长三角地区城市O_3和PM_(2.5)污染特征及影响因素分析

O_3和PM_(2.5)是影响长三角地区空气质量的主要污染物。利用2016年33个城市大气环境监测站6项污染物的小时浓度及4个省会城市的气象数据进行统计分析,研究了该地区O_3和PM_(2.5)浓度的时空分布特征及其影响因素。结果表明:长三角地区O_3年平均浓度为50～73μg·m~(-3),平均为61μg·m~(-3);除芜湖和宣城外,其余31城市均存在不同程度的超标状况,超标率为0.34%～18.86%,平均为5.68%。O_3在5月和9月达到浓度高值;四季O_3日变化均呈单峰型,峰值出现在15∶00,夏季O_3峰值浓度最高值为157μg·m~(-3)。O_3浓度沿海城市整体高于内陆城市;夏季宿迁—淮安—滁州片区O_3污染较重。O_3与NO_2、CO显著负相关,且与NO_2相关性较强;O_3与气温、日照时数显著正相关,与相对湿度、降水呈负相关。PM_(2.5)年平均浓度在25～62μg·m~(-3)范围内,平均为49μg·m~(-3);各城市均出现PM_(2.5)超标,滁州PM_(2.5)超标率最大,为23.91%。PM_(2.5)在3月和12、1月达到浓度峰值;其日变化呈双峰型,09∶00—10∶00和22∶00—23∶00达到峰值。冬季徐州PM_(2.5)浓度最高,为102μg·m~(-3)。PM_(2.5)与NO_2、CO、SO_2、PM_(10)显著正相关,与气温、风速、降水负相关。     

伊宁市降水化学成分及来源分析

降水是大气中主要和次要污染物的重要收集器,是大气中颗粒物和气态污染物最好的清除剂。本文利用离子色谱分析了2011年伊宁市90个降水样品,结果表明,该区降水pH月均值在5.27—7.1之间,年均值为6.26,电导率变化范围为34.64~63μs·cm~(-1),年均值为45.7μs·cm~(-1)。降水中主要离子浓度排序为Ca~(2+)SO_4~(2-)NH_4~+Mg~(2+)Cl~-NO_3~-Na~+F~-K~+,Ca~(2+)是最主要的阳离子,年均值为421.76μeq·L~(-1),SO_4~(2-)是最主要的阴离子,年均值为149.09μeq·L~(-1),表明伊宁降水中的致酸物质主要是硫酸盐。总离子浓度季节变化特征表现为春夏季高,秋冬季低,总离子浓度明显低于乌鲁木齐和沈阳,与北京类似,远高于杭州、广州,表明伊宁降水污染比北方城市轻,但是陆源贡献远大于南方城市。FA平均值为0.003,表明99.7%的降水酸度被碱性成分中和,NF计算结果表明Ca~(2+)的中和能力最强,其次是NH_4~+。从相关分析和因子分析来看,F~-、SO_4~(2-)、NO_3~-、Cl~-、NH_4~+主要受人为源的控制,Ca~(2+)、K~+主要来源于土壤和浮尘,Na~+、Mg~(2+)主要来源为土壤盐碱化、风化、干旱浮尘等自然源。     

成都典型秸秆燃烧季节PM2.5中化学成分研究

采集2012年春季和秋季成都城区的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)样品,分析得到水溶性离子、有机碳(OC)和元素碳(EC)等化学成分。结果表明,春季和秋季PM2.5的浓度分别为101±64μg m~(-3)和88±30μg m~(-3),是环境空气质量标准(GB3095-2012)日均值的1.3倍和1.2倍。基于K~+、OC/EC(OC浓度/EC浓度)和K~+/EC(K~+浓度/EC浓度)指标判别生物质燃烧事件,结果发现春、秋季生物质燃烧期间PM2.5中OC、EC和K~+、Cl~-等成分明显高于非生物质燃烧期;SO_4~(2-)、NH_4~+、Ca~(2+)、Mg~(2+)、NO_3~-、Na~+等其它水溶性离子浓度在生物质燃烧期均有不同程度升高。春、秋季生物质燃烧期间OC浓度分别是非生物质燃烧期的4.2倍和1.8倍,EC为非生物质燃烧期的2.3倍和2.3倍。K~+和Cl~-浓度在春季生物质燃烧期超过平均值的3倍,在秋季生物质燃烧期超过平均浓度的0.8倍和0.9倍。     

降水对河北沧州大气污染物质量浓度的影响

基于2014—2019年河北沧州逐小时气象与环境监测数据,采用风力+背景浓度订正方法,进一步探讨降水对大气污染物浓度的影响。结果表明:风力+背景浓度订正方法可以较好地消除污染物浓度自身的日变化特征,并剔除了风的影响,从而更准确地获取降水对大气污染物浓度的影响。在风力+背景浓度订正条件下,降雨对PM_(2.5)、PM_(10)、NO_2、O_3有较好的清除作用,而对SO_2、CO的清除作用不明显。不同强度降雨对PM_(2.5)、PM_(10)、NO_2均具有较好的清除作用,且清除率随着雨强的增强而增大;雨强小于8.0 mm·h~(-1)的降雨对O_3有显著清除作用,而大于6.0 mm·h~(-1)的降雨对SO_2有清除作用。伴随着降雨的持续,PM_(2.5)、PM_(10)、NO_2、O_3质量浓度不断下降,降雨清除效率也随之降低,当污染物质量浓度降至一定阈值后降雨清除作用不明显。     

2014-2017年华北平原PM_(2.5)组分消光与能见度对比研究（英文）

华北平原大气污染与低能见度状况一直是人们关切的问题.本文通过分析2014-2017年PM_(2.5)化学成分的浓度和消光效果,研究了华北平原典型城市保定市的大气污染特征.结果表明,PM_(2.5)分的年均浓度显示下降趋势,水溶性无机离子,碳质气溶胶和金属元素分别减少了11μg m~(-3),23μg m_(-3)和1796 ng m_(-3).NH_4~+,NO_3~-和SO_4~(2-)是PM_(2.5)污染的主要污染物,三者之和占总离子浓度的82.9%.基于IMPROVE方程对细颗粒物进行重构,在观测期间PM_(2.5)质量浓度平均为93±69μg m~(-3),春季,夏季,秋季和冬季的消光系数分别为373.8±233.6 M m~(-1)±,405.3±300.1 M m~(-1),554.3±378.2 M m~(-1)和1005.2±750.3 M m~(-1).硫酸铵,硝酸铵和有机物对消光的贡献最大,不同季节下占比达55%～77%.通过PM_(2.5)组分进行重构,利用IMPROVE算法计算得到Rbsca,用能见度测量值转换得到Vbsca,二者具有较高的相关性(r2=0.84);但存在Vbsca的高值被低估,Vbsca的低值被高估的现象;特别是当Rbsca 1123 M m~(-1)(对应能见度约小于2.0 km)时,Vbsca的值被低估了17.6%.高浓度PM_(2.5)和高湿度对IMPROVE算法结果有显著的影响.     

乌鲁木齐降水化学成分及来源分析

降水是大气中主要和次要污染物的重要收集器,是清除大气中颗粒物和气态污染物最好的清除剂。为了了解乌鲁木齐降水特征,本文利用离子色谱分析了2010年降水样品,研究表明,该区降水pH值在5.6~7.77之间,年均值为6.19,电导率变化范围为18.7~172.5μs·cm~(-1),年均值为57.73μs·cm~(-1)。降水中主要离子浓度排序为Ca~(2+)SO_4~(2-)NH_4~+Na~+Mg~(2+)Cl~-NO_3~-K~+F~-,Ca~(2+)是最主要的阳离子,SO2-4是最主要的阴离子,表明乌鲁木齐降水中的致酸物质主要是硫酸盐。总离子浓度季节变化特征表现为秋季高,冬季低。FA平均值为0.001,表明99.9%的降水酸度被碱性成分中和,NF计算结果表明Ca~(2+)具有很强的中和能力。从相关分析、富集因子来看,SO_4~(2-)和NO_3~-主要受人为源的控制,K+主要存在于土壤扬尘或生物质燃烧产生的细颗粒物中;Mg~(2+)主要来自陆源的土壤扬尘等;Cl~-主要来自海相输入,生物质燃烧、人类生活污水排放以及化工厂排放对Cl-也有很大贡献。     

APEC会议期间北京地区大气污染物的分析研究

该文利用北京市通州区环保局、北京市环境保护监测中心自动检测网络站点数据及NCEP(1°×1°)再分析资料,分析了2014年北京APEC会议期间各污染物浓度值的变化,分析表明:管控期内PM_(2.5)、NO_3浓度下降明显。PM_(2.5)、NO_2呈现双峰现象,O_3、SO_2呈现单峰态。同时,北京市人口稠密区夜间PM_(2.5)污染较为严重,仍需控制NO_2的排放量。并探究管控措施实施效果,为今后重大活动空气质量保障提供参考。     

广州冬季大气消光系数的贡献因子研究

2008年1月1～31日和2月6～24日在广州城区每天采集一个PM2.5样品,对样品进行有机碳、元素碳及水溶性离子分析,利用美国IMPROVE能见度方程计算得到广州冬季大气消光系数.结果发现:冬季PM2.5 日均值质量浓度为89.0±53.4/μg·m~(-3),OC(Organics Carban)质量浓度为16.9±11.9μg·m~(-3),EC(Element Carbon)质量浓度为5.9±3.4 μg·m～(-3),水溶性离子总浓度为43.9±23.5μg·m~(-3).冬季大气消光系数均值为342±185 Mm~(-1).广州冬季大气消光系数主要贡献者为(NH_4)_2SO_4、NH_4NO_3、POM(Par-ticular organic matter)、EC和NO_2,对消光系数的贡献率分别为36.3%、14.5%、26.6%、17.4%和5.2%.     

2015年上海崇明岛PM2.5和PM10浓度变化特征 及气象因素影响分析

通过对2015年1—12月上海崇明岛崇南地区颗粒物(PM_(2.5)、PM_(10))浓度的连续监测,研究了PM_(2.5)、PM_(10)在不同季节的动态变化特征及与其他因子(SO_2、NO_2、O_3)的相关性,分析了风向风速和降雨对颗粒物浓度的影响。结果表明:崇明岛PM_(2.5)和PM_(10)浓度的季节变化明显,呈现冬季的春季的秋季的夏季的的特征,冬季PM_(2.5)和PM_(10)小时浓度均值分别为0.058 mg/m~3和0.085 mg/m~3,夏季PM_(2.5)和PM_(10)均值分别为0.034 mg/m~3和0.054 mg/m~3。PM_(2.5)和PM_(10)浓度分别与SO_2浓度和NO_2浓度显著正相关,与O_3显著负相关。全年来看,在西南风向时PM_(2.5)和PM_(10)浓度较高,这主要受该方向上游吴淞工业区、宝钢、石洞口电厂、罗店工业区等工业排放影响;从高浓度颗粒物(PM_(2.5)质量浓度≥0.115 mg/m~3)来向看,北和西北风向时出现高浓度颗粒物的频率最高,这主要是受到我国北方采暖季大气颗粒物输送过程对崇明岛区域的脉冲式污染影响所致;PM_(2.5)、PM_(10)实时浓度与相应的风速呈显著负相关。降雨量大于5 mm或持续3 h及以上的连续降雨对大气颗粒物起到显著的湿清除作用,降雨后PM_(2.5)和PM_(10)质量浓度分别降低了68.0%和66.9%,降雨时和雨后PM_(2.5)浓度为0.025～0.033 mg/m~3,均低于我国环境空气PM_(2.5)的一级浓度限值。     

敦煌莫高窟大气颗粒物中水溶性离子变化及来源解析

为探明莫高窟大气颗粒物污染特征,采集了2014年4-12月第16窟及72窟外环境中大气颗粒物PM2.5和PM10~2.5样品,对比分析了样品中水溶性离子变化及影响因素,通过主成分分析法解析了其主要来源。结果表明:(1)窟区主风向为南风,起沙风频率仅为0.01%,不利于污染物的扩散;(2)窟外PM2.5和PM10~2.5、窟内PM2.5和PM10~2.5中水溶性离子总浓度分别为6.1±4.0μg·m^-3、12.2±9.1μg·m^-3、3.7±0.8μg·m^-3和7.5±1.6μg·m^-3,SO₄^2-、Ca²⁺、NO3^-、Na⁺及Cl^-是主要组成,SO42-、NO3-和Ca2+之和在窟外和窟内PM2.5和PM10~2.5中占总离子比例分别为79.24%,76.81%,80.61%及77.74%,二次离子主要来自固定污染源;(3)PM2.5与PM10~2.5中各离子浓度的比值在窟外、内分别为0.33~0.88、0.25~0.94,9种水溶性离子在不同粒径粒子中富集程度有所差异,3-5月的沙尘、7-9月的降雨、11月农村农作物秸秆燃烧及冬季取暖燃煤等对水溶性离子都有一定影响,窟内PM2.5中游客数量与NH4+和NO^3-有一定的相关性(R2=0.27、0.35)、PM10~2.5中游客数量与NH4+有一定的相关性(R2=0.31);(4)沙尘天气下窟外和窟内的PM2.5与PM10-2.5中Cl-、SO₄^2-、Na⁺、K+和Ca²⁺的浓度增加,窟区微环境主要受区域环境气象因素影响,建议极端沙尘天气关闭洞窟;(5)莫高窟大气环境呈碱性;(6)PM2.5和PM10~2.5主要来源于当地秸秆燃烧、二次污染源、土壤沙尘及干涸的大泉河。     

武汉市冬季灰霾期PM2.5中水溶性无机离子的特征

2018年1月,利用颗粒物采样器采集武汉市大气PM_2.5样品并进行水溶性无机离子（F^-、Cl^-、NO₃^-、SO₄^2-、Na⁺、NH₄⁺、K⁺、Mg²⁺、Ca²⁺）的分析.结果表明,NO₃^-、SO₄^2-、NH₄⁺是PM_2.5中最主要的3种水溶性无机离子,除Mg²⁺与Ca²⁺外,PM_2.5与WSⅡs （水溶性无机离子）之间的相关性显著,且移动源贡献占主导地位.阴阳离子平衡表明武汉市冬季灰霾期PM_2.5呈中性或弱酸性.通过混合单粒子拉格朗日综合轨迹模式模拟并采用分层聚类得出了4种主要的后向气流轨迹及相应的PM_2.5和水溶性离子浓度,结果表明区域传输对此次灰霾期影响较大.     

武汉市一次霾污染过程不同发展阶段下水溶性离子来源解析

本文利用气体组分及大气气溶胶在线监测系统(MARGA ADI 2080)观测武汉市2018年1月9—26日大气气溶胶中的8种水溶性离子(NH⁺₄、NO^-₃、SO^2-₄、Cl^-、K⁺、Ca²⁺、Na⁺和Mg²⁺),结合气象要素数据,使用主成分分析(PCA)、正定矩阵因子分析法(PMF)、HYSPLIT后向轨迹模式、潜在源区贡献(PSCF)和浓度权重轨迹(CWT),对霾污染过程中水溶性离子进行了全面的来源解析,探究了霾不同阶段下来源差异和空间分布特征。结果表明:(1)本次霾污染中的8种水溶性离子和4种污染气体,PCA解析出的源和占比分别为二次源和燃煤源的混合源(41.28%)、工业排放和土壤扬尘混合源(27.73%)和机动车排放源(9.63%),PMF解析出的源和占比分别为燃煤与土壤扬尘混合源(18.57%)、机动车排放源(20.74%)、二次源(18.30%)、光化学污染源(22.24%)和燃煤源(20.15%)。(2)霾在不同阶段下水溶性离子和4种污染气体的来源存在差异,在清洁天和霾消散阶段,光化学的贡献最高,占比分别为31.42%和36.07%;在霾发生阶段燃煤与土壤扬尘源的贡献最高,其贡献为40.94%;在霾发展阶段,最大的控制源为二次源,贡献占比为37.51%。(3)此次武汉市霾污染中PM_2.5浓度和NH⁺₄、NO^-₃和SO^2-₄的潜在源区为皖豫鄂三省和赣湘鄂三省交界处。霾污染中PM_2.5的主要影响范围是武汉市南部和北部省份,NO^-₃、NH⁺₄和SO^2-₄的主要影响区域为武汉市东北方向的城市、湖南省和江西省。     

Size-resolved characteristics of inorganic ionic species in atmospheric aerosols at a regional background site on the South African Highveld

Aerosols consist of organic and inorganic species, and the composition and concentration of these species depends on their sources, chemical transformation and sinks. In this study an assessment of major inorganic ions determined in three aerosol particle size ranges collected for 1 year at Welgegund in South Africa was conducted. SO₄^2? and ammonium (NH₄⁺) dominated the PM₁ size fraction, while SO₄^2? and nitrate (NO₃) dominated the PM_1–2.5 and PM_2.5–10 size fractions. SO₄^2? had the highest contribution in the two smaller size fractions, while NO₃^? had the highest contribution in the PM_2.5–10 size fraction. SO₄^2? and NO₃^? levels were attributed to the impacts of aged air masses passing over major anthropogenic source regions. Comparison of inorganic ion concentrations to levels thereof within a source region influencing Welgegund, indicated higher levels of most species within the source region. However, the comparative ratio of SO₄^2? was significantly lower due to SO₄^2? being formed distant from SO₂ emissions and submicron SO₄^2? having longer atmospheric residencies. The PM at Welgegund was determined to be acidic, mainly due to high concentrations of SO₄^2?. PM₁ and PM_1–2.5 fractions revealed a seasonal pattern, with higher inorganic ion concentrations measured from May to September. Higher concentrations were attributed to decreased wet removal, more pronounced inversion layers trapping pollutants, and increases in household combustion and wild fires during winter. Back trajectory analysis also revealed higher concentrations of inorganic ionic species corresponding to air mass movements over anthropogenic source regions.     

Characteristics of the chemical composition and source apportionment of PM2.5 for a one-year period in Wuhan,China

In this study, 123 PM_2.5 filter samples were collected in Wuhan, Hubei province from December 2014 to November 2015. Water- soluble inorganic ions (WSIIs), elemental carbon (EC), organic carbon (OC) and inorganic elements were measured. Source apportionment and back trajectory was investigated by the positive matrix factorization (PMF) model and the hybrid single particle lagrangian integrated trajectory (HYSPLIT) model, respectively. The annual PM_2.5 concentration was 80.5?±?38.2 μg/m³, with higher PM_2.5 in winter and lower in summer. WSIIs, OC, EC, as well as elements contributed 46.8%, 14.8%, 6.7% and 8% to PM_2.5 mass concentration, respectively. SO₄^2?, NO₃^? and NH₄⁺ were the dominant components, accounting for 40.2% of PM_2.5 concentrations. S, K, Cl, Ba, Fe, Ca and I were the main inorganic elements, and accounted for 65.2% of the elemental composition. The ratio of NO₃^?/SO₄^2? was 0.86?±?0.72, indicating that stationary sources play dominant role on PM_2.5 concentration. The ratio of OC/EC was 2.9?±?1.4, suggesting the existence of secondary organic carbon (SOC). Five sources were identified using PMF model, which included secondary inorganic aerosols (SIA), coal combustion, industry, vehicle emission, fugitive dust. SIA, coal combustion, as well as industry were the dominant contributors to PM_2.5 pollution, accounting for 34.7%, 20.5%, 19.6%, respectively.

     

Size distributions and dry deposition fluxes of water-soluble inorganic nitrogen in atmospheric aerosols in Xiamen Bay,China

Size-segregated aerosol particles were collected using a high volume MOUDI sampler at a coastal urban site in Xiamen Bay, China, from March 2018 to June 2020 to examine the seasonal characteristics of aerosol and water-soluble inorganic ions (WSIIs) and the dry deposition of nitrogen species. During the study period, the annual average concentrations of PM₁, PM_2.5, PM₁₀, and TSP were 14.8?±?5.6, 21.1?±?9.0, 35.4?±?14.2 μg m^?3, and 45.2?±?21.3 μg m^?3, respectively. The seasonal variations of aerosol concentrations were impacted by the monsoon with the lowest value in summer and the higher values in other seasons. For WSIIs, the annual average concentrations were 6.3?±?3.3, 2.1?±?1.2, 3.3?±?1.5, and 1.6?±?0.8 μg m^?3 in PM₁, PM_1-2.5, PM_2.5–10, and PM_>10, respectively. In addition, pronounced seasonal variations of WSIIs in PM₁ and PM_1-2.5 were observed, with the highest concentration in spring-winter and the lowest in summer. The size distribution showed that SO₄^2?, NH₄⁺ and K⁺ were consistently present in the submicron particles while Ca²⁺, Mg²⁺, Na⁺ and Cl^? mainly accumulated in the size range of 2.5–10 μm, reflecting their different dominant sources. In spring, fall and winter, a bimodal distribution of NO₃^? was observed with one peak at 2.5–10 μm and another peak at 0.44–1 μm. In summer, however, the fine mode peak disappeared, likely due to the unfavorable conditions for the formation of NH₄NO₃. For NH₄⁺ and SO₄^2?, their dominant peak at 0.25–0.44 μm in summer and fall shifted to 0.44–1 μm in spring and winter. Although the concentration of NO₃–N was lower than NH₄–N, the dry deposition flux of NO₃–N (35.77?±?24.49 μmol N m^?2 d^?1) was much higher than that of NH₄–N (10.95?±?11.89 μmol N m^?2 d^?1), mainly due to the larger deposition velocities of NO₃–N. The contribution of sea-salt particles to the total particulate inorganic N deposition was estimated to be 23.9—52.8%. Dry deposition of particulate inorganic N accounted for 0.95% of other terrestrial N influxes. The annual total N deposition can create a new productivity of 3.55 mgC m^?2 d^?1, accounting for 1.3–4.7% of the primary productivity in Xiamen Bay. In light of these results, atmospheric N deposition could have a significant influence on biogeochemistry cycle of nutrients with respect to projected increase of anthropogenic emissions from mobile sources in coastal region.

     

武汉市源成分谱特征研究

通过采集武汉市土壤风沙尘、建筑水泥尘、城市扬尘、餐饮源、生物质燃烧源、工业煤烟尘和电厂煤烟尘等7类源样品,并分析其碳组分、水溶性离子组分和无机元素组分,建立PM₁₀和PM_2.5源成分谱.研究表明,地壳元素Si、Ca、Al以及Fe等是土壤风沙尘的主要特征组分,其中Si是含量最高的成分,也是土壤风沙尘的标识组分.无组织建筑水泥尘中Si和Ca元素含量较高,将Ca元素作为无组织建筑水泥尘区别其他源类的重要元素,而有组织建筑水泥尘中OC、SO₄^2-含量比无组织建筑水泥尘高.城市扬尘中Ca的含量相对较高,表明城市扬尘受到建筑水泥尘影响较多.生物质燃烧源成分谱中OC的含量远高于成分谱中其他组分,另外Cl^-和K的平均含量也较高,K一般为生物质源的特征元素.     

Atmospheric ionic species in PM2.5 and PM1 aerosols in the ambient air of eastern central India

This study elucidates the characteristics of ambient PM_2.5 (fine) and PM₁ (submicron) samples collected between July 2009 and June 2010 in Raipur, India, in terms of water soluble ions, i.e. Na⁺, NH ₄ ⁺ , K⁺, Mg²⁺, Ca²⁺, Cl^?, NO ₃ ^? and SO ₄ ^2? . The total number of PM_2.5 and PM₁ samples collected with eight stage cascade impactor was 120. Annual mean concentrations of PM_2.5 and PM₁ were 150.9?±?78.6 μg/m³ and 72.5?±?39.0 μg/m³, respectively. The higher particulate matter (PM) mass concentrations during the winter season are essentially due to the increase of biomass burning and temperature inversion. Out of above 8 ions, the most abundant ions were SO ₄ ^2? , NO ₃ ^? and NH ₄ ⁺ for both PM_2.5 and PM₁ aerosols; their average concentrations were 7.86?±?5.86 μg/m³, 3.12?±?2.63 μg/m³ and 1.94?±?1.28 μg/m³ for PM_2.5, and 5.61?±?3.79 μg/m³, 1.81?±?1.21 μg/m³ and 1.26?±?0.88 μg/m³ for PM₁, respectively. The major secondary species SO ₄ ^2? , NO ₃ ^? and NH ₄ ⁺ accounted for 5.81%, 1.88% and 1.40% of the total mass of PM_2.5 and 11.10%, 2.68%, and 2.48% of the total mass of PM₁, respectively. The source identification was conducted for the ionic species in PM_2.5 and PM₁ aerosols. The results are discussed by the way of correlations and principal component analysis. Spearman correlation indicated that Cl^? and K⁺ in PM_2.5 and PM₁ can be originated from similar type of sources. Principal component analysis reveals that there are two major sources (anthropogenic and natural such as soil derived particles) for PM_2.5 and PM₁ fractions.     

Chemical characterization and multivariate analysis of atmospheric PM<Subscript>2.5</Subscript> particles

The new European Council Directive (PE-CONS 3696/07) frames the inhalable (PM₁₀) and fine particles (PM_2.5) on priority to chemically characterize these fractions in order to understand their possible relation with health effects. Considering this, PM_2.5 was collected during four different seasons to evaluate the relative abundance of bulk elements (Cl, S, Si, Al, Br, Cu, Fe, Ti, Ca, K, Pb, Zn, Ni, Mn, Cr and V) and water soluble ions (F⁻, Cl⁻, NO₂ ⁻, NO₃ ⁻, SO₄ ²⁻, Na⁺, NH₄ ⁺, Ca²⁺ and Mg²⁺) over Menen, a Belgian city near the French border. The air quality over Menen is influenced by industrialized regions on both sides of the border. The most abundant ionic species were NO₃ ⁻, SO₄ ²⁻ and NH₄ ⁺, and they showed distinct seasonal variation. The elevated levels of NO₃ ⁻ during spring and summer were found to be related to the larger availability of the NO_x precursor. The various elemental species analyzed were distinguished into crustal and anthropogenic source categories. The dominating elements were S and Cl in the PM_2.5 particles. The anthropogenic fraction (e.g. Zn, Pb, and Cu) shows a more scattered abundance. Furthermore, the ions and elemental data were also processed using principal component analysis and cluster analysis to identify their sources and chemistry. These approach identifies anthropogenic (traffic and industrial) emissions as a major source for fine particles. The variations in the natural/anthropogenic fractions of PM_2.5 were also found to be a function of meteorological conditions as well as of long-range transport of air masses from the industrialized regions of the continent. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

南京市降水化学特征及其来源研究

为了解南京江北地区降水化学特征,分析了2011年3—6月共25个降水日的109个降水样品中的主要水溶性离子,并利用后向轨迹模式探讨了降水气团来源.结果表明:1)南京地区3—6月降水主要受南、北2种气团影响,北方气团降水的主要离子浓度高于南方气团降水.2)海盐示踪法和相关性分析显示,降水中NO₃^-和SO₄^2-主要来自燃煤、工业排放和汽车尾气;Ca²⁺主要来自地壳源;Cl^-主要来自海洋;海洋源和陆源对Mg²⁺和K⁺都有贡献,Mg²⁺的陆源贡献大于海洋源贡献,K⁺受海洋源的影响程度要低于Mg²⁺.3)南、北气团初期降水的各离子浓度高于总降水的各离子浓度,且初期降水的主要离子的富集系数高于总降水.这说明在降水初始阶段,雨水对南京大气中污染物(气态污染物和颗粒物)的云下冲刷去除作用较强,降水的离子浓度最高,局地源对降水离子的贡献较明显.     

湖北2015年冬季PM2.5重污染过程的气象输送条件及日变化特征分析

PM_2.5污染仍然是湖北省冬季大气污染的首要污染类型,且具有明显区域传输特征,重污染过程的空气污染气象条件有别于华北地区,值得关注。采用WRF/Chem不同排放情景下的模拟结果,并结合观测分析,研究了2015年12月—2016年1月湖北省PM_2.5重污染过程的气象输送条件及日变化特征,从大尺度输送条件和局地边界层动力作用分析了外来污染物水平传输、悬浮聚集和向下传输的过程,并解释了该地区观测到的午后PM_2.5浓度特殊峰值的气象成因。结果表明,湖北重污染爆发以区域传输为主,地面观测PM_2.5极值对应10 m风速可达8—10 m/s,边界层0—1 km为较强偏北风输送,污染传输通量极值位于400 m高度附近,为重要传输通道,低空无明显逆温,重污染过程具有“非静稳”边界层气象特征。重污染形成的大尺度输送条件为,长江中下游及北部地区偏北风异常偏强,南部地区风速减缓,使污染物在中游平原堆积,鄂北边界风速越大,越有利污染输送增长。传输性污染主要来自偏北和东北方向的污染源输送,潜在源区贡献主要为途经偏北通道上的豫中、南阳盆地和关中地区,以及途经东北通道上的鲁、皖、苏等部分地区。PM_2.5浓度日变化双峰结构的天气成因不同,21—24时（北京时）峰值为静稳性污染,11—14时峰值为传输性污染。污染输送受大气边界层高度影响,日出前大气边界层高度较低,层结稳定并伴有上升运行,使得低空外来输送悬浮聚集在400 m高度附近;日出后随大气边界层高度升高,静稳层结被破坏,在干沉降作用下高浓度PM_2.5开始向下传输,并在午后地面形成峰值。