Performance and data acquisition from low-cost air quality sensors: a comprehensive review

The rapid increase in urban populations has led to escalating traffic and higher levels of air pollutants, posing significant threats to urban health. In response, there is growing demand for accessible, real-time, and widespread air quality monitoring systems. This review focuses on the potential of low-cost air quality sensors to meet this demand, with emphasis on their ability to provide high-density spatiotemporal data at a lower cost. The paper critically examines current low-cost air quality sensors, including Wireless Sensor Network (WSN) and Internet of Things (IoT)-based solutions, through both field experiments and laboratory studies. A key contribution of this review is the comprehensive evaluation of calibration methods, showing how factors such as temperature and humidity influence sensor performance. The review highlights common challenges like sensor accuracy, cross-sensitivity, and data quality, offering insights into effective strategies such as calibration against reference instruments and advanced data validation techniques. Ultimately, this review underscores the potential of low-cost sensors in revolutionizing air pollution monitoring, while also addressing the practical challenges that must be resolved to fully realize their capabilities.