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.     

城市大气中颗粒物的研究现状及健康效应

综述了大气尘(TSP、PM10、PM2.5)的研究现状,重点围绕TSP、PM10、PM2.5的物理特征、化学组成(有机组成、重金属元素的总含量及其各化学形态分布)及其源解析进行描述,也提及了大气颗粒物与健康的关系.     

南京某县空气、土壤中多环芳烃的分布及来源

采用建立的采样及分析监测程序对南京某县空气、土壤中的多环芳烃进行了调查监测,探讨了多环芳烃在空气、土壤中的分布特征、相关性及可能的污染来源.     

辽河流域浑河沈阳段地表水重点控制有机污染物的筛选

采用美国环保局工业环境实验室提出的化学物质的“潜在危害指数法”,对辽河流域浑河沈阳段地表水和底质中检出的有机污染物的潜在危害进行了排序,参考国内外有毒化学品优先控制名单的筛选原则和方法,结合本流域的实际情况,制定出重点控制有机污染物的评分标准,提出辽河流域浑河沈阳段重点控制有机污染物名单。考虑到个别有机污染物在水中的浓度很低,但在底质中相对含量较高,所以将底质的检测结果也纳入评分标准。     

我国污染源在线监测现状及建议

文章在对国内重点城市污染源在线监测调查研究的基础上 ,分析总结了国内污染源在线监测的经验和教训 ,提出了推进污染源在线监测的建议     

太湖水质氮素质量浓度时空差异特征及影响因素分析

通过对2014—2016年湖体水质中氮素质量浓度分析,结合出入湖总氮浓度、水量、湖体水生生态等影响因素,发现太湖水体中总氮浓度呈现逐年下降的趋势,各监测点位总氮为0.530 mg/L～5.51 mg/L,时空分布不均,差异明显。时间上,总氮浓度表现为春季最高,夏季和秋季最低,且月均值变化曲线呈现出规律的正弦函数波形。空间上,总氮浓度大致表现出由西部湖区向东部湖区递减的趋势,呈现西部湖区﹥北部湖区﹥南部湖区﹥湖心区﹥东部湖区。要改善湖体水质,不仅要切断污染源,而且要加强水生生态功能修复。     

绵阳市环境放射源分布规律

对绵阳市的环境放射源基本情况进行了分析，弄清了放射源的品种、数量。摸索出放射性核素及Ｘ射线装置在地域、行业和剂量方面的分布规律，揭示出环境放射性管理工作的重点。     

兰州市空气质量预报系统研究

从潜势预报、统计预报、数值预报三方面介绍了兰州市空气质量预报系统的研究方法和结构特点,并检验分析了2001年4～10月兰州市空气质量预报,结果表明,此系统具有较强的预报能力,适用于兰州市空气质量预报业务。     

太湖梅梁湾水源水中微囊藻毒素浓度的变化

对太湖梅梁湾水源水中的总藻毒素TMC[(TMC-LR) (TMC-RR)]和胞外藻毒素EMC[(EMC-LR) (EMC-RR)]进行了跟踪检测.结果表明,水体中TMC-RR、TMC-LR、EMC-RR、EMC-LR质量浓度平均分别为1.819 μg/L、1.090 μg/L、0.491 μg/L和0.077 μg/L,无锡市的主要水源地水质已受到微囊藻毒素的污染.提出,应加强水源地水体中微囊藻毒素浓度的监测,确保饮用水的安全.     

污染源监测报告有效期问题的探讨

环境监测是环境管理的基础性工作,污染源监测报告(数据)的有效性是保证环境执法严肃、公正的重要条件,也是是守法企业展示良好社会形象的基本保证。依照国家的相关规定,并结合行业技术规范的要求,探讨污染源监测报告的有效期问题,以利于相关各方严肃、科学、合理地使用污染源监测数据。