Shipping emissions have been considered a significant source of air pollution in the cities along the Yangtze River, with severe impacts on the climate and human health. This study created a complete annual ship emission inventory for the middle reaches of the Yangtze River and assessed its impact on air quality on a regional scale. To estimate the complete emissions, 9 main engine power regression models for different ship types were created to handle those vessels with absent main power data, and a high spatial-temporal resolution annual emission inventory was developed with the activity-based method combined with Automatic Identification System (AIS) data of the full year of 2018. The total emissions of CO2, CO, SO2, NOX, PM2.5, PM10 and HC in middle reaches of the Yangtze River were 5.67 × 105, 1.02 × 103, 5.41 × 102, 1.06 × 104, 2.43 × 102, 2.45 × 102 and 3.52 × 102 tons respectively. Then, the Weather Research and Forecasting with Chemistry (WRF-Chem) model was used to study the dispersion of the ship pollutants in the atmosphere and quantize the impact on the urban area. This research will provide services for the maritime authorities to develop green shipping and emission supervision.@en

To simplify the micro-level CO2 (carbon dioxide) emission calculation model, reduce the dataset quality requirement of the model, and cut down the volume of calculation, a meso-level voyage-based emission model (MeVEM) for inland ships is proposed with their navigation characteristics considered. The navigation characteristics and the main influencing factors of inland ship emissions are analyzed. The main engine power and average speed of the ships are selected as the model inputs. Accurate CO2 emissions are calculated by the use of the micro-level emission model. With that, first-order and second-order polynomial regression models are employed to establish the fitting formula to estimate the emissions per kilometer. To validate the proposed model, the Junshan segment in the middle reaches of the Yangtze River is selected as the study area, and the model parameters are determined to estimate the CO2 emissions. It is found that the model of emission per kilometer (ej, k) established by second-order polynomial regression is more accurate. The results show that the percentage error in the total amount (PETA) of the results estimated by the four proposed models (CO2 emission estimation model for the upstream cargo ships, the downstream cargo ships, the upstream oil tankers, and the downstream oil tankers) are all within ±5%, which verifies the feasibility and applicability of the model. The proposed meso-level model allows us to use a smaller input dataset which is easier to obtain, and estimate CO2 emissions from ships simply and accurately.@en

At present, many maritime applications, such as ship traffic statistics, require fast spatiotemporal query of ship traj ectory data. The existing research is weak in supporting the trajectory spatiotemporal query, and can only realize the spatiotemporal query based on circular and rectangular regions. Moreover, complex spatial indexes and computer clusters are usually required to achieve efficient spatiotemporal query, which undoubtedly increases the storage cost. To solve above problems, this article designs the storage structure and partition strategy of ship trajectory data, and builds attributes multilevel index and H3 spatial index on ship traj ectory data based on ClickHouse database, which not only achieves spatiotemporal query based on the oval, round, and arbitrary polygon area, and improve the efficiency of the spatiotemporal query; At the same time, the data storage space is reduced, so that a common server can manage 10 billion ship trajectory data. The empirical study on a large number of data shows that the storage scheme designed in this paper not only enhances the ability of spatiotemporal query, improves the efficiency of data storage and spatiotemporal query, but also reduces the cost of data storage to a large extent.@en