东平湖DOM荧光特征及与氢氧同位素指标的相关性

本研究在运用三维荧光光谱示踪平水期东平湖DOM荧光特征的基础上,进一步分析其与氢氧同位素指标的相关性,并在此基础上尝试对大气降水的DOC贡献进行估算,以期为东平湖的污染控制及治理提供依据.结果表明,东平湖平水期DOM主要由类蛋白组分（C1和C4）和类腐殖质组分（C2和C3）构成,以类酪氨酸组分C1为主,C1所占比重将近50%.平水期东平湖DOM同时受陆源、内源及大气降水的影响,但以内源输入为主;外源输入中大气降水补给与大汶河输入所占比例相当;大气降水中的溶解性有机碳输入可能会对东平湖DOM产生不可忽视的影响,需进一步系统研究.氢氧稳定同位素与溶解态有机碳以及类腐殖质荧光之间呈显著相关关系,可以在一定程度上指示DOM的变化趋势.     

北京市气温与黑碳和PM2.5对疾病死亡影响的交互效应

通过收集北京市2010~2016年逐日呼吸和心脑血管疾病死亡数据、污染物（BC、PM_2.5、SO₂、O₃和NO₂）日均浓度资料以及同期的气象资料,采用广义相加模型（GAMs）中的主效应模型、非参数二元响应模型和温度分层模型探讨了北京市气温、PM_2.5和BC单效应,以及气温与PM_2.5和BC交互作用分别对呼吸和心脑血管疾病死亡人数的影响.单效应分析结果表明,气温与两种疾病死亡人数的累计暴露-反应关系均呈“J”型分布特征,最适温度为24℃;累计滞后一天情况下PM_2.5和BC的健康效应均最显著,此时PM_2.5和BC浓度每升高四分位间距（IQR）,呼吸系统疾病死亡人数超额增加百分比（ER）分别为2.21%和1.80%,心脑血管疾病死亡人数ER分别为2.02%和1.48%.交互作用的研究结果表明,高温与高浓度的PM_2.5（或BC）对疾病死亡的影响存在协同效应,且高温条件下BC对疾病死亡影响的健康风险大于PM_2.5,当气温大于24℃时,BC和PM_2.5浓度每升高IQR对应的呼吸系统疾病死亡人数ER分别为6.22%和6.17%,心脑血管疾病死亡人数ER分别为5.01%和3.97%.虽然BC只占PM_2.5的一部分,但BC对人群健康的影响不容忽视,应该引起足够的重视.     

长治市沙尘天气污染特征及传输路径解析

以2018年3次沙尘天气为研究对象,分析了PM_2.5和PM₁₀浓度、水溶性离子组分、气象条件和气溶胶光学特征,研究了长治市沙尘天气的典型污染特征和传输路径.结果表明：长治市2018年共8d为受沙尘天气影响日,其中4月份最多（5d）,这与春季大气环流调整,冷暖空气活动频繁有关.沙尘污染发生前一般受西方或西北方冷空气影响,大气层结不稳定,大气环流转为经向环流,平均风速达4m/s以上;沙尘污染过程中PM_2.5/PM₁₀均低于0.3,环境空气中粗颗粒物占比较大,水溶性离子总浓度在PM_2.5中占比下降,Ca²⁺浓度为沙尘污染发生前的4倍以上.冬季沙尘为近地面扩散传输,春夏季沙尘为高空沉降传输,影响范围大,区域传输贡献明显.长治市沙尘重要潜在源区为蒙古国、哈萨克斯坦中部和新疆中北部的荒漠地区,传输路径主要为西北路径和正北路径,其中西北路径为哈萨克斯坦—新疆中北部—内蒙古西部—长治以及新疆中北部—内蒙古西部—长治两条路径,正北路径为蒙古国—内蒙古中部—山西北部—长治.     

乙醇汽油对国六直喷汽油车排放特性的影响

选择5台国六直喷汽油车采用3种不同组分的E10乙醇汽油和1种不含含氧化合物的国VI汽油,按照国六排放标准开展了WLTC循环测试,研究乙醇和基础油组分对污染物排放的影响规律.结果表明,多数国六直喷汽油车的CO、THC和PM排放有所降低,NO_x排放无明显差异,PN排放微增,所有样车的CO、THC和PM平均排放降低为6%、7%和14%.E10乙醇汽油中的重质芳香烃含量增高时,PN排放明显增加,PN排放与PMI指数呈现较强的正相关关系.     

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.     

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.     

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.     

Preliminary air pollution monitoring in San Miguel, Buenos Aires

Passive diffusion samplers were employed in San Miguel(Buenos Aires Metropolitan Area) for a preliminary airpollution monitoring. The highest loads were observedin downtown, compared with an urban background site.Total suspended particulate matter (TSPM) varied from0.257 to 0.033 mg cm^-2 month^-1; dust was examinedfor particle nature and size distribution. A similartrend was observed for nitrogen dioxide (NO₂) andTSPM spatial distribution, suggesting that traffic isthe major pollution source. Sulphur dioxide (SO₂)values were low and rather homogeneous. Levels for theinvestigated pollutants are below EPA's guide linevalues. Geographic (flat area, near to Rio de LaPlata) and climatologic factors (rainfalls andvariable wind directions) contribute to disperse pollutants.