Air Quality Monitoring Technology and Its Benefits for Public Health: A Review
TL;DR
IoT-AI-AI-based air quality monitoring is effective, cost-efficient, and scalable for education, early warning, and pollution control policy support and maximum health impact is achieved through calibration standardization, sensor network expansion, data platform integration, and quadruple helix collaboration.
Abstract
Background: air quality affects public health and ecosystems, especially in urban areas with high emissions from transportation and industry. Objective: to summarize the effectiveness of air quality monitoring technology and its benefits for public health. Methods: a descriptive systematic literature review of 25 articles (2021-2025) from Google Scholar and ResearchGate using the PRISMA framework with keywords related to air quality monitoring, IoT, sensor calibration, and environmental health. Results: the integration of IoT-AI with low-cost sensors and cloud computing provides accurate real-time data for PM₂.₅/PM₁₀, CO₂, and major gases. Predictive models (e.g., LSTM) improve risk projection capabilities; public dashboard systems and automated notifications support early warnings and behavioral changes. In Indonesia, the monitoring network is still limited and uneven; modernization requires standard calibration, expanded coverage, and integrated data governance across stakeholders. Conclusion: IoT-AI-based air quality monitoring is effective, cost-efficient, and scalable for education, early warning, and pollution control policy support. Maximum health impact is achieved through calibration standardization, sensor network expansion, data platform integration, and quadruple helix collaboration (government, academia, industry, and community). Keywords: air quality monitoring, IoT, sensor calibration, environmental health, air quality