Prior research has shown that indoor air quality (IAQ) impacts cognitive performance. At Cal Poly, many older buildings are unable to maintain appropriate IAQ because of their outdated ventilation systems and the increasing number of students in the rooms. This work analyzes the IAQ of different buildings at Cal Poly, with a focus on Building 20. Carbon dioxide, temperature, and relative humidity inside classrooms are collected using an integrated circuit sensor and a microcontroller. A total of 38 hours of data was collected, with 22 of those hours in Building 20 specifically. We find that unlike temperature and relative humidity, CO2 levels routinely exceed 1,000 ppm—a concentration that hinders cognitive function. A questionnaire distributed to Cal Poly students suggests that while students can recognize poor IAQ in classrooms, they erroneously attribute these poor conditions to temperature and humidity instead of CO2. This data is then used to propose a system which can collect long-term data based on optimal placement, storage, and power requirements.

Indoor Air Quality (IAQ) plays a vital role in occupant well-being. Among various factors, CO2 concentration impacts the productivity and cognitive functions of occupants. Different strategies can be utilized to improve IAQ, including context-aware ventilation, air purification technologies, and integration of indoor plants. Existing methods in the literature for reducing CO2 concentrations rely on direct sensing, which requires advanced infrastructure that may prevent scalability. This study investigates a soft sensing approach, utilizing readily accessible features from Building Management System (BMS) to develop predictive models for CO2 concentration, offering a cost-effective alternative to direct sensor-based measurements. We leverage two different datasets to explore the feasibility and accuracy of the soft sensing approach. The first dataset aggregates CO2 data points compiled from existing literature, providing a broad perspective of IAQ variations across various educational settings. The second dataset is a publicly available, high-resolution set of IAQ measurements from several spaces over a month, allowing for detailed model training and testing. By applying machine learning techniques, we developed models that predict CO2 concentrations based on different sets of variables. We observed that the Random Forest model could predict CO2 concentration with a Mean Absolute Error (MAE) of 37.57 by utilizing room temperature, outdoor temperature, and the hour of the day. Moreover, this study assesses the transferability of the predictive models trained on a limited number of data points. We observed that using occupancy percentage results in more transferable models compared to other variable sets. The main contribution of this study to the body of knowledge is the evaluation of the soft sensing approach, which could pave the way for creating more scalable and infrastructure-independent systems to improve indoor air quality in educational facilities. / Master of Science / Indoor air quality is crucial for well-being, especially in schools and universities where students and staff spend much of their day. Among different factors, CO2 concentration plays an important role in students’ cognitive function and productivity. Traditional methods use direct sensors to monitor and operate buildings, which can be expensive and cumbersome. This research investigates a cost-effective way to predict indoor carbon dioxide (CO2) level, called soft sensing, using existing, easily accessible data to create models that predict CO2 levels without requiring extensive hardware for all spaces. We tested our models using two types of data: one collected from published studies on indoor air quality and another from a high-quality public dataset of actual air measurements in educational facilities. By applying machine learning techniques, we developed models that can predict CO2 concentrations based on monitored variables, such as room and outdoor temperature and time of day, thereby bypassing the need for extensive new sensor installations. Our finding shows that the created models are accurate and could decrease our need for extensive infrastructure systems. We also explored how well these models can be applied to other spaces, finding that models based on occupancy rates are more generalizable than others. The key finding of this research is that soft sensing can effectively predict CO2 levels in the educational settings of our case study and can be expanded across environments, making it a potentially scalable solution for improving air quality in educational facilities.

While ozone design values have decreased since 2000, the values measured in Denton Airport South (DEN), an exurban region in the northwest tip of the Dallas-Fort Worth (DFW) metroplex, remains above those measured in Dallas Hinton (DAL) and Fort Worth Northwest (FWNW), two extremely urbanized regions; in addition, all three sites remained in nonattainment of National Ambient Air Quality Standards (NAAQS) ozone despite reductions in measured NOx and CO concentrations. The region's inability to achieve ozone attainment is tied to its concentration of total non-methane organic compounds (TNMOC). The mean concentration of TNMOC measured at DAL, FWNW, and DEN between 2000 and 2018 were 67.4 ± 1.51 ppb-C, 89.31 ± 2.12 ppb-C, and 220.69 ± 10.36 ppb-C, respectively. Despite being the least urbanized site of the three, the TNMOC concentration measured at DEN was over twice as large as those measured at the other two sites. A factor-based source apportionment analysis using positive matrix factorization technique showed that natural gas was a major contributing source factor to the measured TNMOC concentrations at all three sites and the dominant source factor at DEN. Natural gas accounted for 32%, 40%, and 69% of the measured TNMOC concentration at DAL, FWNW, and DEN, respectively. The Barnett Shale region, an active shale gas region adjacent to DFW, is a massive source of unconventional TNMOC emissions in the region. Also, the ozone formation potential (OFP) of the TNMOC pool in DEN were overwhelmingly dominated by slow-reacting alkanes emitted from natural gas sources. While the air pollutant trends and characteristics of an urban airshed can be determined using long-term ambient air quality measurements, this is difficult in regions with sparse air quality monitoring. To solve the lack in spatial and temporal datasets in non-urban regions, various machine learning (ML) algorithms were used to train a computer cluster to predict air pollutant concentrations. Models built using certain ML algorithms performed significantly better than others in predicting air pollutants. The model built using the random forest (RF) algorithm had the lowest error. The performance of the prediction models was satisfactory when the local emission characteristics at the tested site were like the training site. However, the performance dropped considerably when tested against sites with significantly different emission characteristics or with extremely aggregated data points.

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.

Denton, Texas consistently reaches ozone nonattainment levels. This has led to a large focus of air pollution monitoring efforts in the region, with long-range transport being explored as a key contributor. For this study, the University of North Texas Discovery Park campus was chosen as a prospective location for an extensive air quality monitoring station. Sixteen years of ozone and meteorological data for five state-run monitoring sites within a 25 mile radius, including the nearest Denton Airport site, was gathered from TCEQ online database for the month of April for the years 2000 to 2015. The data was analyzed to show a historical, regional perspective of ozone near the proposed site. The maximum ozone concentration measured at the Denton Airport location over the 16 year period was measured at 96 ppb in 2001. Experimental ozone and meteorological measurements were collected at the Discovery Park location from March 26 to April 3 and April 8 to April, 2016 and compared to the Denton Airport monitoring site. A time lag in ozone trends and an increase in peak ozone concentrations at the proposed location were observed at the proposed site in comparison to the Denton Airport site. Historical and experimental meteorological data agreed in indicating that southern winds that rarely exceed 20 miles per hour are the predominant wind pattern. Back trajectories, wind roses, pollution roses, and bivariate plots created for peak ozone days during experimental periods support long range transport as a considerable cause of high ozone levels in Denton. Furthermore, a study of the precursor characteristics at the Denton Airport site indicated the site was being affected by a local source of nitrogen dioxide that was not affecting the proposed location. The differences in the Denton Airport site and the proposed site indicate that further monitoring at Discovery Park would be insightful. An outline of an expansive mobile monitoring station and suggestions for effective utilization are provided to guide future studies in Denton and the surrounding North Texas region.

Spaces with high occupancy density like classrooms are challenging to ventilate and use a lot of energy to maintain comfort. Usually, a compromise is made between low energy use and good Indoor Environmental Quality (IEQ), of which poor IEQ has consequences for occupants’ health, productivity and comfort. Alternative strategies that incorporate elevated air speeds can reduce cooling energy demand and provide occupant’s comfort and productivity at higher operative temperatures. A ventilation strategy, Intermittent Air Jet Strategy (IAJS), which optimizes controlled intermittent airflow and creates non-uniform airflow and non-isothermal conditions, critical for sedentary operations at elevated temperatures, is proposed herein. The primary aim of the work was to investigate the potential of IAJS as a ventilation system in high occupancy spaces. Ventilation parameters such as air distribution, thermal comfort and indoor air quality are evaluated and the system is compared with a traditional system, specifically, mixing ventilation (MV). A 3-part research process was used: (1) Technical (objective) evaluation of IAJS in-comparison to MV and displacement ventilation (DV) systems. (2) An occupant response study to IAJS. (3) Estimation of the cooling effect under IAJS and its implications on energy use. All studies were conducted in controlled chambers. The results show that while MV and DV creates steady airflow conditions, IAJS has  cyclic airflow profiles which results in a sinusoidal temperature profile around occupants. Air distribution capability of IAJS is similar to MV, both having a generic local air quality index in the occupied zone. On the other hand, the systems overall air change rate was higher than a MV. Thermal comfort results suggest that IAJS generates comfortable thermal climate at higher operative temperatures compared to MV. Occupant responses to IAJS show an improved thermal sensation, air quality perception and acceptability of indoor environment at higher temperatures as compared to MV. A comparative study to estimate the cooling effect of IAJS shows that upper HVAC setpoint can be increased from 2.3 – 4.5 oC for a neutral thermal sensation compared to a MV. This implies a substantial energy saving potential on the ventilation system. In general, IAJS showed a potential for use as a ventilation system in classrooms while promising energy savings. / Lokaler där många människor vistas, som t.ex. klassrum, är ofta svåra att ventilera. Att upprätthålla en bra termisk komfort kräver en hög energianvändning. Vanligtvis blir det en kompromiss mellan låg energianvändning och bra kvalitet på inomhusmiljön (IEQ). Dålig IEQ får konsekvenser för människors hälsa, produktivitet och komfort. Alternativa ventilationsstrategier, som använder förhöjda lufthastigheter, kan minska kylbehovet och därmed energianvändningen. I denna avhandling utvärderas en ny ventilationsstrategi, Intermittenta luftstrålar (IAJS), där korta perioder med hög lufthastighet genererar en svalkande effekt, när rummets temperatur upplevs som för hög. Det primära syftet med arbetet var att undersöka potentialen hos IAJS som ett ventilationssystem för klassrum, där den termiska lasten ofta är hög. Strategin jämförs mot traditionella ventilationsprinciper som omblandande ventilation (MV) och deplacerande ventilation (DV). Parametrar som luftdistributionsindex, termisk komfort, luftkvalitet och energibesparing har utvärderats. Alla studier utfördes i klimatkammare. Resultaten visar att medan MV och DV skapar konstanta luftflödesförhållanden genererar IAJS cykliska hastighetsprofiler samt en sinusformad temperaturvariation i vistelsezonen. IAJS klarar att bibehålla ett bra termiskt klimat vid högre operativa temperaturer jämfört med MV. I en jämförelse med ett traditionellt HVAC-system visar beräkningar  att dess börvärde kan höjas från 2.3 till 4.5 °C med bibehållen termisk komfort. Detta indikerar en avsevärd energibesparingspotential vid användande av IAJS.

Characterization and Source Apportionment of Ambient PM2.5 in Atlanta, Georgia: On-Road Emission, Biomass Burning and SOA Impact Bo Yan 260 Pages Directed by Drs. Armistead G. Russell and Mei Zheng Various airborne PM2.5 samples were collected in the metropolitan Atlanta and surrounding areas, which are directly impacted or dominated by on-road mobile and other typical urban emissions, regional transport sources, prescribed burning plumes, wildfire plumes, as well as secondary sources with anthropogenic and biogenic nature in origin. Detailed PM2.5 chemical speciation was conducted including over one hundred of GC/MS-quantified organic compounds, organic carbon (OC), water-soluble organic carbon (WSOC), elemental carbon (EC), ionic species, and tens of trace metals. Day-night, seasonal and spatial variations of PM2.5 characterization were also studied. Contributions of PM2.5 major sources were identified quantitatively through the receptor source apportionment models. These modeling results, especially on-road mobile source contributions and secondary organic carbon (SOC) were assessed by multiple approaches. Furthermore, new season- and location-specific source profiles were developed in this research to reflect real-world and representative local emission characterizations of on-road mobile sources, aged prescribed burning plumes, and wildfire plumes. Secondary organic aerosol (SOA), a major component of PM2.5 in the summer, was also explored for sources and contributions.

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This study aimed to assess the relationships (if any) between air pollutant measurements in the Durban South Basin (DSB) and (i) local meteorology, (ii) community reports of pollution incidents in Durban, and (iii) air quality and meteorology in Cape Town on the days preceding the Durban South Basin events. With the use of daily synoptic charts and various meteorological variables at an hourly resolution, it was established that air pollution events were better associated with local meteorological events than a community complaint database. Visual analyses of graphed meteorological conditions during the course of air pollution events revealed three clear meteorological scenarios associated with these: 1. A pre-frontal scenario; 2. A scenario showing inversion conditions but no approaching front, and generally low wind speeds; and 3. A post-frontal scenario, likely to be associated with stack downwash under higher wind speeds with the passing of a front. ANOVA revealed significant differences between peak PM10 and average PM10 across scenarios, with Scenario 3 showing highest average and peak PM10. At the Wentworth monitoring station, 24.4% of pollution incidents fell under Scenario 1, 64.2% under Scenario 2, and 5.7% under Scenario 3 between 2006 and 2010. A further 5,7% of the air pollution incidents did not fall under these three scenarios. The latter were not associated with fronts, and did not show inversion conditions, and are likely to be associated with intermittent industrial pollution events. Further statistical analysis assessed the relationships (if any) between various meteorological variables, traffic levels and air pollution concentrations at the Wentworth station between 2006 and 2010. Findings show that delta temperature (change in temperature with height) is the strongest explanatory variable with respect to PM10, wind speed the second strongest, and traffic levels the third strongest. On average, PM10 concentrations increased with increasing delta temperature, decreasing wind speed, and increasing traffic levels. The pressure minimum at Durban associated with an approaching front showed a negative relationship with PM10 during pre-frontal events, but this variable was not significant at the 95% confidence level. This tentatively suggests that even when controlling for frontal influences on delta temperature, lower pressure minima (i.e. stronger frontal systems) are associated with higher pollution levels. Pollution maxima at various Cape Town stations and pressure minima in Cape Town prior to the incident in the DSB showed no relationships with incident PM10 levels at Wentworth. As such, pollution concentrations and meteorology in Cape Town as a front approaches do not appear to be effective predictors of pollution conditions in the DSB when the front approaches there. / Thesis (M.Sc.)-University of KwaZulu-Natal, Durban, 2013.