Can people detect the loss of water quality? A field experiment to evaluate the correlation between visual perception and water eutrophication degree

As pessoas podem detectar a perda de qualidade da água? Um experimento de campo para avaliar a correlação entre a percepção visual e o grau de eutrofização da água

João Carlos Nabout Ana Clara Maciel David Jéssica Fagundes Felipe Karine Borges Machado Laurence Carvalho Hélida Ferreira da Cunha About the authors

Abstracts

Abstract

Aim

The quantity and quality of water are essential to many ecosystem services, biodiversity and human well-being. In the present paper, we used a field experiment to evaluate the visual perception of the public regarding the loss of water quality associated with eutrophication and greening of water. We hypothesized that with an increase in eutrophication (i.e. greening of water due to increased Chlorophyll-a), people can detect a loss of water quality and threats to ecosystem services.

Methods

We used an experimental area composed of six mesocosms (500L water tanks) with a gradient of chlorophyll-a varying from clear water (without chlorophyll-a) up to eutrophic mesocosms (very green water). A total of 100 people visited the experimental area in-situ, and 83 people visualized pictures of the mesocosms.

Results

Our results indicated that people were able to detect the loss of water quality associated with increased concentrations of chlorophyll-a, and recognized that these were less suitable for recreational activity and consumption. Moreover, this perception did not vary by gender, formal education, or frequency of visits to aquatic ecosystems.

Conclusions

The results highlight the clear potential of visual public perception to be used as a simple, rapid, early-warning strategy for monitoring programs of water quality and also an approach that strengthens the link between science and society.

Keywords:
Chlorophyll-a; mesocosm; tropical; pictures; interview; citizen science


Resumo

Objetivo

A quantidade e a qualidade da água são essenciais para manutenção de muitos serviços ecossistêmicos, biodiversidade e bem-estar humano. No presente trabalho, utilizamos um experimento de campo para avaliar a percepção visual do público em relação à perda de qualidade da água associada à eutrofização e esverdeamento da água. Nós hipotetizamos que, com um aumento na eutrofização (ou seja, esverdeamento da água devido ao aumento da clorofila-a), as pessoas podem detectar uma perda de qualidade da água e ameaças aos serviços ecossistêmicos.

Métodos

Nós utilizamos uma área experimental composta por seis mesocosmos (caixas d'água de 500L) com gradiente de clorofila-a variando de águas claras (sem clorofila-a) até mesocosmos eutróficos (águas muito verdes). Um total de 100 pessoas visitaram a área experimental in-situ, e 83 pessoas visualizaram imagens dos mesocosmos.

Resultados

Nossos resultados indicaram que as pessoas foram capazes de detectar a perda de qualidade da água associada ao aumento das concentrações de clorofila-a, e reconheceram que estas eram menos adequadas para atividade recreativa e consumo. Além disso, essa percepção não variou por gênero, educação formal ou frequência de visitas aos ecossistemas aquáticos.

Conclusões

Os resultados evidenciam potencial da percepção visual do público como uma estratégia simples, rápida e de alerta precoce para programas de monitoramento da qualidade da água e também uma abordagem que fortalece o vínculo entre ciência e sociedade.

Palavras-chave:
Clorofila-a; mesocosmo; tropical; fotografias; entrevista; ciência cidadã


1. Introduction

Water is an essential resource to the occurrence and maintenance of life (Chaplin, 2001Chaplin, M.F., 2001. Water: its importance to life. Biochem. Mol. Biol. Educ. 29(2), 54-59. http://dx.doi.org/10.1111/j.1539-3429.2001.tb00070.x.
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), and the importance of water availability and quality are clearly recognized by people. Aquatic ecosystems directly generate important ecosystem services, such as fish production and water supply, in addition to indirectly affecting climate regulation (Grizzetti et al. 2016Grizzetti, B., Lanzanova, D., Liquete, C., Reynaud, A., & Cardoso, A.C., 2016. Assessing water ecosystem services for water resource management. Environ. Sci. Policy 61, 194-203. http://dx.doi.org/10.1016/j.envsci.2016.04.008.
http://dx.doi.org/10.1016/j.envsci.2016....
). Moreover, urban lakes are frequently used for recreation and watersport activity. Therefore, water quality impacts human well-being in different ways. Nonetheless, in recent years, many water resources have undergone severe degradation, driven mainly by human activities (Bashir et al., 2020Bashir, I., Lone, F.A., Bhat, R.A., Mir, S.A., Dar, Z.A., & Dar, A.S., 2020. Concerns and threats of contamination of aquatic ecosystems. In: Hankeen, K. R., Bhat, R. A., Quadri, H., eds. Bioremediation and biotechnology. Cham: Springer. http://dx.doi.org/10.1007/978-3-030-35691-0_1.
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). This intense exploitation of water resources has led to changes in pivotal ecosystem services provided by these environments (Green et al., 2015Green, P.A., Vörösmarty, C.J., Harrison, I., Farrell, T., Sáenz, L., & Fekete, B.M., 2015. Freshwater ecosystem services supporting humans: pivoting from water crisis to water solutions. Glob. Environ. Change 34, 108-118. http://dx.doi.org/10.1016/j.gloenvcha.2015.06.007.
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; Culhane et al., 2019Culhane, F., Teixeira, H., Nogueira, A.J.A., Borgwardt, F., Trauner, D., Lillebø, A., Piet, G.J., Kuemmerlen, M., McDonald, H., O’Higgins, T., Barbosa, A.L., van der Wal, J.T., Iglesias-Campos, A., Arevalo-Torres, J., Barbière, J., & Robinson, L.A., 2019. Risk to the supply of ecosystem services across aquatic ecosystem. Sci. Total Environ. 660, 611-621. http://dx.doi.org/10.1016/j.scitotenv.2018.12.346.
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), loss of biodiversity associated with these services (Dudgeon et al., 2006Dudgeon, D., Arthington, A.H., Gessner, M.O., Kawabata, Z.I., Knowler, D.J., Lévêque, C., Naiman, R.J., Prieur-Richard, A.H., Soto, D., Stiassny, M.L.J., & Sullivan, C.A., 2006. Freshwater biodiversity: importance, threats, status and conservation challenges. Biol. Rev. Camb. Philos. Soc. 81(02), 163-182. http://dx.doi.org/10.1017/S1464793105006950.
http://dx.doi.org/10.1017/S1464793105006...
; Vaughn, 2010Vaughn, C.C., 2010. Biodiversity loss and ecosystem function in freshwaters: emerging conclusion and research directions. Bioscience 60(1), 25-35. http://dx.doi.org/10.1525/bio.2010.60.1.7.
http://dx.doi.org/10.1525/bio.2010.60.1....
), as well as changes in the quality and quantity of water consumed by humans (Keeler et al., 2012Keeler, B.L., Polasky, S., Brauman, K.A., Johnson, K.A., Finlay, J.C., O’Neill, A., Kovacs, K., & Dalzell, B., 2012. Linking water quality and well-being for improved assessment and valuation of ecosystem services. Proc. Natl. Acad. Sci. USA 109(45), 18619-18624. http://dx.doi.org/10.1073/pnas.1215991109.
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).

Eutrophication of water bodies, which occurs by the increase in nutrient concentrations mainly due to urban and agricultural development, is one of the most widespread problems in aquatic systems (Jeppesen et al., 2010Jeppesen, E., Moss, B., Bennion, H., Carvalho, L., DeMeester, L., Feuchtmayr, H., Friberg, N., Gessner, M.O., Hefting, M., Lauridsen, T.L., Liboriussen, L., Malmquist, H.J., May, L., Meerhoff, M., Olafsson, J.S., Soons, M.B., & Verhoeven, J.T.A., 2010. Interaction of climate change and eutrophication. In: Kernan, M., Battarbee, R.W., Moss, B., eds. Climate change impacts on freshwater ecosystems. New Jersey: Hoboken Blackwell Publishing. http://dx.doi.org/10.1002/9781444327397.ch6.
http://dx.doi.org/10.1002/9781444327397....
). Higher concentrations of nitrogen and phosphorus in the water can promote algal blooms, besides changing water properties such as colour, odour, and taste (Codd, 2000Codd, G.A., 2000. Cyanobacterial toxins, the perception of water quality, and the prioritisation of eutrophication control. Ecol. Eng. 16(1), 51-60. http://dx.doi.org/10.1016/S0925-8574(00)00089-6.
http://dx.doi.org/10.1016/S0925-8574(00)...
; Smith et al., 2006Smith, V.H., Joye, S.B., & Howarth, R.W., 2006. Eutrophication of freshwater and marine ecosystems. Limnol. Oceanogr. 51(1 Pt 2), 351-355. http://dx.doi.org/10.4319/lo.2006.51.1_part_2.0351.
http://dx.doi.org/10.4319/lo.2006.51.1_p...
; Keeler et al., 2012Keeler, B.L., Polasky, S., Brauman, K.A., Johnson, K.A., Finlay, J.C., O’Neill, A., Kovacs, K., & Dalzell, B., 2012. Linking water quality and well-being for improved assessment and valuation of ecosystem services. Proc. Natl. Acad. Sci. USA 109(45), 18619-18624. http://dx.doi.org/10.1073/pnas.1215991109.
http://dx.doi.org/10.1073/pnas.121599110...
). Cyanobacterial blooms can produce toxins, causing severe risks to human and animal health (Paerl & Otten, 2013Paerl, H.W., & Otten, T.G., 2013. Harmful cyanobacterial blooms: causes, consequences and controls. Microb. Ecol. 65(4), 995-1010. http://dx.doi.org/10.1007/s00248-012-0159-y.
http://dx.doi.org/10.1007/s00248-012-015...
). As a consequence, essential ecosystem services are lost, including fishing, swimming, and drinkable water (Keeler et al., 2012Keeler, B.L., Polasky, S., Brauman, K.A., Johnson, K.A., Finlay, J.C., O’Neill, A., Kovacs, K., & Dalzell, B., 2012. Linking water quality and well-being for improved assessment and valuation of ecosystem services. Proc. Natl. Acad. Sci. USA 109(45), 18619-18624. http://dx.doi.org/10.1073/pnas.1215991109.
http://dx.doi.org/10.1073/pnas.121599110...
).

Numerous strategies have already been developed to detect changes in water quality (Behmel et al., 2016Behmel, S., Damour, M., Ludwig, R., & Rodriguez, M.J., 2016. Water quality monitoring strategies – A review and future perspectives. Sci. Total Environ. 57, 1312-1329. http://dx.doi.org/10.1016/j.scitotenv.2016.06.235.
http://dx.doi.org/10.1016/j.scitotenv.20...
). In the specific case of cyanobacterial blooms, cell counts and cyanotoxin detection have been used for a while, and are included in legislation as a method of assessing water quality (Brasil, 2005Brasil. Conselho Nacional do Meio Ambiente – CONAMA, 2005. Dispõe sobre a classificação dos corpos de água e diretrizes ambientais para o seu enquadramento, bem como estabelece as condições e padrões de lançamento de efluentes, e dá outras providências (Resolução Conama n° 357 de 17 de março de 2005). Diário Oficial da União [da] República Federativa do Brasil, Poder Executivo, Brasília, DF.). However, due to the increasing and rapid deterioration of water resources, other strategies may be necessary to rapidly detect and mitigate the eutrophication process and bloom events. Some initiatives that have emerged involve volunteer citizens and communities in the detection of these events (Jöborn et al., 2005Jöborn, A., Danielsson, I., Arheimer, A., Jonsson, A., Larsson, M.H., Lundqvist, L.J., Löwgren, M., & Tonderski, K., 2005. Integrated water management for eutrophication control: public participation, pricing police, and catchment modeling. Ambio 34(7), 482-488. http://dx.doi.org/10.1579/0044-7447-34.7.482.
http://dx.doi.org/10.1579/0044-7447-34.7...
; Castilla et al., 2015Castilla, E.P., Cunha, D.G.F., Lee, F.W.F., Loiselle, S., Ho, K.C., & Hall, C., 2015. Quantification of phytoplankton bloom dynamics by citizen scientists in urban and peri-urban environments. Environ. Monit. Assess. 187(11), 690. http://dx.doi.org/10.1007/s10661-015-4912-9.
http://dx.doi.org/10.1007/s10661-015-491...
).

Citizen science involves the participation of the population in the development of scientific knowledge. This participation can happen in different ways. Citizen science can act as a tool, in which citizens providing data and information for the development of scientific projects; Citizen science can be seen as a movement that democratizes scientific knowledge and eliminates barriers to access to science; or even with a social aspect, including communities in the production of knowledge and decision-making (Eitzel et al., 2017Eitzel, M.V., Cappadonna, J.L., Santos‐Lang, C., Duerr, R.E., Virapongse, A., West, S.E., Kyba, C., Bowser, A., Cooper, C.B., Sforzi, A., Metcalfe, A.N., Harris, E.S., Thiel, M., Haklay, M., Ponciano, L., Roche, J., Ceccaroni, L., Shilling, F.M., Dörler, D., Heigl, F., Kiessling, T., Davis, B.Y., & Jiang, Q., 2017. Citizen Science Terminology Matters: Exploring Key Terms. Citiz. Sci. Theory Pract. 2(1), 1. http://dx.doi.org/10.5334/cstp.96.
http://dx.doi.org/10.5334/cstp.96...
). In fact, the importance of public perception for monitoring and evaluating water quality has been recognized as a contribution to scientific knowledge (Niinioja et al., 2004Niinioja, R., Holopainen, A.L., Lepistö, L., Rämö, A., & Turkka, J., 2004. Public participation in monitoring programmes as a tool for lakeshore monitoring: the example of Lake Pyhäjärvi, Karelia, Eastern Finland. Limnologica 34(1-2), 154-159. http://dx.doi.org/10.1016/S0075-9511(04)80035-5.
http://dx.doi.org/10.1016/S0075-9511(04)...
; Jollymore et al., 2017Jollymore, A., Haines, M.J., Satterfield, F., & Johnson, M.S., 2017. Citizen science for water quality monitoring: data implications of citizen perspectives. J. Environ. Manage. 200, 456-467. http://dx.doi.org/10.1016/j.jenvman.2017.05.083.
http://dx.doi.org/10.1016/j.jenvman.2017...
), besides increasing the public interest in the conservation of water resources (Gholson et al., 2019Gholson, G.M., Boellstorff, D.E., Cummings, S.R., Wagner, K.L., & Dozier, M.C., 2019. A survey of public perceptions and attitudes about water availability following exceptional drought in Texas. J. Contemp. Water Res. Educ. 166(1), 1-11. http://dx.doi.org/10.1111/j.1936-704X.2019.03297.x.
http://dx.doi.org/10.1111/j.1936-704X.20...
). Thus, local communities can be involved in all stages of management programmes, including water monitoring and protection of drinking water supplies (WHO, 2017World Health Organization – WHO, 2017. Guidelines for drinking water quality: fourth edition incorporating the first addendum. Geneva: WHO.). In Brazil, for example, participation of the local community is required and permitted in the hydrographic committees, as stipulated by law about the Política Nacional dos Recursos Hídricos.

Several factors can determine the public perception of water quality, including colour, smell, and taste (e.g. House, 1996House, M.A., 1996. Public perception and water quality management. Water Sci. Technol. 34(12), 25-32. http://dx.doi.org/10.2166/wst.1996.0295.
http://dx.doi.org/10.2166/wst.1996.0295...
; Doria et al., 2009Doria, M.F., Pidgeon, N., & Hunter, P.R., 2009. Perceptions of drinking water quality and risk and its effect on behaviour: a cross national study. Sci. Total Environ. 407(21), 5455-5464. http://dx.doi.org/10.1016/j.scitotenv.2009.06.031.
http://dx.doi.org/10.1016/j.scitotenv.20...
; Rojas & Megerle, 2013Rojas, L.F.R., & Megerle, A., 2013. Perception of water quality and health risks in the rural area of Medellín. Am. J. Rural Dev. 1, 106-115. https://doi.org/10.12691/ajrd-1-5-2.
https://doi.org/10.12691/ajrd-1-5-2...
). In the case of eutrophication, sensory attributes of the water can be modified (Davies & Shaw, 2010Davies, J.L., & Shaw, G., 2010. Impacts of eutrophication on the safety of drinking and recreational water. In: UNESCO. Desalination and water resources. Paris: UNESCO, Water Health, Encyclopedia of Life Support Systems, vol. 2.), making these aspects one of the first to be perceived by the population, as an indication of water quality alteration. The public, for example, has been able to visually discern the presence of wastewater by colour (e.g. House, 1996House, M.A., 1996. Public perception and water quality management. Water Sci. Technol. 34(12), 25-32. http://dx.doi.org/10.2166/wst.1996.0295.
http://dx.doi.org/10.2166/wst.1996.0295...
), the influence of benthic algae (e.g. Suplee et al., 2009Suplee, M.W., Watson, V., Teply, M., & McKee, H., 2009. How green is to green? Public opinion of what constitutes undesirable algae levels in streams. J. Am. Water Resour. Assoc. 45(1), 123-140. http://dx.doi.org/10.1111/j.1752-1688.2008.00265.x.
http://dx.doi.org/10.1111/j.1752-1688.20...
), and to judge whether or not those environments were suitable for recreational activities according to these characteristics.

Furthermore, contextual indicators (where the water was taken from), previous experience (contact with drinking and contaminated water), influence from other people, cultural, demographic (e.g. gender and educational level) and economic (benefits of improve water quality) factor can also affect the perception of water quality (House, 1996House, M.A., 1996. Public perception and water quality management. Water Sci. Technol. 34(12), 25-32. http://dx.doi.org/10.2166/wst.1996.0295.
http://dx.doi.org/10.2166/wst.1996.0295...
; Doria et al., 2009Doria, M.F., Pidgeon, N., & Hunter, P.R., 2009. Perceptions of drinking water quality and risk and its effect on behaviour: a cross national study. Sci. Total Environ. 407(21), 5455-5464. http://dx.doi.org/10.1016/j.scitotenv.2009.06.031.
http://dx.doi.org/10.1016/j.scitotenv.20...
; Doria, 2010Doria, M.F., 2010. Factors influencing public perception of drinking water quality. Water Policy 12(1), 1-19. http://dx.doi.org/10.2166/wp.2009.051.
http://dx.doi.org/10.2166/wp.2009.051...
; Larson et al., 2011Larson, K.L., Ibes, D.C., & White, D.D., 2011. Gendered perspectives about water risks and policy strategies: A tripartite conceptual approach. Environ. Behav. 43(3), 415-438. http://dx.doi.org/10.1177/0013916510365253.
http://dx.doi.org/10.1177/00139165103652...
; Greenley et al., 2020Greenley, D.A., Walsh, R.G., & Young, R.A., 2020. Economic benefits of improved water quality: public perceptions of option and preservation values. New York: Routledge. http://dx.doi.org/10.4324/9780429049279.
http://dx.doi.org/10.4324/9780429049279...
; Flotemersch & Aho, 2021Flotemersch, J., & Aho, K., 2021. Factors influencing perceptions of aquatic ecosystems. Ambio 50(2), 425-435. http://dx.doi.org/10.1007/s13280-020-01358-0.
http://dx.doi.org/10.1007/s13280-020-013...
). Women generally have a greater perception of the risks offered by water, which can be attributed to the major feeling of vulnerability, different world views, socio-political factors, gender structure (Doria, 2010Doria, M.F., 2010. Factors influencing public perception of drinking water quality. Water Policy 12(1), 1-19. http://dx.doi.org/10.2166/wp.2009.051.
http://dx.doi.org/10.2166/wp.2009.051...
), or by carrying out a considerable part of domestic activities that involve the use of water (Okumah et al., 2020Okumah, M., Yeboah, A.S., & Bonyah, S.K., 2020. What matters most? Stakeholders’ perceptions of river water quality. Land Use Policy 99, 104824. http://dx.doi.org/10.1016/j.landusepol.2020.104824.
http://dx.doi.org/10.1016/j.landusepol.2...
). Some studies indicate that the higher the level of a person`s education, the greater their ability to discern about the quality of the aquatic environment (see reviews Doria, 2010Doria, M.F., 2010. Factors influencing public perception of drinking water quality. Water Policy 12(1), 1-19. http://dx.doi.org/10.2166/wp.2009.051.
http://dx.doi.org/10.2166/wp.2009.051...
; Flotemersch & Aho, 2021Flotemersch, J., & Aho, K., 2021. Factors influencing perceptions of aquatic ecosystems. Ambio 50(2), 425-435. http://dx.doi.org/10.1007/s13280-020-01358-0.
http://dx.doi.org/10.1007/s13280-020-013...
and literature cited), although other studies have failed to find this association (e.g. Ioana-Toroimac et al., 2020Ioana-Toroimac, G., Zaharia, L., Neculau, G., Constantin, D.M., & Stan, F.I., 2020. Translating a river’s ecological quality in ecosystem services: an example of public perception in Romania. Ecohydrol. Hydrobiol. 20(1), 31-37. http://dx.doi.org/10.1016/j.ecohyd.2019.10.005.
http://dx.doi.org/10.1016/j.ecohyd.2019....
). However, contextual indicators and sensory properties can interact with demographic factors and influence the perception of water quality (Doria, 2010Doria, M.F., 2010. Factors influencing public perception of drinking water quality. Water Policy 12(1), 1-19. http://dx.doi.org/10.2166/wp.2009.051.
http://dx.doi.org/10.2166/wp.2009.051...
). Many studies have already been conducted evaluating the public perception of water quality. Early investigations generally assessed public perception by observing the natural aquatic environment (Smith et al., 1991Smith, D.G., Cragg, A.M., & Croker, G.F., 1991. Water clarity criteria for bathing waters based on user perception. J. Environ. Manage. 33(3), 285-299. http://dx.doi.org/10.1016/S0301-4797(91)80030-9.
http://dx.doi.org/10.1016/S0301-4797(91)...
; Smith & Davies-Colley, 1992Smith, D.G., & Davies-Colley, R.J., 1992. Perception of water clarity and colour in terms of suitability for recreational use. J. Environ. Manage. 36(3), 225-235. http://dx.doi.org/10.1016/S0301-4797(05)80136-7.
http://dx.doi.org/10.1016/S0301-4797(05)...
). However, other strategies have emerged over time, including the application of questionnaires by postal surveys (Jones et al., 2006Jones, A.Q., Dewey, C.E., Doré, K., Majowicz, S.E., McEwen, S.A., David, W.T., Eric, M., Carr, D.J., & Henson, S.J., 2006. Public perceptions of drinking water: a postal survey of residents with private water supplies. BMC Public Health 6(1), 94. http://dx.doi.org/10.1186/1471-2458-6-94.
http://dx.doi.org/10.1186/1471-2458-6-94...
; Gholson et al., 2019Gholson, G.M., Boellstorff, D.E., Cummings, S.R., Wagner, K.L., & Dozier, M.C., 2019. A survey of public perceptions and attitudes about water availability following exceptional drought in Texas. J. Contemp. Water Res. Educ. 166(1), 1-11. http://dx.doi.org/10.1111/j.1936-704X.2019.03297.x.
http://dx.doi.org/10.1111/j.1936-704X.20...
), phone surveys (Delpla et al., 2020Delpla, I., Legay, C., Proulx, F., & Rodriguez, M.J., 2020. Perception of tap water quality: assessment of the factors modifying the links between satisfaction and water consumption behavior. Sci. Total Environ. 722, 137786. http://dx.doi.org/10.1016/j.scitotenv.2020.137786.
http://dx.doi.org/10.1016/j.scitotenv.20...
), experiments (Johnson, 2003Johnson, B.B., 2003. Do reports of drinking water quality affect customers’ concerns? Experiments in reports content. Risk Anal. 23(5), 985-988. http://dx.doi.org/10.1111/1539-6924.00375.
http://dx.doi.org/10.1111/1539-6924.0037...
), or pictures (Suplee et al., 2009Suplee, M.W., Watson, V., Teply, M., & McKee, H., 2009. How green is to green? Public opinion of what constitutes undesirable algae levels in streams. J. Am. Water Resour. Assoc. 45(1), 123-140. http://dx.doi.org/10.1111/j.1752-1688.2008.00265.x.
http://dx.doi.org/10.1111/j.1752-1688.20...
), which can facilitate the process of assessing the public perception of water quality.

In this study, we use a field experiment to evaluate the public`s perception of the visual loss of water quality associated with eutrophication and greening of water. Here, we combine the strategy of observing the aquatic environment in-situ and through photographs, seeking to assess people's perception of water quality and whether that perception differs in relation to the type of observational strategy used, or if its perception is affected by demographic factors. We expected that, with the increase in eutrophication (i.e. greening the water with an increase in chlorophyll-a), people can detect the loss of water quality and threats to ecosystem services. Thus, the citizen must be able to act as information providers for monitoring water quality. Specifically, we aimed to investigate the following questions: i) What was the relationship between water quality perception by people with the chlorophyll-a concentration of the water? ii) Does observation of water in-situ and through pictures resulted in similar patterns? iii) Did the relationship between water quality perception and chlorophyll-a vary by gender, level of formal education, and frequency of contact with the aquatic environment? The answers to these questions can help support environmental education and potentially broaden the role of society in monitoring programs of water quality and freshwater ecosystem services, as well as assisting in the production of knowledge and decision making in relation to the use and management of water resources.

2. Material and Methods

2.1. Experimental approach and water quality gradient

The experiment was carried out in the experimental area of the Tropical Aquatic Ecology Group, located on the Campus of the Universidade Estadual de Goiás (UEG), Brazil (see MESOCOSM, 2022Mesocosm, 2022. Tropical Aquatic Ecology Mesocosm [online]. Retrieved in 2021, February 20, from http://mesocosm.org/mesocosm/tropical-aquatic-ecology-mesocosm/.
http://mesocosm.org/mesocosm/tropical-aq...
). The experimental setup ensured that exogenous factors (e.g. sunlight, lake size, trees) did not affect the visual perception of people. The only factor that varied among treatments was water colour as a result of different concentrations of phytoplankton chlorophyll-a.

We used six mesocosms (A, B, C, D, E and F) represented by a 500L water tank each. A eutrophication gradient was established in the mesocosms from clear water (without chlorophyll-a) to eutrophic mesocosms (very green water due to high chlorophyll-a concentration). All mesocosms were initially filled with water from an artesian well without chlorophyll-a. Mesocosm A contained only artesian water. The other mesocosms were filled with a mixture of water from the artesian well (470 liters) and a reservoir (30 liters) containing chlorophyll-a (planktonic algae species). The reservoir water had a low chlorophyll-a concentration of 3.2 µg L-1. The nitrate-NO3 (0.80 mg L-1) and phosphate-PO4 (0.01 mg L-1) concentrations are also low, characterizing it as an oligotrophic environment. However, reservoir water contained different taxonomic groups of planktonic algae (e.g. Cyanobacteria, Zygnemaphyceae, Bacillariophyceae, Cryptophyeae, Chlorophyceae, and Euglenophyceae, see Machado et al., 2019Machado, K.B., Vieira, L.C.G., & Nabout, J.C., 2019. Predicting the dynamics of taxonomic and functional phytoplankton compositions in different global warming scenarios. Hydrobiologia 830(1), 115-134. http://dx.doi.org/10.1007/s10750-018-3858-7.
http://dx.doi.org/10.1007/s10750-018-385...
). Nitrate and phosphate, obtained from solutions of sodium nitrate and potassium phosphate, were added to mesocosms C to F to stimulate algal growth and increase the chlorophyll-a. 0.16 mg L-1 of nitrate and 0.01 mg L-1 of phosphate were added to mesocosms C and D every four days. This was a relatively low concentration that promoted the growth of small algae. Higher concentrations of nitrate and phosphate were added to mesocosms E and F every four days, to stimulate a high algal biomass. For each addition, we increased nutrients by 10% in comparison to the lake’s original concentration, considering the Redfield ratio (Table 1). In mesocosm B, no nutrients were added and this resulted in very low chlorophyll-a concentration. The mesocosms were randomly distributed in the experimental area.

Table 1
Nitrate and phosphate concentrations added to promote eutrophication of E and F mesocosms. The additions were maintained during the interview period (October 29, 2019 to November 12, 2019) seeking to maintain blooming.

Before the interviews, the chlorophyll-a, nitrate, and orthophosphate concentrations of each mesocosm were measured (Table 2). Although we added the same concentration of nutrients to treatments C-D and E-F, the concentration of chlorophyll-a ​​varied very slightly at the start of the experiment. However, we classified these pairs of treatments within the same trophic state (see paragraph below). The Carlson (1977)Carlson, R.E., 1977. A trophic state index for lakes. Limnol. Oceanogr. 22(2), 361-369. http://dx.doi.org/10.4319/lo.1977.22.2.0361.
http://dx.doi.org/10.4319/lo.1977.22.2.0...
index, modified by Lamparelli (2004)Lamparelli, M.C., 2004. Grau de trofia em corpos d’água do Estado de São Paulo: avaliação dos métodos de monitoramento. Instituto de Biociências, Universidade de São Paulo, São Paulo. Retrieved in 2020, June 11, from https://www.teses.usp.br/teses/disponiveis/41/41134/tde-20032006-075813/pt-br.php
https://www.teses.usp.br/teses/disponive...
, was used to classify the trophic state of the mesocosms based on chlorophyll-a concentration (Table 2), moreover, according to Brazilian resolution about the use of the water (Conama resolution 357, of March 17, 2005, Brasil, 2005Brasil. Conselho Nacional do Meio Ambiente – CONAMA, 2005. Dispõe sobre a classificação dos corpos de água e diretrizes ambientais para o seu enquadramento, bem como estabelece as condições e padrões de lançamento de efluentes, e dá outras providências (Resolução Conama n° 357 de 17 de março de 2005). Diário Oficial da União [da] República Federativa do Brasil, Poder Executivo, Brasília, DF.), the mesocosms can be used to human consumption, primary contact recreation, irrigation of vegetables and fruits, aquaculture and fishing (but see the variation among mesocosms in Table 2).

Table 2
Concentration of Nitrate, Orthophosphate, Chlorophyll-a (Chl-a), trophic state and water class of each mesocosms used in experiment. The columns water class indicates the classification of water bodies in accordance with Conama resolution 357, of March17, 2005 and its rectifications.

A multiparameter probe (Manta 2 Eureka) was used to quantify chlorophyll-a concentrations, and nutrients analysis was carried out following the methods described in Golterman et al. (1978)Golterman, H.L., Clymo, R.S., & Ohnstad, M.A.M., 1978. Methods for Physical and Chemical Analysis of Fresh Waters (IBP Handbook, no. 8). Oxford, UK: Blackwell Sci Publ, 213 p.. Photographs of the same mesocosms were used to evaluate the visual perception of the water.

2.2. Visual perception

The public were interviewed through visits to the experimental area (in-situ group) or through analysis of pictures of the mesocosms (picture group) to evaluate their perception of water quality. For the in-situ approach, we selected people that frequent the university (including students, employees, teachers) and the municipality of Anápolis. People were selected seeking to contemplate a variation in age, gender and level of education. The invitation to participants was made in person, verbally. At this moment, we explain the purpose of the project and schedule a visit to the aquatic environments. The visit to the experimental area was performed in groups of up to 10 volunteers at a time, from October 28th until November 14th of 2019. The volunteers were unable to touch in the water and were around 1 meter away from the experiment. The volunteers were always taken to the experimental area during mild sunny days, such as early morning and late afternoon. The interview was performed by the same researcher (ACMD) who explained once again the research goal and the structure of questionnaire to the volunteers at the start. The interviewees were then invited to sign an informed consent form, authorizing us to use the questionnaire data and guaranteeing the interviewee security in terms of confidentiality. The research project was approved by the research ethics committee of the State University of Goiás (CEP 8113 - State University of Goiás - UEG).

During the interview, each interviewee individually observed the water in the mesocosms in-situ and answered the questionnaire, without any exchange of information between the others participants or with the researcher. The questionnaire was composed of: i) personal information (gender, age, and level of formal education); ii) six questions about their perception of the water quality and its potential use (Table 3); iii) information about their number of visits to aquatic environments in the last month (Table 3). The six perception questions were elaborated considering that people use water resources in different ways, for example, for swimming, fishing, sports, or consumption.

Table 3
Questionnaire applied to 100 interviewees seeking to assess their visual perception regarding water quality in the six mesocosms in-situ. We used only question 1 and 5 to 83 interviewees used pictures approach. The questionnaire was applied in Portuguese.

The six questions (item ii of questionnaire) were answered for each mesocosm, and the interviewees chose one option based on a Likert scale varying from 1 to 5, where one represents a very favorable perception about of the water in the mesocosm, and five represents a very negative perception of the water in the mesocosm.

The experiment was repeated using pictures. The pictures used are from the same mesocosms used in in-situ experiment, and each picture was taken at the same angle (superior view) and at a resolution of 15.9 megapixels, using a Nikon camera (model Coolpix P510). For this, we turned the same in-situ questionnaire into an online questionnaire. For this approach, we used only questions 1 and 5 of the in-situ questionnaire. A hyperlink to the form was promoted on social media and emailed to people from Anápolis. This contained the same information disclosed to the in-situ approach participants. The volunteers of the picture approach had the same time to fill out the questionnaire as in the in-situ approach. A total of 87 volunteers completed the online survey.

2.3. Data analysis

The following statistical steps were used to evaluate visual perceptions of the water quality: i) For each person, were obtained the scores of visual perceptions in-situ and through pictures (Likert scale value), then we performed an ANOVA One-Way, to evaluate if the visual perceptions (Likert values), differed between treatments with different water quality; ii) We correlated, to each person, the visual perception (Likert scale) with the chlorophyll-a concentration of each mesocosm. This correlation show how visual perception are changing in response to change in water quality; iii) The correlations registered in-situ and through pictures were compared to evaluate if the perception about the water quality is different when people see water in-situ or through pictures. We used the One-Way ANOVA to compare these two groups; iii) All correlations were investigated to see if they varied among personal traits (e.g. social, educational, and gender). We performed the PERMANOVA to compare the correlations registered among different groups. The experimental and statistical steps are summarized in Figure 1 and detailed below.

Figure 1
Schematic protocol used in the presented study. The mesocosms installed had a strong gradient of eutrophication according to the concentration of Chlorophyll-a (A). People were invited to respond to questionnaires with questions about the water quality of the mesocosms (B). People visualized the mesocosms in the experimental area (in-situ approach) or through pictures. People’s perception of water quality (Likert scale) was analyzed in two ways: One-Way ANOVA comparing the mesocosms, and pearson correlation, to evaluate the relationship of Likert scale with the Chlorophyll-a concentration of the mesocosms (C). We used the result of Pearson correlation to estimate the histogram of r values (D), compare the visual perception obtained in-situ and through pictures (E), and summarized and modeled with predictor variables (gender, formal education, and frequency of visits to aquatic environments) using the PERMANOVA (F).

We used the One-Way ANOVA to determine whether people are capable of detecting changes in water quality. For this, the interviewee score about water quality was the response variables, and the treatments were the mesocosms (six levels of chlorophyll-a). The test significance was assessed using Monte Carlo simulation with 1000 permutations, without the need to check for normality. Tukey's HSD (Honestly Significant Difference) test was used to investigate post-hoc pairwise comparisons among the mesocosms. We used the Bonferroni correction for conservative statistical decision, where the new p-value should be based on the number of statistical tests that have been repeated (i.e. six ANOVAs). Thus, that a ANOVA will be considered significant only if p-value is smaller than 0.008 (calculated considering 0.05/6 tests).

The relationship among water quality (indicated by chlorophyll-a) and interviewee perception was performed by Pearson correlation, where positive values indicated higher scores (i.e. water disgusting perception) were recorded in waters with higher chlorophyll-a. In other words, the highest positive Pearson correlation values indicated that people are capable of detecting the water eutrophication degree. Negative or null Pearson correlation indicated that visual perception of the people are not capable to detect eutrophication degree. We used the Pearson correlation detected by each person in further analysis.

A one-way ANOVA was used to evaluate the similarity between the correlations registered in the in-situ and picture approaches (question ii). The significance was tested through the Monte Carlo simulation with 1000 permutations. The comparison was performed per each question of the in-situ and picture approaches (in this case, only questions 1 and 5). Thus, the Pearson correlation coefficient was the response variable and the predictors, were the type of experimental approach (pictures or in-situ).

The perceptions of the people to detected loss of water quality, indicated by the Pearson correlation coefficient, was modeled in function of personal and self-declared information of the people (question iii). We used the following information in the questionnaire: a) Gender: male or female; b) Level of Formal Education: last formal education that we reclassified into Basic level (up to high school); Undergraduate (up to undergraduate degree, including not complete); Graduate (Master’s and Ph.D. degrees). c) Self-declared information of contact with aquatic environment: number of visits to aquatic environments in the last month. This information indicated the frequency of visits, which had three options: no visit (no frequency); visited once or twice last month (low); and visited more than three times last month (high frequency).

A PERMANOVA (Anderson, 2014Anderson, M.J., 2014. Permutational multivariate analysis of variance (PERMANOVA). Wiley StatsRef: Statistics Reference Online, 1-15. https://doi.org/10.1002/9781118445112.stat07841.
https://doi.org/10.1002/9781118445112.st...
) was used to evaluate if visual perception (r values) varies between gender, formal education, and frequency of visits to aquatic environments. This test compares the mean of Pearson correlation (r) values (centroids) found in each group (e.g. male or female) and determines if the centroids were similar or different. Thus, non-significant values indicated that the mean r values are equivalent for all groups. To perform the PERMANOVA, we used all questions of the questionnaire in a Euclidean distance matrix. The significance was tested using the Monte Carlo simulation with 1000 permutations. We tested the assumption of sphericity of group dispersion (see Anderson et al., 2006Anderson, M.J., Ellingsen, K.E., & McArdle, B.H., 2006. Multivariate dispersion as measure of beta diversity. Ecol. Lett. 9(6), 683-693. http://dx.doi.org/10.1111/j.1461-0248.2006.00926.x.
http://dx.doi.org/10.1111/j.1461-0248.20...
) in the PERMANOVA, which was assured. We used a Principal Component Analysis (PCA) using the Euclidean distance matrix to visualize the groups formed for each predictor variable. We performed an isolated PERMANOVA because some people did not respond all the questions in the questionnaires. Only 68 people answered the frequency of visits to aquatic environments, and 76 answered the questions about gender and formal education.

We performed all statistical analyses using the R software (R Core Team, 2020R Core Team, 2020. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Retrieved in 2020, May 15, from https://www.R-project.org/
https://www.R-project.org/...
). We tested the Pearson correlation, Tukey’s test, and Principal Component Analysis using the functions cor.test, TukeyHSD, and prcomp, of package stats, respectively. The permutational one-way ANOVA was tested using the function perm.oneway.anova in the package wPerm (Weiss, 2015Weiss, N.A., 2015. wPerm: Permutation Tests. R package version 1.0.1. Retrieved in 2020, May 15, from https://CRAN.R-project.org/package=wPerm
https://CRAN.R-project.org/package=wPerm...
); The PERMANOVA was tested using the adonis function, and the assumptions tested using the beta.disper function, both available in the package vegan (Oksanen et al. 2019Oksanen, J., Blanchet, F.G., Friendly, M., Kindt, R., Legendre, P., McGlinn, D., Minchin, P.R., O’hara, R.B., Simpson, G.L., Solymons, P., Stevens, M.H.H., Szoecs, E., & Wagner, H., 2019. Vegan: Community Ecology Package. R Package Version 2.5-6. Retrieved in 2020, May 15, from http://CRAN.R-project.org/package=vegan
http://CRAN.R-project.org/package=vegan...
). The figures were generated using different functions of the packages ggplot2 (Wickham, 2016Wickham, H., 2016. ggplot2: elegant graphics for data analysis. New York: Springer-Verlag.) and ggfortify (Tang et al. 2016Tang, Y., Horikoshi, M., & Li, W., 2016. ggfortify: unified interface to visualize statistical result of popular r packages. R J. 8(2), 478-489. http://dx.doi.org/10.32614/RJ-2016-060.
http://dx.doi.org/10.32614/RJ-2016-060...
).

3. Results

We interviewed a total of 187 people, including pictures (87 people) and in-situ experiment (100 people), where the average age of the interviewees was 26.5 years old (Standard Deviation = 10.2, minimum = 18, maximum = 70 years old). Regarding the gender, we interviewed 83 women (average = 25.1 years old) and 104 men (average = 27.6 years old). These data regarding gender and age were similar to patterns registered locally (IBGE, 2010Instituto Brasileiro de Geografia e Estatística – IBGE, 2010. Censo 2010. Retrieved in 2020, June 20, from https://censo2010.ibge.gov.br/
https://censo2010.ibge.gov.br/...
).

Considering the in-situ experiment, people indicated different “perception scores” (indicated by the Likert scale) to all questions for each mesocosm, where mesocosms A and B had the lowest scores values. These mesocosms had the lowest concentration of chlorophyll-a. Moreover, mesocosms E and F had the highest scores values, as well as the highest concentration of chlorophyll-a (Figure 2). We found significant differences among mesocosms for all questions, even when using the Bonferroni correction (p=0.008): Question 1 (F=42.6; P=0.001); Question 2 (F=34.3; P=0.001); Question 3 (F=54.9; P=0.001); Question 4 (F=24.6; P=0.001); Question 5 (F=29.4; P=0.001); Question 6 (F=53.6; P=0.001). The Tukey’s HSD test showed that mesocosms A and B, respectively ultra-oligotrophic and oligotrophic, had significantly lower Likert scale values than mesocosms C, D, E, and F. The mesocosms C and D are mesotrophic and E, F are eutrophic.

Figure 2
People’s perception of the water quality indicated by mean (triangle) and dispersal of each question (Likert scale), for each mesocosm in the in-situ experiment. The concentration of Chlorophyll-a (chla) of each mesocosm is represented on a gradient scale. The Likert scale varies from 1 (nice feeling about the water quality) to 5 (disgusting feeling about the water quality). The chlorophyll-a concentration in mesocosms varies from 0 (mesocosm A) to 18 μg/L-1 (mesocosm F).

The Pearson’s correlation coefficients (r) were overwhelmingly positive for all of the questions (Figure 3). In fact, the median r values for all questions were similar and indicated high r values (question 1, median = 0.74; question 2, median = 0.7; question 3, median = 0.74; question 4, median = 0.63; question 5, median = 0.69; question 6, median = 0.74). The positive correlation shows that mesocosms with poor water quality (i.e. high chlorophyll-a concentration) received the highest Likert scale values (poor water quality). In other words, most people were able to perceive the change in water quality.

Figure 3
Histogram of Pearson correlation coefficients for each question, considering the 100 people analyzed in the in-situ experiment.

The approach regarding the perception of the water quality through pictures (87 people) showed similar patterns to the in-situ experiment. Thus, we found a positive correlation between the score values and chlorophyll-a concentration (Figure 4). In this approach, we considered only two questions of the questionnaire (questions 1 and 5). Therefore, we observed that for both questions, the correlation coefficients registered in the in-situ experiment were statistically similar to the coefficients registered in the experiment using pictures (Figure 5). We found no significant difference between these two groups: Question 1 (F=3.83; P=0.06), Question 5 (F=0.14; P=0.69).

Figure 4
Histogram of Pearson correlation coefficients for each question, considering the 87 people analyzed in the pictures experiment.
Figure 5
Boxplot comparing the Pearson coefficients of two questions between the experiments in-situ (100 people) and through pictures (87 people).

The PERMANOVA showed no effect of frequency of visits to aquatic environments (F=1.47; P=0.19), gender (F=0.89; P=0.41), and formal education (F=0.77; P=0.51) on the Pearson coefficients (summarized in PCA – Figure 6). In other words, regardless of the number of visits, gender, and level of education, people rated similarly the loss of water quality. These results were similar for the experiment using pictures (Figure 7) with no effects for PERMANOVA (frequency of visits to aquatic environments: F=1.41; P=0.16, gender: F=0.15; P=0.83) and PERMDISPER (frequency of visits to aquatic environments: F=2.61; P=0.07, gender F=0.14; P=0.71).

Figure 6
Principal Component Analysis regarding the correlation between people’s perception and chlorophyll-a concentration of all questions obtained in the in-situ experiment. The polygons represent the groups of people classified by the frequency of visits to aquatic environments (A), gender (B), and level of formal education (C). The frequency of visits was classified in: no visit, low frequency, and high frequency. Gender was classified into Male (M) or Female (F). Formal education was classified into Basic (B), Graduate (G), and Undergraduate (UG).
Figure 7
Principal Component Analysis regarding the correlation between people’s perception and chlorophyll-a concentration of questions obtained in the pictures experiment. The polygons represent the groups of people classified by the frequency of visits to aquatic environments (A) and gender (B). The frequency of visits was classified in: no visit, low frequency, and high frequency. Gender was classified into Male (M) or Female (F). Formal education was classified into Basic (B), Graduate (G), and Undergraduate (UG).

4. Discussion

We have showed that people can detect a loss of water quality. They preferred cleaner water, more suitable for recreational activities and consumption, but did tolerate some level of chlorophyll-a. Moreover, this perception was similar through evaluations of water quality in-situ or through pictures. People’s detection of water quality did not vary by gender, formal education, or frequency of visits to aquatic ecosystems.

People’s perception of water quality indicated that water without (mesocosm A) or with a low concentration of Chlorophyll-a (mesocosm B) was more suitable for recreational activities (e.g. swimming, fishing) and consumption. Moreover, our results indicated that water eutrophication (increase in chlorophyll-a) reduced the attraction of the water to people. Previous studies of coastal waters have shown that people show preferences for “blue” waters over turbid waters (e.g. Smith & Davies-Colley, 1992Smith, D.G., & Davies-Colley, R.J., 1992. Perception of water clarity and colour in terms of suitability for recreational use. J. Environ. Manage. 36(3), 225-235. http://dx.doi.org/10.1016/S0301-4797(05)80136-7.
http://dx.doi.org/10.1016/S0301-4797(05)...
; Smith et al., 1995Smith, D.G., Croker, G.F., & McFarlane, K., 1995. Human perception of water appearance. N. Z. J. Mar. Freshw. Res. 29(1), 29-43. http://dx.doi.org/10.1080/00288330.1995.9516637.
http://dx.doi.org/10.1080/00288330.1995....
; Lee, 2017Lee, L.H., 2017. Appearance’s aesthetic appreciation to inform water quality management of waterscapes. J. Water Resource Prot. 9(13), 1645-1659. http://dx.doi.org/10.4236/jwarp.2017.913103.
http://dx.doi.org/10.4236/jwarp.2017.913...
).

Here, we observed that people readily detect differences in chlorophyll-a concentration among mesocosms and associate increased concentrations with poorer value for use (Angradi et al., 2018Angradi, T.R., Ringold, P.L., & Hall, K., 2018. Water clarity measures as indicators of recreational benefits provided by US lakes: swimming and aesthetics. Ecol. Indic. 93, 1005-1019. http://dx.doi.org/10.1016/j.ecolind.2018.06.001.
http://dx.doi.org/10.1016/j.ecolind.2018...
). Although our questionnaire was limited to just six questions, they covered several possible types of water use by the population and the responses to questions were consistent both in-situ and through photographs. Therefore, considering this association, the visual perception of people can be used as a simple approach to monitor the quality of aquatic ecosystems. Detecting early signals of algal blooms or water eutrophication are current frontiers of biomonitoring programs (e.g. Wilkinson et al., 2018Wilkinson, G.M., Carpenter, S.R., Cole, J.J., Pace, M.L., Batt, R.D., Buelo, C.D., & Kurtzweil, J.T., 2018. Early warning signals precede cyanobacterial blooms in multiple whole-lake experiments. Ecol. Monogr. 88(2), 188-203. http://dx.doi.org/10.1002/ecm.1286.
http://dx.doi.org/10.1002/ecm.1286...
). For this, it is necessary to invest in establishment and maintenance of citizen groups to support high frequency monitoring. Environment protection agencies and researchers can use the rapid, early warning information from people’s perceptions to indicate regions (e.g. urban lakes, rivers, and reservoirs) that show unusual changes in the water colour and use this to target program for monitoring to provide more quantified measures of status and the presence or absence of toxic cyanobacteria

Citizen science has been applied to monitoring species occurrences (Steen et al., 2019Steen, V.A., Elphick, C.S., & Tingley, M.W., 2019. An evaluation of stringent filtering to improve species distribution models from citizen science data. Divers. Distrib. 25(12), 1857-1869. http://dx.doi.org/10.1111/ddi.12985.
http://dx.doi.org/10.1111/ddi.12985...
), water parameters (Jollymore et al., 2017Jollymore, A., Haines, M.J., Satterfield, F., & Johnson, M.S., 2017. Citizen science for water quality monitoring: data implications of citizen perspectives. J. Environ. Manage. 200, 456-467. http://dx.doi.org/10.1016/j.jenvman.2017.05.083.
http://dx.doi.org/10.1016/j.jenvman.2017...
), and algal blooms (Kotovirta et al., 2014Kotovirta, V., Toivanen, T., Järvinen, M., Lindholm, M., & Kallio, K., 2014. Participatory surface algal bloom monitoring in Finland in 2011–2013. Environ. Syst. Res. 3(1), 24. http://dx.doi.org/10.1186/s40068-014-0024-8.
http://dx.doi.org/10.1186/s40068-014-002...
). This association between researchers and society can promote the development of science and increase the environmental awareness of the people, contributing (Johnson et al. 2014Johnson, M.K.F., Hannah, C., Acton, L., Popovici, R., Karanth, K.K., & Weinthal, E., 2014. Network environmentalism: citizen scientists as agents for environmental advocacy. Glob. Environ. Change 29, 235-245. http://dx.doi.org/10.1016/j.gloenvcha.2014.10.006.
http://dx.doi.org/10.1016/j.gloenvcha.20...
; McKinley et al., 2017McKinley, D.C., Miller-Rushing, A.J., Ballard, H.L., Bonney, R., Brown, H., Cook-Patton, S.C., Evans, D.M., French, R.A., Parrish, J.K., Phillips, T.B., Ryan, S.F., Shanley, L.A., Shirk, J.L., Stepenuck, K.F., Weltzin, J.F., Wiggins, A., Boyle, O.D., Briggs, R.D., Chapin 3rd, S.F., Hewitt, D.A., Preuss, P.W., & Soukup, M.A..2017. Citizen science can improve conservation science, natural resource management, and environmental protection. Biol. Conserv. 208, 15-28. http://dx.doi.org/10.1016/j.biocon.2016.05.015.
http://dx.doi.org/10.1016/j.biocon.2016....
). Public involvement in monitoring, vigilance, and evaluation of water quality is relevant because they are often the first to notice changes in water quality and can take immediate action to remedy this problem (WHO, 1997World Health Organization – WHO, 1997. Guidelines for drinking water quality. Geneva: WHO, 2nd ed., Surveillance and control of communities supplies, vol. 3., 2017World Health Organization – WHO, 2017. Guidelines for drinking water quality: fourth edition incorporating the first addendum. Geneva: WHO.). This strategy is relevant, especially in developing countries where it is not always possible to maintain widespread water monitoring programs (Kirschke et al., 2020Kirschke, S., Avellán, T., Bärlund, I., Bogardi, J., Carvalho, L., Chapman, D., Dickens, C.W.S., Irvine, K., Lee, S.B., Mehner, T., & Warner, S., 2020. Capacity challenges in water quality monitoring: understanding the role of human development. Environ. Monit. Assess. 192(5), 298. http://dx.doi.org/10.1007/s10661-020-8224-3.
http://dx.doi.org/10.1007/s10661-020-822...
).

People’s perception of changes in water quality can be affected by social and educational factors (see review in Flotemersch & Aho, 2021Flotemersch, J., & Aho, K., 2021. Factors influencing perceptions of aquatic ecosystems. Ambio 50(2), 425-435. http://dx.doi.org/10.1007/s13280-020-01358-0.
http://dx.doi.org/10.1007/s13280-020-013...
; Ochoo et al., 2017Ochoo, B., Valcour, J., & Sarkar, A., 2017. Association between perceptions of public drinking water quality and actual drinking water quality: a community based explanatory study in Newfoundland (Canada). Environ. Res. 159, 435-443. http://dx.doi.org/10.1016/j.envres.2017.08.019.
http://dx.doi.org/10.1016/j.envres.2017....
; Gifford & Nilsson, 2014Gifford, R., & Nilsson, A., 2014. Personal and social factors that influence pro-environmental concern and behaviour: a review. Int. J. Psychol. 49, 141-157. http://dx.doi.org/10.1002/ijop.12034.
http://dx.doi.org/10.1002/ijop.12034...
). Moreover, it may be necessary to provide formal and non-formal instruction to detect some changes in biological structure (see, for example, Gomes et al., 2019Gomes, M.A.A., Gonçalves, T.V., Teresa, F.B., da Cunha, H.F., Lima, F.P., & Nabout, J.C., 2019. High school students’ knowledge of endangered fauna in the Brazilian Cerrado: a cross-species and spatial analysis. PLoS One 14(4), e0215959. http://dx.doi.org/10.1371/journal.pone.0215959.
http://dx.doi.org/10.1371/journal.pone.0...
). However, we found that formal education and contact with aquatic environments did not affect people’s perception of the loss of water quality. This can be explained by the simplicity of the approach, based on just visual colour. Moreover, these results indicated that vast groups of people could potentially help researchers in monitoring aquatic environments if they can be sufficiently motivated. Despite that, we agree that formal education in environmental courses may increase the level of concordance among the responses of questionnaires, ensuring more confidence for monitoring actions.

Formal instructions (e.g. courses of scientific and environmental education) have been applied to increase the environmental awareness of people (Coertjens et al., 2010Coertjens, L., Boeve-de Pauw, J., De Maeyer, S., & Van Petegem, P., 2010. Do Schools make a difference in their students’ attitudes and awareness? Evidence from PISA 2006. Int. J. Sci. Math. Educ. 3(3), 497-522. http://dx.doi.org/10.1007/s10763-010-9200-0.
http://dx.doi.org/10.1007/s10763-010-920...
). Here, we observed that people that visualized mesocosms in-situ or through pictures showed similar results in detecting the loss of water quality. Although in-situ groups had contact with other water characteristics (e.g. smell), the change in the colour of the water was clearly the most outstanding characteristic for the two groups. Therefore, at least for studies of eutrophication and water greening, scientific and environmental education programmes could use photographs in their activities (c.f. Bloomin’Algae app – UKCEH, 2022UK Centre for Ecology & Hydrology – UKCEH, 2022. Bloomin’ Algae [online]. Retrieved in 2022, March 7, from https://www.ceh.ac.uk/algal-blooms/bloomin-algae.
https://www.ceh.ac.uk/algal-blooms/bloom...
).

5. Conclusions

We recommend further studies to use field experiments to evaluate people’s perceptions of different types of impacts on nature (e.g. toxic algal blooms, global warming, biological invasion). Our results reinforce the potential for people’s visual perception for monitoring aquatic ecosystems impacted by eutrophication. Thus, monitoring agencies and researchers could use that public perception as early-warning strategies for monitoring programs of water quality. Finally, we highlight the growing potential use of mobile phone apps and social media to connect people to researchers, and as a consequence, to connect science with society.

Acknowledgements

This work was supported in part by Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) for the technological and industrial development scholarship received by KBM (process 380171/2019-8), and the PIBIC scholarship received by ACMD. JFF received PIBIC scholarship from the State University of Goiás. ACMD received master scholarship from FAPEG. The experimental area used in presented paper was constructed by financial resource from CNPq and Fundação de amparo à pesquisa de Goiás (FAPEG). JCN and HFC were supported by CNPq productivity fellowships. This paper was developed in the context of National Institutes for Science and Technology (INCT) in Ecology, Evolution and Biodiversity Conservation, and Brazilian Network on Global Climate Change Research (Rede CLIMA). We also thank the comments of two anonymous reviewers on previous versions of the manuscript.

  • Cite as: Nabout, J.C. et al. Can people detect the loss of water quality? A field experiment to evaluate the correlation between visual perception and water eutrophication degree. Acta Limnologica Brasiliensia, 2022, vol. 34, e8.

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Publication Dates

  • Publication in this collection
    21 Mar 2022
  • Date of issue
    2022

History

  • Received
    20 Apr 2021
  • Accepted
    02 Feb 2022
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