北京市大气PM_（2.5）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。     

北京市大气PM_(2.5)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。     

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.     

城市森林大气PM2.5中水溶性无机离子沉降特征

森林被誉为"地球之肺",在防霾治污方面有其独特不可替代的作用,不同树种沉降PM_2.5的功能有很大差别.本文选取代表性城市森林——奥林匹克森林公园为研究对象,设置垂直监测塔观测大气PM_2.5的浓度垂直分布,以考察不同季节城市森林对PM_2.5中各组分的影响.在冬季、春季和夏季各采集PM_2.5样品,分析并计算PM_2.5中Na⁺、NH₄⁺、K⁺、Mg2⁺、Ca2⁺、Cl^-、NO₃^-和SO₄^2-等典型水溶性无机离子的浓度.结果表明,PM_2.5中水溶性无机离子总浓度呈规律性变化特征:冬季((56.90±27.38)μg·m-3)>春季((46.69±12.24)μg·m^-3)>夏季((23.16±8.75)μg·m^-3).其中SO₄^2-和NO₃^-浓度和占PM_2.5主要水溶性无机离子总浓度的50%以上.3个季节中,除冬季外,在春季和夏季,8种离子有明显的垂直方向上的沉降,夏季的沉降速率高于春季,但是春季由于大气颗粒物浓度高,沉降通量高于夏季.NO₃^-和SO₄^2-垂直方向的沉降量在所有可溶性无机离子中最高.植被密度、叶面积指数、气象条件等因素对于PM_2.5的沉降特征有明显影响.     

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.     

2015—2020年海南岛大气PM2.5和PM10的时空分布特征

基于2015—2020年海南省18个市县环境监测数据和气象观测数据,结合Cressman客观差值、相关分析和气候倾向率等统计方法对PM_2.5和PM₁₀质量浓度时空分布特征进行深入分析.结果表明,PM_2.5和PM₁₀质量浓度空间分布上均呈北半部高于南半部的特征,同时2015—2020年均表现为快速下降的变化趋势,趋势系数分别为-0.982(PM_2.5)和-0.935(PM₁₀),通过了99.9%的信度检验.PM_2.5和PM₁₀质量浓度有明显的季节变化特征,冬季质量浓度最高,秋季和春季次之,夏季最低.PM_2.5和PM₁₀质量浓度呈现U形的逐月变化,最低值出现在7月,最高值出现在12月. PM_2.5和PM₁₀质量浓度呈“双峰双谷”型的日变化,受人为活动影响较为显著. PM_2.5和PM₁₀与...     

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_...     

大气PM2.5中重金属的化学形态分布

运用多级连续提取法,对广州市不同季节不同采样高度大气PM2.5中重金属的化学形态和生物有效性进行分析。研究表明,PM2.5中各重金属元素之间的化学形态分布差异较大,Zn、Cd、As、Mn主要分布在F1(可溶态与可交换态)和F2(碳酸盐态、可氧化态与可还原态),绝大部分的Pb以F2存在,Ni和Mo主要分布在F1和F3(有机质、氧化物与硫化物结合态),Cu主要以F2和F3存在,Cr主要分布在F3和F4(残渣态),Co则4种形态平均分布。采样高度对重金属的化学形态分布影响不大,同一采样期内楼顶与地面样品中同一元素的化学形态分布结果比较一致。两个采样季节重金属的形态百分比存在不同程度的变化,2007年春重金属的不稳定态(F1)比例比2006年秋普遍增加,次稳定态(F2、F3)比例减少。在10种重金属中,Cd、Zn、Pb和As的生物有效性系数高(>0.7),属于生物可利用性元素,在环境中的活动性要明显高于其它元素;Mn、Cu、Mo、Co、Ni和Cr元素的生物有效性系数值在0.2～0.6之间,属于潜在生物有效性元素,在环境中比较稳定。     

哈尔滨市大气PM2.5中多环芳香烃污染特征及来源解析

本文旨在分析哈尔滨市两城区(道里区和香坊区)2014年—2019年PM_2.5中16种芳香烃质量浓度变化规律,明确芳香烃中主要的污染及来源.将颗粒物中的多环芳香烃收集于滤膜,滤膜用乙醚/正己烷的混合溶剂提取,提取液经过浓缩、净化后,用具有荧光及紫外检测器的高效液相色谱仪分离检测.通过空气污染人群健康检测系统选取与PM_2.5监测期相同时期的平均气压、平均温度、平均相对湿度、降水量、日照小时数、平均风速等6种气象因素数据,采用Spearman法分析6种气象因素与16种多环芳香烃的相关性.结果表明,道里区PM_2.5平均质量浓度为84.9μg·m^-3,香坊区为86.5μg·m^-3.两城区的PM_2.5与平均气压呈显著正相关,与平均温度、平均相对湿度、降水量、日照小时数呈显著负相关.道里区和香坊区在2014—2019年多环芳香烃平均质量浓度分别为50.7 ng·m^-3、59.5 ng·m^-3.其贡献值由高到低为芘>荧蒽&...     

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...     

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.     

Source apportionment of PM2.5 in Tangshan,China—Hybrid approaches for primary and secondary species apportionment

The new hybrid approaches for the source apportionment of PM_2.5 were proposed. The hybrid approach can be used for source apportionment of secondary species. The metallurgy industry was the biggest contribution source to PM_2.5 of Tangshan. In winter, the contribution from the coal-fired boilers was the largest one. The objective of this paper is to propose a hybrid approach for the source apportionment of primary and secondary species of PM_2.5 in the city of Tangshan. The receptor-based PMF (Positive Matrix Factorization) is integrated with the emission inventory (EI) to form the first hybrid method for the source apportionment of the primary species. The hybrid CAMx-PSAT-CP (Comprehensive Air Quality Model with Extensions – Particulate Source Apportionment Technology – Chemical Profile) approach is then proposed and used for the source apportionment of the secondary species. The PM_2.5 sources identified for Tangshan included the soil dust, the metallurgical industry, power plants, coal-fired boilers, vehicles, cement production, and other sources. It is indicated that the PM_2.5 pollution is a regional issue. Among all the identified sources, the metallurgy industry was the biggest contribution source to PM_2.5, followed by coal-fired boilers, vehicles and soil dust. The other-source category plays a crucial role for PM_2.5, particularly for the formation of secondary species and aerosols, and these other sources include non-specified sources such as agricultural activities, biomass combustion, residential emissions, etc. The source apportionment results could help the local authorities make sound policies and regulations to better protect the citizens from the local and regional PM_2.5 pollution. The study also highlights the strength of utilizing the proposed hybrid approaches in the identification of PM_2.5 sources. The techniques used in this study show considerable promise for further application to other regions as well as to identify other source categories of PM_2.5.     

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.     

北京地区灰霾污染特征

灰霾天气能见度较低,除影响人们日常生产活动和交通运输外,空气中携带的有毒有害细粒子严重危害人们的生命健康。近几年,北京市加大治霾力度,虽取得一定成绩,但灰霾天气仍然频发。为进一步更好地治理北京灰霾,为制定政策提供依据,须了解北京地区灰霾污染特征,因此,对北京市2013年6月到2014年5月的气象观测数据和PM2.5质量浓度进行了统计分析。文章统计了不同强度灰霾的分布,分析了PM2.5质量浓度与能见度的相关关系,在此基础上,研究了PM2.5质量浓度影响能见度变化程度的分界点。研究结果表明：研究期间,北京地区出现灰霾时总计4572 h,发生频率为56.2%,灰霾日总计233 d,频率为64.4%,呈冬季〉春季〉夏季〉秋季;湿霾最易发生在夏季,干霾最易发生在冬季,分别占当季灰霾时的17.6%和59.0%;全年不同强度霾发生小时数呈现轻微霾〉重度霾〉轻度霾〉中度霾,其中,轻微霾时数1625 h,重度霾1163 h,轻度霾1101 h,中度霾683 h;研究期间PM2.5质量浓度呈夏季低冬季高的显著变化趋势,PM2.5日均质量浓度达一级空气质量标准59 d,达二级标准159 d,达标率分别为17.7%和47.74%;PM2.5小时质量浓度与能见度呈负相关性较高的幂函数关系（置信度取99%,P〈0.01）,无高湿条件影响下,空气中细颗粒物对能见度的影响更为直接;北京地区在改善能见度的过程中,通过降低1μg·m-3的PM2.5,使能见度改善大于或远大于1 km的概率仅为18.9%,而在50.4%的时段内仅能使能见度的改善小于或远小于0.1 km。     

Mass concentrations and temporal profiles of PM10, PM2.5 and PM1 near major urban roads in Beijing

Mass concentrations of PM₁₀, PM_2.5 and PM₁ were measured near major roads in Beijing during six periods: summer and winter of 2001, winter of 2007, and periods before, during and after the 2008 Beijing Olympic Games. Since the control efforts for motor vehicles helped offset the increase of emissions from the rapid growth of vehicles, the averaged PM_2.5 concentrations at roadsides during the sampling period between 2001 and 2008 fluctuated over a relatively small range. With the implementation of temporary traffic control measures during the Olympics, a clear “V” shaped curve showing the concentrations of particulate matter and other gaseous air pollutants at roadsides over time was identified. The average concentrations of PM₁₀, PM_2.5, CO and NO decreased by 31.2%, 46.3%, 32.3% and 35.4%, respectively, from June to August; this was followed by a rebound of all air pollutants in December 2008. Daily PM₁₀ concentrations near major roads exceeded the National Ambient Air Quality Standard (Grade II) for 61.2% of the days in the non-Olympic periods, while only for 12.5% during the Olympics. The mean ratio of PM_2.5/PM₁₀ near major roads remained relatively stable at 0.55 (±0.108) on non-Olympic days. The ratio decreased to 0.48 (±0.099) during the Olympics due to a greater decline in fine particles than in coarse-mode PM. The ratios PM₁/PM_2.5 fluctuated over a wide range and were statistically different from each other during the sampling periods. The average ratios of PM₁/PM_2.5 on non-Olympic days were 0.71.     

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.     

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.