BackgroundThe transmission of hemorrhagic fever with renal syndrome (HFRS) is influenced by environmental determinants. This study aimed to explore the association between atmospheric moisture variability and the transmission of hemorrhagic fever with renal syndrome (HFRS) for the period of 1991–2010 in Changsha, China.Methods and FindingsWavelet analyses were performed by using monthly reported time series data of HFRS cases to detect and quantify the periodicity of HFRS. A generalized linear model with a Poisson distribution and a log link model were used to quantify the relationship between climate and HFRS cases, highlighting the importance of moisture conditions. There was a continuous annual oscillation mode and multi-annual cycle around 3–4 years from 1994 to 1999. There was a significant association of HFRS incidence with moisture conditions and the Multivariate El Niño–Southern Oscillation Index (MEI). Particularly, atmospheric moisture has a significant effect on the propagation of HFRS; annual incidence of HFRS was positively correlated with annual precipitation and annual mean absolute humidity.ConclusionsThe final model had good accuracy in forecasting the occurrence of HFRS and moisture condition can be used in disease surveillance and risk management to provide early warning of potential epidemics of this disease.

BackgroundHemorrhagic fever with renal syndrome (HFRS) is an important public health problem in mainland China. HFRS is particularly endemic in Changsha, the capital city of Hunan Province, with one of the highest incidences in China. The occurrence of HFRS is influenced by environmental factors. However, few studies have examined the relationship between environmental variation (such as land use changes and climate variations), rodents and HFRS occurrence. The purpose of this study is to predict the distribution of HFRS and identify the risk factors and relationship between HFRS occurrence and rodent hosts, combining ecological modeling with the Markov chain Monte Carlo approach.MethodsEcological niche models (ENMs) were used to evaluate potential geographic distributions of rodent species by reconstructing details of their ecological niches in ecological dimensions, and projecting the results onto geography. The Genetic Algorithm for Rule-set Production was used to produce ENMs. Data were collected on HFRS cases in Changsha from 2005 to 2009, as well as national land survey data, surveillance data of rodents, meteorological data and normalized difference vegetation index (NDVI).ResultsThe highest occurrence of HFRS was in districts with strong temperature seasonality, where elevation is below 200 m, mean annual temperature is around 17.5°C, and annual precipitation is below 1600 mm. Cultivated and urban lands in particular are associated with HFRS occurrence. Monthly NDVI values of areas predicted present is lower than areas predicted absent, with high seasonal variation. The number of HFRS cases was correlated with rodent density, and the incidence of HFRS cases in urban and forest areas was mainly associated with the density of Rattus norvegicus and Apodemus agrarius, respectively.ConclusionsHeterogeneity between different areas shows that HFRS occurrence is affected by the intensity of human activity, climate conditions, and landscape elements. Rodent density and species composition have significant impacts on the number of HFRS cases and their distribution.

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Accurately quantifying water inundation dynamics in terms of both spatial distributions and temporal variability is essential for water resources management. Currently, the water map is usually derived from synthetic aperture radar (SAR) data with the support of auxiliary datasets, using thresholding methods and followed by morphological operations to further refine the results. However, auxiliary datasets may lose efficacy on large plain areas, whilst the parameters of morphological operations are hard to be decided in different situations. Here, a heuristic and automatic water extraction (HAWE) method is proposed to extract the water map from Sentinel-1 SAR data. In the HAWE, we integrate tile-based thresholding and the active contour model, in which the former provides a convincing initial water map used as a heuristic input, and the latter refines the initial map by using image gradient information. The proposed approach was tested on the Dongting Lake plain (China) by comparing the extracted water map with the reference data derived from the Sentinel-2 dataset. For the two selected test sites, the overall accuracy of water classification is between 94.90% and 97.21% whilst the Kappa coefficient is within the range of 0.89 and 0.94. For the entire study area, the overall accuracy is between 94.32% and 96.7% and the Kappa coefficient ranges from 0.80 to 0.90. The results show that the proposed method is capable of extracting water inundations with satisfying accuracy.

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The multiscale analysis of precipitation teleconnections with climate indices is of great significance to better understand the regional response of the precipitation variability under the different time scales to global climate change. In this study, the inherent cycles of monthly total and extreme precipitations during 1965-2016 in the Yangtze River basin (YRB) and the climate indices influencing their periodic oscillations and long-term trends are investigated by the complete ensemble empirical mode decomposition, lag correlation analysis and stepwise variable selection. Results show that the total and extreme precipitations have experienced increasing long-term trends in the western region of the upper reach of YRB (U-YRB) and most parts of the middle and lower reaches of YRB (ML-YRB), and decreasing trends in the middle region of U-YRB. The identified climate indices significantly affecting total and extreme precipitations are almost identical in the U-YRB and ML-YRB. The sea surface temperature anomalies over East China Sea (ECS), South China Sea (SCS), Kuroshio (KC) and Bay of Bengal (BB) with specific time lags, solar flux with 12-month lag, and the simultaneous global average temperature anomalies (GT) and trans-Niño index have strong linkages with precipitation components under the particular time scales in the YRB. Specifically, the ECS, SCS, KC and BB with specific time lags are identified as effective indicators of periodic oscillations of total and extreme precipitations, while the simultaneous GT is powerful for capturing their long-term trends. The identified climate indices are further demonstrated to provide significant predictive information for total and extreme precipitations and their periodic oscillations. Moreover, their forecast ability is higher in the U-YRB than in the ML-YRB, which detects a strong correlation with the randomness of precipitation. Additionally, monthly total precipitation and its periodic oscillations can be predicted better than extreme precipitation and its periodic oscillations by the identified climate indices.

To organize trajectory data is a challenging issue for both studies on spatial databases and spatial data mining in the last decade, especially where there is semantic information involved. The high-level semantic features of trajectory data exploit human movement interrelated with geographic context, which is becoming increasingly important in representing and analyzing actual information contained in movements and further processing. This paper argues for a novel semantic trajectory model named TOST. It considers both semantic and geographic information of trajectory data happens along network infrastructure simultaneously. In TOST, a flexible intersection-based semantic representation is designed to express movement typically constrained by urban road networks by combining sets of local semantic details along the time axis. A relational schema based on this model was instantiated against real datasets, which illustrated the effectivity of our proposed model.

The Sanshandao Gold Mine is one of the largest gold mines in China, and is almost completely surrounded by seawater. Factors such as water-controlling structures and water-bearing zones that developed in the mine were studied, based on geophysical exploration data and hydrogeological investigations of the wells and drifts of the mine. The sources of recharge (seawater, brine from surface runoff, and Quaternary water) were proportionally determined based on 18 O isotopic and water quality data measured for each drainage point in the pits. Appropriate boundary conditions were defined for the recharge source of the seawater and natural runoff brine. The hydraulic connections among the water-bearing zones were also considered. This work was conducted to prepare for future mine development and allowed us to simulate anticipated ground water flow.