We need to breathe air every moment to sustain life, and the quality of air has a profound impact on human health. However, according to statistics, the number of deaths caused by air pollution worldwide will exceed 7 million in 2023. With the rise of artificial intelligence (AI) technology and its rapid integration with various industries, this situation is ushering in a turning point. According to the official website of the World Economic Forum, many countries are integrating AI, the Internet of Things, and big data into their existing air monitoring systems to track atmospheric changes, warn of pollution risks, and safeguard every breath of humanity in the digital age. Artificial intelligence has a keen eye for identifying dust. To do a good job, one must first sharpen their tools. Traditional air quality monitoring systems are like peering into a tube, while AI and machine learning models are like having a pair of "smart eyes" that can collect, analyze, and process massive amounts of data in real time, accurately identifying changes in pollutant levels in the air. Automated analysis can reduce labor costs and allow air quality information to 'fly into ordinary people's homes'. In addition, the latest research shows that machine learning significantly improves forecast accuracy and corrects the shortcomings of underestimation or overestimation in the past. Based on deep data analysis, it can help governments and businesses seize opportunities, make wise decisions, build health defenses, and protect people from the harmful effects of air pollution. Multiple applications have been launched one after another. Currently, many countries are using AI technology to participate in this' blue sky defense battle '. A team of particle physicists from South Africa has developed an innovative system called 'Ai_r'. Professor Bruce Merado, the team leader and director of the Accelerator Physics Research Institute at the Essamba Accelerator Basic Science Laboratory in South Africa, stated that the device has a unit price of only about $100 and looks like a box with a built-in micro laser that detects particle concentration through the principle of light scattering. The device can be placed on the windowsill, continuously sampled, and uploaded to the cloud in real-time. At present, 20 devices have been put into operation in Johannesburg, with an additional 120 to be deployed, and plans to cover tens of thousands of devices throughout South Africa in the future. The unique feature of Ai_r is that it can not only monitor the current situation, but also predict pollution hotspots, especially for PM2.5 particles that can penetrate deep into the lung blood, which helps authorities to implement precise policies. Scientists from Macau University of Science and Technology and the Chinese Academy of Meteorological Sciences have teamed up to create the "AI Air" system. The system, combined with atmospheric chemical environment models, has been applied in typical cities such as Zhengzhou and Haikou, significantly improving the predictive ability of pollutant concentrations and being able to analyze key meteorological factors under different terrain and climate conditions, demonstrating the potential of AI application in complex environments. The "AirQo" system is serving 16 cities in Africa, using low-cost sensors combined with AI algorithms to provide a basis for health decision-making. Satellite based air quality monitoring has also made breakthroughs. Professor Shi Chong's team of the Institute of Aerospace Information of the Chinese Academy of Sciences cooperated with Japanese scientists to develop the "AIRTrans" algorithm. This system significantly improves the accuracy and efficiency of extracting key aerosol characteristics from multispectral satellite observation data. This AI driven tool has successfully utilized satellites to capture aerosol concentration and size information, becoming an effective solution for pollutant monitoring and early warning systems. By analyzing past datasets, it can also predict pollution trends in specific cities. According to other data, similar AI driven prediction systems have seen their prediction accuracy climb to 92% within 18 months after being applied in China. In addition, Korean researchers have also developed air quality monitoring and warning systems using various algorithms. Despite the broad prospects, the road to AI assisted air quality monitoring still faces some urgent problems that need to be solved. Firstly, there is the challenge of data, as AI model training relies on massive amounts of precise data, and data availability is often limited; Secondly, the cost is crucial, as establishing a system requires a data center and a large amount of power support, which incurs enormous expenses; Finally, there is a shortage of professionals in algorithm development and hardware maintenance, and integrating new systems into old infrastructure is both expensive and complex. Only by overcoming these difficulties can AI monitoring systems operate efficiently, accurately, and economically. In the future, predictive models will become more sophisticated and IoT sensors will become more widespread. AI driven drones can penetrate deep into remote areas to detect pollutants; Smart cities will promote the deployment of low-cost sensor networks and provide continuous real-time feedback on urban pollution levels. Through deep integration with the Internet of Things and big data, coupled with real-time and predictive analysis of AI, air quality monitoring will move towards a new stage of high resolution and efficiency. (New Society)