Description

Environmental monitoring involves the systematic observation, measurement, and analysis of various environmental parameters such as air, water, soil, and biodiversity to assess the health and quality of ecosystems and their interactions with human activities. It encompasses a range of techniques and tools, including sensors, remote sensing, sampling, and data analysis, to monitor changes over time and space. The primary goals of environmental monitoring are to identify environmental hazards, track trends in environmental quality, assess the effectiveness of regulatory measures and management strategies, and ultimately, to support informed decision-making aimed at protecting and preserving the environment for present and future generations.

Establishing the aim of the data collection is critical to ensure that later the data is fit-for purpose. This involves getting clear what the research question is, who should be involved, and how data should be collected, processed, and used.

Why is this relevant?

Understanding why data is collected and by whom, and how it helps to ensure the usefulness for the intended analysis or decision-making process. It helps in preventing biases, errors, and misunderstandings in data interpretation. All of them are important factors to create and increase trust among the different participants and stakeholders of the Citizen Observatory/Citizen Science project.

How can this be done?

Below are some of the considerations that are important when conducting environmental monitoring.

5. Determine the spatial and temporal scales of monitoring: consider the geographic extent over which you need to monitor and how frequently data should be collected. This will depend on factors such as the size of the area of interest, the rate of environmental change, and the availability of resources.

6. Establish robust protocols for data collection, quality assurance and data management: these will depend on the chosen method. Installation guidelines are usually necessary for sensor devices. But also, instructions on where and how to locate the instrument are useful. For wearables, instructions should cover when, where, how and under what circumstances devices should be used. For passive samplers, a time schedule for when to replace the samplers should be provided.

7. Keep it simple: the simpler the instructions are, the easier for people to follow it. If people are overwhelmed or frustrated, you risk losing your precious community. Breaking the protocol in simple steps that are easy to understand and follow. As people are gaining experience, you can include more complex scenarios. Make sure the rules are clear, use engaging visuals and interactions to make sure instructions are clear for everyone, by testing them first.

8. Test small before large implementations: before engaging with a large community in environmental monitoring, start with a small user group representative of your final community. Let them test your instructions and identify potential problems in all the steps.

9. Provide training and support: provide sufficient training and support. This will also help to engage your community. Often there are people in the community that are really skilled and that are prepared to help other people in the neighbourhood.

10. Check data as often as possible: do not wait until the end of the project but check the data early on together with the community. This will allow you to identify early problems such as whether the sensors are sending data, minimizing data loss. Give feedback so participants know if anything needs to be improved.

11. Maintain Engagement: participants' engagement tends to erode over time. Engage with your community to see if they need more assistance. Apart from checking the data, try to analyze the data with them, and revisit the research questions. Are these still the questions they have? Be flexible and make updates as the community is advancing to define new objectives. Discuss with them about the actions they want to do after collecting and analysing the data.

12. Communicate transparently to foster collaboration and support for the monitoring initiative: effective communication is key to building trust and gaining support for environmental monitoring efforts. Keep stakeholders informed about the purpose and progress of the monitoring program, share findings in a clear and accessible manner, and solicit feedback to address concerns and improve the effectiveness of the monitoring effort.

13. Keep the budget in mind: allocate adequate resources including personnel, equipment, and funding. Environmental monitoring requires sufficient resources to be effective. Ensure you have the necessary personnel trained in monitoring techniques, access to appropriate equipment and technology for data collection, and adequate funding to support ongoing monitoring activities. Make sure to understand the budgetary needs for your project.

14. Evaluate the project: when the measurement project is completed, evaluate the project with all participating parties. Did everything go satisfactorily, and why or why not? Revisit the agreements made at the beginning of the project. Have new research questions arisen? See if it is possible to set up a follow-up project.

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Citizen Observatories (COs) involve citizens using modern mobile and web technologies and/or sensors to collect and share data, which enhance Earth Observation systems and official data sources by filling in gaps and adding detail. Often using dedicated IT infrastructure, COs support the flow of data and information between citizens, scientists, and decision-makers, and typically have a distinct focus on influencing decision-making, policy change, and/or environmental governance outcomes.

We have seen that data collected in Citizen Science and COs initiatives are often not regarded as trustful data when it is communicated to decision makers. We have also seen that COs for environmental monitoring tend to engage with participants that feel attracted to technology, leaving behind participants that feel not at ease when it comes to the use of novel technologies. The Toolkit aims to provide guidance to avoid or reduce the technological barriers hindering the widespread adoption of sensing devices and other monitoring systems.

This toolkit is part of the CitiObs project.

CitiObs (Enhancing Citizen Observatories for healthy, sustainable, resilient and inclusive cities) is a EU Project aimed at fostering inclusive, diverse, and stable citizen participation in the observation, monitoring, and protection of urban environments. Through the use of low-cost sensor technologies and wearables, CitiObs supports the observation of air quality and other environmental measures, empowering citizens to take an active role in shaping sustainable urban policies.

Welcome to the CitiObs Environmental Monitoring Toolkit! The primary goal of this Toolkit is to equip Citizen Science/Citizen Observatories practitioners with the necessary tools and guidance materials to overcome barriers hindering the effective use of sensing devices and other monitoring systems.

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Description

Air quality sensing device can measure various air pollution variables, though the specific parameters they can monitor may vary depending on the sensor type and design. Sensing device technologies can be deployed for monitoring both outdoor and indoor air quality, allowing for localized assessments of pollution levels in various environments such as homes, workplaces, schools, and public spaces. Depending on our objective, we need to ensure to select a sensing device that monitors the pollutants that are the most relevant. For example, if we want to monitor pollution from traffic NO2 and PM10 are very relevant parameters, while for wood burning, PM2.5 shall be monitored. It is also important to mention that not all sensing devices are at the same maturity level. Some sensors in the market cannot be used “out of the box” as they require specific calibrations in scientific laboratories and special co-location and calibration against reference instrumentation. More information on calibration is available in: “What is data quality? How can we increase data quality in citizen gathered data?”

Why is this relevant?

Not all sensing devices available on the market are designed to measure the same parameters. Therefore, it is crucial to begin by clearly defining our objectives and the specific questions we aim to address. Next, we must identify the most significant pollutants relevant to our goals and ensure that the selected sensing device can measure those pollutants accurately. It's important to recognize that the quality and precision of measurements depend on the maturity of the sensing element and its integration into the sensing device. Whenever possible, it's advisable to seek information on whether others have tested the sensing device against reference instrumentation. This evaluation can help determine if the desired variable can be measured with sufficient quality and accuracy for our intended purposes.

How can this be done?

Some common air pollution variables that can be measured with sensing devices technologies include:

1. Particulate Matter (PM): sensors can measure particulate matter concentrations in the air, typically categorized by size fractions such as PM₁₀ (particles with a diameter of 10 micrometers or less) or PM2.5 (particles with a diameter of 2.5 micrometers or less).

2. Gases (chemical composition): sensors may detect various gases associated with air pollution, including nitrogen dioxide (NO₂), ozone (O₃), sulfur dioxide (SO₂), carbon monoxide (CO), volatile organic compounds (VOCs), and hydrogen sulfide (H₂S).

3. Environmental metrics: most air quality sensors include meteorological sensors to measure barometric pressure, temperature and humidity levels, which can provide context for interpreting air pollution data and understanding atmospheric conditions.

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Description

A sensing device has various elements: the most important components are of course the sensing elements, but they are not the only ones. Typically, these sensing elements will interface with a microcontroller unit (or units) that collect, process and store the data either locally, or remotely, converting raw sensor readings into digital data for transmission and storage. The device is generally powered with batteries or low-power sources, and the whole setup can be enclosed in protective housing such as 3D printed casing.

Why is this relevant?

Understanding the key components of a sensing device will help us navigate some of the technical aspects when using these devices for environmental monitoring. Having a clear overview of the principles of how devices are built and how they operate will help us make informed choices on the technical solutions that best fit our needs, and how they can be mounted or installed in situ

How can this be done?

The sensing element (often called the sensor) and the sensing device are two different things. The sensing element refers to the actual component responsible for directly detecting the physical property or phenomenon being measured. It could be a small piece of material, or a specific technology designed to react to changes in the environment. For example, in an air pollution sensor, the sensing element might be an electrochemical cell that reacts with specific pollutants in the air.

The sensing device refers to the entire system, which includes the sensing element as well as any additional components necessary for the sensor's operation, such as signal processing, data communication, power management, and user interfaces. The sensing device integrates the sensing element into a functional unit capable of collecting data, processing it, and communicating results to the user or another system. In summary, while the sensing element is the core component responsible for detecting the physical property, the sensing device is the complete system that incorporates the sensing element along with other necessary components for its operation.

The affordability of sensing elements contrasts with the increased costs associated with integrating them into sensing devices due to various factors such as the engineering effort involved, and the overall design to incorporate additional components such as microcontrollers, communication interfaces, and power management systems for full functionality Quality control measures, including calibration processes, are necessary to ensure accurate and reliable sensor readings, contributing to manufacturing expenses. Furthermore, miniaturization and packaging requirements, coupled with the inclusion of features like data logging and wireless connectivity, drive up production costs. Compliance with regulatory standards and certifications also adds complexity and expense to the manufacturing process.

Sensing devices for monitoring air pollution typically include several key components:

1. Sensing element / Environmental sensors: this is the core component responsible for detecting pollutants in the air. Different sensors may use various technologies such as metal oxide semiconductor (MOS) sensors, electrochemical sensors, optical sensors (like laser-based sensors or light scattering sensors) depending on the type of pollutants they are designed to detect. In addition to pollutant detection, some sensing devices may integrate environmental sensors for measuring temperature, humidity, atmospheric pressure, or other relevant parameters. This additional data can provide context for interpreting air pollution levels.

2. Microcontroller or Processor: a microcontroller or processor handles data processing tasks, such as signal conditioning, calibration, and conversion of sensor readings into digital data. It may also control sensor operations and manage communication with external devices.

3. Power supply: sensing devices often use batteries or need to be connected to power supply sources for their operation. Some may also support energy harvesting techniques, such as solar power.

4. Communication interface: to transmit data to a central repository or database, these sensors require communication interfaces such as Wi-Fi, Bluetooth, Zigbee, or LoRa (Long Range). These interfaces enable the sensors to connect to networks or IoT platforms for data sharing and analysis.

5. Enclosure: an enclosure or casing protects the sensor components from environmental factors such as moisture, dust, and physical damage. Enclosures can be made from plastic, metal, or other materials suitable for outdoor or indoor use, and are often 3D-printed to custom fit the Sensing Device

6. Data logging and storage: some sensing devices may include onboard memory or storage capabilities to store data locally in case of communication disruptions. This feature ensures data integrity and enables offline data retrieval for analysis. This is particularly useful for applications where consistent access to a data or internet connection might be challenging.

7. User interface: depending on the application, sensors may feature a built-in or external screen or monitor user interface for configuration, status monitoring, and troubleshooting. This could be as simple as LED indicators or as complex as a graphical user interface (GUI) for more advanced settings. The term user interface also relates to any online or mobile data platform that provides a visual representation of the data gathered from the Sensing Device and the information that can be derived from it, and/or an operational interface with the device itself.

8. Location-detection and GPS chips: These is an additional possible component, especially for portable or wearable devices. GPS chips in mobile sensors enable precise location tracking that can enhance the application of the sensors. For example, in air quality monitoring, it can allow to calculate personal exposure, and identifying the areas with higher and lower exposure to air pollution.

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Description

Environmental monitoring in general, and air quality monitoring in particular, involves multiple actors and stakeholders. Depending on the nature of your organization or project, the nature of your issues and the country where you are based, your ideal interlocutor may be different.

Why is this relevant?

Knowing who is who in environmental monitoring is crucial for Citizen Science initiatives as it enables the identification of key stakeholders, fosters collaborative partnerships, accesses local knowledge and expertise, enhances data quality and credibility, increases community engagement and ownership, supports advocacy and action, and fosters environmental education and awareness. By involving diverse stakeholders in the monitoring process, Citizen Science initiatives can benefit from the expertise and insights of community members, build trust and transparency, and empower communities to address environmental challenges collectively. This approach not only strengthens the scientific rigor and relevance of the monitoring efforts but also promotes environmental stewardship and positive change at the local level.

How can this be done?

To include the right stakeholders in a project, you need to figure out who the potential partners are, and what their contribution may be. This will change depending on your research question, and also on your city and country.

Below is a list of typical actors and their roles:

3. Governments: governments at the local, regional, and national levels play a crucial role in (air quality) policy making. They are usually in charge of implementing measures for protecting the health of humans and the environment. In the European Union (EU), they need to align with the air quality regulations approved at EU level, such as, for example, the Air Quality Directive. Some governments, on the local scale, are more ambitious than just complying with European regulations.

4. Environmental Protection Agencies: Environmental protection agencies are specialized government agencies that are responsible for monitoring air quality. They work for governments. They collaborate with research institutions to assess and develop policies and advise governments in their programs to improve air quality. Some agencies are more open to Citizen Science than others. In the Netherlands and Flanders for example, the environmental agencies actively support Citizen Science data.

5. Research Institutions: research institutions, such as universities and scientific organizations, conduct studies and research on air quality. They develop new monitoring technologies, analyze air pollution data, and provide scientific insights into the causes and impacts of air pollution. More and more of these institutes see Citizen Science as a useful method to do research and engage with society.

6. Non-Governmental Organizations (NGOs): NGOs play a vital role in raising awareness about air pollution issues and advocating for improved air quality. They often work on community-based monitoring initiatives, engage in public education campaigns, and advocate for stronger environmental regulations.

10. The Copernicus Programme: Copernicus is the EU’s flagship Earth observation programme dedicated to monitoring of the Earth and its environment. A core aim of this programme is to provide data and services that are free for all to use and are for the betterment of all European citizens. Copernicus consists of three components that are of relevance to air quality monitoring:

    1. The satellite-based Copernicus space component. This component consists of a series of satellites, dubbed the Sentinels (numbered 1-6), that were specifically designed to support the Copernicus programme with each satellite having a different focus on different aspects of the the Earth’s environment. Sentinel-5P and the planned Sentinel-4 and Sentinel-5 satellite instruments are of most relevance for air quality with their ability to observe nitrogen dioxide and other pollutants. In addition to the Sentinel satellite series, there is a much larger number (20+) of European (managed by either ESA or EUMETSAT) satellites that are considered to contribute to the Copernicus space component.

    2. The Copernicus in-situ component

    3. The Copernicus Atmospheric Monitoring Services CAMS provides a variety of operational services that are used to support air quality monitoring at national and regional levels across Europe. These services are in the form of air quality forecasts and products called analyses and reanalyses that combine ground and space-based observations with modelling output to better enhance the model forecasts. In addition to these air quality forecasting services, CAMS provides a set of tools to help policymakers and air quality experts understand the causes of air pollution across Europe and in specific cities. Furthermore, CAMS provides annual reports summarizing the air quality situation across Europe each year.

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Description

Asking the right questions in environmental monitoring is a crucial step before delving into practical tasks. This is key to identify our research questions and define a clear set of goals. The questions that we ask will impact the methodology and processes we will follow, for instance: how and when to measure, and how many and what type of sensors we need. This section will describe how to go through this reflection process and help us formulate the research question.

Why is this relevant?

Asking the right research questions will define a great portion of our sensing journey, as it will help us understand what the most suitable tools for our purpose are and how to make the best use of them. It will also help us understand who we may be interested in collaborating with and seek collaboration in other groups. Further down the line, this step will help to be mindful of the resources at hand —financial, technical, and human. By having clear questions in mind, we can adapt our work so that it be can realistically achieved within these constraints and will help us work towards our goals.

How can this be done?

A good way to start is addressing the question: what are we worried about? Although the question might look simple at first sight, it involves the development of an understanding of the issue or issues at hand. Next, once we have identified “what we are worried about”, we would like to convert those concerns into a research question and testable hypotheses. This can be done by translating our concerns to simple and manageable questions, such as: how does car traffic affect air quality in my neighbourhood? or, is noise pollution worse on weekdays or weekends? Simple and clear questions will allow us to design a methodology to collect data that can answer those questions. Otherwise, we risk started collecting data, and find at the end that we have not collected the right data to design actions to address “what we are worried about”.

The considerations below can help to formulate the research question:

1. Identify the purpose: understand the specific objectives of the air quality monitoring effort. Are you trying to assess compliance with air quality standards, investigate the sources of pollution, or evaluate the effectiveness of mitigation measures? Tailor your questions to align with these objectives.

2. Define key parameters: determine which air pollutants are of primary concern based on local regulations, health impacts, and environmental significance. Ask questions that focus on monitoring these key parameters, such as particulate matter (PM), nitrogen dioxide (NO2), sulphur dioxide (SO2), ozone (O3), carbon monoxide (CO), and volatile organic compounds (VOCs).

3. Consider sources and pathways: understand the sources and pathways of air pollution in the monitoring area. Ask questions about nearby industrial activities, transportation patterns, natural sources (e.g., wildfires), and indoor sources (e.g., cooking, cleaning) that may contribute to air pollution.

4. Assess exposure and vulnerability: consider who may be most affected by poor air quality and where exposure is highest. Ask questions about sensitive populations (e.g., children, elderly, individuals with respiratory conditions) and vulnerable areas (e.g., near highways, industrial facilities, or disadvantaged communities).

5. Evaluate spatial and temporal variability: recognize that air quality can vary spatially and temporally due to factors such as topography, meteorology, and human activities. Ask questions about the spatial distribution of pollutants, seasonal trends, diurnal patterns, and potential hotspots of pollution.

6. Explore mitigation strategies: inquire about existing or potential mitigation strategies to improve air quality. Ask questions about emission reduction measures, land-use planning, transportation policies, and public health interventions aimed at reducing exposure to air pollution and protecting public health.

7. Map stakeholders that can be engaged: involve relevant stakeholders in the process of formulating questions to ensure that their perspectives and concerns are addressed. Collaborate with government agencies, community groups, industry representatives, and academic experts to develop a comprehensive list of questions that reflect diverse interests and priorities. You can use existing tools for stakeholder mapping to identify “who we may be able to work with”.

8. Allocate adequate resources including personnel, equipment, and funding: environmental monitoring requires sufficient resources to be effective. Ensure you have the necessary personnel trained in monitoring techniques, access to appropriate equipment and technology for data collection, and adequate funding to support ongoing monitoring activities. Refine your questions to find a balance between what you want to do and what you can do with the resources available. You can also identify stakeholders who might support your work.

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Description

The most common installation of air quality monitoring and sensing devices is in fixed physical locations such as next to the window of a residential home, inside the home or in a neighbourhood location such as on a street pole. With the increasing miniaturization of the technology, it is now possible to mount Sensing Devices on vehicles such as bicycles or busses, or even carry them directly as wearables. These mobile-monitoring applications raise new challenges regarding data quality and usability, but open up a whole new range of investigations across multiple locations, and offering spatial continuity not available with fixed monitoring devices. These insights are particularly interesting for understanding the exposure of the individual as they move through a geographical area during their daily activities.

Why is this relevant?

Air pollution might have a very diverse spatio-temporal distribution, meaning that pollutant concentrations can change from one street to another, and from one minute to the next (e.g. when standing in a traffic light the concentrations are higher). Mobile monitoring offers the advantage of capturing spatial and temporal variability in environmental conditions, identifying pollution hotspots, and complementing stationary monitoring efforts, but it presents challenges in maintaining data quality assurance, achieving uniform spatial coverage, ensuring continuous data collection, coordinating logistical complexities, managing costs, interpreting complex data, and integrating diverse datasets for comprehensive analysis. Being aware of the pros and cons of mobile monitoring is crucial before selecting the method that best adapts to our needs, expertise and resources.

How can this be done?

By utilizing mobile platforms such as vehicles or drones equipped with sensors, mobile monitoring can rapidly cover large geographic areas and provide real-time data on air quality, noise levels, and other environmental parameters. This dynamic approach allows for targeted monitoring of pollution hotspots, identification of emission sources, and assessment of pollution dispersion patterns, which may not be feasible with static monitoring stations. Additionally, mobile monitoring enables flexibility in monitoring routes and sampling locations, allowing for adaptive monitoring strategies that respond to changing environmental conditions or emergent pollution events. Furthermore, mobile monitoring can complement static monitoring by providing high-resolution data in areas with limited stationary monitoring infrastructure, thereby enhancing the overall comprehensiveness and accuracy of environmental monitoring efforts.

However, before selecting mobile monitoring over static monitoring, one needs also to be aware of their challenges, including maintaining data quality assurance amidst sensor variability and environmental interference, achieving uniform spatial coverage, ensuring continuous or real-time data collection, coordinating logistical complexities such as route planning and equipment maintenance, managing the resource-intensive nature of campaigns, interpreting complex data due to spatial and temporal variability, and integrating diverse datasets for comprehensive analysis.

There are some crucial points to consider when selecting between mobile and fixed monitoring for air quality:

Spatial and temporal variability: assess the extent of spatial and temporal variability in air pollution levels across the monitoring area to determine whether mobile or fixed monitoring is better suited to capture these fluctuations effectively. With a mobile sensor device, you can cover large spatial areas, but as the sensor is moving you do not have continuous monitoring in any location, so you are blind on those locations until the device comes by again. This is, you miss the temporal information.

Coverage needed: evaluate the need for comprehensive coverage of different locations and pollution sources within the monitoring area to determine whether a fixed network of monitoring stations or mobile monitoring platforms would be more appropriate.

Data quality and interpretation: maintaining data quality in mobile monitoring can be challenging due to factors such as variability in sensor measurements, calibration drift during transportation, and environmental interference (e.g., vibrations, temperature fluctuations). Ensuring consistent data quality across different monitoring routes and platforms requires robust quality assurance procedures and calibration protocols. Moreover, analyzing and interpreting mobile monitoring data can be complex due to factors such as spatial heterogeneity, temporal variability, and potential confounding variables. Integrating mobile monitoring data with other datasets, such as static monitoring data or meteorological observations, may be necessary to provide context and facilitate accurate interpretation. With PM optical sensors this can be problematic as a particle under the effect of non-regular speed of the air flow may “bounce” in the sensor detection chamber and be counted multiple times. And in a more practical way, if you are following a polluting vehicle, you will measure high concentrations during all that time, which results in ‘skewed’ data that show a high level of pollution across a specific space that is not otherwise as highly polluted. Mobility of the sensor also requires an additional level of contextualization of the location of the data gathered to maintain good data quality and trust in the readings– for example, a wearable device will follow the wearer from indoors to outdoors to inside public transportation, over a short period of time. If the context is not well documented, the data will be very difficult to interpret in a usable way. See (Chap 7.3 Why is it important to document context and how does it help better understand collected data?).

Data integration with other information sources: mobile air quality sensors provide high-resolution data on localized pollution levels, whereas traditional stationary monitoring stations offer broader coverage over larger geographic areas. Integrating data from these sources requires methods to reconcile differences in spatial and temporal resolution to create cohesive datasets for analysis. Also, mobile devices might have higher sampling frequency compared to stationary monitoring. Standardizing data formats is crucial when integrating data from diverse sources.

Privacy and security: mobile air quality sensing devices may collect sensitive location-based information from individuals using wearable or smartphone-based monitoring devices. Ensuring privacy protection and data security is paramount to maintain public trust and compliance with data protection regulations.

We can split mobile monitoring in 2 families: vehicle mounted and wearables.

Wearables: are the most popular type of mobile monitoring, used on bikes, at home or during specific activities such as the daily commute to work. Wearable devices offer promising opportunities for advancing air quality monitoring by providing individuals with real-time access to information about air pollution levels and potential health risks. Wearables enable personalized insights into air quality, empowering users to make informed decisions about outdoor activities, travel routes, and exposure reduction strategies. Moreover, wearable air quality sensing devices have the potential to contribute to Citizen Science initiatives by aggregating data from multiple users to create detailed maps of air pollution hotspots and trends. However, challenges such as sensor accuracy, data interpretation, and user adoption need to be addressed to fully harness the potential of wearable devices for air quality monitoring and public health protection.

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Description

A sensing device, often referred to simply as a sensor, low-cost sensor, sensor node, etc., is a technological tool designed to detect and measure physical properties or environmental conditions. Sensors come in various types (e.g. static or mobile) and can be used to monitor a wide range of parameters such as temperature, pressure, humidity, light intensity, motion, sound, chemical composition, and more. In Citizen Science initiatives, a wide range of sensing devices can be employed to engage volunteers in environmental monitoring and data collection efforts. These devices are typically selected based on their ease of use, affordability, and suitability for non-experts.

Why is this relevant?

Identifying the type of sensing devices needed is paramount for effective monitoring due to various factors. Firstly, it ensures the accuracy and precision of measurements by matching the devices’ capabilities with the requirements of the application. Secondly, selecting a device suitable for the monitoring environment guarantees optimal performance under specific conditions such as temperature, humidity, and exposure to contaminants. Additionally, considering factors like measurement range, resolution, response time, and cost ensures that the chosen equipment meets the monitoring objectives efficiently. Moreover, compatibility with existing hardware or software platforms, as well as adherence to application-specific requirements and standards, are critical for seamless integration and regulatory compliance. Overall, choosing the right type of Sensing Device ensures reliable data collection, analysis, and interpretation, ultimately supporting informed decision-making in various fields of monitoring.

How can this be done?

Some common types of sensing devices used in Citizen Science and Citizen Observatories include:

1. Air quality sensors: also referred as air quality low-cost sensors, can measure pollutants such as particulate matter (PM), nitrogen dioxide (NO2), ozone (O3), sulphur dioxide (SO2), and carbon monoxide (CO) in the ambient air. These sensors are often used to assess air pollution levels in communities and identify sources of pollution.

2. Noise sensors: noise sensors can measure ambient noise levels in decibels (dB) and identify levels of noise pollution in urban and rural environments. These sensors are useful for assessing the impact of noise on human health and wildlife.

3. Water quality sensors: handheld or deployable water quality sensors can measure parameters such as pH, dissolved oxygen (DO), conductivity, turbidity, and temperature in bodies of water. Citizen scientists can use these sensors to monitor the health of rivers, lakes, streams, and coastal areas.

4. Weather sensors: weather sensors can measure atmospheric conditions such as temperature, humidity, barometric pressure, wind speed, and wind direction. These sensors are valuable for monitoring local weather patterns and understanding the impacts of climate change on ecosystems.

5. Soil sensors: soil sensors can measure parameters such as moisture content, pH, temperature, and nutrient levels in the soil. Citizen scientists can use these sensors to assess soil health, track changes in agricultural productivity, and monitor the impacts of land use practices.

6. Light pollution sensors: light pollution sensors can measure sky brightness and identify sources of artificial light pollution in urban and suburban areas. These sensors help raise awareness about the impacts of light pollution on astronomy, wildlife, and human health.

Some features of the sensing devices commonly used for engaging people in environmental monitoring are:

1. Real-time monitoring: sensing devices typically offer real-time data collection capabilities, allowing users to access and analyse data immediately for timely decision-making.

2. Portability: sensing devices that are intended to be easily transportable are designed to be lightweight and compact, facilitating monitoring activities across diverse locations and environments. For air quality monitoring for example, there are specifically-designed mobile sensors that can be mounted on cars and bikes, and wearable sensors that can be carried by people while going about their daily activities.

3. Compact size: sensing devices are typically compact and easy to handle, enhancing their usability and enabling deployment in various monitoring scenarios.

4. Battery-powered: many sensing devices operate on battery power, providing flexibility for deployment in remote or off-grid areas without access to external power sources. Some sensing devices are designed to be installed at home and are not battery powered.

5. Wireless connectivity: with options for Bluetooth, Wi-Fi, or cellular connectivity, sensing devices enable seamless data transmission to mobile devices or cloud-based platforms, facilitating real-time data sharing and analysis.

6. Low maintenance requirements: devices should ideally require minimal maintenance, reducing the need for specialized technical expertise and ensuring accessibility for non-experts over extended monitoring periods.

7. User-friendly interfaces: ensure that the use of the device is intuitive for the user, with minimal guidance needed to understand their operation. Sensing devices can usually be connected to an online data platform that provides simplified data visualization that aid the easy interpretation of the data. Data can often be accessed through dedicated mobile apps or mobile web-based dashboards, and devices can come with either built-in or external monitors or screens to easily see current readings

8. Affordability: a defining characteristic of sensing devices in COs is their affordability, making them accessible to individuals, communities, and organizations with limited financial resources, thereby democratizing access to monitoring technology and fostering grassroots participation in environmental monitoring efforts. However, one needs to be aware that the cost of the sensing device is only one part of the total cost of the monitoring, and other costs and fees related to communication, maintenance, electricity, data storage, etc also need to be taken into account.

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Description

Why is this relevant?

Sensing devices are normally part of a larger system, which very likely involve connectivity to a data platform, and maybe other tools and services such as dashboards for visualization. These devices shall be considered as a part integrated in a whole system, from data gathering to data storage and data visualization. Usually, when we acquire a sensing device, that choice will immediately bind us to a certain data platform solution for transmission and storage of the data and will also impact how we can visualize and access our collected data. Thus, it is important to consider all the elements in the system before selecting the best monitoring option.

How can this be done?

Once we have a clear understanding of what these parts are, we need to then look at how we would like our device to be deployed on the field. Does it need to be installed for long periods of time? Are there Wi-Fi communications available? Before doing a major investment, you can always order samples and try them in real life situations, covering different locations and types of users. Go through the whole process from installing the devices to your final dashboard or at least to your centralized database to see if you can spot any unforeseen issue.

The following checklist should cover most of the technical aspects you need to consider:

1. Pollutants measured: evaluate the sensing technology employed by the device, such as electrochemical, optical, or semiconductor-based sensors, to determine its suitability for detecting specific air pollutants of interest. Ensure that the range of air pollutants that the sensor device is capable of detecting, including common pollutants such as particulate matter (PM), nitrogen dioxide (NO2), ozone (O3), sulfur dioxide (SO2), carbon monoxide (CO), and volatile organic compounds (VOCs), are the ones that you need to monitor to answer your research question.

2. Geolocation (GPS): some manufacturers provide built in GPS chips. They may consume some precious energy but in some cases, they are an important asset. Typically, if your project involves moving objects (bus, cars, boat, UAV...), a GPS will come in handy to be able to geolocate the collected data. If your project involves wearables, most of the time it will come with an app that will retrieve location from the smartphone’s GPS. However, you will have to deal with the diversity of smartphones and specific authorizations.

3. Data quality: in some cases, a better sensor performance can be directly linked with an increase in cost, but that’s not always the case when dealing with air quality sensing devices. To keep a complex matter simple, better quality is related to two main aspects: sensor and interface electronics construction. Assess the accuracy and precision of the sensor device's measurements under real-world conditions, considering factors such as calibration, cross-sensitivity, and environmental variability. Check if the sensor device has been used by other initiatives or research institutions. See if there is information available besides the one provided by the manufacturer. A good rule of thumb is to aim for a tested, widespread technical solution, ideally already reviewed by independent bodies.

4. Response time and Detection limit: evaluate the response time of the sensor device, which refers to how quickly it detects changes in pollutant concentrations. Short response times are essential for capturing rapid fluctuations in air quality (critical if the sensor is going to be used as mobile). Determine the lowest concentration of pollutants that the sensor device can reliably detect, as this influences its ability to monitor air quality in areas with low pollutant levels.

5. Data transmission: evaluate how the sensor device transmits data, whether through wired connections, wireless communication protocols (e.g., Wi-Fi, Bluetooth, LoRa), or cellular networks, and assess compatibility with the existing data transmission infrastructure. Most devices will offer one or two data logging options. The most common one is remote data logging, via Wi-Fi communications, but some other options are available such as GSM/LTE or LoRa. In some cases, remote data logging may not be available, and only local storage is possible, in the form of a (micro) SD card. Wi-Fi is generally widely available in many cities, but keep in mind that certain areas might not be covered, or that the types of Wi-Fi networks available may not be supported for the devices (for instance, networks with captive portals such as eduroam (WPA2-Enterprise) are generally not supported). There might also be other challenges associated with Wi-Fi connectivity such as sensor distance or Wi-Fi terminals being turned off. Sensing devices using GSM/LTE or LoRa are becoming more common, but they require additional components, such as pre-paid phone/GSM plans, or an existing LoRa infrastructure available in the area. GSM/LTE is a good option as it is a quite pervasive solution that will get you covered in almost any location; however, consider the corresponding budget for the whole length of your program to pay for the data plans. LoRa and other radio networks are interesting especially if your territory already offers that type of infrastructure, however, some specific constraints must be considered such as the bandwidth fare use.

6. Internal data logging: in some cases, devices will offer internal data storage in the form of a (micro) SD card, or similar. If the device has remote data logging capabilities, this can be seen as a backup solution, but in some cases, it can be the only way to access the data. In general, this feature can be helpful if communication is lost, or it can be an option to store data with relatively low energy consumption, if you operate on solar panel. Make sure you understand how this data is stored as well, for instance, the file format or the capacity of the support.

7. Robustness and durability: consider the environmental conditions in which the sensing device will be deployed, including temperature, humidity, and exposure to outdoor elements, to ensure durability and reliability in harsh environments (extreme temperatures, rain, snow, etc.). Make sure the equipment and its casing are made for this if you want a reliable and durable installation. For instance, electronic devices or boards typically have specified temperature ranges within which they can operate effectively. In environments with more extreme temperatures, such as a village atop a mountain where temperatures can fluctuate significantly, standard consumer-grade devices may not suffice. In such cases, specialized electronic devices designed for military or industrial use, with extended temperature ranges and ruggedized construction, become necessary for reliable data monitoring. Moreover, the enclosures or casings housing these sensing devices play a pivotal role in protecting them from environmental hazards. These enclosures must be designed to withstand water ingress, snow accumulation, UV radiation from direct sunlight, and other external factors that could potentially compromise the integrity of the device. For example, in a scenario where sensors are deployed for environmental monitoring in a forest, the enclosures must be waterproof to protect against rain and snow infiltration. Similarly, in agricultural settings where sensors are exposed to direct sunlight for prolonged periods, UV-resistant enclosures are essential to prevent damage from sun exposure.

9. Readiness to deploy: the diversity of solutions comes with a different set of requirements on each case. Some air quality sensing devices are fully assembled, tested and calibrated by the manufacturer, which will have an impact on the cost. However, in some other cases, there will be devices that require some more involvement, for instance in terms of assembly of the components (for instance, a kit) or calibration, in which case additional equipment will be necessary. It is important to keep this in mind and, if possible, discuss with the device/monitor provider, check if there are guides and clear instructions for this process and assess the resources available within your team, both in terms of personnel, skills and economic resources. There is no rule of thumb but managing many devices (more than 30-50 units) will require significant effort, depending on the complexity, maturity and experience available. Finally, there may need to be other unforeseen requirements, such as computers and software needed to interact with the devices, smartphones for their configuration and so on. All these, including the usability of each of those, should be factored in when considering the various options available, and adapting them to each specific context. It is also important to check the connection to Sensor Data Platform: Evaluate whether the sensor device is compatible with existing sensor data platforms or cloud-based services for data storage, analysis, and visualization; and if the Data Platform provides what you need. Additionally, assess the availability of application programming interfaces (APIs) facilitating interoperability.

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Description

When talking about sensor devices, we may only think about how well they perform, or how many features they have. As important as these aspects are, however, they may not always be the only factors to evaluate. Other factors are to be considered such as the principles under which the devices are designed and manufactured, their data policies, or the existence of an already existing monitoring community, are described in this section. These principles are complementary to the device’s ease of use or cost, which are also important factors to keep in mind in the device selection process.

Why is this relevant?

By considering technical aspects alongside non-technical considerations, we can make more informed choices of sensing devices that not only align with our monitoring goals, but also consider integral factors that can help to sustain the project over time, such as community support, or the absence of vendor locks. This will have direct consequences on our budget, resources needed, and ultimately enhance the effectiveness and long-term impact of our environmental monitoring initiatives.

How can this be done?

When choosing sensing devices for air quality monitoring, several non-technical aspects could be considered to support the choice of a solution for the monitoring initiative:

1. Cost: Assess the initial cost of the device, as well as any additional expenses for maintenance, calibration, assembly, and data management. Consider the overall cost-of the device in relation to the monitoring objectives and available budget, and make sure to consider long term aspects, such as the availability of data free-of-charge in the long term, or specific services with regards to analytics. Finally, consider the cost of replacements, which in the event of an issue, or the end-of-life of certain sensing elements, can have a significant cost.

2. Ease of use: Evaluate the user-friendliness of the device, including the simplicity of its setup and operation processes and data interpretation. If you are working with diverse profiles in terms of experience or dexterity with technology, it’s better to choose devices that are intuitive and accessible to non-technical users.

3. Deployment site: If the device is to be deployed in outdoor or harsh environments, choose devices that are robust and can resist adverse environmental conditions such as extreme temperature, humidity, or dust. If the device is to be carried (or worn) by someone, account for their appearance, size, weight, comfort, and other aspects that can affect the motivation for people to carry the device for a (long) period. Finally, check if the device can be installed where you envision it: if it can be mounted on a street pole, or hung on a wall.

4. Sensor installation: Sensor installation may require some additional equipment and authorizations (if you must do installations on street poles for example). Check that as soon as possible in your project planning. Select the right type of attachments as you can't drill holes everywhere.

5. Data policies: Evaluate how easily the data collected by the device can be accessed, shared, and ultimately analysed, both in the short and long-term. This is directly linked to open data principles, which are to be considered carefully as in many cases there are additional charges or limitations on data access. Consider if data is shared with third parties, and that the data stored is not potentially affecting anyone’s privacy: for instance, that there are methods to anonymize sensitive data like geo-location, or data that could lead to identification of both users or communities unintentionally. Finally, prioritize devices that support data encryption and access control mechanisms to safeguard sensitive information and ensure compliance with data privacy regulations.

6. Black box effect: Assess the transparency of the device’s internal workings and components to mitigate the "black box" effect, where users have limited visibility into how the device operates or how data is processed. Choose devices that provide transparent sensor calibration methods, data processing algorithms, and data validation procedures, allowing users to both understand and validate the process, accuracy and reliability of the final measurements. Prioritize devices with good documentation and open-source firmware, enabling users to audit and verify the device's behaviour, performance, identify potential issues, biases or errors, and build trust in the data collected. From the manufacturer’s perspective, introducing transparency and accountability in sensor operation and data processing can enhance confidence in the reliability and integrity of air quality monitoring data.

7. Community: Assess the potential for using a device that has a strong community behind it. Joining an already existing community might be highly beneficial, for the sheer aspect of joining a collective with a shared experience, which generally provides support to its members. Beyond that, an additional benefit for joining a community-backed project is that, when multiple devices of the same type are used, their data can be easily compared, calibrated, averaged, or corrected. This is because they all function similarly, allowing you to anticipate and understand the results more effectively.

8. Support and additional services: Evaluate the level of support and service provided by the device vendor/monitoring initiative, including technical assistance, warranty coverage, and software updates.

9. (Open) Documentation: Evaluate the availability and quality of documentation provided by the device manufacturer or project, including user manuals, technical specifications, and troubleshooting guides. Choose devices that offer comprehensive and well-documented resources to support users in the setup, operation, and maintenance tasks. Additionally, prioritize devices with open designs and implementations, including schematics, well written and commented source code, and well documented APIs, which enable users to customize and extend the functionality of the device. Open documentation fosters transparency and collaboration, empowering users to contribute to device improvement, share knowledge, and address emerging challenges collectively.

10. Licensing: Evaluate the licensing terms associated with the sensor device, including any proprietary software or firmware, to ensure compatibility with your goals and values. Opt for devices with open-source licenses that promote transparency, collaboration, and community-driven development. Open-source licenses encourage sharing, modification, and redistribution of software and firmware, empowering users to customize and improve the device's functionality while fostering innovation and knowledge exchange within the community. Additionally, prioritize devices with licensing terms that allow for unrestricted use and modification, enabling broader adoption and participation in environmental monitoring initiatives. By selecting devices with appropriate licensing, organizations can promote openness, flexibility, and sustainability in their monitoring efforts, while avoiding potential legal, economic or ethical constraints associated with proprietary technologies.

11. Recycling and repairability: Consider the environmental impact of the device throughout its lifecycle, including its recyclability and repairability. Choose devices that are designed with modular components, easily replaceable parts, and minimal electronic waste generation. Consider the recyclability of the enclosure, or that it does not break any warranty by simply opening it.

12. Scalability and flexibility: Consider the scalability and flexibility of the device for future expansion or adaptation of the monitoring program. If a long-term project commitment is envisioned (+2 years), knowing how that may change will be a deciding factor. Choose devices that can accommodate changes in monitoring objectives, technological advancements, and community needs over time.

13. Business model and project governance: Consider the governance and business model of the project that creates the devices. Many community sensor projects out there may perform at levels that have nothing to envy to commercial ones, and moreover have a philosophy that may be aligned with other principles such as free and open-source practices, open science, open data or FAIR (Findable, Accessible, Interoperable and Reusable) principles. The relationship between intellectual property, business model and quality, is often biased towards considering that better quality is necessarily related to proprietary models, which is not necessarily true.

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Description

A sensor data platform is a comprehensive framework designed to collect, process, store, manage, and utilize data generated by sensing devices deployed in various environments. These platforms are well suited for Citizen Observatories in the field of air quality, and they have been broadly used for this purpose with minor adaptations. Key components include the ingestion of data coming from distributed networks of devices capturing physical parameters, mechanisms for data processing, storage solutions for organizing and archiving data, visualization tools for data representation, security measures to protect data integrity and privacy. Additional features may include the integration with other sources for contextual enrichment and scalability to accommodate growing data volumes.

Why is this relevant?

Sensor data platforms allow collecting, processing and sharing data from distributed networks of sensing devices. However, there are many choices we need to take when designing and implementing such platforms, both on the technical and non-technical side. In this section, we will mostly discuss conceptual choices, such as the ability for the platform to communicate with other platforms (interoperability), or for users to be able to access data in a free and easy way (findable and accessible). These are relevant aspects as the proliferation of platforms in recent years shows both the diversity in approaches, but also the great potential the data on these platforms has when these aspects are well thought through. Other very relevant aspects to consider fall on the technical side, such as transmissions protocols to use, type of databases, API choices, all of which will significantly impact the technical development efforts and the maintainability of the platform in the future.

How can this be done?

The following main components are generic to a typical sensor data platform:

Figure 4. Main components of a sensor data platform

1. Data ingestion: the data ingestion component oversees the interaction with the devices and ingest the data coming from them. The complexity of this block will depend on the transmission protocol and format that the devices send the data in. This block needs to be always running, especially if the sensing devices do not have ways to store data locally.

2. Data processing: data processing pipelines take care of any transformation (typically mathematical) that the sensor data requires before making it to the database (DB). This involves pre-processing data to handle noise and outliers, applying calculations to raw data, or applying algorithms for feature extraction and pattern recognition, as well as more advanced analytics tools (e.g., machine learning models) for predictive analysis and anomaly detection.

3. Data storage: this component will store the data (processed or not) in the database. The database is generally not accessible externally, and its access is managed by the API. This is the core component, as it will store all data coming from the sensing devices, as well as other data (like location, users, or contextual information. The database allows various CRUD operations (Create, Read, Update, Delete).

4. API: The API is the interface that computers will use to interact with the data platform (and ultimately with the database). This interface will typically have various endpoints, where data from different data tables in your database can be presented. The API needs to be able to present data efficiently, interacting with your data storage correctly and in an understandable way for other people to be able to use it.

On the client side, typically, we will find users interacting with the data platform, normally via the API by using web applications or scripts or other platforms (such as data aggregators) that request data from the sensor data platform and potentially perform other operations on it. Sensor data platforms will generally have a data visualisation method, such as dashboards enable users to explore and interact with sensor data through charts, graphs, maps, and other visualizations, allowing them to gain insights and derive meaning from the data, but it’s not mandatory for the platform to be consider as such.

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Description

Sometimes, even if the device manufacturer provides instructions on how to do the installation, those might not meet the needs of the users (language is too technical, need of translation to local language, etc.).

Why is this relevant?

When writing guidance on how to install sensing devices, it’s important to provide clear and comprehensive instructions to ensure the installation is done correctly, minimizing the risk of data loss, and putting in place protocols to help ensuring data quality.

How can this be done?

Some of the aspects that need to be considered to ensure individuals and organizations conduct a correct installation are the following:

1. Preparation:

   1. Begin with a section on pre-installation preparations, including a checklist of items needed.

   2. Specify any tools required for the installation process.

   3. Specify safety procedures for the installation.

2. Site selection:

   1. Provide criteria for selecting suitable monitoring locations.

   2. Consider factors such as proximity to pollution sources, wind direction, elevation and representativeness of the area.

3. Sensor placement:

   1. Clearly describe where to place the sensor for optimal data collection.

   2. Include information on the ideal height and orientation of the device.

   3. Provide information on what to do when the ideal conditions cannot be met.

   4. Include examples of what will be non-correct device placement that might cause wrong data readings.

4. Weather considerations:

   1. Recommend weatherproof enclosures if necessary.

   2. Discuss how to protect the device from adverse weather conditions (e.g. rain, snow, extreme temperatures).

5. Calibration instructions:

   1. Outline step-by-step procedures for calibrating/checking the device before installation.

   2. Include information on how often calibration/checking should be performed.

6. Power supply:

   1. Specify power requirements for the device and provide guidance on connecting to a stable power source.

   2. Include information if the device should be connected indoors or can be connected to an outdoor power supply.

   3. Include information on battery replacement or recharging if applicable.

   4. Include information on expected power consumption.

7. Connectivity instructions:

   1. Explain how to set up and test the devices’ connectivity to networks (e.g. Wi-Fi, cellular).

   2. Include troubleshooting tips for connectivity issues.

   3. Include expected amount of data transmission.

8. Data storage and transmission:

   1. Detail how the device stores and transmits data.

   2. Provide instructions for setting up data storage solutions and configuring data transmission methods, if applicable.

   3. Detail how to access, visualize and download the data.

9. Device type and pollutants:

   1. Specify the type of device being installed and the pollutants it is designed to measure.

   2. Provide information on the detection limits, accuracy and precision of the sensor.

10. Maintenance guidelines:

    1. Outline routine maintenance procedures, including cleaning and sensor replacement.

    2. Include a maintenance schedule to ensure ongoing reliability.

11. Security measures:

    1. Include recommendations for securing the device against tampering or vandalism.

    2. Provide guidance on data security measures, such as encryption.

    3. Provide a description of what type of data will be made open and visible to other people.

12. Data licensing and usage policies:

    1. Clearly specify the licensing and usage policies associated with the data. Define how the data can be used, shared and attributed, ensuring transparency in terms of data ownership and usage rights.

13. Transparency in data processing:

    1. Be transparent about data processing methods and algorithms. Clearly communicate how data is processed and analyzed. Ensure transparency in the entire data lifecycle.

14. Data privacy and ethics:

    1. Explain how you adhere to ethical guidelines and principles, especially concerning data privacy and confidentiality. Clearly communicate the measures taken to protect sensitive information, ensuring transparency in how participant privacy is respected and maintained.

15. Data validation and quality control:

    1. Explain how the data is being validated and corrected.

    2. Explain procedures for recognized anomalies or errors in the data and how to deal with them.

16. Troubleshooting:

    1. Include a troubleshooting section with common issues and their solutions.

    2. Provide contact information for technical support, if needed.

17. Documentation:

    1. Encourage thorough documentation of the installation, including photos and notes.

    2. Include templates for recording installation details for future reference.

    3. Provide instructions on how to report metadata and complementary documentation.

18. Recycling:

    1. Provide information about recycling or disposal recommendations.

    2. Provide information on repurposing. For example, if the air quality sensing device is still in working condition, consider donating it to educational institutions or community groups.

    3. Provide information on secure data disposal if the device stores data, ensuring that any sensitive information is securely wiped before recycling to protect privacy.

19. Accessibility:

    1. Ensure color contrast. Use high color contrast for text and background elements to improve readability, especially for individuals with visual impairments.

    2. Use images and photos to illustrate the installation procedure.

20. Clear and simple language:

    1. Communicate in plain language, avoiding unnecessary jargon.

    2. Define technical terms or provide a glossary to help readers understand complex terms.

21. Translation:

    1. Provide translation of the installation guidelines.

    2. Ensure the translation is accurate.

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Description

A sensor data platform serves as a comprehensive framework tailored to gather, analyze, store, manage, and leverage data emanating from deployed sensing devices across diverse settings. Primarily utilized in environmental monitoring, it is typically associated with an established sensing apparatus. Selecting appropriate hardware involves key considerations. Essential components encompass data ingestion from distributed sensor networks capturing physical parameters, mechanisms for data processing, storage solutions for efficient organization and archival, visualization tools for effective data representation, and security protocols ensuring data integrity and privacy protection. Moreover, additional functionalities may encompass integration with external sources for contextual enhancement and scalability to accommodate expanding data volumes. This segment describes the primary factors to contemplate when opting for a sensor data platform.

Why is this relevant?

Sensor data platforms allow collecting, processing and sharing data from distributed networks of sensing devices. However, there are many factors to consider when choosing what data platform to use (sometimes, this decision will be made for us by choosing a device). These factors can be both technical and non-technical. In this section we highlight some key aspects to consider, as the proliferation of multiple platforms in recent years shows both the diversity in approaches, but also the difficulty to choose the right platform among this diversity.

How can this be done?

Below, we list a series of factors to consider when choosing a platform, classified by data policy and principles, and technical features.

• Data policy and principles:

  • FAIR Data Principles: FAIR (Findable, Accessible, Interoperable, and Reusable) data principles ensure that sensor data is managed in a manner that maximizes its usability and impact. A platform supporting FAIR Data principles will make use of metadata standards and adopt standardized data formats and protocols. This will enable integration with other datasets and analysis tools. Check that the platform defines clearly data sources, collection methods, and processing steps.

  • Security and privacy: make sure that the platforms have means to prevent unauthorized access, data breaches, and privacy violations. This involves checking for encryption, access control mechanisms, authentication, and auditing features to safeguard data integrity and confidentiality throughout the data lifecycle if needed.

  • Regulatory compliance: check for compliance with relevant regulations and standards governing data privacy, security, and environmental monitoring. This includes adhering to laws such as GDPR (General Data Protection Regulation) and ensuring data collection and storage practices align with industry-specific regulations.

  • Data governance, ownership: check data ownership, access rights, and responsibilities. This involves having clear answers to questions about who owns the data collected by devices, who has permission to access it, and how it can be used and shared. Typically, this will be defined by the data and software licenses.

  • Sustainability and long-term viability: review that the platform has a defined lifetime, or it has a plan for deprecation. Projects with low update rate (or none at all) are a red flag.

  • CARE data principles: CARE (Collective Benefit, Authority to Control, Responsibility, and Ethics) data principles prioritize that data collection and usage works towards collective benefits, respect individual rights, and uphold ethical standards. Platforms supporting CARE principles follow the above recommendations in one way or another and prioritize providing individuals with the authority to control their data, implementing measures to ensure data responsibility, promote ethical data practices and mitigate risks associated with data misuse or harm.

• Technical features:

  • Performance and scalability: check (or ask) if there are performance benchmarks for the data platform in question and, if needed, if the scale of your project is suited for the platform itself (i.e. if you are planning to use a large number of devices, make sure that it will not collapse the sensor platform). Consult with the platform providers or designers what safeguards are in place to guarantee data robustness and check that data will be safely stored, no matter what, either on the platform or locally on the sensing device if that fails.

  • Traceability: check if your sensor data will be traceable with metadata such as data source, acquisition timestamp, and processing history. This is key to trace the origins of sensor data and track any transformations or modifications it undergoes. This will allow you to verify the accuracy, reliability, and integrity of sensor data. Additionally, traceability facilitates compliance with regulatory requirements and quality assurance standards, providing assurance regarding the authenticity and trustworthiness of sensor data for various applications and use cases.

  • Data quality assurance: a nice to have feature, is the presence of data quality checks, implemented with data cleaning processes, or error detection and correction mechanisms.

  • Contextualization: check if there is the possibility to enhance data from sensors with contextual information. This is a nice to have feature, improving relevance and usability by providing insights into the conditions under which measurements were made. Contextual factors such as location, environmental conditions, sensor configuration, and operational context help interpret sensor readings and understand their significance. This will also support data quality assessment and validation by enabling anomaly detection, error correction based on contextual discrepancies and facilitate data fusion if your use case requires it.

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Description

Interoperability refers to the ability of different devices, systems, or platforms to work together and exchange information seamlessly, much like how different pieces of a puzzle fit together perfectly. It refers to the ability of different sensing devices and platforms used by CS and CO initiatives to work together seamlessly, despite potential differences in brand, model, or technical specifications.

Why is this relevant?

Increasing interoperability allows citizen scientists to collect and share data using a variety of sensor devices, ensuring compatibility and consistency across monitoring efforts. This interoperability ensures that data collected from diverse sources can be aggregated, analyzed, and compared effectively, enhancing the overall reliability and comprehensiveness of Citizen Science initiatives.

How can this be done?

Increasing interoperability among sensor data infrastructures relies on several pillars:

1. Standardization: developing and adhering to common standards for data formats, communication protocols, and metadata ensures compatibility and interoperability across diverse sensor networks and platforms. Standards such as OGC (Open Geospatial Consortium) Sensor Things API and OGC Sensor Observation Service (SOS) facilitate data exchange and integration between different systems.

2. Semantic interoperability: employing ontologies, vocabularies, and semantic models enables shared understanding and interpretation of sensor data semantics. By formalizing data semantics, disparate sensor data sources can be harmonized and integrated more effectively, enabling richer and more meaningful analysis across heterogeneous environments.

3. Middleware and integration layers: implementing middleware and integration layers facilitates seamless communication and data exchange between heterogeneous sensor systems. Middleware solutions such as message brokers, data brokers, and ESBs (Enterprise Service Buses) provide mediation and transformation capabilities, enabling interoperability between sensors with varying communication protocols and data formats.

4. APIs and Web Services: exposing APIs and web services for accessing and manipulating sensor data enables interoperability with external applications and systems. RESTful APIs and SOAP (Simple Object Access Protocol) web services provide standardized interfaces for querying, retrieving, and interacting with sensor data, facilitating integration with third-party applications and platforms.

5. Data harmonization and fusion: implementing data harmonization and fusion techniques enables the integration of heterogeneous sensor data sources to create unified and consistent datasets. The main aim is to reduce redundances and enable the synthesis of diverse sensor observations into cohesive datasets, enhancing interoperability.

6. Metadata management: establishing robust metadata management practices ensures the availability of comprehensive and standardized metadata describing sensor data characteristics, provenance, and usage. Metadata standards such as ISO 19115 and SensorML facilitate metadata interoperability, enabling efficient data discovery, understanding, and integration across different sensor data infrastructures.

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Description

Besides sensing device technologies, several other methods can be used for environmental monitoring in the context of Citizen Observatories, each with its own advantages and limitations. By employing a combination of these methods, air quality monitoring initiatives can obtain comprehensive and reliable data to assess air quality, identify sources of pollution, and inform decision-making to protect public health and the environment.

Why is this relevant?

For some pollutants, sensing devices are still expensive or their data quality is not good enough for the needs of the COs’ initiative. Understanding the different types of monitoring is crucial for Citizen Science initiatives aimed at engaging volunteers in environmental data collection efforts. By familiarizing themselves with various monitoring methods, Citizen Science practitioners can select approaches that are accessible, cost-effective, and suitable for non-experts. This knowledge enables organizers to design monitoring protocols that align with the skills and resources of citizen scientists while ensuring the collection of meaningful and reliable data. One of the most common alternatives for sensing devices used by citizen scientists is passive sampling.

How can this be done?

Some alternative methods to sensor devices include:

1. Reference and regulatory monitoring stations: These are sophisticated monitoring stations operated by government agencies or research institutions. They use high-quality, calibrated instruments to measure air pollutants according to established regulatory standards. Reference stations provide accurate and reliable data for compliance monitoring and trend analysis but are expensive to operate and maintain, limiting their spatial coverage. Although they cannot be operated by non-experts, their data is usually open and accessible for all to read and use.

3. Passive sampling: Passive sampling involves deploying samplers, such as diffusion tubes or badges, at various locations to collect air samples over a specific period. These samples are then analyzed in a laboratory to measure pollutant concentrations. Passive sampling is cost-effective and suitable for long-term monitoring but may provide less real-time data compared to active monitoring methods. Most common are passive samplers for nitrogen dioxide (NO2), but passive samplers for ammonia (NH3), sulpur dioxide (SO2) or ozone (O3) are also available. The most used passive samplers are the Palmes diffusion tubes, which are particularly useful for dense coverage in a specific geographic region, such as a city. The tubes are about 7 cm long and contain a cap with a chemical on a metal mesh that can bind e.g. NO2 or NH3. By hanging the tube outside for typically a month or four weeks, the pollutant collects in the chemical in the tube. A chemical analysis is done to determine the average NO2 or NH3 concentration at the location where the tube was placed. This measurement method provides monthly averaged concentration values. The analysis takes a few weeks to complete, so results are slow, and the data is not collected automatically. Passive samplers are used by scientists and authorities, but they are also being used in Citizen Science projects where dense coverage is desired or financial resources for Sensing Devices are limited. For NO2, passive samplers have the added value that they are accepted as “indicative” measurement method. This gives the possibility to use them for compliance assurance. The quality of the data produced by NO2 passive samplers is well defined and relatively high. Also, the tubes are very simple to use, do not need electricity or Wi-Fi, and the price per tube is low, around 15 euro per tube including analysis.

4. Collection of air samples: Chemical analysis techniques, such as gas chromatography and mass spectrometry, can be used to quantify air pollutants with high precision and accuracy. These methods are typically performed in laboratory settings using collected air samples. Chemical analysis provides detailed information on pollutant concentrations and composition but may require specialized equipment and expertise. Although collecting air samples usually involves experts, it is possible also to develop protocols for citizens to be able to collect samples.

6. Air quality modelling: Models are not a monitoring method, but they are complementary to monitoring and, in many circumstances, used together to enhance our knowledge about air pollution spatial and temporal distribution. Air quality models simulate the transport, transformation, and dispersion of air pollutants in the atmosphere based on meteorological data, emission inventories, and chemical reactions. These models can predict air pollutant concentrations and assess the impacts of emissions reduction strategies. Air quality modeling provides insights into complex atmospheric processes but requires specialized software and input data. More and more models are also be made open and accessible, with the code published online as well as the data.

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Description

In the context of citizen-collected data, traceability refers to the ability to track and understand the origins, history, and transformations of the data from its collection to its use. It involves documenting and maintaining a clear record of the data's lineage, including information such as its source, collection methods, processing steps, and any changes or modifications it undergoes.

Why is this relevant?

Traceability ensures transparency and accountability in the data collection process, allowing researchers, policymakers, and other stakeholders to understand how the data was obtained and assess its reliability and credibility. It enables users to trace the data back to its original source, verify its authenticity, and evaluate its fitness for use in scientific analysis, decision-making, or policy development. By establishing traceability mechanisms, Citizen Science projects can enhance the integrity and trustworthiness of the data collected by participants.

How can this be done?

Increasing traceability in citizen-collected data involves implementing several key strategies:

1. Data collection protocols: clear guidelines should be provided to participants regarding data collection methods, equipment calibration, sampling techniques, and metadata documentation. This helps maintain data quality and facilitates traceability by ensuring that data collected by different individuals or groups can be compared and integrated reliably. See also “What are the main aspects you need to consider when managing citizen collected data?”

2. Contextualization: capturing contextual information provides valuable context for interpreting the data and it is essential for enhancing traceability. Metadata should include information such as the location, time, and conditions of data collection, as well as details about the equipment used and any relevant environmental factors. See also: “Why is it important to document context and how does it help better understand collected data?”

3. Provenance: Provenance in the context of citizen-collected data refers to the documented history and origin of the data, including its source, collection methods, and any transformations or modifications it has undergone. It provides valuable contextual information about the data's lineage, helping to establish its authenticity, reliability, and trustworthiness.

4. Data quality assurance processes: implementing data quality assurance processes helps identify and address potential issues or errors in citizen-collected data. This may involve data validation checks, outlier detection algorithms, and verifying the accuracy and reliability of observations against for example reference stations. Transparent documentation of quality assurance procedures enhances traceability by providing insights into the data validation process that has been conducted and the rationale behind any data corrections or adjustments applied to the data. See also: “What is data quality? How can we increase data quality in citizen gathered data?”

5. Transparent/Open data sharing practices: promoting open data practices encourages openness and accountability within the Citizen Science community. Data should be shared openly whenever possible, along with associated metadata and documentation. Providing access to raw data enables independent validation and verification by other researchers, enhancing traceability and fostering trust in the data.

6. Community engagement and education: engaging participants in discussions about the importance of traceability and data quality promotes a culture of accountability and responsibility within the Citizen Science community. Training programs, workshops, and educational materials can help raise awareness about the significance of documenting data provenance, metadata, and quality assurance processes. Empowering participants with the knowledge and skills to collect and document high-quality data enhances traceability and strengthens the overall integrity of citizen-collected data.

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Data accessibility refers to the ease with which data can be accessed, retrieved, used, and understood by individuals, systems, or applications. It involves ensuring that data is available and usable by those who need it, promoting openness, transparency, and inclusivity. Making Citizen Science data readily available to non-experts in a clear and understandable format is crucial for maximizing its impact and promoting transparency. Understandable data facilitates participatory decision-making processes where community members can actively engage in discussions, voice concerns, and contribute to solutions based on their understanding of the data.

Why is this relevant?

Accessible and understandable data empower non-experts to make informed decisions about their communities, environment, and personal activities. It promotes awareness and encourages behavioral changes to address local issues. Data transparency is crucial when involving citizen scientists as it builds trust, encourages participation, and increases credibility. When non-experts can comprehend the data, they are more likely to trust the findings and engage in discussions about the implications and possible actions. Accessible data ensures that people of diverse backgrounds, abilities, and expertise can participate in Citizen Science projects.

How can this be done?

Making citizen collected data more accessible and transparent to non-experts involves several key strategies to ensure that the information is easy to understand and use. Some effective approaches are:

1. Clear and simple language:

   1. Avoid jargon and technical terms: explain scientific concepts and findings in plain, simple language that is easily understandable to the public. Define any specialized terms or acronyms used in the data and provide explanations.

   2. Use clear and intuitive visualizations such as charts, graphs, maps and infographics to present the data (e.g. bar charts for comparison, line graphs for trends).

   3. Personal stories can make complex data more relatable and memorable. Present real-life examples, case studies, or narratives that illustrate the importance of the data and how it relates to everyday experiences.

   4. Use of local language.

2. Data platforms and data licensing:

   1. Provide interactive tools and platforms where non-experts can explore data visually. Interactive maps, dashboards and charts allow users to customize their data views, making the information more engaging and accessible.

   2. Host the data on open-access platforms and repositories that allow free and unrestricted access. Platforms like Zenodo, or institutional repositories provide a stable and accessible environment for data sharing.

   3. Clearly define the data licensing terms, specifying how the data can be used, shared, and attributed. Open licenses, such as Creative Commons licenses, promote data accessibility by providing clear guidelines for data usage.

3. Provide context to the data:

   1. Provide information about the research objectives, methods, and the real-world implications of the data on the community or environment. Clearly communicate the methods and protocols used to collect and analyse data.

   2. Provide detailed documentation alongside the data, explaining the context, methodology, and limitations of the data collection process. Clear documentation enhances data accessibility by providing essential background information for interpretation.

   3. Ensure that the data and research findings are openly accessible to the public. Publish data, reports, and research papers in open-access formats, allowing anyone to explore the information without restrictions. Provide access to the raw data for those who want to explore it in more detail.

   4. Explain how to interpret and use the data.

4. Data quality assurance:

   1. Implement robust data quality assurance processes to maintain the accuracy and reliability of the data. Clearly document quality control measures to increase confidence in the data's integrity, enhancing accessibility for users.

   2. If applicable, visualize the uncertainty associated with the data. Uncertainty visualizations help non-experts understand the reliability and limitations of the data, promoting a more nuanced interpretation.

5. Community engagement:

   1. Involve the community in data interpretation and analysis. Organize workshops, webinars, or community events where researchers can interact directly with non-experts. These interactions provide opportunities for participants to ask questions, gain insights, and learn about the data directly from the experts involved in the research. Community engagement fosters a sense of ownership and understanding of the data.

   2. Develop interactive tools, dashboards and simulations that allow users to explore and analyse the data visually. Interactive tools enhance data accessibility by enabling users to customize their data views and derive insights based on their specific interests. Ensure these tools are designed with accessibility features in mind.

   3. Establish feedback mechanisms where non-experts can ask questions, provide feedback, and request clarification. Respond to inquiries promptly and consider incorporating user suggestions to improve the accessibility of the data.

6. Accessible formats:

   1. Ensure that the data and related materials are accessible to people with disabilities. Use accessible formats for visual content, provide alternative text for images, use readable fonts, and ensure compatibility with screen readers for online platforms.

   2. Create visualizations that are easy to interpret and understand. Use clear labels, simple shapes, and high colour contrast. Interactive elements should be keyboard accessible and provide textual descriptions.

   3. Provide captions for videos and transcriptions for audio content to make them accessible to individuals with hearing impairments, and alternative text for the visually impaired. This ensures that everyone can access the information presented in multimedia formats.

   4. Implement keyboard navigation: ensure that all interactive elements, such as buttons and links, can be accessed and activated using a keyboard alone, without requiring a mouse or touch input.

   5. Ensure colour contrast: use high colour contrast for text and background elements to improve readability, especially for individuals with visual impairments. WCAG (Web Content Accessibility Guidelines) provides standards for colour contrast ratios.

   6. If applicable, provide translations of the data and related materials into multiple languages spoken within the community. Consider the linguistic diversity of the target audience. Ensure translations maintain accuracy and cultural relevance.

7. Diverse outreach channels:

   1. Use various communication channels such as social media, blogs, podcasts, and traditional media to reach diverse audiences.

   2. Tailor communication strategies for different demographics.

   3. Emphasize the relevance and importance of science for everyone, regardless of background.

   4. Collaborate with libraries, educational institutions, and local organizations to promote data literacy and provide training on accessing and understanding Citizen Science data.

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A sensor data platform is a comprehensive framework designed to collect, process, store, manage, and utilize data generated by sensing devices deployed in various environments. Key components include the ingestion of data coming from distributed networks of sensors capturing physical parameters, mechanisms for data processing, storage solutions for organizing and archiving data, visualization tools for data representation, security measures to protect data integrity and privacy. Additional features may include the integration with other sources for contextual enrichment and scalability to accommodate growing data volumes.

Why is this relevant?

Sensor data platforms allow collecting, processing, and sharing data from distributed networks of sensing devices. However, there are many choices we need to take when designing and implementing such platforms, both on the technical and non-technical side. In this section, we will mostly discuss conceptual choices, such as the ability for the platform to communicate with other platforms (interoperability), or for users to be able to access data in a free and easy way (findable and accessible). These are relevant aspects as the proliferation of multiple platforms in recent years shows both the diversity in approaches, but also the great potential the data on these platforms has when these aspects are well thought through. Other very relevant aspects to consider fall on the technical side, such as transmissions protocols to use, type of databases, API choices, all of which will significantly impact the technical development efforts and the maintainability of the platform in the future.

How can this be done?

When designing a sensor data platform, several technical and non-technical aspects must be carefully considered to make use of technical development efforts efficiently, ensure the maintainability of the platform, but also consider following conceptual design choices that can make data more findable or interoperable.

Considerations should be accounted for the layers presented on our section about sensor platforms, namely: data ingestion, data storage, data processing, data visualization and API.

2. Data Storage: designing robust data storage and management systems is critical for handling large volumes of sensor data efficiently. Considerations include selecting suitable databases (e.g., relational databases, NoSQL databases), implementing data partitioning and indexing strategies for performance optimization, and ensuring data security and privacy compliance. Consider implementing data duplication and backup systems as well, to avoid data loss. Data storage is a very specialized field of expertise, which requires a certain level of experience to handle such a critical task, guaranteeing that sensor data is stored safely. Consider, however, using well stablished databases with long-term support such as PostgreSQL, Influx DB, or similar options. See the useful resources section to find some guides about the topic.

3. API: API is the interface that computers will use to interact with the data platform(s). This interface will typically have various endpoints, where data from different data-tables in your database can be presented. The API needs to be able to present data efficiently, interacting with your data storage correctly and in an understandable way for other people to be able to use it. Any database allows CRUD operations (Create, Read, Update, Delete), and the API should filter those with authentication and authorization on each operation. Search endpoints help users find information programmatically.. If building from scratch, it is practical to implement a standard API format, such as STA+, which can contribute towards interoperability.

4. Data processing/ Data quality assurance: data processing involves applying quality control algorithms (e.g. removing noise and outliers), as well as calibration/correction algorithms (e.g. correcting the effects of air humidity in air quality sensing, or interferences with other gases, etc). Processing is usually done using statistics and other advanced methods as machine learning models. Consider using well known programming languages and libraries for processing your data. Some languages have open libraries and repositories and are free to use, while others use proprietary licenses.

5. Data visualization and presentation: presenting sensor data in a user-friendly and intuitive manner is essential for facilitating data interpretation and decision-making. Data visualization tools and dashboards enable users to explore and interact with sensor data through charts, graphs, maps, and other visualizations, allowing them to gain insights and derive meaning from the data.

In addition, below, we list some factors that are transversal to these items:

1. Scalability and performance: building a scalable sensor data infrastructure capable of handling growing data volumes and user demands is essential.

2. Data loss prevention: ensuring data will be safely stored is key, regardless of what may happen with any of the components in your platform. In particular, the data ingestion part of your platform should be carefully designed to ensure that data will persist, even if your database is down for a certain period (either intentionally or unintentionally). Consider implementing intermediate buffers for temporary data storage and safeguard mechanisms that check if any component of the platform is down and restore it in case its necessary.

3. Interoperability and integration: facilitating interoperability and seamless integration with existing systems and applications will maximize the utility of sensor data. This involves adhering to open standards and APIs (e.g., OGC SensorThings API+) for data exchange and integration, as well as implementing middleware and data transformation layers for interoperability across heterogeneous environments.

4. Traceability: establishing mechanisms for traceability ensures the provenance and lineage of sensor data throughout its lifecycle. By capturing metadata such as data source, acquisition timestamp, and processing history, organizations can trace the origins of sensor data and track any transformations or modifications it undergoes. This enhances transparency and accountability, enabling stakeholders to verify the accuracy, reliability, and integrity of sensor data and supporting data-driven decision-making processes. Additionally, traceability facilitates compliance with regulatory requirements and quality assurance standards, providing assurance regarding the authenticity and trustworthiness of sensor data for various applications and use cases.

5. Contextualization: incorporating contextual information into sensor data enhances its relevance and usability by providing insights into the conditions under which measurements were made. Contextual factors such as location, environmental conditions, devices’ configuration, and operational context help interpret sensor readings and understand their significance. By capturing and integrating contextual information alongside sensor data, organizations can improve data interpretation, analysis, and decision-making processes. Contextualization also supports data quality assessment and validation by enabling anomaly detection and error correction based on contextual discrepancies. Furthermore, contextual information facilitates data fusion and integration with other datasets, enabling richer and more comprehensive analysis for various applications and domains.

6. FAIR data principles: adhering to the FAIR (Findable, Accessible, Interoperable, and Reusable) data principles ensures that sensor data is managed in a manner that maximizes its usability and impact. This involves implementing metadata standards and conventions to make sensor data findable and accessible through search engines and data catalogues. Additionally, adopting standardized data formats and protocols promotes interoperability, enabling seamless integration with other datasets and analysis tools. Ensuring data quality and documentation facilitates data reuse by providing clear descriptions of data sources, collection methods, and processing steps. By following FAIR data principles, organizations can enhance the discoverability, accessibility, and usability of sensor data, fostering collaboration and knowledge sharing within the scientific community and beyond.

7. Security and privacy: ensuring the security and privacy of sensor data is paramount to protect against unauthorized access, data breaches, and privacy violations. This involves implementing encryption, access control mechanisms, authentication, and auditing features to safeguard data integrity and confidentiality throughout the data lifecycle.

8. Regulatory compliance: ensuring compliance with relevant regulations and standards governing data privacy, security, and environmental monitoring is essential. This includes adhering to laws such as GDPR (General Data Protection Regulation) and ensuring data collection and storage practices align with industry-specific regulations.

9. Data governance and ownership: establishing clear policies and procedures for data governance, including defining data ownership, access rights, and responsibilities, is crucial. This involves addressing questions about who owns the data collected by devices, who has permission to access it, and how it can be used and shared.

10. Licensing and Intellectual Property: addressing licensing and intellectual property considerations is crucial for ensuring legal compliance and protecting proprietary rights. This involves defining the licensing terms and conditions for sensor data, including permissions for use, redistribution, and modification. Choosing appropriate licensing models, such as open-source licenses or proprietary licenses, depending on the organization's goals and requirements, helps clarify the rights and responsibilities of data users and contributors while mitigating legal risks and conflicts. Additionally, establishing mechanisms for tracking and managing intellectual property rights associated with sensor data and related technologies supports innovation and collaboration while safeguarding against unauthorized use or infringement.

11. Sustainability and long-term viability: planning for the long-term sustainability and viability of the sensor data infrastructure is critical. This includes assessing the financial and resource requirements for ongoing maintenance, upgrades, and support, as well as exploring opportunities for revenue generation or cost-sharing models to sustain the infrastructure over time.

12. Open Data policies: incorporating open data principles into the sensor data infrastructure promotes transparency, collaboration, and innovation. This involves adopting policies that encourage the publication of sensor data in open formats and under open licenses, facilitating broader access and reuse by researchers, developers, and the public. Implementing data portals or APIs that provide easy access to sensor data and metadata further enhances transparency and fosters data-driven decision-making.

13. CARE Data Principles: Incorporating CARE (Collective Benefit, Authority to Control, Responsibility, and Ethics) data principles into the sensor data infrastructure ensures that data collection and usage prioritize collective benefits, respect individual rights, and uphold ethical standards. This involves designing data governance frameworks that prioritize collective benefits and societal well-being over individual or organizational interests. Providing individuals with the authority to control their data empowers them to make informed decisions about data sharing and usage, enhancing trust and accountability. Implementing measures to ensure data responsibility, such as data stewardship and accountability mechanisms, promotes ethical data practices and mitigates risks associated with data misuse or harm. By adhering to CARE data principles, organizations can foster a culture of data stewardship, integrity, and respect for individual rights, contributing to the ethical and responsible use of sensor data for societal benefit.

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Data transparency refers to the practice of making data easily accessible, understandable, and usable by the public, stakeholders, and other interested parties. It involves providing clear, accurate, and comprehensive information about the data, including its sources, methods of collection, processing, and analysis, as well as any limitations or uncertainties associated with the data. Transparent data practices aim to promote openness, accountability, and trustworthiness.

Why is this relevant?

Data transparency ensures that data is available for scrutiny and validation, enabling informed decision-making, encouraging collaboration, and fostering public understanding. It is essential for Citizen Science as it enhances the credibility, effectiveness, and impact of Citizen Science initiatives.

How can this be done?

Here are several strategies to ensure transparency in Citizen Science data:

1. Open Data practices and clear data documentation:

   1. Make the collected data openly accessible to the public whenever possible. Publish the data in online repositories, platforms or databases that allow unrestricted access and downloading. Open data practices promote transparency by allowing anyone to scrutinize the information.

   2. Be mindful of the data licenses, this is critical for current and future access to the data. Clearly specify the licensing and usage policies associated with the data. Define how the data can be used, shared and attributed, ensuring transparency in terms of data ownership and usage rights.

   3. Provide detailed documentation about the data, including its sources, collection methods, instrumentation used, and any limitations or uncertainties associated with the data. Clear and comprehensive metadata enhances transparency by offering context for interpreting the data. See also: “Why is it important to document context and how does it help better understand collected data?” and “How do we increase traceability in data collected by citizens?”

2. Data sharing policy, privacy, ethics, ownership, and attribution:

   1. Adhere to ethical guidelines and principles, especially concerning data privacy and confidentiality. Clearly communicate the measures taken to protect sensitive information, ensuring transparency in how participant privacy is respected and maintained.

   2. Clearly define a data sharing policy that outlines the terms under which the data can be shared, used and redistributed.

   3. Specify the ownership of the data and establish guidelines for proper attribution, ensuring credit is given where it’s due.

   4. Define access controls for sensitive data. Transparently communicate who has access to sensitive information and under what circumstances, ensuring data security while maintaining transparency in data access.

   5. Acknowledge the contributions of participants and collaborators transparently. Transparent recognition ensures that contributors are acknowledged publicly, fostering a sense of ownership and pride in their involvement.

3. Quality Assurance:

   1. Use standardized or harmonized data collection protocols and methodologies for ensuring consistency in data collection, making it easier to compare and validate results. Clearly outline the procedures used to collect and process the data, ensuring transparency in the methodology. Standard formats enhance interoperability and facilitate data analysis.

   2. Implement quality control measures to validate the accuracy and reliability of the data. Quality assurance processes, such as data validation, calibration, and peer review, enhance the transparency of the data by ensuring its integrity and credibility. See also: “What is data quality? How can we increase data quality in citizen gathered data?”

   3. Involve participants, researchers and other stakeholders in the data validation and verification process. Collaborative analysis promotes transparency by fostering open dialogue and diverse perspectives in data interpretation. Create interactive data visualizations and dashboards that allow users to explore and interact with the data in an engaging manner, promoting a deeper understanding of the information.

   4. Document how the quality assurance has been done. Clear documentation helps other people willing to use the data to understand the quality and reliability of the data they are working with. Be transparent about data processing methods and algorithms. Clearly communicate how data is processed and analyzed. Ensure transparency in the entire data lifecycle.

4. Community engagement and open communication:

   1. Maintain open and honest communication channels with participants, stakeholders and the public. Clearly communicate the goals, objectives, methodologies, and outcomes of the project. Address questions and concerns openly to build trust and ensure transparency. Transparent communication builds trust and credibility, enhancing the overall transparency of the initiative.

   2. Engage with the community through workshops, seminars, and public meetings. Regular communication with participants and stakeholders fosters transparency by providing updates on project progress, data findings, and any challenges faced during the Citizen Science initiative.

   3. Establish feedback mechanisms that allow participants and the public to provide input, ask questions, and report issues related to the data. Respond to feedback promptly and transparently, demonstrating a commitment to openness and accountability.

5. Data release timelines:

   1. Establish clear timelines for data release. Publicizing these timelines ensures that stakeholders and participants are aware of when the data will be made accessible, promoting transparency in data dissemination.

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Visualization of air pollution data is crucial for understanding complex information and communicating findings effectively. By presenting air quality data in visually intuitive formats such as graphs, maps, and charts, stakeholders can quickly grasp patterns, trends, and relationships within the data. Visualization facilitates the identification of trends, anomalies, and spatial variations in pollution levels, enabling decision-makers to target interventions, allocate resources, and prioritize mitigation strategies accordingly. Moreover, clear and compelling visualizations raise public awareness about air pollution issues, educate communities about the sources and health impacts of pollution, and motivate individuals to take action to protect their health and the environment. Overall, visualizing air pollution data enhances understanding, promotes collaboration, and empowers stakeholders to address air quality challenges more effectively.

This section will compile a list of tools that will help us navigating the complexities of data analysis and visualization, going from simple to use ones, to more advanced data frameworks.

Why is this relevant?

Technical tools should be tailored to their specific purpose and should avoid unnecessary complexities. In the realm of data analysis and visualization, where tasks can often be intricated, selecting the appropriate tool is paramount to staying focused on the primary objective: extracting insights and addressing questions. Typically, due to the substantial volume of data collected or the characteristics of sensing devices, software becomes a necessity at various stages of the process. However, as we'll explore further, not all phases of the process require the same types of tools, and there are instances where software tools may be entirely unnecessary, especially when engaging with non-technical audiences. Additionally, many of these tools are readily available as open-source solutions, minimizing the reliance on complex proprietary software.

How can this be done?

1. Data preparation: in this step, we take the data from its source (i.e. an API, a CSV file, a data stream, etc.) and we convert it, generally, into a format that can be understandable by a machine in an general ways (a spreadsheet, a data table or else). This process generally involves some sort of software, either generic tools such as spreadsheets as MS-Excel and Libre Office, web tools, or more advanced ones such as programming frameworks or scripts. It is generally recommended to perform this task prior to the data analysis itself, generating what we like to call Analysis Ready Data (ARD). ARD can then be used with confidence by both technical and non-technical users, and the tools to be used will greatly depend on the final audience for that data.

   1. Data collection: gather data from various sources, such as databases, spreadsheets, APIs, or sensing devices. Ensure that the data collected is relevant to the analysis objectives and represents the population or phenomena of interest.

   2. Data cleaning: clean the data to remove errors, inconsistencies, and missing or erroneous values that could affect the analysis, leading us to erroneous conclusions. This process is generally complex, and expert knowledge is recommended as there is no standard tool for it. Some recommended tools are listed in the useful resources below.

   3. Data correction: this step involves correcting our data in case there are any known factors that may affect it. For instance, many environmental sensing devices, specially low-cost ones, may suffer from temporal drift and decay (i.e. over time, they don’t measure as well as at the beginning). These effects, if characterized and known, can be corrected, but generally, this process needs to be done by expert hands, or well-crafted automated processes. Some tools are listed in the useful resources section below.

   4. Data Validation: validate the prepared data to ensure that it meets the requirements of the analysis objectives and is free from errors or biases. This may involve conducting Exploratory Data Analysis (EDA) to examine the distributions, correlations, and patterns in the data.

2. Data analysis: the core task of this section. Once we have data in an analysis ready state, we can move onto extracting insights from it. Some common methods are:

   1. Data fusion, data assimilation: integrate data from multiple sources.

   2. Data aggregation: aggregate data spatially or temporally. Select appropriate aggregation functions to calculate summary statistics. Common aggregation functions include sum, count, average, minimum, maximum, median, standard deviation, and percentiles.

   3. Metrics extraction: this process involves using statistical or calculus methods to extract characteristics from the data. In other words, we can represent certain features of our data by using simple and comparable indexes. For instance, these represent what are the minima, maxima or average of our data, or how well sensor data represents our truth values.

3. Data visualization: this involves converting data tables and pure numbers into visual representations of the data. Simple tools such as spreadsheets, can be useful for small amounts of data, but to handle bigger datasets, more capable frameworks may be needed. There is a wide variety of open source and free tools available for this purpose, and we have listed some popular tools in the table below, and there is a tool adapted for each level of expertise and data needs. It is generally coupled with the metrics extraction step below.

CitiObs Tools

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Data quality in the context of environmental monitoring refers to the accuracy, reliability, completeness, and relevance of the data collected and analysed to assess the condition of the environment. In environmental monitoring, data quality is crucial for making informed decisions regarding environmental management, policy development, and regulatory compliance. Official environmental monitoring, also known as regulatory monitoring, requires adherence to standardized protocols, rigorous quality assurance and quality control procedures. In Citizen Science and Citizen Observatories, data quality assurance poses unique challenges. However, with appropriate training, protocols, and quality assurance measures, CS/CO initiatives can produce valuable datasets that contribute to scientific knowledge and inform decision-making.

The quality of data required varies depending on the specific application or purpose for which the data will be used, while high-quality data is preferred and often essential for scientific research and critical decision-making, the required level of data quality depends on the specific application, context, and objectives of the data use.

Why is this relevant?

The relevance of data quality in Citizen Science cannot be overstated. It plays a critical role in the success, credibility, and impact of CS/CO initiatives. That said, it is important to state that for the data to be valuable, we do not always require data of the highest quality, but rather data of known quality. Understanding the trade-offs between data quality and other factors such as cost, time, and resources is crucial for effectively leveraging data to support informed decisions and achieve desired outcomes.

How can this be done?

Understanding data quality from sensing devices, requires us to be familiar with these terms:

1. Accuracy: accuracy is how close a given set of measurements are to their true value. You can assess the accuracy of the sensor measurements by comparing them to reference measurements from validated instruments or established monitoring stations. For air quality, some governments have reference monitoring stations.

2. Precision: precision is a measure for how close the measured data lie together. You can evaluate the precision of the sensor measurements by assessing their repeatability and consistency over time. Repeat measurements under controlled conditions can help determine the sensor's precision and variability. You can also check how different sensors compare to each other by having them measuring at the same location.

3. Cross-sensitivity: sensing devices may exhibit cross-sensitivity to other gases or environmental factors, leading to potential inaccuracies in the measurements. The devices’ performance can vary under different environmental conditions, such as temperature and humidity. Extreme environmental conditions may also affect accuracy and reliability. Understanding the sensor's selectivity and potential for cross-sensitivity is important to avoid misinterpretation of the data.

4. Response time: consider the sensors’ response time, which refers to the time it takes to detect changes in air quality. Faster response times are desirable for capturing rapid changes in pollution levels, especially for mobile monitoring.

5. Signal Drift: sensing devices may exhibit drift over time, leading to gradual changes in baseline readings or sensor response. Checking for sensor drift involves monitoring changes in sensor readings over time and comparing them to expected behavior or reference measurements. Plot the data over time to visually inspect for any gradual shifts or trends in the baseline readings. Drift may manifest as a consistent increase or decrease in sensor values over time.

6. Relative vs absolute data: in some cases, sensors are not accurate enough to provide absolute values (i.e. air quality concentration levels) but they still can provide valuable relative data. Relative data can be useful for detecting trends and patterns in air quality over time (e.g. seasonal trends) or across different locations (e.g. identifying hotspots). A sensor should exhibit stability and consistency in its response over time and under varying environmental conditions. Stability ensures that the sensor's baseline remains unchanged over extended periods, while consistency ensures that the sensors’ responses to similar conditions are reproducible and predictable.

A Citizen Science project can increase data quality when using sensing devices through several strategies:

1. Standardized protocols: clearly define data collection methods, sampling protocols, device deployment techniques, and data recording formats to minimize variability and enhance data quality. Provide comprehensive training and educational resources to citizen scientists to ensure they understand the protocols.

2. Contextualization: provide contextual information to help interpret and contextualize the sensor data collected by citizen scientists. Encourage participants to verify their data and provide feedback on data quality issues or anomalies encountered during data collection. Establish protocols for reporting data discrepancies, sensor malfunctions, or environmental factors that may affect data quality.

3. Data verification and feedback: check routinely the data collected in the project and encourage participants to do the same. Establish communication channels for reporting data discrepancies and sensing device malfunctions and provide timely feedback and support to address these issues.

4. Noise reduction through data averaging: implement techniques for noise reduction by averaging sensor data over time or space. Averaging data over multiple time intervals (e.g., hourly, daily) or spatial locations can help mitigate random fluctuations or short-term variations in readings, resulting in smoother and more stable datasets.

5. Increase provenance: air quality data obtained from government agencies, environmental monitoring networks, or research institutions typically comes with well-established provenance information. While citizen-collected data may lack the institutional backing and formal validation processes of official sources, providing comprehensive provenance information is a good way for enhancing data credibility and usability. Citizen Science projects can increase provenance by documenting sensor specifications, calibration procedures, data collection protocols, and any quality assurance measures and data calibration implemented to ensure data quality and reliability.

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Addressing the lack of confidence in installing an air quality sensing device, especially when users are intimidated by the complexity of technology and scientific aspects, requires a supportive and user-friendly approach. You can create an environment that supports non-experts in overcoming their lack of confidence, making the use of sensor technology more approachable and manageable for individuals with varying levels of technical knowledge.

Why is this relevant?

Empowering non-experts to install air quality sensing devices enhances public engagement in environmental monitoring, fostering a sense of ownership and responsibility for environmental issues. Making installation procedures accessible and user-friendly facilitates participation and involvement. Overall, by addressing the lack of confidence in the use of sensor technology, the field of air quality monitoring becomes more inclusive. It allows individuals with diverse backgrounds and skill sets to contribute to environmental data collection.

How can this be done?

Here are some strategies to help non-experts feel more confident when using sensor technology, from installing a device to understanding the scientific concepts derived from the data:

1. Accessible language and clear and simple instructions:

   1. Ensure that your language is inclusive and accessible to diverse audiences. Understand your audience’s background and tailor your communication accordingly. Consider creating different versions of your message for different audience segments. Avoid exclusive terminology that may alienate certain groups.

   2. Provide step-by-step instructions using plain language. Break down the installation process into manageable and clearly defined steps. Create user manuals that focus on essential information and omit unnecessary technical details. Use visuals and videos to convey the information easily. Visuals can help users to understand the steps more intuitively.

   3. Offer a checklist of items needed before starting the installation. This helps users gather necessary materials and ensures they are adequately prepared.

   4. Connect technical concepts to real-world examples and applications. Show how the information is relevant to everyday life. Use analogies and metaphors to relate technical concepts to familiar, everyday experiences. This helps in making abstract ideas more concrete and relatable. Present technical information in the form of a story. Narratives can engage audiences and make the information more memorable.

   5. Encourage gradual learning by breaking down complex concepts into smaller, more digestible pieces. Encourage users to learn at their own pace and gradually build confidence. Start with the basics and progressively introduce more complex details, this allows the audience to build understanding gradually.

   6. Familiarize yourself with the devices, protocols for measuring, documentation, etc. That way you might be in a better position to understand the challenges and how to troubleshoot the equipment. It is always a good idea to spend some time testing the devices and protocols before using them in real-world situations with participants.

2. Define technical terms to increase accessibility:

   1. Provide clear and concise definitions for technical terms. Consider including a glossary for easy reference.

   2. Ensure that materials are accessible to users with varying levels of technical expertise.

   3. Provide alternative formats for those who may have different learning preferences. Include visual aids, such as diagrams, images, or videos, to illustrate the installation process.

3. Offer support and training online and in person:

   1. If you have a website, anticipate common concerns or questions and address them in a Frequently Asked Questions (FAQ) section.

   2. Provide clear answers to help users overcome potential challenges.

   3. Establish an online support platform or community forum where users can seek assistance from experts or fellow users. Encourage a supportive environment for asking questions.

   4. Offer a chat support or helpline that also allows users to share their experiences and tips with each other.

   5. Engage with the public through workshops, presentations, or community events. Conduct (virtual) training sessions or webinars to guide users through the installation process and data interpretation. Allow for Q&A sessions to address specific concerns.

   6. Hands-on experiences can help individuals grasp technical concepts more effectively.

4. User feedback and positive reinforcement:

   1. Provide positive reinforcement by acknowledging successful installations.

   2. Share success stories or testimonials from users who have successfully installed the sensing devices.

   3. Encourage users to provide feedback. Use feedback to identify areas for improvement and refine user instructions.

   4. Foster a dialogue to address any confusion or misconceptions.

5. Empathy and cultural sensitivity:

   1. Be mindful of cultural differences and adjust your communication style accordingly.

   2. Consider the cultural context of your audience.

6. Collaborate with communicators:

   1. Collaborate with science communicators or experts in science communication.

   2. Collaborate with libraries, educational institutions, and local organizations to promote data literacy and provide training on accessing and understanding Citizen Science data.

Useful resources

Effectively communicating technical and scientific terms to a wide audience involves using clear language, visual aids, and relatable examples. Here are some useful resources to help improve your skills in this area:

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Description

Contextualization in data gathering involves providing additional information surrounding the collected data (e.g., air pollutant levels) to offer a clearer understanding of its significance and implications. This additional information, or context, typically includes details about the circumstances under which the data was collected, the conditions surrounding the observation, and any relevant factors that may influence its interpretation. Documenting context is even more critical when using wearables as often used while moving, measurement context may change a lot in time. Context provides the key to understanding what the data is about.

Why is this relevant?

Adding context to air quality data collected by citizens is crucial for ensuring its accuracy, interpretability, and usefulness. Contextual information, such as the location, time, and potential sources of pollution, helps verify the reliability of the data and aids in its interpretation. This context allows users to understand why certain readings are high or low, making comparisons between different datasets more meaningful and actionable. Overall, contextualizing citizen-collected air quality data enhances its value (i.e. making it more trustable) for a wide range of stakeholders, including policymakers, researchers, public health officials, and the general public.

How can this be done?

Some types of context data are available through online sources or even provided by the sensor device itself. That is for example the case of weather parameters that can be accessed through online meteorological services and some parameters like temperature and relative humidity are usually measured by the sensing device. But very often, context data needs to be reported by the person collecting the data, that is for example the case if the device is indoor or outdoor, heigh of installation, or potential local sources of pollution like smoking or barbecuing. However, it is important to note that nowadays most sensor platforms do not include any entry point for contextual information provided by the sensor users, so the participants might need to keep their own registers in logbooks.

1. Identify the context information you need: when it comes to contextualizing data collected by citizens it is important to assess what information will be needed. This will depend on the objectives, for example if we want to conduct behaviour impact assessment we must make sure that we are going to be able to know “what” happened “when” in order to link it to the pollution levels. Some examples of general context information are:

   1. Location information: include precise location data for each device deployment. This could be GPS coordinates or detailed descriptions of the devices’ placement, such as proximity to roads, industrial areas, or green spaces. Location information helps understand local environmental conditions that might influence air quality.

   2. Time stamps: record the date and time of each data point collected. This allows for tracking changes over time and identifying patterns or trends in air quality fluctuations. This is usually done automatically by the sensing device. When using diffusive samplers, this needs to be done manually.

   3. Surroundings: document nearby sources of pollution, such as factories, traffic congestion, construction sites, agricultural activities, or any unusual polluting activities. Understanding potential sources of contamination helps interpret air quality data more accurately.

2. Identify sources of contextual information: there might be on-line or local sources that can provide you with contextual information to enrich your data. By consulting these (online) sources, you can access a wealth of contextual information relevant for understanding air pollution and its impacts on health and the environment. Some examples are:

   1. Environmental Protection Agencies (EPA): many countries have their own EPA websites where they provide air quality data along with contextual information such as pollution sources, regulations, and health effects. For example, the European Environmental Agency provides comprehensive information on air quality, including data, reports, and educational resources. Data from official reference stations can be accessed through the website and through an API.

   2. Local government: check the websites of local government agencies responsible for environmental protection or public health. These websites often provide air quality data specific to your region along with information about pollution sources, regulations, and initiatives to improve air quality.

   3. Meteorological institutes: the national meteorological institutes often provide information on their website about atmospheric conditions such as temperature, humidity, wind speed, and precipitation, which can influence air quality. Understanding these factors can provide valuable context when interpreting air quality data.

3. Protocols and training: create a form that lists all the context information you want to have collected and train your participants to log this information alongside using the air quality sensor. Be aware that collecting contextual information might be an additional burden, so it is important to keep it simple while still collecting all relevant information. Make a plan on what information can be collected automatically (e.g. providing extra sensors like GPS, luminosity, accelerometer; and accessing online services like google maps) and what data can only be provided by the participant (e.g. singular polluting activities, malfunction of the sensor, spiderwebs, or ice in the sensing units, etc.)

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Description

Citizen Observatories are community-based initiatives where non-experts actively participate in data collection and monitoring of various phenomena, such as environmental challenges as air pollution. Citizen Observatories rely on the collective efforts of people who voluntarily offer their time to gather valuable data (in some cases, participants can receive a monetary counterpart). A challenge usually encountered in Citizen Observatories is data gaps. Those can be temporal data gaps, referring to time series that are not complete in time, or spatial data gaps, referring to lack of data in specific locations. Reducing data gaps in citizen observations can be achieved through various strategies that promote active involvement and participation.

Why is this relevant?

Data gaps and incomplete data limit the utility of the data, as well as our ability to make informed decisions. Without comprehensive data, decisions may be based on biased data. In scientific research, data gaps can limit the scope of studies. Researchers rely on comprehensive data to test hypotheses and draw meaningful conclusions. Moreover, data gaps can perpetuate existing social inequalities. Without data on marginalized or underrepresented communities, these groups may be overlooked in policymaking and resource allocation, exacerbating social disparities and missing opportunities for positive impact.

How can this be done?

List of approaches to encourage citizens to contribute with valuable data and minimize data gaps:

1. Set clear goals, protocols and communicate them:

   1. Raise awareness about the importance of observations and their impact on scientific research, decision-making processes and, most importantly for the community: what is in it for them.

   2. Conduct workshops, seminars, and public awareness campaigns to communicate the objectives, methods, and the benefits of the collected data.

   3. Involve participants in the definition of the goals, that will increase agency, create ownership, and increase the understanding on how the outcomes can have a positive impact in the community.

2. Choose user-friendly technology and offer support and training:

   1. Use/Develop user-friendly mobile apps, online platforms, sensing devices that simplify data collected and submission processes.

   2. Ensure that the technology is accessible to people with varying levels of technical expertise, making it easy for anyone to participate.

   3. Provide training sessions and resources to help participants understand the scientific protocols, data collection methods, and the significance of their contributions. Adapt the language to the audience.

   4. Offer ongoing support through helplines, forums, and community moderators to address questions and issues faced by participants.

   5. Offer regular meetings. Those are important to progressively build on top of previous training and to create dynamic amongst the group. It may be complicated to have all participants at the same time so creating subgroups or having virtual meetings can be a nice way to adapt to their schedule and ensure maximum participation.

   6. Be there when participants need you. Not all the questions will be asked during formal meetings. Provide online support through a forum with FAQ and ideally start a dedicated WhatsApp group (or alike) after making sure you have all user's consent.

3. Keep the Community Engaged:

   1. Organize community events, Citizen Science festivals and meetups to foster a sense of community and belonging.

   2. Identify local community leaders and influencers and encourage them to promote the Citizen Observatory within their networks.

   3. Conduct targeted engagement, ensuring you engage with participants in the areas where data is needed.

   4. Gamification and competition are also a good way to stimulate engagement among the group. Protocols and nudges are also key to creating a framework where participants know what is expected of them, at what moment, for what reasons and for how long.

4. Increase acceptance of the collected data by applying data quality assurance and transparency through open data:

   1. Implement quality control measures to validate citizen-contributed data, ensuring its accuracy and reliability.

   2. Provide training on proper data collection and metadata documentation to enhance the quality and uptake of the observations.

   3. Communicate the data quality expectations, especially when using sensing devices that have large uncertainties.

   4. Foster a culture of transparency by openly sharing collected data, methodologies and research findings with participants and the wider public.

   5. Encourage participants to access and use open data for their own analyses and project.

   6. Provide friendly tools for data analysis so that communities can do their own research.

5. Feedback and Recognition:

   1. Provide regular feedback to participants about the impact of their contributions and the outcomes of the Citizen Science projects.

   2. Recognize and appreciate the efforts of participants through certificates, awards, or other forms of acknowledgement.

   3. Remember that communication goes both ways, and the participants need to be listened to.

6. Inclusivity and Diversity:

   1. Ensure inclusivity by reaching out to diverse communities, including underrepresented groups, and making efforts to involve people from different backgrounds, cultures, and ages.

7. Collaboration and Partnerships:

   1. Collaborate with local organizations, schools, NGOs and research institutions to expand the reach of the initiative.

Useful resources

Here are some useful resources that can help in the endeavor of reducing data gaps in citizen observations and promoting active involvement:

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Description

Data management in the context of sensor data involves ensuring its accuracy, reliability, accessibility, and usability. It includes more technical aspects such as data quality, processing, storage, and visualization, but it also covers concerns such as privacy and ethics, transparency, and questions around data ownership, licensing, and legal compliance.

In a perspective of community projects or collaboration with stakeholders in policy making process, data collected by Citizen Observatories is more than just data. It is the result of the engagement of the community, a common heritage, a digital testimony of the state of the environment and the foundation on top of which discussions, consensus and actions will be built upon.

Why is this relevant?

Data collected within the realm of Citizen Science is frequently disregarded due to perceptions of lower reliability compared to data gathered through more scientific and official means. Nonetheless, by addressing the limitations inherent in citizen-collected data utilizing sensing devices, such data can serve to complement traditional monitoring methods, offering significant insights crucial for informed decision-making and proactive measures.

How can this be done?

Data management involves a series of processes aimed at ensuring the accuracy, reliability, accessibility, and usability of the collected data. Here’s an overview of the key aspects of data management in this context:

1. Data collection: once selected, sensing devices should be deployed and used through protocols established to ensure that data is collected through a structured process to meet pre-defined quality standards.

2. Data quality assurance: ensuring the accuracy and reliability of the collected data is essential. Quality assurance measures include sensor calibration, regular maintenance, and validation against reference instruments or established monitoring stations. Data validation techniques help identify and correct errors or inconsistencies in the dataset.

3. Contextualization: context provides the basis for interpretation, helping you understand what the data means and therefore what you can do, or cannot do with it. Contextualization involves providing additional details surrounding the data, such as the circumstances of collection, environmental conditions, and relevant factors influencing interpretation.

4. Data processing and analysis: processing raw sensor data into meaningful information involves various steps such as data filtering, aggregation, interpolation, and statistical analysis. Data analysis techniques help identify trends, patterns, correlations, and anomalies in the dataset, providing for instance insights into air quality dynamics and pollution sources.

5. Traceability: when managing data, much attention should be paid to traceability issues, from documenting data origins to cover privacy and licensing issues, to bug tracking to identify concerned datasets in the case of a faulty device for example, but also to have a clear understanding of the processing that were applied to the data. Linked to data quality, traceability is a pillar in data trust.

6. Data visualization: presenting air quality data in visually appealing and understandable formats enhances data interpretation and communication. Graphs, charts, maps, and interactive dashboards are commonly used to visualize key metrics, trends, and spatial distributions of pollutants.

7. Transparency and accessibility: making air quality data and analysis accessible to stakeholders, including policymakers, researchers, public health officials, and the general public, promotes transparency and collaboration. Open data portals, APIs, and web-based platforms facilitate data sharing and enable users to access and download the data for analysis or read the outcomes of the analysis.

8. Privacy and ethical considerations: protecting sensitive information and ensuring data privacy are important considerations, particularly when collecting data from personal devices or citizen volunteers. Adhering to privacy regulations and obtaining informed consent from participants is essential to maintain trust and ethical integrity in data management practices.

9. Licensing considerations: define licensing terms and conditions associated with the data to ensure legal compliance, protect intellectual property rights and facilitate appropriate data sharing and reuse.

10. Long-term data preservation: develop strategies for long-term data preservation to ensure the sustainability and usability of air quality datasets for future research, monitoring and policy-making efforts.

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