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Journal

Marine Pollution Bulletin

ISSN:
0025-326X
1
h-index
1
Citations
9
Works
0.00
2-year mean citedness

Trust signals

1 of 3 signals present

Independent positive signals — not a single “predatory/legitimate” verdict, but a converging picture.

  • DOAJ
    No data
    doaj.org
  • OAK list
    No data
    OAK
  • Valid ISSN
    Present
    ISSN

This is an information label, not an official ruling. A missing signal is not a penalty — a regional journal may simply not be in DOAJ.

Trends over time

Performance over time

Citation overview

Publications (bars) and citations (line) by year

  • Publications
  • Citations
01345012010202520262010: Publications 1, Citations 12025: Publications 3, Citations 02026: Publications 5, Citations 0

Citation history

012010202520262010: Citations 12025: Citations 02026: Citations 0

Publication history

013452010202520262010: Publications 12025: Publications 32026: Publications 5

h-index evolution

Cumulative h-index by year

012010202520262010: h-index 12025: h-index 12026: h-index 1

Most-cited works

  1. Allometric relationships of 210Po and 210Pb in mussels and their application to environmental monitoring20101 citations
  2. Safeguarding tourism economies: Managing the financial and ecological challenges of oil spills on coastal destinations20250 citations
  3. Economic and ecological impacts riverine nutrient inputs in Bohai rim coastal zone, China20250 citations
  4. The role of tidal range and seawater pollution in shaping mangrove biomass and carbon stocks20250 citations
  5. Advancing real-time coastal data monitoring: Bio-optical property analysis (chlorophyll-a and TSM) in the Northern Bay of Bengal using Sentinel-3 OLCI, IRS Oceansat-3, and artificial neural networks20260 citations
  6. Predicting coastal subsidence and sea-level scenarios in the Sundarbans Delta using InSAR and artificial intelligence for sustainable coastal management20260 citations
  7. Predicting blue carbon sequestration in Sundarban coastal mangroves: A spatially explicit approach with INVEST and machine learning to advance climate resilience and UN SDG-aligned nature-based climate solutions20260 citations
  8. Corrigendum to “Predicting blue carbon sequestration in Sundarban coastal mangroves: A spatially explicit approach with INVEST and machine learning to advance climate resilience and UN SDG-aligned nature-based climate solutions” [Mar. Pollut. Bull. 226 (2026) 119387]20260 citations
  9. AI-driven prediction of soil trace metal contamination and ecological health in the Sundarbans mangrove ecosystem: Implications for nature-based solutions and the UN SDGs20260 citations
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