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Large-scale citizen science protocol provides high-resolution nitrogen dioxide values while enhancing community knowledge and collective action.

ABSTRACT: We present an already tested protocol from a large-scale air quality citizen science campaign (xAire, 725 measurements, see Ref. [1]). A broad partnership with 1,650 people from communities including 18 primary schools in Barcelona (Spain) provided the capacity to obtain unprecedented high-resolution NO₂ levels. Communities followed the protocol to select measurement points and obtain NO₂ levels from outdoor locations n=671, playgrounds n=31, and inside school buildings (primarily classrooms) n=23. Data was calibrated and annualized with concentration levels from the city´s automatic air quality monitoring reference stations [2].

SUBMITTER: Perello J

PROVIDER: S-EPMC8374703 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Large-scale citizen science protocol provides high-resolution nitrogen dioxide values while enhancing community knowledge and collective action.

Perelló Josep J Targa Jaume J Daher Carolyn C Alonso María M Francis Louise L Masha-Caminals Amanda A Bonhoure Isabelle I Cigarini Anna A Vicens Julià J Carrasco-Turigas Glòria G

MethodsX 20210801

We present an already tested protocol from a large-scale air quality citizen science campaign (xAire, 725 measurements, see Ref. [1]). A broad partnership with 1,650 people from communities including 18 primary schools in Barcelona (Spain) provided the capacity to obtain unprecedented high-resolution NO<sub>2</sub> levels. Communities followed the protocol to select measurement points and obtain NO<sub>2</sub> levels from outdoor locations n=671, playgrounds n=31, and inside school buildings (pr ...[more]

PMID: 34434874

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Project description:BackgroundDisaster citizen science, or the use of scientific principles and methods by "non-professional" scientists or volunteers, may be a promising way to enhance public health emergency preparedness (PHEP) and build community resilience. However, little research has focused on understanding this emerging field and its implications for PHEP. To address research gaps, this paper: (1) assesses the state of disaster citizen science by developing an inventory of disaster citizen science projects; (2) identifies different models of disaster citizen science; and (3) assesses their relevance for PHEP.MethodsWe searched the English-language peer-reviewed and grey literature for disaster citizen science projects with no time period specified. Following searches, a team of three reviewers applied inclusion/exclusion criteria that defined eligible disasters and citizen science activities. Reviewers extracted the following elements from each project: project name and description; lead and partner entities; geographic setting; start and end dates; type of disaster; disaster phase; citizen science model; and technologies used.ResultsA final set of 209 projects, covering the time period 1953-2017, were included in the inventory. Projects were classified across five citizen science models: distributed or volunteer sensing (n = 19; 9%); contributory (n = 98; 47%); distributed intelligence (n = 52; 25%); collaborative research (n = 32; 15%); and collegial research (n = 8; 4%). Overall, projects were conducted across all disaster phases and most frequently for earthquakes, floods, and hurricanes. Although activities occurred globally, 40% of projects were set in the U.S. Academic, government, technology, and advocacy organizations were the most prevalent lead entities. Although a range of technologies were used, 77% of projects (n = 161) required an internet-connected device. These characteristics varied across citizen science models revealing important implications for applications of disaster citizen science, enhancement of disaster response capabilities, and sustainability of activities over time.ConclusionsBy increasing engagement in research, disaster citizen science may empower communities to take collective action, improve system response capabilities, and generate relevant data to mitigate adverse health impacts. The project inventory established a baseline for future research to capitalize on opportunities, address limitations, and help disaster citizen science achieve its potential.

Project description:BackgroundBiodiversity is being lost rapidly and its conservation is thus one of the most urgent tasks today. For biodiversity conservation to be successful, the public needs to gain an awareness and understanding of biodiversity and its importance. Moreover, species experts are needed who have the skills necessary for identifying and recording biodiversity. Previous research showed that citizen science projects can contribute to educating the public about biodiversity. However, it is still unclear how project characteristics connect to participants' knowledge and skills and how citizen science projects should be designed if they are to foster participants' learning.AimWe aimed to investigate specific characteristics of biodiversity citizen science projects that could potentially influence participants' learning. We explored the following project characteristics from both the project coordinators' and the participants' perspectives: information and training provided to participants, social interaction among participants, contact between participants and staff, and feedback and recognition provided to participants.Methods and resultsIn order to examine the extent to which these project characteristics are connected to participants' gains in knowledge and skills, we conducted a comprehensive study across 48 biodiversity citizen science projects in Europe and Australia. We found that participants' perceived gains in knowledge and skills were significantly related to the five project characteristics as reported by the participants: information received by the participants, training received by the participants, social interaction among participants, contact between participants and staff, and feedback and recognition received by the participants.ConclusionWe conclude that by deliberately designing citizen science projects to include features such as interaction and feedback, these projects could achieve higher learning outcomes for the participants. Thereby, suitable modes of communication between projects and their participants are crucial. We provide specific suggestions for the design of biodiversity citizen science projects and for future research on project characteristics and participant outcomes.

Project description:The collective intelligence of crowds could potentially be harnessed to address global challenges, such as biodiversity loss and species' extinction. For wisdom to emerge from the crowd, certain conditions are required. Importantly, the crowd should be diverse and people's contributions should be independent of one another. Here we investigate a global citizen-science platform-iNaturalist-on which citizens report on wildlife observations, collectively producing maps of species' spatiotemporal distribution. The organization of global platforms such as iNaturalist around local projects compromises the assumption of diversity and independence, and thus raises concerns regarding the quality of such collectively-generated data. We spent four years closely immersing ourselves in a local community of citizen scientists who reported their wildlife sightings on iNaturalist. Our ethnographic study involved the use of questionnaires, interviews, and analysis of archival materials. Our analysis revealed observers' nuanced considerations as they chose where, when, and what type of species to monitor, and which observations to report. Following a thematic analysis of the data, we organized observers' preferences and constraints into four main categories: recordability, community value, personal preferences, and convenience. We show that while some individual partialities can "cancel each other out", others are commonly shared among members of the community, potentially biasing the aggregate database of observations. Our discussion draws attention to the way in which widely-shared individual preferences might manifest as spatial, temporal, and crucially, taxonomic biases in the collectively-created database. We offer avenues for continued research that will help better understand-and tackle-individual preferences, with the goal of attenuating collective bias in data, and facilitating the generation of reliable state-of-nature reports. Finally, we offer insights into the broader literature on biases in collective intelligence systems.

Dataset Information

Large-scale citizen science protocol provides high-resolution nitrogen dioxide values while enhancing community knowledge and collective action.

Publications

Large-scale citizen science protocol provides high-resolution nitrogen dioxide values while enhancing community knowledge and collective action.

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