Seasonal trend of ambient PCDD/Fs in Tianjin City, northern China using active sampling strategy

The spatial concentrations, seasonal trends, profiles and congener pairs of ambient polychlorinated dibenzo-p-dioxins （PCDDs） and polychlorinated dibenzofurans （PCDFs） were investigated within a seasonally active sampling scheme during Jun 2008 and Jan 2009 in Tianjin City, northern China. The PCDD/F concentrations ranged 14.2-172 fg I-TEQ/m3 （average 69.3 fg I-TEQ/m3） in summer and （89.8-1.01） x 103 fg I-TEQ/m3 （average 509 fg I-TEQ/m3） in winter, respectively, except for the E-waste dismantling site where much higher values were observed （1.04 x 103 fg I-TEQ]m3 in summer and 7.123 x 103 fg I-TEQ/m3 in winter）. The results indicated a significantly seasonal trend with higher TEQ values in winter as compared with summer, which could be related to increased emission sources and seasonal variations of the atmospheric boundary layer height. 2,3,4,7,8-PeCDF was the dominant contributor to the total PCDD/F toxic equivalents, and 2,3,7,8-TCDD was detected at almost all the sampling sites in winter. Most of the similarly substituted PCDD/F congener pairs exhibited high correlations, suggesting that they might have similar environmental fate or sources. But different seasonal and spatial distributions of PCDD/F concentrations indicated that the emission sources might be intermittent.

Long-term Prediction and Verification of Rainfall Based on the Seasonal Model

Using the seasonal cross-multiplication trend model, monthly precipitation of eight national basic weather stations of Shaanxi Province from 2005 to 2010 was predicted, and the forecast results were verified using the rainfall scoring rules of China Meteorological Administration. The verification results show that the average score of annual precipitation prediction in recent six years is higher than that made by a professional forecaster, so this model has a good prospect of application. Moreover, the level of making prediction is steady, and it can be widely used in long-term prediction of rainfall.

Correlation analysis between the Aral Sea shrinkage and the Amu Darya River

The shrinkage of the Aral Sea,which is closely related to the Amu Darya River,strongly affects the sustainability of the local natural ecosystem,agricultural production,and human well-being.In this study,we used the Bayesian Estimator of Abrupt change,Seasonal change,and Trend(BEAST)model to detect the historical change points in the variation of the Aral Sea and the Amu Darya River and analyse the causes of the Aral Sea shrinkage during the 1950–2016 period.Further,we applied multifractal detrend cross-correlation analysis(MF-DCCA)and quantitative analysis to investigate the responses of the Aral Sea to the runoff in the Amu Darya River,which is the main source of recharge to the Aral Sea.Our results showed that two significant trend change points in the water volume change of the Aral Sea occurred,in 1961 and 1974.Before 1961,the water volume in the Aral Sea was stable,after which it began to shrink,with a shrinkage rate fluctuating around 15.21 km3/a.After 1974,the water volume of the Aral Sea decreased substantially at a rate of up to 48.97 km3/a,which was the highest value recorded in this study.In addition,although the response of the Aral Sea's water volume to its recharge runoff demonstrated a complex non-linear relationship,the replenishment of the Aral Sea by the runoff in the lower reaches of the Amu Darya River was identified as the dominant factor affecting the Aral Sea shrinkage.Based on the scenario analyses,we concluded that it is possible to slow down the retreat of the Aral Sea and restore its ecosystem by increasing the efficiency of agricultural water use,decreasing agricultural water use in the middle and lower reaches,reducing ineffective evaporation from reservoirs and wetlands,and increasing the water coming from the lower reaches of the Amu Darya River to the 1961–1973 level.These measures would maintain and stabilise the water area and water volume of the Aral Sea in a state of ecological restoration.Therefore,this study focuses on how human consumption of recharge runoff affects the Aral Sea and provides scientific perspective on its ecological conservation and sustainable development.

Multi-output Gaussian Process Regression Model with Combined Kernel Function for Polyester Esterification Processes

In polyester fiber industrial processes,the prediction of key performance indicators is vital for product quality.The esterification process is an indispensable step in the polyester polymerization process.It has the characteristics of strong coupling,nonlinearity and complex mechanism.To solve these problems,we put forward a multi-output Gaussian process regression(MGPR)model based on the combined kernel function for the polyester esterification process.Since the seasonal and trend decomposition using loess(STL)can extract the periodic and trend characteristics of time series,a combined kernel function based on the STL and the kernel function analysis is constructed for the MGPR.The effectiveness of the proposed model is verified by the actual polyester esterification process data collected from fiber production.

Prediction of Civil Aviation Passenger Transportation Based on ARIMA Model

The passenger transportation, as an important index to describe the scale of aviation passenger transport, prediction and research, can let us understand the future trend of the aviation passenger transport, according to it, the airline can make corresponding marketing strategy adjustment. Combining with the knowledge of time series let us understand the characteristics of passenger transportation change, the R software is used to fit the data, so as to establish the ARIMA(1,1,8) model to describe the civil aviation passenger transport developing trend in the future and to make reasonable predictions.

Characteristics of gravity signal and loading effect in China

The complex geographical environment in China makes its gravity signals miscellaneous.This work gives a comprehensive representation and explanation in secular trend of gravity change in different regions,the key features of which include positive trend in inner Tibet Plateau and South China and negative trend in North China plain and high mountain Asia（HMA）.We also present the patterns of amplitudes and phases of annual and semiannual change.The mechanism underlying the semiannual period is explicitly discussed.The displacement in three directions expressed in terms of geo-potential spherical coefficients and load Love numbers are given.A case study applied with these equations is presented.The results show that Global Positioning System（GPS） observations can be used to compare with Gravity Recovery and Climate Experiment（GRACE） derived displacement and the vertical direction has a signal-noise-ratio of about one order of magnitude larger than the horizontal directions.

Seasonal variations and the influence of geomembrane liners on the levels of PBDEs in landfill leachates,sediment and groundwater in Gauteng Province,South Africa

In the present study,the seasonal concentrations of polybrominated diphenyl ethers(PBDEs)in leachate and sediment samples,and the influence of geomembrane liners on PBDE levels and the extent of their infiltration into groundwater on selected landfill sites in Gauteng Province,South Africa were determined.Leachate and sediment samples were collected from seven operational landfill sites namely:Goudkoppies,Robinson Deep,Marie Louis,Soshanguve,Onderstepoort,Hatherly and Garankuwa from Johannesburg and Pretoria,in winter and summer.Groundwater samples were collected from monitoring boreholes from two landfill sites.Liquid-liquid and Soxhlet extraction techniques were employed for the extraction of leachate and groundwater,and sediment respectively using dichloromethane.The extracted samples were subjected to column clean up and,thereafter,analysed using gas chromatographyemass spectroscopy(GC-MS).PBDEs selected for the study were:BDE-17,-28,-47,-100,-99,-153,-154,-183 and-209.The P9PBDE concentrations in leachate samples for winter and summer ranged from 0.316e1.36 ng L-1 and 0.560e1.08 ng L-1 respectively.The P9 PBDE concentrations obtained for sediment in winter and summer were 3.00e4.91 ng g-1 and 2.50e3.71 ng g-1 respectively.Winter samples exhibited higher(p<0.05)concentrations for both leachate and sediment samples compared to summer samples.This trend was attributed to high precipitation rate in summer which may have infiltrated into the landfills,subsequently diluting the leachate and sediment samples.In contrast,the winter period is generally dry and PBDEs are,therefore,more likely to be concentrated.The concentrations of PBDEs in leachate and sediment samples were higher in landfill sites with geomembrane liners compared to those without liners.Groundwater samples taken from the vicinity of selected landfill sites without geomembrane liners exhibited high concentrations of P9PBDEs,indicating possible migration of PBDEs from landfill site into groundwater.Pearson correlation(r)and statistical significant t-test(p)for the PBDE congeners versus dissolved organic carbon(DOC)resulted in positive moderate interactions with a statistical significance for most congeners.Suggesting that there is a possible influence of organic carbon on the levels of PBDEs.

Regional drying and wetting trends over Central Asia based on Koppen climate classification in 1961—2015

Central Asia(CA)is one of the most drought-prone regions in the world with complex climate regimes,it is extremely vulnerable to water scarcity.Many studies on drought in CA,as a whole,have been carried out,whereas there is a lack of studies on the drying and wetting trends of different climatic zones within CA.In this study,CA was divided into three different climatic zones based on the Koppen climate classification method,precipitation climatology,and aridity index.These were the temperate continental(Df),dry arid desert(BW),and Mediterranean continental(Ds)climatic zones.The regional drying and wetting trends during the years 1961—2015 were investigated using the monthly gridded Standardized Precipitation Evapotranspiration Index(SPEI).The empirical orthogonal function(EOF)was applied to analyze spatial and temporal variation patterns.EOF mode 1(EOF1)analysis found increasingly wet conditions throughout CA over the duration of the study,and EOF mode 2(EOF2)analysis found a contrast between northern and southern CA:as Df became drier and BW and Ds became wetter.EOF mode 3(EOF3)analysis found a western and eastern inverse phase distribution.The SPEI displayed a decreasing trend from 1961 to 1974 for CA as a whole,before increasing from 1975 to 2015,with a particularly significant increase over the first seven years of that period.On a regional scale,the BW and Ds zones experienced a wetting trend due to increased precipitation during 1961—2015,but the Df zone experienced a drying trend due to reduced evapotranspiration and an increasing temperature,particularly from 1961 to 1978.Thus,the spatio-temporal patterns in dryness and wetness across CA can be categorized according to climatic regions.

Integration of Landsat time-series vegetation indices improves consistency of change detection

Vegetation indices(VIs)were used to detect when and where vegetation changes occurred.However,different VIs have different or even diametrically opposite results,which obstructed the in-depth understanding of vegetation change.Therefore,this study examined the spatial and temporal consistency offive VIs(EVI;NBR;NDMI;NDVI;and NIRv)in detecting abrupt and gradual vegetation changes,and provided an ensemble algorithm which integrated the change detection results of thefive indices to reduce the uncertainty of change detection using a single index.The spatial consistency of thefive indices in abrupt change detection accounted for 50.6%of the study area,but the temporal consistency was low(21.6%).Wetness indices(NBR,NDMI)were more sensitive to negative abrupt changes,greenness indices(EVI,NDVI,NIRv)were more sensitive to positive abrupt changes;and both types of indices were similar in detecting gradual and total changes.The overall accuracy of the ensemble method was 81.60%and higher than that of any single index in abrupt change detection.This study provides a comprehensive evaluation of the spatial and temporal inconsistencies of change detection in model-fitting errors and various types of vegetation changes.The proposed ensemble method can support robust change-detection.

The identification of wildlife-vehicle collision hotspots: Citizen science reveals spatial and temporal patterns

Linear infrastructures(e.g.,roads,railways,pipelines,and powerlines)pose a serious threat to wildlife,due to the risk of wildlife-vehicle collisions(roadkills).The placement of mitigation measures,such as crossing structures,should consider species’life cycles and ecological requirements.Such an assessment would require data collection over large areas,which may be possible by employing citizen science.In this study,we aimed to identify spatiotemporal trends of roadkill occurrence using citizen science data from one of the most urbanized and biodiversityrich regions of Italy.Temporal trends were analyzed using generalized additive models,while landscape patterns were assessed by identifying significant thresholds over land cover gradients,related to increases in relative roadkill abundance,by employing threshold indicator taxa analysis.Our approach recorded a total of 529 roadkills,including 33 different species,comprising 13 mammal,10 bird,6 reptile,and 2 amphibian species.Statistical analysis indicated significant temporal trends for the red fox,the European hedgehog,the stone marten and the European badger,with peaks in roadkill occurrence between the winter and spring months.Relative roadkill abundance increased mostly in landscapes with anthropogenic land cover classes,such as complex cultivations,orchards,or urban surfaces.Our results allowed us to develop a map of potential roadkill risk that could assist in planning the placement of mitigation measures.Citizen science contributions from highly populated areas allowed data collection over a large area and a dense road network,and also directly led to the evaluation of management decisional options.

Annual 30-m land use/land cover maps of China for 1980–2015 from the integration of AVHRR, MODIS and Landsat data using the BFAST algorithm

Annual land use land cover(LULC)change information at medium spatial resolution(i.e.at 30 m)is required in numerous subjects,such as biophysical modelling,land management and global change studies.Annual LULC information,however,is usually not available at continental or national scale due to reasons such as insufficient remote sensing data coverage or lack of computational capabilities.Here we integrate high temporal resolution and coarse spatial resolution satellite images(i.e.,Moderate Resolution Imaging Spectroradiometer(MODIS)and Global Inventory Modelling and Mapping Studies(GIMMS)normalized difference vegetation index(NDVI))with high spatial resolution datasets(China’s Land-Use/cover Datasets(CLUDs)derived from 30-meter Landsat TM/ETM+/OLI)to generate reliable annual nominal 30 m LULC maps for the whole of China between 1980 and 2015.We also test the performance of a statistical based change detection algorithm(Breaks for Additive Seasonal and Trend),originally designed for tracking forest change,in classifying all-type LULC change.As a result,a nominal 30 m annual land use/land cover datasets(CLUD-A)from 1980 to 2015 was developed for the whole China.The mapping results were assessed with a change sample dataset,a regional annual validation sample set and a three-year China sample set.Of the detected change years,75.61%matched the exact time of conversion within±1 year.Annual mapping results provided a detail process of urbanization,deforestation,afforestation,water and cropland dynamics over the past 36 years.The consistent characterization of land change dynamics for China can be further used in scientific research and to support land management for policy-makers.

基于稠密Landsat数据的邛崃山大熊猫栖息地植被变化研究

深入理解大熊猫栖息地植被变化过程及其驱动力,是开展大熊猫栖息地保护和管理的重要基础。该研究利用1986–2018年所有可用的Landsat TM/ETM/OLI影像构建长时间序列归一化植被指数(NDVI),采用BFAST(Breaks For Additive Seasonal and Trend)方法实现大熊猫栖息地植被变化历史检测,从植被累积突变、累积渐变和总变化3个指标揭示植被变化空间分布特征;运用地理探测模型定量评价不同因子(年降水量、年平均气温、高程、坡度、坡向、与河流距离、土壤类型、土地覆盖类型、与道路的距离、与工程扰动区距离)对3种植被变化空间分布的影响。结果表明:1)研究区内植被突变面积比例为9.13%,主要分布于栖息地东部边界附近,2011和2013年植被突变面积较大;2)植被累积突变表现为退化面积占植被累积突变面积的40.17%,植被累积渐变和总变化表明研究区植被呈现改善趋势,改善面积比例分别占研究区的94.58%和97.02%;3)3种植被变化的空间分布主要受年降水量、年平均气温、高程、土壤类型4种因子的影响,植被累积突变、累积渐变和总变化空间分布的最强解释因子分别为年降水量、高程和土壤类型,驱动因子之间的交互作用为相互增强、非线性增强关系。