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®.     

基于聚类分析的颗粒物监测网络优化研究

为了优化香港环境监测网络,收集香港14个监测站2011年1月1日至2015年11月30日的颗粒物PM_(2.5)、PM_(10)的小时数据进行统计分析。对PM_(2.5)进行聚类,并利用日均浓度变化图进行验证,结果表明,可将监测站分为4类(A、B、C、D类),A类位于城市郊区,B类则位于港口附近,且A、B类的PM_(2.5)日变化特征均呈现双峰型分布,峰值分别出现在09:00和21:00。对PM_(10)进行类似分析结果表明,监测站同样可以分为4类,A类位于九龙区,B类则位于港口附近,而且A、B类的PM_(10)日变化双峰分别出现在11:00和20:00左右。说明污染源头及地形的相似致使某些监测站颗粒物浓度的变化出现相同的趋势,导致监测设备的浪费和管理的冗余。建议建立更高效的空气管理系统,将冗余设备转移到其他地区,扩大空气监控区域。对PM_(2.5)/PM_(10)聚类结果表明,将监测站分为4类,B类均属于路边站,C类则位于居民区。同时还发现同类监测站PM_(2.5)/PM_(10)数值变化相同,并且可以用其中一个站的PM_(2.5)和PM_(10)浓度及另一个站的PM_(2.5)或PM_(10)浓度预测PM_(2.5)或PM_(10)浓度,为优化监测资源提供了一种新的思路。     

基于系统聚类方法划分中国PM2.5防治区域

中国各城市细颗粒物(PM_(2.5))环境空气质量差异较大,呈现明显的区域污染特征。合理划分PM_(2.5)污染防治区域、开展区域性大气环境管理,是改善区域空气质量的重要途径。根据2015年全国108个重点城市大气PM_(2.5)的日均浓度数据,使用系统聚类方法对各城市的PM_(2.5)全年污染变化特征进行分析,从而划分出不同防治区域。依据聚类分析的3项原则,综合比较4种不同聚类方法及结果,最后提出可以划分出8个PM_(2.5)污染防治区域:a赣鄂湘接壤地区(长株潭及周边城市),b成渝及周边地区,c粤桂地区,d闽浙沿海城市群,e东三省地区,f长三角地区,g山东及周边地区,h京津冀、山西中北部、陕西关中城市群。     

环境空气质量新标准对珠三角区域站空气质量评价的影响

利用粤港珠三角区域空气质量监控网中天湖、金果湾与万顷沙3个区域站2010年全年SO₂、NO₂、PM₁₀、O₃、PM_2.5与CO自动监测的数据,分析了实施环境空气质量新标准(GB 3095—2012)对这3个子站空气质量评价的影响。研究发现,若采用新标准,万顷沙的NO₂、PM₁₀和PM_2.5年均浓度将不同程度超标。这3个子站空气质量达标率下降7~28个百分点,空气污染指数从91%~99%下降至63%~91%;O₃的引入是导致空气质量达标率下降的最主要的原因;O₃将取代PM₁₀成为最主要的首要污染物,出现频率大于50%,且O₃(8 h)平均浓度的影响大于O₃ (1 h)浓度的影响。PM_2.5的纳入也是导致空气质量达标率下降的重要因素,其超标率为3%(金果湾)~16%(万顷沙)。NO₂标准的收严未对天湖与金果湾空气质量评价造成影响,但导致万顷沙NO₂的超标率从2%上升至10%,且NO₂作为首要污染物的比例达24%。     

中国城市PM2.5空气质量改善分阶段目标研究

中国城市细颗粒物(PM_(2.5))空气质量达标率低,且城市间的污染程度差异较大。为了整体改善PM_(2.5)空气质量,需要针对不同污染程度的城市,制定分阶段改善目标加以考核和管理,研究探讨了城市PM_(2.5)空气质量改善目标体系及不同污染程度城市各阶段目标值。首先运用文献综述法、国内外对比分析法梳理评述了WHO、欧美等发达国家PM_(2.5)的空气质量标准和达标要求,提出中国城市PM_(2.5)空气质量改善的考核目标体系,包括PM_(2.5)浓度目标值或下降率、严重污染天数上限、达标天数下限等指标。通过历史数据分析法研究了2000—2013年美国、日本一些城市和2013—2016年中国74个环保城市PM_(2.5)年均浓度的变化趋势,推论出中国城市PM_(2.5)年均浓度年均下降5%~8%是可能实现的;结合环境保护部及各省市PM_(2.5)污染防治规划,提出PM_(2.5)空气质量改善目标的设定原则和达标天数的回归计算方法;以2014年114个城市PM_(2.5)年均浓度为基数,计算得出不同污染程度城市2020、2025、2030年PM_(2.5)年均浓度年下降率和达标天数的目标值。     

临汾市大气污染物的时间分布特征与源区分析

多年来,临汾市多次名列我国生态环境部公布的空气质量最差的重点城市之列,对其大气污染的时间分布特征和潜在源区进行分析对其环境管理与污染防治具有重要意义。利用2015—2019年临汾市5个国控空气环境质量监测站点的6种空气污染物(SO₂、NO₂、CO、O₃、PM_2.5和PM₁₀)浓度数据和气象观测数据,使用HYSPLIT模型研究了该市空气污染物的时间变化特征、轨迹输送特征和可能的来源。结果表明,PM_2.5和PM₁₀的年均浓度均超过了《环境空气质量标准》(GB 3095—2012)Ⅱ级标准,SO₂仅在2016—2017年超过该标准,其余3种污染物的年均浓度均低于该标准。6种污染物2015—2019年的月均浓度的变化特征表现为O₃浓度呈以6、7月为中心的近似正态分布,SO₂、NO₂和CO以及PM_2.5和PM₁₀浓...     

中国中东部一次大范围重污染过程特征分析

采用嵌套网格空气质量预报模式(NAQPMS)模拟与气象、污染物观测资料相结合的方式,分析了2016年12月影响中国中东部地区的一次重污染过程中PM_(2.5)时空分布特征及来源成因。结果表明,重污染过程中PM_(2.5)具有较明显的时空变化规律,污染呈现一定程度的区域性分布特点,不同地理位置条件下,污染物浓度的累积和传输方式表现出不同的特征,细颗粒物快速二次生成及不利扩散条件下的持续积累可能是此次污染过程的主要原因,不利于污染物扩散的高低空天气形势的配合抑制了污染物的快速消散,为大气污染的形成及维持提供了稳定的大气环境背景,形成了此次污染过程污染浓度高、影响范围大的态势。     

Air Pollution Concentrations of PM2.5, PM10 and NO2 at Ambient and Kerbsite and Their Correlation in Metro City – Mumbai

Ambient concentrations of PM_2.5 and PM₁₀ are of concern with respect to effects on human health and environment. Increased levels of mortality and morbidity have been associated with respirable particulate air pollution. In India, it is not yet mandatory to monitor PM_2.5 levels therefore very limited information is available on PM_2.5 levels. To understand the fine particle pollution and also correlate with PM₁₀ which are monitored regularly in compliance with ambient air quality standards. This study was carried out to monitor PM_2.5, PM₁₀, and NO₂ for about one year in a residential cum commercial area of Mumbai city with a view to understand their correlation. The average PM_2.5 concentration at ambient and Kerbsite was 43 and 69 μg/m³. The correlation coefficients between PM_2.5 and PM₁₀ at ambient and Kerbsite were 0.83 and 0.85 respectively thus indicating that most of the PM_2.5 and PM₁₀ are from similar sources. TSP, PM₁₀ levels exceeded Central Pollution Control Board(CPCB) standard during winter season. PM_2.5 levels also exceeded 24 hourly average USEPA standard during winter season indicating unhealthy air quality.     

山西省大气污染特征及对公众健康的空间影响

利用山西省11个地级市大气环境监测站的PM_2.5、PM₁₀和O₃浓度数据,分析了2015—2020年山西省PM_2.5、PM₁₀和O₃浓度时空变化特征,采用空间计量模型和岭回归方法,分析了空气污染对公众健康的空间影响。结果表明:PM_2.5和PM₁₀年均质量浓度总体下降,两者在2017年最高,2020年最低;O₃年均浓度总体增加。在季节尺度上,PM_2.5和PM₁₀质量浓度在冬季的12月和1月最高,夏季的8月最低;O₃浓度在6月最高。空间上,相较2015年,2020年山西省各地级市PM_2.5污染程度均有改善,其中长治改善效果最好;2020年山西各地级市PM₁₀污染兼有加重和减轻的情形,所有地级市PM_2.5和PM₁₀污染水平均超过国家二级污染浓度限值;2020年山西多数地级市O₃浓度升高。山西公众健康水平具有明显的空间离散特征,PM_2.5和PM₁₀浓度的局部空间自相关特征高度一致,呈现"南高北低"的格局,O₃浓度分布呈"南部高,中北部低"的格局。大气环境质量和经济发展水平均对医疗机构诊疗人数和健康体检人数的变化有正向影响,每万人卫生技术人员数量和公共财政支出比例对公众健康均有负向影响,其中经济发展水平和大气环境质量的影响最显著。山西省PM_2.5治理取得一定成效,但大部分城市PM_2.5和PM₁₀达标率较低,O₃浓度有持续升高的趋势,PM₁₀和O₃污染改善缓慢,深度减排仍面临挑战。PM_2.5和PM₁₀是危害山西公众健康的主要大气污染物,未来需要加强PM_2.5、PM₁₀和O₃的精细化管理及协同治理。     

烟花爆竹禁燃对春节期间东北地区空气质量影响评估

利用2015—2017年春节期间东北地区主要大气污染物(PM_(10)、PM_(2.5)、SO_2、NO_2、CO和O3)质量浓度监测资料及相应气象因子(温度、湿度、风速和气压)观测资料,分析了春节期间烟花爆竹禁燃对东北地区空气质量的影响。结果表明:随着东北地区主要城市禁燃力度的增强,空气质量逐年提升,PM_(2.5)和SO_2浓度逐年大幅度下降。禁燃可明显降低城区PM_(2.5)浓度,而由于春节期间污染源整体减少,城区和城郊监测点PM_(2.5)浓度值差异减小。烟花爆竹对PM_(10)和PM_(2.5)浓度影响高于对气体污染物SO_2、NO_2和CO的影响。此外,气象条件对东北地区春节期间禁燃改善空气质量的效果也有明显影响。因此,结合春节期间的气象条件,在东北地区实施禁燃政策动态调整非常必要。     

环境空气质量新标准对郑州和开封空气质量评价的影响

利用河南省环境空气质量新标准实施一期城市(郑州、开封)2013年的环境空气自动监测数据,分析了实施《环境空气质量标准》(GB 3095—2012)对这2个城市空气质量评价的影响。研究发现,若采用新标准,郑州、开封污染物年均浓度超标情况加重;城市空气质量达标率从51%、71%下降到35%和31%,下降了16和40个百分点;PM2.5的纳入是空气质量达标率下降的最主要原因,大于NO2标准的收严和CO、O3纳入评价指标的影响,其超标率分别为58%(郑州)和63%(开封);且PM2.5取代PM10成为郑州、开封的首要污染物,其作为首要污染物的比例均大于70%。     

Assessment of PM10 Enhancement by Yellow Sand on the Air Quality of Taipei, Taiwan in 2001

The impact of long-range transport of yellow sand from Asian Continent to the Taipei Metropolitan Area (Taipei) not only deteriorates air quality but also poses health risks to all, especially the children and the elderly. As such, it is important to assess the enhancement of PM₁₀ during yellow sand periods. In order to estimate PM₁₀ enhancement, we adopted factor analysis to distinguish the yellow-sand (YS) periods from non-yellow-sand (NYS) periods based on air quality monitoring records. Eight YS events were identified using factor analysis coupling with an independent validation procedure by checking background site values, examining meteorological conditions, and modeling air mass trajectory from January 2001 to May 2001. The duration of each event varied from 11 to 132 h, which was identified from the time when the PM₁₀ level was high, and the CO and NO _x levels were low. Subsequently, we used the artificial neural network (ANN) to simulate local PM₁₀ levels from related parameters including local gas pollutants and meteorological factors during the NYS periods. The PM₁₀ enhancement during the YS periods is then calculated by subtracting the simulated PM₁₀ from the observed PM₁₀ levels. Based on our calculations, the PM₁₀ enhancement in the maximum hour of each event ranged from 51 to 82%. Moreover, in the eight events identified in 2001, it was estimated that a total amount of 7,210 tons of PM₁₀ were transported by yellow sand to Taipei. Thus, in this study, we demonstrate that an integration of factor analysis with ANN model could provide a very useful method in identifying YS periods and in determining PM₁₀ enhancement caused by yellow sand.     

江苏省级区域空气质量数值预报模式效果评估

采用中国科学院大气物理研究所开发的嵌套网格空气质量模式系统(NAQPMS),搭建江苏省级区域空气质量数值预报模式系统,并测试了该系统对2013年夏季江苏省PM2.5质量浓度未来24 h预报以及7 d潜势预测的效果。结果表明,该系统成功应用于江苏省的空气质量预报;所有地市的24 h预报效果均在合理范围内(平均分数偏差小于±60%且平均分数误差小于75%);7 d潜势预测效果比24 h预报效果略差,整体能准确把握PM2.5质量浓度的变化趋势。     

辛集市"十三五"期间空气质量特征分析

重点对河北省辛集市"十三五"期间整体空气质量变化情况以及影响辛集市优良天数的2个重要参数O₃和PM_2.5的污染规律进行了分析。结果表明,辛集市"十三五"期间空气质量改善明显,优良天数整体增加,污染天数整体减少。O₃浓度及其作为首要污染物出现的天数整体呈现上升趋势,对综合指数的贡献率逐年增加;O₃污染高发期主要集中在4—9月,高值区域分布差距较大,但市区污染持续突出。PM_2.5浓度逐年下降,以PM_2.5为首要污染物的天数逐年减少;PM_2.5浓度季节变化特征整体呈现"秋冬高、春夏低"的分布特点,空间分布呈"南北高、中间低"的污染特征。     

Distribution of PM2.5 and PM10-2.5 in PM10 Fraction in Ambient Air Due to Vehicular Pollution in Kolkata Megacity

This research paper aims at establishing baseline PM₁₀ and PM_2.5 concentration levels, which could be effectively used to develop and upgrade the standards in air pollution in developing countries. The relative contribution of fine fractions (PM_2.5) and coarser fractions (PM_10-2.5) to PM₁₀ fractions were investigates in a megacity which is overcrowded and congested due to lack of road network and deteriorated air quality because of vehicular pollution. The present study was carried out during the winter of 2002. The average 24h PM₁₀ concentration was 304 μg/m³, which is 3 times more than the Indian National Ambient Air Quality Standards (NAAQS) and higher PM₁₀ concentration was due to fine fraction (PM_2.5) released by vehicular exhaust. The 24h average PM_2.5 concentration was found 179 μg/m³, which is exceeded USEPA and EU standards of 65 and 50 μg/m³ respectively for the winter. India does not have any PM_2.5 standards. The 24 h average PM_10-2.5 concentrations were found 126 μg/m³. The PM_2.5 constituted more than 59% of PM₁₀ and whereas PM₁₀-PM_2.5 fractions constituted 41% of PM₁₀. The correlation between PM₁₀ and PM_2.5 was found higher as PM_2.5 comprised major proportion of PM₁₀ fractions contributed by vehicular emissions.     

Study of urban atmospheric pollution in Navarre (Northern Spain)

An ambient air quality study was undertaken in two cities (Pamplona and Alsasua) of the Province of Navarre in northern Spain from July 2001 to June 2004. The data were obtained from two urban monitoring sites. At both monitoring sites, ambient levels of ozone, NO_x, and SO₂ were measured. Simultaneously with levels of PM₁₀ measured at Alsasua (using a laser particle counter), PM₁₀ levels were also determined at Pamplona (using a beta attenuation monitor). Mean annual PM₁₀ concentrations in Pamplona and Alsasua reached 30 and 28 μg m⁻³, respectively. These concentrations are typical for urban background sites in Northern Spain. By using meteorological information and back trajectories, it was found that the number of exceedances of the daily PM₁₀ limit as well as the PM₁₀ temporal variation was highly influenced by air masses from North Africa. Although North African transport was observed on only 9% of the days, it contributed the highest observed PM₁₀ levels. Transport from the Atlantic Ocean was observed on 68% of the days; transport from Europe on 13%; low transport and local influences on 7%; and transport from the Mediterranean region on 3% of the days. The mean O₃ concentrations were 45 and 55 μg m⁻³ in Pamplona and Alsasua, respectively, which were above the values reported for the main Spanish cities. The mean NO and NO₂ levels were very similar in both sites (12 and 26 μg m⁻³, respectively). Mean SO₂ levels were 8 μg m⁻³ in Pamplona and 5 μg m⁻³ in Alsasua. Hourly levels of PM₁₀, NO and NO₂ showed similar variations with the typically two coincident maximums during traffic rush hours demonstrating a major anthropogenic origin of PM₁₀, in spite of the sporadic dust outbreaks.     

PM2.5 and PM10 Concentrations from the Qalabotjha Low-Smoke Fuels Macro-Scale Experiment in South Africa

This article presents results from the particulate monitoringcampaign conducted at Qalabotjha in South Africa during the winter of 1997. Combustion of D-grade domestic coal and low-smoke fuels were compared in a residential neighborhood to evaluate the extent of air quality improvement by switchinghousehold cooking and heating fuels.Comparisons are drawn between the gravimetric results from the two types of filter substrates (Teflon-membrane and quartz-fiber) as well as between the integrated and continuous samplers. It is demonstrated that the quartz-fiber filters reported 5 to 10% greater particulate mass than the Teflon-membrane filters, mainly due to the adsorption of organic gases onto the quartz-fiber filters. Due to heating of sampling stream to 50 °C in the TEOM continuous sampler and the high volatile content of the samples, approximately 15% of the particulate mass was lost during sampling.The USEPA 24-hr PM_2.5 and PM₁₀ National Ambient Air Quality Standards (NAAQS) of 65 g m^-3 and 150 g m^-3, respectively, were exceeded on several occasions during the 30-day field campaign. Average PMconcentrations are highest when D-grade domestic coal was used, and lowest between day 11 and day 20 of the experiment when a majority of the low-smoke fuels were phased in. Source impacts from residential coal combustion are also found to be influenced by changes in meteorology, especially wind velocity.PM_2.5 and PM₁₀ mass, elements, water-soluble cations (sodium, potassium, and ammonium), anions (chloride, nitrate, and sulfate), as well as organic and elemental carbonwere measured on 15 selected days during the field campaign. PM_2.5 constituted more than 85% of PM₁₀ at three Qalabotjha residential sites, and more than 70% of PM₁₀ at the gradient site in the adjacent community of Villiers. Carbonaceous aerosol is by far the most abundant component, accounting for more than half of PM mass at the three Qalabotjha sites, and for more than a third of PM mass at the gradient site. Secondary aerosols such as sulfate, nitrate,and ammonium are also significant, constituting 8 to 12% of PM mass at the three Qalabotjha sites and 15 to 20% at the Villiers gradient site.     

新冠肺炎疫情期间冀南地区空气质量时空变化特征

为研究新冠肺炎疫情期间冀南地区空气质量变化规律,明确防疫管控措施对空气质量的具体影响及大气污染物排放特征,笔者综合分析了研究区域的常规监测数据和部分大气超级站的PM_2.5组分数据。结果表明:疫情防控重点区域石家庄市、邢台市空气质量整体好转,细颗粒物和一次排放气态污染物浓度下降明显,PM_2.5来源中燃煤、生物质燃烧源占比上升,机动车尾气源占比下降,体现出交通管制、企业限产和道路工地扬尘管理等环保措施的有效性。疫情防控高风险区域石家庄市藁城区出现了明显的NO₂浓度降低、PM_2.5污染好转现象,而O₃浓度显著升高成为新的特征污染物。结果显示,藁城区综合防疫管控举措对本地一次排放污染物起到了明显抑制作用。疫情防控核心区域藁城区增村镇因实行最严格的封村、限行、停产等措施,人为污染源排放"触底",6项监测指标中除O₃浓度同比、环比均大幅度升高外,其他污染物浓度全时段降低,SO₂和CO昼夜差距缩小,环境质量明显优于周边乡镇。分析认为大规模持续化学消杀可能对O₃浓度升高有影响,此问题需要进一步探讨。     

环境空气质量新旧评价体系及评价结果差异

在分析环境空气质量评价体系的变化的基础上,通过分析实际监测数据说明评价体系变化对评价结果的影响。结果表明:由于NO2和PM10的年平均二级浓度限值收严,用新标准评价全国325个地级以上城市2012年的达标情况,将有164个城市由达标变为不达标;参照《环境空气质量评价技术规范(试行)》评价2012年度地级以上城市的达标情况,达标率由91.4%降为41.8%;不同时段O3的1 h平均浓度值对计算其8 h滑动平均浓度值的贡献不同,可能引起8 h滑动平均浓度值计算结果的差异;由于在计算实时空气质量指数时PM10和PM2.5使用24 h滑动平均浓度值,当空气质量突变时会出现评价结果与实际污染状况不符的情况。     

空气质量AQI实时发布的改进探讨

针对《环境空气质量指数(AQI)技术规定(试行)》(HJ 633-2012)中对空气质量AQI实时发布存在的欠缺,从增加颗粒物1 h浓度的AQI分级浓度限值及颗粒物24 h滑动平均值计算方法改进着手,解决PM2.5和PM10的24 h滑动平均值实时延迟、1 h平均值代替24 h滑动平均值偏高等问题。