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This study aims to understand the effect of the Domestic Emission Control Area (DECA) policy on ambient SO2 and particle components in Shanghai. Online single particle analysis and SO2 measurements from 2015 to 2017 were compared to analyze the long-term variations before and after the DECA policy. Our study showed that there was a significant decrease in SO2 by 27–55% after the implementation of the DECA policy. The number fraction of ship-emitted particles increased along with the increase in ship traffic activity, but the particles tended to contain lower-vanadium content. The elemental carbon component decreased, while the organic carbon components increased after switching oil. One thousand and ninety four ship fuel oil samples were collected. The oil sample analysis confirmed the ambient particle results; sulfur content decreased in domestic ship heavy fuel oils from 2013 to 2018; in the low sulfur fuel oils used after the DECA policy, vanadium was still highly correlated with sulfur as it was in high-sulfur fuels. Our results suggested that heavy fuel oil is still a major part of the low-sulfur ship oils in use. The multiple-component control including organic pollutants regarding low sulfur fuel oils may be necessary for preventing air pollution from ship emissions.

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Objective. Mesothelioma (MESO) is a rare tumor derived from mesothelium cells. The aim of this study was to explore key candidate genes and potential molecular mechanisms for mesothelioma through bioinformatics analysis. Methods. The MESO expression profiles came from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. The differences in the infiltration levels of immune cells between MESO and normal tissues were assessed using CIBERSORT. Differentially expressed genes (DEGs) were identified by comprehensive analysis of multiple datasets. A protein-protein interaction (PPI) network was constructed, and a hub gene COL1A1 was selected for MESO. The expression and mutation of COL1A1 in MESO were analyzed in the cBioPortal database. The correlation between COL1A1 expression and immune cell infiltration was evaluated using the TIMER database. Gene Set Enrichment Analysis (GSEA) of COL1A1 was then performed. Finally, Kaplan-Meier survival analysis was presented to predict the survival times between high and low COL1A1 expression groups for MESO patients. Results. There were distinct differences in the infiltration levels of immune cells between MESO and normal tissues. A total of 118 DEGs were identified by comprehensively analyzing three expression profile datasets. COL1A1, a hub gene, was identified to be highly expressed in MESO compared to normal tissues. COL1A1 genetic mutation occurred in 9% of MESO samples, and amplification was the most common type of mutation. COL1A1 expression was significantly correlated to the infiltration levels of CD4+ T cells, macrophages, and neutrophils. GSEA results indicated that COL1A1 could be involved in key biological processes and pathways like extracellular matrix and PI3K-Akt pathway. Patients with high COL1A1 expression usually experienced shorten overall survival time than those with its low expression. Conclusion. Our findings revealed that COL1A1 could become a potential prognostic biomarker for MESO, which was significantly related to immune cell infiltration.