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.

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.

Even at low concentrations, the criteria air pollutant particulate matter (PM) is an environmental and public health hazard. Emissions levels legislated for modern diesel vehicles are so low (~90% lower than 2003) that it has become difficult to accurately measure PM by the regulatory metric: the mass of particles collected on a filter (i.e., the gravimetric method). Additionally, gravimetric analysis cannot measure real-time emission rates, and therefore is unable to characterize high-emitting transient events (e.g., engine starts, stop-and-go driving). By an alternate method, PM can be estimated by measuring the number-weighted particle size distribution (PSD) and calculating mass with a combination of theoretical and empirical constants (e.g., particle effective density). This integrated particle size distribution (IPSD) method is capable of high measurement sensitivity and real-time resolution. Real-time measurements by the IPSD method require fast-sizing spectrometers, such as the TSI Engine Exhaust Particle Sizer (EEPS), which sizes (between 5.6-560 nm) and counts particles based on their electrical mobility. The EEPS utilizes a unipolar charger to quickly charge particles for sizing and counting, however this mechanism has been shown to produce a less predictable charge distribution than bipolar chargers used in Scanning Mobility Particle Sizer (SMPS) systems – the gold standard 'slow-sizing' spectrometer. Several evaluations have shown deficiencies in EEPS PSD measurements due to charging differences (associated with particle morphology) unaccounted for in the transfer function matrix used to calibrate the EEPS. Specifically, the unipolar charger multiply charges a higher percentage of soot agglomerates (fractal-like particles common in diesel engine exhaust) than bipolar chargers. Because inaccurate PSDs are a primary reason for reported discrepancies between IPSD calculated mass and the gravimetric method, it is important to correct this deficiency in EEPS measurements. Recently, TSI has released additional EEPS calibration matrices ('soot' and 'Compact') which have shown better agreement with SMPS measurements under preliminary test conditions. This study further evaluates the performance of these new matrices relative to the original 'Default' matrix for diesel and biodiesel exhaust particles. Steady-state (75% engine load) emissions were generated by a light-duty diesel engine operating on (1) ultra-low sulfur diesel (ULSD) and (2) 100% soybean biodiesel. Raw EEPS data processed with each matrix were compared to simultaneously collected reference measurements from an SMPS. PSDs were evaluated based on their shape – i.e., multimodal fits of geometric mean diameter (GMD) and geometric standard deviation (GSD) – and concentration at peak particle diameter. For both fuels, all measurements agreed well in terms of the shape of the PSD: primary mode (accumulation) GMD ± 10nm, GSD ± 0.3. For ULSD, EEPS Default, Soot, and Compact concentrations were higher than the SMPS by factors of 1.9, 1.3, and 2.5, respectively. For biodiesel, EEPS Default, Soot, and Compact concentrations were higher than the SMPS by factors of 2.1, 1.7, and 2.4, respectively. Based on these results, the Soot matrix produced acceptable agreement between EEPS and SMPS measurements of ULSD exhaust particles. However, based on the factor of ~2 difference observed here, an additional calibration matrix may be necessary for the EEPS to accurately measure biodiesel exhaust particles. The IPSD method for estimating PM mass was applied to available data sets with corresponding gravimetric measurements (one ULSD transient cycle test and the same biodiesel steady-state test used for PSD evaluation). Real-time PSDs from each of the three EEPS matrices were used in combination with three sets of values assumed for size-dependent particle effective density (representing a range of potential conditions), resulting in nine IPSD estimates of PM mass corresponding to each gravimetric sample (one ULSD, one biodiesel). For the transient ULSD test, a widely used effective density distribution for fractal-like soot agglomerates resulted in good agreement between IPSD estimated mass and the gravimetric measurement (within 9% and 6% for Soot and Compact matrices, respectively). For the steady-state biodiesel test, assuming unit density (1g/cm³ for all particles) resulted in good agreement between IPSD estimated mass and the gravimetric measurement (within 7% and 2% for Soot and Compact matrices, respectively). These results support previous findings that the Soot matrix is currently the best available option for measurement of ULSD exhaust particles by the EEPS and that particle effective density distributions similar to the "fractal-like" one used here are an accurate estimate for ULSD exhaust particles under many conditions. However, based on the discrepancies between the EEPS and SMPS measured biodiesel exhaust PSDs observed here, as well as a current lack of information on the effective density of biodiesel exhaust particles, it is clear that additional research is necessary in order to understand the properties of biodiesel exhaust particles, especially as they relate to electrical mobility measurements and IPSD estimation of PM mass.

This study aimed to develop retrofitted natural ventilation options and control strategies for existing office buildings to improve thermal comfort, indoor air quality and energy consumption. For this purpose, a typical office building was selected in order to identify opportunities and constraints when implementing such strategies. Actual performance of the case study building was evaluated by conducting quantitative and qualitative field measurements including physical measurements and questionnaire surveys. Based on the actual building performance, a combination of Dynamic Thermal Simulation (using IES) and Computational Fluid Dynamics (using PHOENICS) models were built to develop appropriate natural ventilation options and control strategies to find a balance between energy consumption, indoor air quality, and thermal comfort. Several retrofitted options and control strategies were proposed and the best retrofitted natural ventilation options and control strategies were installed in the case study building. Post occupancy evaluation of the case study building after the interventions was also carried out by conducting physical measurements and questionnaire surveys. Post refurbishment measurements revealed that energy consumption and risk of overheating in the refurbished building were reduced by 9% and 80% respectively. The risk of unacceptable indoor air quality was also reduced by 60% in densely occupied zones of the building. The results of questionnaire surveys also revealed that the percentage of dissatisfied occupants reduced by 80% after intervention. Two new products including a Motorized ceiling tile and NVlogIQ , a natural ventilation wall controller, were also developed based on the results of this study.

The theme of the theses is the new legal regulation of air quality protection in Czech Republic, which is mainly represented by the Act no. 201/2012 Coll. on Air Protection. The opening chapters deal with the definitions of the basic terms in this legal area, the characteristic of the main air pollutants and the development of the air protection legal regulation in Czech Republic. The treatise on the new legal regulation is divided by the legal instruments of air quality protection - the individual chapters deal with conceptual, administrative-legal and vindicatory, economical and specific instruments. The final chapter contains the summary of the major changes and the evaluation of the new legal regulation.

Ce travail porte sur l'estimation des incertitudes et la prévision de risques en qualité de l'air. Il consiste dans un premier temps à construire un ensemble de simulations de la qualité de l'air qui prend en compte toutes les incertitudes liées à la modélisation de la qualité de l'air. Des ensembles de simulations photochimiques à l'échelle continentale ou régionale sont générés automatiquement. Ensuite, les ensembles générés sont calibrés par une méthode d'optimisation combinatoire qui sélectionne un sous-ensemble représentatif de l'incertitude ou performant (fiabilité et résolution) pour des prévisions probabilistes. Ainsi, il est possible d'estimer et de prévoir des champs d'incertitude sur les concentrations d'ozone ou de dioxyde d'azote, ou encore d'améliorer la fiabilité des prévisions de dépassement de seuil. Cette approche est ensuite comparée avec la calibration d'un ensemble Monte Carlo. Ce dernier, moins dispersé, est moins représentatif de l'incertitude. Enfin, on a pu estimer la part des erreurs de mesure, de représentativité et de modélisation de la qualité de l'air / This work is about uncertainty estimation and risk prediction in air quality. Firstly, we need to build an ensemble of air quality simulations which can take into account all uncertainty sources related to air quality modeling. Ensembles of photochemical simulations at continental and regional scales are automatically built. Then, these generated ensemble are calibrated with a combinatorial optimization method. It selects a sub-ensemble which is representative of uncertainty or has good resolution and reliability of probabilistic forecasts. Thus, this work show that it is possible to estimate and forecast uncertainty fields related to ozone and nitrogen dioxide concentrations or to improve reliability related to the threshold exceedance prediction. This approach is compared with Monte Carlo ensemble calibration. This ensemble is less representative of uncertainty. Finally, we can estimate the part of the measure error, representativity error and modeling error in air quality

Master of Science / Department of Mechanical and Nuclear Engineering / Mohammad H. Hosni / Each year millions of people travel by commercial aircrafts. The Bureau of Transportation Statistics indicates that about 600 million passengers fly each year in the United States and, of those, roughly 350,000 are international travelers. This number of travelers leaves commercial airliners potentially vulnerable to biological contamination and makes the transmission of diseases a serious threat. The spread of SARS (Severe Acute Respiratory Syndrome) and H1N1 (swine flu) are examples of documented cases. Consequently, considerable research has been and continues to be conducted to study and understand particulate transport mechanisms and dispersion behavior inside aircraft cabins to develop means for detecting, controlling, and removing contaminants from aircraft cabins and to find methods for preventing the aircraft from being used for intentional contaminant deployment. In order to develop means to monitor and control air quality, infectious disease transmission, and particulate transport inside aircraft cabins, an experimental study was conducted to determine the best sensor placement locations for detection and to identify the number of sensors needed to accurately track air quality incidents within a cabin. An 11-row mockup, intended to be representative of a typical wide-body aircraft, was used for the research. The mockup interior is based on the actual dimensions of the Boeing 767 aircraft cabin. Inside the mockup cabin, actual aircraft equipment including seats and air diffusers were used. Each row has seven passenger seats. Particulates were released from different locations in the second row of the mockup cabin. The transported particles were then collected at six different locations in the lateral direction. The best location to place a sensor was defined as the location having the strongest signal (maximum number of particles collected) or the fastest detection time. After determining the best location in the lateral direction, particles were collected at the same location, but in different rows to estimate the differences between the signal strength and the delay time in detecting the signal from row to row. For the later investigation, the particulates were released in Row 2 and in Row 6 as well. For the six locations examined, it was found that the best location for the placement of a sensor in the 11-row mockup in the lateral direction is on the centerline near the cabin floor. Longitudinally, it was found that a sensor may be used for detecting particulates in the same row as the release and a row in front and in back of the release location. For the mockup cabin, a total of 4 sensors was recommended to monitor particulate releases in the 11 row mockup cabin, each of these sensors separated by two rows.

Generation of hydrogen sulfide (H2S) is a common phenomenon from wastewater collection, transport, and treatment processes. Impacts of H2S emissions from wastewater include corrosion and reduction in the service life of wastewater infrastructure, odor nuisance in the community, and health impacts on wastewater operations and maintenance personnel (Neilsen, et al. WEFTEC 2006). Conventional odor control studies performed by municipalities to design their individual odor/corrosion control strategies largely depend on establishing a dilution to detection threshold (D/T) ratio and ascertaining the recognition threshold (R/T) for air samples collected from the study area. These conventional odor studies based on grab samples using R/T and D/T technique using a few days of data have a number of limitations and potentially lead to inaccurate conclusions. However, H2S emission studies using continuous air monitoring is expensive and time consuming. The objective of this research is to understand the feasibility of utilizing emission factors as a tool to predict hydrogen sulfide emissions from headworks of four different Jefferson Parish, Louisiana wastewater treatment plants (WWTP). Proposed model(s) developed for predicting H2S emission factors that depend on wastewater parameters should be convenient for the municipalities to use as the data required is monitored routinely. Use of H2S emission models should assist rapid identification of H2S emission hot spots, optimize H2S control strategies, predict potential health risks, prevent community odor nuisance, and ascertain infrastructure corrosion. This dissertation attempts to; i) develop a research methodology, ii) identify instruments required, iii) generate emission factor ranges and compare their sensitivity to wastewater parameters, iv) generate preliminary empirical emission models based on flow treated, population serviced and area served by a treatment plant for each sampling location and v) provide a roadmap for future research opportunities to refine the models generated as part of this dissertation. Key words: emission model, emission factor, emission ranges, hydrogen sulfide, odor control, air quality, wastewater treatment.

Deterioration of air quality is a growing concern in the world. Air pollution causes serious health problems and also can sometimes result in death. In order to assess air quality, long term and continuous monitoring of pollutant levels in ambient air are needed, such monitoring is often expensive, cumbersome, and resource intensive and so the monitoring programs often fail to succeed. This research focused on designing an ambient air monitoring system by integrating (1) low-cost sensor with a battery, (2) repurposed materials to fabricate all-weather housing for air monitors, and (3) electronics needed to download the data to an on-site secure digital (SD) card, and to push the data wirelessly to the server. This monitoring system was tested at the selected locations in Harvey and Marrero Wastewater treatment plants (WWTPs) by monitoring hydrogen sulfide (H2S) levels. Preliminary analysis was done for few days and also, the results were analyzed.

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.