How IoT-Driven Citizen Science Coupled with Data Satisficing Can Promote Deep Citizen Science

Area of Science:

• Environmental Science
• Computer Science
• Sociology

Background:

• Citizen science (CS) is increasingly vital for environmental monitoring.
• Integrating the Internet of Things (IoT) enhances data collection capabilities.
• Data satisficing presents a novel approach to address data quality challenges in citizen science.

Purpose of the Study:

• To investigate the factors influencing air quality (AQ) data quality in IoT-driven citizen science (IoT-CS).
• To explore methods for making citizen science more inclusive and accessible.
• To assess the viability of data satisficing for overcoming calibration issues in IoT-CS AQ data.

Main Methods:

• A citizen science experiment was conducted in four Pakistani cities with 21 volunteers.
• Quantitative methods were employed to analyze air quality data.
• User engagement workshops were utilized to enhance inclusivity and training.

Main Results:

• Multiple factors affect IoT-CS AQ data quality; good practice guidelines and instrument classification can help.
• User engagement workshops proved effective for inclusivity and training.
• Data satisficing is proposed as a feasible method to detect local AQ variations despite uncalibrated data.

Conclusions:

• Fostering a deep citizen science approach promotes inclusivity and knowledgeable application of science.
• Directly cross-checking low-cost and calibrated sensors may not always be feasible or useful.
• Leveraging data satisficing and advanced cross-checks can yield valuable insights from IoT-CS AQ data.