空气自动监测与连续监测测定值的对比分析

确定空气自动监测与连续监测的相关性,对保证监测数据的连续性、可比性和正确判断环境空气质量的变化规律有重要的作用。对比实验空气的自动监测系统和连续监测系统,两者的监测值存在一定差异。1　实验1 1　分析方法SO2、NO2、PM10的自动监测方法和连续监测方法见表1。表1　SO2、NO2、PM10的自动监测方法和连续监测方法项目自动监测连续监测SO2ISO/CD10498紫外-荧光法GB/T15262-94甲醛吸收副玫瑰苯胺分光光度法NO2ISO7996化学发光法GB/T15435-95Saltzman法PM10β射线法GB6921-86重量法1 2　主要仪器自动监测仪(采样系统为…     

南昌市一次降水自动监测结果的综合分析

利用降水自动监测仪和空气自动监测系统的同步监测结果,分析了一次34 5mm连续降水中全过程的pH、EC和同期SO2、PM10、NO2的变化情况。降水中pH值为7 23～4 08、EC为488 8～7 6μS cm,pH、EC在降水初期变化较大。pH与EC、SO2呈现负相关趋势,降水过程中PM10、NO2基本保持较低水平,SO2变化较大。     

2016年武汉市大气污染物时空分布特征及影响因素

利用统计学和GIS方法对2016年武汉市各区不同污染物的时空分布特征及相关性进行分析。结果表明：武汉市大气污染季节性特征明显，春季和冬季颗粒物（PM2.5、PM10）及NO2污染突出,夏季O3污染严重。污染物空间差异显著，主城区和东西湖区颗粒物及NO2污染严重，郊区O3污染严重。平均气温、平均水汽压与SO2、NO2、CO、PM2.5和PM10均呈显著负相关，而与O3呈显著正相关；降水量与SO2、NO2和CO呈显著负相关。     

被动采样监测环境空气中SO 2和NO 2

用自主研制的采样管开展环境空气中SO2和NO2的被动采样监测.结果表明,被动采样监测结果与自动监测结果高度相关,经回归方程修正后,两者的结果没有显著性差异,被动采样的采样和分析产生的误差得以消除.     

兰州市空气中三项污染物自动与手工监测值比较

通过对自动监测和手动监测兰州市大气中SO2、NO2（NOx）、PM10（TSP）的二组数据分析．在非采暖期二组数据有较大相关性，而在采暖期，SO2和NO2（NOx）具有相关性外，PM10（TSP）则没有相关性．     

环境空气质量综合指数计算方法比选研究

环境空气质量综合指数是进行逐月城市环境空气质量比较和排序的重要方法,提出了4种涵盖SO2、NO2、PM10、PM2.5、CO、O3等6项污染物的综合指数计算方法,基于2013年74个城市逐月污染物浓度数据使用主成分分析方法进行了对比分析。结果表明,综合指数计算方法中污染物统计指标和标准化方法不同对于主要污染物的判定有重要影响,各种计算方法中PM2.5、PM10、O3是出现频率最多的主要污染物;除O3外其他5项污染物逐月统计指标间均有极显著的正相关性,冬季O3统计指标与SO2、NO2、PM10、PM2.5呈显著负相关,夏季则呈显著正相关;主成分分析结果表明,在去除冗余信息后,PM2.5、PM10的权重被相对削弱,SO2、NO2、CO的权重得到相对强化,O3的权重夏季得到强化、冬季被削弱;综合考虑不同方案下主要污染物频率分布情况和PM2.5、PM10、O3权重变化特征,建议计算逐月环境空气质量综合指数时,SO2、NO2、PM10、PM2.5宜以月均值除以年均值标准进行标准化,CO、O3宜以特定百分位数浓度除以日均值标准(或8 h均值标准)进行标准化;该方法可延伸到季、半年和年度的环境空气质量综合指数计算。     

石家庄市大气颗粒物中水溶性无机离子污染特征研究

用超声萃取－离子色谱法分析了石家庄市大气颗粒物中8种水溶性无机离子。结果表明，NO3－、SO2－4、NH4＋及 Ca2＋为主要组分；各个离子的质量浓度均有季节及空间变化差异；不同粒径颗粒物中 SO2－4和 NO3－相关性均很好，NH4＋与 SO2－4、NO3－在细颗粒物中具有良好的相关性，Ca2＋在粗粒子中与 NO3－和 SO2－4的相关性也较好。SO2－4／NO3－质量比季节变化表明，春、夏季固定源与流动源对大气颗粒物贡献相当，秋季流动源贡献较大，冬季固定源贡献较大。PM2．5中SO2与SO2－4、NO2与 NO3－转化率表明，SO2－4、NO3－主要是由二次转化而来。     

空气自动监测中PM2.5与PM10 “倒挂”现象特征及原因

采用不同原理的自动监测仪器在不同季节同时测定PM2.5与PM10,对所得数据中的PM2.5与PM10"倒挂"现象进行分析。结果表明,当PM10采用振荡天平法时,PM2.5与PM10的"倒挂"率较高;冬季和夏季"倒挂"现象发生率明显高于其他季节;造成PM2.5与PM10"倒挂"的原因主要有监测过程中的随机误差,PM2.5与PM10的监测方法原理不同,监测方法之间存在显著差异等。     

大连市区大气气溶胶的无机化学特征分析

通过对大连市的两个采样点从2002年4月至12月三个期间的气溶胶的三种粒径的采样分析,结果表明,大连市区气溶胶中PM10质量浓度约占TSP的50%,PM2.5质量浓度约占TSP的30%;8种可溶性离子在不同粒径气溶胶中所占的比例,随着粒径的减小而增大,冬季的SO42-、NO3-、NH4 在各种粒子中含量高于夏季,沙尘暴期间各种可溶性离子在不同粒径颗粒物中的含量较低;11种常见元素在细粒子中的含量比粗粒子中的含量高,春季各种粒子中的元素含量要高于冬季.     

南通市冬季PM2.5中水溶性离子污染特征

根据南通市2016和2017年冬季大气多参数站自动监测PM2.5数据和在线离子色谱分析仪Marga监测的PM2.5中水溶性离子数据,分析了南通市冬季PM2.5中水溶性离子污染特征。结果表明,南通市2016和2017年冬季,ρ(PM2.5)分别为58和54μg/m 3,均高出其年均值(14μg/m^3);ρ(水溶性离子)总占ρ(PM2.5)百分比分别为74.5%和74.3%;二次离子ρ(NO3^-、SO4^2-和NH4^+)占ρ(PM2.5)百分比分别为66.8%和66.6%;各水溶性离子占比大小依次为:NO3^-、SO4^2-、NH4^+、Cl^-、K^+、Na^+、Ca^2+、Mg^2+。对ρ(NO3^-)/ρ(SO 4^2-)分析表明,移动源已经成为南通市冬季的主要污染源,且呈逐年增强趋势。对氯氧化率和硫氧化率的分析表明,南通市冬季存在较明显的二次污染,SO2的转化程度大于NO2。除Na^+和Mg^2+外,其他离子与PM2.5均呈显著相关性,NO3^-、SO4^2-与NH4^+之间的相关系数最高,Cl^-与除Na^+外的所有阳离子均呈显著相关性。     

天津市基于新标准的空气质量预报模型效果评估

利用2015年环境空气质量监测数据,对天津市OPAQ空气质量统计预报模型预测效果进行验证评估。结果表明,模型对天津市AQI和PM_(2.5)、PM_(10)、O_3、NO——2的预测结果与实测结果具有较好的趋势一致性,且预测时间越临近,拟合度越好,24 h预报的相关系数r全部达到0.8以上。对PM_(2.5)的预报性能明显优于PM_(10)、O_3和NO_2,PM_(2.5)平均值预测略呈正偏差,但重污染预测值偏低约15%;O_3和NO_2预测值呈明显负偏差,O_3峰值预测不足,NO_2预测值整体偏低,均以24 h预报趋势性最好,但负偏差最为突出。     

Air Quality and Monitoring Strategy in the Helsinki Metropolitan Area, Finland

The Helsinki Metropolitan Area Council (YTV) is responsible for air quality monitoring in the Helsinki area. Air quality has been monitored periodically since the late 1950s. An automatic SO2 monitoring network was constructed in 1975 and TSP measurements were added in 1978. Since then the network has been expanded and currently five automatic multicomponent stations form the basis of the network monitoring SO2, NO, NO2, CO, PM10 and O3 concentrations. Manual TSP and PM10 measurements are also conducted. Mobile monitoring units are also being used as well as special measurement campaigns. The effects of air pollution on nature are studied in bioindicator monitoring. An air quality index is used in order to inform the public of the current air quality situation. Changes in air quality are reflected in monitoring strategy. SO2 concentrations have decreased in the past two decades. Annual averages in 1995 were at or below 5 µg/m3. Traffic is the major source for pollutants even though catalytic converters have lowered traffic emissions somewhat. The highest annual average NO2 concentration at an urban site was 49 µg/m3 in 1995, and there has been no clear change in NO2 levels. There has been a decreasing trend in CO concentrations. Maximum annual TSP and PM10 averages in 1995 were 92 and 32 µg/m3, respectively. The highest average lead concentration was 0.01 µg/m3. Elevated concentrations are experienced from time to time. During the spring daily TSP and PM10 concentrations can go up to around 300 and 150 µg/m3, respectively. This is caused by resuspension mainly due to street sanding. Also a major winter NO2 episode occurred in December 1995. The highest hourly NO2 concentrations reached 400 µg/m3.     

Air quality of Prague: traffic as a main pollution source

Political and economical transition in the Central and Eastern Europe at the end of eighties significantly influenced all aspects of life as well as technological infrastructure. Collapse of outdated energy demanding industry and adoption of environmental legislation resulted in seeming improvements of urban environmental quality. Hand in hand with modernization the newly adopted regulations also helped to phase out low quality coal frequently used for domestic heating. However, at the same time, the number of vehicles registered in the city increased. The two processes interestingly acted as parallel but antagonistic forces. To interpret the trends in urban air quality of Prague, Czech capital, monthly averages of PM(10), SO(2), NO(2), NO, O(3) and CO concentrations from the national network of automated monitoring stations were analyzed together with long term trends in fuel consumption and number of vehicles registered in Prague within a period of 1992-2005. The results showed that concentrations of SO(2) (a pollutant strongly related to fossil fuel burning) dropped significantly during the period of concern. Similarly NO(X) and PM(10) concentrations decreased significantly in the first half of the nineties (as a result of solid fuel use drop), but remained rather stable or increased after 2000, presumably reflecting rapid increase of traffic density. In conclusion, infrastructural changes in early nineties had a strong positive effect on Prague air quality namely in the first half of the period studied, nevertheless, the current trend in concentrations of automotive exhaust related pollutants (such as PM(10), NO(X)) needs adoption of stricter measures.     

Analysis of the air pollution climate at a background site in the Po valley

The Po valley in northern Italy is renowned for its high air pollutant concentrations. Measurements of air pollutants from a background site in Modena, a town of 200 thousand inhabitants within the Po valley, are analysed. These comprise hourly data for CO, NO, NO(2), NO(x), and O(3), and daily gravimetric equivalent data for PM(10) from 1998-2010. The data are analysed in terms of long-term trends, annual, weekly and diurnal cycles, and auto-correlation and cross-correlation functions. CO, NO and NO(2) exhibit a strongly traffic-related pattern, with daily peaks at morning and evening rush hour and lower concentrations over the weekend. Ozone shows an annual cycle with a peak in July due to local production; notwithstanding the diurnal cycle dominated by titration by nitrogen oxide, the decreasing long term trend in NO concentration did not affect the long term trend in O(3), whose mean concentration remained steady over the sampling period. PM(10) shows a strong seasonality with higher concentration in winter and lower concentration in summer and spring. Both PM(10) and ozone show a marked weekly cycle in summer and winter respectively. Regressions of PM(10) upon NO(x) show a consistently greater intercept in winter, representing higher secondary PM(10) in the cooler months of the year. There is a seasonal pattern in primary PM(10) to NO(x) ratios, with lower values in winter and higher values in summer, but the reasons are unclear.     

大气颗粒物监测分析及今后研究课题

概述了 PM1 0 和 PM2 .5今后的研究方向。目前我国尚未开展 PM2 .5的常规监测 ,PM1 0 的自动监测中尚存在一些技术问题 ,且尚未公布 PM1 0 自动监测的技术性标准 ,为了满足各级环境监测站的工作需求 ,在附录中介绍了国外关于PM1 0 和 PM2 .5的监测仪器和方法标准 ,并介绍了 2 0 0 0年日本颁布的 PM2 .5自动监测技术规定     

南京市大气微生物调查及其动态研究

调查了南京市主要功能区大气微生物中常见种类的细菌和霉菌类,研究了细菌和霉菌类的生态分级和变化规律,比较了大气微生物与大气监测指标PM10、SO2、NO2的相关性。通过分析2000年10月至2001年8月6个功能区4个季节的监测结果,得出山西路监测点微生物数量最高,并且该点微生物中是以细菌为优势种,其次是霉菌属的马丁霉菌和耐渗透压霉菌。通过生理生化试验鉴定细菌和霉菌类,得出南京市大气微生物中细菌基本以微球菌属为主,霉菌以青霉属、曲霉属为主。大气微生物数量统计结果表明,南京市主要功能区微生物总量(均值)从高到低的顺序为:山西路(商业区)>中华门(交通区)>瑞金路(居住区)>迈皋桥(工业区)>玄武湖(对照点)>草场门(文教区)。大气化学监测指标PM10、SO2、NO2分析结果也表明大气微生物的数量变化与之呈一定的正相关关系,尤其与PM10关系更为密切。     

乌鲁木齐市2011年冬季PM2.5/PM10浓度特征分析

利用乌鲁木齐市PM2.5//PM10自动监测数据,分析PM2.5与PM10的浓度分布特征和时间变化规律。结果表明,按照《环境空气质量标准》(二次征求意见稿)的标准限值,乌鲁木齐市冬季PM2.5污染重于PM10。PM2.5浓度为0.164mg/m3,超过二级年标准限值的3.7倍,超标率为73.9%。PM2.5浓度日变化曲线昼高夜低,呈单峰型,峰值出现在13:00~14:00(北京时间)。PM10中PM2.5所占比例较高,PM2.5/PM10为0.79,相关分析和检验显示PM2.5与PM10的线性相关显著,相关系数为0.92。     

西宁冬季PM2.5和PM10手工与自动监测现状研究

为研究PM_(2.5)和PM_(10)手工与自动监测仪器在高海拔地区的适用性,于2014年12月至2015年1月(冬季)在青海省西宁市开展了为期34 d的监测比对实验。PM监测数据表明:手工监测数据之间都有差异,除了受监测滤膜种类的影响,还存在监测仪器间的系统误差。石英滤膜的PM监测数据都高于聚丙烯滤膜,尤其是PM_(2.5)更为明显,偏高近1/4;石英滤膜与聚丙烯滤膜的PM监测数据具有较好的相关性,PM_(10)监测数据的相关系数为0.97。自动监测数据之间进行了同期比对研究,发现TEOM1405DF(微振荡天平法)和APM-2(β射线法)的PM监测值较低,BAM-1020(β射线法)的PM监测值最高;而Grimm(光散射法)的PM监测值居中。BAM1020配备动态加热系统(DHS),其PM监测数据比没有配备DHS的APM-2偏高40%。基于PM监测比对研究,建议在空气污染严重时加密对各监测仪器的运行维护,并加强长期观测以全面评估PM监测。     

Short-term variation in air quality associated with firework events: a case study

The effect of fireworks on air quality was assessed from the ambient concentrations of various air pollutants (SO2, NO2, PM10 and TSP) during Diwali festival in Hisar city (India), in November 1999. The extensive use of fireworks was found to be related to short-term variation in air quality. During the festival the concentration of SO2 was observed to be increased approximately 10-fold at few sites, whereas the concentrations of NO2, PM10 and TSP increased 2-3 times, compared to the data collected on a typical winter day in December 1999. The maximum NO2 concentration was observed a day after the festival. The diurnal pattern of the above pollutants showed a slight increase in the night. The levels of these pollutants observed during Diwali were found to be moderately high, which can be associated with serious health impacts.