颗粒物分界层Mie散射激光雷达识别的sigmoid算法

基于成都市2013年6月~2015年5月期间由Mie散射激光雷达探测的大气消光系数廓线资料,发现混合层以上在颗粒物消光和分子消光之间一致存在一个S型的过渡区,利用sigmoid函数对此分布形态进行模拟,通过计算该函数上下曲率最大点所在的高度,据此提出了颗粒物分界层Mie散射激光雷达识别的sigmoid算法.针对该算法模拟效果的分析表明,颗粒物分界层过渡区附近大气消光系数理论廓线和实测廓线保持了高度的相关性,二者在春夏秋冬四季的相关系数（R）分别为0.9971±0.0052、0.9935±0.0167、0.9979±0.0038和0.9990±0.0021（均通过α=0.05的显著性检验）.基于sigmoid算法计算的颗粒物分界层过渡区与成都市温江站探空资料得到的逆温层之间存在很好的对应关系.     

天津大气稳定度和逆温特征对PM2.5污染的影响

利用气象铁塔资料分析了逆温频率和强度,采用温差-风速法计算了天津地区大气稳定度,探讨了其相互关系及对PM_2.5浓度月均值和超标日的影响,并对一次重污染过程中大气稳定度和逆温分布特征进行了分析.结果表明,2015年9月~2017年8月A,B,C,D,E和F类大气稳定度发生频率依次为6.7%,11.4%,22.4%,46.1%,11.1%和2.2%,秋冬季节稳定类天气（E,F类）较多,全年白天各时段均以不稳定类大气为主,夜间大气稳定度以中性为主,秋冬季夜间稳定类高达30%~40%.观测期内冬季逆温频率最高,5：00~8：00和21：00~23：00超过90%,冬季逆温强度也最高.随着稳定类大气层结日数的增多,PM_2.5月均值和污染日数均有所增大,同时逐月PM_2.5均值、污染日发生频率均与逆温发生频率呈正相关关系.2016年12月16~21日的一次重污染天气过程显示,PM_2.5受到大气稳定度和逆温发展的影响,霾形成、雾-霾交替和消散等阶段大气稳定度和逆温特征具有显著的不同.大气持续趋于稳定及逆温强度的逐渐增大,对污染生成和维持起了非常重要的作用,污染过程中大气稳定度和逆温特征的精细化分析有助于提升重污染天气预报预警水平.     

Suspended particulate mercury associated with tidal fluxes in a lagoon environment impacted by cinnabar mining activity (northern Adriatic Sea)

The Isonzo River has been demonstrated to be a continuing point source of mercury(Hg)in the Gulf of Trieste although the Idrija mine was last active in 1996. The present study aims to investigate the role of the suspended particulate matter(SPM) associated with tidal fluxes to disperse particulate Hg(PHg) into the Grado coastal lagoon system. PHg concentrations(avg. 3.11 ± 2.62 μg/g, d.w.), notwithstanding the ebb or flood tides, were significantly higher than the local sediment background(0.13 μg/g). The relative affinity of Hg for the particulate phase in surface waters was confirmed by higher average distribution coefficient(Kd) values(5.6–6.7). PHg contents showed the highest values in ebb tide conditions, thus suggesting their origin from the erosion of tidal flats and saltmarshes of the lagoon. When compared to river discharge, high PHg surface concentrations in flood tide are related to rainfall events occurring within the river basin. Results can be used to make an indicative assessment of the amount of Hg bound to SPM which is transported in and out of the lagoon basin following the action of tidal fluxes. A simple estimation provides a negative budget for the Grado lagoon sub-basin which loses between 0.14 and 1.16 kg of PHg during a tidal semi-cycle. This conclusion is in agreement with the evidence of morphological deterioration which has emerged from recent studies on the lagoon environment, and which testifies to a current sedimentary loss from the lagoon into the northern Adriatic Sea.     

大气环境质量综合评价加权灰色关联模型的建立与应用

利用灰色理论建立大气环境质量综合评价加权灰色关联模型 ,运用污染贡献率确定权重系数。与其它评价模型相比 ,加权灰色关联模型还具有排序功能 ,实例表明该模型是简便有效的     

不同区域城市空气O3和PM2.5累积速率时序模型

利用2017—2018年全国7个区域10个典型城市环境空气O₃和PM_2.5浓度数据，统计污染物累积速率，进而采用回归方法拟合污染物浓度及其累积速率的时间序列模型，分析不同区域污染物时序变化特征差异。结果表明：不同区域O₃浓度时序曲线拟合程度总体高于PM_2.5，石家庄O₃拟合程度最高，西安PM_2.5拟合程度最高。以07：00、14：00分别作为O₃、PM_2.5模拟起点是24 h中的最优模型。不同城市夏季O₃小时浓度时序变化曲线均为单峰形态，O₃浓度及累积速率峰值出现时间可能由城市所处经度决定，太原O₃累积最快，西安O₃消解最快。各城市间冬季PM_2.5小时浓度及其累积速率时序变化曲线形态差异较大，沈阳PM_2.5累积和消解均最快。与浓度相比，城市环境空气O₃和PM_2.5累积速率与光照、扩散条件等有更好的时间相关性。     

TSP-PM10-PM2.5-2型中流量大气颗粒物采集系统的开发和应用

自行开发并研制了TSP-PM10-PM2.5-2型中流量TSP、PM10、PM2.5大气颗粒物采集系统,是目前中国唯一可以采集TSP、PM10、PM2.5样品并提供足够的样品量进行大气颗粒物化学成分分析的中流量大气颗粒物采集器.该系统精心设计和加工的限流孔可以保持完全固定的流量,保证切割粒径的稳定,减小采样的误差并方便操作.该系统已经成功地应用于20多个城市和地区大气颗粒物的监测和研究中,为研究大气颗粒物的污染状况和来源提供了有效的技术手段和支持.     

空气颗粒物中铅、镉超声浸取-原子吸收测定方法研究

用超声波震荡方法对大气滤膜样品进行前处理,提取待测元素。研究了浸取液种类、酸度、浸取时间和干扰因素等,确定了颗粒物样品溶液制备的最佳条件,与流动注射在线富集——原子吸收技术结合,建立了大气颗粒物样品测定Pb、Cd方法。结果表明,方法快速、准确,标准样品测定回收率在97％以上,6次测定方目对标准偏差小于2.6％。     

大气环境质量模糊评价的可视化实现

以模糊聚类法为算法基础,运用计算机可视化技术,进行大气环境质量评价初步探索。计算机技术在环境评价中的应用,可以方便数据处理和资源共享。选用武汉市大气环境监测资料,应用该方法评价和分析武汉市大气环境质量,取得了令人满意的结果。     

Interpretation of roadside PM10 monitoring data from Sunderland,United Kingdom

Roadside PM₁₀ has been monitored by Partisol® at three sitesin Sunderland between August 1997 and February 1998. The sites chosen were an inner city kerbside site; a roadside site adjacentto a dual carriageway on the outskirts of Sunderland with an openaspect; and a rural site.The results indicate that there is a seasonal variation in the relationship between the sites in terms of monitored PM₁₀.In the winter there is a poor correlation between the sites whereas in the summer significant correlations are obtained. Of the sites monitored PM₁₀ is consistently highest at the inner city roadside site. During the summer, exceedances of theU.K. 50 g m^-3 standard (DETR, 2000) are associated with conditions suitable for the build-up of photochemical pollutionhowever during the winter period exceedances are recorded duringa variety of weather conditions.At the dual carriageway site PM_2.5 has also been recorded and contributions to measured PM₁₀ are 77% in summer and68% in winter. The results illustrate a number of inconsistencies between this study utilising the Partisol® andothers reporting results where PM₁₀ has been monitored by TEOM®.     

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.