TY - JOUR
T1 - Water pollution scenarios and response options for China
AU - Feng, Haoyuan
AU - Schyns, Joep F.
AU - Krol, Maarten S.
AU - Yang, Mengjie
AU - Su, Han
AU - Liu, Yaoyi
AU - Lv, Yongpeng
AU - Zhang, Xuebin
AU - Yang, Kai
AU - Che, Yue
N1 - Publisher Copyright:
© 2024 The Authors
PY - 2024/3/1
Y1 - 2024/3/1
N2 - China has formulated several policies to alleviate the water pollution load, but few studies have quantitatively analyzed their impacts on future water pollution loads in China. Based on grey water footprint (GWF) assessment and scenario simulation, we analyze the water pollution (including COD, NH3-N, TN and TP) in China from 2021 to 2035 under different scenarios for three areas: consumption-side, production-side and terminal treatment. We find that under the current policy scenario, the GWF of COD, NH3-N, TN, and TP in China could be reduced by 15.0 % to 39.9 %; the most effective measures for GWF reduction are diet structure change (in the consumption-side area), and the wastewater treatment rate and livestock manure utilization improvement (in the terminal treatment area). However, the GWF will still increase in 8 provinces, indicating that the current implemented policy is not universally effective in reducing GWF across all provinces. Under the technical improvement scenario, the GWF of the four pollutants will decrease by 54.9 %–71.1 % via improvements in the current measures related to current policies and new measures in the production-side area and the terminal treatment area; thus, GWF reduction is possible in all 31 provinces. However, some policies face significant challenges in achieving full implementation, and certain policies are only applicable to a subset of provinces. Our detailed analysis of future water pollution scenarios and response options to reduce pollution loads can help to inform the protection of freshwater resources in China and quantitatively assess the effectiveness of policies in other fields.
AB - China has formulated several policies to alleviate the water pollution load, but few studies have quantitatively analyzed their impacts on future water pollution loads in China. Based on grey water footprint (GWF) assessment and scenario simulation, we analyze the water pollution (including COD, NH3-N, TN and TP) in China from 2021 to 2035 under different scenarios for three areas: consumption-side, production-side and terminal treatment. We find that under the current policy scenario, the GWF of COD, NH3-N, TN, and TP in China could be reduced by 15.0 % to 39.9 %; the most effective measures for GWF reduction are diet structure change (in the consumption-side area), and the wastewater treatment rate and livestock manure utilization improvement (in the terminal treatment area). However, the GWF will still increase in 8 provinces, indicating that the current implemented policy is not universally effective in reducing GWF across all provinces. Under the technical improvement scenario, the GWF of the four pollutants will decrease by 54.9 %–71.1 % via improvements in the current measures related to current policies and new measures in the production-side area and the terminal treatment area; thus, GWF reduction is possible in all 31 provinces. However, some policies face significant challenges in achieving full implementation, and certain policies are only applicable to a subset of provinces. Our detailed analysis of future water pollution scenarios and response options to reduce pollution loads can help to inform the protection of freshwater resources in China and quantitatively assess the effectiveness of policies in other fields.
KW - UT-Hybrid-D
KW - Environmental policy
KW - Grey water footprint
KW - Nitrogen
KW - Phosphorus
KW - Water pollution
KW - Chemical oxygen demand
UR - http://www.scopus.com/inward/record.url?scp=85182506205&partnerID=8YFLogxK
U2 - 10.1016/j.scitotenv.2023.169807
DO - 10.1016/j.scitotenv.2023.169807
M3 - Article
C2 - 38211873
AN - SCOPUS:85182506205
SN - 0048-9697
VL - 914
JO - Science of the total environment
JF - Science of the total environment
M1 - 169807
ER -