Jul 2026· Journal of Health Science and Pharmacy· Vol 3, pp. 9-19· 0 citations
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
IoT-based air quality monitoring technology has proven effective, accurate, and accessible, making it useful in preventing the impact of air pollution on public health.
Ni Ketut Susilawati, Nurghany, Tiara Wirdadinaka et al.· Journal of Epidemiology and...· 0 citations
Air pollution is a global environmental and health issue that has adverse effects on climate change and human health and contributes to ecosystem degradation and biodiversity loss. Air quality monitoring is therefore crucial to understand the sources and impacts of air pollution and to assess mitigation interventions. However, the number of operative monitoring stations are usually limited in air quality monitoring networks due to the high cost of the instrumentation used to monitor selected air pollutants. This limits the number of stations and results in spatial gaps. However, air pollution is highly heterogeneous.
The recent development of sensor technology has offered a broad range of gas and particulate matter sensors of small size and relatively low energy consumption and cost. These features make low-cost sensors (LCS) an attractive alternative to increase the spatial coverage of air quality networks and to deploy affordable networks in countries with lower resources. Nonetheless, LCS are faced with limitations such as cross-sensitivity and drift, while sensor networks are characterized by their scale, mixed quality, data volatility, interdependencies and correlations among others. These factors, in conjunction with the inherent complexity of the entire LCS network, lead to the emergence of novel metrological challenges. Moreover, some of the approaches used in sensor networks like the use of sensor redundancy for fault detection and self- or co-calibration, require a more holistic metrological assessment than traditional metrology methods.
These metrological challenges must be addressed to ensure the data quality and comparability of LCS networks. For that purpose, several sensor network metrology techniques are currently under development in the European Partnership on Metrology project "Fundamentals of Sensor Network Metrology" (FunSNM). Some of these techniques include uncertainty propagation in sensor networks using Bayesian approaches and mathematical tools like Laplace tools, sensor fusion, correlation analysis, self- and co-calibration methods using optimised spatial statistics and machine learning algorithms among others. Current outputs of the projects will be included in this poster, showing the essential role of sensor network metrology in advancing the adaptability and reliability of sensor networks.
M. Iturrate-García, M. Zaidan, A. Vedurmudi et al.· 150th anniversary of the Met...· 0 citations
Air pollution has become a major environmental and public health concern due to rapid urbanization, industrial growth, and increasing vehicular emissions. High concentrations of pollutants such as PM2.5, PM10, NO₂, SO₂, CO, and O₃ can significantly impact human health and environmental sustainability. Accurate monitoring and prediction of air quality are therefore essential for effective environmental management and public safety. This paper presents AirAware, a machine learning–based system designed to predict and monitor Air Quality Index (AQI) levels using historical air pollution data and real-time environmental information. The system utilizes the XGBoost algorithm to analyze pollutant parameters and generate accurate AQI predictions and classifications. Data preprocessing techniques such as cleaning, normalization, and SMOTE-based class balancing are applied to improve model performance and ensure reliable predictions across different AQI categories. In addition, the system integrates real-time air pollution data through the OpenWeather API, enabling continuous monitoring of current environmental conditions. The predicted AQI values and pollution trends are displayed through a web-based dashboard, allowing users to visualize air quality patterns and compare real-time data with machine learning predictions. By combining machine learning techniques with real-time data integration, the proposed system provides an effective solution for air quality prediction, monitoring, and environmental awareness.
Neethu Roy, Jeeson Justin· International Journal of Lat...· 0 citations
This review critically examines the role of citizen science in air-pollution monitoring and its integration into environmental health research. It explores how participatory approaches, including low-cost sensors and community co-creation, can enhance spatial resolution, public engagement, and environmental justice, while assessing data quality, health relevance, and scalability. Recent studies document the expanding global deployment of citizen science air quality monitoring networks. Advances in calibration algorithms, wearable technologies, and participatory data-collection methods have strengthened capacity to assess real-time exposure. Several projects demonstrate the increasing technical sophistication and policy relevance of citizen-generated air quality data. Citizen science provides hyperlocal insights into air quality and exposure, strengthens risk communication, and contributes to health-impact assessment. Although challenges remain regarding data reliability, representativeness, and policy uptake, emerging approaches, including AI-assisted calibration and distributed sensor networks, offer promising pathways forward. An integrative framework can support sustained community engagement and evidence-informed action to improve air quality.
Hong Yang· Current Environmental Health...· 0 citations
Particulate matter (PM), particularly PM
2.5
and PM
10
, poses significant threats to air quality and public health worldwide. This study aims to provide a comprehensive review of the global status, trends, sources, health impacts, and management strategies related to PM pollution. Given the rapid industrialization, urbanization, and vehicular emissions in developing regions, understanding PM dynamics is essential for effective policy development and mitigation. The study combines a systematic literature review with a bibliometric analysis using data retrieved from the Web of Science database. A total of 427 documents spanning 1993–2025 were analyzed using the Biblioshiny Package in R Studio and VOSviewer to assess trends, research collaborations, and thematic evolution. Key findings reveal increasing scholarly attention to PM research, particularly in China, India, and the United States of America (USA). South and East Asian countries exhibit the highest PM levels, driven by industrial emissions, vehicular exhaust, and biomass burning. In contrast, regions, such as the EU and the USA, report lower PM concentrations, indicating the effectiveness of stringent regulations. The study identifies gaps in monitoring networks, particularly in remote areas, and emphasizes the growing role of low‐cost sensors, artificial intelligence (AI), and green infrastructure in future air quality management. The findings underscore the urgent need to revise national standards to align with World Health Organization (WHO) guidelines, promote cross‐border collaborations, and implement integrated, science‐based policies. These actions are critical for mitigating PM‐related health risks and advancing global environmental sustainability.
Sushil Kumar, Apurba Koley, S. Balachandran et al.· CLEAN - Soil, Air, Water· 0 citations
This study comprehensively evaluates air pollution in Adana, one of Türkiye’s key industrial, urban, and transportation hubs. Using monitoring data from 2023–2025, the concentrations of major pollutants—PM2.5., PM10, SO2, NO2, CO, and O3—were analyzed. The results were compared with standards set by the World Health Organization (WHO), the European Union (EU), and Türkiye’s national regulations, showing that PM2.5 and PM10 thresholds were frequently exceeded at several stations. A significant finding was that data capture rates remained below 60%, indicating reliability gaps in the monitoring infrastructure and weakening evidence-based policymaking. Primary sources of pollution include coal-fired power plants, the use of low-quality fuels, open burning of agricultural residues, and vehicular emissions. These factors are aggravated by Adana’s geographic and meteorological conditions, such as temperature inversions and low wind speeds, which facilitate pollutant accumulation and increase public health risks. Statistical analyses revealed notable rises in SO2 and particulate matter concentrations during winter due to residential heating, coinciding with higher respiratory illness rates. The study recommends reducing coal dependency, expanding access to natural gas, promoting sustainable transportation, restricting agricultural waste burning, and improving data transparency. The Adana case highlights air pollution as both an environmental and social justice issue.
E. Turan· Doğal Afetler ve Çevre Dergi...· 0 citations
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