Role of secondary aerosols in haze formation in summer in the Megacity Beijing

A field experiment from 18 August to 8 September 2006 in Beijing, China, was carried out. A hazy day was defined as visibility < l0 km and RH (relative humidity) < 90%. Four haze episodes, which accounted for ~ 60% of the time during the whole campaign, were characterized by increases of SNA (sulfate, nitrate, and ammonium) and SOA (secondary organic aerosol) concentrations. The average values with standard deviation of SO₄^2 −, NO₃⁻, NH₄⁺ and SOA were 49.8 (± 31.6), 31.4 (± 22.3), 25.8 (± 16.6) and 8.9 (± 4.1) μg/m³, respectively, during the haze episodes, which were 4.3, 3.4, 4.1, and 1.7 times those in the non-haze days. The SO₄^2 −, NO₃⁻, NH₄⁺, and SOA accounted for 15.8%, 8.8%, 7.3%, and 6.0% of the total mass concentration of PM₁₀ during the non-haze days. The respective contributions of SNA species to PM₁₀ rose to about 27.2%, 15.9%, and 13.9% during the haze days, while the contributions of SOA maintained the same level with a slight decrease to about 4.9%. The observed mass concentrations of SNA and SOA increased with the increase of PM₁₀ mass concentration, however, the rate of increase of SNA was much faster than that of the SOA. The SOR (sulfur oxidation ratio) and NOR (nitrogen oxidation ratio) increased from non-haze days to hazy days, and increased with the increase of RH. High concentrations of aerosols and water vapor favored the conversion of SO₂ to SO₄^2 − and NO₂ to NO₃⁻, which accelerated the accumulation of the aerosols and resulted in the formation of haze in Beijing.     

Chemical characteristics of size-resolved aerosols in winter in Beijing

Size-resolved aerosols were continuously collected by a Nano Sampler for 13 days at an urban site in Beijing during winter 2012 to measure the chemical composition of ambient aerosol particles. Data collected by the Nano Sampler and an ACSM(Aerodyne Aerosol Chemical Speciation Monitor) were compared. Between the data sets,similar trends and strong correlations were observed,demonstrating the validity of the Nano Sampler. PM10 and PM2.5concentrations during the measurement were 150.5 ± 96.0 μg/m3(mean ± standard variation)and 106.9 ± 71.6 μg/m3,respectively. The PM2.5/PM10 ratio was 0.70 ± 0.10,indicating that PM2.5dominated PM10. The aerosol size distributions showed that three size bins of 0.5–1,1–2.5 and 2.5–10 μm contributed 21.8%,23.3% and 26.0% to the total mass concentration(TMC),respectively. OM(organic matter) and SIA(secondary ionic aerosol,mainly SO42-,NO3-and NH4+) were major components of PM2.5. Secondary compounds(SIA and secondary organic carbon) accounted for half of TMC(about 49.8%) in PM2.5,and suggested that secondary aerosols significantly contributed to the serious particulate matter pollution observed in winter. Coal burning,biomass combustion,vehicle emissions and SIA were found to be the main sources of PM2.5. Mass concentrations of water-soluble ions and undetected materials,as well as their fractions in TMC,strikingly increased with deteriorating particle pollution conditions,while OM and EC(elemental carbon) exhibited different variations,with mass concentrations slightly increasing but fractions in TMC decreasing.     

Particle number concentration, size distribution and chemical composition during haze and photochemical smog episodes in Shanghai

The aerosol number concentration and size distribution as well as size-resolved particle chemical composition were measured during haze and photochemical smog episodes in Shanghai in 2009. The number of haze days accounted for 43%, of which 30% was severe （visibility 〈 2 km） and moderate （2 km 〈 visibility 〈 3 km） haze, mainly distributed in winter and spring. The mean particle number concentration was about 17,000/cm3 in haze, more than 2 times that in clean days. The greatest increase of particle number concentration was in 0.5-1μm and 1-10 μm size fractions during haze events, about 17.78 times and 8.78 times those of clean days. The largest increase of particle number concentration was within 50-100 nm and 100-200 nm fractions during photochemical smog episodes, about 5.89 times and 4.29 times those of clean days. The particle volume concentration and surface concentration in haze, photochemical smog and clean days were 102, 49, 15 μm3/cm3 and 949, 649, 206 μm2/cm3, respectively. As haze events got more severe, the number concentration of particles smaller than 50 nm decreased, but the particles of 50-200 nm and 0.5-1μm increased. The diurnal variation of particle number concentration showed a bimodal pattern in haze days. All soluble ions were increased during haze events, of which NH4, SO24- and NO3 increased great/y, followed by Na＋, IC, Ca2＋ and CI-. These ions were very different in size-resolved particles during haze and photochemical smog episodes.     

Composition, source, mass closure of PM2:5 aerosols for four forests in eastern China

PM2.5 aerosols were collected in forests along north latitude in boreal-temperate, temperate, subtropical and tropical climatic zones in eastern China, i.e., Changbai Mountain Nature Reserve (CB), Dongping National Forest Park in Chongming Island (CM), Dinghu Mountain Nature Reserve (DH), Jianfengling Nature Reserve in Hainan Island (HN). The mass concentrations of PM2.5, organic carbon (OC), elemental carbon (EC), water soluble organic carbon (WSOC) as well as concentrations of ten inorganic ions (F-, Cl-, NO3-, SO42-, C2O42-, NH4+, Na+, K+, Ca2+, Mg2+) were determined. Aerosol chemical mass closures were achieved. The 24-hr average concentrations of PM2.5 were 38.8, 89.2, 30.4, 18 μg/m3 at CB, CM, DH and HN, respectively. Organic matter and EC accounted for 21%-33% and 1.3%-2.3% of PM2.5 mass, respectively. The sum of three dominant secondary ions (SO42-, NO3-, NH4+) accounted for 44%, 50%, 45% and 16% of local PM2.5 mass at CB, CM, DH and HN, respectively. WSOC comprised 35%-65% of OC. The sources of PM2.5 include especially important regional anthropogenic pollutions at Chinese forest areas.     

南京北郊冬春季大气能见度影响因子贡献研究

为研究南京北郊气象要素以及气溶胶对大气能见度的影响,利用2014年1~5月的能见度、相对湿度、温度、颗粒物浓度及其化学成分等观测数据,探讨了气溶胶不同化学组分对消光系数的贡献,提出了该地区能见度基于不同参数的拟合方案.结果表明,观测期间平均能见度为(5.78±3.64)km,能见度与相对湿度、PM_(2.5)存在明显的负相关,相关系数分别为-0.66、-0.48.冬季平均消光系数为(398.72±219.88)Mm~(-1),Organic、NH_4NO_3、(NH_4)_2SO_4和EC对消光的贡献率分别为38.81%、27.81%、23.95%和7.15%;春季平均消光系数为(248.36±78.42)Mm~(-1),Orgamic、NH_4NO_3、(NH_4)_2SO_4和EC对消光的贡献率分别为31.59%、24.36%、32.63%和8.64%.对比不同的能见度拟合方案时,基于颗粒物成分的能见度拟合方案优于基于散射系数的.不同相对湿度区间内PM_(2.5)对能见度的影响程度不同,基于PM_(2.5)、相对湿度和温度的能见度拟合方案说明:低相对湿度的条件下,PM_(2.5)对能见度的影响较大;随相对湿度增大,相对湿度成为更为重要的影响因子.     

利用SPAMS研究石家庄市冬季连续灰霾天气的污染特征及成因

2014年11月18~26日石家庄市发生了连续的灰霾天气.利用位于石家庄市大气自动监测站(20 m)的单颗粒气溶胶质谱仪(SPAMS)分析了细颗粒物的化学组成,根据石家庄市大气污染物排放源谱库对主要成分进行了来源解析,并结合颗粒物质量浓度和气象条件研究了该地区冬季灰霾天气成因.结果表明,石家庄市大气细颗粒物来源分为7类,各源示踪离子:燃煤源为Al,工业源为OC、Fe、Pb,机动车尾气源为EC,扬尘源为Al、Ca、Si,生物质燃烧源为K和左旋葡聚糖,纯二次无机源为SO-4、NO-2和NO-3,餐饮源为HOC.灰霾期间大气中主要含有OC、HOC、EC、HEC、ECOC、富钾颗粒、矿物质和重金属等8类颗粒,其中OC和ECOC颗粒最多,分别占到总数的50%和20%以上,OC颗粒主要来自燃煤和工业工艺,ECOC颗粒主要来自燃煤和机动车尾气排放.灰霾发生时含有NH+4、SO-4、NO-2和NO-3等二次离子的颗粒物占比升高,其中含NH+4颗粒增幅最大;EC、OC与NO-3、SO-4、NH+4在灰霾天气下的混合程度均比干净天气高,其中与NH+4的混合程度加剧最为明显.冬季采暖期煤炭的大量燃烧、医化行业工艺过程及机动车尾气等污染源排放的一次气态污染物(SO2、NOx、NH3、VOCs)和一次颗粒物在静稳天气中难以扩散而迅速累积,气态污染物发生二次转化形成硝酸铵、硫酸铵,而颗粒物之间通过碰撞形成二次颗粒物并发生不同程度的混合,从而导致大气能见度下降,以上是石家庄市冬季灰霾形成的主要原因.     

Size distributions of aerosol and water-soluble ions in Nanjing during a crop residual burning event

To investigate the impact on urban air pollution by crop residual burning outside Nanjing, aerosol concentration, pollution gas concentration, mass concentration, and water-soluble ion size distribution were observed during one event of November 4-9, 2010. Results show that the size distribution of aerosol concentration is bimodal on pollution days and normal days, with peak values at 60-70 and 200-300 nm, respectively. Aerosol concentration is 10⁴ cm^-3. nm^-1 on pollution days. The peak value of spectrum distribution of aerosol concentration on pollution days is 1.5-3.3 times higher than that on a normal day. Crop residual burning has a great impact on the concentration of fine particles. Diurnal variation of aerosol concentration is trimodal on pollution days and normal days, with peak values at 03:00, 09:00 and 19:00 local standard time. The first peak is impacted by meteorological elements, while the second and third peaks are due to human activities, such as rush hour traffic. Crop residual burning has the greatest impact on SO₂ concentration, followed by NO₂, O₃ is hardly affected. The impact of crop residual burning on fine particles (< 2.1 μm) is larger than on coarse particles (> 2.1 μm), thus ion concentration in fine particles is higher than that in coarse particles. Crop residual burning leads to similar increase in all ion components, thus it has a small impact on the water-soluble ions order. Crop residual burning has a strong impact on the size distribution of K⁺, Cl^-, Na⁺, and F^- and has a weak impact on the size distributions of NH₄⁺, Ca²⁺, NO₃^- and SO₄^2-.     

Chemical characteristics of fine particles and their impact on visibility impairment in Shanghai based on a 1-year period observation

In this work, a one-year observation focusing on high time resolution characteristics of components in fine particles was conducted at an urban site in Shanghai. Contributions of different components on visibility impairment were also studied. Our research indicates that the major components of PM_2.5 in Shanghai are water-soluble inorganic ions and carbonaceous aerosol, accounting for about 60% and 30% respectively. Higher concentrations of sulfate (SO₄²⁻) and organic carbon (OC) in PM_2.5 occurred in fall and summer, while higher concentrations of nitrate (NO₃⁻) were observed in winter and spring. The mass concentrations of Cl⁻ and K⁺ were higher in winter. Moreover, NO₃⁻ increased significantly during PM_2.5 pollution episodes. The high values observed for the sulfate oxidizing rate (SOR), nitrate oxidizing rate (NOR) and secondary organic carbon (SOC) in OC indicate that photochemical reactions were quite active in Shanghai. The IMPROVE (Interagency Monitoring of Protected Visual Environments) formula was used in this study to investigate the contributions of individual PM_2.5 chemical components to the light extinction efficient in Shanghai. Both NH₄NO₃ and (NH₄)₂SO₄ had close relationships with visibility impairment in Shanghai. Our results show that the reduction of anthropogenic SO₂, NO_x and NH₃ would have a significant effect on the improvement of air quality and visibility in Shanghai.     

Performance and microbial response during the fast reactivation of Anammox system by hydrodynamic stress control

Anaerobic ammonium oxidation （Anammox） has become a promising method for biological nitrogen removal. However, this biotechnology application is always limited due to the low growth rate and biomass yield of Anammox bacteria. This study investigated the process of fast reactivation of an Anammox consortium idled for 2 years uia hydrodynamic stress control. The results showed that the Anammox system was efficiently and quickly reactivated by shortening of the hydraulic retention time （I-IRT） of the reactor from 12 to 6 hr within 68 days of operation. Moreover, at a 4-hr HRT with an influent total nitrogen loading rate of 1.2 kg N/（m3.day）, the reactor maintained high biological performance with an ammonium removal loading rate of 0.52 kg N/（m3.day） and a nitrite removal rate of 0.59 kg N/（m3.day）. In the reactivated Anammox reaction, the stoichiometric coefficients of NH4-N to NOE-N and NH4-N to NO4-N were 1：1.04± 0.08 and 1：0.31 ± 0.03, respectively. The specific Anammox activity and hydrazine oxidoreductase activity, both of which represent the degree of Anammox bacteria present, increased as the hydrodynamic stress increased and were maximally （125.38 ± 3.01 mg N/（g VSS.day） and 339.42 ± 6.83 μmol/（min.g VSS）, respectively） at 4-hr HRT. Microbial response analysis showed that the dominant microbial community was obviously shifted and the dominance of Anammox bacteria was enhanced durinR the hydrodynamic selection.     

海峡西岸经济区大气污染物排放清单的初步估算

以2009年为基准年,结合污染源普查数据、统计年鉴及工业活动、居民生活等多个方面对海峡西岸经济区包括SO2、NOx、PM2.5、VOCs和NH3在内的大气污染物的排放量进行了估算,建立了海西区大气污染物排放清单.结果发现,上述5类污染物基准年的排放量分别为40.67×104、55.84×104、50.57×104、152.26×104和26.18×104t.其中,SO2、NOx及PM2.5的排放主要来自电厂,占排放总量的比例分别为25.58%、34.89%和38.75%;VOCs和NH3的主要排放源分别来自植被排放和养殖业,其贡献量分别为49.12%和47.07%.采用GIS对排放清单进行网格化处理,得出SO2、NOx及PM2.5的高排放强度区域与固定源的空间分布较为一致.此外,结合国家和地方"十二五"发展规划,采用情景分析方法估算了2015年海西区大气污染物的排放清单.与基准年相比,SO2、NOx和NH3的排放量呈下降趋势,PM2.5和VOCs的排放量呈大幅度增加.基准年排放清单的不确定性分析显示,VOCs排放估算的不确定度最大,为225%.     

广州干湿季典型灰霾过程水溶性离子成分对比分析

利用广州气象台2011年地面逐时能见度和相对湿度数据,以及广州番禺南村大气成分站2011年逐时Marga数据、PM数据,对比分析了一次湿季(4—9月)灰霾过程和干季(10月—次年3月)灰霾过程的污染特征.研究表明,相对干季灰霾过程,湿季灰霾过程颗粒物浓度较低,且细粒子所占比例较高;由于湿季较干季光化学反应较为活跃及可能受气象因素的不同影响,导致干湿季灰霾过程颗粒物浓度的总体变化趋势相反;湿季灰霾过程二次无机离子(SO_4~(2-)、NH_4~+和NO_3~-)占PM_(2.5)质量百分比的76%,是PM_(2.5)的主要成分;干季灰霾过程二次无机离子(SO_4~(2-)、NH_4~+和NO_3~-)仅占PM_(2.5)质量百分比的34%;湿季硫氧化率(Sulfur Oxidation Ratio,SOR)、氮氧化率(Nitrogen Oxidation Ratio,NOR)值大于干季,说明二次离子对湿季灰霾的贡献比干季要大;湿季灰霾过程中气溶胶酸性比干季弱.根据相关性分析结果可知,湿季灰霾过程中,NH_4~+主要与SO_4~(2-)结合,Na+主要与Cl-及NO_3~-结合,K+主要与Cl-和NO_3~-结合,极少部分与SO_4~(2-)结合;而在干季灰霾过程中,NH_4~+除了与SO_4~(2-)结合之外,还以NH_4NO_3和NH_4Cl的形式存在,K~+主要与Cl~-和SO_4~(2-)结合,Na+主要与Cl~-及SO_4~(2-)结合.     

Seasonal variation and source apportionment of organic and inorganic compounds in PM2.5 and PM10 particulates in Beijing, China

The distribution and source of the solvent-extractable organic and inorganic components in PM 2.5(aerodynamics equivalent diameter below 2.5 microns),and PM 10(aerodynamics equivalent diameter below 10 microns) fractions of airborne particles were studied weekly from September 2006 to August 2007 in Beijing.The extracted organic and inorganic compounds identified in both particle size ranges consisted of n-alkanes,PAHs(polycyclic aromatic hydrocarbons),fatty acids and water soluble ions.The potential emission sources of these organic compounds were reconciled by combining the values of n-alkane carbon preference index(CPI),%waxC n,selected diagnostic ratios of PAHs and principal component analysis in both size ranges.The mean cumulative concentrations of n-alkanes reached 1128.65ng/m3 in Beijing,74% of which(i.e.,831.7ng/m3) was in the PM 2.5 fraction,PAHs reached 136.45ng/m3(113.44ng/m3 or 83% in PM 2.5),and fatty acids reached 436.99ng/m3(324.41ng/m3 or 74% in PM 2.5),which resulted in overall enrichment in the fine particles.The average concentrations of SO42-,NO3-,and NH4+ were 21.3±15.2,6.1±1.8,12.5±6.1μg/m3 in PM 2.5,and 25.8±15.5,8.9±2.6,16.9±9.5μg/m3 in PM 10,respectively.These three secondary ions primarily existed as ammonium sulfate((NH4)2SO4),ammonium bisulfate(NH4HSO4) and ammonium nitrate(NH4NO3).The characteristic ratios of PAHs revealed that the primary sources of PAHs were coal combustion,followed by gasoline combustion.The ratios of stearic/palmitic acid indicated the major contribution of vehicle emissions to fatty acids in airborne particles.The major alkane sources were biogenic sources and fossil fuel combustion.The major sources of PAHs were vehicular emission and coal combustion.     

Chemical characterization of size-resolved aerosols in four seasons and hazy days in the megacity Beijing of China

Size-resolved aerosol samples were collected by MOUDI in four seasons in 2007 in Beijing. The PM₁₀ and PM_1.8 mass concentrations were 166.0 ± 120.5 and 91.6 ± 69.7 μg/m³, respectively, throughout the measurement, with seasonal variation: nearly two times higher in autumn than in summer and spring. Serious fine particle pollution occurred in winter with the PM_1.8/PM₁₀ ratio of 0.63, which was higher than other seasons. The size distribution of PM showed obvious seasonal and diurnal variation, with a smaller fine mode peak in spring and in the daytime. OM (organic matter = 1.6 × OC (organic carbon)) and SIA (secondary inorganic aerosol) were major components of fine particles, while OM, SIA and Ca^2 + were major components in coarse particles. Moreover, secondary components, mainly SOA (secondary organic aerosol) and SIA, accounted for 46%–96% of each size bin in fine particles, which meant that secondary pollution existed all year. Sulfates and nitrates, primarily in the form of (NH₄)₂SO₄, NH₄NO₃, CaSO₄, Na₂SO₄ and K₂SO₄, calculated by the model ISORROPIA II, were major components of the solid phase in fine particles. The PM concentration and size distribution were similar in the four seasons on non-haze days, while large differences occurred on haze days, which indicated seasonal variation of PM concentration and size distribution were dominated by haze days. The SIA concentrations and fractions of nearly all size bins were higher on haze days than on non-haze days, which was attributed to heterogeneous aqueous reactions on haze days in the four seasons.     

氮、硫输入对河口湿地土壤有机碳矿化的实验研究

通过室内培养实验,研究了氮、硫输入对闽江河口湿地土壤有机碳矿化和土壤理化性质的影响.结果表明:NH_4Cl(N1)、NH_4NO_3(N3)、K_2SO_4(S)和NH_4Cl+K_2SO_4(NS1)处理显著促进了湿地土壤有机碳矿化速率(p0.05),较对照分别提高了76.57%、60.09%、83.20%和52.59%,并且不同处理下土壤有机碳矿化速率均表现为随培养时间的增加而递减.氮、硫输入在不同时间对湿地土壤有机碳矿化的影响不尽一致,在前6 d各处理的促进作用最明显.湿地土壤有机碳累积矿化量在不同处理下均表现为随培养时间逐渐增加,其增长速率在培养初始阶段较快,而后逐渐减慢;不同培养时间有机碳累积矿化量在N1、N3、S和NS1处理下与对照处理间均存在显著差异(p0.05).短期培养结束后,N3、NS1处理显著增加了湿地土壤DOC含量(p0.05);N1、N3、NS1和NH_4NO_3+K_2SO_4(NS3)处理均显著增加了土壤NH_4~+-N含量(p0.05);KNO_3(N2)、N3、NS2和NS3处理均显著增加了土壤NO_3~--N含量(p0.05);S、NS1、NS2和NS3处理均显著增加了土壤SO_4~(2-)含量(p0.05).不同处理下湿地土壤Cl-、pH、EC具有微弱的波动变化特征,但在不同处理组间均不存在显著差异(p0.05).多元回归分析显示,DOC、NH_4~+-N和SO_4~(2-)是氮、硫输入处理下影响闽江河口湿地土壤有机碳矿化速率的主要控制因素.     

应用扩散管测量霾污染期间大气氮硫化合物浓度的方法

活性氮和硫化合物在大气颗粒物形成过程中扮演重要角色,但对它们气相/颗粒相的同步观测结果比较缺乏.本研究尝试基于扩散管的DELTA系统测量氮和硫化合物短时累积浓度,以期捕捉它们在霾污染期间的演变规律.结果表明,DELTA系统收集气态污染物的扩散管中以及颗粒物滤膜上NH_4~+和NO-3空白干扰较小,适用于研究NH_3、HNO_3、NH_4~+和NO-3的日均浓度,可以作为城市环境空气质量监测参数的有效补充;但采样系统中SO_2-4背景含量较高,仅适合监测48 h以上时间尺度的SO_2浓度和周~月尺度SO_2-4浓度,用于大气硫沉降观测.北京2016年5月9日~6月7日观测期间,大气NH_3、HNO_3、NH_4~+和NO-3浓度具有明显的逐日演变规律,呈现出随着风向转变而发生周期性波动的典型特征;这些含氮污染物与PM_(2.5)、CO、SO_2和NO_2浓度的变化规律一致,其来源可能与化石燃料燃烧源有关.污染天NH_3、HNO_3、NH_4~+和NO-3浓度约为清洁天的2倍,但还原性氮和氧化性氮的相态分布在清洁天和污染天无明显差异;整个观测期间,HNO_3/NO-3约为1.2,NH_3/NH_4~+为4.5,春夏之交较高的温度有利于活性氮在气粒平衡过程中偏向于气态形式存在.     

泉州市大气PM2.5中水溶性离子季节变化特征及来源解析

为掌握泉州市大气PM_(2.5)中无机水溶性离子的季节变化特征,于2014年3月~2015年1月同步采集了泉州市5个采样点共116个PM_(2.5)样品.用离子色谱法分析了PM_(2.5)中Na~+、NH_4~+、K~+、Ca~(2+)、Mg~(2+)、F~-、Cl~-、NO_3~-和SO_4~(2-)等9种水溶性无机离子.观测期间,总水溶性离子浓度季节变化特征为春季(14.24±6.43)μg·m~(-3)冬季(8.54±7.61)μg·m~(-3)夏季(4.10±2.67)μg·m~(-3)秋季(3.91±2.58)μg·m~(-3);SO_4~(2-)、NO_3~-和NH_4~+(SNA)是PM_(2.5)中主要的3种离子,占水溶性离子总质量浓度比例分别为春季(90.3±3.3)%、夏季(68.8±11.7)%、秋季(78.9±7.1)%和冬季(74.0±18.4)%,说明春季二次污染较为严重;PM_(2.5)中阴、阳离子电荷平衡分析显示,阴离子相对亏损,大气细颗粒物组分呈弱碱性;春、冬季NH_4~+主要以(NH_4)_2SO_4、NH_4HSO_4和NH_4NO_3等形式存在,而夏、秋季则主要以NH_4HSO_4和NH_4NO_3形式存在;PMF源解析结果表明,泉州市大气PM_(2.5)中水溶性离子主要来自海盐、二次源、建筑扬尘、垃圾焚烧源和生物质燃烧源.     

Photochemical properties and source of pollutants during continuous pollution episodes in Beijing, October, 2011

Beijing sufered from serious air pollution in October, 2011 with the occurrence of three continuous episodes. Here we analyze the pollution status of particulate matter, the relationship between the gaseous pollutants, physical and chemical properties of single particles, and the profile of watersoluble ions in PM2.5during the three episodes. Regional and photochemically aged air masses, which were characterized as having high values of O3and SO2, were hypothesized to have played a dominant role in the first episode. After mixing local air masses with freshly-emitted primary pollutants, the concentration of NOx continued to increase and the size of SO4 2, NO3 and NH4 +in the particle population continued to become smaller. The amount of elemental carbon-rich and organic carbonrich particles in the scaled single particles(0.2–2 μm) and water-soluble K+in PM2.5also increased in the episodes. All the available information suggests that the biomass or fuel burning sources in or around Beijing may have had a huge impact on the last two episodes.     

兰州盆地人为源大气污染物网格化排放清单及其空间分布特征

基于所搜集的兰州盆地各类人为污染源排放大气污染物的活动水平数据及其排放因子,采用"自下而上"的方法建立了2009年兰州盆地(石油化工城市)1 km×1 km的7种(类)大气污染物网格化排放清单,并对其来源和空间分布特征进行了分析研究.结果显示:2009年兰州盆地NOx、SO_2、VOCs、CO、PM_(10)、PM_(2.5)和NH3的排放总量分别为1.2×10~5、8.8×10~4、4.3×10~4、4.1×10~5、9.6×10~4、4.2×10~4和1.4×10~4t;工业燃烧排放是兰州盆地NO_x和SO_2的主要贡献源,分别占其总排放量的85.70%和52.55%;工业非燃烧过程排放是VOCs的最大贡献源,占总排放量的81.25%;工业点源和工业非燃烧过程排放是CO的两大贡献源,分别占其总排放量的33.97%和28.32%;PM_(10)和PM_(2.5)主要来源于工业非燃烧过程,贡献分别为51.09%和55.12%;氮肥使用和禽畜养殖是NH_3排放最大的贡献源,分别占其总排放量的39.20%和30.70%.空间分布特征表现为:以工业源为主要排放源的NO_x、SO_2、VOCs、CO、PM_(10)、PM_(2.5)主要分布在工业和人口最为集中的兰州盆地市区一带,NH_3的排放则主要集中在榆中县和皋兰县交界的农村地区.同时,还对2014年工业燃烧源和道路移动源的7种(类)大气污染物排放量进行了估算,并与2009年进行了排放比较研究.结果表明,2014年工业污染源的7种(类)污染物排放量与2009年相比平均增幅不高,最高不超过30%,但移动源污染物排放量却大幅增加,增幅将近1倍.此外,基于排放因子及活动水平的不确定性,本研究对排放清单的结果进行了不确定性分析,并通过蒙特卡罗模拟对各污染物的排放量进行了评估.本排放清单的建立,不仅填补了兰州盆地大气污染物网格化排放清单的空白,还可为兰州盆地大气污染物排放清单更新、区域环境过程、大气复合污染成因及大气污染预警技术等相关研究提供基本方法手段及基础数据.     

邯郸市PM_(2.5)中水溶性无机离子污染特征及来源解析

本研究通过对邯郸市环境空气中PM2.5样本进行采集和成分检测,分析了该地区PM2.5中水溶性无机离子的污染特征,并结合气象要素(风速、温度)、气态污染物(O3、NO2、SO2、CO)、SOR(硫氧化率)、NOR(氮氧化率)对其主要来源进行了解析.研究结果表明:总水溶性无机离子(TWSII)浓度季节变化特征明显,秋、冬季高于春、夏季.SO2-4、NO-3、NH+4是PM2.5中主要的水溶性无机离子,在TWSII中所占的比例为夏(93.2%)冬(85.6%)秋(85.5%)春(84.0%).春、夏、秋三季PM2.5呈酸性,冬季显碱性.此外还分析得到,SO2-4在四季中均以(NH4)2SO4的形式存在.NO-3在冬季以NH4NO3的形式存在,其余季节中以NH4NO3、HNO3等共存.绝大部分Cl-在冬季以NH4Cl的形式存在,其它季节中以NH4Cl、KCl等的形式存在.均相反应是SO2-4的主要生成途径,夏、冬季也伴随有非均相反应.NO-3的生成以均相反应为主(春、夏、秋),在冬季均相反应与非均相反应同时存在.应用因子分析法解析出4个主因子,其中,工业、燃煤、交通、生物质燃烧等综合源是PM2.5中水溶性无机离子的主要来源.     

2022年北京市城区PM2.5水溶性离子含量及其变化特征

为探究北京市大气细颗粒物（PM_2.5）水溶性离子含量及其变化特征,有针对性地提出污染防治方案,对2022年全年PM_2.5水溶性离子、气态前体物（SO₂、NO₂）和气象因素（温度、RH）进行分析测定.结果表明,北京市城区PM_2.5中占比最高的水溶性离子为NO₃^-、NH₄⁺和SO₄^2-,占PM_2.5的52.7%,ρ（PM_2.5）（33.2 μg·m^-3）和ρ（SNA）（18.9 μg·m^-3）低于历史研究结果,但SNA占比（52.7%）、SOR（0.45）和NOR（0.15）高于历史研究结果,体现出北京市细颗粒物污染得到明显改善,但仍具有较强的二次污染特征.NO₃^-/SO₄^2-为2.2,高于历史及附近省市研究结果,反映出移动源的影响不断扩大.从季节变化上看,PM_2.5呈现秋高夏低的变化特征,秋、春、冬这3个季节NO₃^-的占比最高,夏季SO₄^2-占比最高,而NH₄⁺在各季节占比变化不大.NOR与SOR的季节变化规律几乎相反,反映出二者的转化形成因素存在差异.北京城区SNA的主要存在形式为NH₄NO₃和（NH₄）₂SO₄,其中冬季阴阳离子中和度最高,夏季阳离子NH₄⁺稍显不足,而春秋两季NH₄⁺处于过量状态,北京城区为富氨环境.从污染级别看,水溶性离子质量浓度均随污染加重有不同程度的增长,增长最快的是SNA,其在PM_2.5中占比出现先上升后稳定的变化特征.从空间分布特征来看,中心城区和东南西北部郊区的SNA质量浓度大小均为：NO₃^->SO₄^2->NH₄⁺,体现了以NO₃^-为主导的污染特征;SNA对PM_2.5的贡献率最高的区域发生在东部、中心城区和传输点,表明在中心城区和东部地区二次反应相对活跃,同时区域传输也是二次离子的重要来源.