Spatial and temporal distribution and influencing factor analysis of the malignant tumor mortality rate around the mining area.

Mining activities can threaten residents' health even lives. Integrating spatial empirical Bayesian smoothing, joinpoint regression and spatiotemporal scanning methods, we analyzed aggregations and possible factors of four tumor mortality rates at township and village scales from 2012 to 2016 in Suxian district of Hunan Province, China. Results indicate: (1) Mortality rates were ranked: lung cancer > liver cancer > gastric cancer > colorectal cancer. (2) Lung cancer had a higher five-year mortality rate in the middle; relative risk (RR) of death from lung cancer from 2012 to 2015 in Xujiadong Village was 7.48. Liver cancer had a higher five-year mortality rate in the Middle West; RR in areas centered on Nanta Street with a radius of 9.87 km from 2015 to 2016, was 1.83. Gastric cancer had a higher five-year mortality rate in the east; RR in Xujiadong Village from 2012 to 2014 was 6.9. Five-year mortality rate of colorectal cancer was higher in the northwest; RR in regions centered on Huangcao Village with a radius of 12.11 km in 2016, was 2.88. (3) Pollution from ore mining and smelting, heavy metal and non-metallic, and mine transportation were the main possible factors. This research provides a method reference for studying spatiotemporal patterns of disease in China even the world.

Spatiotemporal Hotspots of Study Areas in Research of Gastric Cancer in China Based on Web-Crawled Literature.

Gastric cancer is a common malignancy worldwide and poses a serious threat to human public health. The difficulty in obtaining epidemiological data limits the development of cross-disciplinary related research. In this study, 99,364 publications on gastric cancer from 1991 to 2019 were obtained using web-crawler technology, and a technical framework for extracting toponyms from these publications was constructed to analyze spatiotemporal hotspots of study areas in gastric cancer research in China. The results showed the following: (1) The accuracy of toponym extraction was greatly improved after eliminating the systematic exclusion words and adding historical toponyms, with a precision of 95.31% and a recall of 94.86%. (2) Gastric cancer research (GCR) and gastric cancer research with toponyms (GCRWT) are attracting increasing amounts of attention. The amount of GCR results published in Chinese and English is gradually leveling off, and the imbalance between those of GCRWT is gradually widening. (3) The spatial distribution of gastric cancer research in China is uneven, and the hotspots are mainly located in the eastern coastal areas. There were huge advances in gastric cancer research at the province/city/county scale in Eastern China, while the central region has only increased research at the county scale. We suggest that gastric cancer research should pay more attention to the central region, which has the highest gastric cancer incidence/mortality. This study provides important clues for research on and investigations of gastric cancer.

Spatial distribution differences in PM2.5 concentration between heating and non-heating seasons in Beijing, China.

Suffered from serious air pollution, Beijing, the capital of China, has implemented multiple measures to reduce the discharge of PM2.5 (particulate matter with aerodynamic diameters of less than 2.5 µm). The average annual PM2.5 concentration of Beijing has shown a continued decline in recent years. However, the improvement was not obvious during the heating season, which had heavier pollution than the non-heating season. Analyzing the spatial distribution of PM2.5 concentrations during heating and non-heating seasons, as well as their spatial differences, is believed to benefit the study of spatial-temporal variation of air pollution and provide scientific reference for the control of air pollution in Beijing. In this study, land use regression (LUR) model was employed to simulate the spatial distribution of PM2.5 concentrations in Beijing during heating and non-heating seasons in 2015. The spatial distribution of the concentration difference between heating and non-heating seasons was analyzed, and the influencing factors were also examined. The results showed that: (1) PM2.5 concentrations during heating and non-heating seasons, as well as their differences, were clearly at a maximum in the south and east of Beijing and at a minimum in the north and west; (2) the area with the biggest concentration difference was situated in a suburban area to the south and east, as well as in outer suburbs to the southeast and northwest; and (3) wind speed, area of transport land and industrial-mining-warehouse land were the main influence factors for the PM2.5 concentration difference in the central, eastern and southern area. Heating activity was not the only cause for the increased PM2.5 concentration during the heating season, vehicle emission, industrial discharge and regional transport of pollutants also played varied roles in PM2.5 pollution in different area.

Assessment of Land-Use and Land-Cover Change in Guangxi, China.

It is increasingly acknowledged that land-use and land-cover change has become a key subject that urgently needs to be addressed in the study of global environmental change. In the present study, supported by the long-time-series of land-use and land-cover data from 1990, 2000, and 2017, we used the land-use transition matrix, Markov chain model and Moran's I to derive detailed information of the spatial patterns and temporal variation of the land-use and land-cover change; additionally, we highlight the deforestation/afforestation conversion process during the period of 1990-2017. The results show that a total of 4708 km2 (i.e., 2.0% of the total area) changed in Guangxi from 1990 to 2017, while 418 km2 of woodland has been lost in this region. The woodland lost (deforestation) and woodland gained (afforestation) were collocated with intensive forest practices in the past 27 years. The conversions from woodland to cropland and from woodland to grassland were the dominant processes of deforestation and afforestation, respectively. Steep slope cropland was one of the major conversion patterns of afforestation after 2000. This result is mainly explained by the implementation of the "Grain for Green Program" policy and the large-scale development of eucalyptus plantations. Further efforts should be made to control deforestation in this area. These findings can also be used as a reference in the formulation and implementation of sustainable woodland management policies.

Comparison of the common spatial interpolation methods used to analyze potentially toxic elements surrounding mining regions.

The appropriate spatial interpolation methods must be selected to analyze the spatial distributions of Potentially Toxic Elements (PTEs), which is a precondition for evaluating PTE pollution. The accuracy and effect of different spatial interpolation methods, which include inverse distance weighting interpolation (IDW) (power = 1, 2, 3), radial basis function interpolation (RBF) (basis function: thin-plate spline (TPS), spline with tension (ST), completely regularized spline (CRS), multiquadric (MQ) and inverse multiquadric (IMQ)) and ordinary kriging interpolation (OK) (semivariogram model: spherical, exponential, gaussian and linear), were compared using 166 unevenly distributed soil PTE samples (As, Pb, Cu and Zn) in the Suxian District, Chenzhou City, Hunan Province as the study subject. The reasons for the accuracy differences of the interpolation methods and the uncertainties of the interpolation results are discussed, then several suggestions for improving the interpolation accuracy are proposed, and the direction of pollution control is determined. The results of this study are as follows: (i) RBF-ST and OK (exponential) are the optimal interpolation methods for As and Cu, and the optimal interpolation method for Pb and Zn is RBF-IMQ. (ii) The interpolation uncertainty is positively correlated with the PTE concentration, and higher uncertainties are primarily distributed around mines, which is related to the strong spatial variability of PTE concentrations caused by human interference. (iii) The interpolation accuracy can be improved by increasing the sample size around the mines, introducing auxiliary variables in the case of incomplete sampling and adopting the partition prediction method. (iv) It is necessary to strengthen the prevention and control of As and Pb pollution, particularly in the central and northern areas. The results of this study can provide an effective reference for the optimization of interpolation methods and parameters for unevenly distributed soil PTE data in mining areas.

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.

Ecological Niche Modeling Identifies Fine-Scale Areas at High Risk of Dengue Fever in the Pearl River Delta, China.

Dengue fever (DF) is one of the most common and rapidly spreading mosquito-borne viral diseases in tropical and subtropical regions. In recent years, this imported disease has posed a serious threat to public health in China, especially in the Pearl River Delta (PRD). Although the severity of DF outbreaks in the PRD is generally associated with known risk factors, fine scale assessments of areas at high risk for DF outbreaks are limited. We built five ecological niche models to identify such areas including a variety of climatic, environmental, and socioeconomic variables, as well as, in some models, extracted principal components. All the models we tested accurately identified the risk of DF, the area under the receiver operating characteristic curve (AUC) were greater than 0.8, but the model using all original variables was the most accurate (AUC = 0.906). Socioeconomic variables had a greater impact on this model (total contribution 55.27%) than climatic and environmental variables (total contribution 44.93%). We found the highest risk of DF outbreaks on the border of Guangzhou and Foshan (in the central PRD), and in northern Zhongshan (in the southern PRD). Our fine-scale results may help health agencies to focus epidemic monitoring tightly on the areas at highest risk of DF outbreaks.

Impacts of road network expansion on landscape ecological risk in a megacity, China: A case study of Beijing.

Road networks affect the spatial structure of urban landscapes, and with continuous expansion, it will also exert more widespread influences on the regional ecological environment. With the support of geographic information system (GIS) technology, based on the application of various spatial analysis methods, this study analyzed the spatiotemporal changes of road networks and landscape ecological risk in the research area of Beijing to explore the impacts of road network expansion on ecological risk in the urban landscape. The results showed the following: 1) In the dynamic processes of change in the overall landscape pattern, the changing differences in landscape indices of various landscape types were obvious and were primarily related to land-use type. 2) For the changes in a time series, the expansion of the road kernel area was consistent with the extension of the sub-low-risk area in the urban center, but some differences were observed during different stages of development. 3) For the spatial position, the expanding changes in the road kernel area were consistent with the grade changes of the urban central ecological risk, primarily because both had a certain spatial correlation with the expressways. 4) The influence of road network expansion on the ecological risk in the study area had obvious spatial differences, which may be closely associated with the distribution of ecosystem types.

Effects of natural factors on the spatial distribution of heavy metals in soils surrounding mining regions.

Various studies have shown that soils surrounding mining areas are seriously polluted with heavy metals. Determining the effects of natural factors on spatial distribution of heavy metals is important for determining the distribution characteristics of heavy metals in soils. In this study, an 8km buffer zone surrounding a typical non-ferrous metal mine in Suxian District of Hunan Province, China, was selected as the study area, and statistical, spatial autocorrelation and spatial interpolation analyses were used to obtain descriptive statistics and spatial autocorrelation characteristics of As, Pb, Cu, and Zn in soil. Additionally, the distributions of soil heavy metals under the influences of natural factors, including terrain (elevation and slope), wind direction and distance from a river, were determined. Layout of sampling sites, spatial changes of heavy metal contents at high elevations and concentration differences between upwind and downwind directions were then evaluated. The following results were obtained: (1) At low elevations, heavy metal concentrations decreased slightly, then increased considerably with increasing elevation. At high elevations, heavy metal concentrations first decreased, then increased, then decreased with increasing elevation. As the slope increased, heavy metal contents increased then decreased. (2) Heavy metal contents changed consistently in the upwind and downwind directions. Heavy metal contents were highest in 1km buffer zone and decreased with increasing distance from the mining area. The largest decrease in heavy metal concentrations was in 2km buffer zone. Perennial wind promotes the transport of heavy metals in downwind direction. (3) The spatial extent of the influence of the river on Pb, Zn and Cu in the soil was 800m. (4) The influence of the terrain on the heavy metal concentrations was greater than that of the wind. These results provide a scientific basis for preventing and mitigating heavy metal soil pollution in areas surrounding mines.

Time Series Analysis of Hemorrhagic Fever with Renal Syndrome: A Case Study in Jiaonan County, China.

Exact prediction of Hemorrhagic fever with renal syndrome (HFRS) epidemics must improve to establish effective preventive measures in China. A Seasonal Autoregressive Integrated Moving Average (SARIMA) model was applied to establish a highly predictive model of HFRS. Meteorological factors were considered external variables through a cross correlation analysis. Then, these factors were included in the SARIMA model to determine if they could improve the predictive ability of HFRS epidemics in the region. The optimal univariate SARIMA model was identified as (0,0,2)(1,1,1)12. The R2 of the prediction of HFRS cases from January 2014 to December 2014 was 0.857, and the Root mean square error (RMSE) was 2.708. However, the inclusion of meteorological variables as external regressors did not significantly improve the SARIMA model. This result is likely because seasonal variations in meteorological variables were included in the seasonal characteristics of the HFRS itself.

The influence of natural factors on the spatio-temporal distribution of Oncomelania hupensis.

BACKGROUND: We analyzed the influence of natural factors, such as temperature, rainfall, vegetation and hydrology, on the spatio-temporal distribution of Oncomelania hupensis and explored the leading factors influencing these parameters. The results will provide reference methods and theoretical a basis for the schistosomiasis control. METHODS: GIS (Geographic Information System) spatial display and analysis were used to describe the spatio-temporal distribution of Oncomelania hupensis in the study area (Dongting Lake in Hunan Province) from 2004 to 2011. Correlation analysis was used to detect the natural factors associated with the spatio-temporal distribution of O. hupensis. Spatial regression analysis was used to quantitatively analyze the effects of related natural factors on the spatio-temporal distribution of snails and explore the dominant factors influencing this parameter. RESULTS: (1) Overall, the spatio-temporal distribution of O. hupensis was governed by the comprehensive effects of natural factors. In the study area, the average density of living snails showed a downward trend, with the exception of a slight rebound in 2009. The density of living snails showed significant spatial clustering, and the degree of aggregation was initially weak but enhanced later. Regions with high snail density and towns with an HH distribution pattern were mostly distributed in the plain areas in the northwestern and inlet and outlet of the lake. (2) There were space-time differences in the influence of natural factors on the spatio-temporal distribution of O. hupensis. Temporally, the comprehensive influence of natural factors on snail distribution increased first and then decreased. Natural factors played an important role in snail distribution in 2005, 2006, 2010 and 2011. Spatially, it decreased from the northeast to the southwest. Snail distributions in more than 20 towns located along the Yuanshui River and on the west side of the Lishui River were less affected by natural factors, whereas relatively larger in areas around the outlet of the lake (Chenglingji) were more affected. (3) The effects of natural factors on the spatio-temporal distribution of O. hupensis were spatio-temporally heterogeneous. Rainfall, land surface temperature, NDVI, and distance from water sources all played an important role in the spatio-temporal distribution of O. hupensis. In addition, due to the effects of the local geographical environment, the direction of the influences the average annual rainfall, land surface temperature, and NDVI had on the spatio-temporal distribution of O. hupensis were all spatio-temporally heterogeneous, and both the distance from water sources and the history of snail distribution always had positive effects on the distribution O. hupensis, but the direction of the influence was spatio-temporally heterogeneous. (4) Of all the natural factors, the leading factors influencing the spatio-temporal distribution of O. hupensis were rainfall and vegetation (NDVI), and the primary factor alternated between these two. The leading role of rainfall decreased year by year, while that of vegetation (NDVI) increased from 2004 to 2011. CONCLUSIONS: The spatio-temporal distribution of O. hupensis was significantly influenced by natural factors, and the influences were heterogeneous across space and time. Additionally, the variation in the spatial-temporal distribution of O. hupensis was mainly affected by rainfall and vegetation.

Probing the spatial cluster of Meriones unguiculatus using the nest flea index based on GIS technology.

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 study used global spatial autocorrelation and spatial hot spot detection methods to describe the relationship between different years and the autocorrelation coefficient of nest flea indexes; it also used a spatial detection method and GIS technology to detect the spatial gathered hot spot of Meriones unguiculatus in the epidemic areas. The results of this study showed that (1) there were statistically significant spatial autocorrelations in the nest flea indexes in 2006, 2012, 2013 and 2014. (2) Most of the distribution patterns of Meriones unguiculatus were statistically significant clusters of high values. (3) There were some typical hot spot regions of plague distributed along the Inner Mongolia plateau, north of China. (4) The hot spot regions of plague were gradually stabilized after increasing and decreasing repeatedly. Generally speaking, the number of hot spot regions showed an accelerated increase from 2005 to 2007, decreased slowly from 2007 to 2008, rapidly increased again after decreasing slowly from 2008 to 2010, showed an accelerated decrease from 2010 to 2011, and ultimately were stabilized after rapidly increasing again from 2011 to 2014. (5) The migration period of the hot spot regions was 2-3 years. The epidemic area of plague moved from southwest to east during 2005, 2007, 2008 and 2010, from east to southwest during 2007 and 2008, from east to west during 2010 and 2011, and from Midwest to east during 2011 and 2014. (6) Effective factors, such as temperature, rainfall, DEM, host density, and NDVI, can affect the spatial cluster of Meriones unguiculatus. The results of this study have important implications for exploring the temporal and spatial distribution law and distribution of the hot spot regions of plague, which can reduce the risk of plague, help support the decision making process for the control and prevention of plague, and form a valuable application for plague research.

Mineral resources prospecting by synthetic application of TM/ETM+, Quickbird and Hyperion data in the Hatu area, West Junggar, Xinjiang, China.

The Hatu area, West Junggar, Xinjiang, China, is situated at a potential gold-copper mineralization zone in association with quartz veins and small granitic intrusions. In order to identify the alteration zones and mineralization occurrences in this area, the Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+), Quickbird, Hyperion data and laboratory measured spectra were combined in identifying structures, alteration zones, quartz veins and small intrusions. The hue-saturation-intensity (HSI) color model transformation was applied to transform principal component analysis (PCA) combinations from R (Red), G (Green) and B (Blue) to HSI space to enhance faults. To wipe out the interference of the noise, a method, integrating Crosta technique and anomaly-overlaying selection, was proposed and implemented. Both Jet Propulsion Laboratory Spectral Library spectra and laboratory-measured spectra, combining with matched filtering method, were used to process Hyperion data. In addition, high-resolution Quickbird data were used for unraveling the quartz veins and small intrusions along the alteration zones. The Baobei fault and a SW-NE-oriented alteration zone were identified for the first time. This study eventually led to the discovery of four weak gold-copper mineralized locations through ground inspection and brought new geological knowledge of the region's metallogeny.

Characterisation of gastric cancer and its relation to environmental factors: a case study in Shenqiu County, China.

This article presents the results of spatial analysis of gastric cancer and its relation to environmental conditions in Shenqiu County, China. Retrospective data on gastric cancer mortality (GCM) were analysed at various spatial scales, with its relation to environmental factors explored at an appropriate scale. The results considered 2 × 2 km(2) grid as the optimal level for characterising GCM due to the highest Moran's I (I = 0.68, p < 0.01). Then, three clustering regions were clearly identified. Meanwhile, GCM was obviously associated with topography (r = -0.11, p < 0.10), farmland (r = 0.11, p < 0.10), population density (r = 0.10, p < 0.10) and river density (r = 0.11, p < 0.10) in the buffered zones. It indicates that spatial grid technique is suitable for characterising GCM in Shenqiu County, and that GCM was geographically associated with environmental conditions. We suggest that preventive measures for controlling the environment-related malignant neoplasm should not be limited in the regions suffering from this disease but be reasonably extended to surrounding areas.

A Rapid Monitoring and Evaluation Method of Schistosomiasis Based on Spatial Information Technology.

Thanks to Spatial Information Technologies (SITs) such as Remote Sensing (RS) and Geographical Information System (GIS) that are being quickly developed and updated, SITs are being used more widely in the public health field. The use of SITs to study the characteristics of the temporal and spatial distribution of Schistosoma japonicum and to assess the risk of infection provides methods for the control and prevention of schistosomiasis japonica has gradually become a hot topic in the field. The purpose of the present paper was to use RS and GIS technology to develop an efficient method of prediction and assessment of the risk of schistosomiasis japonica. We choose the Yueyang region, close to the east DongTing Lake (Hunan Province, China), as the study area, where a recent serious outbreak of schistosomiasis japonica took place. We monitored and evaluated the transmission risk of schistosomiasis japonica in the region using SITs. Water distribution data were extracted from RS images. The ground temperature, ground humidity and vegetation index were calculated based on RS images. Additionally, the density of oncomelania snails, which are the Schistosoma japonicum intermediate host, was calculated on the base of RS data and field measurements. The spatial distribution of oncomelania snails was explored using SITs in order to estimate the area surrounding the residents with transmission risk of schistosomiasis japonica. Our research result demonstrated: (1) the risk factors for the transmission of schistosomiasis japonica were closely related to the living environment of oncomelania snails. Key factors such as water distribution, ground temperature, ground humidity and vegetation index can be quickly obtained and calculated from RS images; (2) using GIS technology and a RS deduction technique along with statistical regression models, the density distribution model of oncomelania snails could be quickly built; (3) using SITs and analysis with overlaying population distribution data, the range of transmission risk of schistosomiasis japonica of the study area can be quickly monitored and evaluated. This method will help support the decision making for the control and prevention of schistosomiasis and form a valuable application using SITs for the schistosomiasis research.

Characterization of dengue epidemics in mainland China over the past decade.

INTRODUCTION: Dengue is an important public health concern in developing countries. As it is increasingly serious in mainland China, its spatiotemporal variations in this region must be further understood. METHODOLOGY: On the basis of the data on dengue cases in 2004-2013 collected from the China Information System for Disease Control and Prevention, examinations of spatiotemporal variations of local, imported, and total dengue cases were conducted to characterize this epidemic at the city scale in China. RESULTS: Local cases in September and October accounted for more than half of the total cases in each year. The cities with more than 50 accumulative local cases were mainly distributed along the southeast coastal areas and southwest border regions of China. In 2004-2013, local dengue transmission (indicated by the number of local cases and the locally infected cities) increased yearly and was closely associated with epidemics (represented by the amount of imported cases and the cities with imported cases). At the city level, local transmission tended to be spatially clustered in the Zhejiang-Fujian coastal area, the Pearl River Delta, and Yunnan-Burma border region, especially in 2005, 2010, 2012, and 2013. CONCLUSIONS: The results showed that China's local dengue transmission is spatially and temporally featured, and that the prevalence of this epidemic is mostly related to imported cases from overseas epidemic areas. This study provides useful support for hygiene authorities of central and local governments to take effective measures to prevent and control this disease.

Integrated Health Risk Assessment of Heavy Metals in Suxian County, South China.

The purpose of this study was to assess soil heavy metal contamination and the potential risk for local residents in Suxian county of Hunan Province, southern China. Soil, rice and vegetable samples from the areas near the mining industrial districts were sampled and analyzed. The results indicate that the anthropogenic mining activities have caused local agricultural soil contamination with As, Pb, Cu and Cd in the ranges of 8.47-341.33 mg/kg, 19.91-837.52 mg/kg, 8.41-148.73 mg/kg and 0.35-6.47 mg/kg, respectively. GIS-based mapping shows that soil heavy metal concentrations abruptly diminish with increasing distance from the polluting source. The concentrations of As, Pb, Cu and Cd found in rice were in the ranges of 0.02-1.48 mg/kg, 0.66-5.78 mg/kg, 0.09-6.75 mg/kg, and up to 1.39 mg/kg, respectively. Most of these concentrations exceed their maximum permissible levels for contaminants in foods in China. Heavy metals accumulate to significantly different levels between leafy vegetables and non-leafy vegetables. Food consumption and soil ingestion exposure are the two routes that contribute to the average daily intake dose of heavy metals for local adults. Moreover, the total hazard indices of As, Pb and Cd are greater than or close to the safety threshold of 1. Long-term As, Pb and Cd exposure through the regular consumption of the soil, rice and vegetables in the investigated area poses potential health problems to residents in the vicinity of the mining industry.

Association between changing mortality of digestive tract cancers and water pollution: a case study in the Huai River Basin, China.

The relationship between the ever-increasing cancer mortality and water pollution is an important public concern in China. This study aimed to explore the association between serious water pollution and increasing digestive cancer mortality in the Huai River Basin (HRB) in China. A series of frequency of serious pollution (FSP) indices including water quality grade (FSPWQG), biochemical oxygen demand (FSPBOD), chemical oxygen demand (FSPCOD), and ammonia nitrogen (FSPAN) were used to characterize the surface water quality between 1997 and 2006. Data on the county-level changing mortality (CM) due to digestive tract cancers between 1975 and 2006 were collected for 14 counties in the study area. Most of investigated counties (eight) with high FSPWQG (>50%) distributed in the northern region of the HRB and had larger CMs of digestive tract cancers. In addition to their similar spatial distribution, significant correlations between FSP indices and CMs were observed by controlling for drinking water safety (DWS), gross domestic product (GDP), and population (POP). Furthermore, the above-mentioned partial correlations were clearly increased when only controlling for GDP and POP. Our study indicated that county-level variations of digestive cancer mortality are remarkably associated with water pollution, and suggested that continuous measures for improving surface water quality and DWS and hygienic interventions should be effectively implemented by local governments.

Spatiotemporal heterogeneity analysis of hemorrhagic fever with renal syndrome in China using geographically weighted regression models.

Hemorrhagic fever with renal syndrome (HFRS) is an important public health problem in China. The identification of the spatiotemporal pattern of HFRS will provide a foundation for the effective control of the disease. Based on the incidence of HFRS, as well as environmental factors, and social-economic factors of China from 2005-2012, this paper identified the spatiotemporal characteristics of HFRS distribution and the factors that impact this distribution. The results indicate that the spatial distribution of HFRS had a significant, positive spatial correlation. The spatiotemporal heterogeneity was affected by the temperature, precipitation, humidity, NDVI of January, NDVI of August for the previous year, land use, and elevation in 2005-2009. However, these factors did not explain the spatiotemporal heterogeneity of HFRS incidences in 2010-2012. Spatiotemporal heterogeneity of provincial HFRS incidences and its relation to environmental factors would provide valuable information for hygiene authorities to design and implement effective measures for the prevention and control of HFRS in China.

Spatial-temporal variation of marginal land suitable for energy plants from 1990 to 2010 in China.

Energy plants are the main source of bioenergy which will play an increasingly important role in future energy supplies. With limited cultivated land resources in China, the development of energy plants may primarily rely on the marginal land. In this study, based on the land use data from 1990 to 2010(every 5 years is a period) and other auxiliary data, the distribution of marginal land suitable for energy plants was determined using multi-factors integrated assessment method. The variation of land use type and spatial distribution of marginal land suitable for energy plants of different decades were analyzed. The results indicate that the total amount of marginal land suitable for energy plants decreased from 136.501 million ha to 114.225 million ha from 1990 to 2010. The reduced land use types are primarily shrub land, sparse forest land, moderate dense grassland and sparse grassland, and large variation areas are located in Guangxi, Tibet, Heilongjiang, Xinjiang and Inner Mongolia. The results of this study will provide more effective data reference and decision making support for the long-term planning of bioenergy resources.