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How Citizen Science can help SIDS reach SDGs

Experiences of open environmental research in Aruba

With the detailed metrics of the Sustainable Development Goals (SDGs), many small communities lack data to measure their baseline status and progress. Small Island Developing States (SIDS) occupy a unique space among UN member states, geographically isolated, small in population, and ecologically unique. With regard to the environment, many SIDS lack the resources to collect reliable data using traditional methods. This project explores the potential of open source, low cost, and automatic technologies to assist in this effort. Here we test options for three SDG indicators (airborne particulate matter, ocean acidity, and green coverage) and one other (seafloor coverage), to track SDG progress in Aruba using citizen science. Preliminary designs for replicable sensor stations, protocols for GIS mapping, and public data that is some of the first for the country have resulted. With further development, this approach can help communities around the world, to track SDG progress and better understand their natural environments.


The Sustainable Development Goals (SDGs) are an ambitious set of goals that look at 17 broad areas of development, each of which is defined by several specific targets, and in turn quantified by one or more indicators. At this basic level, the indicators are the way we can measure our progress towards the SDGs and realize “peace and prosperity for people and the planet”.


In the interest of shedding some light on the mindspace and perspective from which these data collection tools are being developed, we can place it within a context that motivates the need for this type of research. Issues of lacking environmental research, specifically in areas facing significant and existential threats from climate change and pollution, highlight a clear case for the empowerment of small, remote, or low-resource actors to establish a basic scientific understanding of their environment.


Our first step in identifying potential opportunities for citizen science to monitor SDG progress was to go through the list of indicators to identify those that could be measured physically. In Aruba, none of these were being measured at the national level, and there were no plans to do so, making any of them suitable for citizen science efforts.

  • 6.6.1 Change in the extent of water-related ecosystems over time
  • 11.3.1 Ratio of land consumption rate to population growth rate
  • 11.6.2 Annual mean levels of fine particulate matter (e.g.PM2.5 and PM10) in cities (population weighted)
  • 14.1.1 Index of coastal eutrophication and floating plastic debris density
  • 14.3.1 Average marine acidity (pH) measured at agreed suite of representative sampling stations
  • 15.1.1 Forest area as a proportion of total land area
  • 15.3.1 Proportion of land that is degraded over total land area
  • 15.4.2 Mountain Green Cover Index
  • 14.3.1 Average marine acidity (pH)
  • 15.1.1 Forest as a proportion of total land area


The four selected indicators span the two most basic categories of environment, land and sea, each with one parameter that can be continuously monitored using sensors, and one that can be mapped periodically.

Particulate Matter

Air pollution, specifically the concentration of airborne particulates, has been recognized by the WHO as one of the major risk factors contributing to noncommunicable diseases. Two specific measures, PM2.5 and PM10, correspond to the concentration of particles in the air below a certain size in micrograms per cubic meter (μg/m^3), below 2.5μm and 10μm in diameter, respectively. Both of these parameters have internationally recognized target exposure levels, and a third parameter, PM1.0, is now being researched as an important measure of air quality.

Figure 1. EPA AIr Quality Index for PM2.5

Ocean Acidity

Ocean acidification (OA) is directly related to climate change and affects the livelihoods of many Arubans. OA is a measurement of aqueous hydrogen ions in the ocean, which result from reactions between water and carbon dioxide (CO₂), measured as pH. As atmospheric carbon dioxide levels increase, more dissolves into the ocean. This reacts with water to form carbonic acid, which releases hydrogen ions that in turn combine with carbonate in the ocean to form bicarbonate. Many species of coral and other shelled animals need carbonate to form their skeletons, which are made of calcium carbonate. The increased acidity can even lead to their skeletons dissolving, and less carbon ending up being stored in sediments and reefs in the long term.

Green Coverage

Green coverage (GC) is a measure of how many plants there are on land, which is also directly related to climate change. Decreasing levels of vegetation directly accelerates climate change, as the capacity of the planet to capture carbon out of the air and trap it in the greenery and soil is reduced. When areas are deforested they also release previously stored carbon compounding the effect, and made even worse when increased temperatures and extreme weather events lead to droughts and physical destruction of more vegetation.

Seafloor Coverage

Coral reef and seagrass coverage are also directly related to climate change, with increasing temperature and carbon dioxide leading to coral bleaching events, decreased growth, and die-off, and seagrass populations globally form a carbon sink three times that of the Amazon rainforest. Unhealthy or dead reefs also have negative impacts on connected ecosystems and species, including seagrass, mangroves, and people. For many SIDS both coral reef and seagrass meadows are a source of livelihood, driving tourism and fishing industries. A healthy reef ecosystem supports perhaps the greatest density of biodiversity of any ecosystem on the planet.


The environmental justice framework aligns with the potential of open source movements and citizen science, which allow people to collect the data they need to fill the gaps they see, to trust the data, to have access to data, and to be more involved in the conversation.

Particulate Matter

In order to measure airborne particulates, an affordable sensor (PMS5003) was connected to a WiFi-enabled Arduino based microcontroller to continuously measure air quality and transmit readings to an online database. For the pilot, four systems have been built and installed at locations around the island, and a website has been drafted to display the data, both in numerical format and in an animation.

Figure 2. Animation of live particulate matter readings
Figure 3. Graph of air quality readings from Pilot Site 1
Figure 4. Pilot locations of particulate matter monitoring stations

Ocean Acidity

Ocean acidification is being measured using relatively affordable sensors from Atlas Scientific, connected to an Arduino based microcontroller to collect pH, temperature, and dissolved oxygen readings every 10 seconds, and similarly report to an online database through either WiFi or GSM connection. One such monitoring station has been constructed and programmed, is currently being tested at the lab, and will soon be installed for a field pilot test at a pier owned by a local diving company.

Figure 5. Proposed pilot location of ocean monitoring station
Figure 6. Web interface for water quality readings

Green Coverage

In order to calculate green coverage, satellite imagery was used to calculate the Normalized Difference Vegetation Index (NDVI), which can be used to assess how much photosynthesis is occurring in different areas, indicating the presence of plants. Using scripts written in Google Earth Engine, and utilizing Landsat 8 satellite imagery, monthly calculations were done for the island of Aruba to determine which percentage of the land surface has an NDVI above several different thresholds.

Figure 7. Green coverage as a fraction of total land area calculated at several NDVI thresholds

Seafloor coverage

For the ocean maps we took a two pronged approach, on one end using satellite imagery to try to map shallow reef and seagrass systems, and on the other end working to use automated underwater photography coupled with a citizen-trained machine learning algorithm to classify images as different types of seafloor cover. 8. Seafloor mapping zones for drone-based image collection, mapped areas marked in red
Figure 9. Results from a test classification of underwater images using an AI model


This first effort into measuring environmental SDG indicators on a small island has made impressive progress, not only by starting to collect data, but by starting the conversation on the island and engaging people in the process. Using open source tools and citizen science approaches, the project has developed hardware and software protocols and collected sample data showing promise for the approach to continue monitoring these indicators going forward. At the same time, many improvements can be made to the devices and techniques, as well as in spreading the effort to both share the information with the local population, and share the approach with other islands.

Particulate Matter

Looking forward to collecting data over a full year, seasonal changes and wind patterns are going to be of particular interest. In Aruba, the Caribbean hurricane season is accompanied by low winds, and seasons are defined by sudden heavy rains and dry periods, both of which can have significant impacts on airborne particulate matter.

Ocean Acidity

With the ocean acidity monitor, the first step is to get it installed to start collecting preliminary measurements at the pilot location. Once any issues are worked out, a second version will be built to be installed further away from shore, either as a free-floating system or attached to an existing buoy, ideally without setting down an anchor or damaging the ocean environment. Such a monitor will also require a different system for communication and power, using low-power technology to send data and running off solar power. That system can then be replicated and installed around different beaches and coastal locations to collect a representative suite of measurements. This can be done in collaboration with local officials, and result in the ongoing collection of reliable data to monitor SDG progress.

Green Coverage

Initial green coverage calculations provide the basic data of what percentage of Aruba is green during different time periods, but the methods also establish the foundation for other calculations and maps to be produced. This includes more detailed landcover mapping, to track things such as deforestation, desertification, urbanization, changes to wetland extent, and even tracking of sand accumulation and erosion of Aruba’s beaches. With enough mapping, a model can eventually be constructed to extrapolate from historical data to predict how landcover might change in the future under different circumstances, helping to inform how related SDGs might be pursued.

Seafloor coverage

There are three different avenues of development going on around mapping the seafloor. The first is the underwater drone. Currently a manually controlled drone is being used to collect imagery, but an autonomous drone has been prototyped and needs some additional programming, tweaking, and testing in order to get it to the point of collecting images without needing any human control. For both drones, there is a pending challenge to connect collected images with GPS coordinates to enable more accurate mapping. The second avenue of development is the image recognition process. With a combination of programming and human feedback through Zooniverse, the AI model can be trained to improve its accuracy and learn to classify images better as time goes on. The last avenue is in satellite imagery mapping, which also benefits from other mapping efforts. As the drones collect data and as more observations are made to confirm the maps produced by this method, the new information can be fed back into the mapping algorithm to improve the maps it produces.


In an initial exploration, within the space of a year we were able to develop prototypes to collect preliminary data contributing to three SDGs and a broader overview of environmental health in Aruba. This was made possible by a combination of community and technology. Local communities assist in research efforts and share resources and knowledge to help make studies possible. Global communities share a variety of experiences and details on projects that can be readily adapted. Technical advances in affordable environmental sensing, open source code libraries, and global datasets are making environmental monitoring increasingly cheap and accurate. In remote and varying locations such as SIDS, this combination empowers citizen scientists to start collecting data wherever they see a need.

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