Civil and Environmental Engineering Publications

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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.
A global analysis of coastal flood risk to the petrochemical distribution network in a changing climate
(Elsevier, 2022) Capshaw, Kendall M.; Padgett, Jamie E.
The global petroleum distribution network already faces a significant threat of disruption due to annual coastal flooding of major refining centers, which is expected to further increase with the effects of climate change. This study considers the impacts that sea level rise projections might have on the annual flood risk to coastal refineries, and how regional disruptions propagate across the network. Both the annual regional risk in terms of expected production disruption under a range of climate scenarios, as well as the expected production disruption due to a major flood event impacting refining hubs of high importance are assessed throughout the 21st century. These risks are propagated across the network to model the global impact of coastal flood-induced refining disruptions. This analysis provides insights on the relative risks that different climate scenarios and flood events pose globally, informing potential mitigation and adaptation needs of critical facilities. Due to the highly interconnected nature of the global petroleum product distribution network, these results highlight the need for mitigation considerations for even regions with low domestic production disruption risk due to coastal flood hazards, as disruptions in remote regions can have cascading consequences resulting in significant disruption to petroleum product supply around the world. Furthermore, such results can inform decisions regarding technology transitions or energy diversification in light of the new understanding of climate risks to coastal refineries and the global petroleum distribution network.
Global Increase of Antibiotic Resistance Genes in Conjugative Plasmids
(American Society for Microbiology, 2023) Wang, Xiaolong; Zhang, Hanhui; Long, Xiang; Xu, Ximing; Ren, Hongqiang; Mao, Daqing; Alvarez, Pedro J. J.; Luo, Yi
Antibiotic resistance is propagating worldwide, but the predominant dissemination mechanisms are not fully understood. Here, we report that antibiotic resistance gene (ARG) abundance in conjugative plasmids that are recorded in the National Center for Biotechnology Information (NCBI) RefSeq plasmid database is increasing globally, which is likely a key factor in the propagation of resistance. ARG abundance in plasmids increased by 10-fold on a global scale from the year 2000 to the year 2020 (from 0.25 to 2.93 ARG copies/plasmid), with a more pronounced increase being observed in low-to-middle income countries. This increasing trend of plasmid-borne ARGs was corroborated by bootstrap resampling from each year of the NCBI RefSeq plasmid database. The results of a correlation analysis imply that if antibiotic consumption keeps growing at the current rates, a 2.7-fold global increase in the ARG abundance of clinically relevant plasmids may be reached by 2030. High sequence similarities of clinically relevant, conjugative plasmids that are isolated both from clinics and from the environment raise concerns about the environmental resistome serving as a potential ARG maintenance reservoir that facilitates transmission across these ecological boundaries. IMPORTANCE Antibiotic resistance propagation is a significant concern due to its projected impacts on both global health and the economy. However, global propagation mechanisms are not fully understood, including regional and temporal trends in the abundance of resistance plasmids that facilitate antibiotic resistance gene (ARG) dissemination. This unprecedented study reports that ARG abundance in the conjugative plasmids that are recorded in the National Center for Biotechnology Information (NCBI) database and harbor ARGs is increasing globally with antibiotic consumption, especially in low-to-medium income countries. Through network and comparative genomic analyses, we also found high sequence similarities of clinically relevant conjugative resistance plasmids that were isolated from clinical and environmental sources, suggesting transmission between these ecological boundaries. Therefore, this study informs the One Health perspective to develop effective strategies by which to curtail the propagation of plasmid-borne antibiotic resistance.
Enabling accurate and early detection of recently emerged SARS-CoV-2 variants of concern in wastewater
(Springer Nature, 2023) Sapoval, Nicolae; Liu, Yunxi; Lou, Esther G.; Hopkins, Loren; Ensor, Katherine B.; Schneider, Rebecca; Stadler, Lauren B.; Treangen, Todd J.
As clinical testing declines, wastewater monitoring can provide crucial surveillance on the emergence of SARS-CoV-2 variant of concerns (VoCs) in communities. In this paper we present QuaID, a novel bioinformatics tool for VoC detection based on quasi-unique mutations. The benefits of QuaID are three-fold: (i) provides up to 3-week earlier VoC detection, (ii) accurate VoC detection (>95% precision on simulated benchmarks), and (iii) leverages all mutational signatures (including insertions & deletions).
Evaluation of Radar Precipitation Products and Assessment of the Gauge-Radar Merging Methods in Southeast Texas for Extreme Precipitation Events
(MDPI, 2023) Li, Wenzhao; Jiang, Han; Li, Dongfeng; Bedient, Philip B.; Fang, Zheng N.
Many radar-gauge merging methods have been developed to produce improved rainfall data by leveraging the advantages of gauge and radar observations. Two popular merging methods, Regression Kriging and Bayesian Regression Kriging were utilized and compared in this study to produce hourly rainfall data from gauge networks and multi-source radar datasets. The authors collected, processed, and modeled the gauge and radar rainfall data (Stage IV, MRMS and RTMA radar data) of the two extreme storm events (i.e., Hurricane Harvey in 2017 and Tropical Storm Imelda in 2019) occurring in the coastal area in Southeast Texas with devastating flooding. The analysis of the modeled data on consideration of statistical metrics, physical rationality, and computational expenses, implies that while both methods can effectively improve the radar rainfall data, the Regression Kriging model demonstrates its superior performance over that of the Bayesian Regression Kriging model since the latter is found to be prone to overfitting issues due to the clustered gauge distributions. Moreover, the spatial resolution of rainfall data is found to affect the merging results significantly, where the Bayesian Regression Kriging model works unskillfully when radar rainfall data with a coarser resolution is used. The study recommends the use of high-quality radar data with properly spatial-interpolated gauge data to improve the radar-gauge merging methods. The authors believe that the findings of the study are critical for assisting hazard mitigation and future design improvement.
Editorial: Advanced technologies for industrial wastewater reclamation
(Frontiers Media S.A., 2023) Deng, Shihai; Hu, Jiangyong; Ong, Say-Leong; Li, Qilin; Han, Jie
Impact of a natural disturbance on the performance and microbial communities in a full-scale constructed wetland for industrial wastewater treatment
(Frontiers Media S.A., 2023) Hollstein, Marielle; Comerford, Mattheau; Uhl, Michael; Abel, Michael; Egan, Scott P.; Stadler, Lauren B.
Constructed Wetlands (CWs) are a cost-effective, versatile and sustainable choice for wastewater treatment. In these environments, microbial communities play a significant role in pollutant removal. However, little is known about how microbial communities in full-scale CWs contribute to maintaining water quality or how their dynamics change in response to pulse disturbances such as fire or freezes. Furthermore, few studies have examined the relationship between CW microbial community structure and performance in full-scale industrial operations. We characterized the water-column and leaf-litter layer microbial communities in a 110-acre free water surface CW that provides tertiary wastewater treatment to a plastics manufacturing plant. The CW’s sampling campaign was conducted over a 12-month period that included Winter Storm Uri, a 100-year freeze event. Analysis of 16S rRNA gene amplicon sequences revealed that the bacterial communities experienced a temporal shift. There was also a shift in microbial community structure between the influent and the first segment of the CW. However, no differences in microbial community structure were observed in the second segment of the CW. There was a negative association between microbial community diversity and chlorophyll a, as well as microbial community diversity and total suspended solids (TSS); demonstrating an increase in microbial biodiversity as water quality improved throughout the CW. Six months after the freeze, CW performance in terms of removal of water quality constituents began to return to former removal trends. Yet, there was still a significant difference in microbial community structure within the CW relative to the previous year. This suggests CW functional resilience despite a shift in microbial community structure in the wetland.
Frequency independent damped outrigger systems for multi-mode seismic control of super tall buildings with frequency independent negative stiffness enhancement
(Wiley, 2023) Wang, Meng; Sun, Fei-Fei; Koetaka, Yuji; Chen, Lin; Nagarajaiah, Satish; Du, Xiu-Li
Damped outrigger system is effective for improving energy dissipation for tall buildings. However, conventional damped outrigger (CDO) system with viscous damping has two limitations: (i) its maximum damping ratio cannot be improved when outrigger/column stiffness is inadequate; (ii) different modes achieve their maximum damping ratios at different outrigger damping values, and thus the dampers cannot be optimized to simultaneously reduce vibrations of multiple modes of concern to their minimum. In this paper, a purely frequency-independent negative stiffness damped outrigger (FI-NSDO) system is proposed by combining frequency-independent damper (FID) and negative stiffness device (NSD). The damped outrigger with FID can achieve the maximum damping ratio for all modes as compared to frequency-dependent damper like viscous damper. As the NSD has the features of assisting and enhancing motion and frequency-independence, the utilization of NSD will considerably improve the maximum damping ratios when outrigger/column stiffness is inadequate and maintain the frequency-independent feature of the whole system. Therefore, the FI-NSDO has the capability of simultaneously increasing the damping ratios of all target modes to their maximum values. Analysis in frequency domain and time domain, demonstrate that the proposed FI-NSDO performs better in controlling the multi-mode vibration of seismic responses.
Practical negative stiffness device with viscoelastic damper in parallel or series configuration for cable damping improvement
(Elsevier, 2023) Chen, Lin; Liu, Zhanhang; Zou, Yiqing; Wang, Meng; Nagarajaiah, Satish; Sun, Feifei; Sun, Limin
Negative stiffness mechanism has been found able to improve damping performance of dampers on a stay cable which otherwise is limited by the damper installation distance from a cable end. This study provides a practical negative stiffness device (NSD) with adjustable negative stiffness and experiments are performed to validate the negative stiffness effect. The NSD is then combined with a viscoelastic damper in parallel or series for cable damping improvement. Explicit design formulas are derived for optimal design with a target enhancement effect in damping considering the damper described respectively using the Kelvin model and the linear hysteretic damping model. The formulas are verified by analytical and numerical solutions. Parametric analyses show damping enhancement effects of the NSD and it is found more efficient when combined with a damper in series because both deformation amplitudes of the damper and the NSD are further increased in this configuration. Subsequently, case studies are carried out based on two cables of the Sutong Bridge respectively with a shear-type viscous damper and a high damping rubber damper. The results show that the designed NSD can fulfill practical requirements. Particularly, a 100% increase in damping can be achieved by the presented NSD when combined with the damper installed on a cable of 546.9 m long.
Mesoscale Modeling of Distributed Water Systems Enables Policy Search
(Wiley, 2023) Zhou, Xiangnan; Duenas-Osorio, Leonardo; Doss-Gollin, James; Liu, Lu; Stadler, Lauren; Li, Qilin; Nanosystems Engineering Research Center for Nanotechnology-Enabled Water Treatment
It is widely acknowledged that distributed water systems (DWSs), which integrate distributed water supply and treatment with existing centralized infrastructure, can mitigate challenges to water security from extreme events, climate change, and aged infrastructure. However, it is unclear which are beneficial DWS configurations, i.e., where and at what scale to implement distributed water supply. We develop a mesoscale representation model that approximates DWSs with reduced backbone networks to enable efficient system emulation while preserving key physical realism. Moreover, system emulation allows us to build a multiobjective optimization model for computational policy search that addresses energy utilization and economic impacts. We demonstrate our models on a hypothetical DWS with distributed direct potable reuse (DPR) based on the City of Houston's water and wastewater infrastructure. The backbone DWS with greater than 92% link and node reductions achieves satisfactory approximation of global flows and water pressures, to enable configuration optimization analysis. Results from the optimization model reveal case-specific as well as general opportunities, constraints, and their interactions for DPR allocation. Implementing DPR can be beneficial in areas with high energy intensities of water distribution, considerable local water demands, and commensurate wastewater reuse capacities. The mesoscale modeling approach and the multiobjective optimization model developed in this study can serve as practical decision-support tools for stakeholders to search for alternative DWS options in urban settings.
Impregnation of KOAc on PdAu/SiO2 causes Pd-acetate formation and metal restructuring
(Royal Society of Chemistry, 2023) Jacobs, Hunter P.; Elias, Welman C.; Heck, Kimberly N.; Dean, David P.; Dodson, Justin J.; Zhang, Wenqing; Arredondo, Jacob H.; Breckner, Christian J.; Hong, Kiheon; Botello, Christopher R.; Chen, Laiyuan; Mueller, Sean G.; Alexander, Steven R.; Miller, Jeffrey T.; Wong, Michael S.
Potassium-promoted, oxide-supported PdAu is catalytically active for the gas-phase acetoxylation of ethylene to form vinyl acetate monomer (VAM), in which the potassium improves long-term activity and VAM selectivity. The alkali metal is incorporated into the catalyst via wet impregnation of its salt solution, and it is generally assumed that this common catalyst preparation step has no effect on the catalyst structure. However, in this work, we report evidence to the contrary. We synthesized a silica-supported PdAu (PdAu/SiO2, 8 wt% Pd, 4 wt% Au) model catalyst containing Pd-rich PdAu alloy and pure Au phases. Impregnation with potassium acetate (KOAc) aqueous solution and subsequent drying did not cause XRD-detectible changes to the bimetal structure. However, DRIFTS indicated the presence of Pd3(OAc)6 species, which is correlated to up to 2% Pd loss after washing of the dried KOAc-promoted PdAu/SiO2. Carrying out the impregnation step with an AcOH-only solution and subsequent drying caused significant enlargement of the pure Au grain size and generated a smaller amount of Pd3(OAc)6. During co-impregnation of AcOH and KOAc, grain sizes were enlarged slightly, and substantial amounts of K2Pd2(OAc)6 and Pd3(OAc)6 were detected by DRIFTS and correlated to up to 32% Pd loss after washing. Synchrotron XAS analysis showed that approximately half the Pd atoms were oxidized, corroborating the presence of the Pd-acetate species. These results indicate wet-impregnation-induced metal leaching can occur and be substantial during catalyst preparation.
Compound Climate Risk: Diagnosing Clustered Regional Flooding at Inter-Annual and Longer Time Scales
(MDPI, 2023) Amonkar, Yash; Doss-Gollin, James; Lall, Upmanu
The potential for extreme climate events to cluster in space and time has driven increased interest in understanding and predicting compound climate risks. Through a case study on floods in the Ohio River Basin, we demonstrated that low-frequency climate variability could drive spatial and temporal clustering of the risk of regional climate extremes. Long records of annual maximum streamflow from 24 USGS gauges were used to explore the regional spatiotemporal patterns of flooding and their associated large-scale climate modes. We found that the dominant time scales of flood risk in this basin were in the interannual (6–7 years), decadal (11–13 years), and secular bands and that different sub-regions within the Ohio River Basin responded differently to large-scale forcing. We showed that the leading modes of streamflow variability were associated with ENSO and secular trends. The low-frequency climate modes translated into epochs of increased and decreased flood risk with multiple extreme floods or the absence of extreme floods, thus informing the nature of compound climate-induced flood risk. A notable finding is that the secular trend was associated with an east-to-west shift in the flood incidence and the associated storm track. This is consistent with some expectations of climate change projections.
Case studies of multihazard damage: Investigation of the interaction of Hurricane Maria and the January 2020 earthquake sequence in Puerto Rico
(Frontiers, 2023) Hain, Alexandra; Zaghi, Arash E.; Padgett, Jamie E.; Tafur, Anibal
This paper is motivated by the unique findings and observations from reconnaissance visits after the earthquake series in Puerto Rico in January 2020. It aims to discuss the potential interactions of Hurricane Maria and 2020 earthquake series and the considerations they underscore for future field reconnaissance missions. Traditionally, post-disaster damage assessment activities focus on one hazard and overlook the potentially cascading effects of multiple hazards on structures and infrastructure. This paper provides case studies showing the possible interaction of multiple hazards and their cascading effects observed in Puerto Rico. Infrastructure surveyed includes port facilities, buildings (particularly historical structures), and bridge structures. The data collected during the reconnaissance missions reveal how the impacts of Hurricane Maria, along with infrastructure aging and delayed repair and recovery activities, may have influenced the damage level and failure modes observed during the earthquake sequence a few years after. These case studies illustrate the nature of multihazard interactions and how these effects should be documented during post-disaster assessments. Beyond the insights gained from the case studies illustrated in this paper, the field survey instrument is provided as a basis for future reconnaissance studies, and the full set of reconnaissance data collected are published on the NSF funded NHERI DesignSafe cyberinfrastructure. As a result, this work not only provides data from Puerto Rico that can inform future damage and recovery modeling efforts, but also offers survey instruments and a field data collection process that is particularly tailored to cases where multihazard effects are at play.
Snapshot ARG Removal Rates across Wastewater Treatment Plants Are Not Representative Due to Diurnal Variations
(American Chemical Society, 2023) Lou, Esther G.; Ali, Priyanka; Lu, Karen; Kalvapalle, Prashant; Stadler, Lauren B.
To evaluate the threat of the environmental dissemination of antibiotic resistance associated with wastewater treatment plants (WWTPs), the removal efficiency of antibiotic resistance genes (ARGs) during wastewater treatment needs to be assessed. The sample collection strategy is one factor that is often overlooked in study design and most studies on ARGs in wastewater perform grab sampling. Here, we hypothesized that wastewater sampling (i.e., grab and composite sampling) influences the observed ARG concentrations and calculated removal rates across WWTPs. We compared the removal rates calculated based on the two different sampling methods for several genes, including some clinically relevant ARGs (blaNDM-1, blaOXA-1, MCR-1, MCR-5, MCR-10, and qnrA). We conducted summer and winter 24 h sampling campaigns where grab samples were collected every 2 h from the influent, secondary effluent, and final effluent. The snapshot removal rate of each target gene calculated based on the 12 grab samples fluctuated by 0.5–1.6 log in the winter and 0.9–2.7 log in the summer, indicating diurnal variation. Overall, for each target gene, the removal rates calculated based on 24 h composite samples were approximately equal to the median of the 12 snapshot removal rates. Our study confirms the importance of using composite samples to monitor ARGs in wastewater.

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