Spatio-temporal Variation Characteristics of Extreme Climate Events and Their Teleconnections to Large-scale Ocean-atmospheric Circulation Patterns in Huaihe River Basin,China During 1959–2019

Huaihe River Basin(HRB) is located in China’s north-south climatic transition zone,which is very sensitive to global climate change.Based on the daily maximum temperature,minimum temperature,and precipitation data of 40 meteorological stations and nine monthly large-scale ocean-atmospheric circulation indices data during 1959–2019,we present an assessment of the spatial and temporal variations of extreme temperature and precipitation events in the HRB using nine extreme climate indices,and analyze the teleconnection relationship between extreme climate indices and large-scale ocean-atmospheric circulation indices.The results show that warm extreme indices show a significant(P < 0.05) increasing trend,while cold extreme indices(except for cold spell duration) and diurnal temperature range(DTR) show a significant decreasing trend.Furthermore,all extreme temperature indices show significant mutations during 1959-2019.Spatially,a stronger warming trend occurs in eastern HRB than western HRB,while maximum 5-d precipitation(Rx5day) and rainstorm days(R25) show an increasing trend in the southern,central,and northwestern regions of HRB.Arctic oscillation(AO),Atlantic multidecadal oscillation(AMO),and East Atlantic/Western Russia(EA/WR) have a stronger correlation with extreme climate indices compared to other circulation indices.AO and AMO(EA/WR) exhibit a significant(P < 0.05) negative(positive)correlation with frost days and diurnal temperature range.Extreme warm events are strongly correlated with the variability of AMO and EA/WR in most parts of HRB,while extreme cold events are closely related to the variability of AO and AMO in eastern HRB.In contrast,AMO,AO,and EA/WR show limited impacts on extreme precipitation events in most parts of HRB.

Dynamic Multi-Graph Spatio-Temporal Graph Traffic Flow Prediction in Bangkok:An Application of a Continuous Convolutional Neural Network

The ability to accurately predict urban traffic flows is crucial for optimising city operations.Consequently,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to understand complex mobility patterns.Deep learning techniques,such as graph neural networks(GNNs),are popular for their ability to capture spatio-temporal dependencies.However,these models often become overly complex due to the large number of hyper-parameters involved.In this study,we introduce Dynamic Multi-Graph Spatial-Temporal Graph Neural Ordinary Differential Equation Networks(DMST-GNODE),a framework based on ordinary differential equations(ODEs)that autonomously discovers effective spatial-temporal graph neural network(STGNN)architectures for traffic prediction tasks.The comparative analysis of DMST-GNODE and baseline models indicates that DMST-GNODE model demonstrates superior performance across multiple datasets,consistently achieving the lowest Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)values,alongside the highest accuracy.On the BKK(Bangkok)dataset,it outperformed other models with an RMSE of 3.3165 and an accuracy of 0.9367 for a 20-min interval,maintaining this trend across 40 and 60 min.Similarly,on the PeMS08 dataset,DMST-GNODE achieved the best performance with an RMSE of 19.4863 and an accuracy of 0.9377 at 20 min,demonstrating its effectiveness over longer periods.The Los_Loop dataset results further emphasise this model’s advantage,with an RMSE of 3.3422 and an accuracy of 0.7643 at 20 min,consistently maintaining superiority across all time intervals.These numerical highlights indicate that DMST-GNODE not only outperforms baseline models but also achieves higher accuracy and lower errors across different time intervals and datasets.

Role of variation coefficient of stone density in determining success of shock wave lithotripsy in urinary calculi

BACKGROUND Various stone factors can affect the net results of shock wave lithotripsy(SWL).Recently a new factor called variation coefficient of stone density(VCSD)is being considered to have an impact on stone free rates.AIM To assess the role of VCSD in determining success of SWL in urinary calculi.METHODS Charts review was utilized for collection of data variables.The patients were subjected to SWL,using an electromagnetic lithotripter.Mean stone density(MSD),stone heterogeneity index(SHI),and VCSD were calculated by generating regions of interest on computed tomography(CT)images.Role of these factors were determined by applying the relevant statistical tests for continuous and categorical variables and a P value of<0.05 was gauged to be statistically significant.RESULTS There were a total of 407 patients included in the analysis.The mean age of the subjects in this study was 38.89±14.61 years.In total,165 out of the 407 patients could not achieve stone free status.The successful group had a significantly lower stone volume as compared to the unsuccessful group(P<0.0001).Skin to stone distance was not dissimilar among the two groups(P=0.47).MSD was significantly lower in the successful group(P<0.0001).SHI and VCSD were both significantly higher in the successful group(P<0.0001).CONCLUSION VCSD,a useful CT based parameter,can be utilized to gauge stone fragility and hence the prediction of SWL outcomes.

Estimation of ocean primary productivity and its spatio-temporal variation mechanism for East China Sea based on VGPM model

According to calculation results of ocean chlorophyll concentration based on SeaWiFS data by SeaBAM model and synchronous ship-measured data, this research set up an improved model for CaseⅠand CaseⅡwater bodies respectively. The monthly chlorophyll distribution in the East China Sea in 1998 was obtained from this improved model on calculation results of SeaBAM. The euphotic depth distribution in 1998 in the East China Sea is calculated by using remote sensing data of K 490 from SeaWiFS according to the relation between the euphotic depth and the oceanic diffuse attenuation coefficient. With data of ocean chlorophyll concentration, euphotic depth, ocean surface photosynthetic available radiation (PAR), daily photoperiod and optimal rate of daily carbon fixation within a water column, the monthly and annual primary productivity spatio-temporal distributions in the East China Sea in 1998 were obtained based on VGPM model. Based on analysis of those distributions, the conclusion can be drawn that there is a clear bimodality character of primary productivity in the monthly distribution in the East China Sea. In detail, the monthly distribution of primary productivity stays the lowest level in winter and rises rapidly to the peak in spring. It gets down a little in summer, and gets up a little in autumn. The daily average of primary productivity in the whole East China Sea is 560.03 mg/m 2 /d, which is far higher than the average of subtropical ocean areas. The annual average of primary productivity is 236.95 g/m 2 /a. The research on the seasonal variety mechanism of primary productivity shows that several factors that affect the spatio-temporal distribution may include the chlorophyll concentration distribution, temperature condition, the Yangtze River diluted water variety, the euphotic depth, ocean current variety, etc. But the main influencing factors may be different in each local sea area.

Spatio-temporal variation and focal mechanism of the Wenchuan M_S8.0 earthquake sequence

Based on abundant aftershock sequence data of the Wenchuan Ms8.0 earthquake on May 12, 2008, we studied the spatio-temporal variation process and segmentation rupture characteristic. Dense aftershocks distribute along Longmenshan central fault zone of NE direction and form a narrow strip with the length of 325 krn and the depth between several and 40 km. The depth profile （section of NW direction） vertical to the strike of aftershock zone （NE direction） shows anisomerous wedgy distribution characteristic of afiershock concentrated regions; it is related to the force form of the Longmenshan nappe tectonic belt. The stronger aftershocks could be divided into northern segment and southern segment apparently and the focal depths of strong aftershocks in the 50 km area between northern segment and southern segment are shallower. It seems like ＇to be going to rupture＇ segment. We also study focal mechanisms and segmentation of strong aftershocks. The principal compressive stress azimuth of aftershock area is WNW direction and the faulting types of aftershocks at southern and northern segment have the same proportion. Because afiershocks distribute on different secondary faults, their focal mechanisms present complex local tectonic stress field. The faulting of seven strong earthquakes on the Longmenshan central fault is mainly characterized by thrust with the component of right-lateral strike-slip. Meantime six strong aftershocks on the Longmenshan back-range fault and Qingchuan fault present strike-slip faulting. At last we discuss the complex segmentation rupture mechanism of the Wenchuan earthquake.

Spatio-temporal variation and propagation direction of coal fire in Jharia Coalfield,India by satellite-based multi-temporal night-time land surface temperature imaging

In this paper,the spatio-temporal variation and propagation direction of coal fire were studied in the Jharia Coalfield(JCF),India during 2006–2015 through satellite-based night-time land surface temperature(LST)imaging.The LST was retrieved from Advanced Spaceborne Thermal Emission and Reflection Radiometer(ASTER)night-time thermal-infrared data by a robust split-window algorithm based on scene-specific regression coefficients,band-specific hybrid emissivity,and night-time atmospheric transmittance.The LST-profile-based coal fire detection algorithm was formulated through statistical analysis of the LST values along multiple transects across diverse coal fire locations in the JCF in order to compute date-specific threshold temperatures for separating thermally-anomalous and background pixels.This algorithm efficiently separates surface fire,subsurface fire,and thermally-anomalous transitional pixels.During the observation period,it was noticed that the coal fire area increased significantly,which resulted from new coal fire at many places owing to extensive opencast-mining operations.It was observed that the fire propagation occurred primarily along the dip direction of the coal seams.At places,lateral-propagation of limited spatial extent was also observed along the strike direction possibly due to spatial continuity of the coal seams along strike.Moreover,the opencast-mining activities carried out during 2009–2015 and the structurally weak planes facilitated the fire propagation.

Spatio-temporal Variation of Soil Respiration and Its Driving Factors in Semi-arid Regions of North China

Soil respiration （SR） is the second-largest flux in ecosystem carbon cycling. Due to the large spatio-temporal variability of environmental factors, SR varied among different vegetation types, thereby impeding accurate estimation of CO2 emissions via SR. However, studies on spatio-temporal variation of SR are still scarce for semi-arid regions of North China. In this study, we conducted 12-month SR measurements in six land-use types, including two secondary forests （Populus tomentosa （PT） and Robinia pseudoacacia （RP））, three artificial plantations （Armeniaca sibirica （AS）, Punica granatum （PG） and Ziziphusjujuba （Z J）） and one natural grassland （GR）, to quantify spatio-temporal variation of SR and distinguish its controlling factors. Results indicated that SR exhibited distinct sea- sonal patterns for the six sites. Soil respiration peaked in August 2012 and bottomed in April 2013. The temporal coefficient of variation （CI0 of SR for the six sites ranged from 76.98% to 94.08%, while the spatial CV of SR ranged from 20.28% to 72.97% across the 12-month measurement. Soil temperature and soil moisture were the major controlling factors of temporal variation of SR in the six sites, while spatial variation in SR was mainly caused by the differences in soil total nitrogen （STN）, soil organic carbon （SOC）, net photosynthesis rate, and fine root biomass. Our results show that the annual average SR and Q10 （temperature sensitivity of soil respira- tion） values tended to decrease from secondary forests and grassland to plantations, indicating that the conversion of natural ecosystems to man-made ecosystems may reduce CO2 emissions and SR temperature sensitivity. Due to the high spatio-temporal variation of SR in our study area, care should be taken when converting secondary forests and grassland to plantations from the point view of accurately quantifying C02 emissions via SR at regional scales.

Spatio-temporal variations in organic carbon density and carbon sequestration potential in the topsoil of Hebei Province, China

Reliable prediction of soil organic carbon（SOC） density and carbon sequestration potential（CSP） plays an important role in the atmospheric carbon dioxide budget. This study evaluated temporal and spatial variation of topsoil SOC density and CSP of 21 soil groups across Hebei Province, China, using data collected during the second national soil survey in the 1980 s and during the recent soil inventory in 2010. The CSP can be estimated by the method that the saturated SOC content subtracts the actual SOC associated with clay and silt. Overall, the SOC density and CSP of most soil groups increased from the 1980 s to 2010 and varied between different soil groups. Among all soil groups, Haplic phaeozems had the highest SOC density and Endogleyic solonchaks had the largest CSP. Areas of soil groups with the highest SOC density（90 to 120 t C ha^（–1）） and carbon sequestration（120 to 160 t C ha^（–1）） also increased over time. With regard to spatial distribution, the north of the province had higher SOC density but lower CSP than the south. With respect to land-use type, cultivated soils had lower SOC density but higher CSP than uncultivated soils. In addition, SOC density and CSP were influenced by soil physicochemical properties, climate and terrain and were most strongly correlated with soil humic acid concentration. The results suggest that soil groups（uncultivated soils） of higher SOC density have greater risk of carbon dioxide emission and that management should be aimed at maximizing carbon sequestration in soil groups（cultivated soils） with greater CSP. Furthermore, soils should be managed according to their spatial distributions of SOC density and carbon sequestration potential under different soil groups.

Spatio-temporal Variations of Temperature and Precipitation During 1951–2019 in Arid and Semiarid Region, China

Understanding the spatio-temporal variations of temperature and precipitation in the arid and semiarid region of China(ASRC)is of great significance for promoting regional eco-environmental protection and policy-making.In this study,the annual and seasonal spatio-temporal patterns of change in average temperature and precipitation and their influencing factors in the ASRC were analyzed using the Mann-Kendall test,linear tendency estimation,accumulative anomaly and the Pearson’s correlation coefficient.The results showed that both annual average temperature and average annual precipitation increased in the ASRC during 1951–2019.The temperature rose by about 1.93℃and precipitation increased by about 24 mm.The seasonal average temperature presented a significant increase trend,and the seasonal precipitation was conspicuous ascension in spring and winter.The spatio-temporal patterns of change in temperature and precipitation differed,with the southwest area showing the most obvious variation in each season.Abrupt changes in annual and seasonal average temperature and precipitation occurred mainly around the 1990 s and after 2000,respectively.Atmospheric circulation had an important effect on the trends and abrupt changes in temperature and precipitation.The East Asian summer monsoon had the largest impact on the trend of average annual temperature,as well as on the abrupt changes of annual average temperature and precipitation.Temperature and precipitation changes in the ASRC were influenced by long-term and short-term as well as direct and indirect anthropogenic and natural factors.This study identifies the characteristics of spatio-temporal variations in temperature and precipitation in the ASRC and provides a scientific reference for the formulation of climate change responses.

The spatio-temporal variations of frost-free period in China from 1951 to 2012

The frost-free period（FFP）first frost date（FFD） and last frost date（LFD） have been regard as the important climate variables for agricultural production. Understanding the spatio-temporal variations of the FFPFFD and LFD is beneficial to reduce the harmful impacts of climate change on agricultural production and enhance the agricultural adaptation. This study examined daily minimum temperatures for 823 national-level meteorological stationscalculated the values of FFDLFD and FFP for station-specific and region-specific from 1951 to 2012estimated the gradients of linear regression for station-specific moving averages of FFDLFD and FFPand assessed station-specific time series of FFP and detected the abrupt change. The results as follows： at both the station level and the regional levelthe FFP across China decreases with the increase of latitude from south to northand with the increase of altitude from east to west generally. At the station levelthe inter-annual fluctuations of FFDLFD and FFP in south and west agricultural regions are greater than those in north and east. At the regional levelexcluding the QT regiontemporal changes of FFP are relatively small in both the low-latitude and the high-latitude regionsbut for the mid-latitude regions. According to the linear trend gradients of the moving average values of station-specific FFDLFD and FFPFFD was delayedLFD advancedand FFP extended gradually over the 80% of China. Furthermorethe change magnitudes for FFDLFD and FFP in the north and east agricultural regions are higher than that in the southern and western. Among the 659 station-specific time series of FFP examined by the Mann-Kendall test341 stationslocated mainly in the north regionhave one identifiable and significant abrupt change. And at the 341 stations with identified abrupt changesmost（57%） abrupt changes occurred during 1991–2012followed by the periods of 1981–1990（28%）1971–1980（12%）and 1951–1970（3%）. The spatio-temporal variations of FFDLFD and FFP would provide important guidance to agricultural practices.

SPATIO-TEMPORAL VARIATION CHARACTERISTICS OF EXTREMELY HEAVY PRECIPITATION FREQUENCY OVER SOUTH CHINA IN THE LAST 50 YEARS

This paper comprehensively studies the spatio-temporal characteristics of the frequency of extremely heavy precipitation events over South China by using the daily precipitation data of 110 stations during 1961 to 2008 and the extremely heavy precipitation thresholds determined for different stations by REOF, trend coefficients, linear trend, Mann-Kendall test and variance analysis. The results are shown as follows. The frequency distribution of extremely heavy precipitation is high in the middle of South China and low in the Guangdong coast and western Guangxi. There are three spatial distribution types of extremely heavy precipitation in South China. The consistent anomaly distribution is the main type. Distribution reversed between the east and the west and between the south and the north is also an important type. Extremely heavy precipitation events in South China mainly occurred in the summer-half of the year. Their frequency during this time accounts for 83.7% of the total frequency. In the 1960 s and 1980 s, extremely heavy precipitation events were less frequent while having an increasing trend from the late 1980 s. Their climatological tendency rates decrease in the central and rise in the other areas of South China, and on average the mean series also shows an upward but insignificant trend at all of the stations. South China's frequency of extremely heavy precipitation events can be divided into six major areas and each of them shows a different inter-annual trend and three of the representative stations experience abrupt changes by showing remarkable increases in terms of Mann-Kendall tests.

Spatio-temporal variation of spring phenology in Tibetan Plateau and its linkage to climate change from 1982 to 2012

The influence of climate change on vegetation phenology is a heated issue in current climate change study.We used GIMMS-3g NDVI data to detect the spatio-temporal dynamics of the start of the growing season(SGS) over the Tibetan Plateau(TP) from 1982 to 2012 and to analyze its relationship with temperature and precipitation.No significant trend was observed in the SGS at the regional scale during the study period(R^2 = 0.03,P = 0.352).However,there were three time periods(1982-1999,1999-2008 and 2008-2012) with identifiable,distinctly different trends.Regions with a significant advancing trend were mainly scattered throughout the humid and semi-humid areas,whereas the regions with a significant delaying trend were mostly distributed throughout the semi-arid areas.Statistical analysis showed that the response of the SGS to climate change varies spatially.The SGS was significantly correlated with the spring temperature and the start of the thermal growth season(STGS) in the relatively humid area.With increasing aridity,theimportance of the spring temperature for the SGS gradually decreased.However,the influences of precipitation and winter temperature on the SGS were complicated across the plateau.

Assessment of Spatio-Temporal Variations in Water Quality of Shailmari River, Khulna (Bangladesh) Using Multivariate Statistical Techniques

Surface water has become one of the most vulnerable resources on the earth due to deterioration of its quality from diverse sources of pollution. Understanding of the spatiotemporal distribution of pollutants and identification of the sources in the river systems is a prerequisite for the protection and sustainable utilization of the water resources. Multivariate statistical techniques such as Principal Component Analysis (PCA) and Factor Analysis (FA) were applied in this study to investigate the temporal and spatial variations of water quality and appoint the major factors of pollution in the Shailmari River system. Water quality data for 14 physicochemical parameters from 11 monitoring sites over the year of 2014 in three sampling seasons were collected and analyzed for this study. Kruskal-Wallis test showed significant (p < 0.01) temporal and spatial variations in all of the water quality parameters of the river water. Principal component analysis (PCA) allowed extracting the contributing parameters affecting the seasonal water quality in the river system. Scatter plots of the PCs showed the tidal and spatial variation within river system and identified parameters controlling the behavior in each case. Factor analysis (FA) further reduced the data and extracted factors which are significantly responsible for water quality variation in the river. The results indicate that the parameters controlling the water quality in different seasons are related with salinity, anthropogenic pollution (sewage disposal, effluents) and agricultural runoff in pre-monsoon;precipitation induced surface runoff in monsoon;and erosion, oxidation or organic pollution (point and non-point sources) in post-monsoon. Therefore, the study reveals the applicability and usefulness of the multivariate statistical methods in assessing water quality of river by identifying the potential environmental factors controlling the water quality in different seasons which might help to better understand, monitor and manage the quality of the water resources.

SPATIO-TEMPORAL VARIATION OF ACTUAL EVAPOTRANSPIRATION AND ITS RELATION WITH CLIMATE PARAMETERS IN THE PEARL RIVER BASIN,CHINA

Spatio-temporal variation of actual evapotranspiration(ETa) in the Pearl River basin from 1961 to 2010 are analyzed based on daily data from 60 national observed stations. ETa is calculated by the Advection-Aridity model(AA model) in the current study, and Mann-Kendall test(MK) and Inverse Distance Weighted interpolation method(IDW)were applied to detect the trends and spatial variation pattern. The relations of ETa with climate parameters and radiation/dynamic terms are analyzed by Person correlation method. Our findings are shown as follows: 1) Mean annual ETa in the Pearl River basin is about 665.6 mm/a. It has significantly decreased in 1961-2010 at a rate of-24.3mm/10 a. Seasonally, negative trends of summer and autumn ETa are higher than that of spring and winter. 2) The value of ETa is higher in the southeast coastal area than in the northwest region of the Pearl River basin, while the latter has shown the strongest negative trend. 3) Negative trends of ETa in the Pearl River basin are most probably due to decreasing radiation term and increasing dynamic term. The decrease of the radiation term is related with declining diurnal temperature range and sunshine duration, and rising atmospheric pressure as well. The contribution of dynamic term comes from increasing average temperature, maximum and minimum temperatures in the basin. Meanwhile, the decreasing average wind speed weakens dynamic term and finally, to a certain extent, it slows down the negative trend of the ETa.

SPATIO-TEMPORAL VARIATION OF SENSIBLE HEAT FLUX OVER SOUTHERN CHINA

The spatio-temporal variation of surface sensible heat flux(SHF) in southern China(SC) is studied based on the data evaluated from conventional observational meteorological data.There exist prominent increasing trends in all seasonal surface sensible heat fluxes in the western SC and decreasing trends in the central-eastern part of southern China.The variations of surface sensible heat flux in all seasons are dominant on interannual time-scales.The land-air temperature difference and the near-surface wind speed are two key factors for the interannual variations of SHF,but the former is more important.The first two major anomalous patterns of SHF are presented as the region-wide in-phase anomalies and the east-west dipole anomalies,respectively,based on the EOF analysis results.

Atmospheric deposition of polycyclic aromatic hydrocarbons （PAHs） in Shanghai： the spatio-temporal variation and source identification

This study investigated the dry and wet deposition fluxes of atmospheric polycyclic aromatic hydrocarbons （PAHs） in Shanghai, China. The flux sources were traced based on composition and spatio-temporal variation. The results show that wet deposition concentrations of PAHs ranged from 0.07 to 0.67 μg·L-1 and were correlated with temperature （P 〈 0.05）. Dry deposition of PAHs concentrations ranged from 3.60-92.15 μg·L-1 and were higher in winter and spring than in summer and autumn. The annual PAH average fluxes were 0.631 μg·m-2·d-1 and 4.06 μg.m·d-1 for wet and dry deposition, respectively. The highest wet deposition of PAH fluxes was observed in summer, while dry deposition fluxes were higher in winter and spring. Atmospheric PAHs were deposited as dry deposition in spring and winter, yet wet deposition was the dominant pathway during summer. Total atmospheric PAH fluxes were higher in the northern areas than in the southern areas of Shanghai, and were also observed to be higher in winter and spring. Annual deposition of atmospheric PAHs was about 10.8 t in across all of Shanghai. Wet deposition of PAHs was primarily composed of two, three, or four rings, while dry deposition of PAHs was composed of four, five, or six rings. The atmospheric PAHs, composed of four, five, or six rings, primarily existed in the form of particulates. Coal combustion and vehicle emissions were the dominant sources of PAH in the observed area of downtown Shanghai. In suburban areas, industrial pollution, from sources such as coke oven, incinerator, and oil fired power plant, was as significant as vehicle emissions in contributing to the deposition of PAHs.

Spatio-temporal variation of trace elements distributed over surface water of Upper Ganga River Basin in Western Himalayan Region

The rivers draining from the Himalayan range distribute enormous amount of fresh water to the people living in downstream regions.Trace metals flowed with river water can lead to serious impact on ecological system and human health.Nevertheless,the documentation on trace elements of Himalayan rivers is inadequately documented.The current study deals with the spatial and temporal variability of the major and trace elements of Ganga river water in epirhithron,metarhithron and hyporhithron zone belonging to Himalayan segment.Water samples from nineteen monitoring locations were collected in pre-monsoon(May-June),monsoon(AugustSeptember)and post-monsoon(December)seasons and subjected to be assessed for 20 elements(Ag,Al,Ba,Cd,Ca,Cr,Cu,Fe,Ga,K,Mn,Mg,Na,Ni,Pb,Sr,Th,U,Zn,and Zr)using ICP-OES(Inductively Coupled Plasma-Optical Emission Spectrometer).Different water pollution indexes such as HPI(Heavy Metal Pollution Index),MI(Metal Index)and PI(Pollution Index)were used to describe current water quality status at each monitoring station under particular classified ecological zone.The studied stations in hyporhithron zone had the value of Metal Index(MI>1),indicating threshold of warning.Further,the highest values of HPI in hyporhithron zone correspond to poor water quality status.Sites with poor water quality were also found to be contaminated as per the Pollution Index(PI),exhibiting high concentrations for element(Fe).However,the epirhithron and metarhithron zone in Himalayan segment showed excellent water quality mainly contributed from natural sources.Cluster Analysis(CA)and Principal Component Analysis(PCA)were applied to identify the main influential sources for Ganga river water pollution.The Kriging interpolation method was also applied to prepare spatial distribution map of computed indexes(HPI,MI,and PI).With the help of index of local Moran’s I(LMI),identified spatial clusters and spatial outliers revealed the elevated concentration of most elements in hyporhithron zone.The dataset presented in this study would be convenient for government officials in developing more effective management policies and necessary steps to check and monitor the Ganga river water quality.It was also suggested that further investigations in terms of trace elemental sources and their role in self-purification properties of Ganga water can be addressed in future.

Spatio-Temporal Variation of HIV Infection in Kenya

Disease mapping is the study of the distribution of disease relative risks or rates in space and time, and normally uses generalized linear mixed models (GLMMs) which includes fixed effects and spatial, temporal, and spatio-temporal random effects. Model fitting and statistical inference are commonly accomplished through the empirical Bayes (EB) and fully Bayes (FB) approaches. The EB approach usually relies on the penalized quasi-likelihood (PQL), while the FB approach, which has increasingly become more popular in the recent past, usually uses Markov chain Monte Carlo (McMC) techniques. However, there are many challenges in conventional use of posterior sampling via McMC for inference. This includes the need to evaluate convergence of posterior samples, which often requires extensive simulation and can be very time consuming. Spatio-temporal models used in disease mapping are often very complex and McMC methods may lead to large Monte Carlo errors if the dimension of the data at hand is large. To address these challenges, a new strategy based on integrated nested Laplace approximations (INLA) has recently been recently developed as a promising alternative to the McMC. This technique is now becoming more popular in disease mapping because of its ability to fit fairly complex space-time models much more quickly than the McMC. In this paper, we show how to fit different spatio-temporal models for disease mapping with INLA using the Leroux CAR prior for the spatial component, and we compare it with McMC using Kenya HIV incidence data during the period 2013-2016.

Wi-Fi Positioning Dataset with Multiusers and Multidevices Considering Spatio-Temporal Variations

Precise information on indoor positioning provides a foundation for position-related customer services.Despite the emergence of several indoor positioning technologies such as ultrawideband,infrared,radio frequency identification,Bluetooth beacons,pedestrian dead reckoning,and magnetic field,Wi-Fi is one of the most widely used technologies.Predominantly,Wi-Fi fingerprinting is the most popular method and has been researched over the past two decades.Wi-Fi positioning faces three core problems:device heterogeneity,robustness to signal changes caused by human mobility,and device attitude,i.e.,varying orientations.The existing methods do not cover these aspects owing to the unavailability of publicly available datasets.This study introduces a dataset that includes the Wi-Fi received signal strength(RSS)gathered using four different devices,namely Samsung Galaxy S8,S9,A8,LG G6,and LG G7,operated by three surveyors,including a female and two males.In addition,three orientations of the smartphones are used for the data collection and include multiple buildings with a multifloor environment.Various levels of human mobility have been considered in dynamic environments.To analyze the time-related impact on Wi-Fi RSS,data over 3 years have been considered.

Spatio-Temporal Wind Speed Prediction Based on Variational Mode Decomposition

Improving short-term wind speed prediction accuracy and stability remains a challenge for wind forecasting researchers.This paper proposes a new variational mode decomposition(VMD)-attention-based spatio-temporal network(VASTN)method that takes advantage of both temporal and spatial correlations of wind speed.First,VASTN is a hybrid wind speed prediction model that combines VMD,squeeze-and-excitation network(SENet),and attention mechanism(AM)-based bidirectional long short-term memory(BiLSTM).VASTN initially employs VMD to decompose the wind speed matrix into a series of intrinsic mode functions(IMF).Then,to extract the spatial features at the bottom of the model,each IMF employs an improved convolutional neural network algorithm based on channel AM,also known as SENet.Second,it combines BiLSTM and AM at the top layer to extract aggregated spatial features and capture temporal dependencies.Finally,VASTN accumulates the predictions of each IMF to obtain the predicted wind speed.This method employs VMD to reduce the randomness and instability of the original data before employing AM to improve prediction accuracy through mapping weight and parameter learning.Experimental results on real-world data demonstrate VASTN’s superiority over previous related algorithms.