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arXiv 2022年

作者： Fan, Chao Chien, Yu-Heng Mostafavi, Ali Department of Civil and Environmental Engineering Texas A&M University College StationTX77843 United States Department of Industrial and Systems Engineering Texas A&M University College StationTX77843 United States

Ambient exposure to fine particulate matters of diameters smaller than 2.5µm (PM2.5) has been identified as one critical cause for respiratory disease. Disparities in exposure to PM2.5 among income groups at individual residences are known to exist and are easy to calculate. Existing approaches for exposure assessment, however, do not capture the exposure implied by the dynamic mobility of city dwellers that accounts for a large proportion of the exposure outside homes. To overcome the challenge of gauging the exposure to PM2.5 for city dwellers, we analyzed billions of anonymized and privacy-enhanced location-based data generated by mobile phone users in Harris County, Texas, to characterize the mobility patterns of the populations and associated exposure. We introduce the metric for exposure extent based on the time people spent at places with the air pollutant and examine the disparities in mobility-based exposure across income groups. Our results show that PM2.5 emissions disproportionately expose low-income populations due to their mobility activities. People with higher-than-average income are exposed to lower levels of PM2.5 emissions. These disparities in mobility-based exposure are the result of frequent visits of low-income people to the industrial sectors of urban areas with high PM2.5 emissions, and the larger mobility scale of these people for life needs. The results inform about environmental justice and public health strategies, not only to reduce the overall PM2.5 exposure but also to mitigate the disproportional impacts on low-income populations. The findings also suggest that an integration of extensive fine-scale population mobility and pollution emissions data can unveil new insights into inequality in air pollution exposures at the urban scale. Copyright © 2022, The Authors. All rights reserved.

关键词： Population statistics

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Prediction of Mechanical Response of Hexagonal Honeycomb Sps Blast Wall Under Explosive Loading: In-Depth Review and Empirical Formula

SSRN

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SSRN 2023年

作者： Kim, Do Kyun Looi, Chee Kean Cho, Nak Kyun Topa, Ameen Department of Naval Architecture and Ocean Engineering Seoul National University Seoul Korea Republic of Institute of Engineering Research Research Institute of Marine Systems Engineering Seoul National University Seoul Korea Republic of Ocean and Ship Research Group Department of Civil and Environmental Engineering Petronas University of Technology Perak Seri Iskandar Malaysia Department of Maritime Technology Universiti Malaysia Terengganu Kuala Nerus Malaysia Seoul Korea Republic of

Offshore operations consistently grapple with health, safety, and environmental (HSE) challenges, despite comprehensive prevention strategies. Though explosions on offshore structures are rare, their devastating impact cannot be understated. Blast walls, functioning as pivotal passive barriers, play a crucial role in protecting the topsides of these structures by dissipating explosive energy. Sandwich panel structures (SPS), with their lightweight attributes, enhanced bending rigidity, and exceptional energy absorption, present themselves as a cost-effective countermeasure. This study introduces a groundbreaking design guideline, anchored in the LS-Dyna nonlinear finite-element method (NLFEM), tailored for hexagonal SPS offshore blast walls under explosive strains. Our rigorous numerical simulations, encompassing 450 varied scenarios, meticulously evaluated an array of geometric configurations and explosive strengths (spanning from 1kg to 3kg of TNT). From these simulations, crucial parameters affecting maximum mid-span plate deflection were discerned. In addition to these simulations, our research offers in-depth technical reviews, shedding light on the nuances of the subject. An empirical formula was subsequently formulated to anticipate this deflection, with its efficacy being corroborated by FE simulation outcomes. This body of work provides invaluable guidance for refining early-stage offshore blast wall design. © 2023, The Authors. All rights reserved.

关键词： Explosives

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Using Statistical Models to Detect Occupancy in Buildings through Monitoring VOC, CO2 and other environmental Factors

arXiv

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arXiv 2022年

作者： Varnosfaderani, Mahsa Pahlavikhah Heydarian, Arsalan Jazizadeh, Farrokh Link Lab. Department of Engineering Systems and Environment University of Virginia CharlottesvilleVA22904 United States Department of Civil and Environmental Engineering Virginia Polytechnic Institute State University BlacksburgVA24061 United States

Dynamic models of occupancy patterns have shown to be effective in optimizing building-systems operations. Previous research has relied on CO2 sensors and vision-based techniques to determine occupancy patterns. Vision-based techniques provide highly accurate information;however, they are very intrusive. Therefore, motion or CO2 sensors are more widely adopted worldwide. Volatile Organic Compounds (VOCs) are another pollutant originating from the occupants. However, a limited number of studies have evaluated the impact of occupants on VOC level. In this paper, continuous measurements of CO2, VOC, light, temperature, and humidity were recorded in a 17,000 sqft open office space for around four months. Using different statistical models (e.g., SVM, K-Nearest Neighbors, and Random Forest) we evaluated which combination of environmental factors provide more accurate insights on occupant presence. Our preliminary results indicate that VOC is a good indicator of occupancy detection in some cases. It is also concluded that a proper feature selection and developing appropriate global occupancy detection models can reduce the cost and energy of data collection without a significant impact on the accuracy. Copyright © 2022, The Authors. All rights reserved.

关键词： Volatile organic compounds

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Using Machine Learning to Predict 1,2,3-Trichloropropane Contamination from Legacy Non-Point Source Pollution of Groundwater in California's Central Valley

SSRN

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SSRN 2022年

作者： Hauptman, Bambi Hope Naughton, Colleen C. Harmon, Thomas C. University of California Merced Environmental Systems Graduate Group Sustainability Research and Engineering Building SRE #214-01 5200 Lake Rd MercedCA95343 United States University of California Merced Department of Civil and Environmental Engineering Sustainability Research and Engineering Building SRE #214 5200 Lake Rd MercedCA95343 United States University of California Merced Department of Civil and Environmental Engineering Sustainability Research and Engineering Building SRE #435 5200 Lake Rd MercedCA95343 United States

1,2,3-Tricholoropropane (TCP) is an impurity common in nematicides applied to agricultural soils from the 1940s to the 1980s. Evidence from animal studies indicates that TCP is a probable human carcinogen. TCP leaches through the soil into groundwater where it persists and contaminates thousands of wells in North America, Europe, and Asia. In California, TCP contaminates drinking water wells, with the highest levels of TCP beneath agricultural land used to grow grapevines. This study performs a mass balance and evaluates the ability of three types of machine learning tree models to predict 1,2,3-Trichloropropane (TCP) concentration in California’s Central Valley aquifer system: classification and regression tree (CART), Random Forest (RF), and Boosted Regression Trees (BRT). To construct the models, multiple spatial explanatory variables were used, including historical agricultural land use data, irrigation levels, precipitation, soil type, groundwater age estimates, dissolved oxygen levels, and the presence of the co-contaminant nitrate. According to the mass balance of TCP contamination and historical records documenting agricultural land use in California, an estimated 800,000 – 3,300,000 kilograms of TCP (800 – 3,300 metric tons) was applied to farmland. Machine learning model results indicate that precipitation, dissolved oxygen levels, irrigation amount, groundwater age, and the presence of co-contaminant nitrate, are the most important explanatory variables for predicting TCP contamination in groundwater. In addition, the RF model outperforms CART and BRT for predictive performance. Moreover, when considering explanatory variables surrounding wells, using a 1000-meter buffer area offers higher predictive performance over smaller 500-meter buffers and larger 1500-meter buffers. Modeling using decision trees can predict TCP contamination levels in areas where monitoring is unavailable, help target future TCP monitoring efforts, and aid in identifying areas to a

关键词： Contamination

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Skip-WaveNet: A Wavelet based Multi-scale Architecture to Trace Snow Layers in Radar Echograms

arXiv

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arXiv 2023年

作者： Varshney, Debvrat Yari, Masoud Ibikunle, Oluwanisola Li, Jilu Paden, John Gangopadhyay, Aryya Rahnemoonfar, Maryam Department of Information Systems University of Maryland Baltimore County BaltimoreMD21250 United States Department of Computer Science and Engineering Lehigh Univerisity BethlehemPA18015 United States Center for Remote Sensing and Integrated Systems University of Kansas LawrenceKS66045 United States Department of Civil and Environmental Engineering Lehigh Univerisity BethlehemPA18015 United States

Airborne radar sensors capture the profile of snow layers present on top of an ice sheet. Accurate tracking of these layers is essential to calculate their thicknesses, which are required to investigate the contribution of polar ice cap melt to sea-level rise. However, automatically processing the radar echograms to detect the underlying snow layers is a challenging problem. In our work, we develop wavelet-based multi-scale deep learning architectures for these radar echograms to improve snow layer detection. These architectures estimate the layer depths with a mean absolute error of 3.31 pixels and 94.3% average precision, achieving higher generalizability as compared to state-of-the-art snow layer detection networks. These depth estimates also agree well with physically drilled stake measurements. Such robust architectures can be used on echograms from future missions to efficiently trace snow layers, estimate their individual thicknesses and thus support sea-level rise projection models. © 2023, CC BY-NC-SA.

关键词： Sea level

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Optimized Chlorine Bulk Decay Models and a Machine-Learning-Guided Water Quality Responsive Kinetic Model for Residual Chlorine Prediction

SSRN

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SSRN 2023年

作者： Jafari, Iman Luo, Rongmo Lim, Fang Yee Hui, Ng Szu Jiangyong, Hu Department of Civil & Environmental Engineering National University of Singapore 1 Engineering Drive 2 Singapore117576 Singapore Research and Development Department Xylem Water Solutions Singapore Pte Ltd 3A International Business Park Tower B #10-10/18 ICON@IBP Singapore609935 Singapore Department of Industrial Systems Engineering and Management National University of Singapore 1 Engineering Drive 2 Singapore117576 Singapore

The quality of water changes from source to tap, presenting challenges in maintaining consistent water quality across the system. Predicting water quality in distribution systems, including disinfectant residual loss and by-product generation, has been the subject of research since the early 1990s. Although numerous models have been proposed to predict chlorine decay, disputes exist among researchers and experts over the superiority of certain models. Accordingly, this study modified the existing process-based bulk decay models by replacing the initial chlorine concentration parameter with chlorine demand, leading to an improvement in the models' performance. The modification resulted in a 38.03%, 28.02%, 23.11%, and 33.29% average improvement in the First Order Model (FOM), Parallel First Order Model (PFOM), Second Order Model (SOM), and Parallel Second Order Model (PSOM), respectively. The study also introduced an online predictive method based on a machine learning algorithm that predicts the first-order chlorine bulk decay rate by using water quality parameters as inputs. A Gaussian Process Regression model was used to predict the kinetic parameters in FOM, which accurately predicted the test sets for most of the cases. In addition, a new methodology was proposed in this study for predicting residual chlorine in water distribution systems that incorporates the variability of source natural organic matter, operational actions, and water demands. This method seeks to develop high-fidelity and robust water quality predictions that provide operational decision support for optimized distribution system management. In conclusion, this study emphasizes the importance of understanding water quality changes from source to tap and the challenges of maintaining consistent water quality across the system. The study suggests modifying existing models and introducing a novel methodology for predicting residual chlorine in water distribution systems that can improve water qual

关键词： Water quality

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Development of a Hardware-in-the-loop Testbed for Laboratory Performance Verification of Flexible Building Equipment in Typical Commercial Buildings

arXiv

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arXiv 2023年

作者： Chen, Zhelun Lo, L. James Pertzborn, Amanda Grajewski, Gabriel Wen, Jin O'Neill, Zheng Calfa, Caleb Li, Yicheng Bushby, Steven T. Payne, W. Vance Fu, Yangyang Yang, Zhiyao The Department of Civil Architectural & Environmental Engineering Drexel University PhiladelphiaPA United States The Department of Mechanical Engineering Texas A&M University College StationTX United States The Mechanical Systems and Controls Group The HVAC&R Equipment Performance Group The Building Energy and Environment Division of the Engineering Laboratory The National Institute of Standards and Technology GaithersburgMD United States

The goals of reducing energy costs, shifting electricity peaks, increasing the use of renewable energy, and enhancing the stability of the electric grid can be met in part by fully exploiting the energy flexibility potential of buildings and building equipment. The development of strategies that exploit these flexibilities could be facilitated by publicly available high-resolution datasets illustrating how control of HVAC systems in commercial buildings can be used in different climate zones to shape the energy use profile of a building for grid needs. This article presents the development and integration of a Hardware-In-the-Loop Flexible load Testbed (HILFT) that integrates physical HVAC systems with a simulated building model and simulated occupants with the goal of generating datasets to verify load flexibility of typical commercial buildings. Compared to simulation-only experiments, the hardware-in-the-loop approach captures the dynamics of the physical systems while also allowing efficient testing of various boundary conditions. The HILFT integration in this article is achieved through the co-simulation among various software environments including LabVIEW, MATLAB, and EnergyPlus. Although theoretically viable, such integration has encountered many real-world challenges, such as: 1) how to design the overall data infrastructure to ensure effective, robust, and efficient integration;2) how to avoid closed-loop hunting between simulated and emulated variables;3) how to quantify system response times and minimize system delays;and 4) how to assess the overall integration quality. Lessons-learned using the examples of an AHU-VAV system, an air-source heat pump system, and a water-source heat pump system are presented. Copyright © 2023, The Authors. All rights reserved.

关键词： Synthetic apertures

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Stochastic Time-Optimal Trajectory Planning for Connected and Automated Vehicles in Mixed-Traffic Merging Scenarios

arXiv

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arXiv 2023年

作者： Le, Viet-Anh Chalaki, Behdad Tzortzoglou, Filippos N. Malikopoulos, Andreas A. The Department of Mechanical Engineering University of Delaware NewarkDE19716 United States The Systems Engineering Cornell University IthacaNY14850 United States Honda Research Institute USA Inc. Ann ArborMI48103 United States The University of Delaware United States The School of Civil and Environmental Engineering Cornell University IthacaNY14850 United States

Addressing safe and efficient interaction between connected and automated vehicles (CAVs) and human-driven vehicles in a mixed-traffic environment has attracted considerable attention. In this paper, we develop a framework for stochastic time-optimal trajectory planning for coordinating multiple CAVs in mixed-traffic merging scenarios. We present a data-driven model, combining Newell’s car-following model with Bayesian linear regression, for efficiently learning the driving behavior of human drivers online. Using the prediction model and uncertainty quantification, a stochastic time-optimal control problem is formulated to find robust trajectories for CAVs. We also integrate a replanning mechanism that determines when deriving new trajectories for CAVs is needed based on the accuracy of the Bayesian linear regression predictions. Finally, we demonstrate the performance of our proposed framework using a realistic simulation environment. Copyright © 2023, The Authors. All rights reserved.

关键词： Trajectories

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Biochar derived from rice husk: Impact on soil enzyme and microbial dynamics, lettuce growth, and toxicity

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Chemosphere 2024年 349卷 140868页

作者： Akumuntu, Athanasie Hong, Jin-Kyung Jho, Eun Hea Omidoyin, Kehinde Caleb Park, Seong-Jik Zhang, Qianru Zhao, Xin Department of Agricultural Chemistry Chonnam National University Gwangju61186 Korea Republic of Department of Environmental and Energy Engineering Yonsei University Wonju26493 Korea Republic of Department of Agricultural and Biological Chemistry Chonnam National University Gwangju61186 Korea Republic of Department of Bioresources and Rural Systems Engineering Hankyong National University Anseong17579 Korea Republic of State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China the Institute of Agricultural Resources and Regional Planning Chinese Academy of Agricultural Sciences Beijing100081 China Department of Civil and Environmental Engineering Seoul National University Seoul08826 Korea Republic of

This study was set to investigate the effects of rice husk biochar (RHB) on soil characteristics and growth of lettuce (Lactuca sativa). A comprehensive research approach was employed to examine the effect of different RHB concentrations (i.e., 0–1.5%) on soil pH, soil enzyme activities (i.e., alkaline phosphatase, beta-glucosidase, and dehydrogenase), soil microbial community, lettuce growth, and earthworm toxicity. The results showed that, within the studied RHB concentration range, the RHB application did not have significant effects on the soil pH. However, the enzyme activities were increased with increasing RHB concentration after the 28 d-lettuce growth period. The RHB application also increased the abundances of the bacterial genera Massilia and Bacillus and fungal genus Trichocladium having the plant growth promoting abilities. Furthermore, the study revealed that the root weight and number of lettuce leaves were significantly increased in the presence of the RHB, and the growth was dependent on the RHB concentration. The improved lettuce growth can be explained by the changes in the enzyme and microbial dynamics, which have resulted from the increased nutrient availability with the RHB application. Additionally, the earthworm toxicity test indicated that the tested RHB concentrations can be safely applied to soil without any significant ecotoxicity. In conclusion, this study underscores the potential of RHB as a soil amendment with positive effects on crop growth, highlighting the utilization of agricultural byproducts to enhance soil biological quality and plant growth through biochar application. © 2023 Elsevier Ltd

关键词： Lettuce

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Approximate Gibbs Sampler for Efficient Inference of Hierarchical Bayesian Models for Grouped Count Data

arXiv

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arXiv 2022年

作者： Yü, Jin-Zhu Baroud, Hiba Department of Civil Engineering University of Texas at Arlington ArlingtonTX United States Department of Industrial Manufacturing and Systems Engineering University of Texas at Arlington ArlingtonTX United States Department of Civil and Environmental Engineering Vanderbilt University NashvilleTN United States Department of Computer Science Vanderbilt University NashvilleTN United States

Hierarchical Bayesian Poisson regression models (HBPRMs) provide a flexible modeling approach of the relationship between predictors and count response variables. The applications of HBPRMs to large-scale datasets require efficient inference algorithms due to the high computational cost of inferring many model parameters based on random sampling. Although Markov Chain Monte Carlo (MCMC) algorithms have been widely used for Bayesian inference, sampling using this class of algorithms is time-consuming for applications with large-scale data and time-sensitive decision-making, partially due to the non-conjugacy of many models. To overcome this limitation, this research develops an approximate Gibbs sampler (AGS) to efficiently learn the HBPRMs while maintaining the inference accuracy. In the proposed sampler, the data likelihood is approximated with Gaussian distribution such that the conditional posterior of the coefficients has a closed-form solution. Numerical experiments using real and synthetic datasets with small and large counts demonstrate the superior performance of AGS in comparison to the state-of-the-art sampling algorithm, especially for large datasets. © 2022, CC BY.

关键词： Markov chains

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