Dafang Zhuang

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.

Located at the junction between the continental climate region and marine climate region, the Qilian Mountains have experienced significant climate change. Vegetation phenology in the Qilian Mountains is sensitive to climate change. However, the response of vegetation phenology to temperature and precipitation change is still unclear, and the same is true for their interactions. First, we extracted grassland phenological parameters such as SOS (the start of the growing season), EOS (the end of the growing season), and LOS (the length of the growing season) from revised MODIS-NDVI data in the Qilian Mountains during the period from 2000 to 2019. Second, we analyzed change trends of the phenological parameters, temperature, and precipitation. Furthermore, the effects of each meteorological element changes and their interaction on multiple phenological parameters were detected using the GeoDetector method. The result implied that (1) the SOS in most areas except the northwestern mountain region showed an advanced trend (10 d/10a); the EOS showed a delayed trend in the southeast (5 d/10a), and an advanced trend (5 d/10a) in the northwest; the LOS showed an extended trend (10 d/10a) in the southeast, and a shortened trend (5 d/10a) in the northwest. (2) Compared with a single meteorological element in a single period, the interaction of temperature and precipitation in different periods had a higher impact on grassland phenology, with the maximum q-value increasing by about 0.4 for each phenological parameter. (3) The change in the grassland phenology in the Qilian Mountains was inconsistently complete with climate change in the spatial distribution. Our research reveals the response of grassland phenology to the interaction of different meteorological elements in different periods. Compared with a single element, this can reflect the response of vegetation phenology to climate change more comprehensively.

Deep learning (DL) shows remarkable performance in extracting buildings from high resolution remote sensing images. However, how to improve the performance of DL based methods, especially the perception of spatial information, is worth further study. For this purpose, we proposed a building extraction network with feature highlighting, global awareness, and cross level information fusion (B-FGC-Net). The residual learning and spatial attention unit are introduced in the encoder of the B-FGC-Net, which simplifies the training of deep convolutional neural networks and highlights the spatial information representation of features. The global feature information awareness module is added to capture multiscale contextual information and integrate the global semantic information. The cross level feature recalibration module is used to bridge the semantic gap between low and high level features to complete the effective fusion of cross level information. The performance of the proposed method was tested on two public building datasets and compared with classical methods, such as UNet, LinkNet, and SegNet. Experimental results demonstrate that B-FGC-Net exhibits improved profitability of accurate extraction and information integration for both small and large scale buildings. The IoU scores of B-FGC-Net on WHU and INRIA Building datasets are 90.04% and 79.31%, respectively. B-FGC-Net is an effective and recommended method for extracting buildings from high resolution remote sensing images.

Ecological vulnerability evaluations can provide a scientific foundation for ecological environment management. Studies of ecological vulnerability have mainly focused on typical ecologically vulnerable regions with poor natural conditions or severe human interference, and such studies have rarely considered eco-provinces. Taking Jiangsu, an eco-province under construction in China, as the study area, we evaluated the spatiotemporal distributions of ecological vulnerability in 2005, 2010 and 2015 at the kilometer grid scale and analyzed the effects of natural and anthropogenic factors on ecological vulnerability. The pressure state response model (PSR), geographic information systems (GIS), spatial principal component analysis, spatial autocorrelation analysis, and correlation analysis methods were used. The results of the study are as follows: (i) the effects of anthropogenic factors on ecological vulnerability are greater than those of natural factors, and landscape evenness and the land resource utilization degree are the main factors that influence ecological vulnerability. (ii) Jiangsu Province is generally lightly to moderately vulnerable. Slight vulnerability is mainly observed in areas with water bodies. Light vulnerability is concentrated in paddy fields between the Main Irrigation Channel of North Jiangsu and the Yangtze River. Medium, heavy and extreme vulnerability areas are mainly composed of arable and built-up land. Medium vulnerability is mainly distributed to the north of the Main Irrigation Channel of North Jiangsu; heavy vulnerability is scattered to the south of the Yangtze River and in north-western hilly areas; and extreme vulnerability is concentrated in hilly areas; (iii) Ecological vulnerability displays a clustering characteristic. High-high (HH) regions are mainly distributed in heavy and extreme vulnerability regions, and low-low (LL) regions are located in slight vulnerability areas. (iv) Ecological vulnerability has gradually deteriorated. From 2005 to 2010, the vulnerability in hilly areas considerably increased, and from 2010 to 2015, the vulnerability in urban and north-eastern coastal built-up land areas significantly increased. Emphasis should be placed on the prevention and control of ecological vulnerability in high-altitude, urban and coastal areas.

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. 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. 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. 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.

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.

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.

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 km² 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.

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 (FSP_WQG), biochemical oxygen demand (FSP_BOD), chemical oxygen demand (FSP_COD), and ammonia nitrogen (FSP_AN) 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 FSP_WQG (>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.

Solar radiation is an important input for various land-surface energy balance models. Global solar radiation data retrieved from the Japanese Geostationary Meteorological Satellite 5 (GMS-5)/Visible and Infrared Spin Scan Radiometer (VISSR) has been widely used in recent years. However, due to the impact of clouds, aerosols, solar elevation angle and bidirectional reflection, spatial or temporal deficiencies often exist in solar radiation datasets that are derived from satellite remote sensing, which can seriously affect the accuracy of application models of land-surface energy balance. The goal of reconstructing radiation data is to simulate the seasonal variation patterns of solar radiation, using various statistical and numerical analysis methods to interpolate the missing observations and optimize the whole time-series dataset. In the current study, a reconstruction method based on data assimilation is proposed. Using a Kalman filter as the assimilation algorithm, the retrieved radiation values are corrected through the continuous introduction of local in-situ global solar radiation (GSR) provided by the China Meteorological Data Sharing Service System (Daily radiation dataset_Version 3) which were collected from 122 radiation data collection stations over China. A complete and optimal set of time-series data is ultimately obtained. This method is applied and verified in China’s northern agricultural areas (humid regions, semi-humid regions and semi-arid regions in a warm temperate zone). The results show that the mean value and standard deviation of the reconstructed solar radiation data series are significantly improved, with greater consistency with ground-based observations than the series before reconstruction. The method implemented in this study provides a new solution for the time-series reconstruction of surface energy parameters, which can provide more reliable data for scientific research and regional renewable-energy planning.