Scholarly Works, Biological Systems Engineering

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Analyzing multiple-source water usage patterns and affordability in rural central Appalachia
Dudzinski, Emerald; Ellis, Kimberly P.; Krometis, Leigh-Anne H.; Albi, Kate; Cohen, Alasdair (2024-07-18)
Nearly 500,000 American households lack complete plumbing, and more than 21 million Americans are reliant on public drinking water systems with at least one annual health-based drinking water violation. Rural, low-income, and minority communities are significantly more likely to be burdened with unavailable or unsafe in-home drinking water. Lack of access and distrust of the perceived quality of municipally supplied water are leading an increasing number of Americans to rely instead on less regulated, more expensive, and potentially environmentally detrimental water sources, such as roadside springs and bottled water. Previous research studies have stressed the importance of considering the economic burden of all water related expenditures including financial and non-financial water related costs; however, past examinations of water costs have primarily focused on municipal water supplies. We propose an economic model to consider the full economic burden associated with multiple-source water use by incorporating both direct costs (e.g., utility bills, well maintenance, bottled water purchase, payments for water hauling/delivery) and indirect water-related expenditures (e.g., transportation costs to gather water, productivity lost due to time spent collecting). Using data gathered from household surveys along with the economic model, this study estimates the economic burden from two case studies in rural Central Appalachia with persistent water quality concerns: (1) McDowell County, WV (n=15) and (2) Letcher and Harlan Counties, KY (n=9). All surveyed households (n=24) rely on multiple-source water to meet their needs, frequently citing their perception of unsafe in-home tap water. Bottled water was the most common choice for drinking water in both settings (92%, n=24), though roadside spring use was also prevalent in McDowell County, WV (53%, n=15). The results show that multiple-water source use is associated with a large economic burden. Households reliant primarily on bottled water as their drinking water source spent 12.3% (McDowell County, WV) and 5.6% (Letcher and Harlan Counties, KY) of their respective county’s median household income (MHI) on water related expenditures. Households reliant primarily on roadside springs as their drinking water source spent 11.8% (McDowell County, WV) of MHI on water related expenditures. Hence, the vast majority of participating households (92%, n=24) spend above the US water affordability threshold of 2% MHI. The application of this economic model highlights major water affordability concerns in water insecure Appalachian communities and provides a foundation for future studies and enhancements.
Effectiveness of stormwater control measures in protecting stream channel stability
Towsif Khan, Sami; Wynn-Thompson, Theresa; Sample, David; Al-Smadi, Mohammad; Shahed Behrouz, Mina; Miller, Andrew J. (2024-04-23)
While research on the hydrologic impact of different types of stormwater control measures (SCMs) is extensive, little research exists linking urbanization, widespread implementation of SCMs and channel stability in headwater streams. This study evaluated whether the unified stormwater sizing criteria (USSC) regulations in the state of Maryland, USA, which require the use of both end-of-pipe and distributed, smallscale SCMs, protect channel stability. To achieve this goal, a coupled hierarchical modelling approach utilizing the Storm Water Management Model (SWMM) and the Hydrologic Engineering Center River Analysis System 6.3 (HEC-RAS) was developed to predict changes in streamflow and sediment transport dynamics in a first-order gravel-bed, riffle-pool channel. Storm event discretization revealed that 88% of observed storm events during the 16 years (2004–2020) had durations less than 18 h and that the greatest peak flows resulted from storm events with durations less than 24 h. HEC-RAS simulation results also showed that both channel degradation and aggradation, as high as 1.2 m, will likely occur due to regulations which require the use of 24 h duration design storms with a target stormwater detention time rather than bed material sediment transport limits. Overall, this study provides valuable insights into the complex interactions between SCM practises, flow regimes and sediment transport dynamics in heavily urbanized watersheds. It is recommended that SCMs be designed using a continuous simulation model with at least 10 years of continuous rainfall data. Furthermore, to protect channel stability, the SCM design goal should focus on maintaining pre-development sediment transport regimes across a range of flows.
Attomolar-sensitive milk fever sensor using 3D-printed multiplex sensing structures
Kachouei, Matin Ataei; Parkulo, Jacob; Gerrard, Samuel D.; Fernandes, Tatiane; Osorio, Johan S.; Ali, Md. Azahar (Springer Nature, 2025-01-02)
The diagnosis of milk fever or hypocalcemia in lactating cows has a significant economic impact on the dairy industry. It is challenging to identify asymptomatic subclinical hypocalcemia (SCH) in transition dairy cows. Monitoring subclinical hypocalcemia in milk samples can expedite treatment and improve the health, productivity, and welfare of dairy cows. In this study, an attomolar-sensitive sensor is developed using extrusion-based 3D-printed sensing structures to detect the ratio of ionized calcium to phosphate levels in milk samples. The unique geometries of the lateral structure of 3D-printed sensors, along with the wrinkled surfaces, provide a limit of detection down to the attomole (138 am) concentration of the target analyte. The calcium-to-phosphate ratio in milk samples not only provides early disease indications but also enables on-site testing. This highly selective test is validated using real milk and blood samples, and the results are compared with those of commercial meters. This fast response (~10 s) low-cost sensor opens a promising tool for the farm-side diagnostic of dairy cows that can promote best practice management of dairy cows.
Stream restoration that allows for self-adjustment can increase channel-floodplain connectivity
Christensen, Nicholas D.; Prior, Elizabeth M.; Czuba, Jonathan A.; Hession, W. Cully (2024-02-14)
Streams are often “restored” to reduce sediment loading using one or a combination of practices such as livestock exclusion, riparian plantings, and/or bank reshaping and stabilization. Direct comparisons of how these methods affect stream processes, including channel-floodplain connectivity, over time are essential to informing restoration design. (Channel-floodplain connectivity is the ability of a stream to exchange water, sediment, and nutrients with its floodplain at high flows.) To investigate the impact these stream restoration practices have had on channel-floodplain connectivity, we developed a 2-D HEC-RAS hydraulic model for 3 restoration treatments along an urban and agriculturally impacted stream in southwest Virginia, United States. All 3 treatments excluded cattle in 2009. The farthest upstream treatment, Treatment 1, had no other intervention while the other two, Treatments 2 and 3, were regraded and stabilized, then replanted with native species (completed May 2010). The overhanging banks of Treatment 2 were regraded to a slope of 3:1, while those of Treatment 3 had a flat inset floodplain cut into the bank before sloping the banks at 3:1. During the 11-year monitoring timeline, prior work showed the streambanks in Treatment 1 migrated through both outer bank erosion and inner bank deposition with the autogenic creation of inset floodplains, while Treatments 2 and 3 had minimal bank adjustment. The adjusted geometry of Treatment 1 provided higher floodplain volume, channel-floodplain exchange flows, and flow moving across the floodplain than Treatments 2 and 3. Treatment 3 showed some metrics of higher connectivity than Treatment 2, but there was not uniform agreement between metrics. While the hydraulic analysis indicates a higher channelfloodplain connectivity in Treatment 1, active management of Treatments 2 and 3 has reduced the bank erosion rate and accelerated the riparian forest regrowth, providing other benefits including increased shading, wood supply, and vegetation diversity.
Lidar DEM and Computational Mesh Grid Resolutions Modify Roughness in 2D Hydrodynamic Models
Prior, Elizabeth M.; Michaelson, Nathan; Czuba, Jonathan A.; Pingel, Thomas J.; Thomas, Valerie A.; Hession, W. Cully (American Geophysical Union, 2024-07-07)
Topography and the computational mesh grid are fundamental inputs to all two-dimensional (2D) hydrodynamic models, however their resolutions are often arbitrarily selected based on data availability. With the increasing use of drone technology, the end user can collect topographic data down to centimeter-scale resolution. With this advancement comes the responsibility of choosing a resolution. In this study, we investigated how the choice of mesh grid and digital elevation model (DEM) resolutions affect 2D hydrodynamic modeling results, specifically water depths, velocities, and inundation extent. We made pairwise comparisons between simulations from a 2D HEC-RAS model with varying mesh grid resolutions (1 and 2 m) and drone-based lidar DEM resolutions (0.1, 0.25, 0.5, 1, and 2 m) over a 1.5 km reach of Stroubles Creek in Blacksburg, Virginia. The model was rerun for up to ±4% change in floodplain roughness to determine how the DEM and mesh grid changes relate to an equivalent change in roughness. We found that the modeled differences from resolution change were equivalent to altering floodplain roughness by up to 12% for depths and 44% for velocities. The largest differences in velocity were concentrated at the channel-floodplain interface, whereas differences in depth occurred laterally throughout the floodplain and were not correlated with lidar ground point density. We also found that the inundation boundary is dependent on the DEM resolution. Our results suggest that modelers should carefully consider what resolution best represents the terrain while also resolving important riparian topographic features.
Cancer detection in dogs using rapid Raman molecular urinalysis
Robertson, John L.; Dervisis, Nikolaos G.; Rossmeisl, John H. Jr.; Nightengale, Marlie; Fields, Daniel; Dedrick, Cameron; Ngo, Lacey; Issa, Amr Sayed; Guruli, Georgi; Orlando, Giuseppe; Senger, Ryan S. (Frontiers, 2024-02-07)
Introduction: The presence of cancer in dogs was detected by Raman spectroscopy of urine samples and chemometric analysis of spectroscopic data. The procedure created a multimolecular spectral fingerprint with hundreds of features related directly to the chemical composition of the urine specimen. These were then used to detect the broad presence of cancer in dog urine as well as the specific presence of lymphoma, urothelial carcinoma, osteosarcoma, and mast cell tumor. Methods: Urine samples were collected via voiding, cystocentesis, or catheterization from 89 dogs with no history or evidence of neoplastic disease, 100 dogs diagnosed with cancer, and 16 dogs diagnosed with non-neoplastic urinary tract or renal disease. Raman spectra were obtained of the unprocessed bulk liquid urine samples and were analyzed by ISREA, principal component analysis (PCA), and discriminant analysis of principal components (DAPC) were applied using the Rametrix®Toolbox software. Results and discussion: The procedure identified a spectral fingerprint for cancer in canine urine, resulting in a urine screening test with 92.7% overall accuracy for a cancer vs. cancer-free designation. The urine screen performed with 94.0% sensitivity, 90.5% specificity, 94.5% positive predictive value (PPV), 89.6% negative predictive value (NPV), 9.9 positive likelihood ratio (LR+), and 0.067 negative likelihood ratio (LR-). Raman bands responsible for discerning cancer were extracted from the analysis and biomolecular associations were obtained. The urine screen was more effective in distinguishing urothelial carcinoma from the other cancers mentioned above. Detection and classification of cancer in dogs using a simple, non-invasive, rapid urine screen (as compared to liquid biopsies using peripheral blood samples) is a critical advancement in case management and treatment, especially in breeds predisposed to specific types of cancer.
A novel insight on input variable and time lag selection in daily streamflow forecasting using deep learning models
Khatun, Amina; Nisha, M. N.; Chatterjee, Siddharth; Sridhar, Venkataramana (Elsevier, 2024-08)
This study investigates the feasibility of using hybrid models namely Convolutional Neural Network (CNN)-Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN)-Gated Recurrent Unit (GRU), for short-to-medium range streamflow forecasting in the Mahanadi River basin in India. The performance of these hybrid models is compared with that of standalone models. It investigates the impact of selected parameters and associated time lags on the model performance and offers valuable insights into the use of hybrid models for runoff simulation. The hybrid CNN-LSTM model proves to be robust in capturing the overall time series and the typical high peak flows in both the correlation-based and constant lag cases. Also, the upstream discharges play a significant role in improving the streamflow forecasting. Furthermore, the consideration of all input variables with a constant time lag equal to the basin lag time may yield better flood forecasts, even in cases where computational resources are limited.
Real-Time Flood Forecasting using an Integrated Hydrologic and Hydraulic Model for the Vamsadhara and Nagavali basins, Eastern India
Rao, G. Venkata; Nagireddy, Nageswara Reddy; Keesara, Venkata Reddy; Sridhar, Venkataramana; Srinivasan, Raghavan; Umamahesh, N. V.; Pratap, Deva (Springer, 2024-02-23)
Due to recent rainfall extremes and tropical cyclones that form over the Bay of Bengal during the pre- and post-monsoon seasons, the Nagavali and Vamsadhara basins in India experience frequent floods, causing significant loss of human life and damage to agricultural lands and infrastructure. This study provides an integrated hydrologic and hydraulic modeling system that is based on the Soil and Water Assessment Tool model and the 2-Dimensional Hydrological Engineering Centre-River Analysis System, which simulates floods using Global Forecasting System rainfall forecasts with a 48-h lead time. The integrated model was used to simulate the streamflow, flood area extent, and depth for the historical flood events (i.e., 1991–2018) with peak discharges of 1200 m3/s in the Nagavali basin and 1360 m3/s in the Vamsadhara basin. The integrated model predicted flood inundation depths that were in good agreement with observed inundation depths provided by the Central Water Commission. The inundation maps generated by the integrated modeling system with a 48-h lead time for tropical cyclone Titli demonstrated an accuracy of more than 75%. The insights gained from this study will help the public and government agencies make better decisions and deal with floods.
Application of Various Hydrological Modeling Techniques and Methods in River Basin Management
Srivastava, Ankur; Sridhar, Venkataramana; Kumari, Nikul (MDPI, 2025-01-01)
The techniques of hydrological modeling have greatly improved in the recent past and have been instrumental in the management of river basins [...]
Evidence of horizontal gene transfer and environmental selection impacting antibiotic resistance evolution in soil-dwelling Listeria
Goh, Ying-Xian; Anupoju, Sai Manohar Balu; Nguyen, Anthony; Zhang, Hailong; Ponder, Monica A.; Krometis, Leigh-Anne H.; Pruden, Amy; Liao, Jingqiu (Nature Research, 2024-11-19)
Soil is an important reservoir of antibiotic resistance genes (ARGs) and understanding how corresponding environmental changes influence their emergence, evolution, and spread is crucial. The soil-dwelling bacterial genus Listeria, including L. monocytogenes, the causative agent of listeriosis, serves as a keymodel for establishing this understanding. Here, we characterize ARGs in 594 genomes representing 19 Listeria species that we previously isolated from soils in natural environments across the United States. Among the five putatively functional ARGs identified, lin,which confers resistance to lincomycin, is the most prevalent, followed by mprF, sul, fosX, and norB. ARGs are predominantly found in Listeria sensu stricto species, with those more closely related to L. monocytogenes tending to harbor more ARGs. Notably, phylogenetic and recombination analyses provide evidence of recent horizontal gene transfer (HGT) in all five ARGs within and/or across species, likelymediated by transformation rather than conjugation and transduction. In addition, the richness and genetic divergence of ARGs are associated with environmental conditions, particularly soil properties (e.g., aluminum and magnesium) and surrounding land use patterns (e.g., forest coverage). Collectively, our data suggest that recent HGT and environmental selection play a vital role in the acquisition and diversification of bacterial ARGs in natural environments.
The State of the Science and Practice of Stream Restoration in the Chesapeake: Lessons Learned to Inform Better Implementation, Assessment, and Outcomes
Noe, Gregory; Law, Neely; Berg, Joe; Filoso, Solange; Drescher, Sadie; Fraley-McNeal, Lisa; Hayes, Ben; Mayer, Paul; Ruck, Chris; Stack, Bill; Starr, Rich; Stranko, Scott; Thompson, Theresa M. (2024-11-04)
Physical Cell Disruption Technologies for Intracellular Compound Extraction from Microorganisms
Zhao, Fujunzhu; Wang, Zhiwu; Huang, Haibo (MDPI, 2024-09-24)
This review focuses on the physical disruption techniques in extracting intracellular compounds, a critical step that significantly impacts yield and purity. Traditional chemical extraction methods, though long-established, face challenges related to cost and environmental sustainability. In response to these limitations, this paper highlights the growing shift towards physical disruption methods—high-pressure homogenization, ultrasonication, milling, and pulsed electric fields—as promising alternatives. These methods are applicable across various cell types, including bacteria, yeast, and algae. Physical disruption techniques achieve relatively high yields without degrading the bioactivity of the compounds. These techniques, utilizing physical forces to break cell membranes, offer promising extraction efficiency, with reduced environmental impacts, making them attractive options for sustainable and effective intracellular compound extraction. High-pressure homogenization is particularly effective for large-scale extracting of bioactive compounds from cultivated microbial cells. Ultrasonication is well-suited for small to medium-scale applications, especially for extracting heat-sensitive compounds. Milling is advantageous for tough-walled cells, while pulsed electric field offers gentle, non-thermal, and highly selective extraction. This review compares the advantages and limitations of each method, emphasizing its potential for recovering various intracellular compounds. Additionally, it identifies key research challenges that need to be addressed to advance the field of physical extractions.
Ajna: A Wearable Shared Perception System for Extreme Sensemaking
Wilchek, Matthew; Luther, Kurt; Batarseh, Feras A. (ACM, 2024)
This paper introduces the design and prototype of Ajna, a wearable shared perception system for supporting extreme sensemaking in emergency scenarios. Ajna addresses technical challenges in Augmented Reality (AR) devices, specifically the limitations of depth sensors and cameras. These limitations confine object detection to close proximity and hinder perception beyond immediate surroundings, through obstructions, or across different structural levels, impacting collaborative use. It harnesses the Inertial Measurement Unit (IMU) in AR devices to measure users? relative distances from a set physical point, enabling object detection sharing among multiple users across obstacles like walls and over distances. We tested Ajna's effectiveness in a controlled study with 15 participants simulating emergency situations in a multi-story building. We found that Ajna improved object detection, location awareness, and situational awareness, and reduced search times by 15%. Ajna's performance in simulated environments highlights the potential of artificial intelligence (AI) to enhance sensemaking in critical situations, offering insights for law enforcement, search and rescue, and infrastructure management.
Studying the Relationship between Satellite-Derived Evapotranspiration and Crop Yield: A Case Study of the Cauvery River Basin
Anand, Anish; Keesara, Venkata Reddy; Sridhar, Venkataramana (MDPI, 2024-08-05)
Satellite-derived evapotranspiration (ETa) products serve global applications, including drought monitoring and food security assessment. This study examines the applicability of ETa data from two distinct sources, aiming to analyze its correlation with crop yield (rice, maize, barley, soybean). Given the critical role of crop yield in economic and food security contexts, monthly and yearly satellite-derived ETa data were assessed for decision-makers, particularly in drought-prone and food-insecure regions. Utilizing QGIS, zonal statistics operations and time series graphs were employed to compare ETa with crop yield and ET anomaly. Data processing involved converting NRSC daily data to monthly and extracting single-pixel ET data using R Studio. Results reveal USGSFEWS as a more reliable ETa source, offering better accuracy and data continuity, especially during monsoon seasons. However, the correlation between crop yield and ETa ranged from 12% to 35%, while with ET anomaly, it ranged from 35% to 55%. Enhanced collection of satellite-based ETa and crop-yield data is imperative for informed decision-making in these regions. Despite limitations, ETa can moderately guide decisions regarding crop-yield management.
Impact Assessment of Nematode Infestation on Soybean Crop Production Using Aerial Multispectral Imagery and Machine Learning
Jjagwe, Pius; Chandel, Abhilash K.; Langston, David B. (MDPI, 2024-06-24)
Accurate and prompt estimation of geospatial soybean yield (SY) is critical for the producers to determine key factors influencing crop growth for improved precision management decisions. This study aims to quantify the impacts of soybean cyst nematode (SCN) infestation on soybean production and the yield of susceptible and resistant seed varieties. Susceptible varieties showed lower yield and crop vigor recovery, and high SCN population (20 to 1080) compared to resistant varieties (SCN populations: 0 to 340). High-resolution (1.3 cm/pixel) aerial multispectral imagery showed the blue band reflectance (r = 0.58) and Green Normalized Difference Vegetation Index (GNDVI, r = −0.6) have the best correlation with the SCN populations. While GDNVI, Green Chlorophyll Index (GCI), and Normalized Difference Red Edge Index (NDRE) were the best differentiators of plant vigor and had the highest correlation with SY (r = 0.59–0.75). Reflectance (REF) and VIs were then used for SY estimation using two statistical and four machine learning (ML) models at 10 different train–test data split ratios (50:50–95:5). The ML models and train–test data split ratio had significant impacts on SY estimation accuracy. Random forest (RF) was the best and consistently performing model (r: 0.84–0.97, rRMSE: 8.72–20%), while a higher train–test split ratio lowered the performances of the ML models. The 95:5 train–test ratio showed the best performance across all the models, which may be a suitable ratio for modeling over smaller or medium-sized datasets. Such insights derived using high spatial resolution data can be utilized to implement precision crop protective operations for enhanced soybean yield and productivity.
Thermal-RGB Imagery and Computer Vision for Water Stress Identification of Okra (Abelmoschus esculentus L.)
Rajwade, Yogesh A.; Chandel, Narendra S.; Chandel, Abhilash K.; Singh, Satish Kumar; Dubey, Kumkum; Subeesh, A.; Chaudhary, V. P.; Ramanna Rao, K. V.; Manjhi, Monika (MDPI, 2024-06-27)
Crop canopy temperature has proven beneficial for qualitative and quantitative assessment of plants' biotic and abiotic stresses. In this two-year study, water stress identification in okra crops was evaluated using thermal-RGB imaging and AI approaches. Experimental trials were developed for two irrigation types, sprinkler and flood, and four deficit treatment levels (100, 50, 75, and 25% crop evapotranspiration), replicated thrice. A total of 3200 thermal and RGB images acquired from different crop stages were processed using convolutional neural network architecture-based deep learning models (1) ResNet-50 and (2) MobileNetV2. On evaluation, the accuracy of water stress identification was higher with thermal imagery inputs (87.9% and 84.3%) compared to RGB imagery (78.6% and 74.1%) with ResNet-50 and MobileNetV2 models, respectively. In addition, irrigation treatment and levels had significant impact on yield and crop water use efficiency; the maximum yield of 10,666 kg ha⁻¹ and crop water use efficiency of 1.16 kg m⁻³ was recorded for flood irrigation, while 9876 kg ha⁻¹ and 1.24 kg m⁻³ were observed for sprinkler irrigation at 100% irrigation level. Developments and observations from this study not only suggest applications of thermal-RGB imagery with AI for water stress quantification but also developing and deploying automated irrigation systems for higher crop water use efficiency.
Valorization of Seafood Waste for Food Packaging Development
Zhan, Zhijing; Feng, Yiming; Zhao, Jikai; Qiao, Mingyu; Jin, Qing (MDPI, 2024-07-03)
Packaging plays a crucial role in protecting food by providing excellent mechanical properties as well as effectively blocking water vapor, oxygen, oil, and other contaminants. The low degradation of widely used petroleum-based plastics leads to environmental pollution and poses health risks. This has drawn interest in renewable biopolymers as sustainable alternatives. The seafood industry generates significant waste that is rich in bioactive substances like chitin, chitosan, gelatins, and alginate, which can replace synthetic polymers in food packaging. Although biopolymers offer biodegradability, biocompatibility, and non-toxicity, their films often lack mechanical and barrier properties compared with synthetic polymer films. This comprehensive review discusses the chemical structure, characteristics, and extraction methods of biopolymers derived from seafood waste and their usage in the packaging area as reinforcement or base materials to guide researchers toward successful plastics replacement and commercialization. Our review highlights recent advancements in improving the thermal durability, mechanical strength, and barrier properties of seafood waste-derived packaging, explores the mechanisms behind these improvements, and briefly mentions the antimicrobial activities and mechanisms gained from these biopolymers. In addition, the remaining challenges and future directions for using seafood waste-derived biopolymers for packaging are discussed. This review aims to guide ongoing efforts to develop seafood waste-derived biopolymer films that can ultimately replace traditional plastic packaging.
Assessing the Efficacy of Stream Restoration and SCM Retrofitting for Channel Stability in Urbanized Catchments
Towsif Khan, Sami; Thompson, Theresa M.; Sample, David J. (2024-05-29)
The hydrological benefits of catchment-scale implementation of stormwater control measures (SCMs) in mitigating the adverse effects of urbanization are well established. Nevertheless, recent studies indicate that Maryland's stormwater regulations, mandating the combined use of distributed and end-of-pipe SCMs, fall short in maintaining channel stability, despite their effectiveness in reducing runoff from impervious surfaces. The study objective was to evaluate the incremental impact of SCM retrofits and stream restoration on channel stability in a small, urbanized catchment (0.9 sq. km) in Montgomery County, Maryland, USA. This study employed a refined, well-calibrated, coupled hierarchical modeling approach, integrating a watershed-scale Storm Water Management Model (SWMM) with the Hydrologic Engineering Centers River Analysis System (HEC-RAS). A comprehensive methodology was developed using the calibrated SWMM and HEC-RAS models. The modeling results revealed that only retrofitting SCMs with multi-stage outlet structures designed to maintain the pre-development mobility of bed particles may not effectively reduce channel degradation. Conversely, stream restoration practices, including the removal of legacy sediments from the floodplain, significantly mitigated channel instability. Notably, the combination of SCM retrofitting, aimed at matching the sediment transport capacity of the predevelopment state, and stream restoration practices did not yield better results compared to stream restoration alone. This finding suggests that for streams impacted by legacy sediments, floodplain restoration alone might suffice to achieve channel stability, eliminating the need to retrofit SCMs designed under existing regulations.
Cost Comparison for Emerging Technologies to Haul Round Bales for the Biorefinery Industry
Cundiff, John S.; Grisso, Robert D.; Webb, Erin G. (MDPI, 2024-05-30)
Between 20 and 30% of the feedstock delivered cost is the highway hauling. In order to achieve maximum truck productivity, and thus minimize hauling cost, the hauling technology needs to provide for rapid loading and unloading. Three prototype technologies have been proposed to address the hauling issue. The first was developed by Stinger to secure a load of large rectangular bales, and it is identified as the Advanced Load Securing System (ALSS). For this study, the ALSS technology is applied on two trailers hooked in tandem (ALSS-2) loaded with 20 bales each. The second technology (Cable), is a cable system for securing a load of bales (round or rectangular) on a standard flatbed trailer. With the third technology (Rack), bales are loaded into a 20-bale rack at an SSL, and this rack is unloaded as a unit at the biorefinery. Bales remain in the rack until processed, thus avoiding single-bale handling at the receiving facility. A cost comparison, which begins with bales in single-layer ambient storage in SSLs and ends with bales in single file on a conveyor into the biorefinery, was done for the three hauling technologies paired with three load-out technologies. Cost for the nine options ranged from 48.56 USD/Mg (11 load-outs, Cable hauling) to 34.90 USD/Mg (8 loads-outs, ALSS-2 hauling). The most significant cost issue was the reduction in truck cost; 25.54 USD/Mg (20 trucks, Cable) and 15.15 USD/Mg (10 trucks, Rack).
Do Maryland's Stormwater Management Regulations Protect Channel Stability?
Thompson, Theresa M.; Sample, David J.; Al-Samdi, Mohammad; Towsif Khan, Sami; Shahed Behrouz, Mina; Miller, Andrew; Butcher, Jon (2024-06-20)
Webinar for the Maryland Stream Restoration Association. 84 participants

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