Uncertainty assessment of the multilayer perceptron (MLP) neural network model with implementation of the novel hybrid MLP-FFA method for prediction of biochemical oxygen demand and dissolved oxygen: a case study of Langat River

Accurate prediction of the chemical constituents in major river systems is a necessary task for water quality management, aquatic life well-being and the overall healthcare planning of river systems. In this study, the capability of a newly proposed hybrid forecasting model based on the firefly algorithm (FFA) as a metaheuristic optimizer, integrated with the multilayer perceptron (MLP-FFA), is investigated for the prediction of monthly water quality in Langat River basin, Malaysia. The predictive ability of the MLP-FFA model is assessed against the MLP-based model. To validate the proposed MLP-FFA model, monthly water quality data over a 10-year duration (2001–2010) for two different hydrological stations (1L04 and 1L05) provided by the Irrigation and Drainage Ministry of Malaysia are used to predict the biochemical oxygen demand (BOD) and dissolved oxygen (DO). The input variables are the chemical oxygen demand (COD), total phosphate (PO₄), total solids, potassium (K), sodium (Na), chloride (Cl), electrical conductivity (EC), pH and ammonia nitrogen (NH₄-N). The proposed hybrid model is then evaluated in accordance with statistical metrics such as the correlation coefficient (r), root-mean-square error, % root-mean-square error and Willmott’s index of agreement. Analysis of the results shows that MLP-FFA outperforms the equivalent MLP model. Also, in this research, the uncertainty of a MLP neural network model is analyzed in relation to the predictive ability of the MLP model. To assess the uncertainties within the MLP model, the percentage of observed data bracketed by 95 percent predicted uncertainties (95PPU) and the band width of 95 percent confidence intervals (d-factors) are selected. The effect of input variables on BOD and DO prediction is also investigated through sensitivity analysis. The obtained values bracketed by 95PPU show about 77.7%, 72.2% of data for BOD and 72.2%, 91.6% of data for DO related to the 1L04 and 1L05 stations, respectively. The d-factors have a value of 1.648, 2.269 for BOD and 1.892, 3.480 for DO related to the 1L04 and 1L05 stations, respectively. Based on the values in both stations for the 95PPU and d-factor, it is concluded that the neural network model has an acceptably low degree of uncertainty applied for BOD and DO simulations. The findings of this study can have important implications for error assessment in artificial intelligence-based predictive models applied for water resources management and the assessment of the overall health in major river systems.     

Prediction of water quality from simple field parameters

Water quality parameters like temperature, pH, total dissolved solids (TDS), total suspended solids (TSS), dissolved oxygen (DO), oil and grease, etc., are calculated from the field while parameters like biological oxygen demand (BOD) and chemical oxygen demand (COD) are interpreted through the laboratory tests. On one hand parameters like temperature, pH, DO, etc., can be accurately measured with the exceeding simplicity, whereas on the other hand calculation of BOD and COD is not only cumbersome but also inaccurate many times. A number of previous researchers have tried to use different empirical methods to predict BOD and COD but these empirical methods have their limitations due to their less versatile application. In this paper, an attempt has been made to calculate BOD and COD from simple field parameters like temperature, pH, DO, TSS, etc., using Artificial Neural Network (ANN) method. Datasets have been obtained from analysis of mine water discharge of one of the mines in Jharia coalfield, Jharkhand, India. 73 data sets were used to establish ANN architecture out of which 58 datasets were used to train the network while 15 datasets for testing the network. The results show encouraging similarity between experimental and predicted values. The RMSE values obtained for the BOD and COD are 0.114 and 0.983 %, respectively.     

Evaluation of the effect of river style framework on water quality: application of geomorphological factors

The Tarwal River basin with an area of 6560.20 km² is located in the eastern part of Iranian Kurdistan Province. This river crosses the Qorveh and Dehgolan plains and joins the Ghezel Ozan River in Zanjan Province. The importance of this river as a source for drinking water and agricultural and industrial uses in the region necessitates the need for research in this field. The main purpose of this study is to identify the natural features of the riverbed from the perspective of river geomorphology and to investigate their impact on water quality and river self-purification capacity. To achieve this, the river style framework was employed. To investigate the effects of each style framework on the river, a total of 20 samples from the entrance and outlet of styles were obtained using Impact Assessment method and sampling standards which were later analyzed for their quality parameters including T, pH, EC, TDS, TSS, Na, Ca, Mg, K, Cl, F, NO₂, NO₃, SO₄, PO₄, DO, COD and BOD. The results indicated that the changes in the styles lead to changes in water quality and the impact of each style is greater on the physical parameters than the chemical parameters. The river self-purification capacity varied depending on the style. The maximum and the minimum self-purifications occurred in fine-grained Anabranching and low-sinuosity fine-grained styles, respectively.     

Reciprocal influence of Kürtün Dam and wastewaters from the settlements on water quality in the stream Harşit,NE Turkey

This paper aims to reveal the reciprocal influence of Kürtün Dam and wastewaters from the settlements on the water quality in the stream Har?it, NE Turkey. Several key water-quality indicators were measured: water temperature (T), pH, dissolved oxygen (DO), electrical conductivity, water hardness, chemical oxygen demand (COD), ammonium nitrogen (NH₄ ⁺–N), nitrite nitrogen (NO₂ ^?–N), nitrate nitrogen (NO₃ ^?–N), total Kjeldahl nitrogen (TKN), total nitrogen (TN), orthophosphate phosphorus (PO₄ ^3?–P), and methylene blue active substances (MBAS). The monitoring and sampling studies were conducted every 15 days from March 2009 to February 2010 at two stations selected in the upstream and downstream of the Kürtün Dam. It was concluded that the Kürtün Dam Lake had a high-quality water in terms of T, pH, DO, COD, NH₄ ⁺–N, NO₂ ^?–N and NO₃ ^?–N values, but slightly polluted water with respect to TKN, PO₄ ^3?–P, and MBAS according to the Turkish Water Pollution Control Regulation. The dam improved the stream water quality by increasing the DO concentration, and decreasing the NO₂ ^?–N and PO₄ ^3?–P concentrations thanks to its hydraulic residence time despite the wastewater discharge by the nearby settlements. However, the wastewater discharge deteriorated the stream water quality increasing the COD, NH₄ ⁺–N, NO₃ ^––N, and TN concentrations.     

Surface water quality contamination source apportionment and physicochemical characterization at the upper section of the Jakara Basin, Nigeria

The present study investigates the surface water quality of three important tributaries of Jakara Basin, northwestern Nigeria to provide an overview of the relationship and sources of physicochemical and biological parameters. A total of 405 water samples were collected from 27 sampling points and analyzed for 13 parameters: dissolved oxygen (DO), 5-day biochemical oxygen demand (BOD₅), chemical oxygen demand (COD), suspended solids (SS), pH, ammonia-nitrogen (NH₃NL), dissolved solids (DS), total solids (TS), nitrates (NO₃), chloride (Cl), phosphates (PO₄), Escherichia coli (E. coli) and fecal coliform bacteria (FCB). Pearson’s product–moment correlation matrix and principal component analysis (PCA) were used to distinguish the main pollution sources in the basin. Four varimax components were extracted from PCA, which explained 84.86, 83.60, and 78.69 % of the variation in the surface water quality for Jakara, Tsakama, and Gama-Kwari Rivers, respectively. Strong positive loading included BOD₅, COD, NH₃NL, E. coli, and FCB with negative loading on DO attribute to a domestic waste water pollution source. One-way ANOVA revealed that there was no significant difference in the mean of the three water bodies (p?>?0.05). It is therefore recommended that the government should be more effective in controlling the point source of pollution in the area.     

Hydrochemistry and nutrients dynamic in the Suquía River urban catchment’s,Córdoba,Argentina

Suquía River is a medium-sized hydrological system (basin area of ~7,700 km²) that supplies fresh water to Córdoba city, a town of ~1,500,000 inhabitants in central Argentina. This paper examines the present-day hydrochemistry of Suquía River urban catchment analyzing its major and minor dissolved components, and the nutrients variability by means of QUAL-2K modeling software. The Suquía River has bicarbonate-type waters upstream the city and sulfate-type waters right downstream, whereas they exhibit a mixed-to-alkali-type cationic composition. The seasonal analysis of its major dissolved constituents clearly showed a dilution process during the wet season (i.e. austral summer). In the last 20 years, the Suquía River has modified its anionic composition, now showing higher relative concentrations of SO₄ ²⁻ as a consequence of urban activities. However, trace elements dissolved concentrations do not evidence a strong pollution effect. Nutrients [nitrogen species, total phosphorous (TP)] and related parameters, such as biochemical oxygen demand (BOD), and dissolved oxygen (DO), evidence a clear influence of human activities. The QUAL-2K model was used to evaluate the spatial behavior of selected nutrients and associated variables, (i.e. TP, N–NH₄ ⁺, N–NO₃ ⁻, DO, BOD). Nutrient concentrations are affected by point sources of contaminants, particularly domestic waste and sewage, as well as by diffuse agricultural pollution. A calibrated QUAL-2K modeling exercise clearly shows the impact of the Córdoba city’s municipal wastewater treatment plant on the Suquía River water quality.     

Assessing the organic pollution of surface water of Medjerda watershed (NE Algeria)

Water quality in the Northern part of Mellegue-Medjerda watershed (East Algeria) has been adversely affected by important pollutants discharged into the Medjerda wadi without, in most cases, any treatment. Chemical and physical degradation are due to agricultural and industrial practices and domestic wastewaters. Over a three-month period, a study of the low-flow water quality characteristics throughout Medjerda wadi was undertaken. Longitudinal profiles of water quality were constructed using data from fourteen sites. All sewage, agricultural, and industrial inputs were included. Analyzed properties were nutrients (NO₃ ⁻, NO₂ ⁻, NH₄ ⁺, and PO₄ ³⁻), Biochemical oxygen demand after five days (BOD₅), chemical oxygen demand (COD), and dissolved oxygen (DO). Along Medjerda wadi, all values change because of conditions specific to each sampling station. Nitrate was the most important form of nitrogen-element load (94%). Its concentration reached 34.3 mg L⁻¹ at OM₄ point, downstream of domestic wastewater discharges. The spatial evolution of the organic pollution index (OPI) shows that the wastewater effluent constitutes the main source of pollution. Indeed, water quality goes from a moderate pollution state at some sampling stations not or slightly affected by wastewaters discharges to a very strong pollution state (OPI of about 1.75) downstream of the domestic effluents inputs of Souk-Ahras city.     

The influence of topography and land use on water quality of Xiangxi River in Three Gorges Reservoir region

A self-organizing map (SOM) was used to cluster the water quality data of Xiangxi River in the Three Gorges Reservoir region. The results showed that 81 sampling sites could be divided into several groups representing different land use types. The forest dominated region had low concentrations of most nutrient variables except COD, whereas the agricultural region had high concentrations of NO₃N, TN, Alkalinity, and Hardness. The sites downstream of an urban area were high in NH₃N, NO₂N, PO₄P and TP. Redundancy analysis was used to identify the individual effects of topography and land use on river water quality. The results revealed that the watershed factors accounted for 61.7% variations of water quality in the Xiangxi River. Specifically, topographical characteristics explained 26.0% variations of water quality, land use explained 10.2%, and topography and land use together explained 25.5%. More than 50% of the variation in most water quality variables was explained by watershed characteristics. However, water quality variables which are strongly influenced by urban and industrial point source pollution (NH₃N, NO₂N, PO₄P and TP) were not as well correlated with watershed characteristics.     

Ecological characteristics of Vrishabhavathy River in Bangalore (India)

River Vrishabhavathy, a tributary of Cauvery River was studied for 12 physico-chemical parameters at four sites over a distance of 50 km for a period of 2 years (1999–2001) at monthly intervals. Water was faintly alkaline, with pH showing negative correlation with temperature. The dissolved oxygen content increased downstream with negative correlation to biological oxygen demand (BOD), chemical oxygen demand (COD) and turbidity. Bicarbonate alkalinity was very low compared with carbonate alkalinity. Carbonate alkalinity, total hardness, total dissolved solids, total suspended solids, electrical conductivity, BOD and COD decreased downstream, with an upward trend in the middle reaches due to the introduction of raw sewage. The seasonal and yearly trends are also discussed. The river is a sewer collector undergoing self-purification.     

Nutrient removal from activated sludge mixed liquor by wastewater protozoa in a laboratory scale batch reactor

The aim of the study was to investigate the nutrient removal rate of three wastewater protozoan isolates. The study was carried out in a laboratory-scale batch reactor for a period of 120 h. in a four batch study. Aliquot samples were withdrawn from the reactor every 24 h. for the analysis of phosphate, nitrate, nitrite, ammonia, chemical oxygen demand, dissolved oxygen and pH, using standard methods. The results obtained in the different batches among the three isolates showed PO₄ ^2? removal rate ranging from 0.04 to 0.52 mg-PO₄ ^2?/L/h. while NO₃ ^? nitrate removal rates ranged from 0.08 to 0.16 mg-NO₃ ^?/L /h. Also NO₂- and NH₃ rates were observed to range between 0.022 and 0.087 mg-NO₂ ^?/L /h. 0.05 and 0.16 mg-NH₃ ^?/L /h, respectively. For the physicochemical parameters, there was no observed COD decrease; rather there was an increase and this was irrespective of isolates and experimental batches. However, dissolved oxygen concentration decreased drastically (below 1 mg/L) at the end of each batch while pH show a decrease after an initial 24 h. period and thereafter increased. This trend was also irrespective of isolates and experimental batches. Overall, the study has been able to show the effect of the test isolates on nutrient removal rates and other physicochemical parameters (COD, DO and pH) in activated sludge mixed liquor.     

Are people willing to pay for river water quality, contingent valuation

Now a day’s water pollution has caused incoveriences for people whom live near the Pavana river in Pune city, India. The river water quality has deteriorated by major water quality parameters like dissolved oxygen (DO), biological oxygen demand (BOD) and phosphates level. In present study it is tried to find people’s willingness to pay (WTP) for improvement of river water quality. Contingent valuation method (CVM) was utilized for valuation of river water quality in Pavana river. Five categories of users have been chosen and then interviewed: households, farmers, fishermen, washing clothes women, bath taking people. One kilometer from each side of river was covered by researchers for sampling. Mean of willingness to pay was estimated at Rs 17.6 (45 Indian Rupees=$ 1) per family per month. This research shows CVM applicablity and the importance of river quality for Pune city and can effectively be used in developing countries.     

Intelligent modelling of clay compressibility using hybrid meta-heuristic and machine learning algorithms

Compression index Ccis an essential parameter in geotechnical design for which the effectiveness of correlation is still a challenge.This paper suggests a novel modelling approach using machine learning(ML)technique.The performance of five commonly used machine learning(ML)algorithms,i.e.back-propagation neural network(BPNN),extreme learning machine(ELM),support vector machine(SVM),random forest(RF)and evolutionary polynomial regression(EPR)in predicting Cc is comprehensively investigated.A database with a total number of 311 datasets including three input variables,i.e.initial void ratio e0,liquid limit water content wL,plasticity index Ip,and one output variable Cc is first established.Genetic algorithm(GA)is used to optimize the hyper-parameters in five ML algorithms,and the average prediction error for the 10-fold cross-validation(CV)sets is set as thefitness function in the GA for enhancing the robustness of ML models.The results indicate that ML models outperform empirical prediction formulations with lower prediction error.RF yields the lowest error followed by BPNN,ELM,EPR and SVM.If the ranges of input variables in the database are large enough,BPNN and RF models are recommended to predict Cc.Furthermore,if the distribution of input variables is continuous,RF model is the best one.Otherwise,EPR model is recommended if the ranges of input variables are small.The predicted correlations between input and output variables using five ML models show great agreement with the physical explanation.     

Prediction of river flow using hybrid neuro-fuzzy models

The complex nature of hydrological phenomena, like rainfall and river flow, causes some limitations for some admired soft computing models in order to predict the phenomenon. Evolutionary algorithms (EA) are novel methods that used to cover the weaknesses of the classic training algorithms, such as trapping in local optima, poor performance in networks with large parameters, over-fitting, and etc. In this study, some evolutionary algorithms, including genetic algorithm (GA), ant colony optimization for continuous domain (ACO_R), and particle swarm optimization (PSO), have been used to train adaptive neuro-fuzzy inference system (ANFIS) in order to predict river flow. For this purpose, classic and hybrid ANFIS models were trained using river flow data obtained from upstream stations to predict 1-, 3-, 5-, and 7-day ahead river flow of downstream station. The best inputs were selected using correlation coefficient and a sensitivity analysis test (cosine amplitude). The results showed that PSO improved the performance of classic ANFIS in all the periods such that the averages of coefficient of determination, R², root mean square error, RMSE (m³/s), mean absolute relative error, MARE, and Nash-Sutcliffe efficiency coefficient (NSE) were improved up to 0.19, 0.30, 43.8, and 0.13%, respectively. Classic ANFIS was only capable to predict river flow in 1-day ahead while EA improved this ability to 5-day ahead. Cosine amplitude method was recognized as an appropriate sensitivity analysis method in order to select the best inputs.     

Characterisation of the physicochemical qualities of a typical rural-based river: ecological and public health implications

The physicochemical qualities of a typical rural-based river were assessed over a 12-month period from August 2010 to July 2011 spanning the spring, summer, autumn and winter seasons. Water samples were collected from six sampling sites along Tyume River and analysed for total nitrogen, orthophosphate, biochemical oxygen demand (BOD), temperature, pH, dissolved oxygen (DO), electrical conductivity (EC), total dissolved solids (TDS) and turbidity. BOD regimes did not differ significantly between seasons and between sampling points and ranged from 0.78 to 2.76 mg/L across seasons and sampling points, while temperature ranged significantly (P < 0.05) between 6 and 28 °C. Turbidity varied significantly (P < 0.05) from 6 to 281 nephelometric turbidity units while TDS (range 24–209 ppm) and conductivity (range 47.6–408 mg/L) also varied significantly (P < 0.05) across sampling points with a remarkable similarity in their trends. Orthophosphate concentrations varied from 0.06 to 2.72 mg/L across seasons and sampling points. Negative correlations were noted between temperature and the nutrients, DO and temperature (r = ?0.56), and TDS and DO (r = ?0.33). Positive correlations were noted between TDS and temperature (r = 0.41), EC and temperature (r = 0.15), and DO and pH (r = 0.55). All nutrients were positively correlated to each other. Most measured parameters were within prescribed safety guidelines. However, the general trend was that water quality tended to deteriorate as the river flows through settlements, moreso in rainy seasons.     

Monitoring the seasonal dynamics of physicochemical parameters from Atoyac River basin (Puebla), Central Mexico: multivariate approach

Spatiotemporal variations of ten physicochemical parameters in the water quality of Atoyac River basin, Central Mexico, were obtained from 22 sampling sites (66 samples in total) located all along the basin for three different seasons (dry, rainy and winter). Multivariate statistical techniques such as correlation matrix, factor analysis (FA) and cluster analysis (CA) were used as a tool to understand the process. Physicochemical parameters such as temperature (T), pH, conductivity (λ), dissolved oxygen (DO), spectral absorption coefficient (SAC), oxidation–reduction potential (ORP), turbidity, 5-day biochemical oxygen demand (BOD₅), chemical oxygen demand (COD) and total suspended solids (TSS) were analyzed. Extremely high values of pH (10.24), conductivity (1870 µS/cm) and reduced redox potential (?370.1 mV) were observed in the dry season, whereas elevated TSS of 2996 mg/L was detected during the rainy season. The results elucidated high influence from the adjoining industrial, agricultural and urban zones, making the river unsuitable for life. FA generated varifactors, which accounted for cumulative % of 75.04 (dry), 76.22 (rainy) and 79.96 (winter) clearly grouping the external factors responsible for these significant values indicating the source of contamination. Cluster analysis facilitated the ease of classifying the sampling sites based on the similarities of physicochemical parameters. This study carried out in different seasons using multivariate statistical techniques would definitely prove to be an efficient tool for the restoration and establishing the real-time monitoring stations along this important river basin of Mexico.     

Effects of Torul dam on water quality in the stream Harşit,NE Turkey

This paper evaluates the effects of Torul dam on the stream Harşit water quality in terms of 13 physico-chemical parameters in the Gümüşhane Province, Eastern Black Sea Basin, Turkey. For this purpose, a study was fortnightly conducted during the four seasons between March 2009 and February 2010. In two monitoring stations selected in the upstream and downstream of the Torul dam, T, pH, DO and EC were determined in situ, and collected water samples were analyzed for TH, COD, NH₄ ⁺-N, NO₂ ⁻-N, NO₃ ⁻-N, TN, TKN, PO₄ ³⁻-P and MBAS. According to the Turkish Water Pollution Control Regulation (TWPCR), the stream Harşit was classified, and the obtained results were evaluated for the values proposed by Turkish Standard (TS) 266 and World Health Organization (WHO) guidelines. The results showed that the stream Harşit has high-quality water in terms of, T, pH, DO, COD, NH₄ ⁺-N and NO₃ ⁻-N, but slightly polluted water in terms of NO₂ ⁻-N, TKN and PO₄ ³⁻-P, and polluted for MBAS. It was concluded that Torul dam has a positive effect on the stream water quality in terms of decrease in the annual average concentration values. The percent decreases for TH, COD, NH₄ ⁺-N, NO₂ ⁻-N, NO₃ ⁻-N, TN, TKN, PO₄ ³⁻-P and MBAS were 17.1, 20.3, 56.2, 62.6, 11.7, 11.9, 11.4, 17.8 and 71.4, respectively. The reason for these decreases is probably due to the Torul dam reservoir where the water has a hydraulic residence time and the exposure to chemicals by aquatic organisms or populations that ingest the water. Also, statistical analysis shows that there are significant correlations among the studied parameters.     

水库有机污染物生化需氧量-溶解氧和温度的耦合模型研究与应用

生化需氧量溶解氧(BOD5 DO)是反映水体有机污染程度的一个重要指标,而这些指标的浓度变化与水体温度有着密切关系。因此,研究三者之间的耦合数学模型,可以反映水体BOD5 DO迁移分布的规律。文章建立了三者垂向一维耦合数学模型,并给出了稳态情况下精确解的数学表达式且讨论了三者之间的关系,解决了求任意深度在温度影响下BOD5 DO的浓度值,同时还给出了耦合模型非稳态情况下的求解方法。     

A comparative analysis among computational intelligence techniques for dissolved oxygen prediction in Delaware River

Most of the water quality models previously developed and used in dissolved oxygen (DO) prediction are complex. Moreover, reliable data available to develop/calibrate new DO models is scarce. Therefore, there is a need to study and develop models that can handle easily measurable parameters of a particular site, even with short length. In recent decades, computational intelligence techniques, as effective approaches for predicting complicated and significant indicator of the state of aquatic ecosystems such as DO, have created a great change in predictions. In this study, three different AI methods comprising: (1) two types of artificial neural networks (ANN) namely multi linear perceptron (MLP) and radial based function (RBF); (2) an advancement of genetic programming namely linear genetic programming (LGP); and (3) a support vector machine (SVM) technique were used for DO prediction in Delaware River located at Trenton, USA. For evaluating the performance of the proposed models, root mean square error (RMSE), Nash–Sutcliffe efficiency coefficient (NS), mean absolute relative error (MARE) and, correlation coefficient statistics (R) were used to choose the best predictive model. The comparison of estimation accuracies of various intelligence models illustrated that the SVM was able to develop the most accurate model in DO estimation in comparison to other models. Also, it was found that the LGP model performs better than the both ANNs models. For example, the determination coefficient was 0.99 for the best SVM model, while it was 0.96, 0.91 and 0.81 for the best LGP, MLP and RBF models, respectively. In general, the results indicated that an SVM model could be employed satisfactorily in DO estimation.     

Using of neural networks for the prediction of nitrate groundwater contamination in rural and agricultural areas

As a neural network provides a non-linear function mapping of a set of input variables into the corresponding network output, without the requirement of having to specify the actual mathematical form of the relation between the input and output variables, it has the versatility for modeling a wide range of complex non-linear phenomena. In this study, groundwater contamination by nitrate, the ANNs are applied as a new type of model to estimate the nitrate contamination of the Gaza Strip aquifer. A set of six explanatory variables for 139 sampled wells was used and that have a significant influence were identified by using ANN model. The Multilayer Perceptrons (MLP), Radial Basis Function (RBF), Generalized Regression Neural Network (GRNN), and Linear Networks were used. The best network found to simulate Nitrate was MLP with six input nodes and four hidden nodes. The input variables are: nitrogen load, housing density in 500-m radius area surrounding wells, well depth, screen length, well discharge, and infiltration rate. The best network found had good performance (regression ratio 0.2158, correlation 0.9773, and error 8.4322). Bivariate statistical test also were used and resulting in considerable unexplained variation in nitrate concentration. Based on ANN model, groundwater contamination by nitrate depends not on any single factor but on the combination of them.     

Environmental Geochemistry of Swan Lake Inlet,Rongcheng Bay,the Yellow Sea of China

The Swan Lake Inlet, the State Primary Wildlife Protection Area, is a lagoon-inlet system located in the Rongcheng Bay, Shandong Peninsula, China. It has been undergoing development for aquaculture and tourism. In the summer of 1999, a study on the environment of the Swan Lake Inlet was carried out. The concentrations of the major elements and trace elements Fe, Al, Pb, Zn, Cd, Cu, Cr, Mn and P have been measured by ICP-AES and graphite furnace atomic adsorption spectrometry. The sources and distribution of the elements in the Swan Lake Inlet have been discussed. It is concluded that the Swan Lake Inlet has not been subjected to significant environmental pollution. The chemical results show that the dissolved oxygen (DO) contents are generally normal. At some locations DO solubility appears to be >100 %. The BOD₅ ( five-day biochemical oxygen demand) values are generally <4 mg/L and COD (chemical oxygen demand) 3～4 mg/L. The seawater N, P and Si contents are lower than the Class I water type specified by the Chinese National Standard of Water Quality. The low nutrient distribution reflects little discharge from land, therefore lacking of nutrient supply.