<![CDATA[Air quality monitoring is critical in supporting environmental and public health policies, but conventional station-based methods still have limitations in cost and area coverage. Alternatively, low-cost sensor technologies based on the Internet of Things (IoT) and Artificial Intelligence (AI) offer solutions that are more economical, flexible, and capable of real-time monitoring. However, low-cost sensors face the challenge of lower accuracy compared to conventional sensors and require regular calibration for reliable measurement results. This paper analyzes the development of low-cost sensor technology, its effectiveness compared to conventional technology, and its impact on public budget efficiency. The results show that the integration of AI and IoT can improve the accuracy of low-cost sensors, while the citizen science model has the potential to expand monitoring coverage by involving the public in data collection. With the right strategy, low-cost sensors can be an inclusive and sustainable solution to support more cost-effective and data-driven environmental policies. ]]>
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