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Working Paper
Digital Innovation in Citizen Science to
Enhance Water Quality Monitoring in
Developing Countries
Nicholas B. Pattinson, Jim Taylor, Chris W. S. Dickens and P. Mark Graham
210
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IWMI Working Paper 210
Digital Innovation in Citizen Science to Enhance Water
Quality Monitoring in Developing Countries
International Water Management Institute (IWMI)
P. O. Box 2075, Colombo, Sri Lanka
Nicholas B. Pattinson, Jim Taylor, Chris W. S. Dickens and P. Mark Graham
The authors:
Nicholas B. Pattinson is a Research Scientist at GroundTruth cc, Hilton, South Africa, as well as a PhD Biological
Sciences candidate at the Fitzpatrick Institute of African Ornithology, University of Cape Town, Cape Town, South Africa.
Dr. Jim Taylor is a Research Fellow at the University of Kwa-Zulu Natal (UKZN), Pietermaritzburg, South Africa, and a
Researcher within the United Nations University, KZN Regional Centre of Expertise (UNU-RCE), South Africa.
Dr. Chris Dickens is a Principal Researcher within Ecosystems at the International Water Management Institute (IWMI),
Colombo, Sri Lanka.
Dr. Mark Graham is the Director of GroundTruth cc Environment and Engineering, a Research Fellow at UKZN and a
Researcher within the UNU-RCE.
Pattinson, N. B.; Taylor, J.; Dickens, C. W. S.; Graham, P. M. 2023. Digital innovation in citizen science to enhance water
quality monitoring in developing countries. Colombo, Sri Lanka: International Water Management Institute (IWMI). 37p.
(IWMI Working Paper 210). doi: https://doi.org/10.5337/2024.201
/ digital innovation / citizen science / water quality / monitoring / developing countries / freshwater ecosystems / water
resources / water management / decision support / community involvement / data collection / digital technology / sensors
/ databases / smartphones / mobile applications / innovation adoption / big data / Sustainable Development Goals /
Goal 6 Clean water and sanitation / parameters / mitigation /
ISSN 2012-5763
e-ISSN 2478-1134
ISBN 978-92-9090-961-3
Copyright © 2023, by IWMI. All rights reserved. IWMI encourages the use of its material provided that the organization is
acknowledged and kept informed in all such instances. The boundaries and names shown and the designations used on
maps do not imply official endorsement or acceptance by IWMI.
Please send inquiries and comments to IWMI-Publications@cgiar.org
A free copy of this publication can be downloaded at:
https://www.iwmi.org/publications/iwmi-working-papers/
IWMI - iiiWorking Paper 210 - Digital Innovation in Citizen Science to Enhance Water Quality Monitoring in Developing Countries
Acknowledgments
The authors would like to acknowledge Jawoo Koo, leader of the CGIAR Initiative on Digital Innovation, based at the
International Food Policy Research Institute (IFPRI), for his guidance and contributions related to digital innovation.
Project
This research was undertaken as part of the CGIAR Initiative on Digital Innovation.
Collaborators
International Water Management Institute (IWMI)
GroundTruth - Water, Wetlands and Environmental Engineering
Donors
This work was carried out under the CGIAR Initiative on Digital
Innovation, which is grateful for the support of CGIAR Trust Fund
contributors (www.cgiar.org/funders).
INITIATIVE O N
Digital Innovation
IWMI - vWorking Paper 210 - Digital Innovation in Citizen Science to Enhance Water Quality Monitoring in Developing Countries
Contents
Acronyms and Abbreviations vi
Summary vii
Introduction 1
Background 1
Freshwater in Crisis 1
Water Resource Monitoring: The First Step in Mitigating the Crisis 2
Current Systems are Coming Up Short: The Need for Nontraditional Monitoring Methods 3
Digital Technology to Bridge Gaps in Monitoring 4
Citizen Science for Collaborative, Inclusive Water Resource Monitoring (and Management) to Meet SDG 6 5
Integrating Technology and Citizen Science 6
Smartphones and Citizen Science 7
Future Research, Development and Implementation Directions for Smartphone Water Quality Monitoring 9
Examples of Smartphone Applications for Exploration in Developing Countries 11
Conclusions 16
References 17
IWMI - vi Working Paper 210 - Digital Innovation in Citizen Science to Enhance Water Quality Monitoring in Developing Countries
Acronyms and Abbreviations
AI Artificial intelligence
GPS Global Positioning System
miniSASS Stream assessment scoring system
ML Machine learning
ODK Open Data Kit
SDG Sustainable Development Goal
TSS Total suspended solids
WWQA World Water Quality Alliance
IWMI - viiWorking Paper 210 - Digital Innovation in Citizen Science to Enhance Water Quality Monitoring in Developing Countries
Summary
Freshwater systems are adversely affected
disproportionately by the ongoing, global environmental
crisis. The effective and efficient water resource
conservation and management necessary to mitigate
the crisis requires monitoring data especially on water
quality. This is recognized by Sustainable Development
Goal (SDG) 6, particularly indicator 6.3.2., which requires
all United Nations (UN) member states to measure and
report the ‘proportion of water bodies with good ambient
water quality’. However, gathering sufficient data on
water quality is reliant on data collection at spatial and
temporal scales that are generally outside the capacity of
institutions using conventional methods.
Digital technologies, such as wireless sensor networks
and remote sensing, have come to the fore as promising
avenues to increase the scope of data collection and
reporting. Citizen science (which goes by many names,
e.g., participatory science or community-based
monitoring) has also been earmarked as a powerful
mechanism to improve monitoring. However, both modern
digital technologies and citizen science approaches have
drawbacks and limitations. The synergy between the power
of automated, verifiable data collection using modern
technologies, and the power of citizen science to improve
the spatial and temporal resolution of data collection
while engaging and empowering communities, presents an
opportunity to use the best features of each mechanism
to mitigate the shortcomings of the other. Smartphones,
sometimes in conjunction with other sensors, present such
a nexus point, providing a method for citizen scientists to
engage with and use sophisticated modern technology
for water quality monitoring. Smartphones are widely
accessible and equipped for objective, comprehensive and
accurate data collection. The data can also be uploaded
(via internet connections) to large cloud-based databases
with cloud-based computing for data management and
reporting. This paper presents a research synthesis of
technological upgrades or innovations in citizen science
water quality monitoring in developing countries, with
a particular focus on exploring the current status of
modern, smartphone-based, or smartphone-assisted
citizen science tools, and how those tools can be validated
or expanded for SDG reporting in developing countries.
Essentially, the paper aims to briefly summarize the current
standing, reiterate the urgent need for research and action
in water resource monitoring and management, and urge
further engagement with citizen science water quality
monitoring using digital innovations; digital innovations
for smartphones are being rapidly developed, but the
scientific validation for their use in specific circumstances
or regions, as well as their uptake and upscaling, are still
widely lacking.
Globally, there are many options and developments
relevant to citizen science smartphone-based or
smartphone-assisted water quality monitoring. However,
not all modern developments are suitable for deployment
or testing across all socio-ecological environments.
Innovations in smartphone water quality monitoring in
low and middle-income country contexts need to be
low-cost (requiring minimal input costs beyond having a
smartphone), easy-to-use, easily scalable, commercially
available, suited to use by minimally skilled people in
rural and developing areas. Moreover, monitoring all
the parameters (physical, chemical and biological)
that contribute to water quality is highly complex and
outside the scope of what is achievable by most people,
organizations, or even governments. As a result, it is
sensible that water resource monitoring and management
efforts are primarily directed toward addressing the
SDG indicators to align with global goals. The SDG water
quality indicators were chosen as a result of extensive
consultation and research. They are designed to provide
a snapshot of water quality suitable for most regions
and socioeconomic situations worldwide. The SDG 6.3.2.
indicator method employs a water quality index that
integrates basic core water quality parameters; oxygen,
salinity, nitrogen, phosphorus and acidification. Monitoring
algae, temperature and clarity also presents useful options
since they are highly relevant to ambient water quality and
can be monitored cheaply and easily by citizen scientists.
This paper summarizes a non-exhaustive list of examples
of smartphone-based or smartphone-assisted applications
(mobile apps) that are suggested or recommended for
research and implementation in developing countries.
Research and development regarding these options
should aim to validate the accuracy of data collection,
accessibility, ease of use, cost, and the feasibility of
contributing to pathways from data collection to citizen
mobilization and decision-making. Ultimately, once these
options are validated, they can be used to design and
implement monitoring networks around the globe. Well-
designed citizen science water quality monitoring apps
on smartphones can increase community engagement
regarding environmental issues and policy, build
awareness and scientific literacy, and generate large
amounts of data, all at a greatly reduced cost compared
to conventional and modern technological methods. It
is suggested that smartphone-based or smartphone-
assisted citizen science water quality monitoring has the
potential to address critical data and knowledge gaps that
contribute towards reporting on at least SDG 6.3.2 while
fulfilling SDG 6b ‘procedures for participation of local
communities in water and sanitation management’ – a
potential which is still often not realized.
IWMI - 1Working Paper 210 - Digital Innovation in Citizen Science to Enhance Water Quality Monitoring in Developing Countries
Digital Innovation in Citizen Science to Enhance Water
Quality Monitoring in Developing Countries
Nicholas B. Pattinson, Jim Taylor, Chris W. S. Dickens and P. Mark Graham
Introduction
GroundTruth, in conjunction with CGIAR, has engaged
in a research-for-development project involving the
incorporation of real-time natural resource monitoring
data into decision-support systems as per the CGIAR
Initiative on Digital Innovation (DI). The DI seeks to harness
digital technologies for timely decision-making across
food, land and water systems. The theory of change within
DI is designed to address three challenge areas identified
as key bottlenecks in the digital ecosystem: 1) the digital
divide, 2) inadequate information, and 3) limited digital
capabilities.
The project has multiple objectives. This paper presents
the progress on one of the objectives: to conduct research
into technological upgrades or innovations in citizen
science water quality monitoring in developing countries,
with a particular focus on exploring modern, smartphone-
based, or smartphone-assisted citizen science tools.
The primary aims of this research are 1) to briefly
contextualize the current status of freshwater globally,
2) to briefly overview why water resource monitoring is
essential for improved water resource management and
preservation of water resources, 3) to discuss some of the
shortcomings of conventional monitoring methods and
the need for increased worldwide engagement with citizen
science, 4) to identify some of the potential for powerful
synergies between modern technology and citizen science
for water resource monitoring to help achieve Sustainable
Development Goal (SDG) 6 among others, 5) to explore
options (a non-exhaustive list) for integrating smartphones
into citizen science water resource monitoring and
6) to provide recommendations for future research and
implementation of smartphone-based or smartphone-
assisted citizen science water resource monitoring which
is, in many places, still not undertaken with the attention
warranted.
Background
Freshwater in Crisis
Scientific and popular literature is growing rapidly in both
abundance and urgency concerning the ongoing, global
environmental crisis (Harrison et al. 2018; WWF 2020;
Robinson 2023). The most recent update of the global
Living Planet Index showed an average 68% decrease in
population sizes of mammals, birds, amphibians, reptiles
and fish between 1970 and 2016 (WWF 2020). Freshwater
systems are disproportionately affected (Revenga and
Mock 2000; Arthington et al. 2018; Flitcroft et al. 2019;
Pastor et al. 2019; Tickner et al. 2020; Albert et al. 2021).
Freshwater ecosystems are biodiversity hotspots, containing
and supporting approximately 12% of all species on earth
(including 30% of vertebrates) while comprising less
than 2% of the earth’s surface (Abramovitz 1995; Carrizo
et al. 2017). Yet, there has traditionally been a poor
representation of freshwater systems explicitly in policy or
conservation landscapes (Carrizo et al. 2017; Darwall et
al. 2018; Reid et al. 2019). At present, approximately 27%
of all vertebrate species that are dependent on freshwater
systems are threatened with extinction (IUCN 2023), with an
average 84% decline in population of freshwater vertebrates
worldwide since 1970 – a rate twice as high as those in
terrestrial or marine systems (WWF 2016, 2020; Darwall et
al. 2018; Harrison et al. 2018).
It is critical to understand that the problem is not simply
one of biodiversity loss. Human well-being and sustainable
futures are totally dependent on freshwater ecosystems
(Abramovitz 1995; Vörösmarty et al. 2010; Lynch et al.
2023). The essential goods and services provided by
freshwater systems include water treatment (freshwater
systems are the primary receivers and treatment systems
for waste and pollutants), clean drinking water (and its
associated health benefits), fish, fiber, disaster mitigation
(the resilience and adaptability of natural systems is
crucial in the face of climate change), recreation and
intrinsic ‘quality of life’ value (Dyson et al. 2008; Dudgeon
2010; Acreman 2016; Díaz et al. 2018; Lynch et al. 2023).
As a result, the impact of the freshwater crisis is a
catastrophic, direct threat to humans. The World Health
Organization (WHO) estimated approximately 2.1 billion
people do not have regular access to safe and sanitary
water (WHO and UNICEF 2021), while water-borne diseases
from consuming or using unsafe water results in 0.9 – 1.2
million deaths per year (WHO and UNICEF 2017; GBD 2017
Risk Factor Collaborators 2018). With the global population
predicted to increase by 40 – 50% by 2070, the demand
and pressures on freshwater are only set to increase
(Jan et al. 2021). The World Economic Forum (WEF)
Global Risks Report of the top ten biggest risks to society
on Earth, over the next 10 years, identified
IWMI - 2 Working Paper 210 - Digital Innovation in Citizen Science to Enhance Water Quality Monitoring in Developing Countries
‘Freshwater supply’ as number 3 in 2016, ‘Biodiversity
loss and ecosystem collapse’ and ‘Natural resource
crises’ as 4 and 6, respectively in 2023 (WEF 2023).
Risks to fresh water may be described as slow violence
(Nixon 2011). Seemingly imperceptible, the sustained
damage to freshwater rivers and streams will lead to
future catastrophic events. For example, small amounts
of nutrient load gradually accumulate over time, before
reaching a crisis point (Romanelli et al. 2020).
The causes of the freshwater crisis were well-known
since many of the problems today are the same as those
identified over the past three decades, although they are
worsening and being compounded by some emerging,
increasingly complex anthropogenic pressures
(Darwall et al. 2018; Dudgeon 2019; Albert et al. 2021).
Reid et al. (2019), published a review reflecting on how
the freshwater crisis has deepened since Dudgeon et al.’s
(2006) landmark work listed 12 threats that have since
intensified or emerged as new: (i) changing climates;
(ii) e-commerce and invasions; (iii) infectious diseases;
(iv) harmful algal blooms; (v) expanding hydropower; (vi)
emerging contaminants; (vii) engineered nanomaterials;
(viii) microplastic pollution; (ix) light and noise; (x)
freshwater salinization; (xi) declining calcium; and
(xii) cumulative stressors. A year after that review,
Tickner et al. (2020) recognized that the freshwater crisis
had grown so pervasive and intense, that they developed
an ’Emergency Recovery Plan’ to aid in addressing
the critical state of freshwater brought about by the
Anthropocene.
Approximately 82% of the world’s human population
gets its water from upstream areas that are under
immediate and substantial threat of degradation (Green
et al. 2015). Degradation of freshwater systems and
the immense biodiversity they contain, reduces their
capacity to deal with increasing human demand and
threatens the essential goods and services that are
naturally provided by functioning and healthy ecosystems
(Forslund et al. 2009; Green et al. 2015; Abell et al.
2019; Cook et al. 2021). The financial value of the goods
and services provided by natural systems is complex to
quantify, but one global estimate puts this at over USD 4
trillion annually in a ‘ballpark’ attempt to emphasize the
economic incentives for their conservation (Darwall et
al. 2018). Though the exercise of assigning a monetary
value to the often ‘silent’ goods and services is difficult
and in some instances criticized, it is clear that in cases
where water resources have been overexploited and
cease to flow (e.g., Colorado or Indus River), or have
become deeply polluted (e.g., the Ganges River), the loss,
economic and otherwise, to downstream populations
is almost immeasurable (Dudgeon 2010; Sharma et al.
2010). Further, the ecosystem collapses at the Aral Sea
(Micklin 2007) and Azraq Oasis (Whitman 2019) provide
grim examples of the disastrous consequences (e.g.,
loss of fisheries, water supply, biodiversity, tourism, and
cultural heritage) of unabated exploitation and disregard
for freshwater systems (Dudgeon 2019). The concept of
how human societal and economic goals are embedded in
a complex socio-ecological system (Njue et al. 2019;
König et al. 2021) which is reliant on nature and
functioning ecosystems was neatly illustrated by a recent
depiction of the Sustainable Development Goals1 (SDGs)
by the Stockholm Resilience Centre (Figure 1).
Water Resource Monitoring: The First
Step in Mitigating the Crisis
Over the period of the increasingly regular and urgent
literature on the status of freshwater systems, various
regulations, policies, and associated management and
mitigation frameworks or concepts have been developed
across the globe (Green et al. 2015; Albert et al. 2021).
These include, for example, the landmark Water
Framework Directive (WFD 2000) in Europe, the Clean
Water Act in Canada (Government of Ontario 2006), the
Water Act in Australia (Australian Government 2007),
the Clean Water Act in the United States of America,² the
Alliance for Freshwater Life³ (Darwall et al. 2018), the
Intergovernmental Science-Policy Platform on Biodiversity
and Ecosystem Services (IPBES) (Brondizio et al. 2019),
United Nations Framework Convention on Climate Change
(UNFCCC) Glasgow Climate Pact (UNFCCC 2021), United
Nations Environment Programme (UNEP) World Water
Quality Alliance (WWQA), World Water Council, Leaders
Pledge for Nature, the SOLUTIONS project (Brack et al.
2015), and the Convention on Biological Diversity (CBD).
All of these have worked in some degree towards, or
in concert with, the SDGs (Arthington 2021). Generally,
global collaboration on freshwater management takes a
particular focus on SDG6 ‘clean water and sanitation for
all’ (Capdevila et al. 2020; Quinlivan et al. 2020a, 2020b;
White et al. 2020; Gemeda et al. 2021; Hegarty et al. 2021).
One key factor that emerges within these directives,
frameworks, policy recommendations, and the SDGs
concerning improved freshwater resource management
and conservation is the need for monitoring data,
including on water quality (Strobl and Robillard 2008;
Behmel et al. 2016; McKinley et al. 2017; Harrison et al.
2018; Tickner et al. 2020). This focus arises because
implementing efficient and targeted strategies for
management and conservation requires large-scale and
credible data, both to design the strategies and to assess
1 https://sustainabledevelopment.un.org/sdgs
² https://www.epa.gov/laws-regulations/summary-clean-water-act
³ https://allianceforfreshwaterlife.org
https://www.unep.org/explore-topics/water/what-we-do/improving-and-assessing-world-water-quality-partnership-effort
https://www.worldwatercouncil.org/en
https://www.leaderspledgefornature.org/
http://www.solutions-project.eu/
https://www.cbd.int/convention/guide/?id=web4
IWMI - 3Working Paper 210 - Digital Innovation in Citizen Science to Enhance Water Quality Monitoring in Developing Countries
progress (Davids et al. 2019; Bishop et al. 2020; Poisson
et al. 2020; Arthington 2021). Consequently, meeting
SDG 6, especially indicator 6.3.2. which requires all UN
member states to measure and report the ‘proportion of
water bodies with good ambient water quality’ (UNEP and
UN Water 2018), is heavily reliant on water monitoring
data at fine spatial and temporal resolutions (Bonney et
al. 2009; Buytaert et al. 2014; Trouille et al. 2019; Fraisl
et al. 2020).
Current Systems are Coming Up Short:
The Need for Nontraditional Monitoring
Methods
The need for freshwater monitoring data is at odds with
institutional capacities to collect and manage the required
data (O’Grady et al. 2021). Governments and academic
institutions, especially in developing nations, simply do
not have the capacity to develop and implement data
monitoring regimes at the spatial and temporal scale
that are required to meet the SDGs (Freitag et al. 2016;
Carlson and Cohen 2018; Paepae et al. 2021). Traditional
or conventional water resource monitoring involves
manually collecting samples, transporting them to
laboratories (often via intermediary storage), technical
laboratory analysis, reporting, and finally data analysis,
uploading and visualization of data (Park et al. 2020).
While this process is still valuable in many instances, it
typically requires experts at every step, and becomes
expensive and time-consuming, leading to it being done
infrequently at low spatial resolution (Gholizadeh et
al. 2016; Ahmed et al. 2020; Jan et al. 2021; Silva et al.
2022; Zainurin et al. 2022). Consequently, data collected
institutionally are often outdated; uncoordinated in
terms of data collection and handling protocols (thereby
limiting comparability); not representative of fine-scale
(especially of smaller water bodies and streams) or
localized issues; may miss issues that are temporally
distinct such as crop spraying or pollution spills; and
can be slow to influence decision-making (Behmel et al.
2016; König et al. 2021; Manjakkal et al. 2021; Arndt et al.
2022; Wu et al. 2022). These drawbacks greatly detract
from the ability to understand complex catchment- or
fine-scale processes and significantly reduce the power
of trend analysis (Ouma et al. 2018; O’Grady et al. 2021).
In some instances, an institutional unwillingness to
disclose monitoring information can also be prevalent.
This diminishes the agency of independent interested or
affected stakeholders to take appropriate action
(Steyn 2022).
Figure 1. The Sustainable Development Goals (SDGs) ‘wedding cake’, developed by the Stockholm Resilience Centre. The
diagram illustrates how human society and economic goals are embedded in, and reliant on, a foundation of a healthy
and functioning biosphere, including SDGs 6, 13, 14, and 15.
Source: Azote for Stockholm Resilience Centre, Stockholm University Creative Commons License (CC BY-ND 3.0).
IWMI - 4 Working Paper 210 - Digital Innovation in Citizen Science to Enhance Water Quality Monitoring in Developing Countries
Digital Technology to Bridge Gaps in
Monitoring
The power of digital technology to transform conventional
monitoring frameworks has proven to be astounding.
Technological advancements particularly in water quality
monitoring have increased rapidly over the last two
decades (Zulkifli et al. 2018; Park et al. 2020). These
include a huge variety of developments, ranging from
government-run, highly technical, catchment-scale
monitoring networks, to simple, relatively low-cost, in situ
monitoring apparatus (Adu-Manu et al. 2017; Jan et al.
2021). Promising avenues include advancements in, and
connections between, portable laboratories (Silva et al.
2022; Thio et al. 2022), microfluidic techniques (Jaywant
and Arif 2019), wireless sensor networks (Pule et al. 2017;
Rahim et al. 2017; Kishore et al. 2022; Okpara et al. 2022),
remote sensing (Gholizadeh et al. 2016; Leeuw and Boss
2018), microbial fuel cells (Olias and Di Lorenzo 2021),
the internet of things (IoT) (Ullo and Sinha 2020; Ighalo
et al. 2021a; Jan et al. 2021, Manjakkal et al. 2021; Singh
and Ahmed 2021), artificial intelligence (AI) (Ighalo et al.
2021b; O’Grady et al. 2021), and even nanotechnology
(Vikesland 2018; Hairom et al. 2021). New technologies
for monitoring have a range of advantages over traditional
monitoring methods including i) increased spatial and
temporal coverage of basins, reduced sampling and
data collection error (where potential for human error is
minimized); ii) increased ease-of-use for data collection
and handling; iii) reduced requirements for specialized
personnel and facilities for sample collection and analysis;
iv) potential for scalable, common standardized protocols
for up-to-date data collection and management methods;
v) reduced time and expense for sample transport;
vi) reduced time between sampling and data reporting
(including opportunities for real-time reporting);
vii) in-field data collection and reporting; viii) improved
data visualization, reporting and capacity; and ix) reduced
cost (Behmel et al. 2016; Adu-Manu et al. 2017; Pule et al.
2017; Park et al. 2020; Jan et al. 2021; O’Grady et al. 2021;
Arndt et al. 2022; Okpara et al. 2022).
Despite the benefits, new digital technologies are not
without their drawbacks and issues that still need solving.
For example, wireless sensor networks suffer drawbacks
in terms of biofouling, limitations to use in remote areas
in terms of both signal coverage for data transmission
and power supply, sensor drift (incremental sensor data
collection error over time without calibration), high
maintenance costs, electronic waste generation, and data
and physical security issues (Geetha and Gouthami 2016;
Rahim et al. 2017; Manjakkal et al. 2021). Remote sensing
(for example via satellite using Earth Observation) is
still overcoming issues in terms of spatial and temporal
resolution, interference from plants or adverse weather
and improving applicability outside of specifically
validated use cases (Olias and Di Lorenzo 2021). Sensor
networks are also generating enormous datasets which
contribute to modern challenges associated with the age
of ‘big data’ handling in terms of storage, hosting, quality
assurance, control and analytical (human and software-
related) capacities (Strobl and Robillard 2008; Hulbert et
al. 2019; Ighalo et al. 2021a, 2021b; Arndt et al. 2022).
Addressing these challenges carries costs that often
present a significant barrier to longevity, data utility and
reporting (McKinley et al. 2017; Fraisl et al. 2020). Caution
must also be taken not to fall prey to the “data-rich –
information poor syndrome” (Ward et al. 1986), where the
drive to capture ‘big data’ actually undermines or detracts
from focusing on the information and the story data can
provide (Behmel et al. 2016; O’Grady et al. 2021). In sum
globally, especially in the context of developing countries,
many new technologies are ruled out based on limitations
regarding the accessibility, commercial availability, ease-
of-use or technical capacity required for use, potential for
vandalism or theft, and foremost, cost (Zulkifli et al. 2018;
Kishore et al. 2022).
In addition to limitations regarding the uptake and use
of modern, automated, or institutionally run monitoring
technologies, there is also a growing recognition that
top-down institutional monitoring and policy changes
may not be effective, or gain momentum fast enough to
bring about the emergency and drastic changes needed
to monitor, manage, rehabilitate, and conserve freshwater
resources and biodiversity for human and environmental
purposes (Buytaert et al. 2014; Ouma et al. 2018; Paul
et al. 2018; De Filippo et al. 2021; Jordan and Cassidy
2022). As such, there are widespread calls for integrated
water resource management which involves and educates
stakeholders at all levels throughout the process, from
project conceptualization, through data collection, to
management and reporting (Pahl-Wostl et al. 2013; Poff
et al. 2017; Harrison et al. 2018; Pastor et al. 2019; De
Filippo et al. 2021). This is well framed by Arthington et al.
(2021) in a response to Tickner et al.’s (2020) ‘Emergency
Recovery Plan’, who stated “solving complex conflicts about
water use and management, especially in times of scarcity
and uncertainty, requires collaboration and enduring
partnerships among all stakeholders with indigenous,
societal and scientific knowledge, technical expertise,
and credentials at all levels of governance”. Citizen
science (which goes by many names, e.g., participatory
science or community-based monitoring) provides a
powerful mechanism to progress towards meeting these
requirements, whilst contributing to filling critical data
and knowledge gaps to work towards achieving the SDGs
(McKinley et al. 2017; Irwin 2018; UNEP and UN Water 2018;
Fritz et al. 2019; Trouille et al. 2019; UNEP 2019; Bishop et
al. 2020; Capdevila et al. 2020; Fraisl et al. 2020; Poisson
et al. 2020; Queiruga-Dios et al. 2020; Quinlivan et al.
2020a; Dörler et al. 2021; Hegarty et al. 2021; Moczek et al.
2021; Corburn 2022; Kirschke et al. 2022).
https://earthobservations.org/index.php
IWMI - 5Working Paper 210 - Digital Innovation in Citizen Science to Enhance Water Quality Monitoring in Developing Countries
Citizen Science for Collaborative,
Inclusive Water Resource Monitoring
(and Management) to Meet SDG6
The general populace has huge potential to make
large-scale changes and contributions to water quality
monitoring and management, through collective
alterations in behavior, inclusion in the scientific process,
and engagement with policy creators and implementing
agencies (Reid et al. 2019; Capdevila et al. 2020; Cook
et al. 2021). The involvement of citizens in science comes
in many forms, from simply collecting data, through to
citizen-led science where citizens are engaged in research
conceptualization, data collection, analysis, interpretation
and reporting (Buytaert et al. 2014; Graham and Taylor
2018; Schölvinck et al. 2022). Whichever way the system is
set up, citizen science offers data collection and scientific
engagement that is dynamic, decentralized and more
diverse (Hadj-Hammou et al. 2017; Dörler et al. 2021).
In a review of the current and potential contributions of
citizen science to the SDGs, Fraisl et al. (2020) illustrated
that citizen science is already making contributions towards
5 of the SDG indicators, with the potential to meaningfully
contribute to 76 more (covering some aspects of all 17
SDGs). The authors highlighted that there is especially good
potential for the inclusion of citizen science to be highly
impactful for achieving SDG 6, with a range of literature
identifying a strong potential for contributions particularly
to SDG 6.3.2 and SDG 6b ‘procedures for participation of
local communities in water and sanitation management’
(O’Donoghue et al. 2018; Capdevila et al. 2020; Quinlivan
et al. 2020a, 2020b; Taylor et al. 2022; Wu et al. 2022).
Well-designed citizen science can increase the efficiency
of community engagement and awareness building, and
generate large amounts of data which are essential in
bridging current knowledge gaps at a greatly reduced cost
compared to traditional methods (Hadj-Hammou et al.
2017; Fritz et al. 2019; Dörler et al. 2021). This potential
is strongly evident in water quality monitoring, especially
in the ability of citizen science to contribute fine spatial
and temporal resolution data required for pollution
management. For example, pollution has well-known albeit
complex, direct and / or indirect negative consequences
(Amoatey and Baawain 2019; Dudgeon 2019; Mushtaq et
al. 2020). However, the sources of pollution are diverse,
broadly categorized into point (e.g., single origin, ‘end-
of-the-pipe’ sources), and secondary or diffuse (such as
water run-off from cities or agricultural lands), which makes
isolating sources of pollution without sufficient monitoring
data extremely difficult (Behmel et al. 2016; Geetha and
Gouthami 2016; Dudgeon 2019; Zolkefli et al. 2020; Silva et
al. 2022). Through increased monitoring of water quality,
facilitated by citizen science, both point and diffuse sources
of various forms of pollution can be isolated, management
actions can be implemented, and the efficacy of those
actions can be tracked (Chapman 1996; Aitkenhead et al.
2013; Taylor et al. 2013; Altenburger et al. 2015; Forrest et al.
2019; Meyer et al. 2019; Lotz-Sisitka et al. 2022).
Beyond quantitative data generation, there is a range of
other tangible and significant benefits to citizen science
(Jalbert and Kinchy 2016; Jollymore et al. 2017; McKinley
et al. 2017). In some instances, citizen science facilitates
the gathering of valuable qualitative data, such as local
insights into problems or patterns based on indigenous
knowledge (Paul et al. 2018; Bishop et al. 2020; Hegarty
et al. 2021; Lepheana et al. 2021). In this way, citizen
science can give voice to individuals and communities,
especially locals, minorities and those traditionally
marginalized, in a manner that typical quantitative data-
driven science usually does not facilitate (Conrad and
Hilchey 2011; McKinley et al. 2017; Corburn 2022). This
is especially pertinent in developing nations given the
rapid growth of informal and semi-informal urban and
peri-urban districts with limited sanitation and waste
management infrastructure (Corcoran et al. 2010; Adu-
Manu et al. 2017). Citizen science can also be a critical
tool to increase public awareness, scientific literacy and
accountability, mobilize members of the public, engage
in involved education, and foster improved relationships
between policymakers and the public (Bonney et al.
2009; Carlson and Cohen 2018; O’Donoghue et al. 2018;
Graham and Taylor 2018; Capdevila et al. 2020; Queiruga-
Dios et al. 2020; Taylor et al. 2022). As Alender (2016)
puts it, “Citizen science projects generally have several
overlapping goals that yield benefits in three major
categories: outcomes for scientific research such as data
collection; outcomes for participants including education
and new skills; and outcomes for social-ecological systems
such as conservation, stewardship, and policy”. Embedded
in these benefits, citizen science can also lead to long-
term investment in environmental ideologies, as well as
research and policy interest (De Filippo et al. 2021; König
et al. 2021). The quantitative value of these benefits is hard
to estimate or measure, but there is a strong contention
that under the right circumstances, they may be equally
valuable to quantitative data generation (Conrad and
Hilchey 2011; Jalbert and Kinchy 2016).
Citizen science, similar to modern technological
techniques, can have some limitations compared to
traditional monitoring by professionals and scientists
(Hadj-Hammou et al. 2017; Njue et al. 2019). These
include a potential lack of scientific understanding,
inexperience with scientific protocols, a lack of objectivity
and adequate training, unregulated or poor experimental
design, biases in sampling interests and locations,
risks to data collectors (especially at polluted, remote
or otherwise dangerous sites), and irregularity in data
collection, among others (Kolok et al. 2011; Hadj-Hammou
et al. 2017). Cumulatively, these contribute to the largest
barrier to citizen science: a lack of trust from the scientific
community and policymakers (Balázs et al. 2021). Despite
the evidence that citizen science data can be sufficiently
precise and accurate (especially when collected in large
volumes), and are comparable to data collected by trained
professionals and scientists (Holt et al. 2013; Lewandowski
and Specht 2015; Alender 2016; Swanson et al. 2016;
IWMI - 6 Working Paper 210 - Digital Innovation in Citizen Science to Enhance Water Quality Monitoring in Developing Countries
Poisson et al. 2020), citizen science is still broadly viewed
with skepticism regarding its validity (Cohn 2008; Bonney
et al. 2009, 2014; Kolok et al. 2011; Cook et al. 2021). For
example, one review found that less than half of citizen
science monitoring programs reported that their data
were being used for decision-making, as they were largely
viewed as being unreliable due to inconsistent protocols,
insufficient funding and poor communication (Carlson
and Cohen 2018). These findings were also supported
by another recent review that a significant percentage of
citizen science projects in Europe (19%) reported that data
were not passed on to any agencies or authorities (Moczek
et al. 2021). The reality is that for the data generated by
citizen science to be useful in decision-making (both in
research and policy), it must be considered high quality,
trustworthy and legitimate (Buytaert et al. 2014; Hulbert et
al. 2019; Arndt et al. 2022).
Another major barrier to citizen science is built-in; citizen
science generally relies on volunteers (Thornhill et al.
2019; Schölvinck et al. 2022). Consequently, citizen
science needs to factor in volunteer motivation when
designing monitoring projects or research, ensuring that
volunteers find the involvement to be engaging, accessible
and worthy of repetition (Alender 2016; Carlson and
Cohen 2018). The motivation to partake in citizen science,
as well as the type of benefit garnered, will vary according
to region and economic status, among other factors
(Buytaert et al. 2014; Jollymore et al. 2017). For example,
wealthy people or regions might generally engage for the
benefit of learning, scientific or natural enrichment, or
contributing to scientific knowledge, while in developing
regions / poorer people might generally engage to
enhance their well-being, uplift the community, alleviate
pressing issues, or for benefits such as financial rewards
or the promise of remediation interventions based on the
data (Paul et al. 2018; Quinlivan et al. 2020a; Walker et
al. 2020). In many instances funding may prove essential,
given that financial support (e.g., reimbursement for
any costs accrued, such as travel or equipment, for
participation) or reward (e.g., payment for services, gifts)
may be a critical component of sustained and constructive
involvement (Capdevila et al. 2020; Lepheana et al. 2021).
For rural, poverty-stricken, or developing areas, this may
be especially important. Even relatively minor expenses
such as the cost of mobile data required to upload data
may be a bottleneck in data collection, management,
feedback and participation (Weingart and Meyer
2021). However, compared to the costs of conventional
monitoring funding citizen science may provide a far more
cost-effective approach to monitoring for governments.
For example, providing financial incentives for citizen
science monitoring river water clarity, using a cheap
citizen science technique such as the clarity tube
(Dahlgren et al. 2004) could provide high spatial and
temporal resolution data at a fraction of the cost of grab-
sampling and laboratory analysis of suspended solids.
Some of the requirements to get people to engage in
sustained citizen science work are more universal. For
example, participation needs to be as easy as possible;
people will more often volunteer their time and efforts if
the citizen science they are engaging with is as painless
and streamlined as possible (Alender 2016; Scott and
Frost 2017). People also require feedback, which is a
powerful form of reward (Scott and Frost 2017). There is
ample evidence showing that citizen scientists quickly
become demotivated when they cannot see how their
work has an influence at some higher level (e.g., use in
institutional databases, contributing toward decision-
making) or do not receive constructive or positive
feedback of some kind (Conrad and Hilchey 2011;
Capdevila et al. 2020; Dörler et al. 2021). The combination
of these requirements was emphasized by Hulbert et
al. (2019), “Simply, a gap often exists between intention
and behavior. Citizen scientists who initially struggle to
participate in a project are unlikely to try again in the
future. This challenge underscores how critical it is to
tailor an experience that firstly captures the interest of a
potential citizen scientist and then creates a participatory
environment that is both intuitive and rewarding”.
Integrating Technology and Citizen
Science
Clearly, a potential nexus exists between the need for
monitoring data to meet SDG 6, among other global
needs, as well as the strengths and drawbacks of modern
technology and citizen science. Synergy between the
power for automated, verifiable data collection using
modern technologies and the power of citizen science
to improve the spatial and temporal resolution of data
collection, along with associated benefits, presents an
opportunity to use the best features of each to mitigate
the shortcomings of the other.
Citizen science is no stranger to the augmentations
proffered by technological advancement, as Baker (2016)
expresses, “Low-cost, user-friendly technology allows
people across the globe to participate in the scientific
endeavor, and this trend is expected to mushroom far into
the future. Technology is indeed driving citizen science
in ways unimaginable even a decade ago”. Interaction
and integration between citizen science and technology
has already given rise to multiple platforms dedicated
to the coordination, management and dissemination
of information about citizen science over the last two
decades. Well-known examples include the Global
Biodiversity Information Facility10 (GBIF) (Lane and
Edwards 2007), Earthwatch Institute,11 Zooniverse12
10 https://www.gbif.org/
11 https://earthwatch.org
12 https://www.zooniverse.org/
IWMI - 7Working Paper 210 - Digital Innovation in Citizen Science to Enhance Water Quality Monitoring in Developing Countries
(Trouille et al. 2019), SciStarter13 (Hoffman et al. 2017),
CitiSci,14 iNaturalist15 (Nugent 2018), and eBird16 (Sullivan
et al. 2009) – all of which have been associated with
exemplary and encouraging successes. It is also
encouraging that many of these initiatives provide open
access to online data free of charge as a cause for the
common good. Engaging with modern technologies for
citizen science is essential to meet the SDGs and broadly
address many societal and ecological issues, particularly
given the large volume of credible, high-resolution
data required alongside the need for wider involvement
and education in science (Paul et al. 2018; Njue et al.
2019; Trouille et al. 2019; König et al. 2021). Indeed,
Lukyanenko et al. (2020) stated, “Conducting research in
citizen science also heeds the call within the information
science discipline to conduct research that promotes or
supports environmental sustainability through innovative
information technologies”.
Alignment between the objectives of the DI and SDG 6
(among others) creates a large scope for the integration of
modern, accessible, low-cost, real-time tools with citizen
science and initiatives for use in water monitoring and
management. Alignment between SDG 6.3.2 and SDG 6b
in particular, present excellent synergistic opportunities
for technologically upgraded and equipped citizen science
involvement in water quality monitoring and water
resource management (Capdevila et al. 2020; Hegarty et
al. 2021). This is epitomized by the WWQA principle pillar
‘Citizen Engagement’, with the dedicated workstream
‘Citizen Science for SDG 6.3.2’.17
Smartphones and Citizen Science
Smartphones, sometimes in conjunction with other sensors,
present tools to engage with citizen scientists since they are
widely accessible, powerfully equipped for data collection
and have large scope for upscaling to many users (Graham
et al. 2011; Kolok et al. 2011; Buytaert et al. 2016; Rahim et
al. 2017; Ouma et al. 2018; Davids et al. 2019). The concept
is relatively recent, following the rapid pace at which
mobile technologies develop, which now includes extended
battery life, internet connectivity, Wi-Fi, local and cloud-
based data storage, Bluetooth, Global Positioning System
(GPS), accelerometers, gyroscopes, temperature, humidity,
ambient light, fingerprint and heart rate sensors, as well as
powerful cameras, all mediated through simple touchscreen
interfaces (Aitkenhead et al. 2014; Kwon and Park 2017;
Dutta 2019; Kishore et al. 2022). However, Graham et al.
(2011) noted that the potential of mobile phones in data
collection was recognized over a decade ago, “Mobile
phone–based tools have the potential to revolutionize the
way citizen scientists are recruited and retained, facilitating
a new type of ‘connected’ citizen scientist—one who collects
scientifically relevant data as part of his or her daily routine.”
Smartphones enable the collection of large amounts of
potentially more objective, comprehensive (including
metadata such as time, date, identity, location), and
accurate data that can be uploaded (via internet
connections) to large cloud-based databases with cloud-
based computing for initial data management (McKinley et
al. 2017; Njue et al. 2019; Park et al. 2020). Creating a data
acquisition – database and creating a curation pathway
in this manner potentially allows for more automated and
streamlined data management, verification, visualization
and reporting processes (Adu-Manu et al. 2017; Paul et al.
2018; Poisson et al. 2020; O’Grady et al. 2021). This is a vital
process to minimize the collection of data that either never
reaches a database because it is collected manually on
paper and never uploaded to a digital platform, or the data
is stagnant with decreasing relevance in a database which
is not managed or is continually used for real-time reporting
(Strobl and Robillard 2008; Dong et al. 2015). ‘Gamification’
(the process of using game-like elements in a nongaming
context) and AI machine-learning (ML) technology presents
especially exciting avenues of exploration in this regard
(Lowry et al. 2019; Lukyanenko et al. 2020; Ighalo et al.
2021b; Khakpour and Colomo-Palacios 2021). However,
simple auto-verification protocols that flag submissions
outside of expected boundaries for manual checking can
be simple and effective tools to substantially increase data
credibility while reducing manual time and effort
(Njue et al. 2019). This system has already been employed
to a great effect in citizen science projects such as eBird18 or
the Southern African Bird Atlas Project 219 in conjunction with
BirdLasser,20 where submissions have automated protocols for
checking and flagging potential mistakes. Through enabling
more objective, accurate and auto-verified data collection,
smartphones have the potential to mitigate the concerns
(at least partially) about the credibility of citizen science
data (McKinley et al. 2017; König et al. 2021). For example,
smartphones allow the collection and submission of GPS data,
photos and videos to support and help verify data collection.
Much of the data are recorded automatically as well, which
reduces the chance for human error in writing down and
later transcribing information such as the time, date and
location of data collection. Smartphones can also perform a
wealth of other functions related to improving data quality,
such as providing access to training media, real-time support
from project managers or internet connectivity to seek help.
Collectively, these functions might allow improved, easier, and
faster integration and acceptance of citizen science data into
the framework of knowledge generation, and dissemination
involved in publication-policy pathways (Buytaert et al. 2014;
Fritz et al. 2019; Arndt et al. 2022).
13 https://scistarter.org/
14 https://www.citsci.org/
15 https://www.inaturalist.org/
16 https://ebird.org/home
17 https://www.unep.org/explore-topics/water/what-we-do/world-water-quality-alliance-wwqa-partnership-effort/world-water
18 https://ebird.org/home
19 https://sabap2.birdmap.africa/
20 https://www.birdlasser.com/
IWMI - 8 Working Paper 210 - Digital Innovation in Citizen Science to Enhance Water Quality Monitoring in Developing Countries
Another key feature of the interactive nature of internet
connectivity and powerful computing associated with
smartphones is that they enable faster (potentially real
time), easily comprehensible feedback (e.g., through
communication, data visualization, or ‘game-like’ points
or credits) to the user based on potentially large cloud-
hosted datasets (Graham et al. 2011; Geetha and Gouthami
2016). Feedback can serve to empower citizens on the
ground, often as stewards of their environments, with
understanding and agency to take action directly or via
commentary on policy or research structure (McKinley et
al. 2017; Scott and Frost 2017; Carlson and Cohen 2018).
Mobilizing citizen scientists with actionable, real time
data collection and feedback via smartphones may then
work towards two of the most important motivators of
citizen scientists, which are to help the community and
environment and to get outside into protected, healthy
nature (Alender 2016; Jollymore et al. 2017) “People who
are passionate about a subject can quickly locate a relevant
citizen science project, follow its instructions, submit data
directly to online databases, and join a community of peers”
– (Bonney et al. 2014). As a corollary, internet connectivity
on smartphones also enables quick and easy sharing of
information via social media or similar channels. Therefore,
smartphones also present good potential as a platform for
information and data sharing within communities to boost
awareness and scalability (Bonney et al. 2014). This may
facilitate broadscale data collection that uses common,
standardized protocols, maximizing comparability and
usefulness in trend analyses (Strobl and Robillard 2008;
Behmel et al. 2016). It is important that feedback can be
two-way as well; feedback can also be from data collection
frontline users to project management or end users of the
data. This form of feedback from frontline users is useful to
refine data collection protocols, maintain participation and
perspective, and enable project management monitoring
to ensure that the project goals and requirements align
realistically with the goals, capabilities and motivations of
the participants (Walker et al. 2020; Weingart and Meyer
2021). In this way, citizen science may prove critical to
speeding up positive environmental action and meeting the
SDGs (Njue et al. 2019).
Connectivity facilitated by smartphones can also make a
significant contribution to disaster risk management, both
via citizen-driven communication, and access to timely
intervention information for and from authorities (Paul
et al. 2018). For example, the Minister of Environmental
Affairs in South Africa, Barbara Creecy, opened her
2022/2023 National Assembly budget speech lauding the
efforts of citizen scientists / activists, the Enviro-Champs,
for saving lives during flooding in Durban, South Africa,
“Using information from the satellite linked, Flood Early
Warning System she [Mrs Thembisa Nomlala an Enviro-
Champ] and fellow Enviro-Champs were able to save all
but one life, as the Palmiet River washed away 450 homes
in her community” (brackets added by authors) -
(DFFE 2022).21 Geetha and Gouthami (2016) also
demonstrated an example of how real-time connection
via the internet can be used to create a real-time,
customizable ‘dashboard’, inclusive of alerts sent via short
messaging service (SMS), or alternative instant messaging
platforms, on imminent water quality threats.
It is worth noting that caution must be taken when integrating
technology with citizen science (Jalbert and Kinchy 2016).
There are examples where attempts to integrate technology
can be exclusionary, actually reduced understanding and
decreased willingness to engage (Jalbert and Kinchy 2016;
Trouille et al. 2019). For example, using ML to prescreen
images and remove uninteresting or unimportant images
from a camera trap database actually reduced volunteer
engagement with processing (Bowyer et al. 2015). There is
already a lack of diversity in citizen science; often wealthier
people more familiar with science have the time and
resources to participate, while the most vulnerable and
disaffected people most in need of a voice and citizen science
involvement are excluded (Lepheana et al. 2021; Pateman
et al. 2021; Harrisberg 2021). As Walker et al. (2020) stated,
“Participants [are] most likely to live in an advanced economy
and be in the middle class, thus having the education,
technical skills, access to resources and infrastructure and
the free time or the particular leisure pursuits that facilitate
participation. This results in a geographic bias as majority
of the projects are located in North America and Europe”.
Technology can also make data collection technical or
complex in some instances, rendering it too complicated for
broader or sustained involvement “Simplicity is one key to
the success of mass participation citizen science projects.
As the complexity of the protocol increases then the number
of participants is likely to decrease, even though the value
of the data may increase (e.g. because the dataset is more
detailed).” - (Pocock et al. 2014). The lack of representation
of poorer communities may be aggravated by citizen science
that is focused on expensive or highly technical systems,
especially where scientific literacy can be severely limited
(Walker et al. 2020; Weingart and Meyer 2021). Common
examples include devices such as personal weather stations
(e.g., Davis Instruments or NetAtmo) used to collect
precipitation data worldwide for upload and management
in centralized databases such as NetAtmo,22 Weatherlink,23
or Weather Underground,24 or ‘pocket water quality meters’
such as the Horiba LAQUAtwin range25 for easy measurement
of various water quality parameters. These personal weather
stations cost upwards of USD 500 (ZAR 9,000), while a
LAQUAtwin kit (including 4 pocket meters) costs USD 2,080
(ZAR 38,000). As a result, these devices are not accessible to
aspiring citizen scientists who cannot afford them. Naturally,
at the extreme end of the spectrum, machine-automated
data collection cuts people out completely.
21 https://www.dffe.gov. za/speech/creecy_2022 .2023budgetvote
22 https://weathermap.netatmo.com/
23 https://www.weatherlink.com/
24 https://www.wunderground.com/
25 https://www.horiba.com/fra/water-quality/pocket-meters/
IWMI - 9Working Paper 210 - Digital Innovation in Citizen Science to Enhance Water Quality Monitoring in Developing Countries
An interesting twist on the integration of technology
with developing regions is the notion of innovation
‘leapfrogging’, which can actually advance rural and
semi-developed economies more rapidly than developed
regions. Leapfrogging occurs when innovations are picked
up without going through a traditional developmental path
(James 2014). For example, mobile phones spread far
more quickly in some developing countries (e.g., Myanmar,
Kenya, and Uganda) compared to developed nations where
landline connectivity seemed adequate (Cilliers 2021).
Another prime example is in underprivileged townships in
Southern Africa, where the use of innovative citizen science
water quality biomonitoring techniques is often more
extensive and complete than in other more affluent regions
(Taylor and Taylor 2016; Taylor et al. 2022).
Smartphones can assist in combating participation biases
as well as any potential exclusionary practices that would
involve more complicated or expensive technologies, in
at least three ways: first, many modern smartphones are
relatively affordable, accessible and understandable to most
people even in rural and impoverished areas (Aitkenhead
et al. 2014); an estimated more than 6 billion people
(estimates indicate 80 – 90% of the global population) are
in possession of a smartphone (Kishore et al. 2022; Fabio
et al. 2022). Second, smartphones facilitate connection to
the internet, a rich platform for information sharing and
learning to assist and facilitate citizen science in multiple
languages (Quinlivan et al. 2020a). Third, smartphones
undergo thorough, constant modification to make them
evermore user friendly, easy to understand, and robust yet
malleable in terms of the software and computing they can
support (Aitkenhead et al. 2014). In that vein, smartphones
facilitates potential gamification of data collection and
interaction (Scott and Frost 2017). Citizen science platforms
can be designed to be more engaging and ‘game-like’,
to enhance, rather than detract from the data collection
and feedback process, especially among young people
(Morschheuser et al. 2017; Lowry et al. 2019).
The value of smartphones in facilitating, rather than
degrading, involved education (sometimes termed ‘action-
learning’) in citizen science is worth highlighting. Involving
people in the scientific process, as opposed to traditional
top-down teaching, has been recognized as a potent
mechanism for increasing environmental understanding,
building trust and fostering further participation or
sustainable practices (Conrad and Hilchey 2011; Hulbert
2016; Fraisl et al. 2020). For example, Holmes et al. (2019)
and De Filippo et al. (2021) emphasized the distinction
between public involvement (i.e., actively involving people
in research) and public engagement (i.e., raising awareness
of research). The distinction challenges the common
notion (which is usually a misconception) that awareness
translates to action and tangible benefits downstream, and
emphasizes that involvement is correlated with more rapid,
sustained and noticeable effects (Jalbert and Kinchy 2016;
Jordan and Cassidy 2022; Taylor et al. 2022).
Through their combined benefits and capabilities,
smartphones may also motivate more citizen scientists to
engage continuously for longer periods (McKinley et al.
2017). Overall, smartphones (often in conjunction with other
technologies or data collection protocols) help make data
collection easier and more interactive, improve training,
create a sense of agency, facilitate feedback, and increase
diversity of participation in citizen science to bridge poverty
and geographic divides. In this way, smartphones may
contribute towards more citizens collecting data regularly
for longer periods at the fine spatial and temporal scales
necessary to meet the SDGs (Buytaert et al. 2016; Thornhill
et al. 2019; Silva et al. 2022).
Future Research, Development and Implementation Directions for
Smartphone Water Quality Monitoring
Globally, there are many options and developments
relevant to citizen science smartphone-based or
smartphone-assisted water quality monitoring. However,
not all modern developments are suitable for deployment
or testing across all socio-ecological environments.
For example, some smartphone-based or smartphone-
assisted technologies require expensive, or difficult to
operate without training, auxiliary components (Njue et
al. 2019). Many of these are by no doubt powerful, but
presents prohibitive costs to most citizen scientists and
projects in developing regions (Abegaz et al. 2018). Also,
various smartphone-based or smartphone-assisted
developments have only ever reached prototype level
(Kishore et al. 2022). These include among others
the Secchi3000 for measuring turbidity and Secchi
depth (Toivanen et al. 2013), the Mobile Water Kit for
determining total coliform and Escherichia coli in water
(Gunda et al. 2014), a spectrometer for measurement
of water pH (Dutta et al. 2015), an approach to measure
turbidity (Hussain et al. 2016), two approaches to
measure water salinity (Hussain et al. 2017), and a device
(SmartFluo) to measure chlorophyll a fluorescence
(Friedrichs et al. 2017). Exploring these prototype
technologies, especially where they are developed
to become relatively low-cost, might be a promising
avenue for future research. However, most never became
commercially available or easy and affordable to deploy
at a local scale.
IWMI - 10 Working Paper 210 - Digital Innovation in Citizen Science to Enhance Water Quality Monitoring in Developing Countries
Essentially, innovations in smartphone water quality
monitoring in the context of developing countries need
to be low-cost (requiring minimal input costs beyond
having a smartphone), easy-to-use, easily scalable,
commercially available, suitable to be used by minimally
skilled people in rural and developing areas. Considering
that developing countries also have limited resources, it is
also important that the efforts in research, development
and implementation are strategic and efficient. Monitoring
all the parameters (physical, chemical and biological)
that contribute to water quality is highly complex and
outside the scope of what is achievable by most people,
organizations or even governments (Kruse 2018; Zolkefli
et al. 2020; Paepae et al. 2021; Okpara et al. 2022). As a
result, it makes sense that efforts are primarily directed
towards addressing water resource monitoring and
management aimed at achieving the SDG indicators to
align with at least the minimum requirements for global
water quality monitoring. This also provides a good
starting point to standardize collection and reporting
worldwide. The SDG 6.3.2. indicator method employs
a water quality index that integrates basic core water
quality parameters; oxygen, salinity, nitrogen, phosphorus
and acidification (UN Water 2018; Quinlivan et al. 2020b;
Wu et al. 2022). These SDG water quality indicators were
chosen as a result of extensive consultation and research.
They are designed to provide a snapshot of water quality
suitable for most regions and socioeconomic situations
worldwide. However, they acknowledge that where they
indicate problems, further, more in-depth analyses will
be required; they should not and do not replace the
need for monitoring a much wider range of water quality
metrics. Monitoring algae (via chlorophyll or algal cells),
temperature and clarity, also present useful options since
they are highly relevant to ambient water quality and
can be monitored cheaply and easily by citizen scientists
(Dahlgren et al. 2004; Castilla et al. 2015; Ho et al. 2020).
The relevance of each of these parameters to monitoring
ambient water quality is summarised briefly below:
• Acidification: The pH of water specifies how acidic
or alkaline it is. Generally, water is acidic if the pH
is less than 6, and alkaline if pH is more than 8. The
acceptable range for environmental or ambient water
is between 6.5 – 8.5 pH units. The pH of water usually
correlates to electrical conductivity, hardness (the
total calcium and magnesium ion concentration),
sulfates, total dissolved solids and chemical oxygen
demand (Kruse 2018; Ahmed et al. 2020). Monitoring
pH is important since it has various effects on
infrastructure (e.g., corrosion potential of water for
pipes), disinfection efficiency, humans and freshwater
ecosystems (Tibby et al. 2003; Banna et al. 2014; Jan
et al. 2021). Particularly, acidification can have severe
consequences for biota through facilitating changes to
the mobility and toxicity of elements in water, though
any changes in pH can affect ecosystems in complex
ways since the pH tolerance range of species vary
substantially (Tibby et al. 2003).
• Algae: Anaerobic explosive algal blooms causing,
for example, severe depletions of dissolved oxygen
and reductions in visibility and photosynthetic
potential, are a major threat to freshwater systems
worldwide (Sellner et al. 2003). Algal concentration
broadly correlates with the color of water, making
the measurement of chlorophyll-a or the ‘greenness’
of water a proxy for algal concentration (Ouma et al.
2018; Malthus et al. 2020).
• Clarity and turbidity: The measurement of visual
water clarity, in centimeters (cm), has a strong
inverse relationship to total suspended solids
(TSS), thereby providing a powerful proxy for the
measurement of TSS (Davies-Colley and Smith 2001;
Kilroy and Biggs 2002; Ankcorn 2003; Anderson and
Davie 2004; Dahlgren et al. 2004; Ellison et al. 2010;
Ballantine et al. 2015; West and Scott 2016; Johnson
et al. 2018). The TSS content of water is recognized
as one of the most important water quality traits to
monitor (Packman et al. 1999; Rügner et al. 2013;
Mucha and Kułakowski 2016; Sader 2017); high TSS
above naturally occurring levels, is responsible
for, or is directly associated with some of the most
prominent negative impacts of low-quality water (for
reviews, see Cordone and Kelley 1961; Kirk 1985; Ryan
1991; Wood and Armitage 1997; Henley et al. 2000;
Dallas and Day 2004; Bilotta and Brazier 2008;
Kjelland et al. 2015; Schumann and Brinker 2020).
Measurement of visual clarity has been suggested
as preferable to turbidity (Davies-Colley and Smith
2001), since clarity relates intuitively to humans and
directly relates to biological consequences for fish
and birds which perceive relative clarity similarly to
humans (Kilroy and Biggs 2002; Newcombe 2003).
Moreover, clarity is measured in the International
System of Units (SI) as opposed to the more arbitrary
units of turbidity measurement (Davies-Colley et
al. 2014). However, turbidity, a measurement of the
deflection of light, can also be a useful proxy for TSS
in water where light deflection is closely correlated
to TSS (Ankcorn 2003; Sader 2017).
• Nitrogen: Nitrogen comprises 78% of the earth’s
atmosphere. In water, nitrogen is usually fixed
as nitrates (NO3-), nitrites (NO-), ammonia
(NH), or ammonium (NH+), stemming primarily
from atmospheric deposition, agricultural
fertilizer runoff, or industrial waste (Kruse 2018;
Jaywant and Arif 2019). Monitoring would ideally
delineate the specific form of nitrogen present
to offer increased information about sources
and impacts. Nitrogen pollution can contribute
towards nutrient loading and the exponential
proliferation of plankton and algae, leading to
eutrophication (Romanelli et al. 2020). High
nitrogen in drinking water can also directly harm
young animals and humans through restricting
the transportation of oxygen in the blood
(Majumdar 2003; Ozmen et al. 2005).
IWMI - 11Working Paper 210 - Digital Innovation in Citizen Science to Enhance Water Quality Monitoring in Developing Countries
• Oxygen: Dissolved oxygen is a measure of the oxygen
content of water. Dissolved oxygen is critical for
aquatic biota; declining dissolved oxygen affects
biota along a spectrum, from reduced activity and
growth, through reductions in breeding success and
stress, to mortality at low levels for sustained periods
(Ahmed et al. 2020; Jan et al. 2021; Silva et al. 2022).
Dissolved oxygen is influenced by a range of factors
but is highly susceptible to anthropogenic influence
via pollution especially with organic waste and
sewage (Gholizadeh et al. 2016; Kruse 2018).
• Phosphorous: Phosphorous is an essential nutrient
used by plants and microorganisms. It therefore forms
a base for the primary production of animals and
plants. In water, phosphorous is typically in dissolved
forms such as orthophosphates. Similar to nitrogen,
unnaturally high concentrations of phosphorous in
water (usually related to agricultural runoff from
fertilizers) can contribute to nutrient loading and
can cause eutrophication where the ‘nuisance value’
of water is elevated (Park et al. 2020; Silva et al.
2022). Total phosphorous generally correlates to
chlorophyll-a and in some circumstances to water
clarity (Gholizadeh et al. 2016).
• Salinity: Salinity is the concentration of dissolved
salts in soils and water. Unnaturally high or low
salinity can severely, negatively affect aquatic
environments because aquatic organisms generally
have delicate osmotic balances and can only tolerate
specific ranges of salinity (Velasco et al. 2019; Paepae
et al. 2021). In some instances, even relatively small
changes in salinity can have dramatic effects on
organisms if the rate of change in salinity is faster
than their ability to adjust. Salinity is also important
since water desalination is a highly costly process
both financially and in terms of time, energy and
human capital (Jan et al. 2021), while saline irrigation
water is highly detrimental to soil condition, and the
longevity of agricultural lands.
• Temperature: Water temperature is an important
component of water quality since temperature affects
the physicochemical parameters of water (such as
dissolved oxygen potential, electrical conductivity,
and the toxicity of ammonia), and has direct and
indirect effects on aquatic biota (Gholizadeh et al.
2016; Ahmed et al. 2020; Silva et al. 2022).
Examples of Smartphone Applications for
Exploration in Developing Countries
Various smartphone-based or smartphone-assisted
technologies are available which may prove to be useful
in the context of developing countries once validated
for local use. Below, a summarized, non-exhaustive
list of examples that are suggested for research and
implementation in developing countries. These are low-
cost, commercially available, continually supported,
accessible, easy to use and have requirements limited
to functionalities common to almost all smartphones
(such as powerful camera modules). Given the fact
that there is potential for variation in the chemico-
physical parameters and app functionality depending on
geographic location, weather, and operators, it is wise to
make sure that each of the options are locally validated
before being implemented for use in citizen science
monitoring networks:
• The Hydrocolor (Leeuw 2014; Leeuw and Boss
2018) and EyeOnWater26 (formerly Citclops) apps
for measurement of water color, reflectance, and
turbidity. Both apps have been tested for use in
various water systems in several countries with mixed
but promising results (Mahama 2016; Leeuw and Boss
2018; Ouma et al. 2018; Yang et al. 2018; Jovanovic et
al. 2019; Ayeni and Odume 2020; Malthus et al. 2020;
Al-Ghifari et al. 2021; Burggraaff et al. 2022). Some
studies have shown that data collection should be
cognisant of potentially confounding environmental
variables such as cloud cover and wind speed (Ouma
et al. 2018). The most recent version of both apps
have been upgraded to use RAW images instead of
JPEG, aiming to increase data collection accuracy
(Burggraaff et al. 2022).
• Deltares Aquality App27 (formerly Deltares Nitrate
App) in combination with Hach© nitrate test strips.
The Nitrate App allows for automated determination
of nitrate levels based on nitrate test strip results.
The data generated are automatically synced with the
Delta Data Viewer28 to contribute to a global database
of nitrate concentrations in water. One study has
indicated that volunteers using visual methods
produce more accurate results than the Nitrate app
(Topping and Kolok 2021). The Agricultural Research
Council (ARC) and Water Research Commission
(WRC) in South Africa are currently engaged in a joint
project exploring the use of this app in a Southern
African citizen science framework. The app is also
undergoing development at Deltares to increase
its functionality to be able to measure a proxy of
electrical conductivity.
•
The Nutrient App (Push Interactions, Inc.,
developed by the University of Saskatchewan and
Global Water Futures Project [GWF] with the support
of Environment and Climate Change Canada29)
determines nitrate and phosphate concentrations
in water based on automated analysis of in situ test
nitrate (Hach©) and phosphate (API Phosphate
26 https://www.eyeonwater.org/
27 https://www.deltares.nl/en/software/nitrate-app/
28 https://v-web002.deltares.nl/fewsprojectviewer/projectviewer/
29 https://gwf.usask.ca/projects-facilities/nutrient-app.php#Overview
IWMI - 12 Working Paper 210 - Digital Innovation in Citizen Science to Enhance Water Quality Monitoring in Developing Countries
Test Kit)
test strips (Costa et al. 2020). The Nutrient
App measurements are geo-referenced and are
uploaded to a server managed by GWF at the
University of Saskatchewan. Similar to the Nitrate
App, results form part of a global database available
for visualization on a map interface in the app or on
the website.
• Data collection for citizen science projects
such as the Enviro-Champs (Taylor and Taylor
2016; Lepheana et al. 2021) can take place via
customizable form-based data collection tools such
as the Open Data Kit30 (ODK), Cybertracker,31 or
Ushahidi32 (formerly Crowdmap). These use a mobile
app (such as ODK Collect or the Cybertracker app)
that can be custom-built to help citizen science
projects record geo-referenced data (including
photos, video and voice recordings among other
things) on a range of water quality-related topics.
As a result, there is the possibility to explore
simple, low-cost in situ water quality test kit data,
such as those measuring pH, temperature, nitrate,
phosphate and even Escherichia coli (E. coli;
e.g., Praecautio E. coli water test developed by
Microfoodlab), and recording the results using these
apps.
• The Crowdwater33 initiative and app developed at
the University of Zurich gathers citizen science data
on water level, soil moisture, dynamics of temporary
streams, plastic pollution, and other qualitative
data, via a form-based platform.
• The stream assessment scoring system (miniSASS34)
(Graham et al. 2004) citizen science biomonitoring
tool traditionally functions with a pen and paper
survey. However, an app is being developed to aid
in completing a miniSASS survey by providing AI
camera recognition capability to identify freshwater
invertebrates, compute a river health score based on
their tolerance to pollution, and geo-locate the data.
The development is taking place as part of parallel
work in DI.
• The Freshwater Watch program35 (a division of
Earthwatch Europe) is a global initiative to monitor
water quality to aid in SDG indicator reporting. The
Freshwater Watch protocol collects similar data to
those proposed here; data collection uses a water
testing kit (Hach© nitrate and phosphate strips) to
test water chemistry parameters and a small clarity
tube to measure water clarity. Anecdotal, qualitative
data can also be collected regarding any other visual
observations (e.g., waste dumping, observations
of algal blooms). Data are uploaded via the ArcGIS
Survey123 app, which is freely available to use for
Freshwater Watch. The data are automatically
available to, managed and curated by the Freshwater
Watch program based in Europe. Through
collaboration with Freshwater Watch, the data can
be made available to local authorities or agencies
to become locally actionable and useful, in addition
to automatically becoming part of the global
Freshwater Watch database on water quality.36
• Apps such as TurbAqua (Meridian IT Solutions,
developed by the Central Marine Fisheries Research
Institute (CMFRI), iQwtr (BlueLeg Monitor), or Secchi
(developed by Richard Kirby) require Secchi disks,
clarity tubes, or other associated devices for actual
data collection. Therefore, they are redundant to
the use of one of the form-based apps (e.g., ODK,
Cybertracker) listed above; these can also be
designed to record data from clarity tubes etc., as
part of the data input forms. The bloomWatch,37
Bloomin’ Algae38 (UK Centre for Ecology and
Hydrology), and Levävahti (Algae Watch; VTT
Technical Research Centre of Finland, only available
in Finland) (Kotovirta et al. 2014) apps help citizen
scientists record qualitative data about the presence /
absence, and relative scale of algal blooms in local
waters. The information captured by these apps
would be redundant with the use of the form-based
apps listed above, which can be custom-designed
to collect similar data (including photos) on the
presence and extent of algal blooms.
• The Algal estimator mobile application (Ayeni
and Odume 2020) estimates total and cyano-
chlorophyll, ultimately estimating the likelihood
of algal bloom. However, the app requires input
of various river parameters only attainable via
measurements using other, precise and often
expensive in-field analysis, such as determination
of brightness (lux), water temperature at the
surface and bottom of the water, water phosphate
concentration, and chlorophyll a, or dissolved
oxygen and turbidity (Ayeni and Odume 2020). As a
result, this app is unlikely suitable in the context of
citizen science in developing countries.
The apps identified in this report are summarized in
Table 1, with information on their cost, their use, and what
platform they exist on.
30 https://getodk.org/
31 https://cybertracker.org/
32 https://www.ushahidi.com/
33 https://crowdwater.ch/en/start/
34 https://minisass.org/en/
35 https://www.freshwaterwatch.org/
36 https://www.freshwaterwatch.org/pages/community-groups
37 https://cyanos.org/bloomwatch/
38 https://www.ceh.ac.uk/our-science/projects/bloomin-algae
IWMI - 13Working Paper 210 - Digital Innovation in Citizen Science to Enhance Water Quality Monitoring in Developing Countries
Table 1. Summary information on a non-exhaustive selection of smartphone apps available for investigation into their
ability to assist in citizen science water quality monitoring.
App Function Requirements Cost Platform Reference
Determines the reflectance of natural Smartphone with a Free Android and Leeuw and
water bodies. Uses reflectance to camera, GPS, iOS. Can Boss 2018
estimate water turbidity, the gyroscope,andcompass. function
concentration of total suspended solids Also requires an 18% offline.
(TSS), and the backscattering coefficient photographers’ grey
in the red-light spectrum. The data are card as a reference.
saved on the device and can be accessed Instructions for use are
via the HydroColor app or downloaded. provided by the app.
Data are saved as a text file containing
additional information about the
measurement including: latitude, longitude,
date, time, sun zenith, sun azimuth,
phone heading, phone pitch, exposure
values, red-green-blue (RGB) reflectance,
and turbidity.
Determines the color of water based on Smartphone with a Free Android and Ouma et
the Forel-Ule scale. The measurements are camera and GPS. iOS. Can al. 2018;
sent to a central server, validated, stored function Ayeni and
and visible via the EyeOnWater website.a offline. Odume
Results are available to the user as well. 2020
Assists in determination of nitrate levels Smartphone with a Free Android and Topping
based on Hach© nitrate test strip results. camera and GPS. A iOS. Can and Kolok
The data generated are automatically Hach© nitrate test function 2021
synced with the Delta Data Viewerb to strip. offline.
contribute to a global database of nitrate
concentrations in water. Results are
available to the user as well.
Determines nitrate and phosphate Smartphone with a Free Android and Costa et
concentrations in water based on camera and GPS. iOS. Can al. 2020
automated analysis of in situ test nitrate Hach© and API function
(Hach©) and phosphate (API Phosphate Phosphate Test Kit offline.
Test Kit) test strips. Geo-referenced data test strips.
are uploaded to a server managed by the
Global Water Futures (GWF) Project at
the University of Saskatchewan and form
part of a global database available for
visualization on a map interface in the
app or on the website.c
Customizable form-based data capture Smartphone with a USD 169 – Android and https://
tool via the ODK Collect app. Data camera and GPS. USD 429 iOS. Can getodk.
collection can be designed to include Requires auxiliary per month. function org/
photos, videos, voice recording, equipment where offline.
detailed location or tracking data, necessary based on
or any text or picture-based question the desired data.
options. Data are uploaded to the For example, the form
ODK cloud server where they can be can record clarity as
managed or downloaded. measured by a clarity
tube, or a miniSASS
score from a miniSASS
survey.
Open Data Kit (ODK) The Nutrient App HydrocolorDeltares Aquality App EyeOnWater
Continued >>
IWMI - 14 Working Paper 210 - Digital Innovation in Citizen Science to Enhance Water Quality Monitoring in Developing Countries
Table 1. Summary information on a non-exhaustive selection of smartphone apps available for investigation into their
ability to assist in citizen science water quality monitoring. (continued)
App Function Requirements Cost Platform Reference
Customizable form-based data capture Smartphone with a Free Android and https://
tool via the Cybertracker app. Data camera and GPS. iOS. Can cyber
c ollection can be designed to include Similar to ODK function tracker.
photos, videos, voice recording, or any Collect in offline. org/
text or picture-based question options. requirement for
Data can be uploaded to the auxiliary data
Cybertracker cloud server where they collection
can be managed, visualized or equipment.
downloaded.
Customizable crowdsourcing tool. A user Smartphone. Free Android and https://
can create a ‘deployment’ for other users Requires auxiliary for small- iOS. Can www.
to contribute data to or contribute data equipment where scale users. function ushahidi.
to a preexisting deployment. Ushahidi necessary based USD offline. com/
sources data from multiple outlets (e.g., on the desired data. 5,000
SMS, Twitter, email) and collates them for enter
into a geo-referenced database. prise level.
Collects data on water levels in streams or Smartphone with a Free Android and https://
canals using a virtual staff gauge and camera and GPS. iOS. Can crowd
qualitative data on soil moisture, stream function water.ch/
flow, and plastic pollution with an offline. en/start/
allowance for additional anecdotal notes.
The data are uploaded and publicly
available for viewing online on the
website.
Currently in development. Performs all Smartphone with a Free Android and miniSASS.
data capture tasks of a miniSASS survey. camera and GPS. iOS. Can org (under
Additionally, uses artificial intelligence function construc
(AI) to identify aquatic offline. tion)
macroinvertebrates to improve Graham et
identification. Auto-generates a al. 2004
miniSASS score. Data are stored locally,
or uploaded to the miniSASS website,
where they are available for visualization
and downloading.
Data collection uses water testing Smartphone with a Free Android and https://
strips (Hach© nitrate and phosphate camera and GPS. iOS. Can www.
strips) and a small clarity tube to To use Freshwater function freshwater
measure water clarity. Anecdotal, Watch, one must offline. watch.org/
qualitative data can also be collected register a local
regarding any other visual observations group and receive
(e.g., waste dumping, observations of training from a
algal blooms). Data are uploaded via the Freshwater Water
ArcGIS Survey123 app. The data are representative.
automatically available to, managed and
curated by the Freshwater Watch
program based in Europe where they
form part of the global Freshwater
Watch database on water quality.
Freshwater Watch miniSASS Cyber trackerCrowdwater Ushahidi
Continued >>
IWMI - 15Working Paper 210 - Digital Innovation in Citizen Science to Enhance Water Quality Monitoring in Developing Countries
Table 1. Summary information on a non-exhaustive selection of smartphone apps available for investigation into their
ability to assist in citizen science water quality monitoring. (continued)
App Function Requirements Cost Platform Reference
Captures data as measured by the user Smartphone with a Free Android and Menon et
using visual assessment or Secchi disk camera and GPS. iOS. Can al. 2021
depth. Data recorded includes water Also requires a 3D- function
color code (based on the Forel-Ule scale) printed miniature offline.
and Secchi depth, location of Secchi Disk and
measurement and color images of the measuring tape.
water body being sampled.
Measures the Secchi depth and turbidity Smartphone with a Free Android and https://
of water. Photographic data are camera and GPS. iOS. Only d3pcsg2w
uploaded to a centralized server and Requires a specific functions jq9izr.
the Secchi depth and turbidity are container/device that online. cloudfront.
calculated on the server-side. Results needs to be filled with net/files/
are available to the user. the target water. App 84433/
may no longer be download/
supported. 620713/3.
pdf
Records the geolocated measurement Smartphone with a Free Android and Kirby et al.
of Secchi disk depth. Data are uploaded camera and GPS. iOS. Only 2021
to a server. Requires a Secchi disk. functions
Designed for use at sea. online.
Instructions are given
in the app.
Records geo-referenced photos of algal Smartphone with a Free Android and https://
blooms. Data are uploaded and can be camera and GPS. iOS. Only cyanos.
visualized on the website. functions org/bloom
online. watch/
Records geo-referenced photos of algal Smartphone with a Free Android and https://
blooms. Data are uploaded, where they camera and GPS. iOS. Only www.ceh.
go through a verification process before functions ac.uk/our-
becoming available for visualization on a online. science/
global map. projects/
bloomin-
algae
Records geo-referenced photos of algal Smartphone with a Free Android and https://
blooms, water temperature, ice, water camera and GPS. iOS. Only www-jarvi
depth, invasive water plants, jellyfish, Only available in functions wiki-fi.
and rubbish. Data are uploaded, where Finland. online. translate.
they go are available for visualization goog/wiki/
on a global map. Etusivu?_x_
tr_sl=fi&_x_
tr_tl=en&_
x_tr_hl=en
&_x_tr_
pto=sc
Levävahti Bloomin' Algae Bloom Watch TurbAquaSecchi iQwtr
Continued >>
IWMI - 16 Working Paper 210 - Digital Innovation in Citizen Science to Enhance Water Quality Monitoring in Developing Countries
Table 1. Summary information on a non-exhaustive selection of smartphone apps available for investigation into their
ability to assist in citizen science water quality monitoring. (continued)
App Function Requirements Cost Platform Reference
Estimates the likelihood of harmful Requires input Free Android and Ayeni and
algal bloom events. brightness (lux), water iOS. Only Odume
temperature at the functions 2020
surface and bottom of online.
the water, water
phosphate concentration,
and chlorophyll a, or
dissolved oxygen and
turbidity to estimate
chlorophyll a.
Instructions are
provided in the app.
App may no longer be
supported.
Source: Author’s creation.
Notes:
www.eyeonwater.org
https://v-web002.deltares.nl/fewsprojectviewer/projectviewer/
https://gwf.usask.ca/projects-facilities/nutrient-app.php#ViewYourMeasurements
Conclusions
Bridging gaps in data and knowledge, especially in terms
of water quality, has been identified as a necessity in
informing policy and interventions, as well as managing
water for a sustainable future as encapsulated by SDG
indicators/targets (Buytaert et al. 2016; UNEP and UN
Water 2018; UN Habitat and WHO 2018; Flitcroft et al.
2019; Bishop et al. 2020). So far, monitoring data on
water have primarily come from developed countries
and regions, since less developed areas often lack the
resources required to gather, analyze and manage data
(Capdevila et al. 2020; Quinlivan et al. 2020a, 2020b;
Paepae et al. 2021). Southern Africa provides a good
example of these obstacles (Graham and Taylor 2018;
Hulbert et al. 2019). Southern Africa’s freshwater security
is at risk due to scarcity, compounded by poor and aging
infrastructure, a growing population and increasing
demands. These issues are compounded by pollution
pressure, corruption, vandalism and theft, lack of skilled
personnel, as well as climate change (Edokpayi et al.
2017; Boni et al. 2021). Moreover, Southern Africa has
a widespread lack of institutional, financial and human
resources to undertake thorough water quality monitoring
regimes to aid in mitigating the water scarcity and quality
problems (Heyns 2003; Hulbert et al. 2019; Weingart
and Meyer 2021; Mukuyu et al. 2023). The result is that
the most disadvantaged people are at the highest risk,
and often lowest priority, regarding the global freshwater
crisis (Paul et al. 2018; Corburn 2022).
Using modern, low-cost, easy-to-use technologies to
co-create and communicate the knowledge, understanding,
and policy at all levels is a good approach for helping
developing countries recognize, monitor, and preserve
vital freshwater ecosystems (McKinley et al. 2017; Reid
et al. 2019; Arthington 2021; Jordan and Cassidy 2022;
Lynch et al. 2023). While many technologies have been
developed, and the number of publications showing the
power and potential of smartphones and citizen science
have increased, uptake and critical validation are still
scarce. Going forward, we recommend exploration of the
apps above, or others that are suitable, in the context of
developing countries with a focus on scientific validation
and upscaling implementation for water resource
monitoring and SDG reporting. Validation should aim to
assess data collection accuracy, accessibility, ease of use,
cost, and the feasibility to contribute to pathways from
data collection to citizen mobilization and decision-making
(Jollymore et al. 2017; Fritz et al. 2019).
Algal estimator
IWMI - 17Working Paper 210 - Digital Innovation in Citizen Science to Enhance Water Quality Monitoring in Developing Countries
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206 The Link between Small
Reservoir Infrastructure and Farmer-
led Irrigation: Case Study of Ogun
Watershed in Southwestern Nigeria
https://doi.org/10.5337/2022.229
208 Innovations in Water
Quality Monitoring and
Management in Africa:
Towards Developing an African
Water Quality Program (AWaQ)
https://doi.org/10.5337/2023.217
207 State of Water Quality
Monitoring and Pollution
Control in Africa: Towards
Developing an African Water
Quality Program (AWaQ)
https://doi.org/10.5337/2023.216
For access to all IWMI publications, visit: www.iwmi.org/publications/
IWMI Working Paper Series
Working Paper
The Link between Small Reservoir
Infrastructure and Farmer-led Irrigation:
Case Study of Ogun Watershed in
Southwestern Nigeria
Adebayo Olubukola Oke, Olufunke O. Cofie and Douglas J. Merrey
206
205 Environmental Flows in Support
of Sustainable Intensification
of Agriculture in the Letaba
River Basin, South Africa
https://doi.org/10.5337/2022.226
209 A Framework for an African
Water Quality Program (AWaQ)
https://doi.org/10.5337/2024.202
Working Paper
A Framework for an African Water
Quality Program (AWaQ)
Patience Mukuyu, Chris Dickens, Nilanthi Jayathilake, Moshood Tijani,
Deborah V. Chapman and Stuart Warner
209
210 Digital Innovation in Citizen
Science to Enhance Water Quality
Monitoring in Developing Countries
https://doi.org/10.5337/2024.201
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