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Traditional anthropogenic Secondary Organic Aerosol (SOA) research has focused on various combustion sources, including components of emissions from factories, vehicles, and biomass burning. Current models under-predict SOA formation and the oxidation products of non-traditional sources are likely an overlooked source. To this end we studied three major nontraditional sources of emissions for their SOA formation potential: oxygenated Volatile Chemical Products (VCP) and Intermediate Volatile Chemical Products (IVCP), Polycyclic Aromatic Hydrocarbons (PAH), and asphalt heating. Emissions of Volatile Chemical Products and Intermediate Volatility Chemical Products such as cleaning solvents, pesticides, coatings, and personal care products are now larger than emissions from more traditional sources, in part because of control measures on those sources and due to their large emission factors. Various I/VCPs containing oxygen groups such as glycol ethers, esters, and oxygenated aromatics have SOA formation potentials which have not been substantially studied. I/VCPs containing oxygen groups may have a greater probability of undergoing fragmentation and inhibiting intramolecular hydrogen shifts when oxidized, resulting in less polar, lower molecular weight products. As the volatility is inversely dependent on molecular weight and polarity, the addition of these groups may prevent SOA formation. We oxidized common I/VCPs containing oxygen groups under high and low NOx conditions to determine their potential as SOA precursors. We oxidized four glycol ether I/VCPs, two oxygenated aromatic VCPs, and two ester VCPs using an Oxidation Flow Reactor at 50% RH with and without the presence of NOx. All non-aromatic species had SOA mass yields below the detection limit. The two aromatic ring-containing compounds (2-phenoxyethanol and 1- phenoxy-2-propanol) had SOA mass yields of approximately 15%. CIMS data demonstrated this was due to the formation of lower volatility products from the aromatics. SOA composition varied with NOx level despite similar mass yields. Higher oxidation occurred under low NOx, though nitrogen content remained low under high NOx. Overall, this supports the idea that oxygen groups can inhibit SOA formation and that some oxygenated VCPs may be used in consumer products without making SOA. We further tested the two most basic Polycyclic Aromatic Hydrocarbons: 2- methylnaphthalene and naphthalene as a comparison to oxygenated aromatic species. PAHs are of particular interest both for their widespread production from non-traditional sources such as plastic and asphalt heating as well as their health effects as carcinogens. As PAHs are aromatic and relatively high in molecular weight, they have strong potential as SOA precursors, which requires further study. We oxidized the two PAHs 2-methylnaphthalene and naphthalene in an OFR using similar low NOx oxidation levels to the oxygenated VCPs at 50% RH. Both species demonstrated significant SOA yields (g OA/g >0.1) for each OH exposure measured. 2- methylnaphthalene formed SOA yields of ~0.35 g OA/g for all OH exposures, whereas naphthalene formed SOA yields over 0.2 g OA/g only under the two lowest OH exposure conditions, and only 0.11-0.12 g OA/g at higher OH concentrations. The primary differences in products were driven by naphthalene having higher oxidation exposure (> 2 days equivalent exposure) compared to 2-methylnaphthalene (< 2 days equivalent exposure). Nonetheless the additional methyl group in 2-methylnaphthalene did have substantial effects on product formation, primarily by lowering the O:C ratio and forming larger nC = 11 products not formed from naphthalene. Overall, this further demonstrates the ability of aromatic species to generate SOA as well as the importance of the oxidation exposure and overall structure for SOA precursors. Finally, we studied OA formation from asphalt heating. Asphalt is ubiquitous throughout urban areas worldwide. Asphalt is a source of organic compounds spanning a wide range of volatility, from volatile organic compounds to low-volatility species. The emission rate and composition depend strongly on the temperature. For example, Khare et al. showed that emission rates are significantly higher at application temperatures (~130-160°C) than summertime road surface temperatures (~50-70°C), and that the composition of the emissions depends on factors like the presence of UV radiation. Khare et al. also estimated that asphalt paving is a major missing source of intermediate and semivolatile organic compounds in southern California; however, additional studies are necessary to further constrain its contribution to urban particulate matter. In this study, we evaluate the production of primary (POA) and secondary organic aerosols (SOA) from asphalt-related emissions. Fresh roadway asphalt samples were collected from road-paving operations in Pittsburgh and stored in a chemical freezer. Samples were heated at either common application temperature (~130°C) or peak summertime road surface temperature (~55°C). The emitted gas-phase vapors were flushed into a smog chamber containing ammonium sulfate seed particles that served as a condensation sink. SOA was then generated via the photo-oxidation of the emissions under high-NOx conditions typical of urban chemistry. At application temperature, organic vapors were emitted and condensed into POA upon cooling, whereas POA was not formed at summertime temperature. SOA was formed under both conditions, with SOA formation at application temperature partially balanced by POA evaporation. Though application temperature experiments were able to generate substantially greater OA, SOA formation from passive asphalt heating would have a greater effect over time by comparison due to the long lifetime of emissions from asphalt pavements. We further compare the aerosol mass spectra of the laboratory-generated asphalt OA to different OA factors to see if asphalt heating may be an overlooked source in OA inventories. Overall, we demonstrate that asphalt heating both at immediate application and over time is capable of substantial SOA formation. Together, these non-traditional sources have the potential to be major sources of SOA and narrow the gap between models and observations. It is the hope of this work to encourage further study of these and other non-traditional sources of SOA.

EASIUR Organics: Reduced-Complexity Modeling of Organic PM2.5 Health Damages Reduced-complexity models (RCMs) currently do not estimate health damages from organic aerosol or overlook important nuances in the modeling of organic aerosol. EASIUR is a parameterization of health damages built on simulations from chemical transport models. Therefore, EASIUR is well-positioned to incorporate state-of-thescience modeling frameworks for organic aerosol. In this work, we critically expand the EASIUR model to include seasonal and annual marginal health damages for anthropogenic VOCs, semi-volatile POA, and intermediate-VOCs by training regression models on source-tagged simulations from the PMCAMx model. Emissions for OA precursors are individually perturbed at a representative sample of locations in the U.S., and health damages for each species and location are derived for EASIUR model training and testing. When compared with PMCAMx estimates, EASIUR-predicted marginal health damages perform well, with most models having R2 above 0.8 with respect to explaining the spatial variation in health damages, fractional biases ±0.15, and fractional errors below 0.35. Nationally averaged health damages across seasons range from 2,200 to 8,900 $/tonne for high-order alkanes, 24,000 to 110,000 $/tonne for aromatics, 8,500 to 44,000 $/tonne for olefines, 300,000 to 690,000 $/tonne for semivolatile POA, and 51,000 to 120,000 $/tonne for intermediate-VOCs. We estimate total damages from organic aerosol to be $1.8 trillion in 2016. Of these damages, we find that 30% are attributable to biomass burning (including wildfires), 24% are attributable to solvent emissions, and 20% are attributable to mobile sources. We also develop source-specific health damages to facilitate their use in policy analyses. Changes to PM2.5 Health Damages: Robustness of Reduced-Complexity Models for Future Policy Scenarios Reduced-complexity models (RCMs) generally produce PM2.5 health damages by marginally perturbing precursors emissions, and then quantifying the downwind impacts on human mortality. However, marginal health damages may be used to evaluate policies that result in non-marginal emission changes, or policies in future scenarios where the emission baseline differs from those used to develop RCMs. In both cases, marginal health damages from RCMs may not capture potential changes to PM2.5 sensitivity. In this work, we explore changes to PM2.5 sensitivities resulting from future changes to the emission baseline. Simulations from the PMCAMx chemical transport model are used to develop health damages resulting from a -25% emission reduction from 2005 and 2055 emission baselines. Emissions are scaled to match emission changes from 2005 to 2055 as estimated by the RCP4.5 scenario. Month-long simulations for January and July are used to account for seasonal differences in emissions and chemistry. Simulation outputs are used to estimate seasonal and annual nationwide health damages for precursors of PM2.5, including NH3, NOx, SO2, anthropogenic VOCs, and low-, semi-, and intermediate-VOCs. Three classes of anthropogenic VOCs are evaluated, including high-order alkanes, aromatics, and olefins. Emissions of NH3, NOx, SO2, anthropogenic VOCs, and POA change by +24%, -70%, -82%, -42%, and -10% respectively, between the 2005 and 2055 baselines. Changes to annually averaged marginal health damages are -67% for NH3, +16% for SO2, +17% for NOx (not including impacts on OA), -106% for alkanes, -35% for aromatics, -36% for olefins, -19% for IVOCs, and negligible for SVOCs and LVOCs. Changes are modest and within a factor of 2 for most precursors, except for NH3 and alkanes, with the latter contributing little to PM2.5 formation. While health damages for SO2 and NOx change modestly year-round, there are key seasonal differences resulting from seasonal changes to oxidant chemistry. Lower wintertime damages for SO2 and NOx increase +350% and +260%, while higher summertime damages decrease -17% and -36%, respectively. While not included in RCM estimates, impacts of NOx on organic components of PM2.5 become increasingly important due to NOx-limited conditions in the future. Changes in health damages resulting from population changes and increases to wealth can potentially exceed changes resulting from chemistry. Ultimately, we recommend that analysts practice caution when using RCMs to evaluate future scenarios where NH3, or seasonal differences are of concern. Changes to annual health damages tend to be modest for most pollutants, however this is dependent on the cancellation of opposing seasonal effects. This study provides critical guidance for RCM users to better understand uncertainties associated with the use of RCM marginal health damages in future or non-marginal scenarios. Bias Corrections for Species- and Source-Resolved PM2.5 Chemical Transport Model Simulations Using a Geographically Weighted Regression Chemical transport models provide information – albeit with biases – about PM2.5 composition and source that is lacking in observations. Correcting biases in simulated PM2.5 could facilitate their use in exposure assessment, environmental epidemiology, and other applications. Here, we develop a novel dataset of species- and sourceresolved PM2.5 estimates across the continuous U.S. for 2001 and 2010. We use geographically weighted regressions to predict and then correct for the bias in PM2.5 concentration predictions for five chemical species (elemental carbon, organic aerosol, ammonium, nitrate, and sulfate). The regression models are trained using speciated measurements, empirical PM2.5 exposure estimates, CTM predictions, and other geographic information. A 10-fold cross-validation shows minimal bias across all simulated PM2.5 species (-25 – 39% before; 0 – 3 % after) and improved correlations with ground-level monitors for elemental carbon, nitrate, and organic aerosol (R2 : 0.30 – 0.53 before; 0.53 – 0.87 after). GWR also outperforms OLS for all PM2.5 species except elemental carbon, where performance is comparable. Corrected fields also show improved performance in predicting fractional composition. The species- and sourceresolved exposure estimates developed in this study are suitable for use in health analyses of PM2.5 toxicity.

Poor quality of education, high mortality in healthcare and low economic productivity are few of the many plights caused by a lack of access to electricity in sub-Saharan Africa (SSA). On its path to universal electrification (i.e., 100% access to electricity), it is crucial that stakeholders in SSA’s electrification ensure that access to electricity is provided in both a sustainable and just way. A sustainable and just pathway is one where electricity system plans are designed with the needs and wants of the people prioritized, and access to electricity is able to yield long-lasting economic, social and environmental benefits. Researchers and policymakers have sought out least-cost pathways to achieve universal electrification in the sub-continent. While these leastcost approaches have been useful to policymakers in designing electricity system plans, they have left crucial gaps in the literature. First, much of electricity access research has focused on household electrification. Although electrifying the residential sector is useful for helping children study after sunset without having to inhale soot from kerosene lamps, for example, a residential focus misses out on the economic benefit that people may derive from the electrification of more productive uses of electricity. Second, there is a lack of studies that determine how to quantify latent demand for electricity in SSA countries. Such lack of consideration of latent demand has led many utility companies and minigrid developers around the sub-continent to suffer severe financial losses over the years. Third, these least-cost studies do not capture how varying preferences for equality may affect electricity system planning, and do not examine methods to potentially increase such preferences to ensure that access to electricity is provided in an equitable manner. In this dissertation, I seek to address these research gaps accordingly. In Chapter 1, using the agriculture sector in Ethiopia as a case study, I found that the cultivation of wheat would drive the need for larger electricity supply systems, such as grid extension and minigrids, to electrify small-scale pumped irrigation. While such electrification would require a capital investment of about US$10 billion over a 12-year period, it would bolster demand enough to drive down the cost of electricity in rural parts of Ethiopia. In Chapter 2, I review the demand estimation literature in SSA, and present a framework for quantifying latent demand. Applying this framework to Kenya, I show how the commercial and education sectors collectively have at least 126 GWh of latent demand. In Chapter 3, I integrate elicited preferences for equality among decision makers into an electricity system planning model. The results of my discrete choice experiment show that high preferences for equality among decision makers leads to a carbonintensive electricity system in our study. Hence, the least carbon-intensive electricity system may not necessarily be the most equitable electricity system in SSA countries. In Chapter 4, I show how social justice education can be used to increase preferences for equality among decision makers. Finally, I conclude the dissertation by reiterating the need for electricity access research to be more holistic in its methodology to ensure that policymakers and stakeholders in SSA are well-informed about how to attain a more just and sustainable future for Africa.

Severe heatwaves cause tens of thousands of deaths worldwide, and extreme temperatures increase energy use for space-cooling in built environments. The regional heatwave can be amplified locally around built environments due to the thermal and radiative properties of the urban fabric. Therefore, built environments are exposed to excessive heat stressors, and better modeling is needed to identify the complicated interaction between heat stressors and built environments. This thesis aims at three research objectives. The first research develops probabilistic models to enable rapid and efficient forecasts of near-surface temperature with adequate accuracy, motivated by the necessity of highly resolution forecasts for mitigating urban heat risk. The second research develops an approach for quantifying the propagating uncertainty of urban temperature to the energy use of buildings. The study aims to properly consider the heat-induced uncertainty in urban electricity systems at a single-building scale. Finally, extending the previous research, the last research proposes to use an information theory to evaluate the value of accurate temperature modeling to save electricity costs for a building. The overall impact of the study is related to enhancing the resilience of the urban community by leveraging the knowledge of probability and urban informatics.

First, this paper presents a thorough review of water quality data using a rainwater tank, categorizing the data as with and without sedimentation. Data are presented showing minimum, maximum, and mean values for the different parameters. The data measured from several sources reveal that water collected from the tank is much better than the water directly collected from the roof. In addition, to analyse the phenomena through a mathematical model, a hypothetical 5 kL rainwater tank with a 200 m2 roof was modelled with the MUSIC model. The simulations were compared with the measured water quality data from a rainwater tank in Melbourne. In general, we found that MUSIC’s simulations on the mean daily concentrations of total suspended solids (TSS) and total phosphorus (TP) are slight underestimations compared to the measured data from Melbourne. Further MUSIC simulations reveal that significant reductions in the daily maximum concentrations of TSS, TP, and total nitrogen (TN) are expected through a rainwater tank.

Water resources are currently under high pressure due to population growth, urbanization, and changes in climate patterns. Therefore, there is a need for strategies to improve water resources management at all scales. The Food-Energy-Water (FEW) nexus approach has emerged as an alternative for water resources management since it provides a comprehensive management strategy through which interactions among components of food, energy, and water systems can be evaluated. In the Latin American and Caribbean region, studies and reports evaluating the FEW nexus are limited in comparison to other regions in the world. The aim of this work was to investigate the potential for applying a FEW nexus approach to improve water resources management in the Colombian Andean region. In this study, an urban FEW nexus framework was developed for the Otun River Watershed (ORW), a pilot study site located in the Colombian Andean region, based on a characterization of the watershed’s climate, land use land cover, streamflow, and water quality. Results from the characterization revealed that the Pereira/Dosquebradas urban area had the greatest impact on watershed water resources owing to its high water and energy demand. Additionally, the Otun River water quality is mostly affected by this urban area due to the lack of a wastewater treatment facility to decontaminate urban sewage water. The Pereira/Dosquebradas urban area is primarily dependent on food coming from outside of the ORW, thus food production does not have as large an impact on water resources in the watershed. A FEW nexus analysis for 2035showed that water and energy demand could increase by 16% and 30%, respectively, except where a reduction in food production in the ORW is considered, in which case the water demand would remained unchanged. Hydrological modeling of the watershed showed potential changes ranging between -35%and 53%in watershed runoff and -29%and25% in overall water yield for the period 2030to2039, in response to anticipated changes in average annual precipitation ranging from -29% to 6% when comparing to a baseline scenario (2007-2012). Thus, changes in precipitation could affect the volume of water available for residential, industrial, and agricultural activities in the ORW. Moreover, an increase in the number of extreme weather events could cause more floods and landslides. Therefore, recommendations for water resources management in the ORW include reducing water losses in the water distribution systems, adopting water conservation practices, developing GI and decentralized wastewater systems, and implementing urban and peri-urban farming practices. Finally, as water quality is of high concern in the ORW, an assessment was conducted to determine suitable water quality sampling frequencies to meet different water quality monitoring objectives. This analysis used the Western Lake Erie Basin (WLEB) as a case study site as it has long-term, continuous water quality records with data available at least ona daily basis. Daily concentrations for select constituents (suspended solids, total phosphorus, soluble reactive phosphorus, and nitrates+nitrites) were used to create weekly, bi-weekly, monthly, and seasonal subsamples following distinctive rules for day, week, or month of sampling. Results from this evaluation indicate that monthly and seasonal sampling would be sufficient if the objective was to assess the stream’s water quality status. However, if the monitoring objective was related to the examination of water quality trends, weekly and bi-weekly sampling would give better results. Furthermore, differential sampling could be adopted in areas with distinctive characteristics, prioritizing high-resolution sampling (daily, weekly, and bi-weekly) in subareas where the constituents of concern have a high variability (sediments and nutrients)or non-point source pollution has been identified as an issue. The remaining subareas could be sampled on a seasonal or annual basis, with sampling conducted at random to reduce bias. Overall, this study provided an urban FEW nexus framework for the Colombian Andean Region, illustrated the application of this framework in a pilot study site (ORW) under current and future conditions, and presented recommendations for water quality sampling frequency on sites with limited resources to implement a high-resolution water quality monitoring plan.

2021 BE project report

Demand for food, energy and other critical resources is increasing with the growth of the global population. Existing materials and industrial practices cannot meet these demands sustainably. Polymer nanomaterials with densified functional groups and engineered specific physicochemical properties, including size, charge, shape, hydrophobicity and surface to volume ratio can be used to create highly efficient functional nanomaterials for environmental applications,including heavy metal removal from environmental media, resource recovery from waste streams and targeted agrochemical delivery. This thesis develops and tests two kinds of polymer nanomaterials: (1) polymer nanogel sorbents with a high density of chelating groups for efficientand selective heavy metal removal and rare earth element (REE) recovery, and (2) environmentally responsive polymer nanocarriers for controlled and efficient agrochemical delivery. The first objective of this work was to develop and test polymer nanogels with abundant, densely packed thiol chelating groups for mercury removal from both water and hydrocarbons, and for REE recovery from large scale industrial aqueous waste streams such as coal fly ash. Under the first objective, a thiol abundant polymer nanogel was synthesized and the hydrophilic thiol groups and hydrophobic benzene groups in the nanogel enabled its dispersability in both liquid hydrocarbons and water. This unique amphiphilicity and abundance of thiol groups allowed the nanogel to efficiently remove relevant mercury species from both produced water and gas condensate. A phosphate abundant polymer nanogel (PPN) was developed for rare earth elements (REE) recovery from waste streams. The PPN had one of the highest REE sorption capacities (qm)and distribution coefficients (Kd) reported and could concentrate REEs in coal fly ash leachate twenty-one-folds in one sorption-desorption cycle. The second objective of this thesis was to develop polymer nanocarriers with temperature and pH responsive functional groups that enable temperature programmed agent delivery tocombat plant heat stress. A temperature of 40 ��C was used to trigger agent release from poly(acrylic acid)-block-poly(N-isopropyl acrylamide) (PAA-b-PNIPAm) star polymers and high aspect ratio P[BiBEM-g-(PAA-b-PNIPAm)] polymer bottlebrushes in vivo in tomato plants. Both star polymers and bottlebrushes had significant (~20-40%) phloem loading, indicating that a high aspect ratio does not inhibit nanocarrier translocation in plants. Both nanocarriers had near complete uptake into leaf mesophyll and were mainly transported through apoplasts to the vasculature. Near the vascular bundle, nanocarrier was found inside mesophyll cells, suggesting symplastic phloem loading. Overall, these materials can provide nearly 100% efficient uptake ofagrochemicals into leaves and can deliver those agents in response to an environmental stress (temperature), making them highly promising agents for increasing the sustainability of agriculture. The third objective was to design and synthesize a reactive oxygen species (ROS)responsive nanocarrier that alleviates plant stress by scavenging ROS in the leaf while releasing plant nutrients. ROS responsive poly(acrylic acid)-block-poly((2-(methylsulfinyl)ethyl acrylate)- co-(2-(methylthio) ethyl acrylate)) (PAA-b-P(MSEA-co-MTEA)) star polymer (RSP) was synthesized and the ROS scavenging functionality was confirmed both in-vitro and in-vivo. Apart from scavenging ROS, RSP can be loaded with plant nutrients such as Mg2+. After foliar application, RSP penetrated through the leaf epidermis and distributed around the chloroplasts. The foliar applied RSP increased the plant carbon assimilation rate, quantum yield of CO2 assimilation, Rubisco carboxylation rate and photosystem II quantum yield compared to untreated plants under heat and light stress. Mg loaded RSP enhanced carbon assimilation of Mg deficient plants by promoting Rubisco activity. The fourth objective was to determine how size, charge content and hydrophobicity could be tuned for efficient polymer nanocarrier foliar uptake and translocation to specific plant tissues. PAA-b-PMSEA and PAA-b-P(MSEA-co-MTEA) star polymers with well controlled size, negative charge content and hydrophobicity were synthesized, and their leaf uptake and distribution in tomato plants were quantified. Despite the differences in star polymer properties, ~30% of applied star polymers translocated to other plant organs, much higher than traditional agrochemicals (

This thesis provides an assessment of the requirements, obstacles, and pathways to achieving deep decarbonization of the United States (U.S.) passenger vehicle sector. First, in a set of two studies, I investigate the requirements for meeting specific climate targets in the U.S. Light Duty Vehicle (LDV) sector. In the first study, I analyze an 80% carbon emissions reduction target by 2050 relative to 2005, a common midcentury decarbonization benchmark target. I find that decarbonizing U.S. passenger vehicle transport to achieve this target under electrification and automation uncertainty has a travel budget. A travel budget limits and reduces total vehicle travel to improve the likelihood of meeting the midcentury climate target. In the second study, I use the global remaining carbon budget (RCB) for different levels of warming use a contraction and convergence (C&C) framework to allocate them to the U.S., and the assign a portion of that national budget to LDVs. Using these RCB constraints by 2050 as a performance metric, I perform a robust decision making (RDM) analysis on the U.S. LDV fleet by generating thousands of future pathways under different technology and policy scenarios. Clustering analysis of those cases revealed that for a robust LDV decarbonization pathway, immediate and synergistic action for low-carbon electricity and a highly electrified fleet was imperative. Increasing the likelihood of staying below the more stringent 1.5 ��C budget would require additional efforts to reduce the vehicle travel demand via decreasing vehicle miles traveled (VMT) and vehicle sales from now to 2050. Where the first two studies look at the LDV fleet emissions and the enablers for its decarbonization, my third study considers the implications on the U.S. petroleum refining sector given the current reliance on petroleum-based fuels for transport and primarily gasoline for LDVs. I select eight existing individual U.S. refineries and use Linear Programming (LP) optimization models calibrated to approximate actual operation to test the impact of gasoline price reduction on refinery capacity utilization and carbon intensity (CI). I find that refineries with low complexity and/or no access to low-cost heavy crude oils would be the most negatively affected as they will likely shut down earlier. This heterogeneity in refinery response was also illustrated in how their CIs change. The complex refineries operating with low gasoline prices might considerably increase their CI as they process heavier sour crude to minimize gasoline output, however the total absolute emissions will likely decrease due to lower volumes processed. Taken together, these three studies highlight this century���s massive infrastructure challenge pertaining to mitigating climate change. The robust decisions and insights identified in this thesis help inform future policies aiming towards an efficient and equitable transition to a deeply decarbonized passenger mobility future.

The demand for systems and infrastructure that can equitably and efficiently provide food, energy, and water is central to economic development and sustainable growth. Diverse conditions such as growing population, climate change, and access constraints pose a formidable challenge for industrialized and non-industrialized countries. Industrialized countries��� food and energy systems face the threat of unsustainable practices and competition for resources from multiple productive sectors. Meanwhile, the least developed countries struggle with inadequate access to modern agricultural, water, and energy technologies to provide food efficiently and securely. This thesis aims to identify and quantify food production impacts on energy and water consumption via case studies in the United States and East Africa. In both cases, I use integrated biophysical models to estimate the effects of food production (e.g., chicken broiler meat and irrigated crop yields) on energy and water resources consumption using climatological data inputs. For the case study in East Africa, I also assessed the financial viability of pressurized irrigation on a subnational level. Findings suggest that projected future climate change temperatures by mid-century will increase energy demand for cooling, reduce energy demand for heating, and substantially increase water withdrawals for evaporative cooling for industrial chicken broiler production in the Eastern U.S. The results for the case in East Africa indicate that the techno-economic potential of small-scale pressurized irrigation is highest for horticulture, maize, and potato crops grown with improved seeds and at least moderate fertility levels. My results suggest that food production impacts on energy and water demand are climate and site (or geography) dependent. These factors' relative importance depends on operational practices (e.g., input selection), technology types and costs, and fuel prices.

Surfactants self-assemble into diverse aggregate structures like micelles and bilayers. These aggregates play a vital role in applications ranging from wastewater treatment to soil remediation to carbon utilization. Therefore, an understanding of aggregate morphologies in solutions and at interfaces allows us to tailor surfactants for specific applications. To that end, we have used molecular dynamics (MD) simulations to glean atomic-level insights into surfactant aggregation.First, we focus on the self-assembly of Aerosol-OT (AOT), a double-tailed anionic surfactant in aqueous media. Through extensive classical MD simulations, AOT morphologies are generated that are consistent with experimental phase diagrams. Aggregates range from spherical micelles at low concentrations (1 wt.%) to bilayers at higher concentrations (20 wt.%). A transitional biphasic regime is identified at an intermediate concentration (7.2 wt.%); this regime comprises prolate spheroidal and long rod-like micelles. Metrics of micelle shape and size are computed from the moments of inertia tensor. The polydispersity in these metrics are also quantified. The bilayer thickness and area per AOT head group agree with experimental measurements. The simulations also reveal atomic-level mechanistic insights into the early stages of surfactant aggregation. Taken together, these simulations elucidate the structural diversity of AOT aggregates as a function of concentration and temperature thus being complementary to mean-field experiments.Second, we attempt to understand the role of AOT in the valorization of carbon dioxide to calcium carbon ultrafine particles. Through an enhanced sampling MD technique called umbrella sampling, the interactions between a single molecule of AOT and the (10m14) crystal plane of CaCO3 are investigated. These simulations are complemented by a first principles Density Functional Theory (DFT) analysis which is performed through collaboration. DFT identifies the most stable adsorption configurations for AOT-like surrogate molecules and unravels the nature of chemical bonds between these molecules and the (10m14) crystal plane.Finally, preliminary experimental studies pertaining to the synthesis of CaCO3 particles using CO2 and surfactants is discussed. The CaCO3 particles are characterized using X-ray diffraction, scanning electron microscopy, and Raman spectroscopy. The influence of the nature of the surfactant; anionic AOT, or cationic cetyl trimethylammonium bromide (CTAB), on the morphology of CaCO3 particles is discussed.

[When printing, please note that the document is in A5 format, so fits 2 pages in A4]This document is intended as a frame of reference and a quick-start guide for new appointees in Science and Engineering to make sense and success of their first five years following appointment. The secret recipe for rocketing to a professorial appointment will not be found in these pages, but key points and strategies which benefited the authors forms the basis of discussion – these can also be viewed as “I wish I was told this when I started”. Where evidence beyond the anecdotal is available and substantiates claims made to this effect, they are presented. These have been selected from useful resources, which may provide useful further reading.

Children are particularly vulnerable to the health effects of air pollution and as they spend a large proportion of time at school, this is an important environment for children's exposure to air pollution. Understanding the factors that influence indoor air quality in schools is critical for the assessment and control of indoor air pollution. This study analysed the concentration and sources of air pollution at an urban primary school (5–11 years) in Wellington, the capital of New Zealand. Over a three-week period during spring, indoor measures of particulate matter (PM 2.5 , PM 10 ), temperature, humidity, carbon dioxide (CO 2 ) and nitrogen dioxide (NO 2 ) were taken and hourly air particulate matter samples (PM 2.5 , PM 10-2.5 ) were collected inside and outside for elemental speciation analysis. Indoor PM 10 concentrations during the school day were significantly (p < 0.001) higher than outdoor concentrations 30.1 (range 10.0–75.0, SD 1.9) μg m −3 c.f. 8.9 (range

Access to potable water has been identified as a basic human right, yet it is estimated that 2.2 billion people worldwide do not have access to safely managed drinking water. Many of those without access live in regions of the world with abundant sunlight, which can be utilized both directly and indirectly to disinfect drinking water. Directly it can be used in solar water disinfection (SODIS) applications, and indirectly it can be collected by solar panels to power commercially available UV reactors. Herein, we study the potential for direct and indirect water disinfection technologies to be used and adopted in developing countries, with specific insight into their application in the Dominican Republic and Kenya.The amount of available ambient solar UV was both measured and modelled to inform design and modelling of treatment systems, and to understand whether real-time monitoring of ambient UV is required for the operation of systems directly utilizing UV for disinfection. The model both over- and under-predicted measurements of ambient UV, and did so at inconsistent rates, most likely as a result of cloud cover. This indicates that real-time monitoring of ambient UV would most likely be needed for disinfection methods directly using solar UV for inactivation in order to ensure water was always dosed properly.The amount of available ambient solar UV was input into a raytracing model (Photopia, LTI Optics) to simulate the amplification of solar spectral irradiance within a continuous-flow compound parabolic collector (CPC). This informed design improvements that allowed for an increase in flow rate through the system, which was supported by field testing of the reactor. Further, two commercial UV reactors, one utilizing a low-pressure (LP) lamp and the other utilizing an LED source, were tested in the lab to verify their ability to inactivate S. typhimurium LT2. The LP-based device outperformed the LED-based device, which was unable to achieve over 2-log10 units of inactivation under any of the studied conditions.A life cycle assessment was conducted to assess the environmental impact of the three studied UV reactors against traditional chlorination and water delivery methods. Chlorine had the lowest impact in every category under all of the studied conditions, but there have been many barriers reported on the lack of adoption of chlorine. So the next lowest impact technology was evaluated at the community scale, which was the LP reactor. Therefore, the LP reactor was installed in study communities in both the Dominican Republic and Kenya. In the Dominican Republic, the systems suffered from a lack of boots on the ground, and faced technical, social, and economic barriers to adoption. In Kenya, the project suffered from similar constraints, that did not allow for project assessment. This work not only addresses the barriers faced in both of these projects, but provides suggestions for improving similar projects in the future.

The increasing demand for mineral-based resources that face supply risks calls for managing the supply chains for these resources at the regional level. Cobalt is a widely used cathode material in lithium-ion batteries, which form the major portion of batteries used for renewable energy storage - a necessary technology for electrifying mobility and overcoming the challenge of intermittency, thus making renewable energy more reliable and energy generation more sustainable. This necessitates understanding cobalt's supply risks and for the Untied States, identifying sources of cobalt available for future use via recycling or mining. These needs are addressed in this work using single and multiregional input-output (MRIO) analysis in combination with graph theory. An MRIO-based approach is developed to obtain the trade network of cobalt and offer a more expedient way to identify potential critical material sources embodied in commodities made domestically. Commodities containing cobalt were disaggregated from two input-output (IO) models and the trade structure of cobalt at the national and state level was observed and compared. The significance of identified key sectors is measured according to several criteria and differences in sectors highlighted in the national versus subnational networks suggests that analysis at the two regional aggregation levels provides alternative insights. Results from mining the IO networks for cobalt highlight the geographical distribution of its use and industries to further investigate as potential sources for secondary feedstock.

Smartphones, one of the most common consumer electronic devices, are an essential part of daily activities in modern society. Smartphones provide faster communication, easier access to information and many other important services. However, with a compressed product life cycle and growing consumer demand, a significant number of smartphones reach End-of-Life (EoL) annually. At the same time, due to many special physical properties, rare earth, critical and other important metals are essential for the manufacturing of smartphones. Hence, from various economic, resources availability and environmental perspectives, it is crucial to understand how metal content of different smartphones generations change over time. To this end, a high production smartphone series, produced between 2010 and 2015 were considered in the scope of this study. The devices were disassembled, sorted into different components and size reduced. Printed circuit boards assembly, back cameras and NFC antenna/wireless charging chips were then digested using a novel microwave assisted acid digestion method. Inductively Coupled Plasma-Optical Emission Spectroscopy was used to detect and quantify 60 elements. Obtained results indicate that up to 70% of different smartphones components by weight are important recyclable metals. The highest concentration elements, Cu, Ni, Sn, Zn and Fe, accounted for 93.3% while REEs and PGMs collectively accounted for 0.53% of the total recoverable elements by weight. In 2019, the total addressable market value of metals reclamation from only three smartphone components at their end of life is estimated at 298.69 million USD in USA. Gold and Platinum were determined to be the most environmentally critical elements and efforts should made to reduce their use. Finally, and most importantly, smartphones manufacturers should design their products with an extended lifetime due to the high concentration of critical elements used to make them.

Extreme precipitation events have become more frequent over the last several decades, and projections from Global Climate or General Circulation Models (GCMs) suggest that this trend will continue throughout the 21st century due to climate change. Stormwater systems designed in urbanareas to manage precipitation on roadways and protect residents during rainfall events were designed using historical rainfall information that does not reflect observed trends nor future conditions projected by GCMs. Most of these infrastructure systems will continue in service for many more decades, and thus will be exposed to conditions for which they were not designed. Prior research has shown that under climate change, existing stormwater infrastructure might not provide the expected level of protection because of increases in the intensity and frequency of rainfallextremes. Research is needed to understand to what extent and under which conditions are existing and proposed stormwater systems vulnerable to climate change, and what engineered and natural solutions could increase the climate resilience of stormwater systems. Location-specific projected changes in rainfall extremes remain deeply uncertain, sometimes with conflicting change signals fromGCMs projections and numerous downscaling methods to match the spatial resolution required for stormwater engineering design. The type of climate information used and how stakeholders incorporate this information into designs can affect the resilience performance of infrastructure and can result in assets either performing better or worse than originally required or intended.This dissertation contributes to the understanding of the impacts of climate change on stormwater infrastructure assets and advances the exploration of solutions to increase resilience. It characterizes uncertainties in future rainfall projections from commonly-used public sources of downscaled climate projections in an effort to understand stormwater infrastructure climate resilience and preparedness and possible strategies to increase resilience where needed. The research also analyzes the implicationsof data input choices used by local U.S. cities’ climate adaptation plans for infrastructure design decisions. Overall, this research advances the understanding of stormwater infrastructure systems’ vulnerability to climate change and provides valuable and actionable guidance to aid localdecision-makers and stakeholders to increase infrastructure resilience.

Activated carbon (AC) systems and reverse osmosis (RO) systems are commonly used point-of-use (POU) water filtration systems as the last barrier to remove trace-level contaminants in tap water to protect human health. Limited studies have been done to evaluate trace-level manganese and uranium removal in tap water. Additionally, undesired microbial growth in POU systems may reduce treatment efficiencies of POU systems and limited studies have been done to evaluate microbial growth potential in POU systems. The overall research objective of this study was to systematically evaluate the removal of metals and assimilable organic carbon in POU systems. AC systems were operated to 200% of their designed treatment capacities and RO systems were operated for three weeks. The results indicated that AC systems were generally ineffective to remove metals in drinking water, while metals were effectively removed in RO systems. The results showed that calcium and magnesium were not effectively removed in AC systems with removal efficiencies of less than 1%. Various factions of iron were removed with its removal efficiencies in AC systems ranged between 61% and 84%. Copper was effectively removed in AC systems with removal efficiencies greater than 95%, which was possibly related to its low influent concentration in drinking water (

One way to reduce the lifecycle cost and environmental impact of a product in a circular economy is to extend its lifespan by either creating longer-lasting products or managing the product properly during its use stage. Life extension of a product is envisioned to help better utilize raw materials efficiently and slow the rate of resource depletion. In the case of manufacturing equipment (e.g., an electric motor on a machine tool), securing reliable service life as well as the life extension are important for consistent production and operational excellence in a factory. However, manufacturing equipment is often utilized without a planned maintenance approach. Such a strategy frequently results in unplanned downtime, owing to unexpected failures. Scheduled maintenance replaces components frequently to avoid unexpected equipment stoppages, but increases the time associated with machine non-operation and maintenance cost. Recently, the emergence of Industry 4.0 and smart systems is leading to increasing attention to predictive maintenance (PdM) strategies that can decrease the cost of downtime and increase the availability (utilization rate) of manufacturing equipment. PdM also has the potential to foster sustainable practices in manufacturing by maximizing the useful lives of components. In addition, advances in sensor technology (e.g., lower fabrication cost) enable greater use of sensors in a factory, which in turn is producing greater and more diverse sets of data. Widespread use of wireless sensor networks (WSNs) and plug-and-play interfaces for the data collection on product/equipment states are allowing predictive maintenance on a much greater scale. Through advances in computing, big data analysis is faster/improved and has allowed maintenance to transition from run-to-failure to statistical inference-based or machine learning prediction methods. Moreover, maintenance practice in a factory is evolving from equipment “health management” to equipment “wellness” by establishing an integrated and collaborative manufacturing system that responds in real-time to changing conditions in a factory. The equipment wellness is an active process of becoming aware of the health condition and of making choices that achieve the full potential of the equipment. In order to enable this, a large amount of machine condition data obtained from sensors needs to be analyzed to diagnose the current health condition and predict future behavior (e.g., remaining useful life). If a fault is detected during this diagnosis, a root cause of a fault must be identified to extend equipment life and prevent problem reoccurrence. However, it is challenging to build a model capturing a relationship between multi-sensor signals and mechanical failures, considering the dynamic manufacturing environment and the complex mechanical system in equipment. Another key challenge is to obtain usable machine condition data to validate a method. A goal of the proposed work is to develop a systematic tool for maintenance in manufacturing plants using emerging technologies (e.g., AI, Smart Sensor, and IoT). The proposed method will facilitate decision-making that supports equipment maintenance by rapidly detecting a worn component and estimating remaining useful life. In order to diagnose and prognose a health condition of equipment, several data-driven models that describe the relationships between proxy measures (i.e., sensor signals) and machine health conditions are developed and validated through the experiment for several different manufacturing-oriented cases (e.g., cutting tool, gear, and bearing). To enhance the robustness and the prediction capability of the data-driven models, signal processing is conducted to preprocess the raw signals using domain knowledge. Through this process, useful features from the large dataset are extracted and selected, thus increasing computational efficiency in model training. To make a decision using the processed signals, a customized deep learning architecture for each case is designed to effectively and efficiently learn the relationship between the processed signals and the model’s outputs (e.g., health indicators). Ultimately, the method developed through this research helps to avoid catastrophic mechanical failures, products with unacceptable quality, defective products in the manufacturing process as well as to extend equipment service life. To summarize, in this dissertation, the assessment of technical, environmental and economic performance of the AI-driven method for the wellness of mechanical systems is conducted. The proposed methods are applied to (1) quantify the level of tool wear in a machining process, (2) detect different faults from a power transmission mini-motor testbed (CNN), (3) detect a fault in a motor operated under various rotation speeds, and (4) to predict the time to failure of rotating machinery. Also, the effectiveness of maintenance in the use stage is examined from an environmental and economic perspective using a power efficiency loss as a metric for decision making between repair and replacement.

In this study, the Pb(II) adsorption capabilities of the heavy metal tolerant strain of fungus, Aspergillus piperis, were studied. This study involved finding optimal growth conditions using a plating technique, and optimal adsorption conditions using submerged fermentation and fractional factorial experimental design. The adsorption behaviour was then elucidated using isotherm and kinetic models, of which the one surface Langmuir isotherm provided the best fit, with a maximum predicted adsorption capacity of 275.82 mg g−1. The kinetic models suggested that internal mass transfer is the driving force behind the reaction rate. After adsorption, biomass surface characterisation was undertaken using FESEM, EDS, and ATR-FTIR to explain observations. The system was characterised by a cation exchange mechanism with strong carboxyl and organophosphorus group interactions. This study demonstrates that due to the ease of propagation and high adsorption capacity, this locally sourced fungal strain is an ideal adsorbent for industrial Pb(II) bioremediation.

We use data collected as part of a baseline survey in 2012 and a survey 5 months post-intervention in 2014 to assess the short-term outcomes of a water supply intervention in Ribáuè, Mozambique. This intervention included the rehabilitation and expansion of a piped water system, revitalization of water committees, and creation of and capacity building for small-scale private water enterprises. Quantitative results suggest that the intervention led to an immediate significant increase in the use of piped water supply at the expense of unprotected wells and other non-revenue generating forms of unimproved water supply with more than a 2.5-fold increase in the usage of yard taps and water kiosks/standpipes and a two-fold decrease in the use of unprotected wells. Family water consumption also increased by approximately 40 L/d, and the point-of-use treatment of water nearly tripled. Economic opportunities were generated for business and small enterprise owners due to the new water supply infrastructure, and piped water infrastructure had additional positive effects for both public and private sanitation facilities.

The Nampula Province Water, Sanitation, and Hygiene Program is a partnership among the Government of Mozambique, UNICEF, and the Australian Government focused on achieving the United Nations' Millennium Development Goals for water and sanitation in Mozambique and concentrated on five small towns in Nampula Province. Before implementation of water, sanitation, and hygiene (WASH) interventions in these towns, a baseline survey was carried out in 2012. We show how such a survey can be used to characterize the state of the three WASH sub-themes (water, sanitation, hygiene) pre-intervention, suggest possible new emphases of or modifications to proposed interventions, and inform the design of follow-up surveys to best gauge the impact of the interventions.

Dataset for Influence of Synthesis Approach on Controlled Microstructures and Photocatalytic Properties of Ag/AgBr-Activated Carbon Composites on Visible Light Degradation of Tetracycline published in Catalysts

This dissertation focused on examining the potential unintended consequences of sustainability on drinking water quality at the tap. The rising trend in water conservation awareness has given rise to the use of water-efficient appliances and fixtures for residential potable water systems. The first study (Chapter 1) characterized the microbial dynamics at a water-efficient residential building over the course of one year and examined the effects of water stagnation, season, and changes in physicochemical properties on the occurrence of opportunistic pathogen markers. When rainwater harvesting is utilized as an alternative water resource in buildings, a combination of municipal water and rainwater is typically required to meet water demands. However, altering source water chemistry can disrupt pipe scale and biofilm and negatively impact water quality at the distribution level. The second study (Chapter 2) in this dissertation evaluated the potential water quality consequences of using intermittent supplies of municipal water and rainwater within building plumbing systems. Cross-linked polyethylene (PEX) pipes are considered by some to be more sustainable than copper pipes and are commonly installed in building plumbing. The goal of the third study (Chapter 3) was to better understand chemical release from commercially available PEX pipes, to characterize toxicological characteristics of the contact water, and to compare microbial growth potential among the three pipe types. During the COVID-19 pandemic, many commercial and office buildings were closed for extended periods of time, allowing water age to increase over the course of several months. Heightened water age is often associated with an increase in chemical and microbial contamination. The objective of the fourth study (Chapter 4) was to evaluate the impacts of an extended COVID-19 related building closure and stagnation on water quality. The goal of this study was to evaluate the efficacy of flushing and shock chlorination remediation strategies on water quality at the tap. This dissertation contains four chapters and each chapter is a single manuscript. The first two chapters have been published.“Drinking water microbiology in a water-efficient building: Stagnation, seasonality, and physiochemical effects on opportunistic pathogen and total bacteria proliferation.” (Chapter 1) Utilizing a residential building that had been retrofitted with low-flow fixtures, the unintended water quality consequences of increased stagnation in low-flow plumbing were evaluated over a year long period. The study results indicated that microbial growth and potential opportunistic pathogen markers, Legionella and Mycobacterium spp. were detected at higher levels within the home as compared to the municipal water main. Reduced water usage induced longer stagnation times and longer stagnation times were correlated with an increase in Legionella spp., Mycobacterium spp., and total cell counts.“Impacts of Municipal Water−Rainwater Source Transitions on Microbial and Chemical Water Quality Dynamics at the Tap.” (Chapter 2) Altering source water chemistry can disrupt pipe scale and biofilm and negatively impact water quality at the distribution level. Still, it is unknown if similar reactions occur within building plumbing following a transition in source water quality. To date, no prior studies had evaluated the water quality impacts of transitioning between rainwater and municipal groundwater sources in low-flow plumbing. The study revealed that influent water chemistry impacted rates of metal release from plumbing. Because of differences in source water treatment and water chemistry, rainwater and municipal water uniquely interacted with building plumbing and generated distinctively different drinking water chemical and microbial quality profiles. “Contaminant Leaching and Toxicological Assessment of Drinking Water in Contact with Cross-linked Polyethylene (PEX) Pipes.” (Chapter 3) Cross-linked polyethylene (PEX) plastic water pipes are increasingly being installed instead of copper pipes for conventional and green building construction. Unlike metal pipe, PEX pipe is easier to install and not vulnerable to corrosion. However, potential health concerns associated with PEX pipe are: 1.) the organic contaminant release that occurs during its service-life, 2.) increased microbial growth compared to metal pipes, and 3.) compound toxicity. Our study goal was to better understand chemical release from commercially available PEX pipes, to characterize toxicological characteristics of the contact water, and to compare microbial growth potential among the three pipe types at varying chlorine concentrations. Results indicated that PEX contact waters did not affect the neurobehavioral development of zebrafish, but affected development in the zebrafish model. Further studies should be conducted to determine how influent water chemistry impacts carbon migration and the water’s toxicity.“Water quality during the COVID-19 pandemic: The role of flushing and shock chlorination strategies in reducing building water problems.” (Chapter 4) The COVID-19 pandemic led to widespread “stay at home” orders across the United States. As a result, many office buildings, schools, and commercial buildings were left empty, allowing water age within the plumbing to increase dramatically. Heightened stagnation and water age can lead to increased metal leaching from pipe walls, as well as increases in microbial growth and opportunistic pathogen proliferation. Water quality in a large school building was monitored after approximately six months of being closed due to COVID-19 related restrictions. Upon sampling the building, chemical and microbial water quality indicators were affected by the initial six month stagnation period. To monitor the relationship between shock chlorination and water quality at the tap, samples were collected at fixtures at different time points to gain a better understanding of the effects of shock disinfection on drinking water chemistry and microbiology. This study raises concerns with respect to the impact of extended building closures on drinking water quality and the best approach to remediate and monitor water quality issues thereafter.

A neural network model was developed to predict the E. Coli levels and classes in six (6) select Lake Michigan beaches. Water quality observations at the time of sampling and discharge information from two close tributaries were used as input to predict the E. coli. This research was funded by the Indiana Department of Environmental Management (IDEM). A user-friendly Excel Sheet based tool was developed based on the best model for making future predictions of E. coli classes. This tool will facilitate beach managers to take real-time decisions. The nowcast model was developed based on historical tributary flows and water quality measurements (physical, chemical and biological). The model uses experimentally available information such as total dissolved solids, total suspended solids, pH, electrical conductivity, and water temperature to estimate whether the E. Coli counts would exceed the acceptable standard. For setting up this model, field data collection was carried out during 2019 beachgoer’s season. IDEM recommends posting an advisory at the beach indicating swimming and wading are not recommended when E. coli counts exceed advisory standards. Based on the advisory limit, a single water sample shall not exceed an E. Coli count of 235 colony forming units per 100 milliliters (cfu/100ml). Advisories are removed when bacterial levels fall within the acceptable standard. However, the E. coli results were available after a time lag leading to beach closures from previous day results. Nowcast models allow beach managers to make real-time beach advisory decisions instead of waiting a day or more for laboratory results to become available. Using the historical data, an extensive experiment was carried out, to obtain the suitable input variables and optimal neural network architecture. The best feed-forward neural network model was developed using Bayesian Regularization Neural Network (BRNN) training algorithm. Developed ANN model showed an average prediction accuracy of around 87% in predicting the E. coli classes.

This Distinguished Lecture was delivered online on 22 May 2020 as part of the IEEE Aerospace and Electronic Systems Society (AESS) global education initiatives and was organised by RMIT University (Sir Lawrence Wackett Centre, CPS Group) in collaboration with Deakin University (CITECORE Seminar Series), the IEEE Victorian Section, the AESS Avionics Systems Panel and Aviation/Aerospace Australia.

Maize (Zea mays L.) is one of the most important crops worldwide for its critical importance in agriculture, economic stability, and food security. Many agricultural research and commercial breeding programs target the efficiency of this crop, seeking to increase productivity with fewer inputs and becoming more environmentally sustainable and resistant to impacts of climate and other external factors. For the purpose of analyzing the performance of the new varieties and management strategies, accurate and constant monitoring is crucial and yet, still performed mostly manually, becoming labor-intensive, time-consuming, and costly.Flowering is one of the most important stages for maize, and many other grain crops, requiring close attention during this period. Any physical or biological negative impact in the tassel, as a reproductive organ, can have significant consequences to the overall grain development, resulting in production losses. Remote sensing observation technologies are currently seeking to close the gap in phenotyping in monitoring the development of the plants’ geometric structure and chemistry-related responses over the growth and reproductive cycle.For this thesis, remotely sensed hyperspectral imagery were collected, processed and, explored to detect tassels in maize crops. The data were acquired in both a controlled facility using an imaging conveyor, and from the fields using a PhenoRover (wheel-based platform) and a low altitude UAV. Two pixel-based classification experiments were performed on the original hyperspectral imagery (HSI) using Spectral Angle Mapper (SAM) and Support Vector Machine (SVM) supervised classifiers. Feature reduction methods, including Principal Component Analysis (PCA), Locally Linear Embedding (LLE), and Isometric Feature Mapping (Isomap) were also investigated, both to identify features for annotating the reference data and in conjunction with classification.Collecting the data from different systems allowed the identification of strengths and weaknesses for each system and the associated tradeoffs. The controlled facility allowed stable lighting and very high spatial and spectral resolution, although it lacks on supplying information about the plants’ interactions in field conditions. Contrarily, the in-field data from the PhenoRover and the UAV exposed the complications related to the plant’s density within the plots and the variability in the lighting conditions due to long times of data collection required. The experiments implemented in this study successfully classified pixels as tassels for all images, performing better with higher spatial resolution and in the controlled environment. For the SAM experiment, nonlinear feature extraction via Isomap was necessary to achieve good results, although at a significant computational expense. Dimension reduction did not improve results for the SVM classifier.

Stormwater constructed wetlands are one of the most widely used passive stormwater treatment technologies in Australia. They have shown promising results in removal of typical stormwater pollutants such as nutrient and sediments. However, its ability in removing faecal microbial pollutants which might pose potential human health risks remains understudies. This thesis investigated the sources and treatment processes of faecal contamination in wetland systems, by undertaken case studies on two wetlands within the Troups Creek catchments. The findings from this research have the potential to optimise the design and maintenance of wetlands for faecal microbial removal.

In the context of climate change, increasing demands for freshwater make it necessary to manage our water resources in a sustainable way and find innovative ways to extend their life. An integrated water management approach needs to consider anthropogenic water use and reuse which represent major components of the current water cycle. In particular, unplanned, or de facto, indirect water reuse occurs in most of the U.S. river systems; however, there is little real-time documentation of it. Despite the fact that there are national and state agencies that systematically collect data on water withdrawals and wastewater discharges, their databases are organized and managed in a way that limits the ability to combine reported water data to perform large scale analysis about water use and indirect reuse. To better document these issues and to demonstrate the utility of such an analysis, I studied the Wabash River Watershed located in the U.S. Midwest. Existing data for freshwater extraction, use, discharge, and river streamflow were collected, curated and reorganized in order to characterize the water use and reuse within the basin. Indirect water reuse was estimated by comparing treated wastewater discharges with stream flows at selected points within the watershed. Results show that during the low flow months of July-October 2007, wastewater discharges into the Wabash River basin contributed 82 to 121% of the stream flow, demonstrating that the level of water use and unplanned reuse is significant. These results suggest that intentional water reuse for consumptive purposes such as landscape or agricultural irrigation could have substantial ecological impacts by diminishing stream flow during vulnerable low flow periods. This research also completed a time series watershed-scale analysis of water use and unplanned indirect reuse for the Wabash River Watershed from 2009 to 2017. Results document the occurrence of indirect water reuse over time, ranging from 3% to 134% in a water-rich area of the U.S. The time series analysis shows that reported data effectively describe the water use trends through nine years, clearly reflecting both anthropogenic and natural events in the watershed, such as the retirement of thermoelectric power plants, and the occurrence of an extreme drought in 2012. Results demonstrate the feasibility and significance of using available water datasets to perform large scale water use analysis, describe limitations encountered in the process, and highlight areas for improvement in water data management.

Infant exposure to the microbial and allergenic content of indoor floor dust has been shown to play a significant role in both the development of, and protection against, allergies and asthma later in life. Resuspension of floor dust during infant locomotion induces a vertical transport of particles to the breathing zone, leading to inhalation exposure to a concentrated cloud of coarse (> 1μm) and fine (≤ 1μm) particles. Resuspension, and subsequent exposure, during periods of active infant locomotion is likely influenced by gait parameters. This dependence has been little explored to date and may play a significant role in floor dust resuspension and exposure associated with forms of locomotion specific to infants. This study explores associations between infant locomotion dynamics and floor dust resuspension and exposure in the indoor environment. Infant gait parameters for walking and physiological characteristics expected to influence dust resuspension and exposure were identified, including: contact frequency (steps min-1), contact area per step (m2), locomotion speed (m s-1), breathing zone height (cm), and time-resolved locomotion profiles. Gait parameter datasets for standard gait experiments were collected for infants in three age groups: 12, 15, and 19 months-old (m/o). The gait parameters were integrated with an indoor dust resuspension model through a Monte Carlo framework to predict how age-dependent variations in locomotion affect the resuspension mass emission rate (mg h-1) for five particle size fractions from 0.3 to 10 μm. Eddy diffusivity coefficients (m2 s-1) were estimated for each age group and used in a particle transport model to determine the vertical particle concentration profile above the floor.Probability density functions of contact frequency, contact area, locomotion speed, breathing zone height, and size-resolved resuspension mass emission rates were determined for infants in each group. Infant standard gait contact frequencies were generally in the range of 100 to 300 steps min-1 and increased with age, with median values of 186 steps min-1 for 12 m/o, 207 steps min-1 for 15 m/o, and 246.2 steps min-1 for 19 m/o infants. Similarly, locomotion speed increased with age, from 67.3 cm s-1 at 12 m/o to 118.83 cm s-1 at 19 m/o, as did the breathing zone height, which varied between 60 and 85 cm. Resuspension mass emission rates increased with both infant age and particle size. A 19 m/o infant will resuspend comparably more particles from the same indoor settled dust deposit compared to a 15 m/o or 12 m/o infant. Age-dependent variations in the resuspension mass emission rate and eddy diffusivity coefficient drove changes in the vertical particle concentration profile within the resuspended particle cloud. For all particle size fractions, there is an average of a 6% increase in the resuspended particle concentration at a height of 1 m from the floor for a 19 m/o compared to a 12 m/o infant. Time-resolved locomotion profiles were obtained for infants in natural gait during free play establish the transient nature of walking-induced particle resuspension and associated exposures for infants, with variable periods of active locomotion, no motion, and impulsive falls. This study demonstrates that floor dust resuspension and exposure can be influenced by the nature of infant locomotion patterns, which vary with age and are distinctly different from those for adults.

Aerosols are solid or liquid particles that span in size from a few nanometers to tens of micrometers. They are important in both outdoor and indoor environments due to their impact on climate and human health. Many aerosol processes of interest to the environment, health, and filtration are strongly size-dependent. Aerosol particle size distributions (PSDs) provide a basis to better understand outdoor and indoor air pollution sources, evaluate human exposure to air pollution, and aerosol deposition in the human respiratory tract and filters in building ventilation systems. Monitoring the temporal evolution of aerosol PSDs enable for characterization of dynamic aerosol transport and transformation processes, such as direct emissions, nucleation, condensation, coagulation, resuspension, deposition, and filtration. This thesis presents three studies on the PSDs of: (i.) urban aerosols in cities around the world in order to identify geographical trends in the shape and magnitude of PSDs and to frame future research needs for PSD observations at a global-scale, (ii.) synthesized salt particles from a novel thermal aerosol generator to evaluate its suitability for air filter testing, and (iii.) indoor biological particulate matter (bioPM) to characterize transient inhalation exposures of infants and adults to resuspended bioPM from carpet dust induced by crawling and walking. First, this thesis presents the current state-of-knowledge of urban aerosol PSDs by critically analyzing n=793 urban aerosol PSD observations made between 1998 to 2017 in n=125 cities in n=51 countries across the following regions of the world: Africa (AF), Central, South, and Southeast Asia (CSSA), East Asia (EA), Europe (EU), Latin America (LA), North America, Australia, and New Zealand (NAAN), and West Asia (WA). Prominent geographical variations in the shape and magnitude of urban aerosol number and mass PSDs were identified and significant variations in number PSDs were found between cities in EU and NAAN with those in CSSA and EA. Most PSD observations published in the literature are short-term, with only 14% providing data for longer than six months. There is a paucity of PSDs measured in AF, CSSA, LA, and WA, demonstrating the need for long-term aerosol measurements across wide size ranges in many cities around the globe. Inter-region variations in PSDs have important implications for population exposure, driving large differences in the urban aerosol inhaled deposited dose rate received in each region of the human respiratory system. Similarly, inter-region variations in the shape of PSDs impact the penetration of urban aerosols through filters in building ventilation systems, which serve as an important interface between the outdoor and indoor atmospheres. The results of this critical review demonstrate that global initiatives are urgently needed to develop infrastructure for routine and long-term monitoring of urban aerosol PSDs spanning the nucleation to coarse modes. Second, this thesis evaluates a newly designed commercially available thermal aerosol generator for ageing air filters in building heating, ventilation, and air-conditioning (HVAC) systems. The physical characteristics of the synthesized salt aerosol (NaCl and KCl) under different generator operational conditions were evaluated. The shape of the number and mass PSD output of the thermal aerosol generator are similar to those found in outdoor (urban) and indoor air and can be modulated by varying the rate at which the salt stick is fed into the flame. The morphology of the NaCl and KCl particles varied with size, with compact spherical or cubic structures observed below 100 nm and agglomerates observed above 100 nm. The thermal aerosol generator is a cost-effective technique for rapid ageing of HVAC filters with a PSD that more accurately represents, compared to conventional loading dusts, what filters encounter in real HVAC installations. Lastly, this thesis characterizes infant and adult inhalation exposures and respiratory tract deposited dose rates of resuspended bioPM from carpets. Chamber experiments were conducted with a robotic crawling infant and an adult performing a walking sequence. Breathing zone (BZ) size distributions of resuspended fluorescent biological aerosol particles (FBAPs), a bioPM proxy, were monitored in real-time. FBAP exposures were highly transient during periods of locomotion. Both crawling and walking delivered a significant number of resuspended FBAPs to the BZ, with concentrations ranging from 0.5-2 cm-3. Infants and adults are primarily exposed to a unimodal FBAP size distribution between 2 and 6 μm, with infants receiving greater exposures to super-10 μm FBAPs. In just one minute of crawling or walking, 102-103 resuspended FBAPs can deposit in the respiratory tract, with an infant receiving much of their respiratory tract deposited dose in their lower airways. Per kg body mass, an infant will receive nearly four times greater respiratory tract deposited dose of resuspended FBAPs compared to an adult.

Environmental pollution and energy use in the light-duty transportation sector are currently regulated through fuel economy and emissions standards, which typically assess quantity of pollutants emitted and volume of fuel used per distance driven. In the United States, fuel economy testing consists of a vehicle on a treadmill, while a trained driver follows a fixed drive cycle. By design, the current standardized fuel economy testing system neglects differences in how individuals drive their vehicles on the road. As autonomous vehicle (AV) technology is introduced, more aspects of driving are shifted into functions of decisions made by the vehicle, rather than the human driver. Yet the current fuel economy testing procedure does not have a mechanism to evaluate the impacts of AV technology on fuel economy ratings, and subsequent regulations such as Corporate Average Fuel Economy targets. This paper develops a method to incorporate the impacts of AV technology within the bounds of current fuel economy test, and simulates a range of automated following drive cycles to estimate changes in fuel economy. The results show that AV following algorithms designed without considering efficiency can degrade fuel economy by up to 3%, while efficiency-focused control strategies may equal or slightly exceed the existing EPA fuel economy test results, by up to 10%. This suggests the need for a new near-term approach in fuel economy testing to account for connected and autonomous vehicles. As AV technology improves and adoption increases in the future, a further reimagining of drive cycles and testing is required.

In urbanized river basins, sanitary wastewater and urban runoff (non-sanitary water) from urban agglomerations drain to complex engineered networks, are treated at centralized wastewater treatment plants (WWTPs) and discharged to river networks. Discharge from multiple WWTPs distributed in urbanized river basins contributes to impairments of river water-quality and aquatic ecosystem integrity. The size and location of WWTPs are determined by spatial patterns of population in urban agglomerations within a river basin. Economic and engineering constraints determine the combination of wastewater treatment technologies used to meet required environmental regulatory standards for treated wastewater discharged to river networks. Thus, it is necessary to understand the natural-human-engineered networks as coupled systems, to characterize their interrelations, and to understand emergent spatiotemporal patterns and scaling of geochemical and ecological responses. My PhD research involved data-model synthesis, using publicly available data and application of well-established network analysis/modeling synthesis approaches. I present the scope and specific subjects of my PhD project by employing the Drivers-Pressures-Status-Impacts-Responses (DPSIR) framework. The defined research scope is organized as three main themes: (1) River network and urban drainage networks (Foundation-Pathway of Pressures); (2) River network, human population, and WWTPs (Foundation-Drivers-Pathway of Pressures); and (3) Nutrient loads and their impacts at reach- and basin-scales (Pressures-Impacts).Three inter-related research topics are: (1) the similarities and differences in scaling and topology of engineered urban drainage networks (UDNs) in two cities, and UDN evolution over decades; (2) the scaling and spatial organization of three attributes: human population (POP), population equivalents (PE; the aggregated population served by each WWTP), and the number/sizes of WWTPs using geo-referenced data for WWTPs in three large urbanized basins in Germany; and (3) the scaling of nutrient loads (P and N) discharged from ~845 WWTPs (five class-sizes) in urbanized Weser River basin in Germany, and likely water-quality impacts from point- and diffuse- nutrient sources. I investigate the UDN scaling using two power-law scaling characteristics widely employed for river networks: (1) Hack’s law (length-area power-law relationship), and (2) exceedance probability distribution of upstream contributing area. For the smallest UDNs, length-area scales linearly, but power-law scaling emerges as the UDNs grow. While area-exceedance plots for river networks are abruptly truncated, those for UDNs display exponential tempering. The tempering parameter decreases as the UDNs grow, implying that the distribution evolves in time to resemble those for river networks. However, the power-law exponent for mature UDNs tends to be larger than the range reported for river networks. Differences in generative processes and engineering design constraints contribute to observed differences in the evolution of UDNs and river networks, including subnet heterogeneity and non-random branching.In this study, I also examine the spatial patterns of POP, PE, and WWTPs from two perspectives by employing fractal river networks as structural platforms: spatial hierarchy (stream order) and patterns along longitudinal flow paths (width function). I propose three dimensionless scaling indices to quantify: (1) human settlement preferences by stream order, (2) non-sanitary flow contribution to total wastewater treated at WWTPs, and (3) degree of centralization in WWTPs locations. I select as case studies three large urbanized river basins (Weser, Elbe, and Rhine), home to about 70% of the population in Germany. Across the three river basins, the study shows scale-invariant distributions for each of the three attributes with stream order, quantified using extended Horton scaling ratios; a weak downstream clustering of POP in the three basins. Variations in PE clustering among different class-sizes of WWTPs reflect the size, number, and locations of urban agglomerations in these catchments. WWTP effluents have impacts on hydrologic attributes and water quality of receiving river bodies at the reach- and basin-scales. I analyze the adverse impacts of WWTP discharges for the Weser River basin (Germany), at two steady river discharge conditions (median flow; low-flow). This study shows that significant variability in treated wastewater discharge within and among different five class-sizes WWTPs, and variability of river discharge within the stream order Furthermore, I investigate eutrophication risk from spatially heterogeneous diffuse- and point-source P loads in the Weser River basin, using the basin-scale network model with in-stream losses (nutrient uptake).Considering long-term shifts in P loads for three representative periods, my analysis shows that P loads from diffuse-sources, mainly from agricultural areas, played a dominant role in contributing to eutrophication risk since 2000s, because of ~87% reduction of point-source P loads compared to 1980s through the implementation of the EU WFD. Nevertheless, point-sources discharged to smaller streams (stream order < 3) pose amplification effects on water quality impairment, consistent with the reach-scale analyses only for WWTPs effluents. Comparing to the long-term water quality monitoring data, I demonstrate that point-sources loads are the primary contributors for eutrophication in smaller streams, whereas diffuse-source loads mainly from agricultural areas address eutrophication in larger streams. The results are reflective of spatial patterns of WWTPs and land cover in the Weser River basin.Through data-model synthesis, I identify the characteristics of the coupled natural (rivers) – humans – engineered (urban drainage infrastructure) systems (CNHES), inspired by analogy, coexistence, and causality across the coupled networks in urbanized river basins. The quantitative measures and the basin-scale network model presented in my PhD project could extend to other large urbanized basins for better understanding the spatial distribution patterns of the CNHES and the resultant impacts on river water-quality impairment.

A new methodology to quantify minerals criticality is developed—the criticality systems of minerals. This methodology uses game theory modellings and statistical learning methods to determine which minerals are more critical, i.e the combination of a higher supply risk and a higher impact if the supply is restricted, to an national-level mineral end users.

Polyamide based reverse osmosis (PA-RO) membranes are applied for the desalination of halide-containing waters such as seawater and brackish groundwater. They are the industry standard because of their high selectivity and ability to withstand a wide range of pH. However, one of their pitfalls is their propensity to undergo biofouling, which is deterioration due to biological growth. Biofouling is known to dramatically decrease membrane performance and increase energy consumption. In order to overcome biofouling, a disinfectant, typically free chlorine, is applied; however, free chlorine is known to react with the polyamide layer and result in further membrane deterioration and performance loss.One topic that has garnered less attention is the application of chloramines and their interactions with the PA-RO membrane when applied as a biofouling control. Furthermore, the role of halides (e.g. chloride, bromide, and iodide) in the presence of chloramines must be further explored because they are known to react to form secondary species which are reactive toward PA-RO membranes. In Chapter 2, the PA based monomers benzanilide (BA) and N-Methyl-N-phenylbenzamide (N-CH3-BA) were used to model the PA layer. Monomers were exposed to halide containing waters and chloraminated with pre-formed NH2Cl over a wide range of pH. The decay and by-product formation after exposure were evaluated using HPLC-DAD, LC/MS, and/or GC/MS. Results indicated that pH of the system and bromide concentration controlled parent compound decay and brominated by-product formation, where low pH and high bromide concentrations led to the highest formation of brominated by-products.In Chapter 3, commercially made PA-RO membranes (SWC4-LD) were chloraminated or chlorinated with and without halides over a wide pH range. Their performance was evaluated after exposure through flux experiments using a dead-end flow cell. Results indicated that exposure to free chlorine led to the greatest change in flux and monochloramine resulted in the smallest change in flux. During chloramination of the membranes, the reactors containing bromide led to further change in flux than the chloramine only conditions. This was likely due to formation of secondary species that were reactive toward the PA membrane. Furthermore, Chapter 4 summarizes overall research contributions from this work and proposes future work pertaining to this topic.

Due to resource constraints and global climate change, there is an increased need for technological solutions to improve the efficiency and reduce waste of our energy systems. Many of these technological solutions are computationally daunting and therefore require approximate approaches. In this work, we focus on problems on both the demand and generation side of the electrical power system. On the demand side, we investigate the problem of inferring the power consumption of individual loads in a building from aggregate electrical measurements. This problem, also known as Non-Intrusive Load Monitoring or energy disaggregation, involves inference of a latent variable depicting the operational state of individual devices given a set of aggregate observations and so far the existing solutions either require supervised training or make assumptions that limit their applicability and performance in real conditions. On the generation side, we investigate the problem of finding optimal configurations of power generators in a network, also known as the AC-Optimal Power Flow problem. In this setting, because existing solutions usually cast the problem as constrained optimization, non-linear and non-convex constraints that solutions need to adhere to, cause computational difficulties which results in most solvers lacking robustness and speed. What both problems share is the computational difficulty of inferring an optimal binary vector that describes appliance states or generator configurations, respectively. In order to alleviate the computational cost of this inference step that is otherwise NP-hard, we make use of an approximate technique called Variational Inference which translates statistical inference into an optimization problem by minimizing a divergence measure between the true and an auxiliary but tractable distribution. Because the choice of the auxiliary distribution determines the goodness of the approximation andconsidering that for both problems the vector of interest is binary, in this thesis, we introduce an auxiliary distribution that can theoretically approximate any distribution over binary states arbitrarily well. Furthermore, in the case of Non-Intrusive Load Monitoring, because the problem requirestracking appliance states over time and modeling temporal dependencies causes the joint distribution required for Variational Inference to become intractable, computationally efficient strategies to approximate this joint distribution are introduced. We ultimately derive an, under some conditions, asymptotically unbiased algorithm for learning and inference in dynamical systems with binary latent states and cast unsupervised Non- Intrusive Load Monitoring as such a problem. The algorithm shows performance comparable to state-of-the-art competitors but overcomes many of their problems because it is truly unsupervised. In the case of AC-Optimal Power Flow, we reformulate the problem as a learning problem. Specifically, we task an agent to produce optimized generator configurations as a function of a demand assignment to the nodes in the network. The application of Variational Inference allows us to efficiently deal with non-convex generator configurations and to ultimately arrive at an algorithm that produces feasible solutions reliably and fast, i.e. it overcomes the robustness and speed issues of existing algorithms, but at the cost of sub-optimality.

A critical consequence of agriculturally managed lands is the transport of nutrients and sediment to fresh water systems, which is ultimately responsible for a range of adverse impacts on human and environmental health. In the U.S. alone, over half of streams and rivers are classified as impaired, with agriculture as the primary contributor. To address deterioration of water quality, there is a need for reliable tools and mathematical models to monitor and predict impacts to water quantity and quality. Soil water content is a key variable in representing environmental systems, linking and driving hydrologic, climate, and biogeochemical cycles; however, the influence of soil water simulations on model predictions is not well characterized, particularly for water quality. Moreover, while soil moisture estimation is the focus of multiple remote sensing missions, defining its potential for use in water quality models remains an open question. The goal of this research is to test whether updating model soil water process representation or model soil water estimates can provide better overall predictive confidence in estimates of both soil moisture and water quality. A widely-used ecohydrologic model, the Soil and Water Assessment Tool (SWAT), was used to evaluate four objectives: 1) investigate the potential of a gridded version of the SWAT model for use with similarly gridded, remote sensing data products, 2) determine the sensitivity of model predictions to changes in soil water content, 3) implement and test a more physically representative soil water percolation algorithm, and 4) perform practical data assimilation experiments using remote sensing data products, focusing on the effects of soil water updates on water quality predictions. With the exception of the first objective, model source code was modified to investigate the relative influence and effect of soil water on overall model predictions. Results suggested that use of the SWAT grid model was currently not viable given practical computational constraints. While the advantages provided by the gridded approach are likely useful for small scale watersheds (< 500 km2), the spatial resolution necessary to run the simulation was too coarse, such that many of the benefits of the gridded approach are negated. Sensitivity tests demonstrated a strong response of model predictions to perturbations in soil moisture. Effects were highly process dependent, where water quality was particularly sensitive to changes in both transport and transformation processes. Model response was reliant upon a default thresholding behavior that restricts subsurface flow and redistribution processes below field capacity. An alternative approach that removed this threshold and keyed processes to relative saturation showed improvement by allowing a more realistic range of soil moisture and a reduction of flushing behavior. This approach was further extended to test against baseline satellite data assimilation experiments; however, did not conclusively outperform the original model simulations. Nevertheless, overall, data assimilation experiments using a remote sensing surface soil moisture data product from the NASA Soil Moisture Active/Passive (SMAP) mission were able to correct for a dry bias in the model simulations and reduce error. Data assimilation updates significantly impacted flow predictions, generally by increasing the dominant contributing flow process. This led to substantial differences between two test sites, where landscape and seasonal characteristics moderated the impact of data assimilation updates to hydrologic, water quality, and crop yield predictions. While the findings illustrate the potential to improve predictions, continued future efforts to refine soil water process representation and optimize data assimilation with longer time series are needed. The dependence of ecohydrologic model predictions on soil moisture highlighted by this research underscores the importance and challenge of effectively representing a complex, physically-based process. As essential decision support systems rely on modeling analyses, improving prediction accuracy is vital.

The overall goal of this Ph.D. study was to investigate the mobilization of arsenic (As) from sedimentary formations under conditions representative of geologic carbon dioxide storage (GCS) i.e., high pressure, temperature, and salinity. GCS is a promising technology for the mitigation of increasing CO2 emissions in the atmosphere. It primarily involves the capture of CO2 from point sources, followed by transport and injection into deep subsurface formations for long-term storage. Of the potential subsurface formations under consideration in the United States, saline formations, characterized by the presence of high salinity brines, are estimated to have the largest storage capacity. Potential for leakage of injected CO2, native brines, and CO2- saturated brines from these reservoirs exists and may lead to an increase in mineral dissolution from reservoir formations, and leakage pathways. Of particular interest in the risk assessment of GCS is the dissolution and mobilization of toxic metals such as arsenic (As) and lead. The primary mineral source of As in high and low permeability sedimentary formations is arsenopyrite (FeAsS (s)). While the oxidative dissolution of FeAsS (s) has been reported in the literature, the dissolution of FeAsS (s) under anoxic, high salinity conditions of GCS remains unexplored. To conduct dissolution experiments at high pressure, temperature, and salinity, a small-scale plug-flow system capable of measuring dissolution rates without mass transfer limitations was designed and constructed. The capacity of the system in measuring dissolution rates under GCS conditions was validated. The plug-flow system is capable of accurate and rapid measurement of dissolution rates for minerals with slow and moderate dissolution rates, with a maximum rate limitation of 5 x10-5 mol/m2s at a flow rate of 10 ml/min. To enable accurate determination of reaction rates, a method for preparation of uniformly sized arsenopyrite particles free of surface oxides was developed. The method involves sonication of crushed minerals with ethanol, washing with 12N HCl, and 50% ethanol, followed by drying in N2. Analysis of the arsenopyrite surface with X-ray photoelectron spectroscopy revelealed that the method was successful in removing all the oxides of As and S on the surface, while only 12% of Fe was left oxidized. Subsequently, the dissolution of arsenopyrite, galena, and pyrite in low-concentration alkali and alkaline metal chloride solutions under anoxic conditions was investigated. Further, the effect of Na-Ca-Cl brines on the release of arsenic was determined under ambient as well as GCS conditions. The result of these experiments revealed that electrolytes traditionally considered inert, such as NaCl, CaCl2, and MgCl2 are capable of effecting sulfide mineral dissolution. In particular, the dissolution of As increased with increasing cation activity, and the dissolution of sulfur decreased with an increase in chloride ion activity in solution. Dissolution experiments with 1.5M Na-Ca-Cl brines resulted in arsenic dissolution rates in the range of 10-10 to 10-11 mol/m2 s under anoxic conditions. The rate of As release was found to be dependent on the CaCl2 content of these Na-Ca-Cl brines. Upon the introduction of CO2 into the system, the dissolution rate of As decreased and was determined to be in the range of 10-11 to 10-12 mol/m2s. For comparison, the rate of As release from arsenopyrite under oxic conditions is in the range of X to Y mol/m2 s. Finally, dissolution experiments aimed at understanding the release of As from naturally occurring seal rocks of a GCS formation were conducted. A primary seal rock and two secondary seal rocks were obtained from the Cranfield oil field CO2- EOR site in Mississippi. The rock samples were characterized by micro Xray adsorption near edge structure analysis, which revealed that multiple sources of As exist in the reservoir seal rocks studied. Dissolution experiments with seal rocks and anoxic brines of 105g/L NaCl resulted in the dissolution of arsenic in concentrations of 70 to 80 ppb at steady state. Dissolution of CO2 in the brine had no discernible effect on the steady state release concentration of As.

Life Cycle Assessment (LCA) has been applied to help decision-makers understand quantitative environmental effects and impacts through the life stages of a product or process. Matrix-based LCA models are widely incorporated to LCA software tools to simplify the assessment and provide straightforward results. However, these tools do not sufficiently provide the uncertainties that arise from the inventory data as well as from the matrix-based models. To address this problem, in this thesis I use a range method to explore three types of uncertainties (parameter, scenario, and model uncertainties) present in matrix-based LCA models. These three types of uncertainties are assessed separately for two different types of LCA models: the Input-Output- based LCA model, and the process-based LCA models analyzed with matrix methods. IO-based LCA models are studied with the Environmental Input-Output Life Cycle Assessment (EIO-LCA) model, and the US LCI database incorporated to matrix methods is used as an example of process-based LCA models. I selected two demonstrate the results with two environmental effects (greenhouse gas emissions and energy consumptions) and five environmental impacts (global warming, ozone depletion, acidification, eutrophication and ecotoxicity). First, I analyzed the parameter uncertainty in the EIO-LCA model. Publicly available data sources and assumptions are used to estimate the parameter uncertainties of the direct energy consumption in the US industrial sectors. The direct and indirect energy consumption ranges are estimated through the EIO-LCA model. The results show that the parameter uncertainties are generally within -40% to 40% from the default values, with several outliers. Second, I examined the scenario uncertainties in total carbon dioxide emissions by using alternative inputs in the US LCI database. I found that the US LCI database fails to take full advantage of matrix-based methods; when incorporated to matrix-based LCA models, less than 10% of the processes contribute to the indirect environmental effects. The results of scenario uncertainty estimation in the US LCI database show that on average, the total carbon dioxide emissions across all processes are between -30% to -30%. Finally, I addressed the model uncertainty by using different Life Cycle Inventory Assessment (LCIA) methods incorporated in the matrix-based models. The results show that when the US LCI inventories are applied, the uncertainties due to choosing different impact methods are within 5%. This is possibly caused by the incompleteness of the inventories: more than 50% of the characterized substances are excluded in the inventory, resulting in the neglect of some impact values. The results from this study emphasize the importance of estimating uncertainties in matrix-based LCA models. The variability in the LCA results is caused by all three types of uncertainties, as well as the incomplete inventories embedded in the matrix-based LCA models. Future LCA database and software should focus on including uncertainty estimation in the features and improving the inventory data to take full advantage of the matrix-based LCA models.

This work is an attempt to develop a physically realistic model to understand the behavior and failure of rocks, especially under the extreme conditions of High Pressure and High Temperature (HPHT). A platform is laid in the preliminary work where 1D pure and ductile damage models are developed respectively. These models are based on an elasto-plastic model with an additional governing equation incorporated to facilitate the inclusion of damage. This additional governing equation is called the damage evolution equation. In the ductile damage model, it is assumed that the damage is driven and controlled by plasticity. The concepts developed in the preliminary work of 1D modeling are then taken into the study of 3D problems. The main problems studied are: the unconstrained uniaxial compression, the completely constrained uniaxial compression and the dynamic indentation problem. The dynamic indentation problem is the representation of an idealized rock drilling process. The results from the indentation problem are found to be in good qualitative agreement with the experimental results (Abd Al-Jalil, Y.Q 2006).

technical report

"We consider the problem of the interaction of a stationary viscous fluid with an elastic solid that undergoes large deformation. The fluid is modeled by the stationary incompressible Navier- Stokes equations in an Eulerian frame of reference, while a Lagrangian reference frame and large deformation-small strain theory is used for the solid. A variational formulation of the problem is developed that insures satisfaction of continuity of interface tractions and velocities. The variational formulation is approximated by a Galerkin finite element method, yielding a system of nonlinear algebraic equations in unknown fluid velocities and pressures and solid displacements. A Newton-like method is introduced for solution of the discrete system. The method employs a modified Jacobian that enables decomposition into separate fluid and solid subdomains. This domain decomposition avoids possible ill-conditioning of the Jacobian, as well as the need to compute and store geometric coupling terms between fluid and interface shape. The capability of the methodology is illustrated by solution of a problem of the flow-induced large deformation of an elastic infinite cylinder."

With such a vast quantity of space, commercial low-slope roofs offer significant potential for sustainable roofing technology deployment. Specifically, building energy performance can be improved by installing rooftop energy technologies such as photovoltaic (PV) panels, and/or including designs such as white or green roofs instead of traditional black. This research aims to inform and support roof decisions through quantified energy performance impacts across roof choices and photovoltaic technologies. The primary dataset for this research was measured over a 16 month period (May 24, 2011 to October 13, 2012) from a large field experiment in Pittsburgh, Pennsylvania on top of a commercial warehouse with white, black and green roof sections, each with portions covered by polycrystalline photovoltaic panels. Results from the Pittsburgh experiment were extended to three different cities (San Diego, CA; Huntsville, AL; and Phoenix, AZ) chosen to represent a wide range of irradiance and temperature values. First, this research evaluated the difference in electricity production from a green-moss roof and black roof underneath photovoltaic panels to determine if the green roof’s cooler air increases the panel efficiency. Second, separate studies examine 1) average hourly heat flux by month for unobstructed and shaded roof membranes 2) heat flux peak time delay, and 3) air temperature across roof types. Results of this research show green roofs slightly increased (0.8-1.5%) PV panel efficiency in temperatures approximately at or above 25⁰ C (77⁰F) compared to black roofs. However in cool climates, like Pittsburgh, the roof type under the PV panels had little overall impact on PV performance when considering year round temperatures. Instead, roof decisions should place a stronger emphasis on heat flux impacts. The green roof outperformed both black and white roofs at minimizing total conductive heat flux. These heat flow values were used to develop a new, straight-forward methodology to roughly estimate heat flux impacts of different roof types in other climates using ambient temperature and solar irradiance. While managing heat flow is important for building energy performance, roof choices need to include a systems level analysis encompassing a year for the specific region to best quantify the overall energy impacts

Many infrastructure systems in the US such as road networks, bridges, water and wastewater pipelines, and wind farms are aging and their condition are deteriorating. Accurate risk analysis is crucial to extend the life span of these systems, and to guide decision making towards a sustainable use of resources. These systems are subjected to fatigue-induced degradation and need periodic inspections and repairs, which are usually performed through semi-annual, annual, or bi-annual scheduled maintenance. However, better maintenance can be achieved by flexible policies based on prior knowledge of the degradation process and on data collected in the field by sensors and visual inspections. Traditional methods to model the operation and maintenance (O&M) process, such as Markov decision processes (MDP) and partially observable MDP (POMDP) have limitations that do not allow the model to properly include the knowledge available and that may result in nonoptimal strategies for management of infrastructure systems. Specifically, the conditional probabilities for modeling the degradation process and the precision of the observations are usually affected by epistemic uncertainty: this cannot be captured by traditional methods. The goal of this dissertation is to propose a computational framework for adaptive monitoring and control of infrastructures at the system-level and to connect different aspects of the management process together. The first research question we address is how to take optimal sequential decisions under model uncertainty. Second, we propose how to combine decision optimization with learning of the degradation of components and the precision of monitoring system. Specifically, we address the issue of systems made by similar components, where iv transfer of knowledge across components is relevant. Finally, we propose how to assess the value of information in sequential decision making and whether it can be used as a heuristic for system-level inspection scheduling. In this dissertation, first a novel learning and planning method is proposed, called “Planning and Learning for Uncertain dynamic Systems” (PLUS), that can learn from the environment, update the distributions of parameters, and select the optimal strategy considering the uncertainty related to the model. Validating with synthetic data, the total management cost of operating a wind farm using PLUS is shown to be significantly less than costs achieved by a fixed policy or though the POMDP framework. Moreover, when the system is made up by similar components, data collected on one is also relevant in the management of others. This is typically the case of wind farms, which are made up by similar turbines. PLUS models the components as independent or identical and eithers learn the model for each component independently or learn a global model for all components. We extend that formulation, allowing for a weaker similarity among components. The proposed approach, called “Multiple Uncertain POMDP” (MU-POMDP), models the components as POMDPs, and assumes the corresponding model parameters as dependent random variables. By using this framework, we can calibrate specific degradation and emission models for each component while, at the same time, processing observations at the level of the entire system. We evaluate the performance of MU-POMDP compared to PLUS and discuss its potentials and computational complexity. Lastly, operation and maintenance of an infrastructure system rely on information collected on its components, which can provide the decision maker with an accurate assessment of their condition states. However, resources to be invested in data gathering are usually limited and v observations should be collected based on their value of information (VoI). VoI is a key concept for directing explorative actions, and in the context of infrastructure operation and maintenance, it has application to decisions about inspecting and monitoring the condition states of the components. Assessing the VoI is computationally intractable for most applications involving sequential decisions, such as long-term infrastructure maintenance. The component-level VoI can be used as a heuristic for assigning priorities to system-level inspection scheduling. In this research, we propose two alternative models for integrating adaptive maintenance planning based on POMDP and inspection scheduling based on a tractable approximation of VoI: the stochastic allocation model (and its two limiting scenarios called pessimistic and optimistic) that assumes observations are collected with a given probability, and the fee-based allocation model that assumes observations are available at a given cost. We illustrate how these models can be used at component-level and for system-level inspection scheduling. Furthermore, we evaluate the quality of solution provided by pessimistic and optimistic approaches. Finally, we introduce analytical formulas based on the stochastic and fee-based allocation models to predict the impact of a monitoring system (or a piece of information) on the operation and maintenance cost of infrastructure systems.

technical report

This paper introduces a damage diagnosis algorithm for civil structures that uses a sequential change point detection method. The general change point detection method uses the known pre- and post-damage feature distributions to perform a sequential hypothesis test. In practice, however, the post-damage distribution is unlikely to be known a priori, unless we are looking for a known specific type of damage. Therefore, we introduce an additional algorithm that estimates and updates this distribution as data are collected using the maximum likelihood and the Bayesian methods. We also applied an approximate method to reduce the computation load and memory requirement associated with the estimation. The algorithm is validated using a set of experimental data collected from a four-story steel special moment-resisting frame and multiple sets of simulated data. Various features of different dimensions have been explored, and the algorithm was able to identify damage, particularly when it uses multidimensional damage sensitive features and lower false alarm rates, with a known post-damage feature distribution. For unknown feature distribution cases, the post-damage distribution was consistently estimated and the detection delays were only a few time steps longer than the delays from the general method that assumes we know the post-damage feature distribution. We confirmed that the Bayesian method is particularly efficient in declaring damage with minimal memory requirement, but the maximum likelihood method provides an insightful heuristic approach.

Recent advances life cycle assessment methodology provide an opportunity to gain a more holistic understanding of how the mining industry interacts with water resources. A detailed review of assessment methodology and water management in the mining industry was undertaken to identify research needs. Global datasets of water use statistics for mining operations were also developed, and an exhaustive analysis of how global mineral resources and production are spatially distributed across local water and climate contexts was performed. The project has made a significant advance in our understanding of how the global minerals industry interacts with water resources.

We address two problems in this thesis. First, a phase-field model for structural phase transformations in solids and second, a model for dynamic fracture. The existing approaches for both phase transformations and fracture can be grouped into two categories. Sharp-interface models, where interfaces are singular surfaces; and regularized-interface models, such as phase-field models, where interfaces are smeared out. The former are challenging for numerical solutions because the interfaces or crack needs to be explicitly tracked, but have the advantage that the kinetics of existing interfaces or cracks and the nucleation of new interfaces can be transparently and precisely prescribed. The diffused interface models such as phasefield models do not require explicit tracking of interfaces and makes them computationally attractive. However, the specification of kinetics and nucleation is both restrictive and extremely opaque in such models. This prevents straightforward calibration of phase-field models to experiment and/or molecular simulations, and breaks the multiscale hierarchy of passing information from atomic to continuum. Consequently, phase-field models cannot be confidently used in dynamic settings. We present a model which has all the advantages of existing phase-field models but also allows us to prescribe kinetics and nucleation criteria. We present a number of examples to characterize and demonstrate the features of the model. We also extend it to the case of multiple phases where preserving kinetics of each kind of interface is more complex. We use the phase transformation model with certain changes to model dynamic fracture. We achieve the advantage of prescribing nucleation and kinetics independent of each other. We demonstrate examples of anisotropic crack propagation and crack propagation on an interface in a composite material. We also report some limitations of phase-field models for fracture which have not been mentioned in the existing literature. These limitations include dependence of effective crack width and hence the effective surface energy on the crack speed, lack of a reasonable approximation for the mechanical response of cracked region and inability to model large deformations.

Carbon capture, utilization, and storage (CCUS), including enhanced oil recovery (EOR), is one of the most promising mitigation strategies for climate change. CCUS involves the capture of CO2 from point sources and the subsequent injection of that CO2 into geologic storage formations. Depleted oil reservoirs will be the first targets of CCUS due to the economic benefits associated with EOR. EOR operations are expected to produce large volumes of wastewater brines with the crude oil. Brines, which can have high concentrations of salts and dissolved organic compounds; and CO2, which can have dissolved organic compounds, have potential to leak into shallower aquifers. Therefore, fundamental research is needed on the levels of organic compounds in both the reservoir brines and CO2 in case of leakage. This thesis was divided into two parts. Part I was concerned with the aqueous solubility of organic compounds in brines. The presence of dissolved salts typically results in a decrease in organic compound aqueous solubility, this is called the salting-out effect, and it is typically modeled by the Setschenow Equation. Setschenow constants, which are empirical salting-out parameters, are assumed to be additive, meaning that they are applicable in mixed electrolyte solutions. However, this has not been verified by extensive experimental work. For accurate risk assessment modeling, Setschenow constants are needed for NaCl and CaCl2 for hundreds of organic compounds relevant to oil and gas reservoirs. However, there are only ~190 reported NaCl Setschenow constants and ~19 reported CaCl2 Setschenow constants. For the majority of these compounds, the validity of the Setschenow Equation has only been proven up to 0.5 – 1 M NaCl/CaCl2; and has not been extended up to salt concentrations relevant to oil and gas reservoir brines. The first objective of this study was to determine the validity of the Setschenow Equation for selected hydrophobic compounds in the range of 2 – 5 M NaCl, 1.5 – 2 M CaCl2, and in mixed electrolyte brines. The salting-out effect was measured in NaCl, CaCl2, and mixed Na-Ca brines for naphthalene, fluorene, phenanthrene, thiophene, benzothiophene, and dibenzothiophene. In this study, the Setschenow Equation was proven to be valid up to 2 – 5 M NaCl and 1.5 – 2 M CaCl2 for the organic compounds studied here. Setschenow constants were additive for fluorene and thiophene from moderate to high ionic strengths. Results demonstrated that previously determined Setschenow constants measured at low salt concentrations do not need to be re-measured at high salt concentrations. Objective 2 was to determine the validity of the Setschenow Equation for selected hydrophilic compounds up to 5 M NaCl, 2 M CaCl2, and in mixed electrolyte brines. The Setschenow Equation was proven to be valid in predicting the salting-out effect up to those high salt concentrations for three phenol, p-cresol, hydroquinone, 9-hydroxyfluorene, pyrrole, and hexanoic acid. Setschenow constants were additive for p-cresol and 9-hydroxyfluorene up to high ionic strengths. In addition to demonstrating the validity of the Setschenow Equation for these selected organic compounds, both Objective 1 and 2 added to a sparse database of NaCl and CaCl2 Setschenow constants. In Objective 3 models were evaluated, updated, and developed for prediction of Setschenow constants. Two models, a poly-parameter linear free energy relationship (pp-LFER) and a single parameter (sp) LFER, for prediction of NaCl Setschenow constants were evaluated and updated with new NaCl Setschenow constant data from both this study and the literature. The pp-LFER uses the Abraham solvation parameters of the organic compound of interest as inputs and the sp-LFER uses the octanol-water partitioning coefficient of the organic compound to predict NaCl Setschenow constants. Both models produced predictions of Setschenow constants that had good agreement with the experimental NaCl Setschenow constants in this study. The update of these models increased the breadth of organic compounds, and therefore confidence, in these models. In addition, four new models were developed to predict Setschenow constants of four other salts, which include CaCl2, KCl, LiCl, and NaBr. Extensions of this study include determining whether the Setschenow Equation is valid in predicting the salting-out effect for additional organic organics, different salts, and in additional mixed electrolyte systems. Finally, Part II of this study explored the data gaps related to the partitioning of organic compounds from water to sc-CO2. Objective four was to develop new linear partitioning models based on experimental water-sc-CO2 data of selected nitrogen, sulfur, and oxygen containing organic compounds and literature data. There are only ~37 organic compounds that have reported water-sc-CO2 partitioning coefficients; however thousands of partitioning coefficients are needed over a range of temperature and pressure conditions. Therefore, models are needed to accurately predict these partitioning coefficients. Partitioning coefficients over a range of temperatures and pressures were measured for thiophene, pyrrole, and anisole. Those measured partitioning coefficients followed trends based on vapor pressure and aqueous solubility. These partitioning coefficients, along with literature values were used to update a pp-LFER. Five new models based on inputs of vapor pressure, aqueous solubility, and CO2 density were developed to predict water-sc-CO2 partitioning coefficients. Those models were developed using data from this study and literature data. Four of those models are specific to organic compound classes, which include monopolar substituted benzenes, polar substituted benzenes, chlorinated phenols, and nitrogen containing compounds, and the other model is available to any organic compound that has vapor pressure, aqueous solubility, and CO2 density inputs that fall within the specified training range. Possible extensions of this study include further research testing of different groups of organic compounds in water-sc-CO2 systems, co-solvency effects, determining the effects of the salting-out effect in water-sc-CO2 partitioning, and using sc-CO2 for water treatment.

The continuum mechanics of line defects representing singularities due to terminating discontinuities of the elastic displacement and its gradient field is developed. The development is intended for application to coupled phase transformation, grain boundary, and plasticity-related phenomena at the level of individual line defects and domain walls. The continuously distributed defect approach is developed as a generalization of the discrete, isolated defect case. Constitutive guidance for equilibrium response and dissipative driving forces respecting frame-indifference and non-negative mechanical dissipation is derived. A differential geometric interpretation of the defect kinematics is developed, and the relative simplicity of the actual adopted kinematics is pointed out. The kinematic structure of the theory strongly points to the incompatibility of dissipation with strict deformation compatibility., Comment: Contribution to Workshop on Differential Geometry and Continuum Mechanics held at the Intl. Centre for Mathematical Sciences in Edinburgh, 2013. Ed: G. Q Chen, M. Grinfeld, R.J. Knops