Comprehensive Assessment of Seawater Quality Based on an Improved Attribute Recognition Model

The attribute recognition model (ARM) has been widely used to make comprehensive assessment in many engineering fields, such as environment, ecology, and economy. However, large numbers of experiments indicate that the value of weight vector has no relativity to its initial value but depends on the data of Quality Standard and actual samples. In the present study, the ARM is enhanced with the technique of data driving, which means some more groups of data from the Quality Standard are selected with the uniform random method to make the calculation of weight values more rational and more scientific. This improved attribute recognition model (IARM) is applied to a real case of assessment on seawater quality. The given example shows that the IARM has the merits of being simple in principle, easy to operate, and capable of producing objective results, and is therefore of use in evaluation problems in marine environment science.

A discussion on typhoon occurred in the Haikou Bay and impact mechanism on seawater quality

Water quality parameters such as pH, DO, COD, PQ4 - P, SiO3 - Si, NO2 - N, NO3 -N in the Haikou Bay were monitored respectively before and after Typhoon 9618 occurring on Sep. 18, 1996. Based on the statistics of typhoon in the Haikou Bay and numerical calculation of stormy current, the mechanism of water quality variation caused by typhoon is discussed. The typhoon impact on the Haikou Bay usually appears between July and November, most usually between August and October. The monitoring results before a typhoon were different from that. The stormy wave and windstorm cur-rent stir up the sediment in near-shore bottom and make the bottom water mix with the surface water strongly, specially windstorm current with strong velocity at the head of the bay stirs up higher pollutants sediment near sea area of sewage outfall, and heavy rain with typhoon carries the pollutants from land through the Nandu River to the Haikou Bay, so the contents of COD, PO4 - P, NO4 - N, NO3 ~N, SiO3 after a typhoon are higher than those before. Windstorm current is violent, which makes offshore high DO water exchange more frequently with inner bay water and oxygen in the air dissolves in sea water faster, so DO content after typhoon is higher than that before typhoon. This strong action of water exchange also causes lower pH change before and after the typhoon.

Assessing seawater quality with a variable fuzzy recognition model

With the rapid development of the marine economy industry, human exploitation of marine resources is increasing, which is contributing to the growing trend of eutrophication and frequent occurrence of red tide. Accordingly, investigations of seawater quality have attracted a great deal of attention. This study was conducted to construct a seawater environmental quality assessment model based on the variable fuzzy recognition model. The uncertainty and ambiguity of the seawater quality assessment were then considered, combining the monitoring values of evaluation indicators with the standard values of seawater quality. Laizhou Bay was subsequently selected for a case study. In this study, the correct variable model for different parameters was obtained according to the linear and nonlinear features of evaluation objects. Application of the variable fuzzy recognition model for Laizhou Bay, water quality evaluation and comparison with performance obtained using other approaches revealed that the generated model is more reliable than traditional methods, can more reasonably determine the water quality of various samples, and is more suitable for evaluation of a multi-index, multi-level, nonlinear marine environment system; accordingly, the generated model will be an effective tool for seawater quality evaluation.

An Empirical Assessment of Marine Debris, Seawater Quality and Littering in Ghana

A baseline survey was carried out at four beaches along Ghana’s Accra-Tema coastline over a period of sixteen weeks to determine beach quality, seawater quality and the perception of beach users towards littering. A total of 18,241 items of marine debris which weighed 297.59 kg were collected. Plastic materials were the dominant debris, accounting for 63.72% of total debris. Land-based marine debris formed the largest proportion of debris collected (93% of items/m2 and 85 kg/m2). Water quality analysis revealed high mean levels of coliforms and E. coli above World Health Organization (WHO) levels on all four beach locations. A social survey that targeted beach users and some stakeholders revealed a habit of littering and beach users as the main source of litter generation on Ghana’s beaches. Intensive education, continuous monitoring and the enforcement of appropriate policy initiatives remain vital to addressing beach and water quality issues along Ghana’s coastline.

Application of Artificial Neural Network in the Research of the Bohai Bay Eutrophication

In order to research the feasibility of artificial neural network (ANN) in the research of eutrophication of the Bohai Bay in China, an ANN model simulating chlorophyll a, b and c concentrations, concerning temperature, dissolved oxygen, salinity, pH value, chemical oxygen demand (COD), PO43-, NO2-and NO3-factors in the Bohai Bay was presented and validated. After experiencing and training by Matlab, the model′s validation mean square error (MSE) performance is 0.009 985 02. R-squared between estimated and observed concentrations of chlorophyll a, b and c are 0.965 7, 0.998 7 and 0.970 7 respectively, indicating that the estimated value agrees with the observed value well, and the model can be used in the prediction of eutrophication of the Bohai Sea. In order to study the influence of model input factors on chlorophyll concentration (i.e. model outputs), hypothetical scenarios were introduced to show model output responses to variations in input factors. The limitation of temperature, salinity and phosphate that induce red tide in the Bohai Bay was also presented.