Spatial-Temporal Ship Pollution Distribution Exploitation and Harbor Environmental Impact Analysis via Large-Scale AIS Data

Author:

Chen Xinqiang1ORCID,Dou Shuting2,Song Tianqi3,Wu Huafeng2ORCID,Sun Yang2,Xian Jiangfeng1ORCID

Affiliation:

1. Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, China

2. Merchant Marine College, Shanghai Maritime University, Shanghai 201306, China

3. School of Economics and Management, Shanghai Maritime University, Shanghai 201306, China

Abstract

Ship pollution emissions have attracted increasing attention in the maritime field due to the massive growth of maritime traffic activities. It is important to identify the ship emissions (SEs) magnitude and corresponding spatial and temporal distributions for the purposes of developing appropriate strategies to mitigate environment pollution. The aim of this study was to estimate ship pollution emissions with various typical merchant ship types under different sailing conditions. We estimated the emission variation with a ship traffic emission assessment model (STEAM2), and then the ship pollution emission distribution was further visualized using ArcGIS. We collected data from the automatic identification system (AIS) for ships in New York Harbor and further analyzed the spatiotemporal distribution of pollutant emissions from ships. The experimental results demonstrate that the ship pollutant emission volume in the New York Harbor area in 2022 was 3340 t, while the pollution in terms of CO, SO2, CXHX, PM10, NOX, and PM2.5 was 136, 1421, 66, 185, 1384, and 148 t, respectively. The overall SEs from container ships, passenger ships, and tankers account for a large amount of pollution discharge. The pollutant emissions of container ships are significantly greater than that of their counterparts. Moreover, the spatiotemporal distributions of ship pollutant discharge can vary significantly among different ship types and sailing conditions.

Funder

National Natural Science Foundation of China

Publisher

MDPI AG

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