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• Synergistic path planning of multi-UAVs among sailing vessels for the air pollution inspection. • Develop a dynamic multi-objective TSP model. • A heuristic algorithm based on improved PSO is proposed to determine the path reprogramming time in dynamic environment by judging and cutting the "minimal loop". • Experimental results show that our proposed method can efficiently solve the problem. The phenomena of the COVID-19 outbreak and the Arctic Iceberg melting over the past two years make us reconsider the impact our way of life has on the environment and the responsibility of business toward minimizing and potentially eliminating emissions. Increasing ship traffic in ports leads to the growing emission of air pollutants, which influences the air quality and public health in the surrounding areas. The International Maritime Organization (IMO) has adopted relevant regulations (e.g., Annex VI of IMO's pollution prevention treaty (MARPOL) and mandatory energy-efficiency measures) to address ship emissions. To ensure the effective implementation of such regulations and measures, air emission detection and monitoring has become crucial. In this paper, a dynamic multitarget path planning model is developed to realize multi-UAVs (Unmanned Aerial Vehicles) performing synergistic detection of ship emissions in ports. A path planning algorithm under a dynamic environment is developed to establish the model. This algorithm incorporates a Tabu table into particle swarm optimization (PSO) to improve its optimization ability, and it obtains the initial detection route of each UAV based on a "minimum ring" method. This paper describes a multi-UAVs synergistic algorithm to formulate the path reprogramming time in a dynamic environment by judging and cutting the "minimum ring". This finding proves the improved efficiency of air pollution detection by UAVs. It provides useful insights for maritime and port authorities to detect ship emissions in practice and to ensure ship emission reduction for better air quality in the postpandemic era. [ABSTRACT FROM AUTHOR]

Interprovincial electricity transmissions have profoundly affected the emission patterns of air pollutants and greenhouse gases in China. Here, we estimate the air pollution transfer by interprovincial electricity transmission, taking Yangtze River Delta Region (YRDR) of China as an example. The results show that the YRDR received 153.01 billion kWh of electricity from other regions in 2015, accounting for 13.06% of its total electricity consumption; these other regions mainly included Sichuan, Shanxi, Hubei, Henan and Fujian. Air pollutants emissions, including 20.50 kt of SO 2 , 22.40 kt of NO X , 4.30 kt of dust, and 39.23 Mt of CO 2 , were transferred to generation-side regions by electricity transmissions, of which 34% were transferred to Shanxi, 31% to Sichuan, and 29% to Hubei. Then, different schemes to achieve interregional compensation were explored and compared based on previous studies and a 2015 simulation scenario. Direct electricity purchasing can promote renewable energy development but is the most cost, while pollutants emission trading is a scheme to promote the establishment of a reliable and efficient market. It is suggested that integrated schemes will promote collaboration between generation regions and consumption regions to realize sustainable targets on both sides. [ABSTRACT FROM AUTHOR]