Suchergebnisse

With the economic growth and increasing urbanization in the last three decades, the air quality over China has continuously degraded, which poses a great threat to human health. The concentration of fine particulate matter (PM2.5) directly affects the mortality of people living in the polluted areas where air quality is poor. The Beijing-Tianjin-Hebei (BTH) region, one of the well organized urban regions in northern China, has suffered with poor air quality and atmospheric pollution due to recent growth of the industrial sector and vehicle emissions. In the present study, we used the back propagation neural network model approach to estimate the spatial distribution of PM2.5 concentration in the BTH region for the period January 2014–December 2016, combining the satellite-derived aerosol optical depth (S-DAOD) and meteorological data. The results were validated using the ground PM2.5 data. The general method including all PM2.5 training data and 10-fold cross-method have been used for validation for PM2.5 estimation (R2 = 0.68, RMSE = 20.99 for general validation; R2 = 0.54, RMSE = 24.13 for cross-method validation). The study provides a new approach to monitoring the distribution of PM2.5 concentration. The results discussed in the present paper will be of great help to government agencies in developing and implementing environmental conservation policy.

China was attacked by a serious influenza A (H7N9) virus in 2013. The first human infection case was confirmed in Shanghai City and soon spread across most of eastern China. Using the methods of Geographic Information Systems (GIS) and ecological niche modeling (ENM), this research quantitatively analyzed the relationships between the H7N9 occurrence and the main environmental factors, including meteorological variables, human population density, bird migratory routes, wetland distribution, and live poultry farms, markets, and processing factories. Based on these relationships the probability of the presence of H7N9 was predicted. Results indicated that the distribution of live poultry processing factories, farms, and human population density were the top three most important determinants of the H7N9 human infection. The relative contributions to the model of live poultry processing factories, farms and human population density were 39.9%, 17.7% and 17.7%, respectively, while the maximum temperature of the warmest month and mean relative humidity had nearly no contribution to the model. The paper has developed an ecological niche model (ENM) that predicts the spatial distribution of H7N9 cases in China using environmental variables. The area under the curve (AUC) values of the model were greater than 0.9 (0.992 for the training samples and 0.961 for the test data). The findings indicated that most of the high risk areas were distributed in the Yangtze River Delta. These findings have important significance for the Chinese government to enhance the environmental surveillance at multiple human poultry interfaces in the high risk area.

Abstract Background Due to recent emergence, dengue is becoming one of the major public health problems in Nepal. The numbers of reported dengue cases in general and the area with reported dengue cases are both continuously increasing in recent years. However, spatiotemporal patterns and clusters of dengue have not been investigated yet. This study aims to fill this gap by analyzing spatiotemporal patterns based on monthly surveillance data aggregated at district. Methods Dengue cases from 2010 to 2014 at district level were collected from the Nepal government's health and mapping agencies respectively. GeoDa software was used to map crude incidence, excess hazard and spatially smoothed incidence. Cluster analysis was performed in SaTScan software to explore spatiotemporal clusters of dengue during the above-mentioned time period. Results Spatiotemporal distribution of dengue fever in Nepal from 2010 to 2014 was mapped at district level in terms of crude incidence, excess risk and spatially smoothed incidence. Results show that the distribution of dengue fever was not random but clustered in space and time. Chitwan district was identified as the most likely cluster and Jhapa district was the first secondary cluster in both spatial and spatiotemporal scan. July to September of 2010 was identified as a significant temporal cluster. Conclusion This study assessed and mapped for the first time the spatiotemporal pattern of dengue fever in Nepal. Two districts namely Chitwan and Jhapa were found highly affected by dengue fever. The current study also demonstrated the importance of geospatial approach in epidemiological research. The initial result on dengue patterns and risk of this study may assist institutions and policy makers to develop better preventive strategies.

Intro -- Foreword -- Preface -- Contents -- Part IFlooding Identification Method -- 1 Geographical Characteristics of the Study Area -- 1.1 Characteristics of the Ayutthaya Province -- 1.2 Thailand Major Flood Event of 2011 -- 1.3 Environmental Remote Sensing of Flooding Area -- 1.4 Summary -- References -- 2 Datasets and Data Preparation -- 2.1 Remote Sensing Datasets and Preprocessing -- 2.1.1 Image Enhancement or Radiometric Correction -- 2.1.2 Georeferencing or Geometric Correction -- 2.2 Flood Water Quality Data -- 2.3 Morbidity Data -- 2.4 Summary -- References -- 3 Flooding Identification by Vegetation Index -- 3.1 Flooding Identification from Multispectral Images -- 3.2 Flooding Identification from SAR Images -- 3.3 Multi-temporal Remote Sensing Data for Flooding Identification -- 3.4 Summary -- References -- 4 Flooding Identification by Support Vector Machine -- 4.1 Support Vector Machine -- 4.2 Affecting Parameters of the SVM Classifier -- 4.3 Summary -- References -- 5 Improved Support Vector Machine Classifier Through a Particle Filter Algorithm -- 5.1 Linear Dynamics Systems -- 5.1.1 Linear Continuous Systems -- 5.1.2 Discrete Linear Systems -- 5.2 Random Process and Stochastic Systems to Model State Estimation -- 5.2.1 Probability, Random Variables, and Their Statistical Properties -- 5.2.2 Statistical Properties of Random Processes and Random Sequence -- 5.2.3 Linear System Models of Random Processes and Random Sequences -- 5.3 Particle Filter Algorithm -- 5.4 A Support Vector Machine-Based Particle Filter (SVM-PF) -- 5.5 A SVM-PF Applied in the Study Area -- 5.6 Measured Results of the SVM-PF in Water Identification of Flooding Area -- 5.6.1 Reference Dataset -- 5.6.2 Accuracy Assessment -- 5.7 An In-House Classifier for All Land Cover-CANFET -- 5.7.1 Important Theories and Concepts -- 5.7.2 Original CANFET.