城市森林大气PM2.5中水溶性无机离子的观测

PM2.5中水溶性无机组分的浓度及时空分布等已开展了大量的研究.这些研究主要集中在污染较重的地区或者敏感区,比如居民区或者道路边等.而关于我国城市森林生态系统对大气PM2.5及其组分的影响还未开展系统的研究.研究PM2.5中的水溶性离子组分在城市森林系统中的时空分布特征将有助于了解生态系统与大气颗粒物之间的相互影响,对大气污染治理及城市绿化都具有参考意义.因此,本研究对北京市的4个采样点PM2.5中水溶性无机离子组分进行了观测.     

闽南重点城市春季PM2.5中水溶性无机离子特征研究

对闽南地区重点城市春季 PM2.5中水溶性离子的污染状况进行剖析,分析和探讨了闽南地区同城化进程中重点城市PM2.5中水溶性离子污染的共同点和差异点,以期在当前空气污染日益严重的趋势下为闽南地区的大气污染控制提供依据.于2011年春季3月7—16日在福建省闽南地区重点城市厦门城区与郊区、漳州和泉州城区同步用聚丙烯纤维滤膜采集大气PM2.5样品,滤膜经超声萃取后用离子色谱仪分析样品中F-、Cl-、NO3-、SO42-、Na+、K+、NH4+、Ca2+和Mg2+等9种离子组分的质量浓度.结果表明,(1)闽南地区重点城市春季PM2.5的质量浓度变化具有较好的一致性,PM2.5的平均质量浓度为94.14μg·m-3,二次离子SO42-、NO3-和NH4+的质量浓度变化范围为14.66~66.68μg·m-3,平均质量浓度为32.43μg·m-3,占总水溶性离子的83.30%,主要以(NH4)2SO4和NH4NO3形式存在;与国内其他主要城市相比,闽南地区PM2.5中二次离子浓度水平偏高,二次污染严重.(2)NO3-/SO42-和SOR、NOR值均表明闽南地区固定源污染对水溶性离子的贡献大于流动源,但厦门流动源的贡献占了更高的比重;与国内其它地区相比,闽南地区由于春季潮湿多雨,硝酸盐化速率较高,也是导致二次离子污染水平较高的原因之一.(3)作为滨海地区,闽南地区 PM2.5中水溶性离子的主要贡献者主要来自人为源,而非海盐离子.     

江苏沿江城市PM10和PM2.5中水溶性离子特征及来源分析

2012年3月和6月在江苏沿江七市(镇江、常州、无锡、苏州、扬州、泰州和南通)采集空气中PM10和PM2.5样品,运用离子色谱法,分析无机水溶性离子成分,并对其组成、相关性、结合形式和来源解析等方面进行研究.结果表明,春季苏南四市PM10和PM2.5质量浓度低于苏中三市,夏季反之;水溶性离子在PM2.5中所占的比例一般高于PM10,SO2-4、NO-3、NH+4是颗粒物中水溶性离子的主要成分,占总量的80%左右.PM10和PM2.5中的SO2-4和NO-3、NH+4和SO2-4、NO-3之间均具有较好的相关性;PM10中Ca2+和Mg2+显著相关,细粒子中相关性较小.NH+4和SO2-4、NO-3主要以(NH4)2SO4和NH4NO3存在于可吸入颗粒物中.春夏两季,江苏沿江城市PM10和PM2.5中的SOR均大于NOR,SO2在大气中的转化率比NOx的转化率要高;苏南地区PM10和PM2.5中的SOR和NOR高于苏中地区.运用[NO-3]/[SO2-4]的比值法研究颗粒物污染来源,表明春季的污染源主要为流动源,夏季为固定源.运用因子分析法分析颗粒物来源,燃煤、交通运输、生物质燃烧、土壤和建筑地表扬尘是春夏两季江苏沿江城市可吸入颗粒物的主要污染源.     

2018年成都市PM2.5中水溶性无机离子污染特征及来源解析

为了探究成都市PM_2.5水溶性无机离子的污染特征与来源贡献,于2018年1月1日—12月31日利用高分辨率的MARGA对PM_2.5组分展开在线监测,结合同一点位的气态污染物、气象参数监测数据进行分析.结果表明,水溶性无机离子与PM_2.5具有相同的月变化趋势,水溶性无机离子月均浓度为10.35-39.60μg·m^-3,在PM_2.5中的占比为31%—51%,水溶性无机离子是PM_2.5的重要组成部分.NO₃^-在水溶性无机离子中月均占比以12月最高,8月最低,SO₄^2-刚好与之相反.大气长期处于富氨状态,二次离子主要以（NH₄）₂SO₄、NH₄NO₃、NH₄Cl的形式存在,SOR在冬季12月与夏季8月分别出现高值0.61与0.5,但NOR只在冬季出...     

西安冬、夏季PM2.5中水溶性无机离子的变化特征

为探讨西安市冬、夏季水溶性无机离子的季节和空间变化特征,2010年1月和7月分别在西安城区4个站点及上风区高陵(GL)和下风区黑河(HH)连续采集2周的PM2.5样品,使用离子色谱仪分析样品中水溶性无机离子成分.结果表明,PM2.5质量浓度冬季明显高于夏季,空间变化表现为:城区站点浓度均值(172.6μg.m-3)>上风区点GL(98.9μg.m-3)>下风区点HH(81.0μg.m-3).水溶性无机离子浓度总和占PM2.5质量浓度的41.8%,其中SO24-、NO3-和NH4+是水溶性离子的主要成分,分别占总离子质量浓度的35.1%、22.6%和12.2%.Na+、Ca2+和Mg2+在冬、夏季浓度相差不大,而SO24-、NO3-、NH4+以及K+、Cl-等均明显表现为冬季浓度高于夏季.SO24-、NO3-和NH4+在冬、夏季空间变化均表现为城区站点>GL>HH,这3种离子夏季在大气中的主要存在方式为NH4HSO4和NH4NO3,而冬季主要以(NH4)2SO4、NH4NO3和NH4Cl形式存在.NO3-/SO24-的比值为0.64,表明西安市固定源仍是主要污染贡献源,但是移动源所占比例较之前研究有所上升,应采取一定措施控制机动车数量并加强排放监控.     

北京市大气PM2.5中四种水溶性阴离子的水平变化比较

了解北京市城区和郊区大气细颗粒物中的四种水溶性阴离子F-、Cl-、SO42-、NO3-的浓度水平,并分析影响其水平高低的因素。使用聚四氟乙烯滤膜分别采集北京市城区和郊区大气中的PM2.5,用纯水提取后采用离子色谱法测定水溶性阴离子质量浓度。采样期间北京市大气PM2.5、F-、Cl-、SO42-和NO3-质量浓度几何均数分别为55.36、0.02、0.46、6.72和1.09μg·m-3,四种水溶性阴离子质量浓度总和占PM2.5质量浓度的19.14%;同一季节(春季)郊区监测点大气PM2.5、SO42-和NO3-质量浓度显著高于城区监测点;城区大气PM2.5与四种水溶性阴离子质量浓度秋季高于春季,但差异无统计学意义;大气PM2.5与Cl-、SO42-和NO3-质量浓度均高度相关。Cl-、SO42-、NO3-是北京市大气PM2.5的重要组成成分。     

保定市污染天气PM2.5中水溶性离子的特征和源解析

采集了2018年保定市污染天气的PM_2.5样品,采用离子色谱法测定了PM_2.5样品中的水溶性离子（WSIs）,分析了不同季节PM_2.5及其水溶性离子的分布特征,并采用PMF模型对PM_2.5进行了源解析.结果表明,采样期间保定市的PM_2.5浓度为18.4—258.0μg·m^-3,年均值为（91.5±62.5）μg·m^-3;季节规律是冬季(160.6μg·m^-3)>秋季(105.3μg·m^-3)>春季(57.6μg·m^-3)>夏季(53.2μg·m^-3).WSIs年均值为49.20μg·m^-3,占PM_2.5.的63.95%,WSIs的季节规律和PM_2.5的一致.二次离子占水溶性离子的77.12%.湿度和温度与SOR和NOR成正相关.春夏两季水溶性离子主要以Na_...     

天津市2018—2020年春节PM2.5中水溶性无机离子特征及重污染过程分析

为阐明大气污染重点整治和新冠疫情影响下我国华北地区城市春节期间重污染过程PM_2.5中水溶性无机离子变化特征及其影响因素,本研究结合气态前体物浓度和气象要素,对天津市2018—2020年连续3年春节假期的2次重污染过程PM_2.5中主要水溶性无机离子(WSIIs)浓度进行对比分析.结果表明,2018年和2020年春节假期PM_2.5平均浓度(98.32μg·m^-3和137.7μg·m^-3)显著高于2019年(49.97μg·m^-3).PM_2.5平均浓度在污染期Ⅱ(2020年为206.5μg·m^-3)是污染期Ⅰ(2018年98.32μg·m^-3)的2.1倍;2次污染事件中NO₂浓度变化不大,而SO₂浓度在污染期Ⅱ(14.89μg·m^-3)是污染期Ⅰ(30.04μg·m^-3)的49.6%.SNA在WSIIs中占比超...     

西藏日喀则地区夏、冬季大气颗粒物水溶性离子粒径分布特征

为研究日喀则不同粒径大气颗粒物中水溶性无机离子的组成特征,利用Andersen分级撞击式采样器在夏、冬季进行大气颗粒物分级采样,采用离子色谱测定NH+4、Ca2+、K+、Mg2+、Na+、SO2-4、NO-3和Cl-等8种离子浓度.结果表明,夏、冬季总离子浓度分别为9648±3030、4925±1138 ng·m-3.夏季离子浓度高低顺序为Cl-﹥Ca2+﹥NH+4﹥Na+﹥SO2-4﹥NO-3﹥K+﹥Mg2+,Cl-、Ca2+、NH+4和Na+占总离子浓度的比重为74.9%;冬季水溶性无机离子浓度高低顺序为Cl-﹥SO2-4﹥NH+4﹥NO-3﹥Ca2+﹥Na+﹥K+﹥Mg2+,Cl-、SO2-4、NH+4和NO-3占总离子浓度的比重为88.0%.夏、冬季大气颗粒物中所有水溶性无机离子的粒径分布均成双峰型.夏季,大气颗粒物中SO2-4与Mg2+(r=0.951)、Ca2+(r=0.947),NO-3与Mg2+(r=0.904)、Ca2+(r=0.843),在0.01水平(双侧)上均显著相关;冬季,细粒子中K+与Cl-(r=0.740)、SO2-4(r=0.929)及NO-3(r=0.920),在0.01水平(双侧)上显著相关.     

Diverse bacterial populations of PM2.5 in urban and suburb Shanghai,China

• Urban aerosols harbour diverse bacterial communities in Shanghai. • The functional groups were associated with nitrogen, carbon, and sulfur cycling. • Temperature, SO₂, and wind speed were key drivers for the bacterial community. Airborne bacteria play key roles in terrestrial and marine ecosystems and human health, yet our understanding of bacterial communities and their response to the environmental variables lags significantly behind that of other components of PM_2.5. Here, atmospheric fine particles obtained from urban and suburb Shanghai were analyzed by using the qPCR and Illumina Miseq sequencing. The bacteria with an average concentration of 2.12 × 10³ cells/m³, were dominated by Sphingomonas, Curvibacter, Acinetobacter, Bradyrhizobium, Methylobacterium, Halomonas, Aliihoeflea, and Phyllobacterium, which were related to the nitrogen, carbon, sulfur cycling and human health risk. Our results provide a global survey of bacterial community across urban, suburb, and high-altitude sites. In Shanghai (China), urban PM_2.5 harbour more diverse and dynamic bacterial populations than that in the suburb. The structural equation model explained about 27%, 41%, and 20%–78% of the variance found in bacteria diversity, concentration, and discrepant genera among urban and suburb sites. This work furthered the knowledge of diverse bacteria in a coastal Megacity in the Yangtze river delta and emphasized the potential impact of environmental variables on bacterial community structure.     

PM2.5 concentration declining saves health expenditure in China

● Monthly hospitalization expenses are sensitive to increases in PM_2.5 exposure. ● The increased PM_2.5 causes patients with CHD and LRI to stay longer in the hospital. ● The impact of PM_2.5 on total expenses for stroke is greater in southern China. ● Males may be more sensitive to air pollution than females. Air pollution has been a severe issue in China. Exposure to PM_2.5 has adverse health effects and causes economic losses. This study investigated the economic impact of exposure to PM_2.5 pollution using monthly city-level data covering 88.5 million urban employees in 2016 and 2017. This study mainly focused on three expenditure indicators to measure the economic impact considering lower respiratory infections (LRIs), coronary heart disease (CHD), and stroke. The results show that a 10 µg/m³ increase in PM_2.5 would cause total monthly expenses of LRIs, CHD, and stroke to increase by 0.226%, 0.237%, and 0.374%, respectively. We also found that LRI, CHD, and stroke hospital admissions increased significantly by 10%, 8.42%, and 5.64%, respectively. Furthermore, the total hospital stays of LRIs, CHDs, and strokes increased by 2.49%, 2. 51%, and 1.64%, respectively. Our findings also suggest heterogeneous impacts of PM_2.5 exposures by sex and across regions, but no statistical evidence shows significant differences between the older and younger adult subgroups. Our results provide several policy implications for reducing unequal public health expenditures in overpolluted countries.     

嘉善冬季PM2.5化学组分特征及来源分析

为研究嘉兴地区嘉善冬季污染时段和清洁时段PM_2.5化学组分特征,结合气象数据对2019年1月嘉兴市嘉善县善西超级站在线自动监测PM_2.5及化学组分数据、气态污染物(NO₂和SO₂)进行了分析.结果表明,2019年1月嘉善善西超级站污染时段PM_2.5浓度(97.18μg·m^-3)为清洁时段(36.77μg·m^-3)的2.6倍.污染时段水溶性离子浓度(41.58μg·m^-3)较清洁时段(19.82μg·m^-3)高21.76μg·m^-3,但占比有所降低,含碳组分比例增加.OC;EC比值为3.93,可能受到燃煤及机动车排放的共同影响.低风速及高湿有利于NO₂和SO₂等气态污染物进行二次转化,污染时段硫转化率和氮转化率均比清洁时段高,分别增高7.93%和54.11%,说明NOx向硝酸盐二次转化较为明显,导致颗粒物浓度升高.聚类分析结果显示67.34%气流来自北方,且相应的气流轨迹上污染物浓度比周边高,说明污染物存在一定的长距离输送.结合风玫瑰图可以看出,污染主要为本地及其周边的输送,污染物的长距离输送在短时会使污染浓度突增.因此,在重点关注本地及周边污染的同时,偏北气流下的污染物区域输送不可忽视.     

西安市春季大气细粒子的质量浓度及其水溶性组分的特征

为了探讨西安市春季大气细粒污染物的污染水平及水溶性组分的特征及来源,2005年3—5月对西安大气PM2.5进行了观测,并应用离子色谱对其中的水溶性组分进行了分析。结果显示,西安市春季大气PM2.5的质量浓度为159.9μg·m-3。分析的11种阴阳离子(Na 、NH4 、K 、Mg2 、Ca2 、F-、Cl-、Br-、NO2-、NO3-和SO42-)质量浓度占PM2.5的30%,表明水溶性组分是大气细粒子的主要组成之一。NH4 、SO42-和NO3-为水溶性离子的主要组分,其平均质量浓度分别为6.6、20.1和7.6μg·m-3,在总水溶性离子中的百分比分别为12.4%、47.4%和16.9%,SO42-和NO3-质量浓度与能见度有较好的负相关性,表明细粒子中二次气溶胶组分对能见度有显著的影响。阴阳离子的平衡和pH值的测定结果显示,西安市大气细粒子污染物为弱酸性。离子间的相关性分析揭示水溶性离子在颗粒物中主要结合方式为(NH4)2SO4、NH4HSO4、NH4NO3、KHSO4和K2SO4。Mg2 和Ca2 的相关性也较好,其摩尔比率为0.07,小于中国北方沙漠和黄土的平均值(0.15),揭示二次扬尘和建筑扬尘等过程对Ca2 的质量浓度影响较大。计算的NO3-/SO42-质量浓度比值的均值为0.38,说明固定排放源(燃煤)对细粒子中水溶性组分的贡献大于移动排放源(机动车)。     

太原市PM_(2.5)中溴代阻燃剂的污染特征及人体暴露水平

2013年10月至2014年7月,在太原城区,分4个月采集大气细颗粒物,每个月选取4个样品,分析了颗粒物上8种多溴联苯醚(PBDEs)和6种新型溴代阻燃剂(NBFRs)的浓度与组成。结果表明,大气PM2.5样品中PBDEs和NBFRs总浓度算术平均值分别为(10.9±10.3)pg·m-3和(22.3±24.7)pg·m-3,其中BDE-209、HBB和DBDPE是溴代阻燃剂中的主要污染物。季节变化来看,秋季样品中总PBDEs和总NBFRs的浓度要高于其他季节,夏季最低;化合物组成上,秋季样品中BDE-209含量较低,而NBFRs中HBB含量较高。相关分析显示,溴代阻燃剂的变化与颗粒物浓度和有机质的相关性不大,与大气温度与无显著性相关,而主要与气团来源有关。城市儿童的吸入暴露量约为成人的2~3倍,反映出PM2.5中溴代阻燃剂对城镇居民尤其是儿童的潜在健康危害仍不容忽视。     

Local and regional contributions to PM2.5 in the Beijing 2022 Winter Olympics infrastructure areas during haze episodes

• Regional transportation contributed more than local emissions during haze episodes. • Short-range regional transportation contributed the most to the PM_2.5 in the OIAs. • Low wind speeds and low PBLHs led to higher local contributions to Beijing. The 2022 Winter Olympics is scheduled to take place in Beijing and Zhangjiakou, which were defined as OIAs (Olympic infrastructure areas) in this study. This study presents the characteristics and source apportionment of PM_2.5 in the OIAs, China. The entire region of mainland China, except for the OIAs, was divided into 9 source regions, including four regions in the BTH(Beijing-Tianjin-Hebei) region, the four provinces surrounding the BTH and the remaining areas. Using CAMx/PSAT, the contributions of the nine regions to the PM_2.5 concentration in the OIAs were simulated spatially and temporally. The simulated source apportionment results showed that the contribution of regional transportation was 48.78%, and when PM_2.5 concentration was larger than 75 μg/m³ central Hebei was the largest contributor with a contribution of 19.18%, followed by Tianjin, northern Hebei, Shanxi, Inner Mongolia, Shandong, southern Hebei, Henan and Liaoning. Furthermore, the contribution from neighboring regions of the OIAs was 47.12%, which was nearly twice that of long-range transportation. Haze episodes were analyzed, and the results presented the importance of regional transportation during severe PM_2.5 pollution periods. It was also found that they were associated with differences in pollution sources between Zhangjiakou and Beijing. Regional transportation was the main factor affecting PM_2.5 pollution in Zhangjiakou due to its low local emissions. Stagnant weather with a low planetary boundary layer height and a low wind velocity prevented the local emitted pollutants in Beijing from being transported outside, and as a result, local emissions constituted a larger contribution in Beijing.     

Temporal variation of PM2.5-associated health effects in Shijiazhuang,Hebei

•Annual mean PM_2.5 in Shijiazhuang were 87, 95, and 82 µg/m³ in 2015–2017. •Health risk of cardiovascular system was higher than respiratory system. •Premature mortality attributed to PM_2.5 was 5088 people in 2017. •ΔMort and YLL reduced by 84.2% and 84.6% when PM_2.5 reduced to 10 µg/m³. •Health risks due to PM_2.5 were severe in Shijiazhuang in 2015–2017. Shijiazhuang is one of the cities in the North China Plain. In recent decades, this city has experienced high levels of fine particulate matter (PM_2.5), which have potentially significant effects on human health. In this study, the health effects of PM_2.5 exposure in Shijiazhuang were estimated by applying an integrated exposure-response model. Premature mortality, years of life lost (YLL), and the mortality benefits linked to reduced levels of PM_2.5 were quantified for the period 2015–2017. In 2015, 2016, and 2017, cerebrovascular diseases caused the highest premature mortality (2432, 2449, and 2483, respectively), followed by ischemic heart diseases (1391, 1479, and 1493, respectively), lung cancer (639,660, and 639, respectively), and chronic obstructive pulmonary diseases (533, 519, and 473, respectively). Notably, the total number of premature deaths caused by PM_2.5 exposure in Shijiazhuang in 2015, 2016, and 2017 were 4994, 5107, and 5088, respectively. Moreover, the YLL in the same years were 47001, 47880 and 47381, respectively. Interestingly, the YLL per 1000 females was lower than that per 1000 males. Finally, we noted that premature mortality and YLL decreased by 84.2% and 84.6% when the PM_2.5 levels diminished to 10 µg/m³. Overall, the results of this study improve our understanding of how high PM_2.5 concentrations affect human health and suggest the application of more stringent measures in Shijiazhuang to alleviate the associated health risks.