1.3.1 WQIs for Data Generated from Automated Networks
Automated sampling networks generate a limited range of physico-chemical parameters that are measured continuously in specific locations at high temporal frequency.
This process creates a significant volume of data that is eventually stored in data
tables, which are not translated into intelligible information describing the status of
the water body. As not all the existing indices are suitable to deal with data of this
nature, we need to select an index. Terrado et al. [17] proposed different suitable
WQIs to deal with data generated from automated sampling networks, and Table 3.4
summarizes the main characteristics of proposed indices (Tables 3.9 and 3.10).
Figure 3.2 shows the proposed criteria that can be used to compare the different
WQIs [18]. Five proposed indices were selected depending on whether they fulfill
these particular criteria in a good, a fair, or a bad way. Significant parameters, such as
pH, conductivity, turbidity, dissolved oxygen, water temperature, ammonia, nitrates,
chlorides, and phosphates, were considered for proposed WQIs. Different objectives
and a flexible index that allowed use of different parameters were established
depending on various water uses. A higher value on simplicity in programming,
tolerance to missing and erroneous data, and the possibility of the index working
with non-synchronized data were performed. Accordingly, Canadian Council of
Ministers of the Environment (CCME) was selected as the most suitable tool for
categorizing water bodies using data generated by automated sampling stations.
Figures 3.3 and 3.4 illustrate chart for developing WQIs.
For example, British Columbia Ministry of Environment [21] developed formula
for CCME WQI. The index number ranges between 0 (poor water quality) and
100 (excellent water quality), divided into five descriptive categories: Poor: (0–44),
Marginal: (44.1–64), Fair: (64.1–79), Good: (79.1–94) and Excellent: (CCME
94.1–100). Table 3.11, summarizes descriptive index criteria and Table 3.7 illustrates the advantages and disadvantages of CCME WQI index.
There are three elements that can be used for calculation and modification of the
range of index; F 1 (scope), F 2 (frequency) and F 3 (amplitude). F 1 represents the
Table 3.8 (continued)
Parameters/indicators
of water quality
Include as
part of WQI? Reasoning
Bank height ratio
(BHR)
Maybe
This estimate may a more robust measure of potential
sediment pollution during storm flow than simply bank
stability. However, this metric and estimates of bank
stability (a sort of pseudo BEHI) would be a powerful
parameter combination indicating sediment pollution.
Riparian zone width/
vegetation
Yes
We know that riparian conditions (width and type of
vegetation) affect water quality functions. Conventional
chemistry parameters alone do not adequately address
this metric.
Bottom substrate
Maybe
This would address sediment pollution in terms of bed
load and may not be needed if one of the other sediment
related metrics are included.
3 Surface Water Quality and Analysis
79
Automated sampling networks generate a limited range of physico-chemical parameters that are measured continuously in specific locations at high temporal frequency.
This process creates a significant volume of data that is eventually stored in data
tables, which are not translated into intelligible information describing the status of
the water body. As not all the existing indices are suitable to deal with data of this
nature, we need to select an index. Terrado et al. [17] proposed different suitable
WQIs to deal with data generated from automated sampling networks, and Table 3.4
summarizes the main characteristics of proposed indices (Tables 3.9 and 3.10).
Figure 3.2 shows the proposed criteria that can be used to compare the different
WQIs [18]. Five proposed indices were selected depending on whether they fulfill
these particular criteria in a good, a fair, or a bad way. Significant parameters, such as
pH, conductivity, turbidity, dissolved oxygen, water temperature, ammonia, nitrates,
chlorides, and phosphates, were considered for proposed WQIs. Different objectives
and a flexible index that allowed use of different parameters were established
depending on various water uses. A higher value on simplicity in programming,
tolerance to missing and erroneous data, and the possibility of the index working
with non-synchronized data were performed. Accordingly, Canadian Council of
Ministers of the Environment (CCME) was selected as the most suitable tool for
categorizing water bodies using data generated by automated sampling stations.
Figures 3.3 and 3.4 illustrate chart for developing WQIs.
For example, British Columbia Ministry of Environment [21] developed formula
for CCME WQI. The index number ranges between 0 (poor water quality) and
100 (excellent water quality), divided into five descriptive categories: Poor: (0–44),
Marginal: (44.1–64), Fair: (64.1–79), Good: (79.1–94) and Excellent: (CCME
94.1–100). Table 3.11, summarizes descriptive index criteria and Table 3.7 illustrates the advantages and disadvantages of CCME WQI index.
There are three elements that can be used for calculation and modification of the
range of index; F 1 (scope), F 2 (frequency) and F 3 (amplitude). F 1 represents the
Table 3.8 (continued)
Parameters/indicators
of water quality
Include as
part of WQI? Reasoning
Bank height ratio
(BHR)
Maybe
This estimate may a more robust measure of potential
sediment pollution during storm flow than simply bank
stability. However, this metric and estimates of bank
stability (a sort of pseudo BEHI) would be a powerful
parameter combination indicating sediment pollution.
Riparian zone width/
vegetation
Yes
We know that riparian conditions (width and type of
vegetation) affect water quality functions. Conventional
chemistry parameters alone do not adequately address
this metric.
Bottom substrate
Maybe
This would address sediment pollution in terms of bed
load and may not be needed if one of the other sediment
related metrics are included.
3 Surface Water Quality and Analysis
79