应用HYDRUS-1D模型模拟农田土壤水渗漏及硝态氮淋失特征

在定位试验基础上,应用HYDRUS-1D模型对黄淮海平原典型土壤(黄潮土)中土壤水渗漏及硝态氮淋失动态进行了模拟分析。结果表明:在传统水氮管理模式下,黄潮土2m土体深处的土壤水渗漏和硝态氮淋失非常严重,2个轮作期内,土壤水渗漏总量占地表总入水量的23.7%,硝态氮淋失总量占总输入N量的15.9%,冬小麦生长季的硝态氮淋失量大于夏玉米生长季;改良灌溉和改良施肥模式下产生的硝态氮淋失量比传统灌溉和传统施肥模式减少74.7%,节约灌溉水211.5mm、节省施N423.5kg·hm-2。     

石化废水深度处理回用工艺设计

化工废水中含有机物、烃类、石油类、重金属等有毒有害物质,成分复杂,难于生物降解,并且对微生物代谢产生抑制和毒害作用.文章通过研究石化废水二级生化处理后回用的工艺技术,提出以絮凝、氧化、膜分离等组合工艺为主体的石化废水深度处理流程,并对设计参数、工艺调试进行优化分析,为石化废水处理回用开辟途径.     

深床过滤机理及其在水处理中的应用研究与进展

深床过滤既有过滤功能,又具生物处理作用,具有良好的应用前景;本文阐述了深床过滤的最新研究动向,深床过滤过程的数学模型的构建方法,国内外利用深床过滤技术处理微污染水源水、城市生活污水及污水深度处理的实验研究状况.     

基于全隧道方案的渤海海峡跨海通道工程环境影响研究

渤海海峡跨海通道是疏通中国干线交通网战略布局中的咽喉工程,因工程规模浩大,其环境影响广泛而深远.简要回顾了工程建设方案的筛选进程及“深埋式全隧道”方案的环境优势;建议工程环境影响评价采用“协作型”环境影响评价模式,以合作和协调克服各方之间的分歧和矛盾;初步探讨了工程前期调研的主要内容;识别分析了通道工程对近岸海域、出入口城市的主要环境影响.结果表明:与其他方案相比,深埋式全隧道方案对自然人文景观、海洋生态环境、海洋资源开发利用等的影响是最小的,并提出了消除或减缓不良环境影响的对策措施,为通道工程的立项决策提供科学依据.     

深井动压影响下山煤巷围岩变形机理与控制研究

以刘桥一矿Ⅱ66回风下山为工程背景,采用现场实测、实验室实验、数值模拟和工业性试验综合分析了下山煤巷非对称变形破坏机理,提出动压影响下深井软弱煤巷围岩多层次组合控制理论,并给出优化控制措施。研究表明:回风下山变形失稳严重并呈非对称性,围岩完整性较差,强度低。地应力实测显示,回风下山处于高原岩应力区,最大主应力为水平应力且具有明显方向性,与巷道轴线夹角为71°,断面顶底板剪切破坏风险较大;数值模拟显示,回风下山开拓延伸期间,受邻近轨道下山、辅助下山影响显著,4煤回采对其影响较小,6煤工作面回采是下山煤巷非对称失稳的主要诱因;提出以围岩深浅孔注浆为核心,以新型注浆锚索、锚杆为装备基础的高阻让压全断面组合控制理论,并给出具体优化方案。工业性试验显示,下山煤巷围岩变形得到有效控制。     

基于深度学习的大数据空气污染预报

为了更好地反映环境污染变化趋势,为环境管理决策提供及时、全面的环境质量信息,预防严重污染事件发生,开展城市空气质量预报研究是十分必要的.本文针对环境大数据时代下的城市空气质量预报,提出了一种基于深度学习的新方法.该方法通过模拟人类大脑的神经连接结构,将数据在原空间的特征表示转换到具有语义特征的新特征空间,自动地学习得到层次化的特征表示,从而提高预报性能.得益于这种方式,新方法与传统方法相比,不仅可以利用空气质量监测、气象监测及预报等环境大数据,充分考虑污染物的时空变化、空间分布,得到语义性的污染物变化规律,还可以基于其他空气污染预测方法的结果(如数值预报模式),自动分析其适用范围、优势劣势.因此,新方法通过模拟人脑思考过程实现更充分的大数据集成,一定程度上克服了现有方法的缺陷,应用上更加具有灵活性和可操作性.最后,通过实验证明新方法可以提高空气污染预报性能.     

Assessment of deep aquifer groundwater quality in a geographically unique climatic region of Southern Western Ghats,India using water quality indices

Water quality index (WQI) models are generally used in hydrochemical studies to simplify complex data into single values to reflect the overall quality. In this study, deep groundwater quality in the Chittur and Palakkad Taluks of the Bharathapuzha river basin of Kerala, India, was assessed by employing the WQI method developed by the Canadian Council of Ministers of the Environment (CCME). The assessment of overall water quality is indispensable due to the specific characteristics of the study area, such as geography, climate, over-drafting, and prevalent agricultural practices. Forty representative samples were collected from the study area for monsoon (MON) and pre-monsoon (PRM) seasons. The results showed a general increase of contents from MON to PRM. The major cations were spread in the order Ca²⁺>Na+>Mg²⁺>K⁺ and the anions HCO₃⁻>Cl⁻>CO₃²⁻ based on their relative abundance. Among various parameters analysed, alkalinity and bicarbonate levels during MON were comparatively high, which is indicative of carbonate weathering, and 90% of the samples failed to meet the World Health Organization (WHO, 2017)/Bureau of Indian Standards (BIS, 2012) drinking water guidelines. The CCME WQI analysis revealed that nearly 50% of the samples during each season represented good and excellent categories. The samples in the poor category comprised 10% in MON and 15% in PRM. The overall WQI exhibited 15% of poor category samples as well. The spatial depiction of CCME WQI classes helped to expose zones of degraded quality in the centre to eastward parts. The spatial and temporal variations of CCME WQI classes and different physicochemical attributes indicated the influence of common factors attributing to the deep groundwater quality. The study also revealed inland salinity at Kolluparamba and Peruvamba stations, where agricultural activities were rampant with poor surface water irrigation.     

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.     

深部低渗透强突出煤层群首采下保护层确定和效果考察

软岩保护层在深部低渗透强突出煤层群首采保护层选择中具有潜在的应用前景。在阐述下保护层开采覆岩移动与采场裂隙演化及瓦斯运移关系的基础上,以芦岭矿为工程背景,通过FLAC3D数值试验,研究软岩保护层开采后的卸压效果,并和不同层位的薄煤层开采卸压效果对比分析。结果表明,在采厚相同的情况下,开采10煤保护层相对于软岩保护层,卸压程度弱,保护范围小,保护效果差。软岩保护层开采后,被保护层处于弯曲下沉带的下限范围。受采动影响,有大量的离层裂隙生成。考察期范围内上覆被保护层(8、9煤层)瓦斯抽采率达62.9%,表明软岩保护层开采能够对上覆被保护层(8、9煤层)起到显著的卸压效果。研究结果可为其他矿区保护层开采选择提供参考,丰富国内保护层开采实践。     

基于理想点法的邻近基坑高层建筑安全性评价

为更加准确地评价紧邻基坑高层建筑物的安全状况,综合考虑了建筑物稳定性、基坑与建筑之间土体扰动程度和建筑物地基抗扰动能力三方面因素,采用相关系数法选取14个评价指标,构建建筑物安全评价模型。并通过变异系数法和AHP确定各评价指标融合权重,最后采用理想点法对某紧邻基坑的高层建筑物进行安全评价。结果表明,该模型可以有效判断邻近深基坑高层建筑物的安全等级,其评判结果与工程实际相符