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Area of Science:

  • Environmental Science
  • Water Resource Management
  • Machine Learning Applications

Background:

  • China faces a critical groundwater quality crisis linked to economic development and climate change.
  • The full extent and future trajectory of this crisis are not well understood.
  • Groundwater quality is essential for drinking water and agriculture in China.

Purpose of the Study:

  • To develop a machine learning model to map poor groundwater quality (PGQ) across China.
  • To analyze the historical trends and future projections of PGQ from 1980 to 2100.
  • To identify the key drivers contributing to groundwater quality degradation.

Main Methods:

  • Developed a machine learning model integrating natural and socio-economic factors.
  • Constructed annual probabilistic maps of poor groundwater quality (Class V).
  • Analyzed historical data (1980-2020) and projected future scenarios (to 2100).

Main Results:

  • Poor groundwater quality (PGQ) area ratio increased from 17.3% in 1980 to 40.8% in 2020, affecting over 36% of the population.
  • Agricultural discharge was the primary driver, followed by groundwater exploitation and industrial discharge.
  • Future PGQ area ratio projections for 2050 range from 37.9% to 48.3%.

Conclusions:

  • Urgent need for effective water resource management and conservation strategies.
  • Mitigating groundwater degradation is crucial for water security in China and globally.
  • Addressing socio-economic development and climate change impacts on water resources is imperative.