Near-term forecasts of stream temperature using deep learning and data assimilation in support of management decisions

Deep learning (DL) models are increasingly used to make accurate hindcasts of management-relevant variables, but they are less commonly used in forecasting applications. Data assimilation (DA) can be used for forecasts to leverage real-time observations, where the difference between model predictions and observations today is used to adjust the model to make better predictions tomorrow. In this use case, we developed a process-guided DL and DA approach to make 7-day probabilistic forecasts of daily maximum water temperature in the Delaware River Basin in support of water management decisions. Our modeling system produced forecasts of daily maximum water temperature with an average root mean squared error (RMSE) from 1.1 to 1.4°C for 1-day-ahead and 1.4 to 1.9°C for 7-day-ahead forecasts across all sites. The DA algorithm marginally improved forecast performance when compared with forecasts produced using the process-guided DL model alone (0%–14% lower RMSE with the DA algorithm). Across all sites and lead times, 65%–82% of observations were within 90% forecast confidence intervals, which allowed managers to anticipate probability of exceedances of ecologically relevant thresholds and aid in decisions about releasing reservoir water downstream. The flexibility of DL models shows promise for forecasting other important environmental variables and aid in decision-making.     

A multidecadal oscillation in precipitation and temperature series is pronounced in low flow series from Puget Sound streams

In Pacific Northwest streams, summer low flows limit water available to competing instream (salmon) and out-of-stream (human) uses, creating broad interest in how and why low flows are trending. Analyses that assumed linear (monotonic) change over the last ~60 years revealed declining low flow trends in minimally disturbed streams. Here, polynomials were used to model flow trends between 1929 and 2015. A multidecadal oscillation was observed in flows, which increased initially from the 1930s until the 1950s, declined until the 1990s, and then increased again. A similar oscillation was detected in precipitation series, and opposing oscillations in surface temperature, Pacific Decadal Oscillation, and Interdecadal Pacific Oscillation series. Multidecadal oscillations with similar periods to those described here are well known in climate indices. Fitted model terms were consistent with flow trends being influenced by at least two drivers, one oscillating and the other monotonic. Anthropogenic warming is a candidate driver for the monotonic decline, and variation in (internal) climatic circulation for the oscillating trend, but others were not ruled out. The recent upturn in streamflows suggests that anthropogenic warming has not been the dominant factor driving streamflow trends, at least until 2015. Climate projections based on simulations that omit drivers of multidecadal variation are likely to underestimate the range, and rate of change, of future climatic variation.     

基于环境监测实验室的高校实验室安全准入制度信息系统功能优化研究

梳理了高校实验室环境-安全-健康准入制度包含的管理内容,从人员管理、设备管理、试剂管理、实验环境管理角度进行了分析。根据现有实验室安全信息化系统运行情况,探讨了将实验室安全信息化系统与实验室信息化系统、设备管理信息化系统进行数据融合和功能优化的要点,重点分析了设备采购及管理系统、实验室安全教育与考试系统、实验室安全检查系统、试剂管理平台、实验室门禁管理系统在功能上的内在关联,从而通过试剂管理平台、实验室门禁控制系统、设备采购系统实现实验室环境-安全-健康准入制度的强制执行。研究发现,高校与环境监测实验室、院系实验室安全管理部门联合促进实验室安全信息化系统、实验室信息化系统、设备管理信息化系统的数据共享与功能升级,可以丰富实验室安全准入制度的内涵和管理手段,加强实验室安全准入制度的强制执行力,提高实验室安全管理效率。     

基于响应曲面法的遗煤自燃分析与研究*

为研究高瓦斯易自燃煤层不同供风量、高抽巷抽采流量、低抽巷抽采流量3因素对采空区自燃“三带”分布影响规律，选取阳煤五矿8406工作面为研究对象，在数值模拟研究基础上，采用Design Expert软件进行Box Behnken试验设计，构建采空区氧化升温带宽度在3因素、3水平条件下的二次回归响应曲面模型，并对不同条件下采空区氧化升温带宽度进行预测与分析。结果表明:二次回归方程P值为0.001 6，预测模型显著，模型的失拟项为0.606 3，不显著，回归方程具有统计学意义；当供风量为1 500~2 000 m3/min，低抽流量为450~650 m3/min，高抽流量为100~200 m3/min时，对氧化升温带宽度一次项重要度排序为C（高抽巷抽采流量）>A（供风量）>B（低抽巷抽采流量），二次项重要度排序为AC（供风量和高抽巷抽采流量）>AB（供风量和低抽巷抽采流量）>BC（低抽巷抽采流量和高抽巷抽采流量），且AB,AC,BC之间均无交互作用。     

屏蔽服冷却系统对人体表面温度影响的研究*

为科学有效地论证多种方式组合的冷却屏蔽服在不同环境条件下对人体表面温度控制的效果，需要对冷却系统及人体敏感部位发热量进行客观评估。通过对5名健康男性的高温测试，探究人体在不同环境温度下体表温度的变化，得出胸部、背部及额头为热量最高部位，并构建以“人体-降温屏蔽服-外界环境”为主体的冷却系统数值模型，对不同环境中的屏蔽服冷却效果展开研究，分析穿戴冷却屏蔽服时人体躯干部分的温度分布及影响。结果表明:在屏蔽服中靠近胸部、背部部位引入相变材料和风扇，均可帮助人体降低体温，提高舒适度。     

地铁站附属地下餐饮空间噪声特征分析

利用专业噪声测量设备及噪声频谱分析设备,对地铁站附属地下餐饮空间区域噪声进行监测,绘制噪声小时频谱。经分析发现地铁附属地下餐饮空间声环境会受到客流、空间位置及餐铺建筑结构影响,因此在该类空间进行降噪设计中应充分考虑这些因素的影响。     

地表水环境遥感监测关键技术与系统

本文主要是围绕地表水环境遥感检测展开讨论,全面介绍了水环境检测的关键技术和系统,建立改进双峰法的水体分布遥感提取方法,并以具体地区为例进行分析,采用不同方法建立解析方法,从根本上提升反演方法的区域和积极适用性,希望能够对相关人员起到参考性价值。     

“互联网+”智慧环保生态环境多元感知体系发展分析

在大数据技术以及信息化快速稳定持续发展的实践背景之下,"互联网+"智慧环保发展时代的悄然来临,使得对"互联网+"智慧环保多元感知体系的研究与发展工作,逐步被提升到了极其重要的地位。文章结合雄安新区生态环境智慧监测体系建设项目,展开了简要的阐释分析。     

The Water Poverty Index: An application in the Indian context

For developing countries, the proportion of households covered by improved water resources is conventionally used to assess the water stress situation. However, in a developing country like India with a high population growth rate, water demand and supply are considerably mismatched. An agro-based economy with large variations in socio-economic conditions and changing rainfall patterns across the states imposes greater challenge on water resources. Therefore, there is a need to assess the water situation across the country in a holistic manner. This paper proposes application of the Water Poverty Index as a comprehensive policy tool to assess actual water-stress situation across 20 major states in India. This index covers important socio-economic parameters such as access, capacity, use and environment in addition to water resources of each state. The results and findings are expected to be of use to policymakers and implementing agencies. In view of policy formulation, a state performing well on a Water Poverty Index component can act as a benchmark for another state.     

Development of machine learning based prediction models for hazardous properties of chemical mixtures

Lower flammability limit (LFL), upper flammability limit (UFL), auto-ignition temperature (AIT) and flash point (FP) are crucial hazardous properties for fire and explosion hazards assessment and consequence analysis. In this study, a comprehensive prediction model set was constructed by using expanded chemical mixture databases of chemical mixture hazardous properties. Machine learning based gradient boosting quantitative structure-property relationship (GB-QSPR) method is implemented for the first time to improve the model performance and prediction accuracy. The result shows that all developed models have significantly higher accuracy than other regular QSPR models, with the 5-fold cross-validation RMSE of LFL, UFL, AIT, and FP models being 1.06, 1.14, 1.08, and 1.17, respectively. All developed QSPR models can be used to estimate reliable chemical mixture hazardous properties and provide useful guidance in chemical mixture hazard assessment and consequence analysis.