● A study assessing the temperature-injury relationship was conducted among students.● The maximum risks of injury appeared at moderate temperatures.● The temperature effect on outdoor falls was greater in older students. Although studies have suggested that non-optimal temperatures may increase the risk of injury, epidemiological studies focusing on the association between temperature and non-fatal injury among children and adolescents are limited. Therefore, we investigated the short-term effect of ambient temperature on non-fatal falls and road traffic injuries (RTIs) among students across Jiangsu Province, China. Meteorological data and records of non-fatal outdoor injuries due to falls and RTIs among students aged 6–17 were collected during 2018–2020. We performed a time-stratified case-crossover analysis with a distributed lag nonlinear model to examine the effect of ambient temperature on the risk of injury. Individual meteorological exposure was estimated based on the address of the selected school. We also performed stratified analyses by sex, age, and area. A total of 57322 and 5455 cases of falls and RTIs were collected, respectively. We observed inverted U-shaped curves for temperature-injury associations, with maximum risk temperatures at 18 °C (48th of daily mean temperature distribution) for falls and 22 °C (67th of daily mean temperature distribution) for RTIs. The corresponding odds ratios (95% confidence intervals) were 2.193 (2.011, 2.391) and 3.038 (1.988, 4.644) for falls and RTIs, respectively. Notably, there was a significant age-dependent trend in which the temperature effect on falls was greater in older students (P-trend < 0.05). This study suggests a significant association between ambient temperature and students’ outdoor falls and RTIs. Our findings may help advance tailored strategies to reduce the incidence of outdoor falls and RTIs in children and adolescents. 相似文献
● Microplastic (MP) abundance in soil of China was highly heterogeneous.● MP abundance was higher near large rivers and central land affected by monsoons.● MP abundance was correlated with longitude, mulching film, and average temperature.● Factors suitable for predicting MP pollution using models were discussed. Microplastics (MPs) are found worldwide in high abundance, posing a potential threat to ecosystems. Despite the ubiquity of MPs in the environment, very little is known about the regional distribution of MPs and underlying factors affecting this distribution in the field, which likely include human activity, but also features of the environment itself. Here, out of a total of 1157 datapoints investigated in 53 Chinese studies, 9.68% datapoints were removed as outliers in the heterogeneity analysis. This review revealed that the abundance of MPs was highly heterogeneous. In addition, microplastic (MP) distribution maps based on China demonstrated that the highest abundance of MPs tended to occur near large rivers and central land affected by the intersection of two monsoons. The model-fitting and previous studies showed that MP abundance in China was correlated with longitude, agricultural mulching film usage per capita, temperature, and precipitation. However, due to the heterogeneity of MPs and the low matching degree between the current environmental data and the sampling points, this pattern was not as evident as reported in any single study. Factors affecting the distribution of MPs can not be captured by linear relationships alone, and systematic selection of suitable environmental factors and further model optimization are needed to explore the cause of MP pollution in soil. Overall, this review revealed an uneven distribution of MPs and serves as a reference for model prediction to assess and control plastic pollution in natural soil environments. 相似文献
● A machine learning model was used to identify lake nutrient pollution sources.● XGBoost model showed the best performance for lake water quality prediction.● Model feature size was reduced by screening the key features with the MIC method.● TN and TP concentrations of Lake Taihu are mainly affected by endogenous sources.● Next-month lake TN and TP concentrations were predicted accurately. Effective control of lake eutrophication necessitates a full understanding of the complicated nitrogen and phosphorus pollution sources, for which mathematical modeling is commonly adopted. In contrast to the conventional knowledge-based models that usually perform poorly due to insufficient knowledge of pollutant geochemical cycling, we employed an ensemble machine learning (ML) model to identify the key nitrogen and phosphorus sources of lakes. Six ML models were developed based on 13 years of historical data of Lake Taihu’s water quality, environmental input, and meteorological conditions, among which the XGBoost model stood out as the best model for total nitrogen (TN) and total phosphorus (TP) prediction. The results suggest that the lake TN is mainly affected by the endogenous load and inflow river water quality, while the lake TP is predominantly from endogenous sources. The prediction of the lake TN and TP concentration changes in response to these key feature variations suggests that endogenous source control is a highly desirable option for lake eutrophication control. Finally, one-month-ahead prediction of lake TN and TP concentrations (R2 of 0.85 and 0.95, respectively) was achieved based on this model with sliding time window lengths of 9 and 6 months, respectively. Our work demonstrates the great potential of using ensemble ML models for lake pollution source tracking and prediction, which may provide valuable references for early warning and rational control of lake eutrophication. 相似文献