Macao Water Co.

Macau, China

Macao Water Co.

Macau, China
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In Ieong I.,University of Macau | Lou I.,University of Macau | Ung W.K.,Macao Water Co. | Mok K.M.,University of Macau
Environmental Modeling and Assessment | Year: 2015

Principle component regression (PCR), artificial neural network (ANN), and their combination used as data-driven models were selected to apply in this study to predict (based on the current-month variables) and forecast (based on the last 3-month-ahead variables) the phytoplankton dynamics in Macau Main Storage Reservoir (MSR) that is experiencing algal bloom in recent years. The models used the comprehensive 8 years’ monthly water quality data for training and the most recent 3 years’ monthly data for testing. Twenty-four water quality variables including physical, chemical, and biological parameters were involved, and comparisons were made to select the best models that can be applied to MSR. Simulation results revealed that ANN has better accuracy and generalization performance in comparison with PCR both for the prediction and the forecasted model. Using principal component analysis (PCA) for the data, inputs did not show better performance for the ANN, implying that eliminating the uncorrelated variables do not increase the prediction capability for the adopted model. Globally, in contrast with previous studies showing that the hybrid model can handle both linear and nonlinear components of the problems well, the PCR-ANN in this study obtain no better improvement. © 2014, Springer International Publishing Switzerland.


Kong Y.,Macao Water Co. | Lou I.,University of Macau | Zhang Y.,Macao Water Co. | Lou C.U.,Macao Water Co. | Mok K.M.,University of Macau
Hydrobiologia | Year: 2014

Monitoring of cyanobacteria and their toxins are traditionally conducted by cell counting, chlorophyll-a (chl-a) determination and cyanotoxin measurements, respectively. These methods are tedious, costly, time consuming, and insensitive to rapid changes in water quality and cyanobacterial abundance. We have applied and tested an online phycocyanin (PC) fluorescence probe for rapid monitoring of cyanobacteria in the Macau Storage Reservoir (MSR) that is experiencing cyanobacterial blooms. The relationships among cyanobacterial abundance, biovolume, cylindrospermopsin concentration, and PC fluorescence were analyzed using both laboratory and in-the-field studies. The performance of the probe was compared with traditional methods, and its advantages and limitations were assessed in pure and mixed cyanobacterial cultures in the laboratory. The proposed techniques successfully estimated the species including Microcystis and Cylindrospermopsis, two toxic species recently observed in the MSR. During February–November, 2010, the PC probe detected high correlations between PC and cell numbers (R2 = 0.71). Unlike the chl-a content, which indicates only the total algal biomass, the PC pigment specifically indicates cyanobacteria. These results support the PC parameter as a reliable estimate of cyanobacterial cell number, especially in freshwater bodies where the phytoplankton community and structure are stable. Thus, the PC probe is potentially applicable to online monitoring of cyanobacteria. © 2014, Springer International Publishing Switzerland.


Zhang W.,University of Macau | Lou I.C.,University of Macau | Kong Y.,Macao Water Co. | Ung W.K.,Macao Water Co. | Mok K.M.,University of Macau
Desalination and Water Treatment | Year: 2013

Eutrophication analyses of two subtropical storage reservoirs in Macau, the Special Administrative Region of China, namely Main Storage Reservoir (MSR) and Sai Pa Van Reservoir (SPVR), were performed in this study. Totally, 17 monthly water parameters including, five hydrological parameters (precipitation, imported volume, exported volume, water level, and hydraulic retention time), four physical parameters (temperature, pH, turbidity, and conductivity), seven chemical parameters (dissolved oxygen, Ammonium, nitrite, nitrate, total nitrogen (TN), orthophosphate (PO4 3-), and total phosphorus (TP)), and one biological parameter (phytoplankton abundance) were sampled and monitored in 2010. The correlation analysis and principle component regression (PCR), that is, principle component analysis (PCA) followed by multiple linear regression (MLR), were used to simplify the complexity of the relationships and to predict the phytoplankton abundance levels as well. The eutrophication analyses results showed that both reservoirs were in eutrophic status with the trophic state indices of 58-72 for MSR and 51-71 for SPVR, respectively. Phytoplankton abundance in both reservoirs were found to be linearly correlated with turbidity, temperature, and TP, while anti-correlated with conductivity, TN, nitrate, TN/TP, and water level. The PCA showed that three PCs with Eigen value over one, can explain 84.6% of total variation of the water parameters in MSR, while only two PCs can explain 70.8% for SPVR. The MLR models can be used for predicting phytoplankton abundance in the reservoirs with the predictive power of 0.90 in MSR, while only of 0.67 in SPVR. © 2013 Balaban Desalination Publications.


Lou I.,University of Macau | Xie Z.,University of Macau | Kin Ung W.,Macao Water Co. | Meng Mok K.,University of Macau
Adaptation, Learning, and Optimization | Year: 2014

Understanding and predicting dynamic change of algae population in freshwater reservoirs is particularly important, as algae-releasing cyanotoxins are carcinogens that would affect the health of public. However, the high complex nonlinearity of water variables and their interactions makes it difficult in modeling its growth. Recently extreme learning machine (ELM) was reported to have advantages of only requirement of a small amount of samples, high degree of prediction accuracy and long prediction period to solve the nonlinear problems. In this study, the ELMbased prediction and forecastmodels for phytoplankton abundance in Macau Storage Reservoir (MSR) are proposed, in which the water parameters of pH, SiO2, alkalinity, Bicarbonate (HCO3−), dissolved oxygen (DO), total Nitrogen (TN), UV254, turbidity, conductivity, nitrate, total nitrogen (TN), orthophosphate (PO43−), total phosphorus (TP), suspended solid (SS) and total organic carbon (TOC) selected from the correlation analysis of the 23monthly water variables were included, with 8 years (2001-2008) data for training and the most recent 3 years (2009-2011) for testing. The modeling results showed that the prediction and forecast (based on data on the previous 1st, 2nd, 3rd and 12th months) powers were estimated as approximately 0.83 and 0.90 respectively, showing that the ELM is an effective new way that can be used for monitoring algal bloom in drinking water storage reservoir. © Springer International Publishing Switzerland 2014.


Zhang W.,University of Macau | Lou I.,University of Macau | Ung W.K.,Macao Water Co. | Kong Y.,Macao Water Co. | Mok K.M.,University of Macau
Desalination and Water Treatment | Year: 2015

Abstract: In small-scale pumped-storage reservoirs, physical disturbances have been suggested to be one of the main factors influencing phytoplankton structure and water quality. This study presented data on dynamic changes of the phytoplankton structure and the quality of raw water sampled monthly from January 2011 to June 2012, in three locations (with two different water levels each) of a small pumped-storage reservoir of Macau main storage reservoir. The trophic state index, phytoplankton structure indices, and multivariate statistical techniques were applied for assessing trophic state, phytoplankton community, and spatio-temporal variations of the reservoir, respectively. The results showed that the reservoir was categorized as a eutrophic–hypereutrophic reservoir, with the dominance of Cyanophyta in 2011, and of Chlorophyta and Bacillariophyta in 2012. Lowest diversity/evenness and highest dominance happened in June 2011, while highest diversity/evenness and lowest dominance occurred in May 2012. Principle component analysis identified four factors that can explain 80.8% of the total variance of the water quality data, and cluster analysis generated two clusters of spatial similarity among the six sampling points and two clusters of temporal similarity among the 18 months. Discriminant analysis results revealed only three parameters (TP, NO3-N, and Chl-a) that could afford 100% correct assignation in temporal analysis, while no spatial variation was found in spatial analysis. This study highlighted the usefulness of combination of these methods for the evaluation and interpretation of complex water quality data-sets and assessment of pollution level of small-scale eutrophic reservoirs. The results from the study can be used in developing monitoring program of freshwater bodies. © 2014 Balaban Desalination Publications. All rights reserved.


Zhang W.,University of Macau | Lou I.,University of Macau | Ung W.K.,Macao Water Co. | Kong Y.,Macao Water Co. | Mok K.M.,University of Macau
Hydrobiologia | Year: 2014

The Macau storage reservoir (MSR) has experienced algal blooms in recent years, with high levels of Cylindrospermopsis and Microcystis and detectable concentrations of cyanotoxins. To analyze the cyanotoxin-producing genotypes and relate the corresponding cyanotoxins to the water quality parameters, a quantitative real-time polymerase chain reaction was developed and applied to the water samples in three locations of MSR. Cylindrospermopsin polyketide synthetase (pks) gene and a series of microcystin synthetase (mcy) genes were used for identifying and quantifying cylindrospermopsin- and microcystin-producing genes, and the corresponding water parameters were measured accordingly. Our results showed that high concentrations of cylindrospermopsin and low concentrations of microcystin were measured during the study period. There was a strong correlation between the pks gene numbers and cylindrospermopsin concentrations (R2 = 0.95), while weak correlations were obtained between the mcy genes numbers and microcystin concentrations. Furthermore, the pks gene numbers were strongly related to Cylindrospermopsis (R2 = 0.88), cyanobacterial cell numbers (R2 = 0.96), total algae numbers (R2 = 0.95), and chlorophyll-a concentrations (R2 = 0.83), consistent with the dominant species of Cylindrospermopsis among the cyanobacteria existing in MSR. NH4–N (R2 = 0.68) and pH (R2 = 0.89) were the water quality parameters most highly correlated with the pks gene numbers. These results contribute to monitoring for potential cyanotoxins in raw water. © 2014, Springer International Publishing Switzerland.


Zhang W.,University of Macau | Lou I.,University of Macau | Ung W.K.,Macao Water Co. | Kong Y.,Macao Water Co. | Mok K.M.,University of Macau
Frontiers of Earth Science | Year: 2014

Freshwater algal blooms have become a growing concern world-wide. They are caused by a high level of cyanobacteria, predominantly Microcystis spp. and Cylindrospermopsis raciborskii, which can produce microcystin and cylindrospermopsin, respectively. Longtime exposure to these cyanotoxins may affect public health, thus reliable detection, quantification, and enumeration of these harmful algae species has become a priority in water quality management. Traditional manual enumeration of algal bloom cells primarily involves microscopic identification which limited by inaccuracy and time-consumption.With the development of molecular techniques and an increasing number of microbial sequences available in the Genbank database, the use of molecular methods can be used for more rapid, reliable, and accurate detection and quantification. In this study, multiplex polymerase chain reaction (PCR) and real-time quantitative PCR (qPCR) techniques were developed and applied for monitoring cyanobacteria Microcystis spp. and C. raciborskii in the Macau Storage Reservoir (MSR). The results showed that the techniques were successful for identifying and quantifying the species in pure cultures and mixed cultures, and proved to be a potential application for water sampling in MSR. When the target species were above 1 million cells/L, similar cell numbers estimated by microscopic enumeration and qPCR were obtained. Further quantification in water samples indicated that the ratio of the estimated number of cell by microscopy and qPCR was 0.4-12.9 for cyanobacteria and 0.2-3.9 for C. raciborskii. However, Microcystis spp. was not observed by manual enumeration, while it was detected at low levels by qPCR, suggesting that qPCR is more sensitive and accurate. Thus the molecular approaches provide an additional reliable monitoring option to traditional microscopic enumeration for the ecosystems monitoring program. © 2013 Higher Education Press and Springer-Verlag Berlin Heidelberg.

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