As one of the important sources of aerosols, new particle formation events can affect the spatiotemporal dis-tribution of CCN. To analyze the characteristics of new particle formation (NPF) events and their effects on cloud condensation nuclei (CCN) during the summer at Mt.Tian, the data of aerosol size distribution and CCN number concentrations were measured using an aerosol wide-range particle size spectrometer and a cloud condensation nuclei counter from August 4 to 25, 2019, and combined with meteorological data and polycyclic aromatic hydrocarbons (PAHs) data. NPF events at Mt.Tian occurred seven days during the observation period, accounting for 32% of the observation days, and usually occurred at 13:00 and ended at 17:00 (Beijing Time, same below). NPF events particularly influenced the diurnal variations of aerosol number concentrations in different modes. The number concentrations of nucleation and Aitken modes increased rapidly while the accumulation mode increased slowly during 13:00 and 17:00 on NPF days. The peak particle number concentrations in the nucle-ation, Aitken and accumulation modes on NPF days were 5360.1 cm-3, 7185.6 cm-3 and 839.3 cm -3, respec-tively, which were 3.3, 1.8 and 1.7 times higher than those on non-NPF days during 13:00 and 17:00. The meteorological conditions of temperature higher than 20 degrees C, air pressure lower than 806 hPa, relative humidity (RH) lower than 40% and wind speed higher than 2.2 m s- 1 are favorable for the occurrence of NPF events. The NPF events at Mt.Tian can impact the mass concentration of PAHs. More Fluoride (FL) and Phenanthrene (PHE) were generated on NPF days. The mass concentrations of FL and PHE on NPF days were 0.002 & PLUSMN; 0.003 ng m- 3 and 0.020 & PLUSMN; 0.010 ng m- 3, which were 243.54% and 28.44% higher than those on non-NPF days, respectively. NPF events were conducive to increased CCN concentration at Mt.Tian. After the NPF events, the mean number concentrations of CCN at each supersaturation (SS) on NPF days was about 20%-50% higher than on non-NPF days. The contribution of NPF events to the CCN number concentrations was 35.75%, 28.56%, 44.15%, 42.11% and 29.12% at 0.1%, 0.2%, 0.4%, 0.6% and 0.8% SS, respectively.

Based on the hourly monitoring data including meteorological elements and PM2.5 mass concentration in Yancheng from 2017 to 2021, PM2.5 mass concentration variations, influencing factors and source apportionment were studied by the Kolmogorov–Zurbenko filter and Potential Source Contribution Function Analysis (PSCF) method. The results showed that the mass concentration of PM2.5 in Yancheng showed a decreasing trend from 2017 to 2021, with a decline rate of about 33.8% (2017, 44.79 ± 31.22 μg/m3; 2021, 29.66 ± 21.69 μg/m3); the visibility increased by 18.4% (2017, 11.69 ± 6.46 km; 2021,13.8 ± 6.24 km), which is mainly related to emission reduction measures in China. The mass concentration of PM2.5 has significant seasonal variation characteristics, with the highest in winter, reaching 60.61 μg/m3, and the lowest in summer, only 23.11 μg/m3. The diurnal variation of PM2.5 showed a unimodal distribution, and concentration difference is obvious under the influence of land–sea breeze (36.60 μg/m3, easterly wind; 43.57 μg/m3, westerly wind). Meteorological factors have an important impact on the mass concentration of PM2.5, which fluctuates with seasons. It is calculated to have a good fitting relationship between the visibility and PM2.5 concentration, and the correlation decreases with the increase in humidity (−0.71 ~ −0.41). The relatively clean atmosphere under high humidity conditions is also prone to the obstruction to vision. The corresponding PM2.5 concentration varies significantly under different wind directions and wind speeds in Yancheng, and high values mainly come from the northwest–southeast–southwest direction. The potential source regions in autumn are mainly distributed in southwestern Jiangsu and northwestern Zhejiang; the potential source regions in winter are mainly located in southwestern Jiangsu, southern Anhui and northern Jiangxi.

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(NH4+、NO3-、SO42-、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PM2.5浓度和NH4+、NO3-和SO42-的潜在源区为皖豫鄂三省和赣湘鄂三省交界处.霾污染中PM2.5的主要影响范围是武汉市南部和北部省份,NO3-、NH4+和SO42-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.

The source of PM2.5 varies at different stages of urban haze pollution. In addition, there is obvious regional transport of pollutants between urban agglomerations. PM2.5 and its major chemical compositions in a regional haze episode were measured continuously from 16 to 27 November 2018 in Nanjing, China. The types of primary sources resolved by principal component analysis (PCA) and positive matrix factorization (PMF) were similar, and the result of PMF was more refined. The average contribution of each source by PMF was: secondary nitrate (64.01%), secondary sulfate (11.62%), incomplete combustion (4.49%), sea salt (8.61%), biomass burning (6.90%), and crustal dust (4.37%). In different haze stages, the distribution characteristics of air pollutants differed. The concentrations of SO42-, NO3−, NH4+, and black carbon were the highest in the haze developing stage, which was 2.0, 3.1, 3.0, and 2.4 times, respectively, higher than that under clean conditions. The increment of NO3− dominated the development of haze, and the proportion of NO3− from haze generation to development increased by 4.05%. The concentration contributions of secondary nitrate, sea salt, and biomass burning were highest in haze development, secondary sulfate was highest in haze generation, and incomplete combustion was highest in haze dissipation, which was 3.5, 1.8, 3.3, 1.7, and 9.5 times higher than the clean stage, respectively. In the haze episode, the contribution of crustal dust was lower than in the clean stage. Potential source contribution function (PSCF) and concentration weighted trajectory (CWT) revealed that the major source area of air pollutants in Nanjing came from the southeast, and the northwest was the major impact area.

使用TCA08型总碳分析仪观测了南京市2019年4月19日-6月27日大气气溶胶中有机碳(OC)与元素碳(EC)的含量,结合空气质量数据(PM2.5、PM10、SO2、NO2、CO和O3)和气象数据,分析了春夏季含碳气溶胶的污染特征及其影响因素.结果表明,观测期间南京市不同污染物和含碳气溶胶质量浓度春夏季节性差异明显.O3、CO、EC和POC在夏季平均质量浓度分别为109.5、0.74、3.1、2.1 μg·m-3,分别比春季高了32.1％、4.8％、38.0％和 30.0％;而 SO2、NO2,PM2.5、PM10、OC 和 SOC 的质量浓度在春季的平均质量浓度分别为12.0、37.2、31.4、65.4、6.2、4.6 μg·m-3,分别比夏季高了23.3％、24.7％、7.1％、22.8％、26.5％和56.5％.不同空气质量等级下不同碳气溶胶的日变化特征不同.OC和SOC日变化在空气质量为优时日变化平缓,空气质量为良时均为单峰型分布,在轻度污染时均为三峰型分布.EC和POC在空气质量为优时日变化均为双峰型分布,在良和轻度污染下日变化均为单峰型分布.3次污染过程中O3与SOC的相关性不同,相关系数分别为-0.64、-0.66和0.63.3次O3污染过程中含碳气溶胶日变化特征不同.OC和SOC在过程一和过程二中日变化均表现出夜间高白天低的特征;而过程三中昼夜浓度差异不显著.过程一和过程二中EC和POC均呈单峰型分布,峰值均出现在4:00-6:00,但过程三中却均呈显著的双峰型分布,峰值均分别出现在5:00-7:00和21:00-24:00.

Since the Air Pollution Prevention and Control Action Plan (air clean plan) issued in 2013, air quality has been in continuous improvement. The second stage of air clean plan since 2018 was focused on O-3 controlling, but it still didn't decline so significantly as PM2.5. This study conducted a long-term observation on black carbon (BC) and utilized the observational data of other air pollutants (PM2.5, PM10, NO2, SO2, CO and O-3), the meteorological elements and the vertical sounding data of PBL in Nanjing. In the daytime (08:00-20:00), PM2.5 kept decreasing from 2015 to 2020 at the rate of 4.8 mu g.m(-3).a(-1), however, BC increased at the rate of 0.6 mu g.m(-3).a(-1), which has led to the continuous growth of BC/PM2.5 (0.9%.a(-1)). However, during this period, O-3 was relatively stable and, in 2020, it returned below its value in 2015 after slight increases in 2017 and 2018. Meanwhile, the average surface temperature had increased by around 1.0 degrees C during 2015-2019 at the rate of 0.3 degrees C.a(-1). Also, the average height of the inversion layer had increased significantly by 494.0 and 176.7 mat 20:00 and 08:00, whose growth ratio was up to 57% and 25%, respectively. The above observation results have formed a set of chain reactions as follows. The growth of the surface BC caused the surface temperature to rise due to the increasing heating effect of BC. The continuous growth of the surface temperature made it easier for the PBL height to develop, which led to the lift of the inversion layer in the PBL and the larger atmospheric environment capacity. Ultimately, it is conducive to the diffusion of the near surface pollutants, thus helping reduce their concentrations, which offsets the increasing tendency of O-3 and add to the decreasing trend of PM2.5. This phenomenon is the most remarkable in summer, with the fastest increasing rate of temperature (0.8 degrees C.a.(-)(1)) and 0 3 (3.9 mu g.m(-3).a(-1)) during 2015-2019 (excluding 2020 to erase the great effect of COVID-19 lockdown on emissions).

The urban agglomeration of Yangtze River Delta (YRD) is symbol of China's rapid urbanization during the past decades. Urbanization can significantly impact land-cover properties, surface heating, and emissions of air pollutants. To control the spread of COVID-19, China imposed very rigorous restrictions, leading to dramatic reductions in air pollutants (except O3) from satellite and ground-based data. As such, inter-transportation of air pollutants was weak during the lockdown, which was conducive to discuss the impacts of urbanization on the air quality. During the lockdown, the rates of surface PM2.5, PM10, SO2, NO2 and CO reductions in different urban types ranged from 6.6% to 62.4% in the YRD. Urbanization exerted great impacts on the pollutant variations in urban agglomerations despite such large decreases in primary pollution in YRD. Lower values of AOD and tropospheric NO2 columns were noticeably observed over large cities during the lockdown. The extents of surface PM, SO2, NO2 and CO reductions in large cities (first-tier and second-tier) were found to be larger (4.7%–10.6%) than those in small-medium cities (third-tier and fourth-tier) during the lockdown, which was also the case for the extent of the increase (33.0%–53.0%) in O3. PCA analysis revealed that the PM decreases in large cities made greater improvement in the air quality compared with the small cities during lockdown, while the urbanization had non-obvious influence on the photochemical reactions. It is imperative to adopt policies and programs to mitigate the air pollution in urban agglomerations in the fast urbanization process.

As the largest independent east–west-trending mountain in the world, Mt. Tianshan exerts crucial impacts on climate and pollutant distributions in central Asia. Here, the vertical structures of meteorological elements and black carbon (BC) were first derived at Mt. Tianshan using an unmanned aerial vehicle system (UAVS). Vertical changes in meteorological elements can directly affect the structure of the planet boundary layer (PBL). As such, the influences of topography and meteorological elements’ vertical structure on aerosol distributions were explored from observations and model simulations. The mass concentrations of BC changed slightly with the increasing height below 2300 m above sea level (a.s.l.), which significantly increased with the height between 2300–3500 m a.s.l. and contrarily decreased with ascending altitude higher than 3500 m. Topography and mountain–valley winds were found to play important roles in the distributions of aerosols and BC. The prevailing valley winds in the daytime were conducive to pollutant transport from surrounding cities to Mt. Tianshan, where the aerosol number concentration and BC mass concentration increased rapidly, whereas the opposite transport pattern dominated during nighttime.

Despite the yearly decline in PM2.5 in China, surface ozone has been rapidly increasing recently, which makes it imperative to coordinate and control both PM2.5 and ozone in the atmosphere. This study utilized the data of pollutant concentrations and meteorological elements during 2015 to 2018 in Nanjing, China to analyze the daily correlation between black carbon and ozone (CBO), and the distribution of the pollutant concentrations under different levels of CBO. Besides, the diurnal variations of pollutant concentrations and meteorological elements under high positive and negative CBO were discussed and compared. The results show that the percentage of positive CBO had been increasing at the average rate of 7.1%/year, and it was 38.7% in summer on average, nearly twice of that in other seasons (19.2%). The average black carbon (BC), PM2.5 and NO2 under positive CBO was lower than those under negative CBO. It is noticeable that the surface ozone began to ascend when CBO was up to 0.2, with PM2.5 and NO2 decreasing and BC remaining steady. Under negative CBO, pollutant concentrations and meteorological elements showed obvious diurnal variations: BC showed a double-peak pattern and surface ozone, PM2.5, SO2 and CO showed single-peak patterns and NO2 showed a trough from 10:00 to 19:00. Wind speed and visibility showed a single-peak pattern with little seasonal difference. Relative humidity rose first, then it lowered and then it rose. Under positive CBO, the patterns of diurnal variations became less obvious, and some of them even showed no patterns, but just fluctuated at a certain level.

Atmospheric PM2.5-bound polycyclic aromatic hydrocarbons (PAHs) pose a major threat to human health. At present, studies on PAHs in the atmosphere have mostly focused on their concentration levels and source apportionment, whereas studies on the vertical transport of PAHs in the atmosphere are limited. However, the vertical transport of PAHs is important for their diffusion near the ground and their long-range transport at higher altitude. In this study, PM2.5 samples were collected simultaneously at the summit and foot of Mount Tai (MTsummit and MTfoot, respectively) from May to June 2017, and the concentrations of 18 PAHs in the samples were determined. The total concentration of PAHs at MTsummit was 2.406 ng m−3, which was well below the pollution levels of domestic cities, whereas that at MTfoot was as high as 9.068 ng m−3, which was within the range of pollution levels in domestic cities. The total carcinogenic risk for both MTsummit and MTfoot was within the potential risk range. Given the source of PAHs and the diurnal variation of the planetary boundary layer, the PAHs showed opposite diurnal trends at MTsummit and MTfoot. Vertical transport was an important source of daytime PAHs at MTsummit, and the vertical transport efficiency of PAHs decreased with an increasing ring number; this may be due to the combined effects of gas-particle partitioning and chemical reactions. Furthermore, PAHs originating in the surrounding high-emission provinces can affect the Mount Tai area via atmospheric trans-regional transport, and the BaP/BeP ratio is a useful indicator of the transport distance of PAHs.

From November 16 to 28 2018, water-soluble ions in particulate matter and some trace gases in Nanjing City were observed using the online gas composition and aerosol monitoring system MARGA ADI 2080. Combined with meteorological elements and sounding data, the distribution characteristics and day-night differences of pollutants and water-soluble ions during haze, fog, clear, and precipitation processes were analyzed. The results show that the average concentration of PM2.5 varied from 26.9μg·m-3 (precipitation) to 96.4μg·m-3 (haze) while total water-soluble ions varied between 23.7μg·m-3 (precipitation) and 89.7μg·m-3 (haze). The ranked order of ion concentrations was NO3- > NH4+ > SO42- > Cl- > K+ > Ca2+ > Na+ > Mg2+ during haze and fog events, and NO3- > SO42- > NH4+ > Cl- > Ca2+ > K+ > Na+ > Mg2+ during clear weather and precipitation period. The diurnal distributions of water-soluble ions were quite different under the four conditions, although SO42-, NO3-, and NH4+(SNA) were ranked haze > fog > clear > precipitation for both day and night periods. According to the PMF source analysis, secondary sources were the main factors affecting haze; secondary sources, sea salt, and combustion sources were the main pollution sources to foggy conditions; and the removal effect of precipitation on coal-fired sources and secondary sources was more notable than during clear conditions.

Particle size distribution, water soluble ions, and black carbon (BC) concentration in a long-term haze-fog episode were measured using a wide-range particle spectrometer (WPS), a monitor for aerosols and gases (MARGA), and an aethalometer (AE33) in Nanjing from 16 to 27 November, 2018. The observation included five processes of clean, mist, mix, haze, and fog. Combined with meteorological elements, the HYSPLIT model, and the IMPROVE model, we analyzed the particle size distribution, chemical composition, and optical properties of aerosols in different processes. The particle number size distribution (PNSD) in five processes differed: It was bimodal in mist and fog and unimodal in clean, mix, and haze. The particle surface area size distribution (PSSD) in different processes showed a bimodal distribution, and the second peak of the mix and fog processes shifted to a larger particle size at 480 nm. The dominant air masses in five processes differed and primarily originated in the northeast direction in the clean process and the southeast direction in the haze process. In the mist, mix, and fog processes local air masses dominated. NO3− was the primary component of water soluble ions, with the lowest proportion of 45.6% in the clean process and the highest proportion of 53.0% in the mix process. The ratio of NH4+ in the different processes was stable at approximately 23%. The ratio of SO42− in the clean process was 26.2%, and the ratio of other processes was approximately 20%. The average concentration of BC in the fog processes was 10,119 ng·m−3, which was 3.55, 1.80, 1.60, and 1.46 times that in the processes of clean, mist, mix, and haze, respectively. In the different processes, BC was primarily based on liquid fuel combustion. NO3−, SO42−, and BC were the main contributors to the atmospheric extinction coefficient and contributed more than 90% in different processes. NO3− contributed 398.43 Mm−1 in the mix process, and SO42− and BC contributed 167.90 Mm−1 and 101.19 Mm−1, respectively, during the fog process.

In this study, the black carbon (BC) measurements in the atmosphere of Nanjing, China were continuously conducted from 2015 to 2018 using a Model AE-33 aethalometer. By combining dataset of PM2 5, PM10, CO, NO2, SO2, O-3 and meteorological parameters, the temporal variations and the source apportionment of BC were given in this study. The results showed that the PM2 5 mass concentrations decreased in Nanjing, with an average annual rate of variation of 6.50 mu g/(m(3).year). Differently, the annual average concentrations of BC increased with an average annual variation rate of 214.71 ng/(m(3).year). The seasonal variations showed the pattern of BC mass concentrations in winter > autumn > spring > summer. The diurnal variations of BC mass concentrations showed a double-peak in all four seasons. The first peak occurred at approximately 7:00 in spring, summer and autumn and around 8:00 in winter. The second peak took place after 18:00. The average AAE (absorption Angstrom exponent) was 1.26 with a maximum of 1.35 during wintertime and the lowest (1.12) during summertime. In addition, the AAE was smaller in the daytime than that at night, with a minimum occurring between 13:00 and 14:00. BC and visibility show a good power-function relationship at different humidity levels. The average values of the visibility thresholds of the BC mass concentrations in spring, summer, autumn and winter were 1.326, 5.522, 1.340 and 0.708 mu g/m(3), respectively. The greater the relative humidity, the smaller the visibility threshold for the BC mass concentrations was. (C) 2020 The Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences. Published by Elsevier B.V.

使用宽范围粒径谱仪对天山白杨沟风景区2019年8月5-25日10 nm～ 10 μm气溶胶数浓度粒径分布进行观测,结合气象要素数据,分析了天山地区夏季气象条件对气溶胶粒径分布特征的影响.结果表明,夏季天山地区10 nm～10 μm气溶胶数浓度、表面积浓度和体积浓度平均为3539.2 cm-3、116.5 μm2· cm-3和17.6μm3· cm-3.不同降水过程对气溶胶数浓度的影响不同.不同降水过程中气溶胶数浓度谱均为单峰型分布,持续时间长的小雨和毛毛雨对气溶胶数浓度谱谱形的影响较小,而降雨量较强的短时降水过程往往会使得气溶胶粒径谱峰值往大粒径段偏移.降雨过程气溶胶表面积浓度谱和体积浓度谱为多峰型分布,表面积浓度主要集中在30～ 500 nm的细粒子段,体积浓度主要集中在1～10μm的粗粒子段.相对湿度(RH)对核模态气溶胶数浓度和积聚模态气溶胶数浓度的影响较大,对爱根核模态气溶胶数浓度的影响较小.不同相对湿度条件下气溶胶数浓度谱均为单峰型分布.随着相对湿度的增加,气溶胶数浓度谱的峰宽呈现先增加后减小的趋势,这种变化趋势在＜40 nm时更加显著.气溶胶数浓度、表面积浓度和体积浓度随风速风向的分布与能见度随风速风向的分布呈现相反的趋势.

The aerosol size distribution and cloud condensation nuclei (CCN) number concentration were measured using a wide-range particle spectrometer (WPS) and a cloud condensation nuclei counter (CCNC) on Mt. Tian from 31 July to 9 September, 2019. Combined with meteorological data, distribution characteristics of aerosol size and CCN and their influencing factors were analyzed. The results indicated that the mean aerosol number concentration was 5475.6 ± 5636.5 cm−3. The mean CCN concentrations were 183.7 ± 114.5 cm−3, 729.8 ± 376.1 cm−3, 1630.5 ± 980.5 cm−3, 2162.5 ± 1345.3 cm−3, and 2575.7 ± 1632.9 cm−3 at supersaturation levels of 0.1%, 0.2%, 0.4%, 0.6%, and 0.8%, respectively. The aerosol number size distribution is unimodal, and the dominant particle size is 30–60 nm. Affected by the height of the boundary layer and the valley wind, the diurnal variation in aerosol number concentration shows a unimodal distribution with a peak at 17:00, and the CCN number concentration showed a bimodal distribution with peaks at 18:00 and 21:00. The particle size distribution and supersaturation have a major impact on the activation of the aerosol into CCN. At 0.1% supersaturation (S), the 300–500 nm particles are most likely to activate to CCN. Particles of 100–300 nm are most easily activated at 0.2% (S), while particles of 60–80 nm are most likely activated at high supersaturation (≥0.4%). The concentrations of aerosol and CCN are higher in the northerly wind. Ambient relative humidity (RH) has little relationship with the aerosol activation under high supersaturation. According to N = CSk fitting the CCN spectrum, C = 3297 and k = 0.90 on Mt. Tian, characteristic of the clean continental type.

2017年5月21—22日泰山顶发生了一次地形雾过程.利用宽范围粒径谱仪(wide-range particle spectrometer,WPS)和气溶胶单颗粒质谱仪(single particle aerosol mass spectrometer,SPAMS)观测该次雾过程期间泰山顶大气气溶胶粒径分布和化学组成特征,结合PM(大气颗粒物)和气象要素数据,分析了此次雾过程对气溶胶粒径分布和化学组成的影响.此次雾过程分为形成、成熟、消散三个阶段.研究表明:不同阶段雾对不同粒径的气溶胶粒子的清除作用不同.在成熟阶段,硫酸盐与元素碳混合颗粒(EC-sulfate)、二次元素碳颗粒(EC-secondary)和富胺颗粒减少,其他大部分颗粒含量增加.而在消散阶段,这三种颗粒和元素碳(elemental carbon,EC)、富铁颗粒和富钒颗粒增加,其他颗粒物减少.随着雾过程发展,气溶胶粒子谱宽逐渐变窄,小粒径段颗粒迅速减少,大粒径段颗粒逐渐增加,峰值粒径先减小后增加.泰山顶的大气颗粒物主要分为6类:富碳颗粒(47.7％)、富钾颗粒(28.8％)、富钠颗粒(8.9％)、重金属颗粒(10.1％)、富铝颗粒(3.9％)和富铵颗粒(0.7％).从气溶胶粒子谱分布分析,大部分气溶胶粒子集中于0.2～1μm,其中EC-secondary的含量是最多的,单粒径峰值可以达到50％以上.而在各个粒径段,EC-nitrate(硝酸盐与元素碳混合颗粒)的含量几乎都大于EC-sulfate,表明相较于硫化物,EC更易与氮化物结合.

使用MARGA离子在线分析仪ADI 2080对2017年12月27日～2018年1月5日南京市PM2.5化学组分进行连续采样分析,结合气象要素和大气环境监测数据,探讨了霾污染过程中水溶性离子的时间分布特征及其来源特征.结果表明:霾日中南京水溶性离子浓度为121.41μg/m3,是洁净日的3.2倍.霾污染过程中水溶性离子平均浓度大小顺序为NO3->SO42->NH4+>Cl->K+>Ca2+>Mg2+,SNA离子占总水溶性离子浓度的91.97％.霾日中水溶性离子日变化均为三峰型,洁净日中Cl-、SO42-和NH4+的日变化为单峰型,Ca2+为双峰型,K+、Mg2+为三峰型.随着空气污染状况的加重,总水溶性离子在PM2.5中的占比不断减少,空气质量为优时占比95.93％,严重污染时为63.25％.霾日中随着污染加重,NH4+占总离子的比例稳定在23％左右,SO42-占比缓慢减小,NO3-占比不断增大.NOR、SOR的日变化在霾日呈双峰型分布,洁净日则较为平稳.观测期间的水溶性离子主要来源有二次转化、煤烟尘、扬尘以及生物质燃烧.

To explore the variation in characteristics of atmospheric pollutants at different stages of haze, the monitor for aerosols and gases in ambient air (MARGA) was used to observe the concentrations of precursor pollutants (NH3, HNO3, and SO2) and eight water-soluble ions in a regional haze in the Yangtze River Delta region from November 18 to December 07, 2018. Combined with environmental data (PM2.5, NO2, CO, and O3) and meteorological data, the causes of regional haze formation, diurnal variation characteristics of air pollutants, and distribution characteristics of air pollutants in different stages of haze were analyzed. The results showed that the Yangtze River Delta region was mainly controlled by a ridge of high pressure during the haze process and the weather situation was stable, which was conducive to the accumulation of air pollutants. On hazy days, the concentrations of PM2.5, NO2, NO3-, SO42-, NH4+, Cl-, and Na+ were (118.91±39.23), (61.62±26.34), (45.64±16.01), (18.80±8.02), (20.82±7.16), (3.02±2.25), and (0.23±0.22) μg·m-3, respectively, and these were 2.73, 1.63, 2.64, 1.94, 2.50, 2.05, and 2.56 times the levels found on clean days, respectively. The concentration of CO was (1.34±0.39) mg·m-3 on hazy days, which was 1.86 times that found on clean days. Diurnal variation characteristics of different air pollutants were different, as were the distribution characteristics of air pollutants at different haze stages. The concentrations of SO2 was the highest in the haze occurrence stage. The concentrations of PM2.5, NO2, NH3, CO, and SNA were highest in the haze development stage, and the concentrations of O3, Cl-, Na+, and K+ were highest in the haze dissipation stage. The relative contributions of SNA to PM2.5 in different stages of haze could reach 94%-96%, and their growth rate was largest in the development stage. The order of growth rate was NO3- > NH4+ > SO42-. SNA mainly existed in the form of NH4NO3 on clean days and in the occurrence and development stages, and (NH4)2SO4 in the dissipation stage. This haze process was mainly caused by the growth of NO3-, which was mainly generated by gas-phase homogeneous phase reaction, and NO3-contributes 51.06%, 51.85%, and 48.22%, respectively, to PM2.5 in the occurrence, development, and dissipation stages of haze.

基于PM、10nm～10μm气溶胶数谱、水溶性离子和气象要素数据,分析了2017年5月3日～8日一次沙尘远距离输送过程中长三角地区气溶胶粒径分布及其化学组成的污染特征.结果表明,此次沙尘伴随天气系统由北往南的传输过程中,PM的浓度逐渐降低,但是高浓度PM持续时间逐渐增加.沙尘在呼和浩特市影响时间为38h,而在南京的影响时间超过60h.沙尘期间气溶胶数浓度谱的峰值向大粒径段偏移,沙尘和非沙尘期间峰值分别位于33和26nm.表面积浓度谱在非沙尘期间为三峰型分布,但是在沙尘期间为四峰型分布.在沙尘期间PM2.5和PM10中水溶性离子的排序为Ca2+>NH4+>SO42->NO3->Mg2+>Na+>Cl->NO2->K+>F-,非沙尘期间为NH4+>SO42->NO3->Mg2+>Ca2+>Cl->NO2->K+>Na+>F-.沙尘期间不同水溶性离子的浓度变化不同,沙尘天PM2.5和PM10中Ca2+浓度分别是非沙尘天的9.5和13.7倍,Na+分别是非沙尘天的4.4倍和4.6倍.沙尘天PM2.5和PM10中Ca2+占总离子的比例分别为24.7％和24.9％,是非沙尘天的4.9和5.7倍.NO3-在PM10中的占总离子的比例为18.7％,高于非沙尘天(13.9％),但是在PM2.5中占总离子的比例仅为7.9％,低于非沙尘天(13.2％).沙尘天F-、Cl-、SO42-、NH4+和K+离子在PM2.5和PM10中所占总离子的比例均低于非沙尘天.

为了探讨武汉市不同类型大气污染过程中大气污染物变化特征,分析对比了沙尘、秸秆燃烧和霾污染过程中大气污染物(SO2,NO2,CO,O3,PM2.5和PM10)的变化特征及其影响因素.使用HYSPLIT模式计算了不同类型污染过程中气团轨迹,并利用潜在源区贡献(potential source contribution function,PSCF)和浓度权重轨迹(concentration weighted trajectory,CWT)分析方法,揭示了武汉市不同类型污染过程中大气污染物的潜在源区分布及其贡献特性.结果表明,不同类型污染下大气污染物变化不同.沙尘天主要以PM10污染为主,平均浓度为408.8μg/m3,是干净天的5.9倍,PM2.5/PM10仅为29％.霾过程中主要以PM2.5污染为主,平均浓度为182.8μg/m3,是干净天的3.7倍,PM2.5/PM10为90.4％.秸秆燃烧过程中大气污染物浓度均不同程度地增加,其中PM2.5、PM10和SO2的浓度分别为100.2μg/m3,155.4μg/m3和23.7μg/m3,是干净天的1.8倍,1.6倍和1.6倍.表明,不同类型污染下大气污染物的日变化不同,不同类型污染过程中大气污染物的潜在源区差异较大.沙尘期间大气污染物的主要潜在源区为安徽、河南南部、沙尘源区的内蒙古和甘肃等地区.霾过程中大气污染物的主要潜在源区为湖南东北部、湖北东部、安徽西南部、浙江西部、江西北部和河南南部.秸秆燃烧过程中大气污染物的主要潜在源区为安徽、江苏西南部和河南东南部.