济南市PM2.5中二次组分的时空变化特征及其影响因素

为分析济南市PM_2.5中二次组分的时空变化和影响因素,对济南市春季(2019年5月16—25日)、秋季(2019年10月15—24日)和冬季(2019年12月17—2020年1月16日)4个典型点位的PM_2.5样品进行连续采样,并测定了PM_2.5中水溶性离子、有机碳(OC)和元素碳(EC)的含量。结果表明:物流交通区的二次组分质量浓度最高(56.13μg·m^?3),钢铁工业区的二次组分浓度比城市市区高,但是二次组分占比较城市市区低,清洁对照点的浓度和占比最低;济南市4个功能区SO₄^2?和NO₃^?转化率均高于0.1,除清洁对照点外,城市市区、钢铁工业区和物流交通区的SO₄^2?转化率明显高于NO₃^?转化率;济南市春季、秋季和冬季的ρ(NO₃^?)/ρ(SO₄^2?)分别为0.67、2.57和1.98,春季PM_2.5浓度以固定源贡献为主,秋季和冬季以移动源贡献为主;运用ISORROPIA热力学模型分析了含水量和pH对二次组分生成的影响,含水量会随着污染增大而增大,酸度和含水量对二次无机组分的转化机理产生影响,酸度会抑制二次无机组分的生成,而含水量会促进二次组分的生成;后向轨迹聚类分析结果表明,占比最高的轨迹(29.2%)来自东北方向的滨州和东营,基于潜在源贡献因子(WPSCF)和浓度权重轨迹(WCWT)分析PM_2.5中二次组分质量浓度的潜在污染源区域,SO₄^2?的主要贡献源区在济南市区北部的济阳区和东北方向的滨州、东营等,NO₃^?和NH₄⁺的主要贡献源区在济南市区北方向的济阳区、东北方向的章丘区和南方向的莱芜区等。该研究结果可为中国北方城市细颗粒物进一步的治理和防控提供数据支撑和理论依据。     

川南四座城市PM2.5化学组分污染特征及其源解析

为探究川南地区大气气溶胶中化学组分与来源特征,于2015年9月—2016年8月在四川盆地南部4个典型代表城市(泸州、内江、宜宾、自贡)采集了226个PM_2.5样品,对PM_2.5的质量浓度和主要化学组分(水溶性离子和碳质组分)进行测定,并利用颗粒物源解析受体模型对PM_2.5来源进行解析.结果表明:川南地区PM_2.5日均浓度为46.4—68.0μg·m^-3,均高于国家环境空气质量标准年均PM_2.5限值(35.0μg·m^-3).OC、EC和水溶性二次离子(SO₄^2-、NO₃-和NH₄+)分别占PM_2.5质量的15.7%—22.8%、4.2%—6.4%和28.6%—55.8%.PM_2.5及其主要化学组分浓度有显著的季节变化,即冬季浓度显著高于其他季节,夏季浓度最低.泸州除夏季外,其他季节SO₄2-、NO₃-同源性较好;其他城市在冬季,SO₄2-、NO₃-同源性较好.NH₄+主要存在形式为NH₄NO₃、(NH₄)2SO₄、NH₄HSO₄.OC、EC来源复杂,主要为机动车源、煤燃烧源和生物质燃烧源.川南地区PM_2.5的来源主要受8种因子影响,按总体贡献排序依次为:二次硫酸盐、生物质燃烧、工业源、二次硝酸盐、机动车源、煤燃烧、道路尘埃和建筑尘埃.此外,相比较而言,机动车源贡献在泸州市较凸显,煤燃烧源贡献在宜宾市较凸显.     

高山风景区居民区碳质气溶胶污染特征及来源

碳质气溶胶是大气颗粒物的重要组成部分,具有很强的环境和气候效应,是气溶胶科学研究领域的热点.为探究庐山风景区居民区PM_2.5中碳质组分的污染特征及来源,于2019年12月2日—2020年10月31日在庐山风景区居民区进行PM_2.5样品采集,并对其碳质组分有机碳(OC)和元素碳(EC)进行分析.结果表明,观测期间庐山风景区居民区PM_2.5的平均质量浓度为(46.45±18.64)μg·m^-3,其中OC和EC平均质量浓度分别是(4.08±1.61)μg·m^-3和(0.23±0.10)μg·m^-3,占PM_2.5总质量的8.78%和0.50%.且碳质颗粒的污染水平普遍低于城市地区,介于国内其他典型高山背景点之间.采用EC示踪法对PM_2.5中的二次有机碳(SOC)进行估算,发现采样期间SOC的平均浓度为(1.51±1.22)μg·m^-3,占OC的33.2%,表明SOC是PM_2.5...     

天津市夏季PM2.5中碳组分时空变化特征及来源解析

为研究天津市夏季PM_2.5中碳组分的时空变化特征及来源,于2019年7—8月设立2个点位分昼夜采集天津市PM_2.5样品,并测定了其中有机碳(OC)和元素碳(EC)的含量。结果表明,城区PM_2.5、OC和EC浓度日均值分别为(53.4±20.8)μg·m^-3、(8.72±2.56)μg·m^-3和(1.67±0.90)μg·m^-3,郊区PM_2.5、OC和EC浓度日均值分别为(54.2±24.5)μg·m^-3、(7.54±2.50)μg·m^-3和(1.82±1.06)μg·m^-3;白天PM_2.5、OC、EC的平均浓度分别为(47.3±16.1)μg·m^-3、(8.7±2.1)μg·m^-3和(1.5±0.6)μg·m^-3,夜间PM_2.5、OC、EC的平均浓度分别为(60.2±26.2)μg·m^-3、(7.5±2.9)μg·m^-3和(2.0±1.2)μg·m^-3。OC浓度表现为城区高于郊区,白天高于夜间;EC及PM_2.5浓度表现为郊区高于城区,夜间高于白天。OC/EC比值分析得,城区(6.04)高于郊区(5.08);白天(6.58)高于夜间(4.54)。城区OC与EC相关性弱于郊区,白天OC与EC相关性弱于夜间。采用EC示踪法与MRS模型对SOC含量进行估算,得到白天与夜间SOC浓度分别为(5.71±1.35)μg·m^-3和(3.81±1.20)μg·m^-3,白天SOC污染比夜间严重。丰度分析与主成分分析的结果表明,天津市夏季城郊区PM_2.5中碳组分均主要来源于燃煤和机动车尾气排放。     

疫情常态化管控下济南市春节前后PM2.5中二次组分变化特征

为研究新冠肺炎疫情常态化管控下,济南市春节前后PM_2.5中二次组分的变化特征、气粒分配规律及其影响因素,本文对2021年2月1-27日春节前、春节期间和春节后的3个时段济南市区在线监测的水溶性离子、碳组分及气态前体物质量浓度小时数据进行分析.结果表明,2021年疫情常态化管控下济南市春节前后二次组分浓度与2020年同比均明显下降,ρ(NO₃^-)、ρ(SO₄^2-)、ρ(NH₄⁺)和ρ(SOA)分别下降53.09%、58.32%、51.17%和61.84%,其中二次无机组分(NO₃^-、SO₄^2-、NH₄⁺之和)和SOA在PM_2.5中的占比分别为54.07%和8.20%,春节期间PM_2.5及二次组分在10—18时浓度较低,与春节期间白天人为活动相对减少,机动车、建筑工...     

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.     

中国典型沿海城市冬季PM2.5中碳组分的污染特征及来源解析

为研究中国典型沿海城市冬季PM_2.5中碳组分的污染特征及来源,于2018年12月5日—2019年1月30日分别在天津(TJ)、上海(SH)和青岛(QD)同步采集PM_2.5样品。结果表明,天津、上海和青岛PM_2.5的平均浓度分别为(116.96±66.93)、(31.21±25.62)、(74.93±54.60)μg·m^-3,OC和EC的空间分布均为天津(18.69±7.95)μg·m^-3和(4.98±2.08)μg·m^-3>青岛(16.45±8.94)μg·m^-3和(2.01±1.04)μg·m^-3>上海(7.28±3.11)μg·m^-3和(1.05±1.25)μg·m^-3。3个站点的OC和EC均呈现较好的相关性,表明OC和EC具有相似的来源;OC/EC比值范围在2.37—7.53、5.47—46.41和4.77—13.36之间,证明各采样点均存在二次有机碳(SOC)的生成;采用最小R²法(MRS)估算SOC浓度,得到3个采样点SOC的平均质量浓度为(5.09±4.68)、(3.90±1.65)、(4.21±4.31)μg·m^-3,分别占OC总量的27.2%、55.8%和19.5%,其中上海的SOC在OC中的占比最大,说明上海二次有机碳污染较为严重,这主要归因于冬季严重污染源排放和有利的二次转化气象条件,而天津和青岛的碳组分主要来自污染源的直接排放。主成分分析(PCA)结果发现,天津PM_2.5中碳组分主要来源于道路尘、生物质燃烧和机动车尾气,上海PM_2.5中碳组分主要来源于生物质燃烧、道路扬尘和机动车尾气。青岛PM_2.5中碳组分主要来源于道路扬尘、机动车尾气。后向轨迹聚类分析表明,来自西北方向的气团对天津的影响较大,PM_2.5和碳组分的浓度值最大;而对上海而言,主要受北方气溶胶经过海面又传输回上海的气团的影响;青岛站点主要受华北地区污染物和本地排放源的影响。     

青藏高原东缘冬季PM2.5中碳组分特征和来源解析

于2017年冬季12月13—21日在青藏高原东缘理塘地区分昼夜采集PM_2.5样品,并用DRI2001A热光碳分析仪测定了有机碳(OC)和元素碳(EC)的质量浓度,研究青藏高原PM_2.5中碳组分的化学特征及主要来源,以期为理塘地区制定污染排放政策提供参考。结果表明,2017年冬季青藏高原东缘理塘地区PM_2.5平均质量浓度为44.34μg·m^?3,OC和EC的质量浓度为12.72μg·m^?3和3.85μg·m^?3,分别占PM_2.5质量浓度的29.61%和8.96%。通过经验公式,计算得到总碳气溶胶(TCA)质量浓度为24.20μg·m^?3,占PM_2.5的54.84%,说明碳质气溶胶对青藏高原东缘理塘地区PM_2.5有着十分重要的贡献。OC和EC在白天和夜间都有较高的相关性(相关系数分别为0.74和0.91),表明OC和EC的来源基本一致,受燃烧源影响较大。其中白天的相关系数低于夜间,说明青藏高原东缘理塘地区白天碳组分来源相对复杂。昼夜浓度对比显示,青藏高原东缘理塘地区PM_2.5白天和夜间的质量浓度分别为53.88μg·m^?3和33.44μg·m^?3,OC和EC浓度白天高于夜间,表明白天人为排放相对较高。冬季观测期间,PM_2.5中二次有机碳(SOC)昼夜浓度分别为1.11μg·m^?3和3.03μg·m^?3,分别占OC质量浓度的7.09%、26.59%,表明青藏高原东缘理塘城区白天碳组分主要为一次源。利用PMF 5.0软件对理塘城区碳组分进行进一步的解析,结果显示燃煤和生物质燃烧的混合源对总碳(TC)的贡献高达47.84%,占比最高;其次是汽车尾气和柴油车尾气源,贡献率分别为28.62%和23.54%。     

济南市钢铁集聚区大气细颗粒物中碳组分污染特征

为分析钢铁集聚区大气细颗粒物(PM_2.5)中碳组分的污染特征,对济南市钢铁集聚区和市区秋季(2020年10月15日至2020年10月24日)、冬季(2020年12月18日至2021年1月7日)和春季(2021年4月23日至5月2日)环境空气中PM_2.5进行手工采样,利用热光碳分析仪测定了PM_2.5中有机碳(OC)和元素碳(EC)的含量.结果表明,钢铁集聚区秋季OC和EC质量浓度范围分别为5.79—12.56μg·m^-3和1.34—3.44μg·m^-3;冬季OC和EC质量浓度范围分别为3.92—55.54μg·m^-3和0.38—11.39μg·m^-3;春季OC和EC质量浓度范围分别为2.14—4.70μg·m^-3和0.19—1.33μg·m^-3,呈现显著的季节变化,表现为冬季>秋季>春季.钢铁集聚区冬季PM_2.5中OC和EC占比最高,分别为28.11%和5.3...     

济南市柴油型移动源排放颗粒物中碳组分特征和排放量估算

为了识别济南市柴油型移动源排放颗粒物中碳组分特征,采用稀释通道采样器于2021年采集了柴油货车和工程机械尾气排放颗粒物,并对汽油车尾气一并采集对比,分析了尾气排放颗粒物质量浓度和其中的碳组分。结果表明,柴油型移动源排放颗粒物质量浓度明显高于汽油车,且以细颗粒物为主,PM_2.5/PM₁₀数值几乎接近于1.0,其中柴油货车排放颗粒物质量浓度高于工程机械,且随车型增大排放颗粒物质量浓度增大,重型柴油载货车排放PM_2.5和PM₁₀质量浓度最大,分别为4.56×10⁴μg·m^-3和4.71×10⁴μg·m^-3。柴油货车PM_2.5和PM₁₀排放因子范围分别为8.90-21.8 mg·km^-1和9.40-22.5 mg·km^-1,工程机械中破碎机颗粒物排放因子略大于挖掘机,破碎机PM_2.5和PM₁₀     

辽宁西南典型城市冬季大气细颗粒物水溶性无机离子污染特征及来源解析

本研究于2018年12月3日-2019年1月1日在辽宁省西南典型城市葫芦岛市和朝阳市分别布设3个城区采样点,在区域传输点龙屯水库布设1个采样点,采集大气细颗粒物PM2.5样品(n=201).使用离子色谱检测样品中的Na+、Mg2+、Ca2+、K+、NH4+、SO42-、F-、Cl-和NO3-的质量浓度.观测期间PM2....     

Relations between indoor and outdoor PM2.5 and constituent concentrations

Factors impacting indoor-outdoor relations are introduced. Sulfate seems a fine tracer for other non-volatile species. Particulate nitrate and ammonium desorb during outdoor-to-indoor transport. OC load increases during the transport due to sorption of indoor SVOCs. Outdoor PM_2.5 influences both the concentration and composition of indoor PM_2.5. People spend over 80% of their time indoors. Therefore, to assess possible health effects of PM_2.5 it is important to accurately characterize indoor PM_2.5 concentrations and composition. Controlling indoor PM_2.5 concentration is presently more feasible and economic than decreasing outdoor PM_2.5 concentration. This study reviews modeling and measurements that address relationships between indoor and outdoor PM_2.5 and the corresponding constituent concentrations. The key factors in the models are indoor-outdoor air exchange rate, particle penetration, and deposition. We compiled studies that report I/O ratios of PM_2.5 and typical constituents (sulfate (SO₄^2-), nitrate (NO₃^-), ammonium (NH₄⁺), elemental carbon (EC), and organic carbon (OC), iron (Fe), copper (Cu), and manganese (Mn)). From these studies we conclude that: 1) sulfate might be a reasonable tracer of non-volatile species (EC, Fe, Cu, and Mn) and PM_2.5 itself; 2) particulate nitrate and ammonium generally desorb to gaseous HNO₃ and NH₃ when they enter indoors, unless, as seldom happens, they have strong indoor sources; 3) indoor-originating semi-volatile organic compounds sorb on indoor PM_2.5, thereby increasing the PM_2.5 OC load. We suggest further studies on indoor-outdoor relationships of PM_2.5 and constituents so as to help develop standards for healthy buildings.     

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.