Deep learning architecture for air quality predictions

With the rapid development of urbanization and industrialization, many developing countries are suffering from heavy air pollution. Governments and citizens have expressed increasing concern regarding air pollution because it affects human health and sustainable development worldwide. Current air quality prediction methods mainly use shallow models; however, these methods produce unsatisfactory results, which inspired us to investigate methods of predicting air quality based on deep architecture models. In this paper, a novel spatiotemporal deep learning (STDL)-based air quality prediction method that inherently considers spatial and temporal correlations is proposed. A stacked autoencoder (SAE) model is used to extract inherent air quality features, and it is trained in a greedy layer-wise manner. Compared with traditional time series prediction models, our model can predict the air quality of all stations simultaneously and shows the temporal stability in all seasons. Moreover, a comparison with the spatiotemporal artificial neural network (STANN), auto regression moving average (ARMA), and support vector regression (SVR) models demonstrates that the proposed method of performing air quality predictions has a superior performance.     

Modeling urban solar energy with high spatiotemporal resolution: A case study in Toronto,Canada

This research presents a method to determine the maximum potential for the capturing of solar radiation on the rooftop of buildings in an urban environment. This involves the modeling of solar energy potential and comparison to historical building energy demand profiles through the use of 3-D solar simulation software tools and geographic information systems (GIS). The objective is to accurately identify the amount of surface area that is suitable for solar photovoltaic (PV) installations and to estimate the hourly PV electricity generation potential of existing building rooftops in an urban environment. This study demonstrates a viable approach for modeling urban solar energy and offers valuable information for electricity distributors, policy makers, and urban energy planners to facilitate the substantial design of a green built environment. The developed methodology is comprised of three main sections: (1) determination of suitable rooftop area, (2) determination of the amount of incident solar radiation available per rooftop, and (3) estimation of hourly solar PV electricity generation potential. A case study was performed using this method for Ryerson University, located in Toronto, Canada. It was found that solar PV could supply up to 19% of the study area’s electricity demands during peak consumption hours. The potential benefits of solar PV was also estimated based upon hourly greenhouse gas emission intensity factors as well as Time-of-Use (TOU) savings through the Ontario Feed-in-Tariff (FIT) program, which allows for better representation of the positive impacts of solar technologies.     

核电站温排水卫星遥感监测应用研究

选取我国 HJ－1 B卫星红外相机为遥感数据源，在介绍了温排水卫星遥感监测的技术流程和基本原理之后，重点论述了海表温度反演的算法和基准温度提取的基本原则。以2013年1月17日大亚湾核电站和2013年5月22日田湾核电站2景 HJ－1 B红外相机数据为应用实例，说明了卫星遥感监测可作为开展核电站温排水影响监测与热污染评价的首选技术方向和主要监测手段，阐述了其在核电站温排水影响后评估中的作用和意义。     

Distribution and controls of heavy metals and organic matter in the surface sediments of the Southern Yellow Sea,China

Environmental Science and Pollution Research - Accurate and effective determination of the source of heavy metals is essential for the treatment of marine ecological environments. This article...     

PbO_2复合电极预处理甲苯二胺废水

通过涂覆—热分解法与电沉积法制备了β-PbO_2/α-PbO_2/SnO_2-Sb2O3/Ti复合电极(PbO_2复合电极),采用X射线衍射(XRD)、扫描电子显微镜(SEM)、循环伏安法(CV)、线性极化法(LSV)和加速寿命试验对电极进行表征。将PbO_2复合电极用于处理甲苯二胺(TDA)废水,考察了电解质浓度、电流密度对TDA降解效果的影响。实验结果表明:α-PbO_2呈梭状,β-PbO_2呈花菜状,多层结构的PbO_2电极利于提高电极的稳定性和活性;PbO_2复合电极的析氧电位(1.9 V)明显高于TDA的氧化电位(1.28 V),其使用寿命长达486 d;在电流密度为60 m A/cm2、Na2SO4质量浓度为10 g/L、电解时间为240 min的条件下,对COD为4 791.74 mg/L、TDA质量浓度为486.4mg/L的废水进行处理,TDA去除率高达97.3%,COD去除率可达88.1%。     

羟基乙叉二膦酸镀铜废液的处理及Cu~(2+)的高附加值资源化回收

采用Na BH4还原法将羟基乙叉二膦酸(HEDP)镀铜废液中的Cu~(2+)制备成纳米铜粉,并采用聚丙烯酰胺(PAM)对还原反应后的废液进行絮凝处理。研究了n(Cu~(2+))∶n(Na BH4)、还原反应温度、还原反应时间及PAM添加量对废液中剩余Cu~(2+)质量浓度的影响,并对回收的纳米铜粉进行了XRD和TEM表征。实验结果表明:当n(Cu~(2+))∶n(Na BH4)=4∶6、还原反应温度为50℃、还原反应时间为2 h时,废液中剩余Cu~(2+)质量浓度降低至1.1 mg/L,Cu~(2+)还原率达99.99%;可获得粒径为20~45 nm的近球型、高纯度、由多晶组成的纳米铜粉;当PAM添加量为10 mg/L时,废液中剩余Cu~(2+)质量浓度降至0.35 mg/L以下,达到GB 21900—2008《电镀污染物排放标准》(小于0.5 mg/L)的要求。     

Analysis of urban carbon emission efficiency and influencing factors in the Yellow River Basin

Environmental Science and Pollution Research - The Yellow River Basin is an energy-rich area. The low-carbon development of the Yellow River Basin is one of the ways to achieve ecological...     

有机磷阻燃剂的环境暴露与动物毒性效应

有机磷阻燃剂(OPFRs)逐渐替代了危害较大的多溴联苯醚(PBDEs),因此使得人类及其他生物更易暴露于这种有机物中。有研究表明,部分有机磷酸酯具有致癌性,因而使人们对其毒性的问题也日益关注。本文概述了有机磷阻燃剂的环境暴露水平,总结了近年来从体外与体内实验2个方面动物毒性效应的研究。目前研究发现诸多地区的大气、土壤和水体中的有机磷阻燃剂总含量水平相对较低;仅高浓度暴露才会对不同动物体造成一定程度的损伤,而远大于环境浓度的低浓度暴露几乎无损伤效应。最后,对有机磷阻燃剂毒性效应的未来研究重点进行了展望。     

絮团粘性分维数的概念及其分析模型

水处理中的絮团具有复杂的形态结构,分维数是描述这种结构的有力工具。通过分析固液体系的粘度与絮团分维数之间的关系,提出了絮团粘性分维数的新概念及粘性分维数测定的数学物理模型,并进行了实验验证。与常用的质量分维数、边界分维数、面积分维数、体积分维数、表面积分维数等相比,絮团的粘性分维数及其测定模型具有易于测量、可同时表征不同类型絮团、可定量分析絮凝产品组成等特点,从而为絮团形态结构的定性及定量研究开辟了新的途径。     

Effect of heavy metals (Cu, Pb, and As) on the ultrastructure of Sargassum pallidum in Daya Bay, China

Concentrations of Cu, Pb, and As were determined in seawater, surface sediment, Sargassum pallidum collected from the Daya Bay, China. The influence of metal contamination on the marine alga was investigated at chemical and ultrastructural level. Mean concentrations of Cu (19.44 mg kg^?1) and Pb (33.99 mg kg^?1) were found to be high in sediment, whereas concentration of As (122.29 mg kg^?1) in S. pallidum was higher than that in water and sediment. The ultrastructure of S. pallidum cells was anomalous and aberrant. Energy-dispersive x-ray spectroscopic analysis revealed that the nanometal particles in the form of comparatively high-electron density substance diffused in the cell structures constituted by Cu, Pb, As, etc. There is a remarkable similarity or correspondence in the anomalous elements between the geochemistry and the botanic cell, and the heavy metals have potential hazardous effect on the ocean ecology system in Daya Bay.