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April 27, 2026Ecological Chemistry and Engineering S0 citationsOpen Access

Machine Learning Analysis of Coastal Water Pollution in China: Drivers and Complex Relationships

DSDehong SunSZSenwei ZhengYYYinhui Yu

Key Points

  • The study aims to explore the complex nonlinear effects of various factors on seawater pollution in coastal regions of China.
  • Utilized a Generalised Additive Model (GAM) to analyze panel data from 11 Chinese coastal provinces from 2018 to 2023.
  • Investigated the relationship between socioeconomic, pollution control, and technological factors on Chemical Oxygen Demand Emissions (CODE).
  • Chemical Oxygen Demand Emissions exhibited a pattern of initial increase, decrease, and stabilization, with significant spatial variability.
  • Zhejiang had the highest CODE emissions, while Tianjin recorded the lowest emissions.
  • The analysis revealed distinct nonlinear relationships between most examined variables and CODE.

Abstract

Abstract This study investigates the nonlinear effects of socioeconomic, pollution control, and technological innovation factors on seawater pollution using a Generalised Additive Model (GAM) and panel data from 11 Chinese coastal provinces (2018-2023). Key findings indicate: (1) Chemical Oxygen Demand Emissions (CODE) from direct marine discharge sources showed a temporal pattern of “initial increase, subsequent decrease, and gradual stabilisation”, with significant spatial heterogeneity - Zhejiang recorded the highest emissions, Tianjin the lowest; (2) GAM revealed significant nonlinear relationships between most variables and CODE; (3) Factor impacts exhibited distinct range-dependent characteristics. These findings provide a scientific basis for identifying key pollution sources and formulating differentiated control strategies, leading to targeted policy recommendations.

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Cite This Study

Sun et al. (2026) studied this question.

synapsesocial.com/papers/69eefd82fede9185760d426fhttps://doi.org/10.2478/eces-2026-0003
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