京津冀地区钢铁行业协同减排成本-效益分析

京津冀地区是我国钢铁行业布局最集中的区域,也是大气污染治理的重点区域之一.分析京津冀地区钢铁行业各类治污手段中长期减排的成本-效益,对于选择最经济有效的减排路径、加快推动该地区大气环境质量达标意义重大.基于能源环境、环境分布、人群健康效益评价等模块构建多模型耦合方法,以2015年为基准年,以每5 a为时间节点,设计了京津冀地区钢铁行业规模-结构、规模-技术、规模-末端治理、综合减排4种协同减排情景,计算各情景下2015—2030年京津冀地区钢铁行业主要污染物(SO₂、NO_x、PM₁₀、PM_2.5、CO₂)的减排成本与效益,比较获取治污减排的最优路径方案.结果表明:①基于减排成本计算,规模-末端治理减排情景成本最低,分别为规模-结构、规模-技术减排情景投入的15.18%、23.94%;综合减排情景下治污减排潜力最大,但综合成本最高.②基于环境税效益评价方法、人群健康效益评价方法计算显示,人群健康效益评价方法计算的减排效益高于环境税效益评价方法,表明污染减排的潜在人群健康效益更高;基于两种减排效益方法,4种协同减排情景中综合减排效益分别为10.78×108、76.14×108元,高于规模-结构、规模-技术、规模-末端治理减排情景效益.③基于环境税效益评价方法,4种协同减排情景的效益-成本比表现为规模-末端治理减排情景(0.46) >规模-技术减排情景(0.24) >综合减排情景(0.15) >规模-结构减排情景(0.10);基于人群健康效益评价方法4种协同减排情景的效益-成本比依次为规模-末端治理减排情景(8.35) >综合减排情景(1.07) >规模-结构减排情景(0.57) >规模-技术减排情景(0.65),表明规模-末端治理减排情景的减排路径最优.研究显示,京津冀地区钢铁行业应基于环境质量底线目标要求,综合考虑减排潜力、减排成本与收益,以规模-末端治理为主要途径,选取协同减排的最优路径.      

Adjusting sowing date and cultivar shift improve maize adaption to climate change in China

Mitigation and Adaptation Strategies for Global Change - This study investigates the impact of climate change on spring and summer maize (Zea mays) yield and evaluates several adaptation measures...     

Seasonal variation and size distribution of biogenic secondary organic aerosols at urban and continental background sites of China

Size-resolved biogenic secondary organic aerosols(BSOA) derived from isoprene and monoterpene photooxidation in Qinghai Lake, Tibetan Plateau(a continental background site) and five cities of China were measured using gas chromatography/mass spectrometry(GC/MS). Concentrations of the determined BSOA are higher in the cities than in the background and are also higher in summer than in winter. Moreover, strong positive correlations(R2= 0.44–0.90) between BSOA and sulfate were found at the six sites,suggesting that anthropogenic pollution(i.e., sulfate) could enhance SOA formation,because sulfate provides a surface favorable for acid-catalyzed formation of BSOA. Size distribution measurements showed that most of the determined SOA tracers are enriched in the fine mode( 3.3 μm) except for cis-pinic and cis-pinonic acids, both presented a comparable mass in the fine and coarse( 3.3 μm) modes, respectively. Mass ratio of oxidation products derived from isoprene to those from monoterpene in the five urban regions during summer are much less than those in Qinghai Lake region. In addition, in the five urban regions relative abundances of monoterpene oxidation products to SOA are much higher than those of isoprene. Such phenomena suggest that BSOA derived from monoterpenes are more abundant than those from isoprene in Chinese urban areas.     

高速铁路选线对生态环境的影响分析研究

高速铁路选线要充分考虑对生态环境的影响,在路基、桥涵、隧道等形式通过环境敏感点时,要从工程技术和生态环境保护的角度出发,避免高速铁路建设和运营对当地的水资源环境、自然保护区造成破坏。根据高速铁路周边的环境信息,在路基填土高度上结合地形、地质和水文自然特征确定合理的填土高度。在高速铁路跨越生态保护区和湖泊等线路中,贯彻生态选线的原则,准确确定桥涵净空,在工程设计、施工上采用系列的生态保护措施。     

绿色建筑的节能环保设计思路分析研究

绿色建筑具有透光好、保温性与通风性好等优点,为了实现节能减排,提高宜居性,进行绿色建筑的节能环保设计,考虑低碳节能效果,在建筑外墙使用抹整体式保温系统,楼顶使用现浇整体式保温系统进行建筑的保温墙设计,考虑建筑的通风性,建筑使用通透性的设计方案,设计空中楼顶花园,提高隔热防晒效果的同时,提高绿化覆盖面积。合理布局楼高和楼间距,提高建筑的采光性,设计景观生物廊道,采用底层架空式结构实现自然通风和采光,实现绿色建筑的节能优化设计。     

Analysis of pollutant levels in central Hong Kong applying neural network method with particle swarm optimization

Air pollution has emerged as an imminent issue in modernsociety. Prediction of pollutant levels is an importantresearch topic in atmospheric environment today. For fulfillingsuch prediction, the use of neural network (NN), and inparticular the multi-layer perceptrons, has presented to be acost-effective technique superior to traditional statisticalmethods. But their training, usually with back-propagation (BP)algorithm or other gradient algorithms, is often with certaindrawbacks, such as: 1) very slow convergence, and 2) easilygetting stuck in a local minimum. In this paper, a newlydeveloped method, particle swarm optimization (PSO) model, isadopted to train perceptrons, to predict pollutant levels, andas a result, a PSO-based neural network approach is presented. The approach is demonstrated to be feasible and effective bypredicting some real air-quality problems.     

柴油机尾气中PAH_s的分析

本文概述了柴油机尾气中ＰＡＨｓ的分析方法，着重论述了采样及预处理方法，特别对预处理方法进行了深入的探讨。另外，通过对柴油机两种不同工况下产生的尾气进行分析后发现，柴油机转速越高，其排放尾气中ＰＡＨｓ的含量就越低。     

Environmental and biological monitoring of methyl ethyl ketone (MEK)

This study was conducted to evaluate the usefulness of various biological parameters for monitoring of workers exposed to methyl ethyl ketone (MEK). Fifty male workers from a large magnetic videotape factory participated in this study. Personal air samples were collected using 3M organic vapor monitors and analysed for MEK by gas chromatography with flame ionisation detector (FID). 10 mL of urine; blood (1 mL) and exhaled air were also collected at the end of an 8-hour workshift. The headspace GC method was applied for measurement of urinary and blood MEK. MEK in expired air was analysed directly by using a GC/FID.The correlation coefficients (r) between environmental MEK and all other biological parameters measured show significant positive relationships. The r for environmental MEK and urine MEK was 0.84; for blood 0.73 and for breath 0.64. The correlation coefficients between blood and urine was 0.72; blood and breath was 0.88 and urine and breath 0.60. These findings suggest that measurements of unmetabolised MEK in blood, exhaled air and urine can be used for biological monitoring of MEK exposure. Nevertheless, laboratory methodological assessment is in favour of measuring urinary MEK as it is non-invasive and does not have to be analysed immediately after collection.     

Analysis of BTEX and other substituted benzenes in water using headspace SPME-GC-FID: method validation

The analysis of BTEX and other substituted benzenes in water samples using solid phase microextraction (SPME) and quantification by gas chromatography with flame ionization detection (GC-FID) was validated. The best analytical conditions were obtained using PDMS/DVB/CAR fibre using headspace extraction (HS-SPME) at 50 [degree]C for 20 min without stirring. The linear range for each compound by HS-SPME with GC/FID was defined. The detection limits for these compounds obtained with PDMS/DVB/CAR fibre and GC/FID were: benzene (15 ng L(-1)), toluene (160 ng L(-1)), monochlorobenzene (54 ng L(-1)), ethylbenzene (32 ng L(-1)), m-xylene (56 ng L(-1)), p-xylene (69 ng L(-1)), styrene (35 ng L(-1)), o-xylene (42 ng L(-1)), m-dichlorobenzene (180 ng L(-1)), p-dichlorobenzene (230 ng L(-1)), o-dichlorobenzene (250 ng L(-1)) and trichlorobenzene (260 ng L(-1)). This headspace SPME-GC-FID method was compared with a previously validated method of analysis using closed-loop-stripping analysis (CLSA). The headspace SPME-GC-FID method is suitable for monitoring the production and distribution of potable water and was used, in field trials, for the analysis of samples from main intakes of water (surface or underground) and from the water supply system of a large area (Lisbon and neighbouring municipalities).     

Using improved neural network model to analyze RSP,NOx and NO2 levels in urban air in Mong Kok,Hong Kong

As the health impact of air pollutants existing in ambient addresses much attention in recent years, forecasting of airpollutant parameters becomes an important and popular topic inenvironmental science. Airborne pollution is a serious, and willbe a major problem in Hong Kong within the next few years. InHong Kong, Respirable Suspended Particulate (RSP) and NitrogenOxides NO_x and NO₂ are major air pollutants due to thedominant diesel fuel usage by public transportation and heavyvehicles. Hence, the investigation and prediction of the influence and the tendency of these pollutants are ofsignificance to public and the city image. The multi-layerperceptron (MLP) neural network is regarded as a reliable andcost-effective method to achieve such tasks. The works presentedhere involve developing an improved neural network model, whichcombines the principal component analysis (PCA) technique and theradial basis function (RBF) network, and forecasting thepollutant levels and tendencies based in the recorded data. Inthe study, the PCA is firstly used to reduce and orthogonalizethe original input variables (data), these treated variables arethen used as new input vectors in RBF neural network modelestablished for forecasting the pollutant tendencies. Comparingwith the general neural network models, the proposed modelpossesses simpler network architecture, faster training speed,and more satisfactory predicting performance. This improvedmodel is evaluated by using hourly time series of RSP, NO_x and NO₂ concentrations collected at Mong Kok Roadside Gaseous Monitory Station in Hong Kong during the year 2000. By comparing the predicted RSP, NO_x and NO₂ concentrationswith the actual data of these pollutants recorded at the monitorystation, the effectiveness of the proposed model has been proven.Therefore, in authors' opinion, the model presented in the paper is a potential tool in forecasting air quality parameters and hasadvantages over the traditional neural network methods.