Analysis of long term water quality variations driven by multiple factors in a typical basin of Beijing-Tianjin-Hebei region combined with neural networks

Water environment can be affected by multiple natural and human factors. This study aimed to explore the impacts on water quality from various driving factors including land use, landscape, social economy and climate in a typical basin of Beijing-Tianjin-Hebei Region combined with neural networks like self-organizing map and back propagation artificial neural network based on the water quality monitoring data. And redundancy analysis as well as gray correlation analysis were also adopted to explore the relationships between water quality and landscape indices as well as socioeconomic factors, respectively. The results showed that different land uses had different impacts on water quality. Agricultural additions in cropland had a great impact on surrounding water environment. The forest and grassland purified water to some degree, while the vegetation purification became weaker when there was serious pollution. The fragmentated landscape patches with high patch density caused by human activities also worsened water quality because its retention of pol-lutants was weakened. Besides, the per capita disposable income of rural residents and the per capita disposable urban income had the highest correlation degrees with most water quality indexes in all socioeconomic factors. In addition, temperature rise would promote dissolved oxygen consumption and eutrophication. Increasing precipitation brought pollutants into water via rainfall contains, nutrient transportation and soil erosion. The main conclusions were that both agricultural activities and urban development had impacts on water environ-ment, and the vegetation purification became less obvious in heavily polluted areas. In addition, the rising temperature and precipitation disturbed water quality under the background of global warming. This study is helpful for water pollution management and it will give enlightenment for the water environment protection under the combining effects of human disturbance, economic development and climate change.

Automated summary

This study aimed to explore the impacts on water quality from various driving factors including land use, landscape, social economy and climate in a typical basin of Beijing-Tianjin-Hebei Region. Several neural network methods and statistical analyses were used to analyze the relationships between water quality and landscape indices as well as socioeconomic factors. The results showed that different land uses had different impacts on water quality, and both agricultural activities and urban development had significant effects on water environment.