A real-time,dynamic early-warning model based on uncertainty analysis and risk assessment for sudden water pollution accidents

A real-time, dynamic, early-warning model (EP-risk model) is proposed to cope with sudden water quality pollution accidents affecting downstream areas with raw-water intakes (denoted as EPs). The EP-risk model outputs the risk level of water pollution at the EP by calculating the likelihood of pollution and evaluating the impact of pollution. A generalized form of the EP-risk model for river pollution accidents based on Monte Carlo simulation, the analytic hierarchy process (AHP) method, and the risk matrix method is proposed. The likelihood of water pollution at the EP is calculated by the Monte Carlo method, which is used for uncertainty analysis of pollutants’ transport in rivers. The impact of water pollution at the EP is evaluated by expert knowledge and the results of Monte Carlo simulation based on the analytic hierarchy process. The final risk level of water pollution at the EP is determined by the risk matrix method. A case study of the proposed method is illustrated with a phenol spill accident in China.     

GIS-based source identification and apportionment of diffuse water pollution: perfluorinated compound pollution in the Tokyo Bay basin

To efficiently reduce perfluorinated compound (PFC) pollution, it is important to have an understanding of PFC sources and their contribution to the pollution. In this study, source identification of diffuse water pollution by PFCs was conducted using a GIS-based approach. Major components of the source identification were collection of the monitoring data and preparation of the corresponding geographic information that was extracted from a constructed GIS database. The spatially distributed pollution factors were then explored by multiple linear regression analysis, after which they were visually expressed using GIS. Among the 35 PFC homologues measured in a survey of the Tokyo Bay basin, 18 homologues were analyzed. Pollution by perfluorooctane sulfonate (PFOS) was explained well by the percentage of arterial traffic area in the basin, and the 84% variance of the measured PFOS concentration was explained by two geographic variables, arterial traffic area and population. Source apportionment between point and nonpoint sources was conducted based on the results of the analysis. The contribution of PFOS from nonpoint sources was comparable to that from point sources in several major rivers flowing into Tokyo Bay. Source identification and apportionment using the GIS-based approach was shown to be effective, especially for ubiquitous types of pollution, such as PFC pollution.     

Use of selected chemical markers in combination with a multiple regression model to assess the contribution of domesticated animal sources of fecal pollution in the environment

Human and animal wastes are major sources of environmental pollution. Reliable methods of identifying waste sources are necessary to specify the types and locations of measures that best prevent and mitigate pollution. This investigation demonstrates the use of chemical markers (fecal sterols and bile acids) to identify selected sources of fecal pollution in the environment. Fecal sterols and bile acids were determined for pig, horse, cow, and chicken feces (10-26 feces samples for each animal). Concentrations of major fecal sterols (coprostanol, epicoprostanol, cholesterol, cholestanol, stigmastanol, and stigmasterol) and bile acids (lithocholic acid, deoxycholic acid, cholic acid, chenodeoxycholic acid, ursodeoxycholic acid, and hyodeoxycholic acid) were determined using a gas chromatography and mass spectrometer (GC-MS) technique. The fecal sterol and bile acid concentration data were used to estimate parameters of a multiple linear regression model for fecal source identification. The regression model was calibrated using 75% of the available data validated against the remaining 25% of the data points in a jackknife process that was repeated 15 times. The regression results were very favorable in the validation data set, with an overall coefficient of determination between predicted and actual fecal source of 0.971. To check the potential of the proposed model, it was applied on a set of simulated runoff data in predicting the specific animal sources. Almost 100% accuracy was obtained between the actual and predicted fecal sources. While additional work using polluted water (as opposed to fresh fecal samples) as well as multiple pollution sources are needed, results of this study clearly indicate the potential of this model to be useful in identifying the individual sources of fecal pollution.     

巢湖入湖河流沉积物中有机磷的形态分级研究

为识别巢湖流域污染物的特征、来源及其沉积物有机磷各形态分布与富营养化的关系，测定了7条巢湖入湖河流沉积物中有机磷各形态的含量，分析不同污染类型人湖河流沉积物中有机磷各形态分布的差异及与其他因素间的相关性。研究发现，不同污染类型人湖河流沉积物中水土保持控制型河流沉积物中有机磷各组分的相对含量顺序为残渣态Po〉富里酸-Po〉HCl-Po〉胡敏酸-Po〉NaHCO3-Po，平均的相对比例为7．5：3．1：1．9：1．5：1．0，而城市污染控制型和面源污染控制型河流沉积物中有机磷各组分的相对含量顺序恰好相同，面源污染控制型河流沉积物Po各形态含量低于城市污染控制型和水土保持控制型河流。中活性P。和OM、TP、Pi、Po、TN、NaHCO3-Pi、NaOH—Pi呈正相关，非活性Po与Po、NaOH-Pi呈显著正相关关系，反映了中活性Po很容易转化为生物可利用磷和非活性Po，且非活性Po仍然具有潜在的生物活性。     

Occurrence and sources of selected phenolic endocrine disruptors in Ria de Aveiro,Portugal

Background, aim and scope  

Ria de Aveiro (Portugal) is a shallow coastal lagoon of high economic and ecological importance. Hardly any data on its chemical pollution by polar organic pollutants are available in literature. This study focused on the presence and sources of a series of phenolic endocrine-disrupting compounds (EDCs) in this area, including parabens, alkylphenolic compounds and bisphenol-A (BPA). A number of possible sources of pollution are present in the area, including the large harbours present in the lagoon, the city of Aveiro and the rivers discharging into the area. A recently constructed submarine wastewater outfall, located a few kilometres from the lagoon inlet has also been suggested as a possible source of pollution to Ria de Aveiro in several publications. The aim of the current field study was to investigate the occurrence and main sources of phenolic endocrine disruptors in Ria de Aveiro.     

An inverse Gaussian plume approach for estimating atmospheric pollutant emissions from multiple point sources

A method is developed for estimating the emission rates of contaminants into the atmosphere from multiple point sources using measurements of particulate material deposited at ground level. The approach is based on a Gaussian plume type solution for the advection–diffusion equation with ground-level deposition and given emission sources. This solution to the forward problem is incorporated into an inverse algorithm for estimating the emission rates by means of a linear least squares approach. The results are validated using measured deposition and meteorological data from a large lead–zinc smelting operation in Trail, British Columbia. The algorithm is demonstrated to be robust and capable of generating reasonably accurate estimates of total contaminant emissions over the relatively short distances of interest in this study.     

Conservative heavy metal total discharge schemes

Currently, according to Taiwan’s Water Pollution Control Act, the environmental control of waterbodies and water quality depends on the effluent standards and the standard of water quality in the rivers. The Act demands that each stationary pollution source comply with the effluent standard before being discharged into the rivers, and that the overall water quality in the river shall not exceed the declared standard of water quality. To improve the condition of the waterbodies and water quality of the rivers, the Environmental Protection Administration (EPA) in Taiwan has made stricter regulations concerning the discharge standard. Such regulations will help to reduce the weight of individual pollutants discharged; the discharged wastewater, however, will still gradually worsen the water quality of the rivers even after complying with the effluent standard since some of the pollutant dischargers may decrease the concentration of pollutants by diluting the water before discharging; thus, the total weight of metals discharged in the rivers will not be reduced, and the water quality in the areas where the pollutant sources are concentrated will not thereby be significantly improved. To protect the irrigation water and farmlands from being polluted by discharged heavy metals in industrial wastewater, the EPA started controlling the sources in accordance with the total quantity control (TQC) as defined in the Water Pollution Control Act, in the hope of perfecting the environmental protection of waterbodies and water quality, as well as ensuring clean water sources without any pollution for the rivers, land, and people.     

Artificial neural network for the identification of unknown air pollution sources

Artificial neural networks (ANN), whose performances to deal with pattern recognition problems is well known, are proposed to identify air pollution sources. The problem that is addressed is the apportionment of a small number of sources from a data set of ambient concentrations of a given pollutant. Three layers feed-forward ANN trained with a back-propagation algorithm are selected. A test case is built, based on a Gaussian dispersion model. A subset of hourly meteorological conditions and measured concentrations constitute the input patterns to the network that is wired to recover relevant emission parameters of unknown sources as outputs. The rest of the model data are corrupted adding noise to some meteorological parameters and we test the effectiveness of the method to recover the correct answer. The ANN is applied to a realistic case where 24 h SO₂ concentrations were previously measured. Some of the limitations of the ANN approach, together with its capabilities, are discussed in this paper.     

Combining the multivariate statistics and dual stable isotopes methods for nitrogen source identification in coastal rivers of Hangzhou Bay,China

Coastal rivers contributed the majority of anthropogenic nitrogen (N) loads to coastal waters, often resulting in eutrophication and hypoxia zones. Accurate N source identification is critical for optimizing coastal river N pollution control strategies. Based on a 2-year seasonal record of dual stable isotopes (\({\updelta}^{15}\mathrm{N}-{\mathrm{NO}}_3^{\hbox{-} }\) and \({\updelta}^{18}\mathrm{O}-{\mathrm{NO}}_3^{\hbox{-} }\)) and water quality parameters, this study combined the dual stable isotope-based MixSIAR model and the absolute principal component score-multiple linear regression (APCS-MLR) model to elucidate N dynamics and sources in two coastal rivers of Hangzhou Bay. Water quality/trophic level indices indicated light-to-moderate eutrophication status for the studied rivers. Spatio-temporal variability of water quality was associated with seasonal agricultural, aquaculture, and domestic activities, as well as the seasonal precipitation pattern. The APCS-MLR model identified soil + domestic wastewater (69.5%) and aquaculture tailwater (22.2%) as the major nitrogen pollution sources. The dual stable isotope-based MixSIAR model identified soil N, aquaculture tailwater, domestic wastewater, and atmospheric deposition N contributions of 35.3 ±21.1%, 29.7 ±17.2%, 27.9 ±14.5%, and 7.2 ±11.4% to riverine \({\mathrm{NO}}_3^{\hbox{-} }\) in the Cao’e River (CER) and 34.4 ±21.3%, 29.5 ±17.2%, 27.4 ±14.7%, and 8.7 ±12.8% in the Jiantang River (JTR), respectively. The APCS-MLR model and the dual stable isotope-based MixSIAR model showed consistent results for riverine N source identification. Combining these two methods for riverine N source identifications effectively distinguished the mix-source components from the APCS-MLR method and alleviated the high cost of stable isotope analysis, thereby providing reliable N source apportionment results with low requirements for water quality sampling and isotope analysis costs. This study highlights the importance of soil N management and aquaculture tailwater treatment in coastal river N pollution control.

     

A dynamic contaminant fate model of organic compound: a case study of Nitrobenzene pollution in Songhua River, China

A one-dimensional dynamic contaminant fate model, coupling kinematic wave flow option with advection-dispersion-reaction equation, has been applied to predict Nitrobenzene pollution emergency in Songhua River, China that occurred on November 13, 2005. The model includes kinetic processes including volatilization, photolysis and biodegradation, and diffusive mass exchange between water column and sediment layer as a function of particles settling and resuspension. Four kinds of quantitative statistical tests, namely Nash-Sutcliffe efficiency, percent bias, ratio of root-mean-square to the standard deviation of monitoring data and Theil’s inequality coefficient, are adopted to evaluate model performance. The results generally show that the modeled and detected concentrations exhibit good consistency. Flow velocity in the river is most sensitive parameter to Nitrobenzene concentration in water column based on sensitivity analysis of input parameters. It indicates flow velocity has important impact on both distribution and variance of contaminant concentration. The model performs satisfactory for prediction of organic pollutant fate in Songhua River, with the ability to supply necessary information for pollution event control and early warning, which could be applied to similar long natural rivers.     

Refining emission rate estimates using a coupled receptor–dispersion modeling approach

Receptor modeling techniques like chemical mass balance are used to attribute pollution levels at a point to different sources. Here we analyze the composition of particulate matter and use the source profiles of sources prevalent in a region to estimate quantitative source contributions. In dispersion modeling on the other hand the emission rates of various sources together with meteorological conditions are used to determine the concentrations levels at a point or in a region. The predictions using these two approaches are often inconsistent. In this work these differences are attributed to errors in emission inventory. Here an algorithm for coupling receptor and dispersion models is proposed to reduce the differences of the two predictions and determine the emission rates accurately. The proposed combined approach helps reconcile the differences arising when the two approaches are used in a stand-alone mode. This work is based on assuming that the models are perfect and uses a model-to-model comparison to illustrate the concept.     

Breakthrough Curves Characterization and Identification of an Unknown Pollution Source in Groundwater System Using an Artificial Neural Network (ANN)

Contamination of groundwater constrains its uses and poses a serious threat to the environment. Once groundwater is contaminated, the cleanup may be difficult and expensive. Identification of unknown pollution sources is the first step toward adopting any remediation strategy. The proposed methodology exploits the capability of a universal function approximation by a feed-forward multilayer artificial neural network (ANN) to identify the sources in terms of its location, magnitudes, and duration of activity. The back-propagation algorithm is utilized for training the ANN to identify the source characteristics based on simulated concentration data at specified observation locations in the aquifer. Uniform random generation and the Latin hypercube sampling method of random generation are used to generate temporal varying source fluxes. These source fluxes are used in groundwater flow and the transport simulation model to generate necessary data for the ANN model-building processes. Breakthrough curves obtained for the specified pollution scenario are characterized by different methods. The characterized breakthrough curves parameters serve as inputs to ANN model. Unknown pollution source characteristics are outputs for ANN model. Experimentation is also performed with different number of training and testing patterns. In addition, the effects of measurement errors in concentration measurements values are used to show the robustness of ANN based methodology for source identification in case of erroneous data.     

The impact of trajectory starting heights on the MURA trajectory source apportionment (TSA) method

Trajectory source apportionment (TSA) methods have been used in many research projects to attempt to identify the sources of pollution. Hybrid Single Particle Lagrangian Integrated Trajectories (HYSPLIT) is a popular model for use in various TSA methods. One of the options in this model is to choose a starting height. Very little research is available to assist a user in making this choice. This paper evaluates starting heights of 10, 50, 100, 250, and 500 m on the accuracy of the Multi-Receptor (MURA) method using artificial sources for three different simulations. It was found that using ensembles of trajectories in the MURA method appear to average out most of the biases found from different trajectory starting heights up to the 500 m tested.     

Retrieval of the aerosol size distribution in the complex anomalous diffraction approximation

This contribution reports some recently achieved results in aerosol size distribution retrieval in the complex anomalous diffraction approximation (ADA) to MIE scattering theory. This approximation is valid for spherical particles that are large compared to the wavelength and have a refractive index close to 1.The ADA kernel is compared with the exact MIE kernel. Despite being a simple approximation, the ADA seems to have some practical value for the retrieval of the larger modes of tropospheric and lower stratospheric aerosols.The ADA has the advantage over MIE theory that an analytic inversion of the associated Fredholm integral equation becomes possible. In addition, spectral inversion in the ADA can be formulated as a well-posed problem. In this way, a new inverse formula was obtained, which allows the direct computation of the size distribution as an integral over the spectral extinction function. This formula is valid for particles that both scatter and absorb light and it also takes the spectral dispersion of the refractive index into account.Some details of the numerical implementation of the inverse formula are illustrated using a modified gamma test distribution. Special attention is given to the integration of spectrally truncated discrete extinction data with errors.     

Hierarchical space-time modelling of PM10 pollution

In this paper, we propose a hierarchical spatio-temporal model for daily mean concentrations of PM₁₀ pollution. The main aims of the proposed model are the identification of the sources of variability characterising the PM₁₀ process and the estimation of pollution levels at unmonitored spatial locations. We adopt a fully Bayesian approach, using Monte Carlo Markov Chain algorithms. We apply the model on PM₁₀ data measured at 11 monitoring sites located in the major towns and cities of Italy's Emilia-Romagna Region. The model is designed for areas with PM₁₀ measurements available; the case of PM₁₀ level estimation from emissions data is not handled. The model has been carefully checked using Bayesian p-values and graphical posterior predictive checks. Results show that the temporal random effect is the most important when explaining PM₁₀ levels.     

A statistical model for FEV1 response to arbitrary dynamic ozone exposure conditions

Lung function response to inhaled ozone at ambient air pollution levels is known to be a function of ozone concentration, exposure duration, and minute ventilation. Most data-driven exposure-response models address exposures under static condition (i.e., with a constant ozone concentration and exercise pattern). Such models are simplifications, as both ambient ozone concentrations and normal human activity patterns change with time. The purpose of this study was to develop a dynamic model of response with the advantages of a statistical model (a relatively simple structure with few parameters). A previously proposed mechanistic model for changes in specific airways resistance was adapted to describe the percent change in forced expiratory volume in one second (FEV1). This model was then reduced using the fit to three existing exposure-response data sets as criterion. The resulting model consists of a single linear differential equation together with an algebraic logistic equation. Under restricted static conditions the model reduces to a logistic model presented earlier by the authors.     

Bayesian inference for source determination with applications to a complex urban environment

The problem of determining the source of an emission from the limited information provided by a finite and noisy set of concentration measurements obtained from real-time sensors is an ill-posed inverse problem. In general, this problem cannot be solved uniquely without additional information. A Bayesian probabilistic inferential framework, which provides a natural means for incorporating both errors (model and observational) and prior (additional) information about the source, is presented. Here, Bayesian inference is applied to find the posterior probability density function of the source parameters (location and strength) given a set of concentration measurements. It is shown how the source–receptor relationship required in the determination of the likelihood function can be efficiently calculated using the adjoint of the transport equation for the scalar concentration. The posterior distribution of the source parameters is sampled using a Markov chain Monte Carlo method. The inverse source determination method is validated against real data sets acquired in a highly disturbed flow field in an urban environment. The data sets used to validate the proposed methodology include a water-channel simulation of the near-field dispersion of contaminant plumes in a large array of building-like obstacles (Mock Urban Setting Trial) and a full-scale field experiment (Joint Urban 2003) in Oklahoma City. These two examples demonstrate the utility of the proposed approach for inverse source determination.     

Modification of chemical oxygen demand monitoring in the Yellow River, China, with a high content of sediments.

This paper presents a modification of chemical oxygen demand (COD) monitoring giving a better indication of the pollution level compared with the conventional COD method for rivers with a high content of sediments. The correlation between the sediment organic carbon and COD was investigated using sediments sampled in the middle Yellow River, China. Partitioning of the sediment organic carbon between the water and sediment phases was also investigated using batch experiments, with the sediment concentration varying from 20 to 400 g/L. As a result, the COD modification equations are proposed for both turbid water (mixture of water and sediment) and supematant water (filtrate using a 0.45-microm membrane). The modified COD in turbid water and supernatant water could be 40 and 10% less than the monitored COD values, respectively. These results may have a significant influence on the assessment of water quality class in the Yellow River.     

Characterization of heavy metal concentrations in the sediments of three freshwater rivers in Huludao City, Northeast China

Wuli River, Cishan River, and Lianshan River are three freshwater rivers flowing through Huludao City, in a region of northeast China strongly affected by industrialization. Contamination assessment has never been conducted in a comprehensive way. For the first time, the contamination of three rivers impacted by different sources in the same city was compared. This work investigated the distribution and sources of Hg, Pb, Cd, Zn and Cu in the surface sediments of Wuli River, Cishan River, and Lianshan River, and assessed heavy metal toxicity risk with the application of two different sets of Sediment Quality Guideline (SQG) indices (effect range low/effect range median values, ERL/ERM; and threshold effect level/probable effect level, TEL/PEL). Furthermore, this study used a toxic unit approach to compare and gauge the individual and combined metal contamination for Hg, Pb, Cd, Zn and Cu. Results showed that Hg contamination in the sediments of Wuli River originated from previous sediment contamination of the chlor-alkali producing industry, and Pb, Cd, Zn and Cu contamination was mainly derived from atmospheric deposition and unknown small pollution sources. Heavy metal contamination to Cishan River sediments was mainly derived from Huludao Zinc Plant, while atmospheric deposition, sewage wastewater and unknown small pollution were the primary sources for Lianshan River. The potential acute toxicity in sediment of Wuli River may be primarily due to Hg contamination. Hg is the major toxicity contributor, accounting for 53.3-93.2%, 7.9-54.9% to total toxicity in Wuli River and Lianshan River, respectively, followed by Cd. In Cishan River, Cd is the major sediment toxicity contributor, however, accounting for 63.2-66.9% of total toxicity.