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.     

河北省不同盐渍化土壤类型的微生物多样性与种群结构

为了探索盐渍化土壤中微生物多样性及群落构成，有效筛选盐渍土壤中耐盐微生物菌群.采用高通量测序技术对采集的河北省滨海盐渍土（原生盐渍化）、设施盐渍土（次生盐渍化）和高产粮田（健康土壤）3个生境的耕层土壤样本细菌和真菌多样性、群落结构、网络关系及其影响因子进行测定.结果表明，与大田土壤相比，设施土壤中OM、AP、AK、TS和EC显著升高，滨海盐渍土壤的TS和EC显著升高，其他养分指标则显著降低.细菌α多样性依次为：设施盐渍土>高产粮田>滨海盐渍土，真菌α多样性则为高产粮田显著高于设施盐渍土和滨海盐渍土.在门和属水平上分析盐渍化土壤的菌群结构，细菌群落中绿弯菌门（Chloroflexi）及其菌属和真菌群落中子囊菌门（Ascomycota）及其中有益菌Trichocladium和病原菌Fusarium为盐渍化土壤中的优势微生物类群.土壤EC和TS两个盐分因子是对细菌和真菌菌群分布贡献最大的因子，与绿弯菌门中unclassified_A4b和unclassified_Chloroflexi以及变形菌门中unclassified_α-Proteobacteria等细菌菌属和子囊菌门中Trichocladium、unclassified_Chaetomiaceae、Crassicarpon、Cephaliophora和Sodiomyces等真菌菌属呈显著正相关.研究结果为盐渍化土壤修复所需的微生物资源筛选提供了理论依据.     

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.     

皂素和柠檬酸复合淋洗土壤中的钍

采用振荡淋洗和土柱动态淋洗实验研究了皂素和柠檬酸复合对土壤中钍的淋洗性能。实验结果表明:将质量浓度为20 g/L的皂素与浓度为20 mmol/L的柠檬酸以体积比1∶10配制复合淋洗液对污染土壤中钍的去除效果最佳,淋洗3.0 h,钍去除率为84.98%;准一级动力学方程能更好地描述复合淋洗液对钍的淋洗动力学特征;动态淋洗实验中当复合淋洗液用量为1 600 mL时,钍的累积去除量为73.66 mg/kg;淋洗后土壤中钍的酸可提取态、氧化结合态和有机结合态含量分别减少了79.63%、38.13%和20.34%,残渣态含量变化不大;淋洗后土壤中钍的稳定性增加。     

土壤污染物的生态毒理效应和风险评估研究进展

环境生态风险评估（ERA）流程已经被纳入全球环境政策中，既用于规范新化学物质的授权和营销（前瞻性环境生态风险评估），也用于评估潜在的污染场地（回顾性环境生态风险评估）。将土壤生态毒理学应用于风险评估，能阐明有毒物质对土壤生态系统中生命有机体的危害程度与范围。笔者主要介绍了应用评估因子法和物种敏感度分布法对基于效应数据进行的外推与估算，并综述了欧美等主要国家和地区的土壤生态风险评估框架、相关法律法规及其实施情况等，为中国开展土壤污染物生态毒理效应和风险评估等相关研究提供参考。     

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。     

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。     

Preliminary studies on potential remediation of acid mine drainage‐impacted soils by amendment with drinking‐water treatment residuals

Mining operations result in a wide range of environmental impacts: acid mine drainage (AMD) and acid sulfate soils being among the most common. Due to their acidic pH and high soluble metal concentrations, both AMD and acid sulfate soils can severely damage the local ecosystems. Proper post‐mining management practices are necessary to control AMD‐related environmental issues. Current AMD‐impacted soil treatment technologies are rather expensive and typically not environmentally sustainable. We conducted a 60‐day bench‐scale study to evaluate the potential of a cost‐effective and environment‐friendly technology in treating AMD‐impacted soils. The metal binding and acid‐neutralizing capacity of an industrial by‐product, drinking water treatment residuals (WTRs) were used for AMD remediation. Two types of locally generated WTRs, an aluminum‐based WTR (Al‐WTR) and a lime‐based WTR (Ca‐WTR) were used. Highly acidic AMD‐impacted soil containing very high concentrations of metals and metalloids, such as iron, nickel, and arsenic, was collected from the Tab‐Simco coal mine in Carbondale, Illinois. Soil amendment using a 1:1 Al‐ and Ca‐WTR mix, applied at 5 and 10 percent rates significantly lowered the soluble and exchangeable fractions of metals in the AMD‐impacted soil, thus lowering potential metal toxicity. Soil pH increased from an extremely acidic 2.69 to a near‐neutral 6.86 standard units over the 60‐day study period. Results from this preliminary study suggest the possibility of a successful scale‐up of this innovative, cost‐effective, and environmentally sustainable technology for remediating AMD‐impacted acid sulfate soils.