金沙江上游水电站应对白格堰塞湖灾害的措施及经验

2018年10月、11月,西藏自治区昌都市江达县白格村两次发生山体滑坡造成金沙江断流并形成白格堰塞湖,给金沙江上游沿岸人民生产生活设施造成巨大安全威胁和超过百亿元的经济损失。华电金沙江上游水电开发有限公司组织所属企业及参建单位快速应对,采取撤离避让、工程防护、人工干预、生产恢复,推动后续处置、风险评估等措施,最大限度减轻了对金沙江上游叶巴滩、拉哇、巴塘、苏洼龙等在建水电站的灾害损失和后续影响,为类似灾害的处置提供借鉴经验。     

战略性稀有金属资源绿色高值利用技术进展

从资源紧缺、环境污染、产品低端等方面，总结了我国钒、钛、镍、钴、锂稀有金属资源在战略性新兴产业迅猛发展时代所面临的国家重大需求，回顾了领域近年来通过升级和变革传统稀有金属资源利用技术取得的主要进展和成就。其中煤基钠化冶炼、高温碳化?低温氯化、亚熔盐氧化等非常规介质强化手段是实现钒钛磁铁矿中钒、钛绿色高值高效利用的核心；盐酸常压浸出?低温选择性水解?共沉淀新技术是一种低成本、短流程高效高值利用红土镍矿全组分的先进技术代表；双功能协同复合萃取原理是实现高镁盐湖卤水的锂资源绿色利用的关键基础。基于固废资源化和源头减废两个思路，现阶段战略性金属资源的利用技术初步解决了环境污染和资源利用率低的问题，但仍存在资源绿色利用基础原理匮乏、产品科技含量低等普遍问题。以战略性产业关键材料为导向的绿色高值利用技术的应用基础研究是稀有金属相关绿色产业战略性发展的重要科技保障，是未来重要的前沿研究方向。     

数据中心湖水源自然冷却系统现场性能测试分析

为解决数据中心冷却系统能耗高、能源利用效率低的问题,自然冷却技术得到了广泛应用。以东江湖数据中心湖水源自然冷却系统为测试对象,详细测试了系统的实际运行性能,并讨论了其节能性,旨在为数据中心自然冷却系统运行设计提供实际数据支撑。测试结果表明:湖水源自然冷却系统的综合性能系数高达11,相比国家政策要求(新建大型数据中心PUE≤1.3),其能耗可进一步降低16%;此外,末端精密空调能耗占自然冷却系统总能耗50%以上,是自然冷却进一步降低能耗的关键。     

A comparative assessment of local municipal food policy integration in the Netherlands

ABSTRACT

Local governments around the world increasingly engage in food governance, aiming to address food system challenges such as obesity, food waste, or food insecurity. However, the extent to which municipalities have actually integrated food across their policies remains unknown. This study addresses this question by conducting a medium-n systematic content analysis of local food policy outputs of 31 Dutch municipalities. Policy outputs were coded for the food goals and instruments adopted by local governments. Our analysis shows that most municipalities integrate food to a limited extent only, predominantly addressing health and local food production or consumption. Furthermore, municipalities seem hesitant to use coercive instruments and predominantly employ informative and organizational instruments. Nonetheless, a small number of municipalities have developed more holistic approaches to address food challenges. These cities may prove to be a leading group in the development of system-based approaches in Dutch local food policy.     

Understanding the impact of High Speed Railway on urban innovation performance from the perspective of agglomeration externalities and network externalities

Transportation infrastructure and innovation play an important role in promoting regional economic growth. However, previous studies have mainly focused on the economic agglomeration by increased accessibility, but ignored the important on inter-city economic linkages and the network externalities it generated. Based on the externality perspective, this paper uses the propensity score matching model (PSM-DID) and spatial econometric model to analyze the heterogeneous impact of HSR opening on urban innovation performance and compare the differential effects of agglomeration externalities and network externalities on knowledge spillovers. The empirical results suggest the following: (1) The opening of HSR significantly improves urban innovation performance, but there is significant spatial heterogeneity. (2) The opening of HSR not only promotes the agglomeration of production factors to cities, but also strengthens inter-city economic connections, thus improving urban innovation performance through agglomeration externalities and network externalities. However, compared to agglomeration externalities, network externalities have a superior strength of impact on urban innovation performance. (3) From the dynamic effect perspective, the opening of HSR has a hysteresis effect on urban innovation of 2–3 years, and differences exist between cities.     

Soft sensing of water depth in combined sewers using LSTM neural networks with missing observations

Information and communication technologies combined with in-situ sensors are increasingly being used in the management of urban drainage systems. The large amount of data collected in these systems can be used to train a data-driven soft sensor, which can supplement the physical sensor. Artificial Neural Networks have long been used for time series forecasting given their ability to recognize patterns in the data. Long Short-Term Memory (LSTM) neural networks are equipped with memory gates to help them learn time dependencies in a data series and have been proven to outperform other type of networks in predicting water levels in urban drainage systems. When used for soft sensing, neural networks typically receive antecedent observations as input, as these are good predictors of the current value. However, the antecedent observations may be missing due to transmission errors or deemed anomalous due to errors that are not easily explained. This study quantifies and compares the predictive accuracy of LSTM networks in scenarios of limited or missing antecedent observations. We applied these scenarios to an 11-month observation series from a combined sewer overflow chamber in Copenhagen, Denmark. We observed that i) LSTM predictions generally displayed large variability across training runs, which may be reduced by improving the selection of hyperparameters (non-trainable parameters); ii) when the most recent observations were known, adding information on the past did not improve the prediction accuracy; iii) when gaps were introduced in the antecedent water depth observations, LSTM networks were capable of compensating for the missing information with the other available input features (time of the day and rainfall intensity); iv) LSTM networks trained without antecedent water depth observations yielded larger prediction errors, but still comparable with other scenarios and captured both dry and wet weather behaviors. Therefore, we concluded that LSTM neural network may be trained to act as soft sensors in urban drainage systems even when observations from the physical sensors are missing.     

Predicting pollutant removal in constructed wetlands using artificial neural networks(ANNs)

Growth in urban population,urbanisation,and economic development has increased the demand for water,especially in water-scarce regions.Therefore,sustainable approaches to water management are needed to cope with the effects of the urbanisation on the water environment.This study aimed to design novel configurations of tidal-flow vertical subsurface flow constructed wetlands(VFCWs) for treating urban stormwater.A series of laboratory experiments were conducted with semi-synthetic influent stormwater to examine the effects of the design and operation variables on the performance of the VFCWs and to identify optimal design and operational strategies,as well as maintenance requirements.The results show that the VFCWs can significantly reduce pollutants in urban stormwater,and that pollutant removal was related to specific VFCW designs.Models based on the artificial neural network(ANN) method were built using inputs derived from data exploratory techniques,such as analysis of variance(ANOVA) and principal component analysis(PCA).It was found that PCA reduced the dimensionality of input variables obtained from different experimental design conditions.The results show a satisfactory generalisation for predicting nitrogen and phosphorus removal with fewer variable inputs,indicating that monitoring costs and time can be reduced.     

西藏水电开发是应对治理揠塞湖的重要举措

西藏区域地质构造发育,又属于高震区,在局部强降雨强降雪等恶劣小气候条件下,自然灾害频发,在高山深谷的江河上尤其容易发生堰塞湖。2018年10月17日凌晨5:00左右,西藏林芝加拉村下游约6km处(大峡谷核心区、无人区)雅鲁藏布江(以下简称“雅江”)左岸发生大规模泥石流,造成干流断流,形成影响很大的雅江加拉村堰塞湖。堰塞湖的治理一直是个世界难题,本文从2018年10月17日治理分析加拉堰塞湖的实践,阐述从水电开发角度,在灾害区上游建设高坝大库拦水调蓄,水库下游再采取截弯取直方式引走来水,作为防治雅江大峡谷河段今后可能出现大型堰塞湖重要治理手段,防止大型堰塞湖造成水淹城乡和溃堰水淹下游灾难的发生,从而达到既推进雅江水电开发,又较好治理了大峡谷河段自然灾害,实现把水电开发建成“功在当代,利在千秋”的减灾防灾工程。     

中国城市天然气消费驱动因素分析

为推动我国能源革命,培育天然气成为主体能源之一,有必要进一步研究我国天然气消费量的发展趋势,探寻影响天然气消费量的驱动因素。为此,采用迪氏对数指标分解法,构建城市天然气消费量分解模型,将驱动因素分解为空间扩张、管网密度、人口密度、人口城镇化、居民天然气气化率、能源消费弹性、天然气替代、经济增长和管道规模等9个效应,并定量分析各效应对我国城市天然气消费量变动的贡献率。研究结果表明:(1)各省天然气消费量驱动因素存在着较大的差异;(2)管道密度效应、管网规模效应、经济增长效应、居民天然气气化率效应、空间扩张效应、能源消费弹性效应能显著推动城市天然气消费量的增长,而人口密度和人口城镇化效应的影响则并不显著。进而根据分效应贡献率和市场特征,将中国30个省区市天然气市场划分为成熟型、培育型、经济发展型和资源约束型等4种类型,并分析了各类型市场天然气消费的核心驱动因素,以期为因地制宜开展天然气产业规划和政策制定提供决策依据。     

Anaerobic methane oxidation and aerobic methane production in an east African great lake (Lake Kivu)

We investigated CH₄ oxidation in the water column of Lake Kivu, a deep meromictic tropical lake with CH₄-rich anoxic deep waters. Depth profiles of dissolved gases (CH₄ and N₂O) and a diversity of potential electron acceptors for anaerobic CH₄ oxidation (NO₃^?, SO₄^2?, Fe and Mn oxides) were determined during six field campaigns between June 2011 and August 2014. Denitrification measurements based on stable isotope labelling experiments were performed twice. In addition, we quantified aerobic and anaerobic CH₄ oxidation, NO₃^? and SO₄^2? consumption rates, with and without the presence of an inhibitor of SO₄^2?-reducing bacteria activity. Aerobic CH₄ production was also measured in parallel incubations with the addition of an inhibitor of aerobic CH₄ oxidation. The maximum aerobic and anaerobic CH₄ oxidation rates were estimated to be 27?±?2 and 16?±?8?μmol/L/d, respectively. We observed a difference in the relative importance of aerobic and anaerobic CH₄ oxidation during the rainy and the dry season, with a greater role for aerobic oxidation during the dry season. Lower anaerobic CH₄ oxidation rates were measured in presence of molybdate in half of the measurements, suggesting the occurrence of linkage between SO₄^2? reduction and anaerobic CH₄ oxidation. NO₃^? consumption and dissolved Mn production rates were never high enough to sustain the measured anaerobic CH₄ oxidation, reinforcing the idea of a coupling between SO₄^2? reduction and CH₄ oxidation in the anoxic waters of Lake Kivu. Finally, significant rates (up to 0.37?μmol/L/d) of pelagic CH₄ production were also measured in oxygenated waters.