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1.0 Introduction: An Analysis Of Impact Of Water Quality And Heavy Metals On Ashfield Flat Environment
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The Ashfield flat project was a major project in Perth, Western Australia. But, there were environmental impacts on the project area due to that project. In this report, a statistical analysis had been conducted to analyse the impact of the project. This study aims to measure the environmental impact statistically.
2.0 Background
The Ashfield flat project was a major project in Perth, Western Australia. As the project was built on a wetland, which previously contains a huge amount of biodiversity. The site was also located adjacent to the Perth River (Perez, & Martin, 2023). Thus, different animals, fishes, and other waterborne species, and various other species. The compounds like metals, and other associated materials used for construction had caused an ample amount of damage to the biodiversity. This study had examined these factors through a collection of raw data from the ground and conducting a statistical analysis. After that compare the results to the standards.
3.0 Methodology
The method followed in this research is based on the primary method. In this method, researchers generally carried out the process of research by collecting data (Paull, 2020). The different components of the methodology followed in this research is being given below.
3.1 Research philosophy
The philosophy of the research is based on “interpretivism”. As, the research is a primary one, which is based on the quantitative method, this philosophical approach was followed.
3.2 Research approach
The approach for this research analysis was based on the “Inductive approach”. This approach involves the formulation of a research hypothesis, which was then analysed using various statistical means.
3.3 Research design
The design of the research is primary. In this design, the researchers generally prepared a research hypothesis, which they would analyse through the use of different statistical methods (Byrnes & Rose, 2019). The collection of data would had been done through the use of various methods like surveys, collection of samples, and interviews among others.
3.4 Condition of field
The condition at the field was good. The researchers were allowed to take samples from soil, sediments of the soil, river water, water of the drainage system of the area easily. They also had to taken permission from the local authorities (Ehrhart, 2021). But, after the granting of the permission, there was relatively fewer problems regarding the process of the research.
At the laboratory, after the collection of samples from the field, the researchers had worked in a team effort. They had distributed the samples among the various teams, who were doing work on specific tasks.
3.4 Data collection method
The researchers followed the primary data collection method. They collected data by conducting research on the soil, water, and sediments of the area. After the collection of data from the ground, they had analysed those samples in the laboratory using various biochemical and other tests (Ojstršek, et al., 2021). After that, they prepared the datasets according to the reports of those tests for further statistical analysis.
3.5 Data analysis method
For the analysis of data, they conducted different statistical tests. These tests include frequency analysis, and regression tests among others.
Research Question
- How do the water quality and existing heavy metals affect the eDNA variables?
4.0 Results
The result of the tests were given below.
4.1 For Water Quality
4.1.1 Frequency test
The result of the frequency distribution of the data regarding water samples were given in the table below.
Figure 1: Frequency statistics table
(Source: Self-created in SPSS)
From the frequency analysis of the variables, it had been found that there were a total 229 number of samples, from the year 2019 to 2023. There were no missing values in those datasets. The mean value of the variable pH was 7.196201. Similarly, the mean values of variables Fe, Na, Mn, P and S were 26.643598, 2976.962760, 0.244434, 0.188300 and 244.121627. The median values of the variables were 7.050000, 0.680196, 508.497200, .0159848, 0.080331 and 84.00000 (Arivalagan, 2022). The mode values of the variables were 7.0000, 0.0000, 0.0000, 0.0000, 0.0000 and 92.0000.
4.1.2 Graphs
The following figures shows the graphs of various variables of the water quality category.
Figure 2: Line graph of Fe in water quality
(Source: Self-created in SPSS)
The above figure shows the line graph of the variable “Fe” in the category of water quality. It shows the maximum number of 7 was observed for Fe level 0.0000. Also, for the Fe level of 1.0500 and 1.900, there are two peaks for the count of 5.
Figure 3: Line graph of pH in water quality
(Source: Self-created in SPSS)
The above figure shows the line graph of the variable “pH” in the category of water quality. It shows the maximum number of 40 was observed for pH level between 6.9700 and 7.0300.
Figure 4: Line graph of Na in water quality
(Source: Self-created in SPSS)
The above figure shows the line graph of the variable “Na” in the category of water quality. It shows the maximum number of 22 was observed for Na level near 0.0000.
Figure 5: Line graph of P in water quality
(Source: Self-created in SPSS)
The above figure shows the line graph of the variable “P” in the category of water quality. It shows the maximum number of 32 was observed for P level near 0.0000 (Cheung, et al. 2022). There were small peaks of number 4 and 5 for P level 0.0200, 0.0958 and 0.9900.
4.1.3 Regression analysis
The results of regression analysis is given in the figures below.
Figure 6: Model summary of regression analysis
(Source: Self-created in SPSS)
The first table shows the summary of the regression analysis. The R-value for the analysis was found to be 0.504.
Figure 7: Coefficients table of regression analysis
(Source: Self-created in SPSS)
The table above shows the results of the values of standard error, with statistical significance of the regression analysis of the variables. The significance is between 0.000 to 0.007. The standard error values for the pH, Fe, Na and P were 0.118, 0.001, 0.000 and 0.283. The dependent variable which was selected was “Year”.
Figure 8: Table showing the ANOVA analysis of the variables
(Source: Self-created in SPSS)
The table above shows the ANOVA test values for the selected variables under the category of water quality. The sum of the square value of the regression analysis was found to be 107.972. The residual value was found to be 317.425 (James, et al., 2019). The significance level was 0.000.
Figure 9: Histogram of the variables of regression analysis
(Source: Self-created in SPSS)
The above figure shows the histogram chart of the regression analysis of the category of “water quality”. The plotting was done against the frequency and regression standard residual.
4.2 For sedimentation
4.2.1 Frequency test
Figure 10: Frequency statistics table
(Source: Self-created in SPSS)
The table above shows the results of statistics of the frequency of variables. From the frequency analysis of the variables, it had been found that there were a total 358 number of samples, from the year 2019 to 2023. There were no missing values in those datasets. The mean value of the variable pH was 6.137905. Similarly, the mean values of variables Fe, Mn, Na, P and S were 39740.92151, 143.853911, 13163.65621, 552.282133 and 6496.576786. The median values of the variables were 6.205000, 38286.50000, 81.000000, 10363.50000, 419.000000 and 2979.000000 (Ezemba, 2019). The mode values of the variables were 7.0000, 532.0000, 46.0000, 221.0000, 170.0000 and 486.0000.
4.2.2 Graphs
Figure 11: Line graph of pH in sedimentation
(Source: Self-created in SPSS)
The above figure shows the line graph of the variable “pH” in the category of sedimentation. It shows the maximum number of 33 was observed for pH level at 7.0100. Also, another peak of number 28 was found near the pH level 6.0000.
Figure 12: Line graph of Mn in sedimentation
(Source: Self-created in SPSS)
The above figure shows the line graph of the variable “Mn” in the category of sedimentation. It shows there were multiple peaks, which indicates the level of Mn was spread throughout the years of samples.
Figure 13: Line graph of Na in sedimentation
(Source: Self-created in SPSS)
The above figure shows the line graph of the variable “Na” in the category of sedimentation (Paull, 2020). It shows the maximum number of 2.0 was observed for Na level at 295.48, 4117.00, near 5000.00 and between 7070.00 and 8222.00.
Figure 14: Line graph of S in sedimentation
(Source: Self-created in SPSS)
The above figure shows the line graph of the variable “S” in the category of sedimentation. It shows there were multiple peaks of numbers at different S levels of the samples collected between 2019 and 2023.
4.2.3 Regression analysis
The following table shows the results of the regression analysis.
Figure 15: Model summary of regression analysis
(Source: Self-created in SPSS)
The table above shows the results of the summary of the variables that were involved in the regression analysis. The R-value for the analysis was found to be 0.349.
Figure 16: Coefficients of regression analysis
(Source: Self-created in SPSS)
The table above shows the results of the values of standard error, with statistical significance of the regression analysis of the variables. The significance is between 0.000 to 0.454. The standard error values for the pH, Fe, Mn, Na and P were 0.088, 0.000, 0.000, 0.000 and 0.000 (Barber, 2020). The dependent variable which was selected was “Year”.
Figure 17: Table showing the ANOVA analysis of the variables
(Source: Self-created in SPSS)
The table above shows the ANOVA test values for the selected variables under the category of sedimentation. The sum of the square value of the regression analysis was found to be 94.779. The residual value was found to be 682.375. The significance level was 0.000.
4.3 eDNA analysis
4.3.1 Frequency test
Figure 18: Frequency statistics table
(Source: Self-created in SPSS)
The table above shows the results of statistics of the frequency of variables. From the frequency analysis of the variables, it had been found that there were a total 24 number of samples, from the year 2019 to 2023. There were no missing values in those datasets. The mean value of the variable pH was 7.626667. Similarly, the mean values of variables Ca, Mn, Na, P and S were 162.371667, 0.122500, 2387.921667, 0.155833 and 265.591667 (Dufty, 2020). The median values of the variables were 7.3925, 56.887500, 0.067500, 118.952500, 0.115000 and 39.29. The mode values of the variables were 6.9800, 44.99, 0.0200, 17.3900, 0.0200 and 8.9200.
4.3.2 Graphs
Figure 19: Line graph of pH according to eDNA samples
(Source: Self-created in SPSS)
The above figure shows the line graph of the variable “pH” in the category of sedimentation. It shows there were multiple peaks of numbers at different pH levels of the samples collected between 2019 and 2023. For the sample AF2, the pH was 6.98 and the count was 4.
Figure 20: Line graph of Na according to eDNA samples
(Source: Self-created in SPSS)
The above figure shows the line graph of the variable “Na” in the category of sedimentation. It shows there were multiple peaks of numbers at different Na levels of the samples collected between 2019 and 2023.
4.3.3 Regression analysis
Figure 21: Model summary of regression analysis
(Source: Self-created in SPSS)
The table above shows the results of the summary of the variables that were involved in the regression analysis. The R-value for the analysis was found to be 1.0.
Figure 22: Coefficients of regression analysis
(Source: Self-created in SPSS)
The table above shows the results of the values of standard error, with statistical significance of the regression analysis of the variables. The significance is between 0.000 to 0.454. The standard error values for the pH, Fe, Mn, Na and P were 0.000. The dependent variable which was selected was “Year”.
Figure 23: Table showing the ANOVA analysis of the variables
(Source: Self-created in SPSS)
5.0 Discussion
The results of the samples collected from Ashfield flatlands in Perth, Australia shows that there are higher levels of contamination in the water by the presence of heavy metals. The samples were collected from the year 2019 to 2023. It affects the liveable species in that area as well. It could be analysed by the level of eDNA as well. There were various previously published pieces of literature on this subject as well. Heavy metals like sulfur, manganese, phosphorus, sodium, and various others change the pH level of the water of that area, which were harmful for the species of that area (Shah, 2021). In this research analysis, the impact of the presence of various heavy metals were analysed through statistical tests.
In the frequency analysis, the analysis of variables like pH, Na, Mn, P, S and Fe were done. The results showed the number of samples collected over the years and their amount of presence in the nearby waters. It was found that, there was a presence of heavy metals above the permissible limit suggested by the World Health Organisation. These were established by the graphs that were generated after the analysis.
The regression analysis was carried out to process and estimate the relationship between the set of variables which were being analysed. The result of the regression analysis had shown the standard error values for the pH, Fe, Na and P were 0.118, 0.001, 0.000 and 0.283 for the water quality. This means the variables were linked to each other, and they had a significant presence inside the water. Although the level of significance was 0.000, which indicates that there should not be any concrete conclusion to be drawn from the results.
For the sedimentation category, the result of the regression analysis had shown the standard error values for the pH, Fe, Mn, Na and P were 0.088, 0.000, 0.000, 0.000 and 0.000. This indicates that the samples were not linked to each other and should be analysed individually. The graphs indicate there were specific peaks at some values for each of the variables. The pH values were peaked at 5, 6 and 7 values. Similarly, the Na values peaked at 295.48, 4117.00, near 5000.00 and between 7070.00 and 8222.00. These are exceptionally high amounts to be found in water and soil sediments (Ncbi.nlm.nih.gov, 2020). The high level of these molecules in soil and water in the Ashfield area had a profound impact on the species of that area. These can be seen from the eDNA numbers for the each category and variable.
The results were also in line with the previously published articles which analyse the impact on the species in a particular area with an increasing value of heavy metals, and depreciation of water quality. As per the study conducted in 2009, during the developmental phase of the Ashfield project, the result of field and laboratory testing had found that due to the project, there were high levels of toxic heavy metals like lead, manganese, iron, phosphorus, sulfur and other present in that area (Webster, 2022). The researchers of that study count the ratio of chloride and sulfate, pH and other heavy metals. They had also found a huge amount of heavy metals, toxic wastes, and plastics as well on that site. These ultimately reduce the BOD and COD levels of the Perth River water and reduce the number of species in that area. Since the area is located in a highly sensitive ecological park area, the project had an overall profound impact on the environment of the city.
6.0 Conclusion
In conclusion of the research and statistical analysis, it can be said that there could be an adverse effect on the species present in the area due to the depreciation of water quality in that area. The depreciation of water quality could be linked to top the presence of heavy metals in huge amount in the river water and soil sediments of the area. The presence of heavy metals like Fe, Mn, Na, P, S and others had been found in the samples, which also change the pH, BOD and COD levels of the river water. These facts could be found after the laboratory analysis of the collected samples. The presence of heavy metals could lead to a reduction in the number of species in the river water, which could be estimated by eDNA variables. Thus, it can be said that the water quality and existing heavy metals affect the eDNA variables, which indicates a reduction of the number of species.
References
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