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.     

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。     

基于关联性及趋势性分析的AVHRR NDVI及#br# MODIS NDVI数据产品比较

针对AVHRR NDVI和MODIS NDVI数据集中不同数据产品间的差异和区域适应性问题，利用2003~2013年重叠时间段MODIS Terra/Aqua的NDVI Collection 5(C5)和Collection 6(C6)产品以及AVHRR NDVI中的GIMMS NDVI3g产品，选择汉江流域典型区域进行数据对比分析。通过关联指数IOAs(Index of Association)来评价各数据在季节波动上的一致性；采用Mann Kendall检验分析各数据的长期趋势差异。结果表明，在季节性变化上，同一卫星不同版本产品之间的关联性相对较高。在年际变化上，GIMMS产品相对MODIS 产品，仅能检出部分NDVI变化趋势。在地表覆被异质性相对较高地区，GIMMS产品的长期变化趋势检出率较低。MODIS Terra的NDVI C5产品受传感器退化影响，表现出相对较高的减少趋势。 关键词： GIMMS NDVI；MODIS NDVI；Mann Kendall检验；趋势分析     

石家庄市春季大气颗粒物的铅污染特征

为了解石家庄市2016年春季大气颗粒物的铅污染特征及来源,利用单颗粒气溶胶质谱仪(SPAMS),分析了大气中含铅颗粒的化学成分。结果表明: 研究期间大气环境中含铅颗粒数浓度共出现11次跳跃式升高,跳跃时间段内石家庄均处于轻度污染过程。从成分分析来看,含铅颗粒分为纯铅颗粒、Pb与K(Pb-K)、OC(Pb-OC)、Cl(Pb-Cl)、混合颗粒等八大类。观测结果表明:Pb-K颗粒最多,占到含铅颗粒的84.4%；其次为纯铅颗粒,占比为13.0%。与石家庄市污染源谱库比对进行来源解析,得到Pb-K颗粒主要来自生活垃圾焚烧源, 纯铅颗粒主要来自工业源。结合石家庄市大气污染源排放清单和后向气流轨迹分析,推测含铅颗粒可能来自市区西南方向某区县的生活垃圾焚烧企业。     

初始温度和压力对排污空间甲烷-空气混合物爆燃特性影响的模拟研究

市政排污空间作为城市公共基础设施的重要组成部分，易积聚可燃气体形成爆炸性环境。结合排污空间的特殊环境条件，采用Fluidyn-MP多物理场数值模拟软件，建立了20 L球形爆炸罐分析模型，通过改变初始温度和初始压力，对排污空间甲烷-空气混合物爆燃特性及其变化规律进行模拟研究。结果表明:初始温度升高导致甲烷-空气混合物最大爆炸压力降低，缩短了到达最大爆炸压力的时间；初始压力增加导致最大爆炸压力急剧升高，并延长了到达最大爆炸压力的时间；最大爆炸压力对初始压力的敏感程度远大于初始温度的影响。此外，随着初始温度和初始压力的升高，爆炸火焰平均传播速度增加，而火焰传播速度对初始温度较敏感。     

松花江野生鱼类重金属残留分析与评价

以松花江流域的野生鱼类为研究对象,在松花江流域选取松林、大顶子山、佳木斯3个具有代表性的点位,在2014—2016年的每年5月底6月初采集鲤鱼和鲫鱼样品,3年内共制备45个鲤鱼单样,44个鲤鱼混样,30个鲫鱼混样,共计119个。采用电感耦合等离子体-质谱法对鱼类肌肉组织中残留的As、Cd、Cr、Pb、Hg 5种重金属进行检测。检测结果显示,As、Cd、Cr、Pb、Hg 5种重金属全部检出,检测结果全部达标,达标率为100%。根据检测结果采用单因子指数法和金属污染指数法等评价了松花江流域鱼体重金属残留状况及食用安全性,评价结果为松花江流域鱼类重金属残留量相对较少,鱼体健康,食用安全。     

Growth of Zostera japonica in different sediment habitats of the Yellow River estuary in China

Environmental Science and Pollution Research - The estuarine delta of the Yellow River is a region of strong land-ocean-human interactions that undergoes a unique evolutionary process. The delta is...     

嘉陵江有毒鱼类的分布研究

为了丰富人们对有毒鱼类的认知,结合8年的调查数据和已有资料对嘉陵江中有毒鱼类的种类、分布和食性进行研究。研究结果发现:在嘉陵江中有毒鱼类共计18种,隶属于4目6科17属,随着分类水平的变化(从目、科、属到种),有毒鱼类在不同分类级别下所占的比例逐渐降低。嘉陵江有毒鱼类中仅圆口铜鱼(Coreius guichenoti)和鳜(Siniperca chuatsi)分布于中下游,其余16种有毒鱼类分布于整个嘉陵江干流。Jaccard’s相似性分析表明各江段有毒鱼类分布的相似性极高,卡方(χ~2)检验结果显示各江段有毒鱼类分布无显著性差异(p0.05)。嘉陵江分布的有毒鱼类共4类,包括卵毒鱼类2种,血清毒鱼类1种,胆毒鱼类9种以及刺毒鱼类7种,其中鲇(Silurus asotus)既属于卵毒鱼类也属于刺毒鱼类,胆毒鱼类全部来源于鲤科鱼类,暂没有发现皮肤黏液毒鱼类。这18种有毒鱼类共包含7种食性类型,主要以杂食性和肉食性类型为主,分别占有毒鱼类种类数的38.89%和33.33%。通过本研究可知,有毒鱼类在嘉陵江中分布广泛,有毒鱼类种类和类别较多。该研究促进了人们对嘉陵江有毒鱼类相关知识的了解,为进一步研究嘉陵江有毒鱼类奠定了基础。     

Robust Regression with Data-Dependent Regularization Parameters and Autoregressive Temporal Correlations

We introduce robust procedures for analyzing water quality data collected over time. One challenging task in analyzing such data is how to achieve robustness in presence of outliers while maintaining high estimation efficiency so that we can draw valid conclusions and provide useful advices in water management. The robust approach requires specification of a loss function such as the Huber, Tukey’s bisquare and the exponential loss function, and an associated tuning parameter determining the extent of robustness needed. High robustness is at the cost of efficiency loss in parameter loss. To this end, we propose a data-driven method which leads to more efficient parameter estimation. This data-dependent approach allows us to choose a regularization (tuning) parameter that depends on the proportion of “outliers” in the data so that estimation efficiency is maximized. We illustrate the proposed methods using a study on ammonium nitrogen concentrations from two sites in the Huaihe River in China, where the interest is in quantifying the trend in the most recent years while accounting for possible temporal correlations and “irregular” observations in earlier years.