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.     

Biological conversion pathways of sulfate reduction ammonium oxidation in anammox consortia

• The SRAO phenomena tended to occur only under certain conditions. • High amount of biomass and non-anaerobic condition is requirement for SRAO. • Anammox bacteria cannot oxidize ammonium with sulfate as electron acceptor. • AOB and AnAOB are mainly responsible for ammonium conversion. • Heterotrophic sulfate reduction mainly contributed to sulfate conversion. For over two decades, sulfate reduction with ammonium oxidation (SRAO) had been reported from laboratory experiments. SRAO was considered an autotrophic process mediated by anammox bacteria, in which ammonium as electron donor was oxidized by the electron acceptor sulfate. This process had been attributed to observed transformations of nitrogenous and sulfurous compounds in natural environments. Results obtained differed largely for the conversion mole ratios (ammonium/sulfate), and even the intermediate and final products of sulfate reduction. Thus, the hypothesis of biological conversion pathways of ammonium and sulfate in anammox consortia is implausible. In this study, continuous reactor experiments (with working volume of 3.8L) and batch tests were conducted under normal anaerobic (0.2≤DO<0.5 mg/L) / strict anaerobic (DO<0.2 mg/L) conditions with different biomass proportions to verify the SRAO phenomena and identify possible pathways behind substrate conversion. Key findings were that SRAO occurred only in cases of high amounts of inoculant biomass under normal anaerobic condition, while absent under strict anaerobic conditions for same anammox consortia. Mass balance and stoichiometry were checked based on experimental results and the thermodynamics proposed by previous studies were critically discussed. Thus anammox bacteria do not possess the ability to oxidize ammonium with sulfate as electron acceptor and the assumed SRAO could, in fact, be a combination of aerobic ammonium oxidation, anammox and heterotrophic sulfate reduction processes.     

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。     

Effect of pyrolysis and oxidation characteristics on lauric acid and stearic acid dust explosion hazards

The effect of pyrolysis and oxidation characteristics on the explosion sensitivity and severity parameters, including the minimum ignition energy MIE, minimum ignition temperature MIT, minimum explosion concentration MEC, maximum explosion pressure P_max, maximum rate of pressure rise (dP/dt)_max and deflagration index K_st, of lauric acid and stearic acid dust clouds was experimentally investigated. A synchronous thermal analyser was used to test the particle thermal characteristics. The functional test apparatuses including the 1.2 L Hartmann-tube apparatus, modified Godbert-Greenwald furnace, and 20 L explosion apparatus were used to test the explosion parameters. The results indicated that the rapid and slow weight loss processes of lauric acid dust followed a one-dimensional diffusion model (D1 model) and a 1.5 order chemical reaction model (F1.5 model), respectively. In addition, the rapid and slow weight loss processes of stearic acid followed a 1.5 order chemical reaction model (F1.5 model) and a three-dimensional diffusion model (D3 model), respectively, and the corresponding average apparent activation energy E and pre-exponential factor A were larger than those of lauric acid. The stearic acid dust explosion had higher values of MIE and MIT, which were mainly dependent on the higher pyrolysis and oxidation temperatures and the larger apparent activation energy E determining the slower rate of chemical bond breakage during pyrolysis and oxidation. In contrast, the lauric acid dust explosion had a higher MEC related to a smaller pre-exponential factor A with a lower amount of released reaction heat and a lower heat release rate during pyrolysis and oxidation. Additionally, due to the competition regime of the higher oxidation reaction heat release and greater consumption of oxygen during explosion, the explosion pressure P_m of the stearic acid dust was larger in low concentration ranges and decayed to an even smaller pressure than with lauric acid when the concentration exceeded 500 g/m³. The rate of explosion pressure rise (dP/dt)_m of the stearic acid dust was always larger in the experimental concentration range. The stearic acid dust explosion possessed a higher P_max, (dP/dt)_max and K_st mainly because of a larger pre-exponential factor A related to more active sites participating in the pyrolysis and oxidation reaction. Consequently, the active chemical reaction occurred more violently, and the temperature and overpressure rose faster, indicating a higher explosion hazard class for stearic acid dust.     

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.     

肥煤自燃全过程热动力学特征参数研究

实验测定了林西矿肥煤样品30~900℃煤自燃全过程热动力学特征参数,得出:TG/DTG曲线显示煤样DTG初始临界温度45℃,干裂温度122℃,活性温度195℃,增速温度265℃,质量极大值温度342℃,着火温度465℃,最大热失重速率温度515℃和燃尽温度690℃;DSC曲线显示,煤样初始放热温度60℃、最大热释放速率温度511℃。结合TG-DTG-DSC曲线综合分析可知,煤温达到510℃左右时煤样反应最剧烈。由煤自燃标志气体测定实验系统得出:煤温130℃后CO,CO 2释放量迅速增加,210℃增加速度下降;CH 4,C 2 H 6含量变化具有规律性且两者变化相近;C 2 H 4出现温度为130℃;C 2 H 4/C 2 H 6比值在190~350℃有较强的规律性,呈上升趋势且上升速度较快;350℃之后,CH 4,C 2 H 6,C 2 H 4体积分数均开始急剧增大;C 2 H 4/CO与C 2 H 4/CO 2变化趋势大致相同,在130~350℃时缓慢增长,达到350℃后比值呈指数形式上升。经拟合曲线,得到活化能的3个突变点温度:70,180,220℃,其中180℃与交叉点温度相吻合。通过以上研究,得到了肥煤自燃全过程的热力学特征参数,为实际生产中防治煤自燃提供了理论依据。     

基于Landsat 8遥感数据的天津市地表温度反演

以Landsat 8遥感数据为数据源,进行天津市地表温度反演研究。首先采用单通道算法反演地表温度,并利用均值标准差法进行温度分级。然后建立不同温度等级面积比例的估算模型。再通过随机样点,从不同温度等级和土地覆盖类型2个角度,分别建立并比较不同类样点的地表温度与各指数的拟合模型。结果表明:次高温区域面积比例与人口密度、人均GDP都具有较高的决定系数;地表温度与NDVI、BAEM的二元线性回归决定系数高于地表温度与单一指数的决定系数;将样点分类后,低温点与MNDBI的决定系数高于其他温度等级样点,水域和植被样点与各种指数的决定系数高于其他地物类型样点。     

钙钛矿型催化剂催化湿式氧化处理煤气化废水纳滤浓缩液

采用溶胶-凝胶法制备了Mn掺杂钙钛矿型催化剂LaFexMn1-xO3，并以其为催化剂催化湿式双氧水氧化处理煤气化废水纳滤浓缩液。采用XRD，SEM，FTIR技术对催化剂进行了表征。表征结果显示：制备的催化剂均具有标准的钙钛矿型结构，其中，LaFe0.9Mn0.1O3的结构稳定，比表面积大。实验结果表明：制备的催化剂中LaFe0.9Mn0.1O3的催化活性最高，且稳定性好，连续使用5次后催化活性未见明显减弱；在H2O2投加量3.0 g/L、n（H2O2）∶n（LaFe0.9Mn0.1O3）=12∶1、反应温度160 ℃、反应压力1 MPa、浓缩液pH 3、反应时间60 min的最优条件下，COD、UV254和TOC的去除率分别达到80.9%、95.2%和68.0%，BOD5/COD由0.02提升至0.40，可生化性大幅提高。