However, from mid to late-summer, dust transported by the Saharan Air Layer moved through the North Texas region periodically, influencing the typical monitoring pattern exhibited between low-cost sensors and reference instruments. Traditional modeling strategies were adapted to develop a new approach to calibrating low-cost particulate sensors. In this study, data collected by sensors was split according to a novel dust filter into dust and non-dust subsets prior to modeling. This approach was compared with building a single model from the data, as is typically done in other studies. Random forest and multiple linear regression algorithms were used to train models for both strategies. The best performing split-model strategy, the multiple linear regression models split according to dust and non-dust subsets (combined R2 = 0.65), outperformed the best performing single-model strategy, a random forest model (R2 = 0.49). The results from this study indicate that low-cost sensor performance can be greatly influenced by the presence of dust, and that adaptive strategies, like the ones presented in this paper, are necessary when calibrating sensors in environments that may experience pollution from inconsistent sources throughout the year.

Air quality in general is a hot topic that is significantly linked to respiratory diseases that have been causing a decrease in life expectancy around the globe. Over the past three decades, European cities have done air quality monitoring using one or two air quality instruments per 249 thousand inhabitants. The instruments are big, rigid and expensive. This thesis focuses on new ways of air quality monitoring using air quality sensors that are small, cheap and flexible. An investigatory approach was used through taking insights from citizens to get an understanding of what air pollutants to measure, where to measure and how to present the information in ways that are clear and easy to understand. From a survey of 82 participants, results showed that 74% of the people are concerned about air quality and they would like to receive information in ways that are easy to understand like colours (red for bad or green for good), numbers (µg/m3) and graphs. The citizens identified carbon dioxide (40.6%), particulate matter (25%), nitrogen oxide (18.8%) and ozone (9.4%) as pollutants that need critical attention which can be measured in places that they spend a lot of time in for example playgrounds and parks as well as near sources of pollution like busy roads and industries. The survey was followed by an analysis of eight sensors that were put in the urban environment measuring PM1, PM2.5 and PM10 together with environmental factors (temperature and humidity) to find how the information can be used for citizen air quality awareness and if was accurate enough. The sensors could accuracy detect particulate matter variations from all the places. All environmental factors (temperature, humidity and wind) significantly affected particulate matter (p ≤ 0.05) variations. However, associations with particulate matter were weak to moderate (r = 0.02 to 0.46) which were influenced by the surroundings in the locations of the sensors. A strong correlation with municipal refence instruments and the ability to detect pollution variations is enough accuracy for sensors to be used for awareness which the sensors did. In conclusion, the sensors can be used for air quality monitoring and it is important to do a background study of the area including the number of people in an area and sources of pollution if they are to be used for citizen awareness. Therefore, using sensors will bring more awareness in air quality monitoring by measuring air pollution concentrations in localised places that municipal air quality instruments cannot do.

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The impact that the indoor environment has on productivity is a topic that has been investigated in numerous studies. There are a variety of different methods that have been used to evaluate productivity with. There are quantitative methods and there are qualitative ones, and both have been used in the literature as indicators or real productivity. The quantitative ones are for instance short arithmetical or linguistic performance tests or measurements of the actual quantitative output of a job. Qualitative assessments of productivity consist of different ways of allowing the subjects to rate their own productivity. Both these two approaches of evaluating productivity are claimed to be subject to different issues, and the question of which way is preferable is a matter of contention among the researchers. The quantitative approach is claimed to be unable to reflect the complex and qualitative output of many modern jobs, while the qualitative one is believed to be highly influenced by bias. This master’s degree project has investigated the associations between the two approaches and conducted a qualitative assessment of the impact of the indoor environment on the productivity in a university building in Stockholm. Numerous studies have been reviewed that include both quantitative evaluations of productivity and qualitative evaluations of the indoor environment. Qualitative evaluations are for instance evaluations of environmental satisfaction, as well as evaluations of healthiness and productivity. The relationship between the quantitative measurements and the subjective evaluation is indeed complex. However, there appears to be a consistency to some extent between the two, and the trend seems to indicate that occupants who are more productive are also more satisfied with the indoor environment or perceive themselves to be healthier or more productive. A working hypothesis has been formulated; that subjective evaluations of the indoor environment may act as indicators of productivity. This approach has been used in a university building in Stockholm, where the productivity of the students has been evaluated through a survey, together with physical measurements of the indoor environment. The survey is designed based on the current literature within the field. It has a large emphasis on productivity, with several questions concerning it directly and indirectly. The physical parameters that were measured were radiant temperature, air velocity, relative humidity, CO2- concentration and sound pressure level. Considerable correlations were observed between perceived productivity and environmental satisfaction, perceived environmental control and between different ways of evaluating productivity subjectively. The correlations between the physical measurements and the subjective evaluations were in general considerably weaker than the ones between the different subjective parameters. The correlations between the mean CO2-concentration and productivity was weak, and similar findings were obtained concerning sound pressure level. This emphasise the importance of heeding the opinions of the occupants while evaluating the performance of a building, as physical measurements alone appear to be unable to reflect the users’ perspective reliably. The correlation between the thermal parameters (evaluated by the PMV- value) and the subjective evaluations were, on the other hand, considerably stronger. This may indicate that the thermal parameters are among the most influential ones in creating a productive workplace. Furthermore, the study discusses different methods that have been used to evaluate productivity with. It discusses their weaknesses and strengths and what elements they contain that may be used for future studies of productivity.

This work is aimed to study the capabilities of CMAQ and WRF-Chem models for predicting the PM10 and PM2.5 pollution in the west side of Saudi Arabia. To do this fairly, one-month simulations (April, 2021) are done in both models using same initial and boundary conditions, meteorology and anthropogenic emissions. Unique configurations in both models allow to compare differences in the chemical processes and natural emissions estimation of each model. Simulated PM (PM10 and PM2.5) surface concentrations and AOD are compared with available observations to assess models’ performance. Moreover, CMAQ is used to study a real air pollution episode generated by a fire in the Rabigh Electricity Power Station between April 8 and April 11, 2021.

This thesis is designed to introduce beginning and experienced heating, ventilation and air conditioning (HVAC) engineers to common indoor air quality (IAQ) problems and solutions. The bulk of the work is a literature review of common pollutants, pollutant sources, HVAC equipment and systems, and remediation techniques. Pollutants covered include fungi, bacteria, dust mites, viruses, biofilms, microbiological volatile organic compounds (MVOC?s), volatile organic compounds (VOC?s), carbon dioxide, ozone, and radon. The HVAC systems covered are ventilation, direct expansion (DX), desiccant dehumidification, and system filters. The remediation techniques discussed are proper hygiene and maintenance, increased ventilation, humidity control, and proper selection of building materials.

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