provide useful information to managers to know better about how to improve water
quality. Selle et al. [4] utilized spatial–temporal patterns of scores technique for
surface water, springs, and deep groundwater from the wells in order to understand
the processes governing water quality at catchment scales. The study demonstrated
the potential analysis to identify dominant processes at catchment scales. Chung and
Yoo [5] designed a wireless sensor network (WSN) with deployed field servers to
detect water pollution in streams, rivers, and coastal areas. The proposed system can
be efficiently performed to monitor the variation of the water quality data in streams,
rivers, and coastal areas in real time. Hatvani et al. [6] used Dynamic Factor Analysis
method to determine the driving background factors of a river located in an agricultural watershed to separate the role of the diffuse and point source nutrient loads. In
this study, Dynamic Factor Analysis was applied to the time series (1978–2006) of
21 response parameters measured in its watershed. The study concluded that, with
the aid of Dynamic Factor Analysis, the superimposed effects of the socio-economic
changes which began in the mid-1980s, and the introduction of advanced wastewater
treatment in the river catchment in the early 1990s, could be separated and their
relative importance assessed. Chen et al. [7] prepared a comparative study of surface
water quality for the major rivers and lakes in china. Data from 33,612 observations
for the major rivers and lakes between 2012 and 2018 was used to evaluate the
performance of ten learning models (seven traditional and three ensemble models) to
explore the potential key water parameters for future model prediction. Busico et al.
[8] utilized a multivariate statistical analysis to investigate a novel hybrid method for
the effect of anthropogenic pollutions on groundwater in Italy. Jahin et al. [9]
developed irrigation water quality index for surface water in Egypt by using multivariate analysis. Weerasinghe and Handapangoda [10] investigated the analysis of
physiochemical parameters of surface water in Sri Lanka. In this study, two-way
ANOVA, followed by Tukey’s pairwise comparison, were used to assess the spatial
and temporal variability. Carstens and Amer [11] perform spatio-temporal analysis
study of urban changes and surface water quality in southeast Louisiana. The study
reported that the high levels of fecal coliform were consistent with increased
urbanization in water bodies. Khan et al. [12] investigated the effect of chemical
and microbiological quality of sea water on reverse osmosis membrane and on
fouling of RO membrane modification in RO sea water desalination plant in Saudi
Arabia. Won et al. [13] evaluated the microbiological quality of tow irrigation canals
and four surface reservoirs located in Ohio, USA. The study reported that the level of
Escherichia coli in irrigation canals was higher than that in reservoirs and increased
during heavy rain season. According to Texas commotion on environmental quality
[14, 15], the Surface Water Quality Monitoring (SWQM) Program reported that
around 1800 samples were collected from different surface water sites statewide to
characterize physical, chemical, and biological parameters in order to identify
emerging problems and evaluate the effectiveness and trends of water quality
program. Standard values and criteria of surface water quality and monitoring has
been adopted by Colorado department of public health and are presented in
Tables 3.1, 3.2, and 3.3.
66
H. A. Aziz et al.
quality. Selle et al. [4] utilized spatial–temporal patterns of scores technique for
surface water, springs, and deep groundwater from the wells in order to understand
the processes governing water quality at catchment scales. The study demonstrated
the potential analysis to identify dominant processes at catchment scales. Chung and
Yoo [5] designed a wireless sensor network (WSN) with deployed field servers to
detect water pollution in streams, rivers, and coastal areas. The proposed system can
be efficiently performed to monitor the variation of the water quality data in streams,
rivers, and coastal areas in real time. Hatvani et al. [6] used Dynamic Factor Analysis
method to determine the driving background factors of a river located in an agricultural watershed to separate the role of the diffuse and point source nutrient loads. In
this study, Dynamic Factor Analysis was applied to the time series (1978–2006) of
21 response parameters measured in its watershed. The study concluded that, with
the aid of Dynamic Factor Analysis, the superimposed effects of the socio-economic
changes which began in the mid-1980s, and the introduction of advanced wastewater
treatment in the river catchment in the early 1990s, could be separated and their
relative importance assessed. Chen et al. [7] prepared a comparative study of surface
water quality for the major rivers and lakes in china. Data from 33,612 observations
for the major rivers and lakes between 2012 and 2018 was used to evaluate the
performance of ten learning models (seven traditional and three ensemble models) to
explore the potential key water parameters for future model prediction. Busico et al.
[8] utilized a multivariate statistical analysis to investigate a novel hybrid method for
the effect of anthropogenic pollutions on groundwater in Italy. Jahin et al. [9]
developed irrigation water quality index for surface water in Egypt by using multivariate analysis. Weerasinghe and Handapangoda [10] investigated the analysis of
physiochemical parameters of surface water in Sri Lanka. In this study, two-way
ANOVA, followed by Tukey’s pairwise comparison, were used to assess the spatial
and temporal variability. Carstens and Amer [11] perform spatio-temporal analysis
study of urban changes and surface water quality in southeast Louisiana. The study
reported that the high levels of fecal coliform were consistent with increased
urbanization in water bodies. Khan et al. [12] investigated the effect of chemical
and microbiological quality of sea water on reverse osmosis membrane and on
fouling of RO membrane modification in RO sea water desalination plant in Saudi
Arabia. Won et al. [13] evaluated the microbiological quality of tow irrigation canals
and four surface reservoirs located in Ohio, USA. The study reported that the level of
Escherichia coli in irrigation canals was higher than that in reservoirs and increased
during heavy rain season. According to Texas commotion on environmental quality
[14, 15], the Surface Water Quality Monitoring (SWQM) Program reported that
around 1800 samples were collected from different surface water sites statewide to
characterize physical, chemical, and biological parameters in order to identify
emerging problems and evaluate the effectiveness and trends of water quality
program. Standard values and criteria of surface water quality and monitoring has
been adopted by Colorado department of public health and are presented in
Tables 3.1, 3.2, and 3.3.
66
H. A. Aziz et al.