南京市PM2.5物理化学特性及来源解析

在夏、冬两季,分别在南京市4个站点进行为期7天的气溶胶PM2.5采样,同步采集并分离主要排放源的PM2.5样品,用X射线荧光光谱仪(XRF)分析得到气样及源样中PM2.5的化学成分,对南京市PM2.5的物理化学特性、富集因子进行了分析,并应用化学质量平衡法(CMB)计算各类源对气溶胶PM2.5的贡献。结果表明,南京市PM2.5的夏、冬平均值分别为69.1、139.5μg.m-3,PM2.5/PM10的全年平均值为63.9%;富集成分中,S、As、Zn、Pb等主要来源于人为污染源,Na则主要来源于海洋。来源解析的结果表明,各类污染源对南京市气溶胶PM2.5的贡献率分别为:扬尘37.28%、煤烟尘30.34%、硫酸盐9.87%、建筑尘7.95%、汽车尘2.98%、冶炼尘2.57%、其他源9.01%。作者还对扬尘中的PM2.5进行了来源解析。     

南京市主城区大气颗粒物来源探讨

在2005-05-03——05-27期间,用Anderson九级采样器在南京市两个采样点采集大气气溶胶样品,同时进行了部分排放源的采集。用X射线—荧光光谱仪(XRF)分析得到气样及源样中PM10的化学成分,分析了南京市大气气溶胶的元素质量谱分布,进行了PM10的富集因子分析,并应用化学质量平衡法(CMB)计算各类源对气溶胶PM10的贡献。结果表明,各类污染源对南京市气溶胶PM10的贡献率分别为:建筑尘(35.45%)、煤烟尘(22.13%)、土壤尘(20.27%)、硫酸盐(5.43%)、汽车尘(4.61%)、海盐(1.91%)、冶炼尘(1.69%)、其它源(8.51%)。文中还结合了南京市TSP和PM2.5的来源解析结果,分析了南京市不同粒径气溶胶颗粒物的污染特征。     

上海东滩大气颗粒物长期变化及影响源区分析北大核心CSCD

根据2008—2015年上海崇明东滩大气成分观测站(以下简称东滩站)大气颗粒物(PM)观测数据,分析其浓度水平、变化趋势、影响气团和潜在源区。结果表明,2008—2015年东滩站PM质量浓度的长期变化趋势不显著,但细粒子(PM_(2.5))比例不断升高。PM_(2.5)/PM_(10)从0.84上升至0.92,表明二次气溶胶占比趋于增加。对8年大样本数据进行后向轨迹聚类,发现东滩站主要受大陆型、海洋型、大陆/海洋混合型气团影响,三者所占比率分别为32.0%、38.8%、29.3%。海洋型气团中PM_(2.5)本底质量浓度为11~15μg·m^(-3),而大陆型气团中PM_(2.5)本底质量浓度的季节差异显著,在29~56μg·m^(-3)波动,对东滩站具有明显的输入效应。东滩站PM_(2.5)的潜在源区随季节变化,秋季和冬季主要受华北、黄淮、苏皖影响,春季收缩至苏皖和浙江北部,夏季则转换至长三角南部的浙江及浙闵沿海。总体而言,上海及周边的苏锡常、杭嘉湖对东滩PM_(2.5)浓度贡献最显著,来自渤海、黄海近海污染回流的贡献也不可忽视。     

不同人为源排放清单对大气污染物浓度数值模拟的影响:以浙江省为例

应用大气化学模式WRF-Chem(Weather Research and Forecast-Chemistry),分别选用亚洲排放源清单INTEX-B(Intercontinental Chemical Transport Experiment-Phase B)、REASv2.1(Regional Emission inventory in Asia version 2.1)以及全球排放源清单HTAP_v2(Hemispheric Transport of Air Pollution version 2),对浙江省2013年12月进行模拟,分别记为IN、RE和HT试验,研究人为源排放清单对大气污染物浓度数值模拟的影响。结果表明,3组试验合理的反映出PM2.5(空气动力学当量直径小于等于2.5μm的颗粒物,即细颗粒物)、PM10(空气动力学当量直径小于等于10μm的颗粒物,即可吸入颗粒物)和NO_2近地面浓度的时空分布特征,相关系数为0.5~0.8,85%以上的模拟值落在观测值的0.5~2倍范围内,但对SO_2近地面浓度模拟较差。IN、RE、HT试验对PM2.5和PM10的模拟偏差均成递减趋势,约为30%、16%和6%,HT试验的模拟值更加接近观测。INTEX-B清单中PM2.5的一次排放与二次气溶胶前提物SO_2均高于REAS与HTAP清单,因此会导致更多的硫酸盐生成,从而进一步增加PM2.5浓度。HTAP_v2清单中较低的NH3排放会抑制硝酸盐的生成,从而有助于降低PM2.5浓度。3个清单的基准年与模拟年的差异对SO_2浓度模拟的准确性影响更大,INTEX-B清单中SO_2排放量明显高于REASv2.1与HTAP_v2清单,尤其在浙北和沿海工业发达地区,导致IN试验模拟的SO_2在这些地区存在明显高估。3组试验模拟的NO_2浓度偏差最小且更为接近(-8%~4%),主要原因是3个清单在浙江省的NOx排放十分一致。从3组试验结果之间的差异程度来看,浙江省范围内PM2.5、PM10、SO_2和NO_2逐日浓度模拟值之间的平均差异程度分别约为14%、15%、51%和16%,最大差异程度分别为69%、78%、137%和132%。月均浓度与逐日浓度的平均差异程度基本一致,但最大差异程度明显更低。总体来看3组试验模拟的PM2.5、PM10与NO_2的差异程度明显低于SO_2。     

基于WRFDA-Chem三维变分同化系统的中国PM2.5和PM10资料同化试验北大核心CSCD

将大气化学三维变分同化系统WRFDA_Chem引入睿图—化学环境气象数值预报系统(RMAPS-Chem),利用2016年11月地面观测细颗粒物(PM2.5)和颗粒物(PM10)逐小时质量浓度资料进行同化预报试验:6 h循环同化结果表明,WRFDA-Chem对初始场PM2.5和PM10的模拟偏差和相关性有显著改善,均方根误差(RMSE)减小40%左右,相关性提高0.27~0.37;同化对预报改进能持续24 h以上,PM2.5(PM10)浓度预报RMSE降低25%(10%),相关性提升14%(25%);加密同化频次(逐小时循环同化)进一步改进预报效果。未来需要进一步开展同化数据质量控制方案研究以优化业务预报效果,并在深入理解模式不确定性和偏差来源的情况下,进一步开展模式和同化系统的协同发展。     

降水和风对泰安地区PM2.5浓度的影响及区域传输研究

利用泰安市2018—2019年降水、风和PM2.5逐小时观测数据,分析了降水和风对PM2.5浓度的影响,并对PM2.5进行了源解析.结果表明:降水对PM2.5有一定清除作用,降雨日PM2.5平均质量浓度较非降雨日平均降低约7.2％,秋冬季节最为显著.降水对PM2.5的清除率与降水强度、降水前PM2.5初始浓度及降水时间...     

大气可吸入颗粒物解析技术研究进展

大气颗粒物源解析技术在环境管理中发挥着越来越重要的作用。本文从受体模型以及源解析技术发展的趋势两方面介绍了大气颗粒物源解析技术的研究进展。     

大气可吸入颗粒物解析技术研究进展

大气颗粒物源解析技术在环境管理中发挥着越来越重要的作用.本文从受体模型以及源解析技术发展的趋势两方面介绍了大气颗粒物源解析技术的研究进展.     

秦皇岛2019年冬季重污染过程PM2.5来源数值模拟

秦皇岛地处河北省东北部,是环渤海重要的港口城市,在近几年京津冀地区减排效果较好的情况下,于2019年1月出现了多次持续细颗粒物（PM2.5）污染过程。因此本文利用耦合了数值源解析模块ISAM（Integrated Source Apportionment Method）的区域空气质量模式RAMS-CMAQ（Regional Atmospheric Modeling System–Community Multiscale Air Quality）,对2019年1月秦皇岛地区PM2.5进行模拟,并将PM2.5质量浓度高于（低于）75 μg m－3的时段划分为污染（清洁）时段,分别探讨了两个时段本地排放源对秦皇岛市PM2.5质量浓度的贡献情况,并且进一步探讨了秦皇岛各区县及外地排放源对秦皇岛市4个国控环境监测站点（第一关站、北戴河站、市监测站、建设大厦站）PM2.5质量浓度的区域传输特征。结果表明,秦皇岛地区PM2.5质量浓度整体呈“南高北低”式分布。清洁时段,PM2.5质量浓度受本地贡献较大,青龙县、卢龙县大部分地区贡献为40%～50%,海港区、抚宁区、北戴河区、第一关区及昌黎县大部分地区贡献在60%以上;4个国控环境监测站点受跨界输送贡献占34.7%～41.6%。污染时段,秦皇岛市本地贡献相对于清洁时段整体下降10%左右,当地大气污染受到跨界区域传输影响增加;而在4个国控站中,北戴河站、第一关站受到跨界输送贡献分别下降1.0%和2.3%;市监测站、建设大厦站受到跨界输送贡献分别上升2.9%和2.0%。     

青岛市大气颗粒物污染特征及潜在来源分析

利用山东青岛2017年1月至2020年12月的大气颗粒物质量浓度、常规气象观测资料以及全球数据同化系统(Global Data Assimilation System,GDAS)数据,研究了该地区大气颗粒物的污染特征,基于拉格朗日混合单粒子轨道模型(Hybrid Single Particle Lagrangian Integrated Trajectory Model,HYSPLIT)和轨迹统计(TrajStat)软件对青岛市大气颗粒物的传输路径进行了研究,运用潜在源贡献因子分析法(Potential Source Contribution Function,PSCF)和浓度权重轨迹分析法(Concentration Weighted Trajecto‐ry,CWT)对其潜在源区和浓度贡献进行了分析。研究结果表明：(1)青岛市PM_2.5质量浓度年均值为35.3μg·m^-3,冬季最高,春、秋次之,夏季最低。PM_2.5质量浓度年超标率分别为8.22%,7.40%,11.51%和7.38%,重污染日仅出现在冬季,夏季从未出现...     

Source identification of PM2.5 particles measured in Gwangju, Korea

The UNMIX and Chemical Mass Balance (CMB) receptor models were used to investigate sources of PM_2.5 aerosols measured between March 2001 and February 2002 in Gwangju, Korea. Measurements of PM_2.5 particles were used for the analysis of carbonaceous species (organic (OC) and elemental carbon (EC)) using the thermal manganese dioxide oxidation (TMO) method, the investigation of seven ionic species using ion chromatography (IC), and the analysis of twenty-four metal species using Inductively Coupled Plasma (ICP)-Atomic Emission Spectrometry (AES)/ICP-Mass Spectrometry (MS). According to annual average PM_2.5 source apportionment results obtained from CMB calculations, diesel vehicle exhaust was the major contributor, accounting for 33.4% of the measured PM_2.5 mass (21.5 μg m^− 3), followed by secondary sulfate (14.6%), meat cooking (11.7%), secondary organic carbon (8.9%), secondary nitrate (7.6%), urban dust (5.5%), Asian dust (4.4%), biomass burning (2.8%), sea salt (2.7%), residual oil combustion (2.6%), gasoline vehicle exhaust (1.9%), automobile lead (0.5%), and components of unknown sources (3.4%). Seven PM_2.5 sources including diesel vehicles (29.6%), secondary sulfate (17.4%), biomass burning (14.7%), secondary nitrate (12.6%), gasoline vehicles (12.4%), secondary organic carbon (5.8%) and Asian dust (1.9%) were identified from the UNMIX analysis. The annual average source apportionment results from the two models are compared and the reasons for differences are qualitatively discussed for better understanding of PM_2.5 sources.Additionally, the impact of air mass pathways on the PM_2.5 mass was evaluated using air mass trajectories calculated with the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) backward trajectory model. Source contributions to PM_2.5 collected during the four air mass patterns and two event periods were calculated with the CMB model and analyzed. Results of source apportionment revealed that the contribution of diesel traffic exhaust (47.0%) in stagnant conditions (S) was much higher than the average contribution of diesel vehicle exhaust (33.4%) during the sampling period. During Asian dust (AD) periods when the air mass passed over the Korean peninsula, Asian dust and secondary organic carbon accounted for 25.2 and 23.0% of the PM_2.5 mass, respectively, whereas Asian dust contributed only 10.8% to the PM_2.5 mass during the AD event when the air mass passed over the Yellow Sea. The contribution of biomass burning to the PM_2.5 mass during the biomass burning (BB) event equaled 63.8%.     

Origin and source regions of PM10 in the Eastern Mediterranean atmosphere

A set of daily PM₁₀ (n = 281) samples collected from April 2001 to April 2002 at a rural site (Erdemli), located on the coast of the Eastern Mediterranean, were analyzed applying Mass Closure (MC), absolute principal factor analysis (APFA) and Positive Matrix Factorization (PMF) to determine source contributions. The results from the three techniques were compared to identify the similarities and differences in the sources and source contributions. Source apportionment analysis indicated that PM₁₀ were mainly originated from natural sources (sea salt + crustal ≈ 60%) whilst secondary aerosols and residual oil burning accounted for approximately 20% and 10% of the total PM₁₀ mass, respectively. Calculations for sulfate showed that on average 8% and 12% of its total concentration were originated from sea salt and biogenic emissions, respectively. However, the contribution by biogenic emissions may reach up to a maximum of ~ 40% in the summer. Potential Source Contribution Function (PSCF) analysis for identification of source regions showed that the Saharan desert was the main source area for crustal components. For secondary aerosol components the analysis revealed one source region, (i.e. the south-Eastern Black Sea), whereas for residual oil, Western Europe and the western Balkans areas were found to be the main source regions.     

不同降水强度对PM2.5的清除作用及影响因素

云和降水过程是大气污染物的重要清除途径,但由于降水过程和大气污染颗粒物本身的复杂性,目前降水过程对大气污染物的清除机制及影响因素有待深入研究。该文利用2014年3月—2016年7月在北京地区连续观测的PM_2.5和降水数据,研究了不同降水强度对PM_2.5的清除率,以及雨滴谱、风速和降水持续时间对PM_2.5清除率的影响。研究表明:降水强度越大,对PM_2.5清除效率越高。小雨、中雨和大雨对PM_2.5清除率平均值分别为5.1%,38.5%和50.6%。小雨不但对PM_2.5的清除率最低,而且对PM_2.5的清除效果也存在很大差异,约50%的小雨个例中PM_2.5质量浓度出现减小情况,而另外50%的小雨个例中,PM_2.5质量浓度出现增加情况。在持续时间长或地面风速增大的情况下,小雨也表现出较高的清除率。在中雨和大雨情况下,PM_2.5质量浓度均出现明显减小情况。但降水持续时间和风速对中雨和大雨的清除率影响较小,这是由于中雨和大雨一般在较短时间内即可清除大部分PM_2.5,因此,对降水的持续时间和风速大小不敏感。     

Influence of traffic emissions on the composition of atmospheric particles of different sizes—Part 2: SEM–EDS characterization

In urban areas traffic is the major contributor to atmospheric particulate matter and exposure to these particles currently represents a serious risk to human health. The attention has been recently focused more on the particles of smaller sizes (PM_2.5) which penetrate deeper in respiratory system causing severe health effects. Therefore, more information on PM_2.5 should be provided, namely concerning morphological and chemical characterization. Aiming further evaluation of the impact of traffic emissions on public health, this work evaluated the influence of traffic on the chemical and morphological characteristics of PM₁₀ and PM_2.5, collected at one site influenced by traffic emissions and at one reference site. Chemical and morphological characteristics of 1,000 individual particles were determined by scanning electron microscopy combined with energy dispersive spectrometer (SEM–EDS). Cluster analysis (CA) was used to identify different types of particles that occurred in PM, aiming the identification of the respective emission sources. Traffic PM_2.5 were dominated by particles composed of Fe oxides and alloys (67%) which were related to traffic emissions (this percentage was 3.7 times higher than at the background site); in PM_2.5–10 the abundance of Fe oxides and alloys were 20% and 0% for the traffic and background sites, respectively. Background PM_2.5 were mainly constituted by aluminum silicates (63%) related to natural sources (this percentage was 2.5 times higher than at the traffic site); the abundances of aluminum silicates in PM_2.5–10 were 74% and 73% for traffic and background sites, respectively. It was concluded that traffic emissions were mainly present in PM_2.5 (the percentage of particles associated to these emissions was 3.4 times higher than in PM_2.5–10), while coarse particles were dominated by material of natural origin (the percentage of particles associated was 1.2 and 3.0 times higher than in PM_2.5 for traffic and background sites, respectively). Previous results obtained by proton induced X-ray emission (PIXE) were consistent with SEM–EDS analysis that showed to be very useful to complement elemental analysis of different PM_2.5 and PM_2.5–10.     

Influence of traffic emissions on the composition of atmospheric particles of different sizes – Part 1: concentrations and elemental characterization

Epidemiological studies initially considered the impact of total solid particles on human health, but according to the acquired knowledge about the worse effect of smaller particles, those studies turned to consider the impact of PM₁₀. However, for the last decade PM_2.5 began to be more important, once as they are smaller they can penetrate deeper in the lungs, being possible their trapping in alveoli and worse effects on human health. Therefore, more information on PM_2.5 should be provided namely concerning the levels and elemental composition. Considering the relevance of traffic on the emission of particles of small sizes, this work included the detailed characterization of PM₁₀ and PM_2.5, sampled at two sites directly influenced by traffic, as well as at two reference sites, aiming a further evaluation of the influence of PM₁₀ and PM_2.5 on public health. The specific objectives were to study the influence of traffic emission on PM₁₀ and PM_2.5 characteristics, considering concentration, size distribution and elemental composition. PM₁₀ and PM_2.5 samples were collected using low-volume samplers; the element analyses were performed by particle induced X-ray emission (PIXE). At the sites influenced by traffic emissions PM₁₀ and PM_2.5 concentrations were 7–9 and 6–7 times higher than at the background sites. The presence of 17 elements (Mg, Al, Si, P, S, Cl, K, Ca, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn and Pb) was determined in both PM fractions; particle metal contents were 3–44 and 3–27 times higher for PM₁₀ and PM_2.5, respectively, than at the backgrounds sites. The elements originated mostly from anthropogenic activities (S, K, V, Mn, Ni, Zn and Pb) were predominantly present in PM_2.5, while the elements mostly originated from crust (Mg, Al, Si and Ca) predominantly occurred in PM_2.5–10. The results also showed that in coastal areas sea salt spray is an important source of particles, influencing PM concentration and distributions (PM₁₀ increased by 46%, PM_2.5/PM₁₀ decreased by 26%), as well as PM compositions (Cl in PM₁₀ was 11 times higher).     

Compositions and source apportionments of atmospheric aerosol during Asian dust storm and local pollution in central Taiwan

A study has been carried out on water soluble ions, trace elements, as well as PM_2.5 and PM_2.5–10 elemental and organic carbon samples collected daily from Central Taiwan over a one year period in 2005. A source apportionment study was performed, employing a Gaussian trajectory transfer coefficient model (GTx) to the results from 141 sets of PM_2.5 and PM_2.5–10 samples. Two different types of PM₁₀ episodes, local pollution (LOP) and Asian dust storm (ADS) were observed in this study. The results revealed that relative high concentrations of secondary aerosols (NO₃⁻, SO₄²⁻ and NH₄⁺) and the elements Cu, Zn, Cd, Pb and As were observed in PM_2.5 during LOP periods. However, sea salt species (Na⁺ and Cl⁻) and crustal elements (e.g., Al, Fe, Mg, K, Ca and Ti) of PM_2.5–10 showed a sharp increase during ADS periods. Anthropogenic source metals, Cu, Zn, Cd, Pb and As, as well as coarse nitrate also increased with ADS episodes. Moreover, reconstruction of aerosol compositions revealed that soil of PM_2.5–10 elevated approximately 12–14% in ADS periods than LOP and Clear periods. A significantly high ratio of non-sea salt sulfate to elemental carbon (NSS-SO₄²⁻/EC) of PM_2.5–10 during ADS periods was associated with higher concentrations of non-sea-salt sulfates from the industrial regions of China. Source apportionment analysis showed that 39% of PM₁₀, 25% of PM_2.5, 50% of PM_2.5–10, 42% of sulfate and 30% of nitrate were attributable to the long range transport during ADS periods, respectively.     

Source identification of PM2.5 in an arid Northwest U.S. City by positive matrix factorization

Spokane, WA is prone to frequent particulate pollution episodes due to dust storms, biomass burning, and periods of stagnant meteorological conditions. Spokane is the location of a long-term study examining the association between health effects and chemical or physical constituents of particulate pollution. Positive matrix factorization (PMF) was used to deduce the sources of PM_2.5 (particulate matter ≤2.5 μm in aerodynamic diameter) at a residential site in Spokane from 1995 through 1997. A total of 16 elements in 945 daily PM_2.5 samples were measured. The PMF results indicated that seven sources independently contribute to the observed PM_2.5 mass: vegetative burning (44%), sulfate aerosol (19%), motor vehicle (11%), nitrate aerosol (9%), airborne soil (9%), chlorine-rich source (6%) and metal processing (3%). Conditional probability functions were computed using surface wind data and the PMF deduced mass contributions from each source and were used to identify local point sources. Concurrently measured carbon monoxide and nitrogen oxides were correlated with the PM_2.5 from both motor vehicles and vegetative burning.     

Terra和Aqua卫星MODIS 3 km AOD与北京PM_(2.5)对比分析

本文利用2014年全年北京市12个空气质量监测站的逐小时PM_(2.5)地面观测资料,以及Terra卫星和Aqua卫星的MODIS 3 km气溶胶光学厚度(AOD)产品,分析了地面PM_(2.5)和两颗卫星AOD的时空分布特征,并在时空匹配的基础上,建立了AOD与PM_(2.5)浓度之间的回归模型。结果表明:PM_(2.5)浓度在城区高、郊区低,最低值位于定陵站,城区站和郊区站的逐时PM_(2.5)浓度的日变化分别呈"双峰型"和"单峰型";两颗卫星AOD数值也均是城区高、郊区低,沿山区的边界有明显的AOD梯度,且城区上午Terra卫星的AOD高于下午Aqua卫星的AOD,而郊区上、下午的AOD基本相同;Aqua卫星AOD与PM_(2.5)的确定系数(R2)较Terra卫星AOD与PM_(2.5)的确定系数平均高0.11,且城区站点两颗卫星AOD与PM_(2.5)相关性均较郊区站点AOD与PM_(2.5)相关性偏高;综合来看,Aqua卫星的AOD与城区的PM_(2.5)相关系数最高,即Aqua卫星的AOD更适于监测和反演城区地面的PM_(2.5)。     

Reconstructed light extinction coefficients using chemical compositions of PM<Subscript>2.5</Subscript> in winter in Urban Guangzhou,China

The objective of this study was to reconstruct light extinction coefficients (b ext ) according to chemical composition components of particulate matter up to 2.5 μm in size (PM 2.5 ). PM 2.5 samples were collected at the monitoring station of the South China of Institute of Environmental Science (SCIES, Guangzhou, China) during January 2010, and the online absorbing and scattering coefficients were obtained using an aethalometer and a nephelometer. The measured values of light absorption coefficient by particle (b ap ) and light scattering coefficient by particle (b sp ) significantly correlated (R 2 > 0.95) with values of b ap and b sp that were reconstructed using the Interagency Monitoring of Protected Visual Environments (IMPROVE) formula when RH was <70%. The measured b ext had a good correlation (R 2 > 0.83) with the calculated b ext under ambient RH conditions. The result of source apportionment of b ext showed that ammonium sulfate [(NH 4 ) 2 SO 4 ] was the largest contributor (35.0%) to b ext , followed by ammonium nitrate (NH 4 NO 3 , 22.9%), organic matter (16.1%), elemental carbon (11.8%), sea salt (4.7%), and nitrogen dioxide (NO 2 , 9.6%). To improve visibility in Guangzhou, the effective control of secondary particles like sulfates, nitrates, and ammonia should be given more attention in urban environmental management.     

Geochemistry of regional background aerosols in the Western Mediterranean

The chemical composition of regional background aerosols, and the time variability and sources in the Western Mediterranean are interpreted in this study. To this end 2002–2007 PM speciation data from an European Supersite for Atmospheric Aerosol Research (Montseny, MSY, located 40 km NNE of Barcelona in NE Spain) were evaluated, with these data being considered representative of regional background aerosols in the Western Mediterranean Basin. The mean PM₁₀, PM_2.5 and PM₁ levels at MSY during 2002–2007 were 16, 14 and 11 µg/m³, respectively. After compiling data on regional background PM speciation from Europe to compare our data, it is evidenced that the Western Mediterranean aerosol is characterised by higher concentrations of crustal material but lower levels of OM + EC and ammonium nitrate than at central European sites. Relatively high PM_2.5 concentrations due to the transport of anthropogenic aerosols (mostly carbonaceous and sulphate) from populated coastal areas were recorded, especially during winter anticyclonic episodes and summer midday PM highs (the latter associated with the transport of the breeze and the expansion of the mixing layer). Source apportionment analyses indicated that the major contributors to PM_2.5 and PM₁₀ were secondary sulphate, secondary nitrate and crustal material, whereas the higher load of the anthropogenic component in PM_2.5 reflects the influence of regional (traffic and industrial) emissions. Levels of mineral, sulphate, sea spray and carbonaceous aerosols were higher in summer, whereas nitrate levels and Cl/Na were higher in winter. A considerably high OC/EC ratio (14 in summer, 10 in winter) was detected, which could be due to a combination of high biogenic emissions of secondary organic aerosol, SOA precursors, ozone levels and insolation, and intensive recirculation of aged air masses. Compared with more locally derived crustal geological dusts, African dust intrusions introduce relatively quartz-poor but clay mineral-rich silicate PM, with more kaolinitic clays from central North Africa in summer, and more smectitic clays from NW Africa in spring.