Research and Publications - Department of Civil, Construction & Environmental Engineering

Permanent URI for this collection


Recent Submissions

Now showing 1 - 20 of 38
  • Item
    How did the COVID-19 pandemic affect road crashes and crash outcomes in Alabama?
    (Pergamon, 2021) Adanu, Emmanuel Kofi; Brown, David; Jones, Steven; Parrish, Allen; University of Alabama Tuscaloosa
    With the rising number of cases and deaths from the COVID-19 pandemic, nations and local governments, including many across the U.S., imposed travel restrictions on their citizens. This travel restriction order led to a significant reduction in traffic volumes and a generally lower exposure to crashes. However, recent preliminary statistics in the US suggest an increase in fatal crashes over the period of lockdown in comparison to the same period in previous years. This study sought to investigate how the pandemic affected road crashes and crash outcomes in Alabama. Daily vehicle miles traveled and crashes were obtained and explored. To understand the factors associated with crash outcomes, four crash-severity models were developed: (1) Single-vehicle (SV) crashes prior to lockdown order (Normal times SV); (2) multi-vehicle (MV) crashes prior to lockdown order (Normal times MV); (3) Single-vehicle crashes after lockdown order (COVID times SV); and (4) Multi-vehicle crashes after lockdown order (COVID times MV). The models were developed using the first 28 weeks of crashes recorded in 2020. The findings of the study reveal that although traffic volumes and vehicle miles traveled had significantly dropped during the lockdown, there was an increase in the total number of crashes and major injury crashes compared to the period prior to the lockdown order, with speeding, DUI, and weekends accounting for a significant proportion of these crashes. These observations provide useful lessons for road safety improvements during extreme events that may require statewide lockdown, as has been done with the COVID-19 pandemic. Traffic management around shopping areas and other areas that may experience increased traffic volumes provide opportunities for road safety stakeholders to reduce the occurrence of crashes in the weeks leading to an announcement of any future statewide or local lockdowns. Additionally, increased law enforcement efforts can help to reduce risky driving activities as traffic volumes decrease.
  • Item
    Safety and health management response to COVID-19 in the construction industry: A perspective of fieldworkers
    (Elsevier, 2022) Nnaji, Chukwuma; Jin, Ziyu; Karakhan, Ali; University of Alabama Tuscaloosa; University of New Mexico; University of Baghdad
    The COVID-19 outbreak has significantly impacted the construction industry. The pandemic can exacerbate an already dire safety and health situation in the industry and negatively impact construction employees and employers. The present study investigates the safety and health measures implemented by construction firms in the United States (US), their effectiveness and usefulness, and workers' satisfaction with these COVID-19 measures. A questionnaire survey was developed and distributed to construction fieldworkers in the US to collect their perspectives on the implemented COVID-19 measures in the construction industry. A total of 187 valid responses were received and analyzed to achieve the aim of the study. Results revealed that strategies implemented to increase social distance and minimize group gathering to 10 persons in certain workstations were perceived to be substantially more effective than job-site screening strategies. Furthermore, smaller contractors implemented fewer safety measures and perceived them to be significantly less effective than those used by medium- and large-sized contractors. Fieldworkers were favorably disposed toward using technologies, such as video-conferencing apps and wearable sensing devices, to slow the spread of COVID-19 on construction job sites. The present study contributes to the body of knowledge by identifying safety and health measures to mitigate the spread of COVID-19 in construction. Practically, the study findings provide valuable insights to inform the successful implementation of safety strategies in the construction industry during a pandemic. The results are crucial for industry practitioners responsible for developing and revising pre- and post-pandemic safety and health plans.
  • Item
    Does Global Progress on Sanitation Really Lag behind Water? An Analysis of Global Progress on Community- and Household-Level Access to Safe Water and Sanitation
    (PLOS, 2014) Cumming, Oliver; Elliott, Mark; Overbo, Alycia; Bartram, Jamie; University of London; London School of Hygiene & Tropical Medicine; University of Alabama Tuscaloosa; University of North Carolina; University of North Carolina Chapel Hill
    Safe drinking water and sanitation are important determinants of human health and wellbeing and have recently been declared human rights by the international community. Increased access to both were included in the Millennium Development Goals under a single dedicated target for 2015. This target was reached in 2010 for water but sanitation will fall short; however, there is an important difference in the benchmarks used for assessing global access. For drinking water the benchmark is community-level access whilst for sanitation it is household-level access, so a pit latrine shared between households does not count toward the Millennium Development Goal (MDG) target. We estimated global progress for water and sanitation under two scenarios: with equivalent household-and community-level benchmarks. Our results demonstrate that the "sanitation deficit" is apparent only when household-level sanitation access is contrasted with community-level water access. When equivalent benchmarks are used for water and sanitation, the global deficit is as great for water as it is for sanitation, and sanitation progress in the MDG-period (1990-2015) outstrips that in water. As both drinking water and sanitation access yield greater benefits at the household-level than at the community-level, we conclude that any post-2015 goals should consider a household-level benchmark for both.
  • Item
    Development of a field testing protocol for identifying Deepwater Horizon oil spill residues trapped near Gulf of Mexico beaches
    (PLOS, 2018) Han, Yuling; Clement, T. Prabhakar; Auburn University; University of Alabama Tuscaloosa
    The Deepwater Horizon (DWH) accident, one of the largest oil spills in U.S. history, contaminated several beaches located along the Gulf of Mexico (GOM) shoreline. The residues from the spill still continue to be deposited on some of these beaches. Methods to track and monitor the fate of these residues require approaches that can differentiate the DWH residues from other types of petroleum residues. This is because, historically, the crude oil released from sources such as natural seeps and anthropogenic discharges have also deposited other types of petroleum residues on GOM beaches. Therefore, identifying the origin of these residues is critical for developing effective management strategies for monitoring the long-term environmental impacts of the DWH oil spill. Advanced fingerprinting methods that are currently used for identifying the source of oil spill residues require detailed laboratory studies, which can be cost-prohibitive. Also, most agencies typically use untrained workers or volunteers to conduct shoreline monitoring surveys and these worker will not have access to advanced laboratory facilities. Furthermore, it is impractical to routinely fingerprint large volumes of samples that are collected after a major oil spill event, such as the DWH spill. In this study, we propose a simple field testing protocol that can identify DWH oil spill residues based on their unique physical characteristics. The robustness of the method is demonstrated by testing a variety of oil spill samples, and the results are verified by characterizing the samples using advanced chemical fingerprinting methods. The verification data show that the method yields results that are consistent with the results derived from advanced fingerprinting methods. The proposed protocol is a reliable, cost-effective, practical field approach for differentiating DWH residues from other types of petroleum residues.
  • Item
    A knowledge graph-based method for epidemic contact tracing in public transportation
    (Pergamon, 2022) Chen, Tian; Zhang, Yimu; Qian, Xinwu; Li, Jian; Tongji University; University of Alabama Tuscaloosa
    Contact tracing is an effective measure by which to prevent further infections in public transportation systems. Considering the large number of people infected during the COVID-19 pandemic, digital contact tracing is expected to be quicker and more effective than traditional manual contact tracing, which is slow and labor-intensive. In this study, we introduce a knowledge graph-based framework for fusing multi-source data from public transportation systems to construct contact networks, design algorithms to model epidemic spread, and verify the validity of an effective digital contact tracing method. In particular, we take advantage of the trip chaining model to integrate multi-source public transportation data to construct a knowledge graph. A contact network is then extracted from the constructed knowledge graph, and a breadth first search algorithm is developed to efficiently trace infected passengers in the contact network. The proposed framework and algorithms are validated by a case study using smart card transaction data from transit systems in Xiamen, China. We show that the knowledge graph provides an efficient framework for contact tracing with the reconstructed contact network, and the average positive tracing rate is over 96%.
  • Item
    Integrated socio-environmental vulnerability assessment of coastal hazards using data-driven and multi-criteria analysis approaches
    (Nature Portfolio, 2022) Tanim, Ahad Hasan; Goharian, Erfan; Moradkhani, Hamid; University of South Carolina Columbia; University of Alabama Tuscaloosa
    Coastal hazard vulnerability assessment has been centered around the multi-variate analysis of geo-physical and hydroclimate data. The representation of coupled socio-environmental factors has often been ignored in vulnerability assessment. This study develops an integrated socio-environmental Coastal Vulnerability Index (CVI), which simultaneously combines information from five vulnerability groups: biophysical, hydroclimate, socio-economic, ecological, and shoreline. Using the Multi-Criteria Decision Making (MCDM) approach, two CVI (CVI-50 and CVI-90) have been developed based on average and extreme conditions of the factors. Each CVI is then compared to a data-driven CVI, which is formed based on Probabilistic Principal Component Analysis (PPCA). Both MCDM and PPCA have been tied into geospatial analysis to assess the natural hazard vulnerability of six coastal counties in South Carolina. Despite traditional MCDM-based vulnerability assessments, where the final index is estimated based on subjective weighting methods or equal weights, this study employs an entropy weighting technique to reduce the individuals' biases in weight assignment. Considering the multivariate nature of the coastal vulnerability, the validation results show both CVI-90 and PPCA preserve the vulnerability results from biophysical and socio-economic factors reasonably, while the CVI-50 methods underestimate the biophysical vulnerability of coastal hazards. Sensitivity analysis of CVIs shows that Charleston County is more sensitive to socio-economic factors, whereas in Horry County the physical factors contribute to a higher degree of vulnerability. Findings from this study suggest that the PPCA technique facilitates the high-dimensional vulnerability assessment, while the MCDM approach accounts more for decision-makers' opinions.
  • Item
    Post-pandemic shared mobility and active travel in Alabama: A machine learning analysis of COVID-19 survey data
    (Elsevier, 2023) Xu, Ningzhe; Nie, Qifan; Liu, Jun; Jones, Steven; University of Alabama Tuscaloosa
    The COVID-19 pandemic has had unprecedented impacts on the way we get around, which has increased the need for physical and social distancing while traveling. Shared mobility, as an emerging travel mode that allows travelers to share vehicles or rides has been confronted with social distancing measures during the pandemic. On the contrary, the interest in active travel (e.g., walking and cycling) has been renewed in the context of pandemic-driven social distancing. Although extensive efforts have been made to show the changes in travel behavior during the pandemic, people's post-pandemic attitudes toward shared mobility and active travel are under-explored. This study examined Alabamians' post-pandemic travel preferences regarding shared mobility and active travel. An online survey was conducted among residents in the State of Alabama to collect Alaba-mians' perspectives on post-pandemic travel behavior changes, e.g., whether they will avoid ride-hailing services and walk or cycle more after the pandemic. Machine learning algorithms were used to model the survey data (N = 481) to identify the contributing factors of post-pandemic travel preferences. To reduce the bias of any single model, this study explored multiple machine learning methods, including Random Forest, Adaptive Boosting, Support Vector Machine, K-Nearest Neighbors, and Artificial Neural Network. Marginal effects of variables from multiple models were combined to show the quantified relationships between contributing factors and future travel intentions due to the pandemic. Modeling results showed that the interest in shared mobility would decrease among people whose one-way commuting time by driving is 30-45 min. The interest in shared mobility would increase for households with an annual income of $100,000 or more and people who reduced their commuting trips by over 50% during the pandemic. In terms of active travel, people who want to work from home more seemed to be interested in increasing active travel. This study provides an understanding of future travel preferences among Alabamians due to COVID-19. The information can be incorporated into local trans-portation plans that consider the impacts of the pandemic on future travel intentions.
  • Item
    Cement substitution with secondary materials can reduce annual global CO2 emissions by up to 1.3 gigatons
    (Nature Portfolio, 2022) Shah, Izhar Hussain; Miller, Sabbie A.; Jiang, Daqian; Myers, Rupert J.; Imperial College London; University of California Davis; University of Alabama Tuscaloosa
    Population and development megatrends will drive growth in cement production, which is already one of the most challenging-to-mitigate sources of CO2 emissions. However, availabilities of conventional secondary cementitious materials (CMs) like fly ash are declining. Here, we present detailed generation rates of secondary CMs worldwide between 2002 and 2018, showing the potential for 3.5 Gt to be generated in 2018. Maximal substitution of Portland cement clinker with these materials could have avoided up to 1.3 Gt CO2-eq. emissions (similar to 44% of cement production and similar to 2.8% of anthropogenic CO2-eq. emissions) in 2018. We also show that nearly all of the highest cement producing nations can locally generate and use secondary CMs to substitute up to 50% domestic Portland cement clinker, with many countries able to potentially substitute 100% Portland cement clinker. Our results highlight the importance of pursuing regionally optimized CM mix designs and systemic approaches to decarbonizing the global CMs cycle.
  • Item
    UA_L-DoTT: University of Alabama's large dataset of trains and trucks
    (Elsevier, 2022) Eastepp, Maxwell; Faris, Lauren; Ricks, Kenneth; University of Alabama Tuscaloosa
    UA_L-DoTT (University of Alabama's Large Dataset of Trains and Trucks) is a collection of camera images and 3D LiDAR point cloud scans from five different data sites. Four of the data sites targeted trains on railways and the last targeted trucks on a four-lane highway. Low light conditions were present at one of the data sites showcasing unique differences between individual sensor data. The final data site utilized a mobile platform which created a large variety of viewpoints in images and point clouds. The dataset consists of 93,397 raw images, 11,415 corresponding labeled text files, 354,334 raw point clouds, 77,860 corresponding labeled point clouds, and 33 timestamp files. These timestamps correlate images to point cloud scans via POSIX time. The data was collected with a sensor suite consisting of five different LiDAR sensors and a camera. This provides various viewpoints and features of the same targets due to the variance in operational characteristics of the sensors. The inclusion of both raw and labeled data allows users to get started immediately with the labeled subset, or label additional raw data as needed. This large dataset is beneficial to any researcher interested in machine learning using cameras, LiDARs, or both. The current dataset is publicly available at UA_L-DoTT (C) 2022 The Author(s). Published by Elsevier Inc.
  • Item
    Severity analysis of crashes involving in-state and out-of-state large truck drivers in Alabama: A random parameter multinomial logit model with heterogeneity in means and variances
    (Elsevier, 2022) Okafor, Sunday; Adanu, Emmanuel Kofi; Jones, Steven; University of Alabama Tuscaloosa
    The trucking sector contributes significantly to the economic vitality of the United States. Large trucks are primarily used for transporting goods within and across states. Despite its economic importance, large truck crashes constitute public safety concerns. To minimize the consequences, there is a need to understand the factors that contribute to the severity outcomes of truck-involved crashes. Since many large truck drivers transport goods across several states, the driver-centered crash factors are expected to differ between in-state and out-of-state drivers. For this reason, this study developed two random parameters multinomial logit models with heterogeneity in means and variances to examine the factors contributing to the severity of crashes involving in-state and out-of-state large truck drivers in Alabama. The study was based on the 2016-2020 large truck crashes in Alabama. After data cleaning and preparation, it was observed that approximately 20% of in-state and 23% of out-ofstate large truck crashes were fatigue-related. There were more speeding related crashes (12.4%) among in-state large truck drivers, but the contribution of speeding to crash severity outcomes was only significant in the out-ofstate model. More crashes related to red light running violation (14.2%) were observed among out-of-state drivers, pointing to the fundamental issues of fatigue and unfamiliarity with the operations of signalized intersections in Alabama. The study contributes to the literature on large truck crashes by uncovering the nuances in crashes involving in-state and out-of-state large truck drivers. Despite the seeming similarity in factors that influence crash outcomes, this study provides the basis for truck drivers' training and communication campaigns on the differences that may exist in roadway characteristics from state to state. Also, policy formulations and strategies that prioritizes the well-being of the large truck drivers and creates a better working condition for them should be explored.
  • Item
    Rainfall-induced hydroplaning risk over road infrastructure of the continental USA
    (PLOS, 2022) Salvi, Kaustubh Anil; Kumar, Mukesh; University of Alabama Tuscaloosa
    Extreme rainfall causes transient ponding on roads, which increases the risk of vehicle accidents due to hydroplaning (HP), a phenomenon characterized by reduced friction between the pavement surface and the tires of moving vehicles. Before mitigation plans are drawn, it is important to first assess the spatio-temporal patterns of hydroplaning risk (HpR). This study quantifies HpR over the entire continental USA considering the coupled role of precipitation characteristics and pavement properties. Results show the southern United States to be a primary hotspot of HpR. About 22% of road sections experiencing HpR exhibit an increasing trend in the annual occurrence of HP events with time, indicating a riskier future ahead. Alarmingly, road sections that either experience higher HpR or increasing trend in annual occurrences of HP events are the ones with sizeable traffic. These results emphasize the need to prioritize HP-aware road design, traffic management, and signage in regions with high or fast-evolving risks.
  • Item
    An in-depth analysis of head-on crash severity and fatalities in Ghana
    (Cell Press, 2023) Adanu, Emmanuel Kofi; Agyemang, William; Lidbe, Abhay; Adarkwa, Offei; Jones, Steven; University of Alabama Tuscaloosa
    Head-on collisions are often linked to more serious injuries compared to other types of crashes, due to the intense impact they cause. In low-and middle-income countries, these collisions frequently involve high occupancy public transportation vehicles, leading to higher fatality rates per crash. Given the high risk of injury and potential for multiple casualties, this study delves into the factors influencing the outcomes of head-on crashes and the number of fatalities in Ghana. The study analyzed six years of historical head-on collision data from Ghana and developed two models to address the issue. The injury-severity analysis was performed using a random param-eter multinomial logit with heterogeneity in means and variances approach and aimed to identify the factors that have a significant impact on the severity of injuries sustained in head-on colli-sions, while the random parameters negative binomial fatality count model was designed to examine the factors that contribute to the number of fatalities in these crashes in the country. Results showed that head-on collisions with drivers over 65, buses, motorcycles, and those be-tween 25 and 65 years of age were more likely to result in fatalities. Speeding and vehicle malfunctions were also found to be significant contributing factors to fatal head-on collisions. Head-on crashes involving minibuses and incidents where the driver was attempting to overtake another vehicle were found to be more likely to result in a higher number of fatalities. The results of this study uncover an intriguing interaction between human-related elements and socioeco-nomic factors, which pose obstacles to the Government's endeavor to upgrade the major high-ways in the country. Additionally, the increasing need for transportation has led to the presence of vehicles on the roads that may not meet safety standards. Consequently, it is no surprise that several of the study's findings align with expectations. Nevertheless, within the specific context of Ghana, these findings furnish compelling data-driven evidence supporting the adoption and implementation of the safe systems approach as a means to tackle fatal head-on collisions in the country.
  • Item
    The perceived effectiveness and hidden inequity of postpandemic fiscal stimuli
    (National Academy of the Sciences, 2022) Zhang, Yaxin; Zheng, Xinzhu; Jiang, Daqian; Luo, Huilin; Guo, Kaidi; Song, Xinke; Wang, Can; Tsinghua University; China University of Petroleum; University of Alabama Tuscaloosa
    The world has committed trillions in fiscal expenditures to reboot the economy in the post-COVID-19 era. However, the effectiveness and the equity impacts of current fiscal stimuli are not fully understood. Using an extended adaptive regional input-output model, we assess the short-term impacts (2020 through 2022) of feasible stimuli on the global economy and the labor market. Our findings show that the stimuli pledged by 26 countries, i.e., 2.4 trillion euros in total, are effective in keeping the recession short and shallow by saving 53 million to 57 million jobs (compared to the no-stimulus scenario). However, the stimuli exacerbate income inequity at the global scale if we define "equity" as those who suffer more from the pandemic should receive more assistance. Low-skilled workers in these countries, who suffer more from the pandemic than high-skilled workers, benefit 38 to 41% less from the job-creation effects of the current fiscal stimuli. As an alternative, low-carbon stimuli can achieve a balance between effectiveness and equity at the global level. Low-carbon stimuli save 55 million to 58 million jobs and decrease income inequality by 2 to 3% globally compared to the currently pledged stimuli. Country-level situations are more complicated, as modifying the current stimuli to achieve more "greenness" brings win-win in effectiveness and equity in some countries, while in the others, more greenness and equity are at the expense of less job savings. Our findings underscore the need to consider the overlooked trade-offs between effectiveness, equity, and greenness, both globally and nationally, when designing further postpandemic fiscal stimuli.
  • Item
    Prediction of cost and schedule performance in post-hurricane reconstruction of transportation infrastructure
    (PLOS, 2023) Safapour, Elnaz; Kermanshachi, Sharareh; Rouhanizadeh, Behzad; University of New Orleans; University of Texas Arlington; University of Alabama Tuscaloosa
    This study aimed to develop predictive models that could be used to estimate the cost and schedule performance of reconstruction of transportation infrastructure damaged by hurricanes and to determine the predictors that are robustly connected to the developed models. Stepwise multiple linear regression and extreme bound analysis (EBA) were used to develop the models and determine the robust and fragile predictors, respectively. The results demonstrated that seven cost performance predictors and nine schedule performance predictors accounted for Adjusted R-Squared of 92.4% and 99.2%, respectively. The results of the EBA revealed that four cost and seven performance predictors were robustly connected to the developed cost and schedule performance predictive models. It was concluded that increases in laborers' wages, the number of inspections, information and data management, and addressing safety and environmental issues prior to a project's execution were predictors of both the cost and schedule performance of reconstruction projects. The outcomes of this study provide knowledge and information that will be helpful to decision-makers who are responsible for mitigating delays and cost overruns, and effectively allocating their limited resources available following a disaster.
  • Item
    Mobile device use while crossing the street: Utilizing the theory of planned behavior
    (Pergamon, 2019) Piazza, Andrew J.; Knowlden, Adam P.; Hibberd, Elizabeth; Leeper, James; Paschal, Angelia M.; Usdan, Stuart; University of Alabama Tuscaloosa; Worcester State University
    Every year, thousands of pedestrians are killed and tens-of-thousands are nonfatally injured as a result of traffic crashes. The year 2016 holds the record for the most pedestrians killed in one year since 1990. Mobile device use while crossing the street has been associated with unsafe crossing behaviors and gait abnormalities, potentially increasing the risk of pedestrian injury or death. Expanding upon the small body of literature, the present study utilized the theory of planned behavior to guide the development of a questionnaire used to collect data from 480 adults on predictors of intentions to use a mobile device while crossing the street. Questionnaire development involved one round of expert panel review (N = 4), subsequent pilot testing of a revised questionnaire, and a test-retest reliability assessment. Results demonstrate that attitude toward the behavior, subjective norm, and perceived behavioral control significantly predicted the intention to use a mobile device while crossing the street in this population. Such a questionnaire can be used in the design and evaluation of TPB-based inter-ventions to decrease distracted mobile device use while crossing the street.
  • Item
    How can we reform the STEM tenure system for the 21st Century?
    (National Academy of the Sciences, 2022) Clement, T. Prabhakar; University of Alabama Tuscaloosa
  • Item
    Block-level vulnerability assessment reveals disproportionate impacts of natural hazards across the conterminous United States
    (Nature Portfolio, 2023) Yarveysi, Farnaz; Alipour, Atieh; Moftakhari, Hamed; Jafarzadegan, Keighobad; Moradkhani, Hamid; University of Alabama Tuscaloosa
    The global increase in the frequency, intensity, and adverse impacts of natural hazards on societies and economies necessitates comprehensive vulnerability assessments at regional to national scales. Despite considerable research conducted on this subject, current vulnerability and risk assessments are implemented at relatively coarse resolution, and they are subject to significant uncertainty. Here, we develop a block-level Socio-Economic-Infrastructure Vulnerability (SEIV) index that helps characterize the spatial variation of vulnerability across the conterminous United States. The SEIV index provides vulnerability information at the block level, takes building count and the distance to emergency facilities into consideration in addition to common socioeconomic vulnerability measures and uses a machine-learning algorithm to calculate the relative weight of contributors to improve upon existing vulnerability indices in spatial resolution, comprehensiveness, and subjectivity reduction. Based on such fine resolution data of approximately 11 million blocks, we are able to analyze inequality within smaller political boundaries and find significant differences even between neighboring blocks. Introduces a precise, machine-learning-based Socio-Economic-Infrastructure Vulnerability index for natural hazards that uncovers stark variations in vulnerability at the block level emphasizing crucial information for risk-informed decision making.
  • Item
    Greening China Naturally
    (Springer, 2011) Cao, Shixiong; Sun, Ge; Zhang, Zhiqiang; Chen, Liding; Feng, Qi; Fu, Bojie; McNulty, Steve; Shankman, David; Tang, Jianwu; Wang, Yanhui; Wei, Xiaohua; United States Department of Agriculture (USDA); United States Forest Service; Beijing Forestry University; Chinese Academy of Sciences; Research Center for Eco-Environmental Sciences (RCEES); Cold & Arid Regions Environmental & Engineering Research Institute, CAS; University of Alabama Tuscaloosa; Marine Biological Laboratory - Woods Hole; Chinese Academy of Forestry; Research Institute of Forest Ecology, Environment and Protection, CAF; University of British Columbia; University of British Columbia Okanagan
    China leads the world in afforestation, and is one of the few countries whose forested area is increasing. However, this massive "greening" effort has been less effective than expected; afforestation has sometimes produced unintended environmental, ecological, and socioeconomic consequences, and has failed to achieve the desired ecological benefits. Where afforestation has succeeded, the approach was tailored to local environmental conditions. Using the right plant species or species composition for the site and considering alternatives such as grassland restoration have been important success factors. To expand this success, government policy should shift from a forest-based approach to a results-based approach. In addition, long-term monitoring must be implemented to provide the data needed to develop a cost-effective, scientifically informed restoration policy.
  • Item
    Simulating PFAS adsorption kinetics, adsorption isotherms, and nonideal transport in saturated soil with tempered one-sided stable density (TOSD) based models
    (Elsevier, 2021) Zhou, Dongbao; Brusseau, Mark L.; Zhang, Yong; Li, Shiyin; Wei, Wei; Sun, HongGuang; Zheng, Chunmiao; Hohai University; University of Arizona; University of Alabama Tuscaloosa; Nanjing Normal University; Southern University of Science & Technology
    Reliable quantification of per- and polyfluoroalkyl substances (PFAS) adsorption and mobility in geomedia provides critical information (i.e., evaluation and prediction) for risk characterization and mitigation strategy development. Given the limited PFAS data available and various competing theories for modeling pollutant kinetics, it is indispensable to better understand and quantify the adsorption and transport of PFAS in geomedia using generalized models built upon a consistent physical theory. This study proposed a universal physical law (called the tempered stable law) in PFAS adsorption/transport by interpreting PFAS adsorption kinetics and nonideal transport as a nonequilibrium process dominated by adsorption/desorption with multiple rates following the tempered one-sided stable density (TOSD) distribution. This universal TOSD function led to novel TOSD-based models which were then tested by successfully simulating PFAS adsorption kinetics, adsorption isotherms, and nonideal transport data reported in the literature. Model comparisons and extensions were also discussed to further check the feasibility of the TOSD models and their adaptability to capture PFAS transport in more complex geomedia at all scales.
  • Item
    Comparison of RUSLE and MMF Soil Loss Models and Evaluation of Catchment Scale Best Management Practices for a Mountainous Watershed in India
    (MDPI, 2020) Das, Susanta; Deb, Proloy; Bora, Pradip Kumar; Katre, Prafull; Punjab Agricultural University; University of Alabama Tuscaloosa; Indira Gandhi Krishi Vishwavidyalaya (IGKV)
    Soil erosion from arable lands removes the top fertile soil layer (comprised of humus/organic matter) and therefore requires fertilizer application which affects the overall sustainability. Hence, determination of soil erosion from arable lands is crucial to planning conservation measures. A modeling approach is a suitable alternative to estimate soil loss in ungauged catchments. Soil erosion primarily depends on soil texture, structure, infiltration, topography, land uses, and other erosive forces like water and wind. By analyzing these parameters, coupled with geospatial tools, models can estimate storm wise and annual average soil losses. In this study, a hilly watershed called Nongpoh was considered with the objective of prioritizing critical erosion hazard areas within the micro-catchment based on average annual soil loss and land use and land cover and making appropriate management plans for the prioritized areas. Two soil erosion models namely Revised Universal Soil Loss Equation (RUSLE) and Modified Morgan-Morgan-Finney (MMF) models were used to estimate soil loss with the input parameters extracted from satellite information and automatic weather stations. The RUSLE and MMF models showed similar results in estimating soil loss, except the MMF model estimated 7.74% less soil loss than the RUSLE model from the watershed. The results also indicated that the study area is under severe erosion class, whereas agricultural land, open forest area, and scrubland were prioritized most erosion prone areas within the watershed. Based on prioritization, best management plans were developed at catchment scale for reducing soil loss. These findings and the methodology employed can be widely used in mountainous to hilly watersheds around the world for identifying best management practices (BMP).