Chemical composition and source apportionment of PM10 and PM2.5 in different functional areas of Lanzhou, China

To elucidate the air pollution characteristics of northern China, airborne PM_10(atmospheric dynamic equivalent diameter ≤ 10 μm) and PM_(2.5)(atmospheric dynamic equivalent diameter ≤ 2.5 μm) were sampled in three different functional areas(Yuzhong County,Xigu District and Chengguan District) of Lanzhou, and their chemical composition(elements, ions, carbonaceous species) was analyzed. The results demonstrated that the highest seasonal mean concentrations of PM_10(369.48 μg/m~3) and PM_(2.5)(295.42 μg/m~3) were detected in Xigu District in the winter, the lowest concentration of PM_(2.5)(53.15 μg/m~3) was observed in Yuzhong District in the fall and PM_10(89.60 μg/m~3) in Xigu District in the fall.The overall average OC/EC(organic carbon/elemental carbon) value was close to the representative OC/EC ratio for coal consumption, implying that the pollution of Lanzhou could be attributed to the burning of coal. The content of SNA(the sum of sulfate, nitrate,ammonium, SNA) in PM_(2.5)in Yuzhong County was generally lower than that at other sites in all seasons. The content of SNA in PM_(2.5)and PM_10 in Yuzhong County was generally lower than that at other sites in all seasons(0.24–0.38), indicating that the conversion ratios from precursors to secondary aerosols in the low concentration area was slower than in the area with high and intense pollutants. Six primary particulate matter sources were chosen based on positive matrix factorization(PMF) analysis, and emissions from dust, secondary aerosols, and coal burning were identified to be the primary sources responsible for the particle pollution in Lanzhou.     

Chemical characterization and source identification of PM2.5 in Luoyang after the clean air actions

Luoyang is a typical heavy industrial city in China, with a coal-dominated energy structure and serious air pollution. Following the implementation of the clean air actions, the physicochemical characteristics and sources of PM_2.5 have changed. A comprehensive study of PM_2.5 was conducted from October 16, 2019 to January 23, 2020 to evaluate the effectiveness of previous control measures and further to provide theory basis for more effective policies in the future. Results showed that the aerosol pollution in Luoyang in autumn and winter is still serious with the average concentration of 91.1 μg/m³, although a large reduction (46.9%) since 2014. With the contribution of nitrate increased from 12.5% to 25.1% and sulfate decreased from 16.7% to 11.2%, aerosol pollution has changed from sulfate-dominate to nitrate-dominate. High NO₃⁻/SO₄²⁻ ratio and the increasing of NO₃⁻/SO₄²⁻ ratio with the aggravation of pollution indicating vehicle exhaust playing an increasingly important role in PM_2.5 pollution in Luoyang, especially in the haze processes. Secondary inorganic ions contributed significantly to the enhancement of PM_2.5 during the pollution period. The high value of Cl⁻/Na⁺ and EC concentration indicate coal combustion in Luoyang is still serious. The top three contributor sources were secondary inorganic aerosols (33.3%), coal combustion (13.6%), and industrial emissions (13.4%). Close-range transport from the western and northeastern directions were more important factors in air pollution in Luoyang during the sampling period. It is necessary to strengthen the control of coal combustion and reduce vehicle emissions in future policies.     

Chemical characterization and source apportionment of atmospheric submicron particles on the western coast of Taiwan Strait, China

Taiwan Strait is a special channel for subtropical East Asian Monsoon and its western coast is an important economic zone in China. In this study, a suburban site in the city of Xiamen on the western coast of Taiwan Strait was selected for fine aerosol study to improve the understanding of air pollution sources in this region. An Aerodyne high-resolution time-of-flight aerosol mass spectrometer(HR-To F-AMS) and an Aethalometer were deployed to measure fine aerosol composition with a time resolution of 5 min from May 1to 18, 2015. The average mass concentration of PM1 was 46.2 ± 26.3 μg/m~3 for the entire campaign. Organics(28.3%), sulfate(24.9%), and nitrate(20.6%) were the major components in the fine particles, followed by ammonium, black carbon(BC), and chloride. Evolution of nitrate concentration and size distribution indicated that local NOx emissions played a key role in high fine particle pollution in Xiamen. In addition, organic nitrate was found to account for 9.0%–13.8% of the total measured nitrate. Positive Matrix Factorization(PMF)conducted with high-resolution organic mass spectra dataset differentiated the organic aerosol into three components, including a hydrocarbon-like organic aerosol(HOA) and two oxygenated organic aerosols(SV-OOA and LV-OOA), which on average accounted for 27.6%,28.8%, and 43.6% of the total organic mass, respectively. The relationship between the mass concentration of submicron particle species and wind further confirmed that all major fine particle species were influenced by both strong local emissions in the southeastern area of Xiamen and regional transport through the Taiwan Strait.     

Cross-boundary transport and source apportionment for PM2.5 in a typical industrial city in the Hebei Province, China: A modeling study

Cross-boundary transport of air pollution is a difficult issue in pollution control for the North China Plain. In this study, an industrial district (Shahe City) with a large glass manufacturing sector was investigated to clarify the relative contribution of fine particulate matter (PM_2.5) to the city's high levels of pollution. The Nest Air Quality Prediction Model System (NAQPMS), paired with Weather Research and Forecasting (WRF), was adopted and applied with a spatial resolution of 5 km. During the study period, the mean mass concentrations of PM_2.5, SO₂, and NO₂ were observed to be 132.0, 76.1, and 55.5 μg/m³, respectively. The model reproduced the variations in pollutant concentrations in Shahe at an acceptable level. The simulation of online source-tagging revealed that pollutants emitted within a 50-km radius of downtown Shahe contributed 63.4% of the city's total PM_2.5 concentration. This contribution increased to 73.9±21.2% when unfavorable meteorological conditions (high relative humidity, weak wind, and low planetary boundary layer height) were present; such conditions are more frequently associated with severe pollution (PM_2.5 ≥ 250 μg/m³). The contribution from Shahe was 52.3±21.6%. The source apportionment results showed that industry (47%), transportation (10%), power (17%), and residential (26%) sectors were the most important sources of PM_2.5 in Shahe. The glass factories (where chimney stack heights were normally < 70 m) in Shahe contributed 32.1% of the total PM_2.5 concentration in Shahe. With an increase in PM_2.5 concentration, the emissions from glass factories accumulated vertically and narrowed horizontally. At times when pollution levels were severe, the horizontally influenced area mainly covered Shahe. Furthermore, sensitivity tests indicated that reducing emissions by 20%, 40%, and 60% could lead to a decrease in the mass concentration of PM_2.5 of of 12.0%, 23.8%, and 35.5%, respectively.     

Fine particulate matter (PM2.5/PM1.0) in Beijing, China: Variations and chemical compositions as well as sources

Particulate matter (i.e., PM_1.0 and PM_2.5), considered as the key atmospheric pollutants, exerts negative effects on visibility, global climate, and human health by associated chemical compositions. However, our understanding of PM and its chemical compositions in Beijing under the current atmospheric environment is still not complete after witnessing marked alleviation during 2013–2017. Continuous measurements can be crucial for further air quality improvement by better characterizing PM pollution and chemical compositions in Beijing. Here, we conducted simultaneous measurements on PM in Beijing during 2018–2019. Results indicate that annual mean PM_1.0 and PM_2.5 concentrations were 35.49 ± 18.61 µg/m³ and 66.58 ± 60.17 µg/m³, showing a positive response to emission controls. The contribution of sulfate, nitrate, and ammonium (SNA) played an enhanced role with elevated PM loading and acted as the main contributors to pollution episodes. Discrepancies observed among chemical species between PM_1.0 and PM_2.5 in spring suggest that sand particles trend to accumulate in the range of 1–2.5 µm. Pollution episodes occurred accompanied with southerly clusters and high formation of SNA by heterogeneous reactions in summer and winter, respectively. Results from positive matrix factorization (PMF) combined with potential source contribution function (PSCF) models showed that potential areas were seasonal dependent, secondary and vehicular sources became much more important compared with previous studies in Beijing. Our study presented a continuous investigation on PM and sources origins in Beijing, which provides a better understanding for further emission control as well as a reference for other cities in developing countries.     

华南沿海地区夏初PM2.5水溶性离子特征及来源解析

2018年6月7日—7月10日,利用在线气体和气溶胶成分监测仪(IGAC)在珠海市沿海站对PM_2.5中水溶性离子浓度和气体开展连续观测分析.结果发现,夏初沿海地区水溶性离子处于较低水平,SO₄^2-、NH₄⁺、NO₃^-、Cl^-、Na⁺、Ca²⁺、K⁺、Mg²⁺浓度分别为4.78、1.87、1.16、0.92、0.37、0.27、0.11和0.11μg·m^-3,其中,代表海洋来源的Na⁺和Cl^-浓度与珠江口东海岸的深圳沿海地区相当. Na⁺和Cl^-呈明显的白天高、夜晚低的日变化特征,与海盐排放在海陆风环流下的输送有关.基于天气形势分析、气团来源分析和PMF来源解析方法研究了观测期间发生的两次污染过程,一次是受到强热带风暴外...     

An integrated chemical mass balance and source emission inventory model for the source apportionment of PM2.5 in typical coastal areas

The source apportionment of PM_2.5 is essential for pollution prevention.In view of the weaknesses of individual models,we proposed an integrated chemical mass balancesource emission inventory(CMB-SEI) model to acquire more accurate results.First,the SEI of secondary component precursors(SO₂,NO_x,NH₃,and VOCs) was compiled to acquire the emission ratios of these sources for the precursors.Then,a regular CMB simulation was executed to obtain the contribut...     

东莞市PM1中重金属元素的污染特征及来源解析

采集了2011年8月—2012年7月间东莞市不同区域两点(A:生活区;B:工业区)的PM2.5～10、PM1～2.5和PM1样品,并用ICP-MS分析了颗粒物上Pb、Cu、Zn、As、Cd、V、Mn、Cr、Hg和Al等10种元素,重点研究了PM1中除Al外其它9种重金属元素的污染特征.分析结果显示,工业区B点PM1中的重金属污染明显较生活区A点严重,9种重金属元素在B点PM1中的浓度是A点的2.3～4.4倍.Zn和Pb是A、B两点PM1中主要的重金属元素,同时各重金属元素质量占PM1质量百分比范围为0.0008%～0.3530%.粒径分布、富集因子分析的结果显示,大部分重金属元素主要富集在PM1中,且主要受人为源的影响.因子分析源解析结果表明,A、B两点PM1主要来源于4类污染源:燃煤源、机动车/工厂燃油源、冶金化工源、土壤尘.污染源特征分析表明,偏北风时东莞地区PM1受局地源远距离输送影响更严重,偏南风时受本地污染源影响更严重.     

邯郸市区PM2.5污染特征及来源解析

对邯郸市区内邯郸钢铁集团（邯钢）、邯郸市环境监测中心（环保局）、河北工程大学（矿院）3个点位4个季节代表月大气PM_2.5样品进行采集,并对其离子、元素、碳质组分进行测试分析;利用基于排放清单、受体模型与空气质量模型相结合的综合来源解析方法,对邯郸市区大气PM_2.5贡献来源进行分析.结果表明：邯郸市区PM_2.5年均浓度为85.5μg/m³,秋冬季浓度明显高于春夏季,邯钢点位浓度略高于矿院和环保局;PM_2.5中占比较高的组分为NO₃^-、SO₄^2-、POA、SOA和NH₄⁺,分别占15.7%、14.5%、13.2%、12.2%和12.4%,具有明显的二次污染和有机污染特征,冬季二次组分和有机组分占比略高于其他季节,环保局点位一次有机气溶胶（POA）和二次有机气溶胶（SOA）占比略高于矿院和邯钢;冶金和扬尘是PM_2.5最主要的贡献来源,贡献率分别为27.0%和18.7%,冶金源在春夏季的贡献比例高于秋冬季,在邯钢点位的贡献率明显高于环保局和矿院.     

哈尔滨市供暖期PM2.5组分特征及来源解析

在哈尔滨市2014年1—3月的供暖期间对城区、郊区及周边农村地区的室内外PM_2.5样品进行了同时采集,分析了样品中碳质组分、水溶性离子及无机元素后,通过颗粒物热力学模型计算了颗粒物原位酸度,并通过基于标记的正矩阵分解（PMF）模型对室内外颗粒物的来源进行了表征.计算结果表明,3个地点室外PM_2.5原位酸度均低于室内,且室内外颗粒物原位酸度均为市区最高.PMF结果表明,哈尔滨市区、郊区及农村地区二次源对室外PM_2.5的贡献均排第3位.交通源对市区及郊区的贡献在16%~20%,对于农村地区则是最弱的影响因素.生物质燃烧是农村地区室内外PM_2.5的首要来源;燃煤和工业排放则是市区室内外PM_2.5的主要来源;工业排放是郊区室外PM_2.5的首要来源,与郊区的石化及金属工业有密切联系.因此,为提升哈尔滨市供暖期的空气质量,在进行农村散煤与生物质燃烧治理,推进农村地区清洁能源利用的同时,应多措并举注重城市交通状况改善和促进燃煤锅炉与工业超低排放技术的升级改造,促进区域协同治理.     

Chemical characteristics, oxidative potential, and sources of PM2.5 in wintertime in Lahore and Peshawar, Pakistan

The chemical characteristics, oxidative potential, and sources of PM_2.5 were analyzed at the urban sites of Lahore and Peshawar, Pakistan in February 2019. Carbonaceous species, water soluble ions, and metal elements were measured to investigate the chemical composition and sources of PM_2.5. The dithiothreitol (DTT) consumption rate was measured to evaluate the oxidative potential of PM_2.5. Both cities showed a high exposure risk of PM_2.5 regarding its oxidative potential (DTT_v). Carbonaceous and some of the elemental species of PM_2.5 correlated well with DTT_v in both Lahore and Peshawar. Besides, the DTT_v of PM_2.5 in Lahore showed significant positive correlation with most of the measured water soluble ions, however, ions were DTT-inactive in Peshawar. Due to the higher proportions of carbonaceous species and metal elements, Peshawar showed higher mass-normalized DTT activity of PM_2.5 compared to Lahore although the average PM_2.5 concentration in Peshawar was lower. The high concentrations of toxic metals also posed serious non-carcinogenic and carcinogenic risks to the residents of both cities. Principle component analysis coupled with multiple linear regression was applied to investigate different source contributions to PM_2.5 and its oxidative potential. Mixed sources of traffic and road dust resuspension and coal combustion, direct vehicle emission, and biomass burning and formation of secondary aerosol were identified as the major sources of PM_2.5 in both cities. The findings of this study provide important data for evaluation of the potential health risks of PM_2.5 and for formulation of efficient control strategies in major cities of Pakistan.     

重庆万州城区大气PM2.5中正构烷烃污染特征及来源分析

采用大流量气溶胶采样器采集了重庆市万州城区2013年夏季和冬季大气中PM_(2.5)样品,并运用气相色谱-质谱联用技术对PM_(2.5)中22种(C12~C33)正构烷烃的含量进行了测定,进而对万州城区PM_(2.5)中正构烷烃的污染特征及来源进行了分析.结果表明,万州城区夏、冬季大气PM_(2.5)中均检测出C12~C33正构烷烃,主峰碳均为C29和C31.夏、冬季PM_(2.5)中正构烷烃日均总浓度分别为158.70 ng·m-3和257.20 ng·m-3,碳优势指数CPI分别为1.63和1.82,CPI1分别为0.61和0.67,CPI2分别为1.83和1.96,植物蜡参数Wax C平均值分别为53.44%和55.53%.万州城区大气细颗粒物中n-alkanes受到来源于陆源高等植物蜡的排放等生物源及化石燃料燃烧等人为源的共同影响,且生物源的影响较大.     

Chemical formation and source apportionment of PM2.5 at an urban site at the southern foot of the Taihang mountains

The region along the Taihang Mountains in the North China Plain (NCP) is characterized by serious fine particle pollution. To clarify the formation mechanism and controlling factors, an observational study was conducted to investigate the physical and chemical properties of the fine particulate matter in Jiaozuo city, China. Mass concentrations of the water-soluble ions (WSIs) in PM_2.5 and gaseous pollutant precursors were measured on an hourly basis from December 1, 2017, to February 27, 2018. The positive matrix factorization (PMF) method and the FLEXible PARTicle (FLEXPART) model were employed to identify the sources of PM_2.5. The results showed that the average mass concentration of PM_2.5 was 111 μg/m³ during the observation period. Among the major WSIs, sulfate, nitrate, and ammonium (SNA) constituted 62% of the total PM_2.5 mass, and NO₃^? ranked the highest with an average contribution of 24.6%. NH₄⁺ was abundant in most cases in Jiaozuo. According to chemical balance analysis, SO₄^2?, NO₃^?, and Cl^? might be present in the form of (NH₄)₂SO₄, NH₄NO₃, NH₄Cl, and KCl. The liquid-phase oxidation of SO₂ and NO₂ was severe during the haze period. The relative humidity and pH were the key factors influencing SO₄^2- formation. We found that NO₃^? mainly stemmed from homogeneous gas-phase reactions in the daytime and originated from the hydrolysis of N₂O₅ in the nighttime, which was inconsistent with previous studies. The PMF model identified five sources of PM_2.5: secondary origin (37.8%), vehicular emissions (34.7%), biomass burning (11.5%), coal combustion (9.4%), and crustal dust (6.6%).     

汾渭平原复杂地形影响下冬季PM2.5污染分布特征、来源及成因分析

汾渭平原受其复杂地形特征及产业结构影响,和京津冀、长三角地区一起被列为大气污染重点防治区域.本研究应用2014—2019年冬季中国环境监测总站汾渭平原各城市的六大空气污染物逐小时数据,结合欧洲中心ERA-5数据,利用HYSPLIT后向轨迹模型及T-model斜交旋转主成分分析法（PCT）,揭示过去6年汾渭平原冬季颗粒物浓度演变规律,厘清汾渭平原复杂地形影响下大气污染来源特征、潜在源区及成因,识别影响汾渭平原冬季空气污染的主要天气系统类型.HYSPLIT模拟结果表明,冬季喇叭口地形城市主要受本地和邻近区域污染源影响;山区盆地地形城市更易受到100~300 km距离以内污染源的传输影响,其中,来自陕北的气团对其影响最大;峡谷地形城市更易受到300~600 km范围内污染源的传输影响;平原地形城市的污染物浓度受区域传输的影响较大.影响汾渭平原冬季颗粒物重污染的天气系统可分为高压前部型、高压后部型、均压场型及低压倒槽型,其中,高压前部型是汾渭平原冬季重污染时段最易出现的天气形势.     

Source apportionment of PM2.5 and visibility in Jinan, China

Atmospheric extinction is impacted by the chemical composition of particles. To better understand the chemical composition of PM_2.5 (particles with diameters of less than 2.5 μm) and its relationship with extinction, one-month sampling campaigns were carried out in four different seasons from 2013 to 2014 in Jinan, China. The seasonal average concentrations of PM_2.5 were 120.9 (autumn), 156.6 (winter), 102.5 (spring), and 111.8 μg/m³ (summer). The reconstructed PM_2.5 chemical composition showed that sulfate, nitrate, chlorine salt, organic matter (OM), mineral dust, elemental carbon (EC) and others accounted for 25%, 14%, 2%, 24%, 22%, 3% and 10%, respectively. The relationship between the chemical composition of PM_2.5 and visibility was reconstructed by the IMPROVE method, and ammonium sulfate, ammonium nitrate, OM and EC dominated the visibility. Seven main sources were resolved for PM_2.5, including secondary particles, coal combustion, biomass burning, industry, motor vehicle exhaust, soil dust and cooking, which accounted for 37%, 21%, 13%, 13%, 12%, 3% and 1%, respectively. The contributions of different sources to visibility were similar to those to PM_2.5. With increasing severity of air pollution, the contributions of secondary particles and coal combustion increased, while the contribution of motor vehicle exhaust decreased. The results showed that coal combustion and biomass burning were still the main sources of air pollution in Jinan.     

东北亚冬季PM2.5水溶性离子空间分布特征及来源

为探讨东北亚冬季PM_2.5水溶性离子空间分布特征及来源,测定了2017~2018年沈阳冬季PM_2.5水溶性离子浓度.结果显示：沈阳冬季PM_2.5水溶性离子平均质量浓度为28.5±11.9μg/m³,二次离子（SO₄^2-、NO₃^-和NH₄⁺）的浓度最高,分别占总水溶性离子质量浓度的31.0%、22.4%和19.2%.运用离子化学计量学关系、相关性和主成分分析,探讨了沈阳冬季PM_2.5水溶性离子的可能来源.并整合了东北亚冬季（中国东北、韩国、日本）近20a来PM_2.5水溶性离子数据,发现沿着东亚冬季风,东北亚冬季PM_2.5水溶性离子浓度从中国东北,经韩国海岸、韩国和济州岛,日本海岸至日本整体呈下降趋势,在韩国和日本出现局部上升,且在不同区域,不同水溶性离子占比明显不同.其中,韩国冬季PM_2.5中SO₄^2-、Ca²⁺和K⁺受外来源影响显著,NO₃^-和NH₄⁺主要来自本地源,Cl^-、Na⁺和Mg²⁺主要来自本地源或海源;日本中部冬季PM_2.5中SO₄^2-、NO₃^-、NH₄⁺和K⁺主要来自本地源,Cl^-、Ca²⁺、Na⁺和Mg²⁺主要来自本地源或海源.     

Water-soluble matter in PM2.5 in a coastal city over China: Chemical components, optical properties, and source analysis

Although marine and terrestrial emissions simultaneously affect the formation of atmospheric fine particles in coastal areas, knowledge on the optical properties and sources of water-soluble matter in these areas is still scarce. In this work, taking Qingdao, China as a typical coastal location, the chemical composition of PM_2.5 during winter 2019 was analyzed.Excitation-emission matrix fluorescence spectroscopy was combined with parallel factor analysis model to explain the component...     

北京混合功能区夏冬季细颗粒物组分特征及来源比较

于2014年8月和12月,选择北京某城市混合功能区,分别手工采集一个月的环境空气PM_2.5样品,实验室方法测定滤膜中的元素碳/有机碳、9种可溶性离子、16种无机元素等20余种化学组分,采用CMB模型对夏冬两季PM_2.5来源进行分析.结果表明,夏季PM_2.5日均质量浓度为73μg/m³,低于《环境空气质量标准》,而冬季平均值为111μg/m³,高于夏季和标准限值.冬季OC和EC浓度均高于夏季,且OC/EC比值升高,OC和EC呈线性相关,提示二者有相同来源.NO₃^-、SO₄^2-、NH₄⁺是北京混合功能区3种主要可溶性离子,且夏季生成量较高;冬季Cl^-显著升高与燃煤排放有关.Si、Ti、Fe、Zn、Al等元素质量浓度在0.1~10μg/m³浓度水平,Pb、Cu、Mn、Cr、Ba、Sb等在10~10²ng/m³浓度水平,V、Ni、Co、Mo、Cd等在0.1~10ng/m³浓度水平.且冬季各个元素浓度均高于夏季.CMB模型初步解析结果表明,夏季和冬季颗粒物的来源变化明显,夏季二次硫酸盐、机动车和二次硝酸盐贡献率居前三位,而冬季则为燃煤、机动车和扬尘.     

成都城区PM2.5季节污染特征及来源解析

于2009—2010年各季节典型月在成都城区采集了大气PM_2.5样品,对PM_2.5的质量浓度及其主要化学成分(含碳组分、水溶性无机离子和元素)进行了测定. 结果显示:成都城区PM_2.5平均质量浓度高达(165.1±85.1)μg·m^-3,是国家环境空气质量标准年均PM_2.5限值的4.7倍. OC、EC和水溶性二次离子(SO₄^2-,NO₃^-和NH₄⁺)的平均浓度分别为(22.6±10.2)μg·m^-3,(9.0±5.4)μg·m^-3和(62.8±44.3)μg·m^-3,分别占PM_2.5浓度的13.7%、5.5%和38.0%. PM_2.5及其主要化学成分浓度季节特征明显,即秋冬季高于春夏季. 利用正交矩阵因子分析(PMF)对成都城区PM_2.5的来源进行解析,结果表明,土壤尘及扬尘、生物质燃烧、机动车源和二次硝酸盐/硫酸盐的贡献率分别为14.3%、28.0%、24.0%和31.3%. 就季节变化而言,生物质燃烧源贡献率在四个季节均维持在较高水平;土壤尘及扬尘的贡献率在春季显著提高;机动车源的贡献率在夏季中表现突出;而二次硝酸盐/硫酸盐的贡献率在秋冬季中则最为显著.     

烟花爆竹燃放对大气污染物及PM2.5组分影响

为了解烟花爆竹燃放对保定市大气污染物和PM_2.5中水溶性离子及有机碳(OC)、元素碳(EC)浓度的影响,对保定市春节期间大气污染物和颗粒物组分的浓度特征进行了分析,并评估了烟花爆竹的贡献.结果表明:2019年春节期间烟花爆竹集中燃放期PM_2.5、PM10、SO2、NO2、CO平均浓度比非集中燃放期分别增加了1.3、1.0、1.1、0.4、0.02倍;保定市春节期间禁燃措施施行后,除夕、初一2d污染物平均浓度、最高浓度和高浓度持续时间均明显下降,集中燃放期烟花爆竹燃放对PM_2.5、PM10和SO2浓度贡献量从50%左右(2018年、2017年)下降至30%左右(2019年),其中SO2贡献量下降幅度超过PM_2.5和PM10;组分分析表明,接待中心站点(主城区)、涿州站点(区县建成区)烟花爆竹燃放期K⁺、Mg²⁺、Cl^-浓度在水溶性离子中的总占比分别为39.3%、51.1%,比非燃放期的占比显著上升;烟花爆竹燃放对PM_2.5