Objectives: The current study was designed to investigate the relationship between the soil arsenic (As) concentration and the mortality from Alzheimer's disease (AD) in mainland China. Study design: Ecological study. Methods: Twenty-two provinces and 3 municipal districts in mainland China were included in this study. The As concentrations in soil in 1990 was obtained from the China State Environmental Protection Bureau; the data on annual mortality of AD from 1991 to 2000 were obtained from the National Death Cause Surveillance Database of China. Using these data, we calculated the spearman correlation coefficient between soil As concentration and AD mortality, and the relative risk (RR) between soil As levels and AD mortality by quartile-dividing study groups. Results: The spearman correlation coefficient between As concentration and AD mortality was 0.552 (p = 0.004), 0.616 (p = 0.001) and 0.622 (p = 0.001) in the A soil As (eluvial horizon), the C soil As (parent material horizon), and the Total soil As (A soil As + C soil As), respectively. When the A soil As concentration was over 9.05 mg/kg, 10.40 mg/kg and 13.10 mg/kg, the relative risk was 0.835 (95 % CI: 0.832, 0.838), 1.969 (95 %CI: 1.955, 1.982), and 2.939 (95 % CI: 2.920, 2.958), respectively; when the C soil As reached 9.45 mg/kg, 11.10 mg/kg and 13.55 mg/kg, the relative risk was 4.349 (95 % CI: 4.303, 4.396), 6.108 (95 % CI: 6.044, 6.172), and 9.125 (95 %CI: 9.033, 9.219), respectively. No correlation was found between lead, cadmium, and mercury concentration in the soil and AD mortality. Conclusion: There was an apparent soil As concentration dependent increase in AD mortality. Results of this study may provide evidence for a possible causal linkage between arsenic exposure and the death risk from AD.
Introduction Impaired lung function is independently associated with higher rates of disability, however, few studies have examined the association between lung function and functional limitation. This study aimed to assess this association and dose-response relationship in older adults. Methods Data from the National Health and Nutrition Examination Survey (2007–2012) was used as a cross-sectional study. Lung function was determined by Forced expiratory volume in 1 second (FEV1) and forced vital capacity (FVC). Functional limitation in older adults was identified by six self-reported questions on physical function. 3070 adults aged 60 and over were enrolled in our study. Logistic regression models and restricted cubic spline models were applied to examine the association between lung function and the risk of functional limitation. Results FEV1 and FVC were inversely associated with the risk of functional limitation. In the full adjusted model, compared with the lowest tertile of FEV1, the odds ratios (95% confidence intervals) of functional limitation for tertile 2 and tertile 3 were 0.5422 (0.3848–0.7639) and 0.4403 (0.2685–0.7220), and the odds ratios (95% confidence intervals) of functional limitation for tertile 2 and tertile 3 of FVC were 0.5243 (0.3503–0.7848) and 0.3726 (0.2072–0.6698). Furthermore, an inverse association persisted after stratified analysis by gender and sensitivity analysis. Dose-response analyses showed that the odds of functional limitation declined with increase in FEV1 and FVC in a nonlinear manner. Conclusions Lung function was inversely associated with functional limitation among older adults.
Previous wildfire risk assessments have problems such as subjectivity of weight allocation and the linearization of statistical models, resulting in generally low robustness and low generalization ability of fire risk assessment models. Therefore, in this paper, we explored the potential of integration machine learning algorithms to build wildfire risk assessment models. Based on analyzing fire data’s spatial and temporal distribution, we selected 10 triggering factors of topography, meteorology, vegetation, and human activities, using frequency ratio (FR) to provide uniform data representation of triggering factors. Next, we used the Bayesian optimization (BO) algorithm to perform hyperparametric optimization solutions for various machine learning models: support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost). Finally, we constructed an integration machine learning algorithm to acquire a fire risk grading map and the importance evaluation corresponding to each triggering factor. For validation purposes, we selected Liangshan Prefecture in Sichuan Province as the specific study area and obtained MCD64A1 burned area product to extract the extent of burned areas in Liangshan Prefecture from 2011 to 2020. The accuracy, kappa coefficient, and area under curve (AUC) were then applied to assess the predictive power and consistency of the fire risk classification maps. The experimental analysis showed that among the three models, FR-BO-XGBoost had the best performance in wildfire risk assessment in the Liangshan region (AUC = 0.887), followed by FR-BO-RF (AUC = 0.876) and FR-BO-SVM (AUC = 0.820). The feature importance result indicated that the study area’s most significant effects on wildfires were precipitation, NDVI, land cover, and maximum temperature. The proposed method avoided the subjective weighting and model linearization problems. Compared with the previous methods, it automatically acquired the importance of the triggering factors to the wildfire, which had certain advantages in wildfire risk assessment, and was worthy of further promotion.
Vegetation net primary productivity (VNPP) is the main factor in ecosystem carbon sink function and regulation of environmental processes. However, NPP data products have data missing in some areas, which affects the availability and overall accuracy level of data. Therefore, we adopted the Factor Analysis Backpropagation neural network model (FA-BP model) to acquire a high-accuracy and high-reliability NPP result without missing or empty areas by using a series of easily accessible datasets, such as meteorological data and remote sensing data. We selected the western Sichuan region as the study area and carried out a VNPP time series estimation from 2000 to 2016. Comparative simulations also verify the accuracy of the time series estimation results: The Pearson correlation r of VNPP prediction results ranged from 0.807 to 0.917, the mean absolute error ranged from 29.1 to 38.9, the root mean square error was between 37.3 and 51.8, and the mean relative error varies from 0.10 to 0.14. Further analysis shows that the spatial pattern of estimated VNPP during the past 17 years in western Sichuan shows a decreasing trend from southeast to northwest. Besides, the VNPP time series is generally on an upward trend in this period. The increasing and decreasing areas of VNPP values in the study area accounted for 81.42% and 18.58%, respectively. Moreover, we find that temperature dominates the change of VNPP in the whole western Sichuan region. The data processing method and experimental results presented in this paper can provide a reference for accurately acquiring VNPP and related studies on natural resources and climate change.
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