Based on the observation of a complete hydrological year from June 2014 to May 2015, the temporal and spatial variations of the main inorganic nitrogen (MIN, referring to NO3^--N, NO2^--N, NH4^+-N) in surface water an...Based on the observation of a complete hydrological year from June 2014 to May 2015, the temporal and spatial variations of the main inorganic nitrogen (MIN, referring to NO3^--N, NO2^--N, NH4^+-N) in surface water and groundwater of the Li River and the Yuan River wetland succession zones are analyzed. The Li River and the Yuan River are located in agricultural and non-agricultural areas, and this study focus on the influence of surface water level and groundwater depth and precipitation on nitrogen pollution. The results show that NO3^--N in surface water accounts for 70%-90% of MIN, but it does not exceed the limit of national drinking water surface water standard. Groundwater is seriously polluted by NH4^+-N.Based on the groundwater quality standard of NH4^+-N, the groundwater quality in the Li River exceeds Class III water standard throughout the year, and the exceeding months' proportion of Yuan River reaches 58.3%. Compared with the Yuan River, MIN in groundwater of the Li River shows significant temporal and spatial variations owing to the influence of agricultural fertilization. The correlation between the concentrations of MIN and surface water level is poor, while the fitting effect of quadratic correlation between NH4^+-N concentration and groundwater depth is the best (R^2=0.9384), NO3^--N is the next (R^2=0.5128), NO2^--N is the worst (R^2=0.2798). The equation of meteoric water line is δD =7.83δ^18O+12.21, indicating that both surface water and groundwater come from atmospheric precipitation. Surface infiltration is the main cause of groundwater NH4^+-N pollution. Rainfall infiltration in non-fertilization seasons reduces groundwater nitrogen pollution, while rainfall leaching farming and fertilization aggravate groundwater nitrogen pollution.展开更多
When groundwater pollution occurs,to come up with an efficient remediation plan,it is particularly important to collect information of contaminant source(location and source strength)and hydraulic conductivity field o...When groundwater pollution occurs,to come up with an efficient remediation plan,it is particularly important to collect information of contaminant source(location and source strength)and hydraulic conductivity field of the site accurately and quickly.However,the information can not be obtained by direct observation,and can only be derived from limited measurement data.Data assimilation of observations such as head and concentration is often used to estimate parameters of contaminant source.As for hydraulic conductivity field,especially for complex non-Gaussian field,it can be directly estimated by geostatistics method based on limited hard data,while the accuracy is often not high.Better estimation of hydraulic conductivity can be achieved by solving inverse groundwater problem.Therefore,in this study,the multi-point geostatistics method Quick Sampling(QS)is proposed and introduced for the first time and combined with the iterative local updating ensemble smoother(ILUES)to develop a new data assimilation framework QS-ILUES.It helps to solve the contaminant source parameters and non-Gaussian hydraulic conductivity field simultaneously by assimilating hydraulic head and pollutant concentration data.While the pilot points are utilized to reduce the dimension of hydraulic conductivity field,the influence of pilot points’layout and the ensemble size of ILUES algorithm on the inverse simulation results are further explored.展开更多
文摘Based on the observation of a complete hydrological year from June 2014 to May 2015, the temporal and spatial variations of the main inorganic nitrogen (MIN, referring to NO3^--N, NO2^--N, NH4^+-N) in surface water and groundwater of the Li River and the Yuan River wetland succession zones are analyzed. The Li River and the Yuan River are located in agricultural and non-agricultural areas, and this study focus on the influence of surface water level and groundwater depth and precipitation on nitrogen pollution. The results show that NO3^--N in surface water accounts for 70%-90% of MIN, but it does not exceed the limit of national drinking water surface water standard. Groundwater is seriously polluted by NH4^+-N.Based on the groundwater quality standard of NH4^+-N, the groundwater quality in the Li River exceeds Class III water standard throughout the year, and the exceeding months' proportion of Yuan River reaches 58.3%. Compared with the Yuan River, MIN in groundwater of the Li River shows significant temporal and spatial variations owing to the influence of agricultural fertilization. The correlation between the concentrations of MIN and surface water level is poor, while the fitting effect of quadratic correlation between NH4^+-N concentration and groundwater depth is the best (R^2=0.9384), NO3^--N is the next (R^2=0.5128), NO2^--N is the worst (R^2=0.2798). The equation of meteoric water line is δD =7.83δ^18O+12.21, indicating that both surface water and groundwater come from atmospheric precipitation. Surface infiltration is the main cause of groundwater NH4^+-N pollution. Rainfall infiltration in non-fertilization seasons reduces groundwater nitrogen pollution, while rainfall leaching farming and fertilization aggravate groundwater nitrogen pollution.
基金This work was supported by the Belt and Road Special Foundation of the State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering(No.2019nkzd01)National Natural Science Foundation of China(42077176).
文摘When groundwater pollution occurs,to come up with an efficient remediation plan,it is particularly important to collect information of contaminant source(location and source strength)and hydraulic conductivity field of the site accurately and quickly.However,the information can not be obtained by direct observation,and can only be derived from limited measurement data.Data assimilation of observations such as head and concentration is often used to estimate parameters of contaminant source.As for hydraulic conductivity field,especially for complex non-Gaussian field,it can be directly estimated by geostatistics method based on limited hard data,while the accuracy is often not high.Better estimation of hydraulic conductivity can be achieved by solving inverse groundwater problem.Therefore,in this study,the multi-point geostatistics method Quick Sampling(QS)is proposed and introduced for the first time and combined with the iterative local updating ensemble smoother(ILUES)to develop a new data assimilation framework QS-ILUES.It helps to solve the contaminant source parameters and non-Gaussian hydraulic conductivity field simultaneously by assimilating hydraulic head and pollutant concentration data.While the pilot points are utilized to reduce the dimension of hydraulic conductivity field,the influence of pilot points’layout and the ensemble size of ILUES algorithm on the inverse simulation results are further explored.
