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While copper (Cu) is vital for life, it can quickly become toxic at some concentrations and impact growth, reproductive, and immune health of organisms. In aquatic environments, it is thought that colloids and dissolved organic matter play a vital role in determining the toxicity of metals, like Cu, to organisms in an environment. Therefore, one way to measure the severity of toxic metal effects on an aquatic ecosystem is to evaluate an organism’s mortality rate to increasing concentrations of the metal in question. We conducted an ecotoxicological study to deduce freshwater shrimps’ (Caridina (sp.)) response to environmental Cu pollution in artificial and true pond water. To conduct the study, we followed the Aquatic Ecotoxicology Laboratory Project procedure. Our experiment had 36 replicates, 18 replicates of pond water and 18 replicates of artificial pond water; Water sources were analyzed by EAL. Metals and salts were analyzed by Inductively Coupled Plasma – Mass Spectrometry (ICP-MS). Water quality measurements (pH, conductivity, temperature, dissolved oxygen concentration) were recorded at the beginning and end of the experiment. Our hypothesis was that the Caridina (sp.) in artificial pond water would be more sensitive to increasing copper concentrations, while the Caridina (sp.) in the true pond water would be more resistant to the copper’s effects. In artificial pond water, average LC10= 0.5327, LC50= 1.1433, LC90= 1.17537; average pond water LC10= 0.0447, LC50= 0.1013, and LC90= 0.1573. Standard deviation values from LC50 for artificial pond water was 0.029194748 and 0.136639428 in pond water. ANOVA test conducted for the artificial pond water (p<0,05m=, F= 3.13, DF=2, 17) and true pond water (p<0.05, F=4.32, DF= 2,17) prove to be significant. Our hypothesis was correct, the shrimp in the true pond water were more resistant to Cu toxicity. Overall, this data could be used to further confirm the validity of dissolved organic matter and colloids in removing toxic metals from a contaminated environment.
Contaminating an ecosystem has the potential to affect organisms, the ecosystem itself, and the surrounding areas; currently, ecosystem contamination is considered to be an environmental disturbance (Araújo, C. V., Pereira, K. C., & Blasco, J., 2018).In our current cultural climate, it is necessary to reduce our pollutive habits; the use of potentially toxic elements, such as copper (Cu), has led to global environmental pollution (McKinley, K., McLellan, I., Gagné, F., & Quinn, B., 2019). Often, people do not anticipate the anthropogenic influence they have on susceptible environments. For example, the pollution of aquatic environments with metals like Cu is most common near localized human activity; Cu has been largely associated with human influence (Birch et al., 2015; Deheyn and Latz, 2006; McLellan et al., 2013). Further, the Division of Toxicology (2004) reports 640,000,000,000 grams of Cu were released into the environment by industries in 2000.
At high concentrations, Cu is toxic and can adversely impact organismal, ecosystem, and environmental health; Cu can have negative impacts on growth, reproduction, and immune functions of aquatic invertebrates, even at sublethal levels (McLellan et al., 2013; Schamphelaere et al., 2007). The toxic effects occur when the rate of uptake exceeds organismal detoxification and excretion rates (Rainbow, 1996). Cu pollution is extensive in aquatic habitats; it is a growing concern worldwide because of the use of Cu in industrial manufacturing and agricultural practices (Järup, 2003).Cu can also have synergistic effects with other stressors and can bioaccumulate (Zhou et al., 2008). One place in which Cu can bioaccumulate is in sediments, which are sinks for pollutants (Fleming C.A. & Trevors J.T., 1989).
While it can be toxic, Cu is vital in trace amounts to support and maintain functions in aquatic ecosystems; Cu is essential for life and has an important function as a cofactor for many enzymes (Tchounwou et al., 2012, Zhu et al., 2014). Cu enters the environment through releases from the mining of Cu and other metals and from factories that make or use Cu metal or Cu compounds (Division of Toxicology, 2004). Cu can also enter the environment through waste dumps, domestic waste water, combustion of fossil fuels and wastes, wood production, phosphate fertilizer production, and natural sources, such as dust, soils, volcanoes, decaying vegetation, forest fires, and sea spray (Division of Toxicology, 2004). When Cu is released, it can become strongly attached to the organic material and other components during natural purification processes in aquatic systems (Division of Toxicology, 2004; Fleming C.A. & Trevors J.T., 1989). Fleming C.A. & Trevors J.T. (1989) found when Cu in readily soluble forms is moved from the water column into sediments, such as clay or sand, it may not move far; when Cu is released into water, the Cu that dissolves can be carried in particles suspended in the water, like dissolved organic matter (DOM), or colloids, which are “nanoparticles and macromolecules in the 1 nm to 1 μm size range, with at least one dimension less than 100 nm in size, and have the ability to scatter light” (IUPAC, 2001).
DOM plays a critical role in the cycling of metals and the mobility of colloidal particles in aquatic environments; it controls environmental reactions and the availability of metals to exposed organisms (Aiken, G. R., Hsu-Kim, H., & Ryan, J. N., 2011). In environmental scenarios where dissolved DOM exceeds metal ion concentrations in water, DOM binds to dissolved metal ions (Aiken, G. R., Hsu-Kim, H., & Ryan, J. N., 2011). In aquatic ecosystems, colloids are composed of natural or dissolved organic matter with trace amounts of metals (Santschi, P. H., 2018). Colloids occupy the intermediate place between suspensions and solutions (Lower, 2019). Metal ions can complex with colloids, which remove them from ecosystems; this changes the rate of transport of metal ions, which is usually a slow step (Leeuwen, H. P., & Buffle, J., 2009). So, if Cu binds with DOM or colloids, it could easily be removed from the system during Cu cycling.
In order to explore the toxic effects of copper pollution on aquatic organisms, we decided to conduct an ecotoxicological experiment. Ecotoxicology is concerned with toxic effects of chemical agents on living organisms, especially on ecological entities such as populations and communities within ecosystems, including their integration with the environment (Balling, H., 2009; Segner, H., 2011). To determine the effect of pollutants on aquatic ecosystems, it is common for ecotoxicology experiments to expose invertebrates, like shrimp, to heavy metals (Borgmann et al., 2005). For example, Soltanian (2007) used Artemia (sp.) as a marine model test organism for eco-toxicity studies because of its ready availability, low cost, adaptability to adverse conditions. This is how we decided to conduct our study. We employed an ecotoxicological “forced exposure” experimental approach to discern the toxicity of increasing copper concentrations in natural and artificial aquatic systems to Caridina (sp.). Our hypothesis was that the Caridina (sp.) in artificial pond water would be more sensitive to increasing copper concentrations while the Caridina (sp.) in the true pond water would be more resistant to the copper’s effects.
Our procedure followed the Aquatic Ecotoxicology Laboratory Project procedure throughout this study. Organisms used throughout this study followed the university’s code for animal experimental ethics. Five 1L control test containers and 5 1L containers of each copper concentration dose (nominal concentrations= 0.005ppm, 0.01ppm, 0.05ppm, 0.1ppm, and 0.2ppm for artificial pond water, 0.005ppm, 0.01ppm, 1.00ppm, 2.00ppm, and 3.00ppm for pond water), were created from lowest to highest concentrations as to not contaminate samples. This created 5 replicate 1L containers for each of the Cu concentration doses, totaling 36 replicates, 18 replicates of pond water and 18 replicates of artificial pond water. Artificial pond water was created following Collins et al. (2016). 10mL samples were taken from each concentration, 3 drops of nitric acid were added to digest metals in samples and taken to the EAL for analysis. Water sample Analysis was performed according to APHA (2017) ‘Standard Methods for the Examination of Water & Wastewater’, 23rd Edition. Metals and salts were analyzed by Inductively Coupled Plasma – Mass Spectrometry (ICP-MS). Water quality measurements (pH, Conductivity, Dissolved Oxygen, Temperature) were recorded at the beginning and end of the experiment. Caridina (sp.) were collected from the retention pond on campus; one shrimp was placed in each of the containers after the concentration preparation. Caridina (sp.) mortality was monitored at 24, 48, 72, and 96 hours. Dead organisms were removed from containers throughout the study. At the end of the 96 hours, the containers were emptied and organisms were frozen to avoid introducing contaminated organisms to the environment. LC data was collected via SPSS Probit analysis.
ICP-MS results from EAL concluded differences between dose and actual copper levels for both artificial and pond water. Average standard deviation of copper concentration in artificial pond water (A) 0.00420519 and pond water (B) 0.01992702 (Table 1). Water quality averages for initial treatment were found to be pH= 7.25, Conductivity= 221.86 µS, Dissolved Oxygen= 8.83 mg/L, and Temperature= 22.37 °C (Table 2). In the final treatment, the water quality averages were found to be pH= 7.25, Conductivity= 221.07 µS, Dissolved Oxygen= 8.67 mg/L, and Temperature 21.72 °C (Table 2). In artificial pond water, the average LC10= 0.5327, LC50= 1.1433, and LC90= 1.17537 (Table 3). The average LC10 in pond water was 0.0447, LC50= 0.1013, and LC90= 0.1573 (Table 3). Standard deviation values from LC50 for artificial pond water was 0.029194748 and 0.136639428 in pond water (Graph 1). The ANOVA test conducted for the artificial pond water proved statistically significant (p<0,05m=, F= 3.13, DF=2, 17); ANOVA test for true pond water prove to be significant, as well (p<0.05, F=4.32, DF= 2,17).
Table 1. AverageWater Parameter Analysis Results and standard deviations comparing nominal (Dose) and actual concentration (Copper (mg/L)) of artificial (A) and true (B) pond water.
Table 2. Average of water quality measurements of initial (A) and final (B) artificial pond water and initial (C) and final (D) pond water data.
Table 3. Final Average LC10, LC50, and LC90 values and standard deviations between the two water types.
Graph 1. Experimental water types compared to averaged LC50 estimate values.
We completed three independent tests for each water type. Results of each experiment were slightly different due to natural variability in biological responses. Nominal and measured Cu concentrations were comparable but varied slightly. Measured Cu concentrations were used in the SPSS Probit analysis because it was the concentration experienced by the shrimp throughout the study. Water quality did not change significantly throughout the experiment; the physicochemical variables (pH, Conductivity, Dissolved Oxygen, Temperature) were maintained within acceptable ranges for aquatic life throughout the study and did not vary greatly (Flores, 2019). Single factor ANOVA test concluded the values were found to be statistically significant for both artificial pond water (p< 0.05) and pond water (p< 0.05). LC50 standard deviation comparisons between water types prove mortality rate was at higher concentrations for pond water, as expected; therefore, our hypothesis was correct: Caridina (sp.) in artificial pond water were more sensitive to increasing copper concentrations while Caridina (sp.) in true pond water were more resistant to the copper’s effects.
On species sensitivity curves, our results support Flores (2019), who stated a safe Cu LC50 for Caridina (sp.) was 0.072ppm. The next most sensitive shrimp species Flores tested, Neocaridina denticulata, had a LC50 ppm value of 0.370; therefore, Caridina (sp.) is an extremely sensitive test species, so it protects the less sensitive organisms in the ecosystem. Williams et al. (1991) agreed Caridina (sp.) is an “extremely sensitive shrimp”; therefore, it was a valuable test species to use for our ecotoxicology experiment.
Because invertebrates have Cu based enzymes for oxygen transport, Cu is readily incorporated into their system. However, Cu, which is speciation dependent on DOM and colloid complexation, can decrease in bioavailability, causing a risk for these Cu dependent organisms; the change in speciation also has the potential to be toxic to some organisms (Lage, 1996). The chemical species of a metal can affect its toxicity by influencing its absorption, distribution, and biotransformation; Lage (1996) found that Copper (II) produced pronounced toxic effects (European Virtual Institute for Speciation Analysis, 2019). Further, physicochemical parameters such as DOM and colloids, dictate the effects at the organism level; the presence of colloids reduces toxicity of Cu by influencing its overall speciation via complexation, therefore decreasing its bioavailability (Witters, 2010). This is because Whiting (2011) found that colloids and DOM remove heavy metals from ecosystems; Kim et al. (1999) found that copper toxicity to C. dubia decreased with increasing copper‐DOM interaction (Kim et al. 1999).
In order to improve this experiment, we would more accurately match nominal and measured copper concentrations in order to determine true toxic effects of the nominal copper concentrations. Further, increasing the number of replicates in order to increase our number of confidence intervals would give our experiment more validity; we would also run the experiment longer for this same reason. The number of replicates performed and the small sample size could not accurately give us confidence limits, or determine error bounds. Moreover, we would measure the DOM and colloid concentrations in the true pond water; in this experiment, we assumed that there was DOM and colloids in the true pond water, and that this led to a difference between the water types. Moreover, we used a “forced exposure” experiment; this triggers escape behavior that may have the potential to increase the metabolism of the organism, incorporating more Cu into the shrimp’s system, and increasing its rate of mortality (Araújo et al. 2018). Using a different type of ecotoxicology experimental method or a larger test container may give more valid results because the shrimp would not be exhibiting the avoidance behavior response, and thus, incorporating more Cu into its system.
Overall, our hypothesis was correct: the Caridina (sp.) in artificial pond water were more sensitive to increasing Cu concentrations while the Caridina (sp.) in the true pond water were more resistant to the Cu’s effects. We believe that this is because of the DOM and colloids in the true pond water binding the Cu and removing it from the system, while the Cu in the artificial pond water does not have anything to bind to, so the Caridina (sp.) was forced to deal with the Cu’s toxic effects. Adding more replicates and running this experiment for a longer period of time would give this experiment more validity; nevertheless, our experiment confirms the validity of dissolved organic matter and colloids in removing toxic metals from a contaminated environment.
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