如何让“人工智能+”更好赋能大气污染治理？

Core Insights - The "14th Five-Year Plan" has shown significant progress in air pollution control, with PM2.5 average concentration expected to drop to 29.3 µg/m³ by 2024, a decrease of 20.7 µg/m³ from 2015, nearing the 2035 target of below 25 µg/m³ [1] - Recent pollution events highlight the complexity and long-term nature of air quality improvement, necessitating more precise identification of issues and targeted governance measures [1] - The integration of artificial intelligence into ecological governance is emphasized as a direction for advancing precise air pollution control [1] Industry Challenges - The application of AI in air pollution prevention faces challenges, including the need for multi-source data integration and the development of operational business platforms [2] - The development costs for air pollution tracing models range from hundreds of thousands to millions, creating barriers for economically disadvantaged regions [2] - The potential for resource waste exists if similar platforms are developed independently across regions, necessitating a solution to bridge the cost gap and create accessible tools [2] Proposed Solutions - A public product supply model for "AI + air pollution governance" is needed, focusing on building a unified AI model library and algorithm platform for standardized monitoring and forecasting [2][3] - This model would allow for the reuse of technological achievements, particularly benefiting economically disadvantaged areas by providing equal access to smart governance tools [3] - Enhancements in air quality prediction and pollution source analysis capabilities are essential for effective emergency measures and environmental law enforcement [3] Collaborative Framework - The government should take a leading role in top-level design, planning, standard setting, and data platform regulation, while local governments should identify specific application scenarios [4] - Research institutions and universities should focus on model development and algorithm innovation, providing foundational technology for public products [4] - Companies should concentrate on practical applications and customized solutions for local governments, facilitating the implementation of AI technologies in air pollution governance [4]