Exploring the Use of Phytoplankton for Water Pollution Monitoring

This essay investigates the application of phytoplankton as a bio-indicator for monitoring water pollution. It explores various methodologies, including multispectral image processing and diversity indices, to assess water quality. The essay highlights the effectiveness of phytoplankton in evaluating pollution levels and discusses the impact of environmental stressors on phytoplankton communities. It examines different research studies that have utilized phytoplankton for water pollution assessment, such as the use of the Integrated Phytoplankton Index (IPI) and Shannon-Wiener Diversity index. While traditional techniques like diversity indices have drawbacks, the introduction of multispectral imaging offers improvements. The essay concludes by emphasizing the importance of further research using multispectral imaging techniques to enhance the use of phytoplankton for water pollution monitoring, leading to promising and applicable methods for real-world scenarios.

Running head: USE OF PHYTOPLANKTON FOR WATER POLLUTION MONITORING
Use of phytoplankton for water pollution monitoring
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USE OF PHYTOPLANKTON FOR WATER POLLUTION MONITORING
Environmental pollution has become one of the greatest global concerns and efforts have
been started to be taken on both national and global level in order to properly measure the level
of pollution and be able to counteract it (Singh et al. 2013). According to Qu and Fan (2010),
biological monitoring has emerged as an impactful method for conservational analysis using the
bio-indicators for protecting and preserving the integrity of the national ecosystems. Bio-
indicators can be defined as an individual or a group of species which can help in monitoring the
qualitative status of the environment. Among the various different kinds of bio-indicators
available for measuring the water pollution, phytoplankton has emerged as a beneficial and
useful method (Xu et al. 2017). This paper will attempt to explore literature on the topic of use of
phytoplankton for the monitoring of water pollution.
There have been many research studies that have focused on exploring the effectiveness
of evaluating phytoplankton as a bio-indicator for measuring the level of water pollution in many
cases. Exploring further, it has to be mentioned that there are various different methodologies
that are routinely utilized for the evaluation of the level of water pollution in different regions.
As for example, the article by Petruk et al. (2015), a technique of multispectral image processing
of the phytoplankton has been explored for the environmental control of the water pollution.
Petruk et al. (2015) in the article have attempted to evaluate the improvements that have been
made to the bio- monitoring process of the water pollution using the phytoplankton. The
proposed technique identifies phytoplankton particles that are carried out through comparison
array multispectral images by the use of Bayesian classifier of solving function based on
Mahalanobis distance. The authors have stated that the phytoplankton is present as a component
of poly-disperse environment in the aquatic system and in case of monitoring the aquatic
pollution is via the quantitative co-relation between the volume concentrations of the

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phytoplankton particles by using the multispectral imaging of the suspended phytoplankton
particles. As per Srebotnjak et al. (2012), the measurement technique of the television
multispectral imaging of the different water bodies are a reliable technique for measuring and
evaluating the water pollution. The methodological technique used is a measuring control
multispectral television ecological state of water bodies using different phytoplankton
parameters. The first task is sampling of the phytoplankton, followed by identification of
quantitative and qualitative structure of micro-algal cells, the gathered data is then compared to
standardized values, then multispectral television measuring flow analysis is performed to
measure the continuous phytoplankton activity using a microscope and CCD-TV camera that
captures and analyses the image of particles of phytoplankton species in certain specialized
processors in real time (Petruk et al. 2015).
The article by Spetharis and Tsirtis (2010), discusses different ecological quality scales
using phytoplankton for implementing water framework directive in the Eastern Mediterranean.
The authors have stated that structural changes in the poly-dispersed environment of the
phytoplankton communities serve as an important metric for the European Water Framework
Directive, assessing the water quality and level of pollution. Among the various assessments
performed, the article also used an Integrated Phytoplankton Index or IPI utilizing three different
phytoplankton metrics such as chlorophyll a, abundance and diversity. The author have stated
that establishing synthetic indices such as the IPI has proved to be very helpful to perform
critical assessment of the quality of coastal water when implementing quality scales for water
framework derivatives (Spatharis and Tsirtsis 2010).
Another article by Gharib et al. (2011), has focused on assessment of phytoplankton
along with other environmental variables as bio-indicators of water quality assessment and

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pollution control for the beaches of Matrouh. For this article, authors have assessed the water
quality and extent of any pollution utilizing the phytoplankton abundance and structure of the
community. Exploring the methodology further, surface water samples have been observed over
a period of 2009 to 2010. The study also utilized Shanon-Wiener Diversity index to assess the
water quality and the result indicated the water to be between clean and moderately polluted.
Hence, it can be mentioned that phytoplankton indexing is a very useful technique for measuring
level of water pollution and assessing the water quality (Qu and Fan 2010).
On a similar note, Shekhar et al, (2008) have explored the use of phytoplankton indexing
to assess and identify the extent of water pollution with respect to industrial waste. This article
has made use of the Shannon-Weaver diversity index and surface phytoplankton to study the
quality of the water of Bhadra river that receives Mysore paper mill and Iron and steel mill
waste. This authors have focused on the idea that species diversity index can be a very useful
metric for measuring water pollution as the various species respond differently to the
environmental stressors produced by pollution. However, as critiqued by Xu et al. (2017), have
stated that seasonal changes in distribution, large uncontrollable sample size and lack of response
to all kinds of pollutants are a few drawbacks of the traditional techniques such as phytoplankton
diversity indices.
Although, the article by Jiang et al. (2014) the applicability of the Phytoplankton as bio-
indicators for measuring the water quality and extent of pollution has been correlated with the
spatial and seasonal variation of different phytoplankton community. Authors have utilized the
process of canonical correspondence analysis revealed that the environmental stressors such as
oxidation reduction potential, water temperature, orthophosphate, and dissolved oxygen, have a
significant impact on the composition of the Phytoplankton community, especially in Lake

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Chaohu. However, the manual phytoplankton diversity measurement is undoubtedly a very time
taking procedure which also prone to different bias as well (Jiang et al. 2014).
On a concluding note, it has to be mentioned that phytoplankton communities, their
distributions and structural characteristics have been used for monitoring the level of water
pollution for the past decade, and the essay has successfully highlighted that most of these
methods utilized the diversity indexes as the only mode of analysis. However, large sample size,
seasonal distribution, and lack of response to all kinds of pollutants can be considered
considerable drawbacks of the use of indices. However, the introduction of multispectral imaging
technique undoubtedly minimizes the restrictions of using manual phytoplankton indices. Hence,
there should be further research utilizing the multispectral imaging technique to evaluate the use
of phytoplankton for monitoring water pollution and assessing water quality so that promising
and applicable techniques can be discovered and implemented in real world scenario.

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References:
Gharib, S.M., El-Sherif, Z.M., Abdel-Halim, A.M. and Radwan, A.A., 2011. Phytoplankton and
environmental variables as a water quality indicator for the beaches at Matrouh, south-eastern
Mediterranean Sea, Egypt: an assessment. Oceanologia, 53(3), pp.819-836.
Jiang, Y.J., He, W., Liu, W.X., Qin, N., Ouyang, H.L., Wang, Q.M., Kong, X.Z., He, Q.S.,
Yang, C., Yang, B. and Xu, F.L., 2014. The seasonal and spatial variations of phytoplankton
community and their correlation with environmental factors in a large eutrophic Chinese lake
(Lake Chaohu). Ecological Indicators, 40, pp.58-67.
Petruk, V., Kvaternyuk, S., Yasynska, V., Kozachuk, A., Kotyra, A., Romaniuk, R.S. and
Askarova, N., 2015, December. The method of multispectral image processing of phytoplankton
processing for environmental control of water pollution. In Optical Fibers and Their
Applications 2015 (Vol. 9816, p. 98161N). International Society for Optics and Photonics.
Qu, J. and Fan, M., 2010. The current state of water quality and technology development for
water pollution control in China. Critical Reviews in Environmental Science and
Technology, 40(6), pp.519-560.
Shekhar, T.S., Kiran, B.R., Puttaiah, E.T., Shivaraj, Y. and Mahadevan, K.M., 2008.
Phytoplankton as index of water quality with reference to industrial pollution. Journal of
Environmental Biology, 29(2), p.233.
Singh, U.B., Ahluwalia, A.S., Sharma, C., Jindal, R. and Thakur, R.K., 2013. Planktonic
indicators: a promising tool for monitoring water quality (early-warning signals). Ecol Environ
Conserv, 19(3), pp.793-800.

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Spatharis, S. and Tsirtsis, G., 2010. Ecological quality scales based on phytoplankton for the
implementation of Water Framework Directive in the Eastern Mediterranean. Ecological
Indicators, 10(4), pp.840-847.
Srebotnjak, T., Carr, G., de Sherbinin, A. and Rickwood, C., 2012. A global Water Quality Index
and hot-deck imputation of missing data. Ecological Indicators, 17, pp.108-119.
Xu, Y., Li, A.J., Qin, J., Li, Q., Ho, J.G. and Li, H., 2017. Seasonal patterns of water quality and
phytoplankton dynamics in surface waters in Guangzhou and Foshan, China. Science of the
Total Environment, 590, pp.361-369.