Knowing the vertical distribution of ambient particulate matter (PM) will help port authorities choose the optimal dust-suppression measures to reduce PM concentrations. In this study, we used an unmanned aerial vehicle (UAV) to assess the vertical distribution (0–120 m altitude) of PM in a dry bulk port along the Yangtze River, China. Total suspended particulates (TSP), PM10, and PM2.5 concentrations at different altitudes were measured at seven sites representing different cargo-handling sites and a background site. Variations in results across sites make it not suitable to characterize the vertical distribution of PM concentration at this port using simple representative distributions. Bulk cargo particle size, fog cannon use, and porous fence all affected the vertical distribution of TSP concentrations but had only minor impacts on PM10 and PM2.5 concentrations. Optimizing porous fence layout according to weather conditions and cargo demand at port have the most potential for mitigating PM pollution related to port operation. As ground-based stations cannot fully measure vertical PM distributions, our methods and results represent an advance in assessing the impact of port activities on air quality and can be used to determine optimal dust-suppression measures for dry bulk ports.

As a typical low-carbon transportation mode, inland navigation requires less land, produces less pollution, and consumes less energy, but carries more goods. To scientifically evaluate the green performance of inland navigation using the method of LCIA to analyze input and output inventories, the paper constructs a green degree evaluation index system of inland navigation, quantifies the indexes with the Fuzzy Analytic Hierarchy Process (F-AHP), and uses a linear weighted sum method to calculate the final comprehensive evaluation. The results show that inland navigation has some green properties but cannot meet the requirements of China's low-carbon society. The paper puts forward some advanced measures on basic attributes and environmental attributes.