聚乙烯醇增强p(AA-co-CTS)规整吸附介质的制备及对Sb(Ⅴ)的吸附性能

以复配过硫酸钾-抗坏血酸为氧化还原引发剂,丙烯酸(AA)、壳聚糖(CTS)为功能性单体,以聚乙烯醇(PVA)为互穿线型高分子,利用溶剂结晶成孔技术和低温引发聚合制备了具有高强度、规整型吸附介质PVA-p(AA-co-CTS),探究了其对Sb(Ⅴ)的吸附性能。聚合体系最佳配方:AA与CTS总质量分数为7%,其中CTS与AA的质量比为0.18~0.2,PVA的质量分数为1.5%~2.0%,PVA-p(AA-co-CTS)介质整体成型于反应器中,孔隙连续,孔径范围为100~800μm,湿样抗压缩强度为0.48 MPa;孔隙内表面富含的-OH、-NH2及-COOH基团对Sb(Ⅴ)有强吸附能力。最佳吸附工艺为流量6 mL/min,温度293.15 K,初始浓度200 mg/L,最大吸附量76.77 mg/g。当操作温度在293.15 K时,Thomas拟合方程R2=0.9995,KTh=1.7595 mL/(mg·min)。富含功能基团规整型吸附介质通过低温结晶聚合一步成功制备,可取代传统散装吸附树脂、离子交换树脂用于Sb(Ⅴ)的吸附分离。

BP neural networks and random forest models to detect damage by Dendrolimus punctatus Walker

The construction of a pest detection algorithm is an important step to couple"ground-space"characteristics,which is also the basis for rapid and accurate monitoring and detection of pest damage.In four experimental areas in Sanming City,Jiangle County,Sha County and Yanping District in Fujian Province,sample data on pest damage in 182 sets of Dendrolimus punctatus were collected.The data were randomly divided into a training set and testing set,and five duplicate tests and one eliminating-indicator test were done.Based on the characterization analysis of the host for D.punctatus damage,seven characteristic indicators of ground and remote sensing including leaf area index,standard error of leaf area index(SEL)of pine forest,normalized difference vegetation index(NDVI),wetness from tasseled cap transformation(WET),green band(B2),red band(B3),near-infrared band(B4)of remote sensing image are obtained to construct BP neural networks and random forest models of pest levels.The detection results of these two algorithms were comprehensively compared from the aspects of detection precision,kappa coefficient,receiver operating characteristic curve,and a paired t test.The results showed that the seven indicators all were responsive to pest damage,and NDVI was relatively weak;the average pest damage detection precision of six tests by BP neural networks was 77.29%,the kappa coefficient was 0.6869 and after the RF algorithm,the respective values were 79.30%and 0.7151,showing that the latter is more optimized,but there was no significant difference(p>0.05);the detection precision,kappa coefficient and AUC of the RF algorithm was higher than the BP neural networks for three pest levels(no damage,moderate damage and severe damage).The detection precision and AUC of BP neural networks were a little higher for mild damage,but the difference was not significant(p>0.05)except for the kappa coefficient for the no damage level(p<0.05).An"over-fitting"phenomenon tends to occur in BP neural networks,while RF method is more robust,providing a detection effect that is better than the BP neural networks.Thus,the application of the random forest algorithm for pest damage and multilevel dispersed variables is thus feasible and suggests that attention to the proportionality of sample data from various categories is needed when collecting data.

Spatiotemporal analysis of vegetation variability and its relationship with climate change in China

This paper investigated spatiotemporal dynamic pattern of vegetation,climate factor,and their complex relationships from seasonal to inter-annual scale in China during the period 1982–1998 through wavelet transform method based on GIMMS data-sets.First,most vegetation canopies demonstrated obvious seasonality,increasing with latitudinal gradient.Second,obvious dynamic trends were observed in both vegetation and climate change,especially the positive trends.Over 70%areas were observed with obvious vegetation greening up,with vegetation degradation principally in the Pearl River Delta,Yangtze River Delta,and desert.Overall warming trend was observed across the whole country(>98%area),stronger in Northern China.Although over half of area(58.2%)obtained increasing rainfall trend,around a quarter of area(24.5%),especially the Central China and most northern portion of China,exhibited significantly negative rainfall trend.Third,significantly positive normalized difference vegetation index(NDVI)–climate relationship was generally observed on the de-noised time series in most vegetated regions,corresponding to their synchronous stronger seasonal pattern.Finally,at inter-annual level,the NDVI–climate relationship differed with climatic regions and their long-term trends:in humid regions,positive coefficients were observed except in regions with vegetation degradation;in arid,semiarid,and semihumid regions,positive relationships would be examined on the condition that increasing rainfall could compensate the increasing water requirement along with increasing temperature.This study provided valuable insights into the long-term vegetation–climate relationship in China with consideration of their spatiotemporal variability and overall trend in the global change process.

Relationship between spatial heterogeneity and wavelength in multisensor airborne images

Variogram has been utilized to exploring the spatial heterogeneity of remote sensing images,especially its association with spatial resolution.However,very few attentions have been drawn on evaluating the spatial heterogeneity of multisensor airborne imagery and its relationship with spectral wavelength.Therefore,an investigation was carried out on multisensor airborne images to determine the relation between spatial heterogeneity and spectral wavelength for woodland,grass,and urban landscapes by applying variogram modeling.The airborne thematic mapper(ATM),compact airborne spectrographic imager(CASI),and Specim AISA Eagle airborne images at Harwood Forest,Monks wood,Cambridge,and River Frome areas,UK,were utilized.Results revealed that(1)the red band contained greater spatial variability than near-infrared wavelengths and other visible wavebands;(2)there was a steep gradient at the red edge in reference to its spatial variability of multisensor airborne images;(3)only for natural landscape such as woodland and grass,near-infrared waveband contains greater within-scene variations than the blue and green bands;(4)compared with the discrepancy of spatial resolution introduced by multisensor images(ATM,CASI,and Eagle),the specific landscape and spectral bands were more important in determining heterogeneity by means of original visible,near-infrared bands,and normalized difference vegetation index(NDVI).These findings remained us to be caution when combining and interpreting spatial variability and spatial structures derived from airborne images with different spatial resolution and spectral wavelength.Additionally,the outcomes of this study also have considerable implications in terms of designing and choosing suitable images for different applications.

A virtual geographic environment for dynamic simulation and analysis of tailings dam failure

It is of great significance for disaster prevention and mitigation to carry out disaster simulations for dam failure accidents in advance,but at present,there are few professional systems for disaster simulations of tailings dams.In this paper,we focused on the construction of a virtual geographic environment(VGE)system that provides an effective tool for visualizing the dam-break process of a tailings pond.The dam-break numerical model of the tailings dam based on computational fluid dynamics(CFD)was integrated into the VGE system.The infrastructure of the VGE was supported by a 3-D geographic information system(GIS)with a user-friendly interface for the initiation,visualization,and analysis of the dynamic process of tailings dam failure.Key technologies,including the integration of numerical models,rendering of large-scale scenes,and optimizations of disaster simulation and visualization,were discussed in detail.In the prototype system,information on the run-out path,travel distance,etc.can be obtained to visually describe the flow motion released by two dam failure cases.The simulation results showed that the VGE can be used for the multidimensional,dynamic and interactive visualization of dam-break disasters,and can also be useful for assessing the risk associated with tailings dams.