Changes in the impacts of ship emissions on PM <sub>2.5</sub> and its components in China under the staged fuel oil policies
Bibliographic record
Abstract
The issue of air pollution caused by ship emissions is becoming prominent with the increasing global shipping activities. China has carried out fuel oil policies in three stages in the past few years to meet the requirements of the global low sulfur regulation by the International Maritime Organization (IMO). However, the impacts of staged policies on air quality in China are not sufficiently understood. This study firstly updated the ship emission inventory including PM 2.5 components based on field and on-board measurements under the staged fuel oil policies. Then, the impacts of ship emissions on PM 2.5 and its gas precursors and primary and secondary components in China from 2017 to 2021 were revealed by using the Weather Research and Forecasting (WRF) model and the Community Multiscale Air Quality (CMAQ) model. In the model domain, the 99th percentile of the shipping-related PM 2.5 concentrations was reduced by 19.5 % and then by 35.6 % due to the policy shifts. Ship emissions increased the PM 2.5 concentrations up to 3.8 µg m −3 in 2017 and 2.6 µg m −3 in 2021. The areas with high concentration levels widely distributed over offshore waters in 2017 and shrunk to some parts of China's coast in 2021. The contributions of ship emissions to the PM 2.5 concentrations over China's main port cities ranged from 3.0 % to 17.4 % in 2017 and 2.5 % to 10.3 % in 2021. In these cities, the change rates of the concentrations of PM 2.5 , SO42-, NO3-, NH4+, carbonaceous aerosols, V, and Ni related to ship emissions from 2017 to 2021 were −32.7 %, −74.0 %, +11.0 %, −27.5 %, −76.9 %, −90.3 %, and −38.4 %, respectively. NO3- constituted 54.6 % of the shipping-related PM 2.5 in 2021. Our findings suggest that it is important to consider both transport pathways and secondary aerosol formation mechanisms to combat the PM 2.5 pollution caused by shipping in different regions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".