A general regression neural network model,combined with an interative algorithm(GRNNI)using sparsely distributed samples and auxiliary environmental variables was proposed to predict both spatial distribution and vari...A general regression neural network model,combined with an interative algorithm(GRNNI)using sparsely distributed samples and auxiliary environmental variables was proposed to predict both spatial distribution and variability of soil organic matter(SOM)in a bamboo forest.The auxiliary environmental variables were:elevation,slope,mean annual temperature,mean annual precipitation,and normalized difference vegetation index.The prediction accuracy of this model was assessed via three accuracy indices,mean error(ME),mean absolute error(MAE),and root mean squared error(RMSE)for validation in sampling sites.Both the prediction accuracy and reliability of this model were compared to those of regression kriging(RK)and ordinary kriging(OK).The results show that the prediction accuracy of the GRNNI model was higher than that of both RK and OK.The three accuracy indices(ME,MAE,and RMSE)of the GRNNI model were lower than those of RK and OK.Relative improvements of RMSE of the GRNNI model compared with RK and OK were 13.6%and 17.5%,respectively.In addition,a more realistic spatial pattern of SOM was produced by the model because the GRNNI model was more suitable than multiple linear regression to capture the nonlinear relationship between SOM and the auxiliary environmental variables.Therefore,the GRNNI model can improve both prediction accuracy and reliability for determining spatial distribution and variability of SOM.展开更多
The characteristics of effluent organic matter(EfOM) from a wastewater treatment plant(WWTP) during ozonation were investigated using excitation and emission matrix(EEM)spectra, Fourier transform infrared spectroscopy...The characteristics of effluent organic matter(EfOM) from a wastewater treatment plant(WWTP) during ozonation were investigated using excitation and emission matrix(EEM)spectra, Fourier transform infrared spectroscopy(FT-IR) and high-performance size exclusion chromatography(HPSEC) at different ozone dosages. The selectivity of ozonation towards different constituents and functional groups was analysed using two-dimensional correlation spectra(2D-COS) probed by FT-IR, synchronous fluorescence spectra and HPSEC.The results indicated that ozonation can destroy aromatic structures of EfOM and change its molecular weight distribution(MWD). According to 2D-COS analysis, microbial humiclike substances were preferentially removed, and then the protein-like fractions. Terrestrial humic-like components exhibited inactivity towards ozonation compared with the above two fractions. Protein-like substances with small molecular weight were preferentially reacted during ozonation based on 2D-COS probed by HPSEC. In addition, the selectivity of ozone towards different functional groups of EfOM exhibited the following sequence:phenolic and alcoholic C\O groups > aromatic structures containing C_C double bonds >aliphatic C\H. X-ray photoelectron spectroscopy(XPS) further elucidated the preferential reaction of aromatic structures in EfOM during ozonation.展开更多
In agricultural systems,the regular monitoring of Soil Organic Matter(SOM)dynamics is essential.This task is costly and time-consuming when using the conventional method,especially in a very fragmented area and with i...In agricultural systems,the regular monitoring of Soil Organic Matter(SOM)dynamics is essential.This task is costly and time-consuming when using the conventional method,especially in a very fragmented area and with intensive agricultural activity,such as the area of Sidi Bennour.The study area is located in the Doukkala irrigated perimeter in Morocco.Satellite data can provide an alternative and fill this gap at a low cost.Models to predict SOM from a satellite image,whether linear or nonlinear,have shown considerable interest.This study aims to compare SOM prediction using Multiple Linear Regression(MLR)and Artificial Neural Networks(ANN).A total of 368 points were collected at a depth of 0-30 cm and analyzed in the laboratory.An image at 15 m resolution(MSPAN)was produced from a 30 m resolution(MS)Landsat-8 image using image pansharpening processing and panchromatic band(15 m).The results obtained show that the MLR models predicted the SOM with(training/validation)R^(2)values of 0.62/0.63 and 0.64/0.65 and RMSE values of 0.23/0.22 and 0.22/0.21 for the MS and MSPAN images,respectively.In contrast,the ANN models predicted SOM with R2 values of 0.65/0.66 and 0.69/0.71 and RMSE values of 0.22/0.10 and 0.21/0.18 for the MS and MSPAN images,respectively.Image pansharpening improved the prediction accuracy by 2.60%and 4.30%and reduced the estimation error by 0.80%and 1.30%for the MLR and ANN models,respectively.展开更多
Metal binding of organic ligands can definitely affect its environmental behavior in waters,while information on the binding heterogeneity with different organic ligands is still lacked till now.In this study,the bind...Metal binding of organic ligands can definitely affect its environmental behavior in waters,while information on the binding heterogeneity with different organic ligands is still lacked till now.In this study,the binding of zinc with organic matters associated with cyanobacterial blooms,including dissolved organic matters（DOM） and attached organic matters（AOM）,were studied by using fluorescence quenching titration combined with two-dimensional correlation spectroscopy（2D-COS）.Metal-induced fluorescent quenching was obviously observed both for DOM and AOM,indicating the formation of metal-ligand complexes.Compared with the one-dimensional spectra,2D-COS revealed the sequences of metal-ligand interaction with the following orders：276 nm 〉 232 ran for DOM and232 nm 〉 276 nm for AOM.Furthermore,the modified Stern-Volmer model showed that the binding constant（logKM） of 276 nm in DOM was higher than that of 232 nm（4.93 vs.4.51）,while AOM was characterized with a high binding affinity for 232 nm（log KM：4.83）.The ranks of log KM values were consistent with the sequential orders derived from 2D-COS results both for the two samples.Fluorescence quenching titration combined with 2D-COS was an effective method to characterize the metal-ligand interaction.展开更多
基金The article is supported by National Key Research and Development Projects of P.R.China(No.2018YFD0600100).
文摘A general regression neural network model,combined with an interative algorithm(GRNNI)using sparsely distributed samples and auxiliary environmental variables was proposed to predict both spatial distribution and variability of soil organic matter(SOM)in a bamboo forest.The auxiliary environmental variables were:elevation,slope,mean annual temperature,mean annual precipitation,and normalized difference vegetation index.The prediction accuracy of this model was assessed via three accuracy indices,mean error(ME),mean absolute error(MAE),and root mean squared error(RMSE)for validation in sampling sites.Both the prediction accuracy and reliability of this model were compared to those of regression kriging(RK)and ordinary kriging(OK).The results show that the prediction accuracy of the GRNNI model was higher than that of both RK and OK.The three accuracy indices(ME,MAE,and RMSE)of the GRNNI model were lower than those of RK and OK.Relative improvements of RMSE of the GRNNI model compared with RK and OK were 13.6%and 17.5%,respectively.In addition,a more realistic spatial pattern of SOM was produced by the model because the GRNNI model was more suitable than multiple linear regression to capture the nonlinear relationship between SOM and the auxiliary environmental variables.Therefore,the GRNNI model can improve both prediction accuracy and reliability for determining spatial distribution and variability of SOM.
基金supported by the National Key Technology Support Program (No.2014BAC13B06)the National Natural Science Foundation of China (Nos.51708443,51378414)+2 种基金the National Key Research and Development Program of China (No.2016YFC0400701)the China Postdoctoral Science Foundation (No.2017M623326XB)the Program for Innovative Research Teams in Shaanxi (No.2013KCT-13)
文摘The characteristics of effluent organic matter(EfOM) from a wastewater treatment plant(WWTP) during ozonation were investigated using excitation and emission matrix(EEM)spectra, Fourier transform infrared spectroscopy(FT-IR) and high-performance size exclusion chromatography(HPSEC) at different ozone dosages. The selectivity of ozonation towards different constituents and functional groups was analysed using two-dimensional correlation spectra(2D-COS) probed by FT-IR, synchronous fluorescence spectra and HPSEC.The results indicated that ozonation can destroy aromatic structures of EfOM and change its molecular weight distribution(MWD). According to 2D-COS analysis, microbial humiclike substances were preferentially removed, and then the protein-like fractions. Terrestrial humic-like components exhibited inactivity towards ozonation compared with the above two fractions. Protein-like substances with small molecular weight were preferentially reacted during ozonation based on 2D-COS probed by HPSEC. In addition, the selectivity of ozone towards different functional groups of EfOM exhibited the following sequence:phenolic and alcoholic C\O groups > aromatic structures containing C_C double bonds >aliphatic C\H. X-ray photoelectron spectroscopy(XPS) further elucidated the preferential reaction of aromatic structures in EfOM during ozonation.
文摘In agricultural systems,the regular monitoring of Soil Organic Matter(SOM)dynamics is essential.This task is costly and time-consuming when using the conventional method,especially in a very fragmented area and with intensive agricultural activity,such as the area of Sidi Bennour.The study area is located in the Doukkala irrigated perimeter in Morocco.Satellite data can provide an alternative and fill this gap at a low cost.Models to predict SOM from a satellite image,whether linear or nonlinear,have shown considerable interest.This study aims to compare SOM prediction using Multiple Linear Regression(MLR)and Artificial Neural Networks(ANN).A total of 368 points were collected at a depth of 0-30 cm and analyzed in the laboratory.An image at 15 m resolution(MSPAN)was produced from a 30 m resolution(MS)Landsat-8 image using image pansharpening processing and panchromatic band(15 m).The results obtained show that the MLR models predicted the SOM with(training/validation)R^(2)values of 0.62/0.63 and 0.64/0.65 and RMSE values of 0.23/0.22 and 0.22/0.21 for the MS and MSPAN images,respectively.In contrast,the ANN models predicted SOM with R2 values of 0.65/0.66 and 0.69/0.71 and RMSE values of 0.22/0.10 and 0.21/0.18 for the MS and MSPAN images,respectively.Image pansharpening improved the prediction accuracy by 2.60%and 4.30%and reduced the estimation error by 0.80%and 1.30%for the MLR and ANN models,respectively.
基金funded by the National Natural Science Foundation of China(Nos.51479187,51209192)the China Postdoctoral Science Foundation(Nos.2014T70505+1 种基金2013M 540438)the PAPD,and the State Key Laboratory of Pollution Control and Resource Reuse Foundation(No.PCRRF13011)
文摘Metal binding of organic ligands can definitely affect its environmental behavior in waters,while information on the binding heterogeneity with different organic ligands is still lacked till now.In this study,the binding of zinc with organic matters associated with cyanobacterial blooms,including dissolved organic matters（DOM） and attached organic matters（AOM）,were studied by using fluorescence quenching titration combined with two-dimensional correlation spectroscopy（2D-COS）.Metal-induced fluorescent quenching was obviously observed both for DOM and AOM,indicating the formation of metal-ligand complexes.Compared with the one-dimensional spectra,2D-COS revealed the sequences of metal-ligand interaction with the following orders：276 nm 〉 232 ran for DOM and232 nm 〉 276 nm for AOM.Furthermore,the modified Stern-Volmer model showed that the binding constant（logKM） of 276 nm in DOM was higher than that of 232 nm（4.93 vs.4.51）,while AOM was characterized with a high binding affinity for 232 nm（log KM：4.83）.The ranks of log KM values were consistent with the sequential orders derived from 2D-COS results both for the two samples.Fluorescence quenching titration combined with 2D-COS was an effective method to characterize the metal-ligand interaction.
