Civil and Environmental Engineering Publications

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Emergence of microplastics in African environmental drinking water sources: A review on sources, analysis and treatment strategies
(Elsevier, 2024) Adewuyi, Adewale; Li, Qilin; NSF Nanosystems Engineering Research Center for Nanotechnology-Enabled Water Treatment
The emergence of microplastics (MPs) as microcontaminants in environmental drinking water sources is a problem in Africa that requires immediate action. Therefore, this review focused on understanding the sources of MPs in African water systems, treatment strategies, analytical methods for identification and quantification, and Africa's pollution index. From the findings, the source of MPs in African water systems was attributed to unregulated importation of plastic products, poor waste management, lack of awareness, poor environmental value system and the inability of local polymer industries to adjust to new policies on plastic management. Most studies identified microfibers and microbeads to be the primary sources of plastics that break down to MPs in African drinking water sources, with polystyrene (PS), polypropylene (PP), and polyethylene (PE) being frequently detected. Current methods for identification, and quantification of MPs in most studies conducted in Africa were not developed in Africa but was adopted from developed countries and, in some cases, modified to meet specific analytical requirements. More studies are necessary for in-depth understanding of the fate and pollution index of MP in African environmental water systems. Furthermore, the interaction between MP and other pollutants in the water system still needs to be better understood. This review suggests membrane and rapid sand filtration methods as promising methods that may be considered for removing MPs from water systems in Africa.
Emergency of per- and polyfluoroalkyl substances in drinking water: Status, regulation, and mitigation strategies in developing countries
(Elsevier, 2024) Adewuyi, Adewale; Li, Qilin; NSF Nanosystems Engineering Research Center for Nanotechnology-Enabled Water Treatment
The detection of per- and polyfluoroalkyl substances (PFAS) in water presents a significant challenge for developing countries, requiring urgent attention. This review focuses on understanding the emergence of PFAS in drinking water, health concerns, and removal strategies for PFAS in water systems in developing countries. This review indicates the need for more studies to be conducted in many developing nations due to limited information on the environmental status and fate of PFAS. The health consequences of PFAS in water are enormous and cannot be overemphasized. Efforts are ongoing to legislate a national standard for PFAS in drinking water. Currently, there are few known mitigation efforts from African countries, in contrast to several developing nations in Asia. Therefore, there is an urgent need to develop economically viable techniques that could be integrated into large-scale operations to remove PFAS from water systems in the region. However, despite the success achieved with removing long-chain PFAS from water, more studies are required on strategies for eliminating short-chain moieties in water.
Point-of-use filtration units as drinking water distribution system sentinels
(Springer Nature, 2024) Bai, Weiliang; Xu, Ruizhe; Podar, Mircea; Swift, Cynthia M.; Saleh, Navid B.; Löffler, Frank E.; Alvarez, Pedro J. J.; Kumar, Manish
Municipal drinking water distribution systems (DWDSs) and associated premise plumbing (PP) systems are vulnerable to proliferation of opportunistic pathogens, even when chemical disinfection residuals are present, thus presenting a public health risk. Monitoring the structure of microbial communities of drinking water is challenging because of limited continuous access to faucets, pipes, and storage tanks. We propose a scalable household sampling method, which uses spent activated carbon and reverse osmosis (RO) membrane point-of-use (POU) filters to evaluate mid- to long-term occurrence of microorganisms in PP systems that are relevant to consumer exposure. As a proof of concept, POU filter microbiomes were collected from four different locations and analyzed with 16S rRNA gene amplicon sequencing. The analyses revealed distinct microbial communities, with occasional detection of potential pathogens. The findings highlight the importance of local, and if possible, continuous monitoring within and across distribution systems. The continuous operation of POU filters offers an advantage in capturing species that may be missed by instantaneous sampling methods. We suggest that water utilities, public institutions, and regulatory agencies take advantage of end-of-life POU filters for microbial monitoring. This approach can be easily implemented to ensure drinking water safety, especially from microbes of emerging concerns; e.g., pathogenic Legionella and Mycobacterium species.
Olivar: towards automated variant aware primer design for multiplex tiled amplicon sequencing of pathogens
(Springer Nature, 2024) Wang, Michael X.; Lou, Esther G.; Sapoval, Nicolae; Kim, Eddie; Kalvapalle, Prashant; Kille, Bryce; Elworth, R. A. Leo; Liu, Yunxi; Fu, Yilei; Stadler, Lauren B.; Treangen, Todd J.
Tiled amplicon sequencing has served as an essential tool for tracking the spread and evolution of pathogens. Over 15 million complete SARS-CoV-2 genomes are now publicly available, most sequenced and assembled via tiled amplicon sequencing. While computational tools for tiled amplicon design exist, they require downstream manual optimization both computationally and experimentally, which is slow and costly. Here we present Olivar, a first step towards a fully automated, variant-aware design of tiled amplicons for pathogen genomes. Olivar converts each nucleotide of the target genome into a numeric risk score, capturing undesired sequence features that should be avoided. In a direct comparison with PrimalScheme, we show that Olivar has fewer mismatches overlapping with primers and predicted PCR byproducts. We also compare Olivar head-to-head with ARTIC v4.1, the most widely used primer set for SARS-CoV-2 sequencing, and show Olivar yields similar read mapping rates (~90%) and better coverage to the manually designed ARTIC v4.1 amplicons. We also evaluate Olivar on real wastewater samples and found that Olivar has up to 3-fold higher mapping rates while retaining similar coverage. In summary, Olivar automates and accelerates the generation of tiled amplicons, even in situations of high mutation frequency and/or density. Olivar is available online as a web application at https://olivar.rice.edu and can be installed locally as a command line tool with Bioconda. Source code, installation guide, and usage are available at https://github.com/treangenlab/Olivar.
Electrothermal mineralization of per- and polyfluoroalkyl substances for soil remediation
(Springer Nature, 2024) Cheng, Yi; Deng, Bing; Scotland, Phelecia; Eddy, Lucas; Hassan, Arman; Wang, Bo; Silva, Karla J.; Li, Bowen; Wyss, Kevin M.; Ucak-Astarlioglu, Mine G.; Chen, Jinhang; Liu, Qiming; Si, Tengda; Xu, Shichen; Gao, Xiaodong; JeBailey, Khalil; Jana, Debadrita; Torres, Mark Albert; Wong, Michael S.; Yakobson, Boris I.; Griggs, Christopher; McCary, Matthew A.; Zhao, Yufeng; Tour, James M.
Per- and polyfluoroalkyl substances (PFAS) are persistent and bioaccumulative pollutants that can easily accumulate in soil, posing a threat to environment and human health. Current PFAS degradation processes often suffer from low efficiency, high energy and water consumption, or lack of generality. Here, we develop a rapid electrothermal mineralization (REM) process to remediate PFAS-contaminated soil. With environmentally compatible biochar as the conductive additive, the soil temperature increases to >1000 °C within seconds by current pulse input, converting PFAS to calcium fluoride with inherent calcium compounds in soil. This process is applicable for remediating various PFAS contaminants in soil, with high removal efficiencies ( >99%) and mineralization ratios ( >90%). While retaining soil particle size, composition, water infiltration rate, and cation exchange capacity, REM facilitates an increase of exchangeable nutrient supply and arthropod survival in soil, rendering it superior to the time-consuming calcination approach that severely degrades soil properties. REM is scaled up to remediate soil at two kilograms per batch and promising for large-scale, on-site soil remediation. Life-cycle assessment and techno-economic analysis demonstrate REM as an environmentally friendly and economic process, with a significant reduction of energy consumption, greenhouse gas emission, water consumption, and operation cost, when compared to existing soil remediation practices.
Enhanced solar and wind potential during widespread temperature extremes across the U.S. interconnected energy grids
(IOP Publishing, 2024) Singh, Deepti; Bekris, Yianna S.; Rogers, Cassandra D. W.; Doss-Gollin, James; Coffel, Ethan D.; Kalashnikov, Dmitri A.
Several recent widespread temperature extremes across the United States (U.S.) have been associated with power outages, disrupting access to electricity at times that are critical for the health and well-being of communities. Building resilience to such extremes in our energy infrastructure needs a comprehensive understanding of their spatial and temporal characteristics. In this study, we systematically quantify the frequency, extent, duration, and intensity of widespread temperature extremes and their associated energy demand in the six North American Electric Reliability Corporation regions using ERA5 reanalysis data. We show that every region has experienced hot or cold extremes that affected nearly their entire extent and such events were associated with substantially higher energy demand, resulting in simultaneous stress across the entire electric gird. The western U.S. experienced significant increases in the frequency (123%), extent (32%), duration (55%) and intensity (29%) of hot extremes and Texas experienced significant increases in the frequency (132%) of hot extremes. The frequency of cold extremes has decreased across most regions without substantial changes in other characteristics. Using power outage data, we show that recent widespread extremes in nearly every region have coincided with power outages, and such outages account for between 12%–52% of all weather-related outages in the past decade depending on the region. Importantly, we find that solar potential is significantly higher during widespread hot extremes in all six regions and during widespread cold extremes in five of the six regions. Further, wind potential is significantly higher during widespread hot or cold extremes in at least three regions. Our findings indicate that increased solar and wind capacity could be leveraged to meet the higher demand for energy during such widespread extremes, improving the resilience and reliability of our energy systems in addition to limiting carbon emissions.
Empirical Fragility Analysis of Haitian Reinforced Concrete and Masonry Buildings
(MDPI, 2024) Laguerre, Marc-Ansy; Salehi, Mohammad; Desroches, Reginald
This study develops empirical fragility curves for concrete and masonry buildings in Haiti, utilizing data from the 2021 earthquake. A dataset of 3527 buildings from the StEER database, encompassing a diverse range of building types, is used. These buildings types include reinforced concrete structures with masonry infills, confined masonry buildings, reinforced masonry bearing walls, and unreinforced masonry bearing walls. Shakemaps from the USGS are utilized to assess the earthquake’s intensity at each building, with the peak ground acceleration (PGA) as the intensity measure. Damage is classified into five distinct states: no damage, minor, moderate, severe, and partial or total collapse. For each of these states, the corresponding probabilities of exceedance are calculated, and log-normal cumulative distribution functions were fitted to those data to produce empirical fragility curves. The results show a notable similarity in performance among the four types, each having high probability of failure even under low-intensity earthquakes. Total fragility curves (including all four building types) are developed subsequently and they are convolved to the probabilistic seismic hazard map of Haiti to assess the seismic risk. This includes estimating the annual probability of partial/total collapse and the probability of partial/total collapse in the event of 475-year and 2475-year earthquakes. The results indicate a significant risk, with up to 64% probability of collapse in certain areas for the 2475-year earthquake and a probability of collapse of 15% for a 475-year earthquake. These findings underscore the critical vulnerability of Haiti’s buildings to seismic events and the urgent need for their retrofit.
Enabling efficient regional seismic fragility assessment of multi-component bridge portfolios through Gaussian process regression and active learning
(Wiley, 2024) Ning, Chunxiao; Xie, Yazhou; Burton, Henry; Padgett, Jamie E.
Regional seismic fragility assessment of bridge portfolios must address the embedded uncertainties and variations stemming from both the earthquake hazard and bridge attributes (e.g., geometry, material, design detail). To achieve bridge-specific fragility assessment, multivariate probabilistic seismic demand models (PSDM) have recently been developed that use both the ground motion intensity measure and bridge parameters as inputs. However, explicitly utilizing bridge parameters as inputs requires numerous nonlinear response history analyses (NRHAs). In this situation, the associated computational cost increases exponentially for high-fidelity bridge models with complex component connectivity and sophisticated material constitutive laws. Moreover, it remains unclear how many analyses are sufficient for the response data and the resulting demand model to cover the entire solution space without overfitting. To deal with these issues, this study integrates Gaussian process regression (GPR) and active learning (AL) into a multistep workflow to achieve efficient regional seismic fragility assessment of bridge portfolios. The GPR relaxes the probability distribution assumptions made in typical cloud analysis-based PSDMs to enable heteroskedastic nonparametric seismic demand modeling. The AL leverages the varying standard deviation to select the least but most representative bridge-model-ground-motion sample pairs to conduct NRHA with much-improved efficiency. Both independent and correlated multi-output GPRs are proposed to deal with bridge portfolios with seismic demand correlations among multiple components (column, bearing, shear key, abutment, unseating, and joint seal). Considering a single benchmark highway bridge class in California as the case study, the AL-GPR framework and the associated component-level fragility results are investigated in terms of their efficiency, accuracy, and robustness. The fragility results show that 70 AL-selected samples would enable the GPR to derive bridge-specific fragility models comparable to the ones using the multiple stripes analysis approach with 1950 ground motions considered for each individual bridge. The AL-GPR model also successfully captures the physics of how bridge span length, deck area, column slenderness, and steel reinforcement ratio would change the damage state exceedance probabilities of different bridge components. The efficiency of AL stems from the fact that, with the multi-output independent GPR, a stable and reliable fragility model can be achieved using 50 AL-selected samples compared to at least 270 randomly chosen samples. The proposed methodology advances the state of the art in enabling more efficient and reliable regional seismic fragility assessment of multi-component bridge portfolios.
Damping Enhancement Solution for Wind Turbines Through Amplifying Damping Transfer Systems
(World Scientific, 2024) Wang, Meng; Lu, Hai-Qiang; Wang, Pi-Guang; Nagarajaiah, Satish; Du, Xiu-Li
This paper proposed a novel amplifying damping transfer system (ADTS) as a new damping enhancement solution for high-rise structures like wind turbines. The proposed ADTS can transfer the upper rotation of turbine tower to its bottom with damping amplification mechanism. Hence, viscous damper can be installed on wind turbines in a very convenient and efficient way. The dynamic characteristics of wind turbines equipped with ADTS were parametrically investigated concerning the influence of the damping, stiffness, and position of the ADTS based on complex frequency analysis. It was found that each mode has a maximum damping ratio, which is affected by the ADTS stiffness and position. The optimal ADTS position of the first mode is about 0.7 H (turbine height), and the optimal positions of the second mode are at 0.3 H and 0.86 H. The proposed ADTS considerably attenuated both drift and acceleration responses of wind turbines caused by winds and earthquakes. For example, as compared to the optimized tuned mass damper, ADTS further decreases the displacement (acceleration) of wind turbine tower by about 22% (38%).
Crykey: Rapid identification of SARS-CoV-2 cryptic mutations in wastewater
(Springer Nature, 2024) Liu, Yunxi; Sapoval, Nicolae; Gallego-García, Pilar; Tomás, Laura; Posada, David; Treangen, Todd J.; Stadler, Lauren B.
Wastewater surveillance for SARS-CoV-2 provides early warnings of emerging variants of concerns and can be used to screen for novel cryptic linked-read mutations, which are co-occurring single nucleotide mutations that are rare, or entirely missing, in existing SARS-CoV-2 databases. While previous approaches have focused on specific regions of the SARS-CoV-2 genome, there is a need for computational tools capable of efficiently tracking cryptic mutations across the entire genome and investigating their potential origin. We present Crykey, a tool for rapidly identifying rare linked-read mutations across the genome of SARS-CoV-2. We evaluated the utility of Crykey on over 3,000 wastewater and over 22,000 clinical samples; our findings are three-fold: i) we identify hundreds of cryptic mutations that cover the entire SARS-CoV-2 genome, ii) we track the presence of these cryptic mutations across multiple wastewater treatment plants and over three years of sampling in Houston, and iii) we find a handful of cryptic mutations in wastewater mirror cryptic mutations in clinical samples and investigate their potential to represent real cryptic lineages. In summary, Crykey enables large-scale detection of cryptic mutations in wastewater that represent potential circulating cryptic lineages, serving as a new computational tool for wastewater surveillance of SARS-CoV-2.
Bivariate Processes Evolutionary Power Spectral Density Estimation Using Energy Spectrum Equations
(IOP Publishing, 2024) Spanos, Pol D.; Matteo, Alberto Di; Zhang, Hanshu; Yue, Qingxia
In this paper a novel procedure is developed for evolutionary cross power spectra (ECPS) estimation of bivariate nonstationary stochastic processes. Specifically, the ECPS is determined by estimating the statistical moments of energy-like response quantities of lightly damped linear filters excited by nonstationary stochastic processes. In this context, a smoothing procedure is incorporated by using the Savitzky-Golay (S-G) moving average filter to obtain reliable ECPS based even from a limited number of available records. Further, a refinement of the approach is proposed relying on polynomial based functions of the system output. Several numerical examples, including nonstationary processes with known spectra, and historic accelerograms are used to assess the reliability and accuracy of the proposed procedure.
Online trend estimation and detection of trend deviations in sub-sewershed time series of SARS-CoV-2 RNA measured in wastewater
(Springer Nature, 2024) Ensor, Katherine B.; Schedler, Julia C.; Sun, Thomas; Schneider, Rebecca; Mulenga, Anthony; Wu, Jingjing; Stadler, Lauren B.; Hopkins, Loren
Wastewater surveillance has proven a cost-effective key public health tool to understand a wide range of community health diseases and has been a strong source of information on community levels and spread for health departments throughout the SARS- CoV-2 pandemic. Studies spanning the globe demonstrate the strong association between virus levels observed in wastewater and quality clinical case information of the population served by the sewershed. Few of these studies incorporate the temporal dependence present in sampling over time, which can lead to estimation issues which in turn impact conclusions. We contribute to the literature for this important public health science by putting forward time series methods coupled with statistical process control that (1) capture the evolving trend of a disease in the population; (2) separate the uncertainty in the population disease trend from the uncertainty due to sampling and measurement; and (3) support comparison of sub-sewershed population disease dynamics with those of the population represented by the larger downstream treatment plant. Our statistical methods incorporate the fact that measurements are over time, ensuring correct statistical conclusions. We provide a retrospective example of how sub-sewersheds virus levels compare to the upstream wastewater treatment plant virus levels. An on-line algorithm supports real-time statistical assessment of deviations of virus level in a population represented by a sub-sewershed to the virus level in the corresponding larger downstream wastewater treatment plant. This information supports public health decisions by spotlighting segments of the population where outbreaks may be occurring.
Generalized Damped Outrigger Systems for Suppressing Multimode Vibrations of Tall Buildings
(World Scientific, 2024) Wen, Yongkui; Chen, Lin; Nagarajaiah, Satish
Damped outrigger is a viable means for reducing dynamic responses of tall buildings. This study focuses on generalized damped outrigger (GDO) systems. A GDO is composed of a damper for energy dissipation, a negative stiffness device and an inerter for damping enhancement. The GDO system incorporates GDOs at different floors of the tall building optimized to varied structural modes. Frequency equation of a tall building simplified as a cantilever beam with multiple GDOs is first derived by complex modal analysis. A finite different model of such a system is used for verification. Parametric analyses are then performed to compare damping effects of different GDO systems. It is found that a negative stiffness damped outrigger (NSDO) or inerter damped outrigger (IDO) needs to be optimized for maximizing damping of a specific mode. GDOs, respectively, tuned to different modes can largely improve the multimode damping effects. The optimal parameters of the GDOs are slightly different from those in the case when they are installed separately. With both negative stiffness and nonzero inertance, a GDO still needs to be tuned to a specific mode because multimode damping is sensitive to the damper coefficient. The combination of an NSDO optimized to the first mode and an IDO tuned to a higher mode seems the best solution. The IDO additionally improves the first mode damping provided by the NSDO and the two-mode damping is not sensitive to the damper coefficient of the NSDO. The findings are confirmed through seismic response analyses of a tall building with different GDO systems.
Detecting plumes in mobile air quality monitoring time series with density-based spatial clustering of applications with noise
(Copernicus Publications, 2023) Actkinson, Blake; Griffin, Robert J.
Mobile monitoring is becoming an increasingly popular technique to assess air pollution on fine spatial scales, but methods to determine specific source contributions to measured pollutants are sorely needed. One approach is to isolate plumes from mobile monitoring time series and analyze them separately, but methods that are suitable for large mobile monitoring time series are lacking. Here we discuss a novel method used to detect and isolate plumes from an extensive mobile monitoring data set. The new method relies on density-based spatial clustering of applications with noise (DBSCAN), an unsupervised machine learning technique. The new method systematically runs DBSCAN on mobile monitoring time series by day and identifies a subset of points as anomalies for further analysis. When applied to a mobile monitoring data set collected in Houston, Texas, analyzed anomalies reveal patterns associated with different types of vehicle emission profiles. We observe spatial differences in these patterns and reveal striking disparities by census tract. These results can be used to inform stakeholders of spatial variations in emission profiles not obvious using data from stationary monitors alone.
Hybrid method for full-field response estimation using sparse measurement data based on inverse analysis and static condensation
(Elsevier, 2022) Pal, Ashish; Meng, Wei; Nagarajaiah, Satish; Smalley-Curly Institute
In structural health monitoring, measuring the accurate and spatially dense response near critical locations of the structure can be advantageous to estimate damage to the structure. Due to several physical restrictions or limitations of the sensing method, it may not always be possible to generate reliable data at critical locations. In this study, a hybrid method is presented that makes use of the measured displacement data and finite element (FE) model of the structure to predict dense full-field response. The presented method can incorporate unknown boundary conditions and unknown body forces by applying correction/fictitious forces to match predicted and measured responses. Using static condensation followed by inverse analysis, these additional forces are found by setting up a least square problem. Due to the problem being ill-posed, L2-penalty is used to control the prediction error. Numerical simulation of a plate subjected to body force showed an accurate prediction of full-field response except for a few boundary locations. To handle this, the proposed method is used in conjunction with linear interpolation near boundary locations. The method is validated in a laboratory experiment for a plate with a notch having displacement measured using Digital Image Correlation (DIC). On comparing strains calculated using predicted displacements, FEM, and DIC, the predicted strains show better agreement with the FEM than DIC. This affirms that the proposed hybrid technique can be used at critical locations where DIC fails to provide reliable strain data.
Battery metal recycling by flash Joule heating
(AAAS, 2023) Chen, Weiyin; Chen, Jinhang; Bets, Ksenia V.; Salvatierra, Rodrigo V.; Wyss, Kevin M.; Gao, Guanhui; Choi, Chi Hun; Deng, Bing; Wang, Xin; Li, John Tianci; Kittrell, Carter; La, Nghi; Eddy, Lucas; Scotland, Phelecia; Cheng, Yi; Xu, Shichen; Li, Bowen; Tomson, Mason B.; Han, Yimo; Yakobson, Boris I.; Tour, James M.; Welch Institute for Advanced Materials; NanoCarbon Center; Applied Physics Program; Smalley-Curl Institute
The staggering accumulation of end-of-life lithium-ion batteries (LIBs) and the growing scarcity of battery metal sources have triggered an urgent call for an effective recycling strategy. However, it is challenging to reclaim these metals with both high efficiency and low environmental footprint. We use here a pulsed dc flash Joule heating (FJH) strategy that heats the black mass, the combined anode and cathode, to >2100 kelvin within seconds, leading to ~1000-fold increase in subsequent leaching kinetics. There are high recovery yields of all the battery metals, regardless of their chemistries, using even diluted acids like 0.01 M HCl, thereby lessening the secondary waste stream. The ultrafast high temperature achieves thermal decomposition of the passivated solid electrolyte interphase and valence state reduction of the hard-to-dissolve metal compounds while mitigating diffusional loss of volatile metals. Life cycle analysis versus present recycling methods shows that FJH significantly reduces the environmental footprint of spent LIB processing while turning it into an economically attractive process.
Public Health Interventions Guided by Houston’s Wastewater Surveillance Program During the COVID-19 Pandemic
(Sage, 2023) Hopkins, Loren; Ensor, Katherine B.; Stadler, Lauren; Johnson, Catherine D.; Schneider, Rebecca; Domakonda, Kaavya; McCarthy, James J.; Septimus, Edward J.; Persse, David; Williams, Stephen L.
Since the start of the COVID-19 pandemic, wastewater surveillance has emerged as a powerful tool used by public health authorities to track SARS-CoV-2 infections in communities. In May 2020, the Houston Health Department began working with a coalition of municipal and academic partners to develop a wastewater monitoring and reporting system for the city of Houston, Texas. Data collected from the system are integrated with other COVID-19 surveillance data and communicated through different channels to local authorities and the general public. This information is used to shape policies and inform actions to mitigate and prevent the spread of COVID-19 at municipal, institutional, and individual levels. Based on the success of this monitoring and reporting system to drive public health protection efforts, the wastewater surveillance program is likely to become a standard part of the public health toolkit for responding to infectious diseases and, potentially, other disease-causing outbreaks.
Lower bound of structural damage to achieve practical identifiability of nonlinear models in seismic structural health monitoring
(Wiley, 2024) Hernandez, Eric M.; Erazo, Kalil
This paper examines the effect of structural damage on the practical/computational identifiability of the parameters that define nonlinear models of building structures subjected to earthquake-induced base motions. The objective is to determine the level of physical damage expected to successfully estimate the nonlinear parameters of restoring force models. For this purpose, the analyses aim to determine if the parameters that define a hysteretic (Bouc-Wen type) model can be identified within a predefined level of accuracy from accelerations measured during seismic events that cause minor damage. The identified model is then interrogated to determine if it can provide accurate predictions of the response and damage level experienced during strong ground motions that cause moderate-to-severe damage. The damage model adopted is a Park–Ang type model and the unscented Kalman filter is used for parameter estimation. The results are verified using simulated two-dimensional building models and validated using experimental data from a large-scale shake table test.
Niobium Oxide Photocatalytically Oxidizes Ammonia in Water at Ambient Conditions
(SciELO, 2024) Elias, Welman; Clark, Chelsea; Heck, Kimberly; Arredondo, Jacob; Wang, Bo; Toro, Andras; Kürtib, László; Wong, Michael; Nanosystems Engineering Research Center for Nanotechnology-Enabled Water Treatment
Ammonia contamination in water is a significant environmental issue since it is toxic and leads to eutrophication. Photocatalysis has been investigated as a strategy for ammonia degradation but can potentially form toxic nitrite (NO2–) and nitrate (NO3–) byproducts. This work reports on the ability of niobium oxide (Nb2O5) to photocatalytically oxidize aqueous-phase ammonia (NH3). Whereas as-synthesized Nb2O5 showed little catalytic activity (< 1% NH3 conversion after 6 h of UV-C irradiation, at room temperature and atmospheric pressure, and under O2 headspace), Nb2O5 treated in basic solution (OH-Nb2O5) was able to photocatalytically degrade NH3 at ca. 9% conversion after six hours, with ca. 70% selectivity to the desired N2, with a first-order rate constant of ca. 12 times higher than the as synthesize catalyst (1.6 × 10–3 min–1 vs. 2.0 × 10–2 min–1). Raman spectroscopic analysis indicated the presence of terminal Nb=O species after base treatment of Nb2O5, implicating them as catalytically active sites. These results underscore how a simple structural modification can significantly affect photocatalytic activity for aqueous ammonia oxidation.
Leveraging mesh modularization to lower the computational cost of localized updates to regional 2D hydrodynamic model outputs
(Taylor & Francis, 2023) Garcia, M.; Juan, A.; Doss-Gollin, J.; Bedient, P.
Hydrodynamic model outputs are used in urban flood risk modelling, flood alert systems, and Monte Carlo hazard assessment. This study tackles an under-explored challenge wherein regular updates to the spatial characteristics of the watershed – due to factors such as changing land use – alter the watershed’s response to rainfall forcing, thus rendering existing model outputs obsolete. Because state-of-the-art hydrodynamic models are computationally expensive, frequently re-running simulations can be costly. Modularization addresses this problem by requiring re-computation only for a limited domain affected by the land use changes. This article introduces a novel approach by modularizing the 2D domain into independent sub-domains before (‘discrete’) and after (‘abstract’) the numerical computations. Using the Hydrologic Engineering Center River Analysis System (HEC-RAS) 2D model of a large urban watershed in Houston as an illustrative and generalizable testbed, we show that both the discrete and abstract modularization closely approximates the results from re-running the entire model. The computational cost of modularization scales linearly with model size for memory requirements as storing the solution on the interior boundaries (discrete) or throughout the domain (abstract) are necessary. This trade-off of memory for computation may facilitate advances in surrogate modelling or Monte Carlo flood risk assessment.

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