Huaicheng Guo

Factor Analysis and Dynamics of Water Quality of the Songhua River, Northeast China

The water quality of the Songhua River (Northeast China) was of concern, especially in the 1980s and early 1990s, and there weremany studies carried out on this aspect. However, only one or several independent water quality indiceswere used to consider the effects of pollutants on thewater quality, for instance, heavy metal and toxic organicpollutants. The combined effects of multiple indices wereseldom studied. In this article, based on the environmentalmonitoring data, the water quality of the Songhua River wasanalyzed using factor analysis, which comprehensivelyconsidered six indices of water quality of each monitoringsection. The results showed that the main pollutants hadchanged to nitrogenous pollutants originated from nonpointsources, and water quality was variable in differenthydrological periods. The results also showed that themethod was comprehensive and efficient in analyzing thedynamics of water quality.

Water quality modeling for load reduction under uncertainty: A Bayesian approach

A Bayesian approach was applied to river water quality modeling (WQM) for load and parameter estimation. A distributed-source model (DSM) was used as the basic model to support load reduction and effective water quality management in the Hun-Taizi River system, northeastern China. Water quality was surveyed at 18 sites weekly from 1995 to 2004; biological oxygen demand (BOD) and ammonia (NH(4)(+)) were selected as WQM variables. The first-order decay rate (k(i)) and load (L(i)) of the 16 river segments were estimated using the Bayesian approach. The maximum pollutant loading (L(m)) of NH(4)(+) and BOD for each river segment was determined based on DSM and the estimated parameters of k(i). The results showed that for most river segments, the historical loading was beyond the L(m) threshold; thus, reduction for organic matter and nitrogen is necessary to meet water quality goals. Then the effects of inflow pollutant concentration (C(i-1)) and water velocity (v(i)) on water quality standard compliance were used to demonstrate how the proposed model can be applied to water quality management. The results enable decision makers to decide load reductions and allocations among river segments under different C(i-1) and v(i) scenarios.

Scientometric analysis of geostatistics using multivariate methods

Multivariate methods were successfully employed in a comprehensive scientometric analysis of geostatistics research, and the publications data for this research came from the Science Citation Index and spanned the period from 1967 to 2005. Hierarchical cluster analysis (CA) was used in publication patterns based on different types of variables. A backward discriminant analysis (DA) with appropriate statistical tests was then conducted to confirm CA results and evaluate the variations of various patterns. For authorship pattern, the 50 most productive authors were classified by CA into 4 groups representing different levels, and DA produced 92.0% correct assignment with high reliability. The discriminant parameters were mean impact factor (MIF), annual citations per publication (ACPP), and the number of publications by the first author, for country/region pattern, CA divided the top 50 most productive countries/regions into 4 groups with 95.9% correct assignments, and the discriminant parameters were MIF, ACCP, and independent publication (IP); for institute pattern, 3 groups were identified from the top 50 most productive institutes with nearly 88.0% correct assignment, and the discriminant parameters were MIF, ACCP, IP, and international collaborative publication; last, for journal pattern, the top 50 most productive journals were classified into 3 groups with nearly 98.0% correct assignment, and its discriminant parameters were total citations, impact factor and ACCP. Moreover, we also analyzed general patterns for publication document type, language, subject category, and publication growth.

A dynamic optimization approach for nonrenewable energy resources management under uncertainty

Abstract This paper introduces an integrated dynamic optimization approach for nonrenewable energy (NRE) resources management under uncertainty. A hybrid inexact chance-constrained mixed-integer linear programming (ICCMILP) method is proposed, with an objective of maximizing economic return under constraints of resources availability and environmental regulations. In its solution process, the ICCMILP is transformed into two deterministic submodels, which correspond to the upper and lower bounds for the desired objective function value. Interval solutions, which are feasible and stable in the given decision space, can then be obtained by solving the two submodels sequentially. Thus, decision alternatives can be generated by adjusting decision variable values within their solution intervals. The obtained solutions are useful for decision makers to optimally allocate limited NRE resources over time for acquiring maximized benefit. Meanwhile, regional air quality could be maintained to keep the communities from health damage. Results of a hypothetical case study indicate that reasonable solutions for dynamic planning of NRE resources allocation in a regional system have been obtained. A number of decision alternatives were generated based on the ICCMILP solutions as well as the projected applicable conditions.

Quantitative evaluation of lake eutrophication responses under alternative water diversion scenarios: a water quality modeling based statistical analysis approach.

China is confronting the challenge of accelerated lake eutrophication, where Lake Dianchi is considered as the most serious one. Eutrophication control for Lake Dianchi began in the mid-1980s. However, decision makers have been puzzled by the lack of visible water quality response to past efforts given the tremendous investment. Therefore, decision makers desperately need a scientifically sound way to quantitatively evaluate the response of lake water quality to proposed management measures and engineering works. We used a water quality modeling based scenario analysis approach to quantitatively evaluate the eutrophication responses of Lake Dianchi to an under-construction water diversion project. The primary analytic framework was built on a three-dimensional hydrodynamic, nutrient fate and transport, as well as algae dynamics model, which has previously been calibrated and validated using historical data. We designed 16 scenarios to analyze the water quality effects of three driving forces, including watershed nutrient loading, variations in diverted inflow water, and lake water level. A two-step statistical analysis consisting of an orthogonal test analysis and linear regression was then conducted to distinguish the contributions of various driving forces to lake water quality. The analysis results show that (a) the different ways of managing the diversion projects would result in different water quality response in Lake Dianchi, though the differences do not appear to be significant; (b) the maximum reduction in annual average and peak Chl-a concentration from the various ways of diversion project operation are respectively 11% and 5%; (c) a combined 66% watershed load reduction and water diversion can eliminate the lake hypoxia volume percentage from the existing 6.82% to 3.00%; and (d) the water diversion will decrease the occurrence of algal blooms, and the effect of algae reduction can be enhanced if diverted water are seasonally allocated such that wet season has more flows.

Mixed uncertainty analysis of polycyclic aromatic hydrocarbon inhalation and risk assessment in ambient air of Beijing

This article presents the application of an integrated method that estimates the dispersion of polycyclic aromatic hydrocarbons (PAHs) in air, and assesses the human health risk associated with PAHs inhalation. An uncertainty analysis method consisting of three components were applied in this study, where the three components include a bootstrapping method for analyzing the whole process associated uncertainty, an inhalation rate (IR) representation for evaluating the total PAH inhalation risk for human health, and a normally distributed absorption fraction (AF) ranging from 0% to 100% to represent the absorption capability of PAHs in human body. Using this method, an integrated process was employed to assess the health risk of the residents in Beijing, China, from inhaling PAHs in the air. The results indicate that the ambient air PAHs in Beijing is an important contributor to human health impairment, although over 68% of residents seem to be safe from daily PAH carcinogenic inhalation. In general, the accumulated daily inhalation amount is relatively higher for male and children at 10 years old of age than for female and children at 6 years old. In 1997, about 1.73% cancer sufferers in Beijing were more or less related to ambient air PAHs inhalation. At 95% confidence interval, approximately 272-309 individual cancer incidences can be attributed to PAHs pollution in the air. The probability of greater than 500 cancer occurrence is 15.3%. While the inhalation of ambient air PAHs was shown to be an important factor responsible for higher cancer occurrence in Beijing, while the contribution might not be the most significant one.

AN INDEPENDENT VARIABLE CONTROLLED GREY FUZZY LINEAR PROGRAMMING APPROACH FOR WASTE FLOW ALLOCATION PLANNING

This paper proposes an independent variable controlled grey fuzzy linear programming (IVC-GFLP) approach to address uncertainty in optimization processes. The IVC-GFLP method improves upon established grey linear programming (GLP) and ordinary grey fuzzy linear programming (GFLP) methods by introducing independent control variables into model formulations. These variables enable the model to address the independent characteristics of constraint uncertainty well. In this paper, the IVC-GFLP approach is applied to a hypothetical case study of municipal solid waste management. Included comparisons between the IVC-GFLP and GLP/GFLP solutions indicate that the IVC-GFLP approach can provide more realistic and applicable solutions than its counterparts.

Enhanced-interval linear programming

An enhanced-interval linear programming (EILP) model and its solution algorithm have been developed that incorporate enhanced-interval uncertainty (e.g., A±, B± and C±) in a linear optimization framework. As a new extension of linear programming, the EILP model has the following advantages. Its solution space is absolutely feasible compared to that of interval linear programming (ILP), which helps to achieve insight into the expected-value-oriented trade-off between system benefits and risks of constraint violations. The degree of uncertainty of its enhanced-interval objective function (EIOF) would be lower than that of ILP model when the solution space is absolutely feasible, and the EIOFs expected value could be used as a criterion for generating the appropriate alternatives, which help decision-makers obtain non-extreme decisions. Moreover, because it can be decomposed into two submodels, EILPs computational requirement is lower than that of stochastic and fuzzy LP models. The results of a numeric example further indicated the feasibility and effectiveness of EILP model. In addition, EI nonlinear programming models, hybrid stochastic or fuzzy EILP models as well as risk-based trade-off analysis for EI uncertainty within decision process can be further developed to improve its applicability.

Analysis of cyanobacteria bloom in the Waihai part of Dianchi Lake, China

article i nfo Article history: Blue-green algae (BGA) bloom is a typical phenomenon in eutrophied lakes. However, up to now, no environmental mechanism has been commonly accepted. Systematic and complete data sets of BGA blooms and environmental factors without any missing data are rare, which seriously affected previous studies. In this study, a bootstrapping based multiple imputation algorithm (EMB) was first applied to reconstruct a complete data set from the available data set with missing data, hence forming a basis for quantitatively relating BGA bloom to contributing factors. Then, the probability of BGA bloom outbreak was simulated using a binomial (or binary) logistic regression model, which is an effective tool for recognizing key contributing factors. The results suggest that 1) the outbreak frequency or probability of BGA bloom tends to first increase and then decrease with a turning point between June and September each year; 2) air temperature, relative humidity, and precipitation were significant positive factors correlated with outbreak frequency, whereas wind speed and the number of sunshine hours were negative factors; 3) water temperature had a strong positive effect on the probability of BGA bloom outbreak, whereas other water quality factors, such as concentrations of organics and nutrients, were not so significant. However, water quality factors, such as NO3-N, SD, pH, NH4-N, COD and DO, still need to be concerned, which had a potential to aggravate the outbreak of BGA bloom in Dianchi Lake, if they were out of control.

REILP Approach for Uncertainty-Based Decision Making in Civil Engineering

The civil and environmental decision-making processes are plagued with uncertain, vague, and incomplete information. Interval linear programming ILP is a widely applied mathematical programming method in assisting civil and environmental decision making under uncertainty. However, the existing ILP decision approach is found to be ineffective in generating operational schemes for practical decision support due to a lack of linkage between decision risk and system return. In addition, the interpretation of the ILP solutions represented as the lower and upper bounds of decision variables can cause problems of infeasibility and nonoptimality in the resulted implementation schemes. This study proposed a risk explicit ILP REILP approach to overcome the limitations of existing ILP approaches. The REILP explicitly reflects the tradeoffs between risk and system return for a decision-making problem under an interval- type uncertainty environment. A risk function was defined to enable finding solutions which maximize system return while minimizing system risk, hence leading to crisp solutions that are feasible and optimal for practical decision making. A numerical experiment on land-use decision making under total maximum daily load was conducted to illustrate the REILP approach. The model results show that the REILP approach is able to efficiently explore the interval uncertainty space and generate an optimal decision front that directly reflects the tradeoff between decision risks and system return, allowing decision makers to make effective decision based on the risk-reward information generated by the REILP modeling analysis.