Jiping Jiang

Monthly water quality forecasting and uncertainty assessment via bootstrapped wavelet neural networks under missing data for Harbin, China.

In this paper, bootstrapped wavelet neural network (BWNN) was developed for predicting monthly ammonia nitrogen (NH4+–N) and dissolved oxygen (DO) in Harbin region, northeast of China. The Morlet wavelet basis function (WBF) was employed as a nonlinear activation function of traditional three-layer artificial neural network (ANN) structure. Prediction intervals (PI) were constructed according to the calculated uncertainties from the model structure and data noise. Performance of BWNN model was also compared with four different models: traditional ANN, WNN, bootstrapped ANN, and autoregressive integrated moving average model. The results showed that BWNN could handle the severely fluctuating and non-seasonal time series data of water quality, and it produced better performance than the other four models. The uncertainty from data noise was smaller than that from the model structure for NH4+–N; conversely, the uncertainty from data noise was larger for DO series. Besides, total uncertainties in the low-flow period were the biggest due to complicated processes during the freeze-up period of the Songhua River. Further, a data missing–refilling scheme was designed, and better performances of BWNNs for structural data missing (SD) were observed than incidental data missing (ID). For both ID and SD, temporal method was satisfactory for filling NH4+–N series, whereas spatial imputation was fit for DO series. This filling BWNN forecasting method was applied to other areas suffering “real” data missing, and the results demonstrated its efficiency. Thus, the methods introduced here will help managers to obtain informed decisions.

High-capacity adsorption of aniline using surface modification of lignocellulose-biomass jute fibers.

Pyromellitic dianhydride (PMDA) modified jute fiber (MJF) were prepared with microwave treatment to generate a biosorbent for aniline removal. The characterization of the biosorbent was investigated by SEM, BET and FT-IR analysis to discuss the adsorption mechanism. The studies of various factors influencing the adsorption behavior indicated that the optimum dosage for aniline adsorption was 3g/L, the maximum adsorption capacity was observed at pH 7.0 and the adsorption process is spontaneous and endothermic. The aniline adsorption follows the pseudo second order kinetic model and Langmuir isotherm model. Moreover, the biosorbent could be regenerated through the desorption of aniline by using 0.5M HCl solution, and the adsorption capacity after regeneration is even higher than that of virgin MJF. All these results prove MJF is a promising adsorbent for aniline removal in wastewater.

Evaluation and selection of emergency treatment technology based on dynamic fuzzy GRA method for chemical contingency spills

A robust scheme to address emergency pollution accident is becoming more and more important with the rise of the frequency and intensity of the emergency pollution accidents. Therefore, it is crucial to select an appropriate technology in an emergency response to chemical spills. In this study, an evaluation framework based on dynamic fuzzy GRA method has been developed to make forward optimum scheme for the selection of emergency treatment technology. Dynamic analysis and linguistic terms are used to evaluate alternatives to improve efficiency of emergency treatment procedures by addressing the vagueness and ambiguity in decision making. The method was then applied in a case study to evaluate emergency arsenic treatment technology and demonstrate its applicability and feasibility in emergency arsenic pollution under two scenarios associated with different arsenic levels. Therefore, not only the results can be used for selecting emergency treatment technology, but also help decision-makers identify desired decisions for contaminant mitigation with a quick response and cost-effective manner.

A global assessment of climate-water quality relationships in large rivers: an elasticity perspective.

To uncover climate-water quality relationships in large rivers on a global scale, the present study investigates the climate elasticity of river water quality (CEWQ) using long-term monthly records observed at 14 large rivers. Temperature and precipitation elasticities of 12 water quality parameters, highlighted by N- and P-nutrients, are assessed. General observations on elasticity values show the usefulness of this approach to describe the magnitude of stream water quality responses to climate change, which improves that of simple statistical correlation. Sensitivity type, intensity and variability rank of CEWQ are reported and specific characteristics and mechanism of elasticity of nutrient parameters are also revealed. Among them, the performance of ammonia, total phosphorus-air temperature models, and nitrite, orthophosphorus-precipitation models are the best. Spatial and temporal assessment shows that precipitation elasticity is more variable in space than temperature elasticity and that seasonal variation is more evident for precipitation elasticity than for temperature elasticity. Moreover, both anthropogenic activities and environmental factors are found to impact CEWQ for select variables. The major relationships that can be inferred include: (1) human population has a strong linear correlation with temperature elasticity of turbidity and total phosphorus; and (2) latitude has a strong linear correlation with precipitation elasticity of turbidity and N nutrients. As this work improves our understanding of the relation between climate factors and surface water quality, it is potentially helpful for investigating the effect of climate change on water quality in large rivers, such as on the long-term change of nutrient concentrations.

Removal of As(III) from water using modified jute fibres as a hybrid adsorbent

Many studies have concentrated on the removal of arsenic from water using granular mineral fine particles. However, very little research has focused on the preparation of materials which aim to be applied to the situations of arsenic pollution emergencies in rivers or lakes. In this study, jute fibres were modified by loading iron oxyhydroxide (which was demonstrated to be mainly α-FeOOH) to produce an effective hybrid adsorbent (Fe-JF) with the advantages of an excellent arsenic removal effect and easy retrieval from rivers or lakes. The jute fibres were firstly esterified with succinic anhydride to graft with carboxyl groups in order to enhance the loading amount of iron(III), the maximum iron(III) loading on Fe-JF reached 208.2 ± 0.2 mg g−1 while the density of grafted carboxyl groups was 2.78 mmol g−1. The maximum adsorption capacity for As(III) reached 12.66 mg g−1 while the density of carboxyl groups was 2.21 mmol L−1. Meanwhile, the iron leaching amount was 0.178 mg L−1 which could meet the requirement of the standard limit of iron in drinking water (China, 0.3 mg L−1). Influential factors, such as pH, contact time and coexisting anions are investigated in this study. The column experiments showed that the breakthrough point declined from 2300 BV (bed volume) to 1200 BV when EBCT (empty-bed contact time) decreased from 3.5 min to 1.8 min. The Adams–Bohart model was adopted to describe the continuous flow system.

A Bayesian method for multi-pollution source water quality model and seasonal water quality management in river segments

Abstract Excessive pollutant discharge from multi-pollution resources can lead to a rise in downriver contaminant concentration in river segments. A multi-pollution source water quality model (MPSWQM) was integrated with Bayesian statistics to develop a robust method for supporting load ( I ) reduction and effective water quality management in the Harbin City Reach of the Songhua River system in northeastern China. The monthly water quality data observed during the period 2005–2010 was analyzed and compared, using ammonia as the study variable. The decay rate ( k ) was considered a key factor in the MPSWQM, and the distribution curve of k was estimated for the whole year. The distribution curves indicated small differences between the marginal distribution of k of each period and that water quality management strategies can be designed seasonally. From the curves, decision makers could pick up key posterior values of k in each month to attain the water quality goal at any specified time. Such flexibility is an effective way to improve the robustness of water quality management. For understanding the potential collinearity of k and I , a sensitivity test of k for I 2i (loadings in segment 2 of the study river) was done under certain water quality goals. It indicated that the posterior distributions of I 2i show seasonal variation and are sensitive to the marginal posteriors of k . Thus, the seasonal posteriors of k were selected according to the marginal distributions and used to estimate I 2i in next water quality management. All kinds of pollutant sources, including polluted branches, point and non-point source, can be identified for multiple scenarios. The analysis enables decision makers to assess the influence of each loading and how best to manage water quality targets in each period. Decision makers can also visualize potential load reductions under different water quality goals. The results show that the proposed method is robust for management of multi-pollutant loadings under different water quality goals to help ensure that the water quality of river segments meets targeted goals.

Applying high-frequency surrogate measurements and a wavelet-ANN model to provide early warnings of rapid surface water quality anomalies

It is critical for surface water management systems to provide early warnings of abrupt, large variations in water quality, which likely indicate the occurrence of spill incidents. In this study, a combined approach integrating a wavelet artificial neural network (wavelet-ANN) model and high-frequency surrogate measurements is proposed as a method of water quality anomaly detection and warning provision. High-frequency time series of major water quality indexes (TN, TP, COD, etc.) were produced via a regression-based surrogate model. After wavelet decomposition and denoising, a low-frequency signal was imported into a back-propagation neural network for one-step prediction to identify the major features of water quality variations. The precisely trained site-specific wavelet-ANN outputs the time series of residual errors. A warning is triggered when the actual residual error exceeds a given threshold, i.e., baseline pattern, estimated based on long-term water quality variations. A case study based on the monitoring program applied to the Potomac River Basin in Virginia, USA, was conducted. The integrated approach successfully identified two anomaly events of TP variations at a 15-minute scale from high-frequency online sensors. A storm event and point source inputs likely accounted for these events. The results show that the wavelet-ANN model is slightly more accurate than the ANN for high-frequency surface water quality prediction, and it meets the requirements of anomaly detection. Analyses of the performance at different stations and over different periods illustrated the stability of the proposed method. By combining monitoring instruments and surrogate measures, the presented approach can support timely anomaly identification and be applied to urban aquatic environments for watershed management.

A two-stage optimization model for emergency material reserve layout planning under uncertainty in response to environmental accidents

In the emergency management relevant to pollution accidents, efficiency emergency rescues can be deeply influenced by a reasonable assignment of the available emergency materials to the related risk sources. In this study, a two-stage optimization framework is developed for emergency material reserve layout planning under uncertainty to identify material warehouse locations and emergency material reserve schemes in pre-accident phase coping with potential environmental accidents. This framework is based on an integration of Hierarchical clustering analysis - improved center of gravity (HCA-ICG) model and material warehouse location - emergency material allocation (MWL-EMA) model. First, decision alternatives are generated using HCA-ICG to identify newly-built emergency material warehouses for risk sources which cannot be satisfied by existing ones with a time-effective manner. Second, emergency material reserve planning is obtained using MWL-EMA to make emergency materials be prepared in advance with a cost-effective manner. The optimization framework is then applied to emergency management system planning in Jiangsu province, China. The results demonstrate that the developed framework not only could facilitate material warehouse selection but also effectively provide emergency material for emergency operations in a quick response.

Engineering risk assessment for emergency disposal projects of sudden water pollution incidents

Without an engineering risk assessment for emergency disposal in response to sudden water pollution incidents, responders are prone to be challenged during emergency decision making. To address this gap, the concept and framework of emergency disposal engineering risks are reported in this paper. The proposed risk index system covers three stages consistent with the progress of an emergency disposal project. Fuzzy fault tree analysis (FFTA), a logical and diagrammatic method, was developed to evaluate the potential failure during the process of emergency disposal. The probability of basic events and their combination, which caused the failure of an emergency disposal project, were calculated based on the case of an emergency disposal project of an aniline pollution incident in the Zhuozhang River, Changzhi, China, in 2014. The critical events that can cause the occurrence of a top event (TE) were identified according to their contribution. Finally, advices on how to take measures using limited resources to prevent the failure of a TE are given according to the quantified results of risk magnitude. The proposed approach could be a potential useful safeguard for the implementation of an emergency disposal project during the process of emergency response.

Screening of pollution control and clean-up materials for river chemical spills using the multiple case-based reasoning method with a difference-driven revision strategy.

In-depth filtering of emergency disposal technology (EDT) and materials has been required in the process of environmental pollution emergency disposal. However, an urgent problem that must be solved is how to quickly and accurately select the most appropriate materials for treating a pollution event from the existing spill control and clean-up materials (SCCM). To meet this need, the following objectives were addressed in this study. First, the material base and a case base for environment pollution emergency disposal were established to build a foundation and provide material for SCCM screening. Second, the multiple case-based reasoning model method with a difference-driven revision strategy (DDRS-MCBR) was applied to improve the original dual case-based reasoning model method system, and screening and decision-making was performed for SCCM using this model. Third, an actual environmental pollution accident from 2012 was used as a case study to verify the material base, case base, and screening model. The results demonstrated that the DDRS-MCBR method was fast, efficient, and practical. The DDRS-MCBR method changes the passive situation in which the choice of SCCM screening depends only on the subjective experience of the decision maker and offers a new approach to screening SCCM.