UCLA Civil and Environmental Engineering

We appraised factors and processes related to human activities and high water, subsidence, and seismicity. Farming and drainage of peat soils caused subsidence, which contributed to levee internal failures. Subsidence rates decreased with time, but still contributed to levee instability. Modeling changes in seepage and static slope instability suggests an increased probability of failure with decreasing peat thickness. Additional data is needed to assess the spatial and temporal effects of subsidence from peat thinning and deformation. Large-scale, state investment in levee upgrades (> $700 million since the mid-1970s) has increased conformance with applicable standards; however, accounts conflict about corresponding reductions in the number of failures.

Modeling and history suggest that projected increases in high-flow frequency associated with climate change will increase the rate of levee failures. Quantifying this increased threat requires further research. A reappraisal of seismic threats resulted in updated ground motion estimates for multiple faults and earthquake-occurrence frequencies. Estimated ground motions are large enough to induce failure. The immediate seismic threat, liquefaction, is the sudden loss of strength from an increase in the pressure of the pore fluid and the corresponding loss of inter-particle contact forces. However, levees damaged during an earthquake that do not immediately fail may eventually breach. Key sources of uncertainty include occurrence frequencies and magnitudes, localized ground motions, and data for liquefaction potential.

Estimates of the consequences of future levee failure range up to multiple billions of dollars. Analysis of future risks will benefit from improved description of levee upgrades and strength as well as consideration of subsidence, the effects of climate change, and earthquake threats. Levee habitat ecosystem benefits in this highly altered system are few. Better recognition and coordination is needed among the creation of high-value habitat, levee needs, and costs and benefits of levee improvements and breaches.

As part of a One Health approach, the World Health Organization (WHO) has deemed extended beta-lactamases-producing Escherichia coli (ESBL-Ec) as an appropriate proxy for antimicrobial resistance (AMR) in human, animal, and environmental samples. Traditional methods for ESBL-Ec quantification involve a labor-intensive process of membrane filtration, culturing in the presence and absence of antibiotics, and colony confirmation. The emerging modified IDEXX method utilizes IDEXX Colilert-18 test kits, recognized by the USEPA for the enumeration of total coliforms and E. coli in water samples, modified with cefotaxime for measurement of ESBL-Ec in environmental samples. However, this method has yet to be validated for ocean or sewage-contaminated water and has not been compared against the plate-based method with mTEC for surface water. In this study, ESBL-Ec in ocean and river waters of the Tijuana River Estuary were analyzed by three methods: membrane filtration using mTEC plates (as outlined in USEPA Method 1603), membrane filtration using TBX plates (as outlined in the WHO Tricycle Protocol), and Colilert-18 spiked with cefotaxime (Hornsby et al., 2023). Levels of ESBL-Ec were elevated in the Tijuana River Estuary and nearby ocean samples, as high as 2.2 × 106 CFU/100 mL. The modified IDEXX method correlated with membrane filtration methods using selective mTEC (r = 0.967, p < 0.001, n = 14) and TBX (r = 0.95, p < 0.001, n = 14) agars. These results indicate that the modified IDEXX method can be used as a more accessible alternative to the traditional culturing methods as a screening tool for antibiotic resistance in urban aquatic environments. Advantages of the IDEXX-based method including portability, lower Biosafety Level requirements, fewer dilutions to stay within the dynamic range, greater ease of maintaining sterility during analysis, and less required staff training are discussed. Future studies into the validity of the modified IDEXX method compared to qPCR and metagenomic sequencing are needed.

Gases and dissolved black carbon (DBC) formed during pyrolysis of nitrogen-rich feedstock would affect atmospheric and aquatic environments. Yet, the mechanisms driving biomass gas evolution and DBC formation are poorly understood. Using thermogravimetric-Fourier transform infrared spectrometry and two-dimensional correlation spectroscopy, we correlated the temperature-dependent primary noncondensable gas release sequence (H2O → CO2 → HCN, NH3 → CH4 → CO) with specific defunctionalization stages in the order: dehydration, decarboxylation, denitrogenation, demethylation, and decarbonylation. Our results revealed that proteins in feedstock mainly contributed to gas releases, and low-volatile pyrolytic products contributed to DBC. Combining mass difference analysis with Fourier transform ion cyclotron resonance mass spectrometry, we showed that 44-60% of DBC molecular compositions were correlated with primary gas-releasing reactions. Dehydration (-H2O), with lower reaction energy barrier, contributed to DBC formation most at 350 and 450 °C, whereas decarboxylation (-CO2) and deamidization (-HCNO) prevailed in contributing to DBC formation at 550 °C. The aromaticity changes of DBC products formed via gas emissions were deduced. Compared to their precursors, dehydration increased DBC aromaticity, while deamidization reduced the aromaticity of DBC products. These insights on pyrolytic byproducts help predict and tune DBC properties via changing gas formed during biochar production, minimizing their negative environmental impacts.

On 6 February 2023, a devastating earthquake of magnitude 7.7 struck the southeastern part of Turkey, followed by multiple aftershocks. The sequence of earthquakes caused extensive damage in Southern Turkey and Northern Syria. This study evaluates the destructive potential of ground shaking resulting from the Pazarcik mainshock by developing spatial distribution of inelastic demand spectra across the region by using the recorded ground motions. These spectra provide an additional step toward more realistic estimates of the damage potential of ground shaking than the traditional elastic response spectra. Given the recorded ground motions, we also developed simulated ground-motion time series at numerous un-instrumented sites. We used these recorded and simulated motions to estimate ductility demands across the affected area. Constant-ductility spectra were derived using recording stations within 100 km of the fault rupture. The results indicated that in the near-fault area, and for structural periods of 0.5 and 1.0 s, the yield strength demand at a ductility level of 3 exceeded the levels specified in the local seismic design code for a 475-year return period. Our findings demonstrate that with more refinement and efficiency of the ground-motion simulation approach, the development of near-real-time spatial distribution of ductility demand is a promising approach for rapid seismic damage assessment of earthquakes.

Infectious diseases are a major global public health concern. Precise modeling and prediction methods are essential to develop effective strategies for disease control. However, data imbalance and the presence of noise and intensity inhomogeneity make disease detection more challenging. Goal: In this article, a novel infectious disease pattern prediction system is proposed by integrating deterministic and stochastic model benefits with the benefits of the deep learning model. Results: The combined benefits yield improvement in the performance of solution prediction. Moreover, the objective is also to investigate the influence of time delay on infection rates and rates associated with vaccination. Conclusions: In this proposed framework, at first, the global stability at disease free equilibrium is effectively analysed using Routh-Haurwitz criteria and Lyapunov method, and the endemic equilibrium is analysed using non-linear Volterra integral equations in the infectious disease model. Unlike the existing model, emphasis is given to suggesting a model that is capable of investigating stability while considering the effect of vaccination and migration rate. Next, the influence of vaccination on the rate of infection is effectively predicted using an efficient deep learning model by employing the long-term dependencies in sequential data. Thus making the prediction more accurate.

Los Angeles coastal waters are an ecologically important marine habitat and a famed recreational area for tourists. Constant surveillance is essential to ensure compliance with established health standards and to address the persistent water quality challenges in the region. Remotely sensed datasets are increasingly being applied toward improved detection of water quality by augmenting monitoring programs with spatially intensive and accessible data. This study evaluates the potential of satellite remote sensing to augment traditional monitoring by analyzing the relationship between in situ and satellite-derived turbidity data. Field measurements were performed from July 2021 to March 2024 to build synchronous matchup datasets consisting of satellite and field data. Correlation analysis indicated a positive relationship between satellite-derived and field-measured turbidity (R2 = 0.451). Machine learning models were assessed for predictive accuracy, with the random forest model achieving the highest performance (R2 = 0.632), indicating its robustness in modeling complex turbidity patterns. Seasonal trends revealed higher turbidity during wet months, likely due to stormwater runoff from the Ballona Creek watershed. Despite limitations from cloud cover and spatial resolution, the findings suggest that integrating satellite data with machine learning can enhance large-scale, efficient turbidity monitoring in coastal waters.

Mineral crystal formation poses a challenge on surfaces (e.g., heat exchangers, pipes, membranes, etc.) in contact with super-saturated fluids. Applying alternating currents (AC) to such surfaces can prevent surface crystallization under certain conditions. Here, we demonstrate that ion displacement induced by periodic charging and discharging of the electrical double layer (EDL) inhibits both heterogeneous and homogeneous nucleation (and crystal growth) of CaCO₃. Titanium sheets (meant to simulate metallic heat exchanger surfaces) are immersed in super-saturated CaCO₃ solutions with a saturation index >11. We show that at relatively high AC frequencies, incomplete EDL formation leads to an alternating electric field that propagates far into the bulk solution, inducing rapid ion migration that overwhelms the Brownian motion of ions. Electrochemical characterization reveals EDL charging/discharging under AC conditions that greatly inhibits precipitation. Operating at 4 V_pp, 0.1-10 Hz reduces turbidity by over 96% and reduces CaCO₃ coverage on the metal plates by over 92%. Based on electrokinetic and crystallization models, the ion displacement velocity (exceeding the mean Brownian velocity) and displacement length disrupts ion collision and crystal nucleation. Overall, the technique has potential for preventing mineral crystal formation in heat exchangers and many other industrially relevant systems.

As anthropogenic activities continue to introduce various contaminants into the environment, the need for effective monitoring and bioremediation strategies is critical. Fungi, with their diverse enzymatic arsenal, offer promising solutions for the biotransformation of many pollutants. While conventional research reports on ligninolytic, oxidoreductive, and cytochrome P450 (CYP) enzymes, the vast potential of fungi, with approximately 10 345 protein sequences per species, remains largely untapped. This review describes recent advancements in fungal proteomics instruments as well as software and highlights their detoxification mechanisms and biochemical pathways. Additionally, it highlights lesser-known fungal enzymes with potential applications in environmental biotechnology. By reviewing the benefits and challenges associated with proteomics tools, we hope to summarize and promote the studies of fungi and fungal proteins relevant in the environment.