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.     

Characteristics and driving factors of marsh changes in Zhalong wetland of China

Zhalong National Nature Reserve in the northeast of China is a large wetland and a habitat of hundreds species of fauna and flora. The rare red-crowned crane is one kind of endangered birds in it. Recently, Zhalong wetland is shrinking and it encounters many problems including occasional fires, bad water quality, human activities, etc. In order to find out a proper way to protect and restore the wetland, this study, using a geographic information system, the global positioning system and remote sensing techniques, analyses the spatial characteristics of the changes in marsh landscape pattern and examines the driving factors for these changes. Data sources include 8 Landsat Thematic Mapper satellite images of Zhalong area in the period of 1986–2002 and the investigation information on site. Based on the analysis of changes of marsh area and annual precipitation during the 16 years, it is found that there is a close correlation between annual precipitation and marsh area. It means that climate is one of driving factors of marsh pattern changes. To understand influences of other kinds of land uses on marsh spatial distribution in Zhalong wetland, this paper analyses the relationship between marsh and different kinds of land uses, such as water surface, residential area, farmland, salina land and grass land, respectively. According to the patch analysis theory, a fragmental index and a fractal dimension of the marsh are calculated with perimeter-area method. The results indicate that the marsh pattern is affected by human activities significantly. In addition, the location alteration of marsh centroid point over the 16 years is studied. The movement trace of marsh centroid point is concerned with different hydrological situation in different areas of the wetland. In summary the characteristics of the marsh landscape pattern evolution during the 16 years are affected by multiple driving factors. The main driving factors are climate, human activities, distribution of other kinds of land uses and hydrological situation in different areas.     

长江源区浮游植物群落结构及分布特征

地处青藏高原腹地的长江源区是长江流域的水源涵养地和生态系统的天然屏障,同时也是自然生态系统最敏感、生态环境十分脆弱的地区,浮游植物作为水体生态系统中的初级生产者,其群落结构对反映水体质量具有重要的指示作用。为探究长江源区水环境特性和浮游植物群落特征,于2018年3月份与10月份对长江源区10个典型河段的水环境因子与浮游植物群落组成进行了系统调查。结果显示:10月份各调查河段流速、浊度、水温及电导率等水环境参数均比3月份高;同一月份中,楚玛尔河的浊度、盐度、电导率等水环境参数均显著高于其余河段;两个月份各调查河段的pH、溶解氧以及亚硝酸盐氮等指标差异不显著。采样调查共鉴定出浮游植物4门28属58种,其中以硅藻、绿藻和蓝藻物种数占优,分别占总数的79.3%、10.4%和8.6%。调查期间,长江源区浮游植物密度在6.06×10~4～39.90×10~4 cells·L~(-1)之间,平均生物量为0.59 mg·L~(-1)。浮游植物优势种有美丽颤藻(Oscillatoria formosa)、普通等片藻(Diatoma vulgare)、脆杆藻(Fragilaria sp.)、针杆藻(Synedra sp.)、舟形藻(Navicula sp.)、桥弯藻(Cymbella sp.)、小球藻(Chlorella sp.)等。两个月份Shannon-Wiener多样性指数(H’)均值分别为3.06和3.12,Margalef丰富度指数(D)均值为1.17和1.52,Pielou均匀度指数(J)均值为0.90和0.82。结合水体营养盐指标、浮游植物密度及多样性指数等指标对水质评价,长江源区水质总体呈无污染或轻度污染状态。     

基于ARAMIS体系的某原油罐区风险评估

随着我国原油罐区规模的逐渐扩大,原油罐区安全管理也在经受着严峻的挑战。以西北某原油罐区为例,建立了基于ARAMIS体系原油罐区风险评估模型,从识别危险设备出发,找出对应设备的关键事件,建立事故树和事故后果蝴蝶结图,从而建立完整的事故场景,并对事故场景进行风险后果严重度分析;然后考虑到欧盟国家与我国国情存在明显的差异,邀请国内相关专家对人员、环境、设施的相对重要性程度重新进行评估,并根据层次分析法计算得到人员、环境、设施脆弱性指标的权重值,构建了适合我国原油罐区特性的系统脆弱性计算方程;最后,基于风险后果严重度和系统脆弱性评估结果,计算得到系统的风险值,并确定原油罐区系统的风险水平。结果表明：相较于欧盟研究中心调查打分法,我国调查打分法对人员脆弱性指标的权重有所提高,导致计算得到的原油罐区系统整体风险值略高,体现我国对原油泄漏严重后果的重视程度更高,也进一步体现了引入层次分析法并据此制定适于我国国情的原油罐区风险评价方法的必要性。该风险评估结果可为原油罐区火灾事故模拟和制定应急预案提供数据支持和决策依据。     

关于环境保护法律法规修订的新观点

为了保护人类健康不受恶劣环境的侵害,就必须对依法保护环境进行相关研究。提出了环境与健康是人类健康生存头等大事的观点,总结中国主要环境保护法律法规的不足,提出新的修订措施。提出6条保护环境的新法规建议供环境保护法修订时参考应用,主要包括:1)环境与健康及保护法规的宣传教育立法;2)医疗卫生机构设立环境医学防治学科立法;3)国家普及设立环境污染监测和预报系统立法;4)人民政府环境保护行政主管部门应扩展和延伸立法;5)国家在任何单位均应设立环境污染保护行政主管科室立法;6)国家设立外国在华企业环境保护行政主管部门立法。     

Integrated factor analysis of water level variation in geographically isolated ponds

Environmental Science and Pollution Research - Small geographically isolated ponds provide a multitude of ecological functions and services, but water table fluctuations alter the magnitude of...     

Distribution of radionuclides in the guano sediments of Xisha Islands, South China Sea and its implication

Several natural and anthropogenic radionuclides (²¹⁰Pb, ²²⁶Ra and ¹³⁷Cs) in guano-phosphatic coral sediments and pure guano particles collected from Ganquan, Guangjin, Jinqing and Jinyin Islands of the Xisha archipelago, South China Sea, were analyzed. The Constant Initial Concentration (CIC) model and the Constant Rate of Supply (CRS) model were applied for age calculation. The average supply rate of ²¹⁰Pb was 126 Bq m⁻² a⁻¹, very close to the flux of northern hemisphere average (125 Bq m⁻² a⁻¹). The activities of anthropogenic radionuclides in the sediments were very low, indicating that human nuclear tests did not notably impact this region. The main source of radionuclides in the sediments was from atmospheric precipitation, and the organic matter derived from plant and produced by nutrient-rich guano could further enhance them.     

高电压电容吸附法处理模拟含盐废水

针对电容吸附除盐法的缺点,通过对电极进行绝缘处理,开发了一种新型高电压电容吸附除盐技术。实验结果表明：增大外加电压,有利于除盐效率的提高;外加电压为60V时,NaCl去除率可达90%以上;增大废水流速对吸附效果不利;反应温度对处理效果的影响不大;在NaCl质量浓度为80mg/L、外加电压为60V、反应温度为25℃、废水流速为36m/s、反应时间为30min的条件下,经一级处理和二级处理后出水的NaCl质量浓度分别为39mg/L和16mg/L,NaCl去除率分别为51.3%和80.0%。     

N_2O的氮、氧稳定同位素真空制样系统探讨

氮、氧稳定同位素指标在N2 O源汇识别、全球源汇质量估算中具有独特的定量作用。本文主要讨论N2 O的氮、氧稳定同位素真空制样系统的基本原理、总体设计和技术难点 ,为建立N2 O氮、氧稳定同位素真空制样系统提供理论基础。     

Pollution characteristics and health risk assessment of polycyclic aromatic hydrocarbons and nitrated polycyclic aromatic hydrocarbons during heating season in Beijing

Polycyclic aromatic hydrocarbons(PAHs) and their nitrated derivatives(NPAHs) attract continuous attention due to their outstanding carcinogenicity and mutagenicity. In order to investigate the diurnal variations, sources, formation mechanism, and health risk assessment of them in heating season, particulate matter(PM) were collected in Beijing urban area from December 26, 2017 to January 17, 2018. PAHs and NPAHs in PM were quantitatively analyzed via gas chromatography-mass spectrometry(GC-MS). ...