Ship emissions and reduction potential under domestic policies and international cooperation: A case study in Hainan, China.

Ship emissions contribute considerably to air pollution and are expected to decline under domestic policies and international cooperation such as green shipping corridors (GSCs). However, evaluation of the emission reduction potential by the Domestic Emission Control Area (DECA) policy and GSC cooperation is still lacking. Here, a series of multi-year high spatiotemporal ship emission inventories around Hainan, a representative island province of China, were developed with the state-of-the-art Shipping Emission Inventory Model.

Insights into transportation CO emissions with big data and artificial intelligence.

The ever-increasing stream of big data offers potential for deep decarbonization in the transportation sector but also presents challenges in extracting interpretable insights due to its complexity and volume. This overview discusses the application of transportation big data to help understand carbon dioxide emissions and introduces how artificial intelligence models, including machine learning (ML) and deep learning (DL), are used to assimilate and understand these data. We suggest using ML to interpret low-dimensional data and DL to enhance the predictability of data with spatial connections across multiple timescales.

Ship emission variations during the COVID-19 from global and continental perspectives.

The COVID-19 pandemic and the International Maritime Organization's (IMO) 2020 fuel-switching policy have profoundly impacted global maritime activities, leading to unprecedented changes in shipping emissions. This study aimed to examine the effects from different scales and investigate the underlying drivers. The big data model Ship Emission Inventory Model (SEIM) was updated and applied to analyze the spatiotemporal pattern of global ship emissions as well as the main contributors in 2019 and 2020.

Advancing shipping NO pollution estimation through a satellite-based approach.

Estimating shipping nitrogen oxides (NO) emissions and their associated ambient NO impacts is a complex and time-consuming task. In this study, a satellite-based ship pollution estimation model (SAT-SHIP) is developed to estimate regional shipping NO emissions and their contribution to ambient NO concentrations in China. Unlike the traditional bottom-up approach, SAT-SHIP employs satellite observations with varying wind patterns to improve the top-down emission inversion methods for individual sectors amidst irregular emission plume signals.

Altitude-dependent gaseous emissions from freight trucks along the China-Pakistan Economic Corridor in Pakistan.

Recent increases in emissions from freight transport have caused strong concerns about air quality in Pakistan, following the rapid development of projects related to the China-Pakistan Economic Corridor (CPEC). This study reported the first measurements of on-road truck emissions in Pakistan and investigated their dependence on altitude along CPEC routes. Emissions from 70 trucks were measured on CPEC highways located in Islamabad (540 m above sea level), Sost (2800 m above sea level), and at the Khunjerab Pass (4693 m above sea level).

Impacts of vehicle emission on air quality and human health in China.

The growing of vehicle population aggravates air pollution and threatens human health. In this study, based on the refined whole-process vehicle emission inventory considering volatile organic compounds (VOCs) evaporation emission, the CAMx model was applied to comprehensively quantify the impacts of the vehicle sector on the annual and seasonal concentrations of PM and O in China. Also, the health risks caused by long-term exposure to PM and O were evaluated.