Browsing by Author "An, Chunjiang"

Now showing 1 - 18 of 18

Open Access
Chloride Salts Removal by non Planted Constructed Wetlands Receiving Synthetic Brines from Belle Plaine Potash Mining
(Faculty of Graduate Studies and Research, University of Regina, 2015-03) Chairawiwut, Warawut; McMartin, Dena; Azam, Shahid; Ng, Kelvin Tsun Wai; An, Chunjiang; Gilles, Jon
Four pilot-scale constructed wetlands (CWs) were employed to study the fate and transport of the two dominant chloride salts (NaCl and KCl) receiving the synthetic brine. The characteristics of the brine solution are made up with a 10:1 concentration ratio between NaCl and KCl based on data obtained from Belle Plaine potash mine site. The multi-layer soils were designed to function as a main salt filtering component comprising of Regina Clay (grain size <0.002 mm), Brick Sand (grain size <4.75 mm) and sharp gravels (grain size between 6.3-19.1 mm) despite the clay layers contained a poor compact condition (Db =1.4, <1.6 g/cm3). The volume of void space in the compacted clay was reduced to approximately half the natural state following application of pressure (4,505 cm3 to 2,743 cm3). The CW systems were operated by the 16-day format (batch 1-3) and the 4-day format (batch 4). During the 16-day format experiments, the best K+ removal rate was recorded on Cell2 as 92.1±63.4% (4.6±5.3mg/l) while Cell 4 contributed the maximum removal of Na+ and Cl- as 44.8±76.7% (53.2±92.4mg/l) and 50.5±109.6% (85.3±184.3.4mg/l). In the 4-day format operation, the chloride breakthrough curve was discovered in all treatment CW cells, excluding the control. Moreover, the curve stated at less than 6 hours (C/C0 > 0.05) and approximately reached the break point (C/C0 > 0.95) after passing 48 hours. Then the clay media was exhausted and lost its ability to remove Cl-. However, the study was not able to determine the breakthrough curves in neither Na+ nor K+ cases during batch experiment 4 due to unidentified ending points on both cations.
Open Access
Development of an Integrated Hydro-Climatic Systems Analysis Framework and its Application to the Athabasca River Basin, Canada
(Faculty of Graduate Studies and Research, University of Regina, 2016-09) Cheng, Guanhui; Huang, Guo (Gordon); Yang, Boting; Veawab, Amornvadee; An, Chunjiang; Xu, Chongyu
Climate change has profound impacts on regional hydrological characteristics in large unregulated continental river basins (LUCRiBs) such as the Athabaasca River Basin (ARB), Canada. A systematic analysis of these impacts is confronted with many challenges. For instance, the performances of general circulation models (GCMs) vary with many factors, e.g. climate variables, geographic locations, temporal scales, and evaluation measures. Mesoscale atmospheric features can barely be provided by coarse-resolution GCMs. Filling this gap by statistical downscaling is further challenged by redundant computations, resulting from spatial climatic similarities, and the complexities of data uncertainties, nonlinear correspondences, normality prerequisites, and multivariate dependencies. Climatic projection may lack a solid GCM-evaluation foundation and a high spatial resolution. These complexities in downscaling may also exist and be coupled with massive computations in integer optimization in hydrological simulation. Furthermore, an integration of these challenges would decrease the reliability of long-term streamflow forecastings for guiding socio-economic development and eco-environmental conservation over LUCRiBs such as the ARB under climate change. To fill the gap of few effective techniques, an integrated hydro-climatic systems analysis framework is developed and applied to the ARB. This framework includes six modules. (a) The multi-dimensional performances of CMIP5 GCMs and their ensemble are evaluated. (b) The climate over the ARB is classified by recursive dissimilarity and similarity inferences. (c) The spatial resolution of GCM is enhanced by recursive multivariate principal-monotonicity inferential downscaling based on (a) and (b). (d) High-resolution climatic projection under four representative concentration pathways (RCPs) are generated by coupling (a) to (c). (e) The correspondence between climate and streamflow is reproduced by Bayesian principalmonotonicity inference based on (b). (f) Modules (d) and (e) are integrated for streamflow forecasting under climate change. A series of findings are revealed while methodological reliability is verified. For instance, the multi-model ensemble has a relatively high modeling accuracy. The climatic conditions over the ARB are classified into 20 classes based on their dissimilarity and similarity. The overall downscaling accuracies are relatively high for temperature and acceptable for precipitation although varying with multiple factors. At the scale of octo-decades, daily minimum temperature would increase by 1.7, 2.3, 2.1 and 3.0 􀔨, daily maximum temperature by 1.4, 1.8, 1.6 and 2.2 􀔨, and daily total precipitation by 0.03, 0.07, 0.08 and 0.16 mm under RCPs 2.6, 4.5, 6.0 and 8.5, respectively. The approach in module (e) is effective at capturing the temporal variability and the multi-year averages of streamflow and the uncertainties of climatestreamflow correspondences. Streamflow tends to increase at the upper and middle reaches and decline at the lower one. The increments of streamflow would be the highest in March and the decrements would be dominated by less flow in July or Summer. Either RCP scenarios or modeling biases are significant for the temporal variability and trends and are insignificant for the overall magnitudes of streamflow. The methods and findings in this study would be helpful for gaining insights into coupled climatic and hydrological systems over the ARB, evaluating the impacts of climate change, guiding regional socio-economic development and eco-environmental conservation, and promoting developations of more advanced climatic and hydrometeorological systems analysis methods.
Open Access
Development of Fractional Programming Methods for Environmental Management Under Uncertainty
(Faculty of Graduate Studies and Research, University of Regina, 2016-09) Wang, Shudong; Huang, Guo (Gordon); Azam, Shahid; An, Chunjiang
Rapid economic development and population growth has accelerated environmental degradation and resource scarcity. There is growing recognition of the importance of environmental conservation and sustainable development. Sustainable environmental management may benefit from integrating a variety of factors into decision-making processes, such as economic, environmental, social, technical, legislative, and political considerations. Moreover, environmental systems are often involved in a multitude of uncertainties, which significantly intensifies the complexity of systems analysis. In this thesis, a set of fractional programming methods are developed to solve multiobjective environmental management problems under uncertainties. Factorial analysis is introduced to explore the interactions of uncertain system parameters and quantify their interactive effects on system performance. The proposed methods include (1) an inexact fractional credibility-constrained programming method for sustainable municipal solid waste management, (2) a generalized fuzzy fractional programming method for air quality management, and (3) an inexact mixed-integer sequential factorial fractional programming method for sustainable municipal solid waste management in the City of Regina, Canada. The models can reflect the multi-objective characteristic of environmental management and address the conflicts between economic and environmental objectives without weighing them. The proposed models are able to maximize environmental benefits and obtain maximum system efficiency with minimal system costs. The objective is to optimize the ratio of environmental benefits to system costs rather than considering them separately, which provides a practical way to solve efficiency issues in environmental management. Moreover, the interactions obtained with factorial analysis may reveal implicit interrelationships between uncertain parameters and help decision makers gain insight into a complex environmental system.
Open Access
Development of Inexact Optimization Methods for Planning Environmental Management Systems Under Multiple Uncertainties
(Faculty of Graduate Studies and Research, University of Regina, 2014-12) Zhai, Yuanyuan; Huang, Guo (Gordon); Ng, Tsun Wai Kelvin; An, Chunjiang; Dai, Liming; Yao, Yiyu
Environmental planning is becoming a key component in environmental management due to rapid socio-economic development and steadily increasing population. In real-world applications, uncertainties and complexities are frequently involved in environmental management systems, where decision makers need sound management approaches in order to allocate their limited resources to different competing end users. Thus, more innovative approaches are required to deal with multiple uncertainties. As well more in-depth information in regard to the individual and interactive effects among multiple input parameters on the system performance is needed. In this research, a dual-interval two-stage mixed-integer inexact-chance-constrained linear programming (DITMIC) method was developed for the planning of flood-diversion management systems. DITMIC did not only reflect uncertainties expressed as intervals, dual intervals, interval-valued probability distributions but also incorporated pre-regulated diversion policies into its optimization process. Various policy scenarios were generated and they were associated with varied economic penalties if the promised targets were violated. Furthermore, DITMIC assisted in analyzing dynamic features of capacity-expansion schemes when it came to decision-making. Various flood-diversion patterns and capacity-expansion schemes were obtained under different risk levels. The reliability of satisfying the system constraints under uncertainty was also examined. This enabled decision makers to choose the most desired outcome relative to their preference and perception of future conditions. To better enable insights on the detailed effects from uncertain parameters as well as their interactions on the system objective, a factorial dual-interval programming (FDIP) method was developed for planning municipal waste management systems. Through the integration of factorial analysis and dual-interval linear programming into a general framework, the FDIP method could handle uncertainties existing in the left- and right-hand sides of the objective function, as well as in the associated constraints. Moreover, it had the advantages of identifying influential parameters along with their joint effects on the system output. Impact factors as well as their interactive effects have been identified and analyzed for the lower and upper bounds of the system output, which could further provide valuable information on their effects on the system solutions when it came to decision-making.
Open Access
Development of multi-stage fractional programming methods for electric power system planning under uncertainty
(Faculty of Graduate Studies and Research, University of Regina, 2016-12) Wang, Lin; Huang, Guo (Gordon); An, Chunjiang; Mehran, Babak
For electric power systems, although satisfying soaring power demand is critical for regions and countries, climate change mitigation by reducing carbon emissions is one of major challenges. Consequently, the primary obstacle for electric power system management is to tackle the trade-off between economic and environmental issues. Moreover, electric power systems exhibit a great number of complexities, associated with uncertainties and dynamics in system components, parameters and their interactions, such as future end-user demand, emission amounts and system violation. Therefore, efficient programming techniques are desired to deal with these uncertainties and complexities of the dual-objectives for maximizing economic profits and minimizing carbon emissions simultaneously. In this research, a set of multi-stage fractional programming approaches were proposed for electric power system management. The associated approaches included: (a) a multi-stage chance-constrained fractional programming (MCFP) approach for carbon-emission management; (b) a multi-stage joint-probabilistic left-hand-side chance-constrained fractional programming (MJCFP) approach for planning the electric power system in Saskatchewan, Canada; and (c) a stochastic risk-aversion dual-objective programming (SRDP) approach for electric power system management in Ontario, Canada. The proposed approaches can solve ratio-optimization problems with random information, reflect trade-offs between objectives and system reliability, and depict stochastic uncertainties and dynamics by introducing chance-constrained programming (CCP) technique into the fractional programming (FP) framework with a multi-layer scenario tree. To gain better insights on left-hand-side multi-randomness issues, especially when a joint-probabilistic requirement is imposed on multiple emission constraints, the MJCFP method was further developed to enhance the capabilities of MCFP approach. Furthermore, based on previous optimization approaches, the SRDP approach was improved by taking into consideration of financial risk management against extremely low profits conditions.
Open Access
Development of Stochastic Factorial Programming Methods for Water Resources Management Under Uncertainty
(Faculty of Graduate Studies and Research, University of Regina, 2016-05) Zhou, Yang; Huang, Guo (Gordon); Wu, Peng; Deng, Dianliang; An, Chunjiang; Qin, Xiaosheng
Freshwater is a limited and vital natural resource on earth. It also plays a key role in advancing social and economic development. Despite its importance, freshwater is still one of the most vulnerable resources that have not been managed in a proper manner. There is growing recognition in public and private sectors that freshwater resource needs to be effectively conserved, managed, and distributed. This aspiration makes water resources management a prevalent topic from regional to national levels. The need for sustainable water resources management has become even more urgent with the status quo of global water scarcity. In this dissertation, a set of factorial-analysis-based mathematical programming methods has been proposed to support water resources management under uncertainty. The proposed methods include: (1) the factorial fuzzy programming (FFP) method which can deal with uncertain parameters with fuzzy information and quantify the main and interactive effects of fuzzy parameters on system responses, (2) the dual factorial fuzzy programming (DFFP) method which can deal with dual uncertainties in terms of the fuzziness in modelling parameters and the variability in α-cut levels, (3) the factorial dual-objective two-stage stochastic programming (FDTSP) method which can address dual-objective optimization problems under uncertainty and provide detailed uncertainty analysis, and (4) the multi-level factorial dual-objective programming (MFDP) method which can investigate the non-linear effects of uncertain parameters on system responses and help decision makers gain improved insight into a system. The main contributions of this research include: (1) the development of a set of innovative methods for supporting integrated and sustainable water resources management under uncertainty and (2) the development of regional planning models with regard to the economic efficiency of water use for rural counties in China. The FFP and DFFP methods improve existing fuzzy mathematical programming methods by enabling factorial analysis and the fuzzy vertex method to be seamlessly integrated within a general modelling framework. The two methods are applied to illustrative flood diversion planning and water resources management cases respectively to demonstrate the applicability of the methods. The FDTSP and MFDP methods improve existing linear fractional programming methods by allowing data uncertainty to be considered in the modelling process and the effects of uncertain parameters to be quantified in systems analysis. The two methods are applied to develop regional planning models for Makit County and Xingshan County, China respectively to support rural sustainable development.
Open Access
Development of Two-Stage Fractional Programming Methods for Environmental Management Under Uncertainty
(Faculty of Graduate Studies and Research, University of Regina, 2014-12) Zhou, Xiong; Huang, Guo H.; An, Chunjiang; Young, Stephanie; Mehrandezh, Mehran
Due to the increasing contamination and resource-scarcity issues, environmental systems management is essential to socio-economic development. However, formulating relevant policies and strategies is often associated with a variety of complexities. It is necessary for decision makers to identify desired management plans to reflect multiobjective features that involve a trade-off between environmental protection and economic development. Moreover, these complexities will be further intensified by multiple formats of uncertainties existent in the related factors and parameters, as well as their interrelationships. Therefore, efficient system analysis techniques for supporting multiobjective environmental systems management under such complexities are required. In this dissertation research, a set of two-stage fractional programming methods were developed for managing environmental systems under uncertainty, including (a) a twostage fractional programming method for managing multiobjective waste management systems, (b) a two-stage chance-constrained fractional programming method for sustainable water quality management under uncertainty, and (c) a dynamic chanceconstrained two-stage fractional programming method for planning regional energy systems in the province of British Columbia, Canada. The proposed multiobjective optimization methods could address the conflicts between two objectives (e.g. economic and environmental effects) without the demand of subjectively setting a weight for each objective. Economic penalties were taken into consideration as corrective measures against any arising infeasibility caused by a particular realization of uncertainty, such that a linkage to pre-regulated policy targets was established. Furthermore, the methods facilitated an in-depth analysis of the interactions between economic cost and system efficiency. The developed methods could provide desired decision alternatives for managing environmental systems under various conditions.
Open Access
Emergency Evacuation Management for Nuclear Power Plant Accidents Under Multiple Uncertainties
(Faculty of Graduate Studies and Research, University of Regina, 2017-01) Guo, Li; Huang, Guo (Gordon); Yang, Boting; An, Chunjiang; Wu, Peng; Zhang, Zhou; Xiao, Huining
Nuclear power accidents are one of the most dangerous disasters posing a lethal threat to human health and have detrimental effects lasting for decades. Therefore, emergency evacuation is important to minimize injuries and prevent lethal consequences resulting from a nuclear power accident. An evacuation management system in response to a nuclear power plant accident, involves a number of processes with a variety of socio-economic and environmental implications. These processes may be influenced by a number of factors, such as availability of traffic vehicles employed, number of evacuees, destinations of evacuees, limitations of evacuation times, budgets for evacuation, shelter and hospital capacities, and environmental regulations of the areas involved. Extensive uncertainties exist in the evacuation process and the associated factors. Hence, innovative inexact optimization approaches may be proposed to account for various uncertainties existing in an evacuation planning system. In this dissertation, a series of inexact optimization approaches will be proposed to reflect compound uncertainties existing in emergency evacuation planning systems. In detail, (i) an interval-based evacuation management (IBEM) model will be developed in response to a nuclear-power plant accident; (ii) an inexact gradient-based fuzzy chance constrained programming (IGFCCP) method will be proposed to balance a decision maker’s optimistic and pessimistic preferences and applied to the planning of the evacuation scheme for the Qinshan Nuclear Power Site (QNPS); (iii) an inexact fuzzy stochastic chance constrained programming (IFSCCP) approach will be developed to address various uncertainties and optimize the planning of the evacuation scheme for the QNPS; (iv) an inexact mixed-integer credibility-constrained de Novo programming (IMICDNP) method will be developed to analyze the trade-offs between conflicting objectives under uncertainty. The major accomplishments of this research are summarized as follows: (i) A set of inexact optimization approaches are proposed to deal with uncertainties in emergency evacuation planning systems. The uncertainties, expressed in various forms such as interval, fuzzy, fuzz random variables, can be well reflected through the developed IBEM, IGFCCP, IFSCCP, IMCDP methods. Optimal decision alternatives for emergency evacuation planning are obtained through the proposed approached under various uncertain conditions. (ii) The applicability of the proposed approaches are demonstrated through a real-case study of emergency evacuation management at the Qinshan Nulcear Power Site (QNPS), which is the first nuclear power plant in China. Under consideration of various system complexities and uncertainties, potential.
Open Access
Enhanced Physicochemical Processes for the Treatment of petroleum-Contaminated Systems
(Faculty of Graduate Studies and Research, University of Regina, 2015-09) Zhao, Shan; Huang, Guo H.; Ng, Kelvin Tsun Wai; An, Chunjiang; Deng, Dianliang; Song, Lianfa
In Canada, environmental issues caused by petroleum-contaminated sites are becoming a major concern. Therefore, effective physicochemical remediation technologies are desired for produced water treatment and groundwater remediation in oil fields. In this dissertation research, the feasibility of treating produced water using synthetic polymers combined with natural diatomite was evaluated. Using diatomite as an adsorbent and a coagulant aid, this study provided an economical and enhanced approach for utilizing diatomite in the clean-up of produced water. A pilot-scale electrocoagulation process was developed for enhanced removal of hardness, chemical oxygen demand, and turbidity to mitigate the scaling and fouling of Reverse Osmosis membranes. Response surface methodology was employed to refine operating parameters and to evaluate individual/interactive effects of parameters on pollutant recovery. The modification of palygorskite with gemini surfactants enhanced phenanthrene retention in solid particles from aqueous phase. The effects of solution chemistry on phenanthrene sorption to modified palygorskite were systematically studied. The effectiveness of gemini modified palygorskite as the novel remediation material in polycyclic aromatic hydrocarbon contaminated water remediation was revealed and examined. A multi-level fuzzy-factorial inference approach was proposed to elucidate the sorption behavior of phenanthrene on palygorskite modified with gemini surfactants. Fuzzy vertex analysis discretized the design factors with triangular membership functions into multiple deterministic levels. Examination of curvature effects of factors revealed the nonlinear complexity inherent in the sorption process. The potential interactions among experimental factors were detected, which was meaningful for providing a deep insight into the sorption mechanisms under the influences of factors at different levels. The enhancement of soil retention for phenanthrene was investigated through the sorption barriers created by binary mixture of cationic gemini and nonionic surfactants. The research addressed the sorption characteristic and mechanism of gemini surfactant in complex soil system using a developed Two-step Adsorption and Partition Model. The sorption barrier substantially enhanced the soil retention capabilities for phenanthrene, while the sorption of gemini was inhibited by the increasing nonionic surfactant dose. The interactions among water, soil, surfactant, and contaminant in petroleumcontaminated systems have been revealed. This research can provide reference on the implementation of remediation technologies at petroleum-contaminated sites.
Open Access
High-Resolution Regional Climate Downscaling and Probabilistic Projection for Impact Assessment – A Canadian Case Study
(Faculty of Graduate Studies and Research, University of Regina, 2015-09) Wang, Xiuquan; Huang, Guo H.; Yang, Xue-Dong; Maqsood, Imran; An, Chunjiang; Chen, Jing Ming
Human-induced climate change has been regarded as one of the most pressing issues around the world because it often leads to severe, widespread, and irreversible consequences. Assessing the potential impacts of climate change is essential and critical for developing appropriate mitigation and adaptation strategies against the changing climate. In this research, a series of approaches and methodologies have been proposed for dealing with the challenges in climate change impact assessment due to the lack of highresolution climate projections and the difficulty in quantifying the uncertainties associated with future climate projections. The proposed approaches and methodologies have been applied to the Province of Ontario, Canada to demonstrate their effectiveness in generating probabilistic and high-resolution regional climate scenarios. Specifically, a new statistical downscaling tool, named SCADS, has been developed to help perform rapid development of downscaled scenarios under current and future climate forcing conditions. The SCADS uses a cluster tree to effectively deal with continuous and discrete variables, as well as nonlinear relations between large-scale atmospheric variables and local surface ones. A hybrid downscaling approach by coupling the PRECIS model and the SCADS model has been proposed to construct high resolution climate projections for studying climate change impacts at local scales. The coupled approach was applied for projecting the future climate over Ontario at a fine resolution of 10 km. A Bayesian hierarchical model has been developed to quantify the uncertainties of regional climate projections in a statistical framework based upon a limited number of explicit assumptions for prior distributions. By feeding the observations for current climate and the PRECIS ensemble simulations into the Bayesian model, probabilistic projections of future climatic changes over Ontario have been developed. The likely changes in temperature and precipitation as well as extreme precipitation events across the Province of Ontario were evaluated to help understand its local climate’s response to global warming. A public climate change data portal, named Ontario CCDP, have been established to ensure impact researchers and decision makers have free access to the high-resolution climate projections, thus supporting further impact studies and development of climate mitigation and adaptation strategies.
Open Access
Immobilization of Tetrabromobisphenol A On Pinecone-Derived Biochar
(Faculty of Graduate Studies and Research, University of Regina, 2017-08) Shen, Jian; Huang, Guo (Gordon); An, Chunjiang; Ng, Kelvin; Peng, Wei
In the past decades, Tetrabromobisphenol A (TBBPA), one of brominated flame retardants (BFR), has been widely used in a variety of commercial and industry applications to improve fire resistance, and it is also commonly used in fire safety of laminates in printed circuit boards, plastics in electrical and electronic equipment. Due to its large part of world production of BFRs for covering around 60% of the total BFR market and the use of TBBPA is currently not restricted in many countries including America and China, TBBPA is of great concern as a relatively persistent organic pollutant released through manufacturing, recycling, and disposal of various fabrics and materials. Moreover, many studies suggest that TBBPA at environmentally relevant concentrations can induce a variety of adverse health effects including cytotoxicity, immunotoxicity, hepato-toxicity, disruption of thyroid homeostasis, and has potential to disrupt estrogen signaling. The aqueous toxicity of TBBPA has been demonstrated that the acute 48-h LC50 (lethal concentration of 50%) for Daphnia magna was reported to be 0.96 mg/L, and 96-h LC50 for fish ranged from 0.40 to 0.54mg/L. The acute oral toxicity of TBBPA for mammals is low. However, the International Agency for Research on Cancer has recently upgraded this flame retardant to group 2A (probably carcinogenic to humans) which was based on sufficient evidence of carcinogenicity in experimental animals and strong mechanistic evidence in humans. The immobilization of TBBPA from aqueous solution by pinecone-derived biochars was investigated. The surface structures and functional groups of biochars produced at different temperatures were characterized through synchrotron-assisted FTIR analysis. The adsorption isotherms were well described by the Langmuir model. The adsorption capacity of TBBPA varied as pH and TBBPA initial concentration changed. The influences of inorganic fertilizer ions (NH4+, PO43− and NO3− ) on the immobilization of TBBPA by pinecone biochars were revealed through fractional-factorial assisted analysis. The results indicated the main effects include negative effects of PO43− , positive effects of NH4+ and insignificant effects of NO3− ions in immobilization and there are interactions among these ions, pH and biochar properties. Functionality and surface interaction between biochar and TBBPA were investigated under analysis of kinetic models, contact times, surface composition and synchrotron-based FTIR analysis. The results indicated that the interaction between TBBPA on biochar was mainly caused by hydroxyls. Their interactive effects of acid functional groups could be an important role in the adsorption of TBBPA. Moreover, under the high initial concentration, the adsorption process can be stabilized by other oxygen-containing groups. In addition, this study also revealed that π-π interactions could have insignificant impacts on TBBPA adsorption. The results can help understand migration patterns of TBBPA and analyze the immobilization on biochars in the presence of inorganic fertilizer ions. It will have important implications for environmental risk assessment and wastewater treatment.
Open Access
A Market-Based Arid-Region Water Resources Planning Model: Application to the Guhai Water Distribution System, China
(Faculty of Graduate Studies and Research, University of Regina, 2015-08) Li, Kailong; Huang, Guo H.; An, Chunjiang; Jin, Yee-Chung; Yao, Yiyu
The arid regions in China, which own a pivotal position in national agriculture production, have confronted water supplying crisis triggered by the rapid growth of local economy. The Arid Zone of Ningxia Hui Autonomous Region (AZN) is one of the arid regions that severely suffer from water shortage. Since 1970s, local people has been relying on the water delivered from the Yellow River by water distribution systems, among which Guhai Water Distribution System (GWDS) is the largest and earliest one. Due to the limited capacity of GWDS and the decreasing precipitation caused by changing climate, the water availability in AZN is becoming scarcer. In recent years, emerging industrial sectors and growing food demands have become the major driving forces for water conservation in AZN, especially for those areas covered by GWDS. Development of an Integrated Water Resources Management (IWRM) approach has been placed as a priority in the government agenda with a combination of technology innovation, institutional development and system consideration. However, inherent complexities and uncertainties in natural, social and economic contexts are posing great challenges for decision makers to bring out a comprehensive water management strategy. Therefore, this research aims to develop a Market-Based Arid-Region Water Resources Planning (MAWRP) model for GWDS, with a focus on water trading under uncertainty. The MAWRP model encompassed a board perspective of technological, political and social factors to reflect the tradeoffs between food production and gains from water trading in the GWDS. This model is useful for exploring the full potential of water conservation through a combination of three approaches, including cropping pattern optimization, irrigation infrastructure improvement and water trading. The results show that the proposed method can help generate optimal cropping patterns, water trading rules, and improved irrigation infrastructure areas under various uncertainties. Moreover, decision makers can obtain useful information to formulate reasonable water allocation strategies and to design the most beneficial subsidization policies through the comparison of various policy scenarios with different water prices and opportunity cost of water.
Open Access
Multi-Level Factorial Sensitivity Analysis of REGCM4 Physical Scheme Combinations Over Canada
(Faculty of Graduate Studies and Research, University of Regina, 2019-01) Song, Tangnyu; Huang, Guo (Gordon); An, Chunjiang; Veawab, Amornvadee; Deng, Dianliang
Climate change has been considered as one of the most important problems around the world since it always causes severe, extensive and irreversible consequences. It is thus desired to generate reliable high-resolution climate projections for supporting impact assessments under changing climate conditions. In this study, a multi-level factorial design has been proposed to conduct the sensitivity analysis of RegCM4 physical scheme combinations. In detail, the major contributing factors for four climatic variables are selected based on the predetermined contribution level; then the F-test has been applied to evaluate the statistical significance of each individual physical scheme or the interaction among multiple physical schemes. The proposed design has been applied to Canada to illustrate its effectiveness. The results indicate that there exist considerable spatial and temporal simulated differences when choosing varied physical scheme combinations. Although the single physical schemes have dominant influences on simulated differences, the effects explained by their interactions can not be neglected. Moreover, the PBL scheme, moisture scheme and land surface model are found to be the dominant factors of temperature, precipitation and wind speed simulations, respectively. Furthermore, the vegetation cover conditions may affect the cumulus convective activities to some extent, accounting for the spatial and temporal statistical significance variations of cumulus convective scheme. The obtained results could help screen out the most sensitive physical schemes or interactions considering specific region and season. On this basis, the computational costs of follow-up long-term simulations are considered affordable, which further assist in selecting the optimized ii physical scheme combination and then generating reliable high-resolution climate projections over Canada.
Open Access
Physiochemical and Biological Behaviors of Organic Contaminants in Multi-Phase Media Under Enhanced Treatment Conditions
(Faculty of Graduate Studies and Research, University of Regina, 2013-05) An, Chunjiang; Huang, Guo (Gordon); Zeng, Fanhua; Deng, Dianliang; Veawab, Amornvadee; Liu, Lei
The rapid increase of anthropogenic activities has led to the ubiquitous presence of organic contaminants. There is an urgent need for understanding the fate and behavior of organic contaminants in the environment. In this dissertation research, the physiochemical and biological behaviors of organic contaminants in multi-phase media have been investigated. Considerable research efforts have been made in characterizing the transport of polycyclic aromatic hydrocarbons (PAHs) at solid-liquid interface, as well as exploring the remediation of pollutants through solvent extraction and composting. The effects of short-chain organic acids (SCOAs) on the behaviors of PAHs in soil-water system have been systemically investigated. The amendment of SCOAs could influence the adsorption and desorption behaviors of pyrene in soil-water system. The mobility of phenanthrene in soil could be also enhanced by introducing organic acids. The biosurfactant-enhanced remediation of PAH-contaminated soil in the presence of SCOAs has been studied. The results supported the combined use of biosurfactant and SCOAs could further enhance the desorption of phenanthrene from soil into aqueous phase. The quantity and species of organic acids could affect the tendency of phenanthrene distribution in the presence of biosurfactant. The adsorption of phenanthrene on fly ash in solid-liquid system was investigated. The adsorption of phenanthrene onto fly ash showed significant stepwise pattern. The developed stepwise multisite Langmuir model can help well simulate the adsorption process. The solution chemistry played an important role in the distribution of phenanthrene in fly ash-water system. The performance of food waste composting in the presence of fly ash and uric acid has been investigated. Different trends of degradation intensity, O2 uptake amount, microbial population and C/N ratio were observed in the series with additional fly ash and uric acid. The results can be used to understand the environmental patterns and mechanisms of organic contaminant behaviors in a complex system. They have important implications for environmental risk assessment and pollution control at contaminated sites.
Open Access
Projecting Regional Climate Change through Statistical and Dynamical Downscaling Techniques
(Faculty of Graduate Studies and Research, University of Regina, 2017-08) Lu, Chen; Huang, Guo (Gordon); An, Chunjiang; wu, Peng; Zeng, Fanhua
Climate change has comprehensive and profound influences on every aspect of the natural and human systems such as the hydrological systems, ecosystems, food systems, infrastructure, human health and livelihoods. Not only are the impacts diverse in expressions, they are also geographically heterogeneous. In order to manage and reduce the risk of climate change impacts with the consideration of its heterogeneity, region-specific mitigation and adaptation strategies needs to be taken. The foundation of regional climate impact studies and the subsequent design of regional mitigation and adaption strategies is the projection of future regional or local climate. To this end, this study focuses on exploring how the regional or local climate is affected in the context of global warming, using both statistical and dynamical downscaling techniques. Specifically, (1) the stepwise cluster analysis (SCA) is used for downscaling of the local climate of the City of Toronto, and (2) the RegCM is used for the dynamical downscaling of the regional climate over China. From the statistical downscaling study of the temperature of the City of Toronto, the capability of the SCA for capturing the relationship between the global atmospheric variables and the local surface variables is demonstrated. Geophysical Fluid Dynamics Laboratory (GFDL) data of the historical period, representative concentration pathways (RCP4.5 and RCP8.5) is used for the projection of the future local climate. The results show that the future daily maximum, mean and minimum temperature (Tmax, Tmean, and Tmin) of the City of Toronto is likely to increase, with the speed of increase higher under RCP8.5 than under RCP4.5. The Tmin is projected to have a slightly larger increase than the Tmax. Significant increasing trend can be found under both scenarios for the entire 80- year future periods for Tmax, Tmean, and Tmin. In terms of the seasonal variations, large temperature increase happens in summer under RCP4.5, while it happens in both summer and winter under RCP8.5. In terms of the extreme climate, the occurrence of extreme cold weather will decrease and extreme warm weather increase, whether the index is percentile-based or value-based. The diurnal temperature range will decrease, which is consistent with the aforementioned conclusions. Potentially beneficial for the agriculture, the growing season length is projected to increase for the City of Toronto. For the dynamical downscaling study of the regional climate over China, the results indicate that RegCM is capable of reproducing the spatial distributions of temperature and precipitation over China. Particularly, the high temperature centers in the Tarim Basin and the Sichuan Basin, which GFDL fails to capture, are reasonably represented by RegCM. RegCM also demonstrates good performance in eliminating the unrealistic high-precipitation center between the Tibetan Plateau and the Sichuan Basin produced by GFDL. Future projections from RegCM suggest that an increase of 2 °C in mean temperature is expected in China by the end of the twenty-first century under RCP4.5 while an increase of 4 °C would be seen under RCP8.5. The Tibetan Plateau is likely to expect the largest increase in temperature in China. The magnitude of increase in minimum temperature is apparently higher than that of mean and maximum temperature. In comparison, the annual total precipitation over China is projected to increase by 7% by the end of the twenty-first century under RCP4.5 and by 9% under RCP8.5. The projected changes in precipitation show apparent spatial variability due to the influences of local topography and land cover/use.
Open Access
Saskatchewan Electrical Power Systems Planning under Stochastic Conditions and Federal Climate Change Regulations
(Faculty of Graduate Studies and Research, University of Regina, 2016-08-05) Pan, Siyue; Huang, Guo (Gordon); An, Chunjiang; Zeng, Fanhua
Greenhouse gas (GHG) emissions from industrial activities are the key drivers of climate change impacts in many regions around the world. In 2012, Environment Canada and Climate Change published its first GHG emissions performance standards, the Reduction of Carbon Dioxide Emissions from Coal-fired Generation of Electricity Regulations, to set up the performance standards for coal-fired generation units to reduce GHG emissions in Canada. Saskatchewan is concerned about the impact of the published regulations on future electricity generation system planning and GHG. Specifically, the regulations result in a shift from existing coal-fired generation units towards other high efficient power generation units or carbon capture and storage technologies. In this research, chance-constrained programming (CCP) and fractional programming (FP) approaches have been applied to provide recommendations and options for Saskatchewan’s power system optimization, GHG emissions reduction planning, and risk analysis under the Canada Reduction of Carbon Dioxide Emissions from Coal-fired Generation of Electricity Regulations. A number of policy and decision options for Saskatchewan’s power system have been obtained from three designed scenarios, which are based on comprehensive consideration of the GHG emission targets, social impacts, and regional economic and environmental impacts. More importantly, multi-layer interrelationships among multiple electrical system components have been examined in this research.
Open Access
Slow Sand Filtration Treatment of Agriculturally-Impacted Water
(Faculty of Graduate Studies and Research, University of Regina, 2017-09) Chen, Weixin; McMartin, Dena; An, Chunjiang; JPeng, Wei; Weger, Harold
Agricultural runoff and influence to surface water is a widely experienced problem across the world, particularly in treating that water for potable consumption. Although many effective methods have been researched, there are few that can be considered suitable for rural regions, in-home and on-site water treatment, and extremely smallscale design without requiring significant chemical use, operator skills and sophistication, and large-scale to be economically viable. This research focuses on the use of slow sand filtration (SSF) as one of the most effective methods for potable water treatment that can meet all of the above criteria. The experiments were designed using rapid small scale column testing theory, which is a directly scalable approach to developing a robust and effective system for treating water. The SSF laboratory experiments were divided into two parts: preliminary experiments and full-scale experiments. The preliminary experiments were designed to test the removal efficiency of agriculturally-associated concentrations of orthophosphate and iron. The full-scale experiments were design simulate the realistic SSF plant which can test the removal efficiency with different concentration of additive in raw water. Raw water samples were collected from Wascana Lake as representative of complex water quality associated with both rural and urban contamination and agricultural influence. In the experiments, these raw water samples are subjected to several operational, water quality and environmental factors, including temperature, pH and filtration rate, to determine the impacts on removal efficiency and overall performance of the SSFs. The results of the preliminary experiments indicate that 25°C and alkaline conditions had the most significant impact on orthophosphate removal. Factors such as lower temperature (5°C) and acidic condition created an inhibited impact on the results, leaving higher concentration of orthophosphate in the effluent. For iron removal, the pH, temperature and filtration rate were all noted to impact removal efficiency. Higher temperature (25°C), increased alkalinity, and higher filtration rate resulted in higher effluent concentrations of iron. The results of the full-scale experiments indicate the interaction of phosphate, nitrate, iron and humic acids in SSF. The data show that increasing phosphate and nitrate in the raw water can slightly improve the performance of nitrate and phosphate removal but no obvious effect in humic acids and iron removal. The higher iron concentration can help improve humic acids removal. The experimental results clearly demonstrate that temperature and pH control in water treatment design are essential for high performance and robust and reliable production of high quality potable water.
Open Access
Stochastic Modeling and Risk Management for Water Resources Systems Under Changing Climatic Conditions
(Faculty of Graduate Studies and Research, University of Regina, 2016-06) Li, Zhong; Huang, Guo (Gordon); Yang, Xue Dong; Ng, Kelvin; An, Chunjiang; Cai, Ximing
Water resources are indispensable for the sustainable development of the human society. A variety of hydrological modeling and water resources management tools based on simulation and optimization have been developed to address the current water issues worldwide. However, there are many challenges arising from climate change, human disturbances and enormous uncertainties and complexities. Thus, there is a global need for advanced methodologies that can support the modeling and management of water resources systems in an effective and efficient way. In this dissertation research, a spectrum of methods have been developed to deal with the stochastic modeling and risk-based management problems for water resources systems. These methods include: (i) a Stepwise Clustered Hydrological Inference (SCHI) model that can establish the complex nonlinear relationships between climatic conditions and streamflow for hydrological forecasting; (ii) a flexible and effective hydro-climatic modeling framework based on the Providing Regional Climates for Impacts Studies (PRECIS) modeling system and stepwise cluster analysis for hydrological modeling under the changing climatic conditions; (iii) a Stepwise-cluster-analysis-based Probabilistic Collocation Expansion (SPCE) method for the stochastic simulation and forecast of hydrologic time series; (iv) a hydrologic frequency analysis framework based on change point analysis and Bayesian parameter estimation to deal with the nonstationarity and uncertainties in hydrological risk analysis; (v) an Interval-parameter Two-stage Fuzzy Stochastic Integer Programming (ITFSIP) model for risk-based flood diversion management under multiple uncertainties. The proposed methods have been applied to the Xiangxi River Watershed in China and the Grand River Watershed in Canada, in order to demonstrate their capabilities and performances in precipitation-runoff modeling, climate change impact analysis, uncertainty quantification, frequency analysis, and systematic water resources and risk management. The major contribution of this research lies in the development of innovative approaches for tackling various uncertainties and complexities in the hydrological cycle and water resources systems. This research can provide scientific and practical bases for robust hydrological modeling and reliable water resources management.