Improving spatial representation of global ship emissions inventories

Area of Science:

• Environmental Science
• Atmospheric Science
• Oceanography

Background:

• Ship activity data sets like ICOADS and AMVER exhibit distinct spatial and statistical sampling biases.
• These biases can significantly impact the accuracy of ship emissions inventories and atmospheric models.

Purpose of the Study:

• To develop and demonstrate a method for improving the representativeness of global ship traffic data.
• To address sampling biases in ship activity data for more accurate emissions and modeling.

Main Methods:

• Utilized the International Comprehensive Ocean-Atmosphere Data Set (ICOADS) for analysis.
• Implemented a method to trim over-reporting vessels, augment data, and account for ship heterogeneity to mitigate sampling bias.
• Compared ICOADS and AMVER data, noting potential underreporting in coastal areas.

Main Results:

• The developed method improves the global-proxy representativeness of ICOADS data.
• Identified potential underreporting in both ICOADS and AMVER for coastwise shipping, suggesting regional inventories may be more locally accurate.
• The enhanced ICOADS data set is proposed as a suitable proxy for top-down global ship traffic analysis.

Conclusions:

• The improved ICOADS data set offers a more reliable global ship traffic proxy for top-down approaches.
• Combining ICOADS, AMVER, and other proxies can enhance uncertainty analyses for global ship air-emissions impacts.
• Addressing data biases is crucial for accurate environmental assessments of maritime activities.