Temporal and spatial distribution characteristics in the natural plague foci of Chinese Mongolian gerbils based on spatial autocorrelation.

BACKGROUND: The nest flea index of Meriones unguiculatus is a critical indicator for the prevention and control of plague, which can be used not only to detect the spatial and temporal distributions of Meriones unguiculatus, but also to reveal its cluster rule. This research detected the temporal and spatial distribution characteristics of the plague natural foci of Mongolian gerbils by body flea index from 2005 to 2014, in order to predict plague outbreaks. METHODS: Global spatial autocorrelation was used to describe the entire spatial distribution pattern of the body flea index in the natural plague foci of typical Chinese Mongolian gerbils. Cluster and outlier analysis and hot spot analysis were also used to detect the intensity of clusters based on geographic information system methods. The quantity of M. unguiculatus nest fleas in the sentinel surveillance sites from 2005 to 2014 and host density data of the study area from 2005 to 2010 used in this study were provided by Chinese Center for Disease Control and Prevention. RESULTS: The epidemic focus regions of the Mongolian gerbils remain the same as the hot spot regions relating to the body flea index. High clustering areas possess a similar pattern as the distribution pattern of the body flea index indicating that the transmission risk of plague is relatively high. In terms of time series, the area of the epidemic focus gradually increased from 2005 to 2007, declined rapidly in 2008 and 2009, and then decreased slowly and began trending towards stability from 2009 to 2014. For the spatial change, the epidemic focus regions began moving northward from the southwest epidemic focus of the Mongolian gerbils from 2005 to 2007, and then moved from north to south in 2007 and 2008. CONCLUSIONS: The body flea index of Chinese gerbil foci reveals significant spatial and temporal aggregation characteristics through the employing of spatial autocorrelation. The diversity of temporary and spatial distribution is mainly affected by seasonal variation, the human activity and natural factors.

Evaluation of relative water use efficiency (RWUE) at a regional scale: a case study of Tuhai-Majia Basin, China.

Enhancing water use efficiency (WUE) is the key approach to maintain sustainable water resource supply. Due to the complexity of the water cycle, accurate estimation of WUE at the regional scale is a challenging task. Here we presented a framework of relative water use efficiency (RWUE). According to the linkage between RWUE and land use types, assessment of WUE at a regional scale could be performed operationally. This approach was evaluated in a study area, Tuhai-Majia Basin, North China. Based on remote sensing-derived evapotranspiration (ET) and land use data, regional WUE were assessed accordingly. The mean RWUE of agriculture, ecosystem and total basin in 2005 was 60.12, 30.07 and 62.5%, respectively. Spatial analysis showed that the agricultural WUE played the dominant role in water-saving of the study area; water management of unused land (RWUE of 2005 was 5.46%), especially wetland protection and other unused land development, will contribute significantly to ecological RWUE improvement. Temporal analysis indicated that there was considerable inter-annual variability in RWUE time series profiles. The agricultural interlude period might be important for enhancing WUE in the Tuhai-Majia Basin. In general, the results indicated that the RWUE-based method was an efficient and simple method to evaluate WUE at regional scale.

[Risk assessment of the farmland and water contamination with the livestock manure in Anhui province].

Basing on the data of livestock in 2001-2009 in Anhui province, the farmland pollution loading and water equal standard pollution loading of livestock manure were calculated utilizing the discharge rate of livestock manure. In addition, the risk assessment was evaluated on the livestock pollution in farmland and water bodies in this province. The industrial production of animal manure of this industry in 2008-2009 in Anhui amounted to 0.67 billion tons, and the averaged farmland loading of livestock manure, N, and P were 16.2 t x hm(-2), 83.8 kg x hm(-2), and 34.5 kg x hm(-2), respectively. The overall averaged risk constant of livestock manure loading in farmland was 0.36 (approximately risk level I). As to the water bodies, the averaged equal standard pollution loading was 7.03. However, significant differences were observed for the farmland and water contamination with livestock manure in different areas of Anhui, suggesting that some areas might receive much higher doses than the averaged amounts. The contamination weakened comparing with that in 2001-2002. But there was a trend of increase for P pollution. According to the information in 2008-2009, the farmland and water bodies in the areas of Hefei, Suzhou, and Bengbu still borne the livestock manure contamination. Results of this work provide some useful information for the water and farmland environmental protection in Huaihe river basin in Anhui province.

[Study on canopy spectral characteristics of paddy polluted by heavy metals].

Because of frequent mining, heavy metals are brought into environment like soils, water and atmosphere, resulting heavy metal contamination in the agricultural region beside mines. Heavy metals contamination causes vegetation stress like destruction of chloroplast structure, chlorophyll content decrease, blunt photosynthesis, etc. Spectral responses to changes in chlorophyll content and photosynthesis make it possible that remote sensing is applied in monitoring heavy metals stress on paddy plants. Field spectroradiometer was used to acquire canopy reflectance spectra of paddy plants contaminated by heavy metals released from local mining. The present study was conducted to (1) investigate discrimination of canopy reflectance spectra of heavy metal polluted and normal paddy plants; (2) extract spectral characteristics of contaminated paddy plants and compare them. By means of correlation analysis, sensitive bands (SB) were firstly picked out from canopy spectra. Secondly, on the basis of these sensitive bands, normalized difference vegetation indices (NDVI) were established, and then red edge position (REP) was extracted from canopy spectra via curve fitting of inverted Gaussian model. As a result of correlation analysis, 460, 560, 660 and 1 100 nm were considered respectively as sensitive band for Pb, Zn, Cu and As concentration in paddy leaves. Furthermore, heavy metal concentrations (Pb, Zn, Cu and As) were significantly correlated with NDVIs (Pb, NDV(510, 810); Zn, NDVI(510, 870; Cu, NDVI(660, 870); As, NDVI(510, 810)). Heavy metals were also significantly correlated with REP, however, the inflexion termed as spectral critical value (SCV) between low and high heavy metals concentrations should be considered during applying REP in remote sensing monitoring. Moreover, NDVI and REP are much better than SB in terms of capability of expressing spectral information. Therefore, heavy metals contamination in paddy plants can be remotely monitored via ground spectroradiometer when NDVI and REP are selected as spectral characteristics.

[Analysis of visible and near-infrared spectra of As-contaminated soil in croplands beside mines].

Soil samples in the depth from 0 to 20 cm were scooped from agricultural region beside mines and prepared for determination of As concentration, Fe concentrations and organic matter content. At the same time they were scanned by mobile hyperspectral radiometer for visible and near-infrared spectra. Savitzky-Golay filter was used to smooth noises in spectrum curve because of some low signal-to-noise ratios in some regions of visible and near-infrared light, and all the spectra were resampled with the spectral interval of 10 nm. Before principal component regression and partial least square regression models were constructed for predicting As concentration, Fe concentrations and OM content, several spectral preprocessing techniques like first/second derivative (F/SD), baseline correction (B), standard normalized variate (SNV), multiplicative scatter correction (MSC) and continuum removal (CR) were used for promotion of models' robustness and predicting performance. For limited samples, cross validation was carried out by repeated leave-one-out procedure, and root mean square error of prediction (RMSEP) was used for validating the prediction ability of constructed models. In this study principal component regression models behave better than partial least square regression models in representing regressing ability, reducing risk of over-fitting with less factors and ensuring models' accuracy and pertinences (relative RMSEP and R2). Preprocessing techniques of SNV, MSC and CR improve obviously the prediction ability of models for As concentration, Fe concentrations and OM content with relative RMSEP equal to 0.3040, 0.1443 and 0.1712, with number of factors equal to 5, 3 and 3, respectively. The analysis of regression vectors of selected optimal PCR models shows that several important wavelengths are simultaneously taken and helpful for prediction performance: 450, 1,000, 1,400, 1,900, 2,050, 2,200, 2,250, 2,400 and 2,470 nm. Application of the calibrated models to soil contamination of croplands is promising. Concentrations of soil contaminants and contents of other matter can be determined by reflectance spectroscopy with high spectra resolution, which would provide potent reference for remote sensing monitoring of soil and environmental quality.