南京市一次霾污染过程中水溶性离子分布特征

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PM_2.5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^- > SO₄^2- > 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^-、SO₄^2-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PM_2.5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,SO₄^2-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.     

北京地区大气温湿廓线对气溶胶垂直分布的影响

目的分析北京地区大气温湿廓线对气溶胶垂直分布的影响。方法利用北京地区2017年9月至2018年8月每日两次(08时和20时)的气象探空、地面PM_(2.5)浓度和气溶胶激光雷达消光系数资料,分析不同污染条件下大气温湿廓线与气溶胶消光系数廓线的关系。结果地面PM_(2.5)浓度和210m气溶胶消光系数的相关系数达到0.77。春季、秋季和冬季污染条件下的近地面消光系数约是清洁条件下的5倍,夏季污染条件下的近地面消光系数约是清洁条件下的3倍。相比清洁条件下,污染条件下各季节的大气温度垂直递减率偏小,并且低层大气相对湿度偏大。结论大气温度廓线代表大气层结稳定性,影响气溶胶的扩散高度,而相对湿度廓线与气溶胶吸湿增长密切相关,两者对气溶胶消光系数的垂直分布都有重要影响。     

间接蒸发冷却新风机组净化室内PM2.5特性

为有效降低室内PM2.5,指导间接蒸发冷却新风机组的设计选型,推导了新风机组净化效力和净化能效评价指标的数学表达式,并利用自行研制的间接蒸发冷却新风机组,分析了2种过滤器在不同运行工况下净化PM2.5的特性。结果表明,MERV 7+16和MERV 7+13的计数效率均随颗粒物粒径增加而提高,且前者的计数效率高于后者;由于滤材的不同,MERV 7+16的初阻力更小。MERV 7+16的渗透系数更小,其净化效力也要大于MERV 7+13;由于PM10在室内的自然沉降作用更显著,过滤器对PM10的净化效力明显大于PM2.5和PM1。将室内新风负荷作为新风机组能耗的一部分,在对比天津和南宁地区新风机组的净化能效时发现,由于两地区冬季室外温度存在明显差异,导致处于天津地区的新风机组若要在冬季获得同等的洁净空气量,需付出更多的能耗代价。     

秸秆焚烧对玉溪市大气PM10的影响解析研究

以水溶性钾为示踪元素,解析秋收季节秸秆焚烧对玉溪市大气PM10的贡献.结果表明,2001年秋收季节(10月)水溶性钾浓度(7.68μg/m3)比夏季水溶性钾浓度(2.38μg/m3)高3.2倍,与7月至12月份PM10浓度变化呈显著相关,相关系数为0.810.很显然,秋收季节秸秆焚烧对玉溪市大气PM10有负面影响.     

Particle emissions of a railway line determined by detailed single particle analysis

The goal of this study was to identify and quantify particles emitted from railway traffic. For that purpose PM10 samples were collected near a busy railway line using a wind direction and speed controlled sampling equipment consisting of five devices. Measurements taken perpendicular to the railway lines at 10, 36 and 120 m distance enable an identification and separation of particles caused by the railway traffic from background particles. Morphology and chemistry of more than 11,000 particles were analysed by computer controlled scanning electron microscopy (CCSEM). Based on chemical composition five particle classes are defined and assigned to their sources. The mass of the individual particles is determined by multiplying their volumes, calculated based on their morphology with a density assigned specifically to each particle class. The density of the particle classes is derived from their chemical composition. To estimate the PM10 contributions of the railway lines, the mass of PM10 at 120 m (background, not influenced by the railway lines) is subtracted from the mass of PM10 at 10 m. The emissions of the railway lines are dominated by ‘iron’ particles, which contribute 2.9 μg m⁻³ or 67% to the railway related PM10. In addition, ‘aluminium’ and ‘calcium’ particles contribute also to the railway related PM10 (1.0 μg m⁻³ or 23% for the ‘aluminium’ and 0.4 μg m⁻³ or 10% for the ‘calcium’ particles). These particles are assigned to abrasion of the gravel bed and re-suspension of mineral dust.Long-term gravimetric results of the contribution of iron to the mass of railway related PM10 from a study performed earlier at the same site are in good agreement with the data presented in this study.     

PM10冲击采样器切割头设计参数对切割粒径的影响

本文从颗粒在气流中的受力情况分析入手 ,得到冲击采样器中喷嘴喷出后颗粒的运动轨迹方程以及运动轨迹与采样器切割头设计参数的关系式。分析了PM10空气采样器的切割原理 ,并利用数值计算研究颗粒在不同的流量、喷嘴尺寸及喷嘴与冲击板的距离下的运动轨迹 ,从而得到切割头设计参数对切割粒径的影响     

PM2.5 chemical source profiles for vehicle exhaust, vegetative burning, geological material, and coal burning in Northwestern Colorado during 1995

PM_2.5 (particles with aerodynamic diameters less than 2.5 μm) chemical source profiles applicable to speciated emissions inventories and receptor model source apportionment are reported for geological material, motor vehicle exhaust, residential coal (RCC) and wood combustion (RWC), forest fires, geothermal hot springs; and coal-fired power generation units from northwestern Colorado during 1995. Fuels and combustion conditions are similar to those of other communities of the inland western US. Coal-fired power station profiles differed substantially between different units using similar coals, with the major difference being lack of selenium in emissions from the only unit that was equipped with a dry limestone sulfur dioxide (SO₂) scrubber. SO₂ abundances relative to fine particle mass emissions in power plant emissions were seven to nine times higher than hydrogen sulfide (H₂S) abundances from geothermal springs, and one to two orders of magnitude higher than SO₂ abundances in RCC emissions, implying that the SO₂ abundance is an important marker for primary particle contributions of non-aged coal-fired power station contributions. The sum of organic and elemental carbon ranged from 1% to 10% of fine particle mass in coal-fired power plant emissions, from 5% to 10% in geological material, >50% in forest fire emissions, >60% in RWC emissions, and >95% in RCC and vehicle exhaust emissions. Water-soluble potassium (K⁺) was most abundant in vegetative burning profiles. K⁺/K ratios ranged from 0.1 in geological material profiles to 0.9 in vegetative burning emissions, confirming previous observations that soluble potassium is a good marker for vegetative burning.     

关中典型城市及农村夏季PM2.5的化学组成对比

于2016年7~8月采集了陕西省西安市（城市）及蔺村（农村）夏季昼夜PM_2.5样品,分析其有机碳（OC）、元素碳（EC）和无机离子等化学组分的含量,探讨关中平原城市和农村地区PM_2.5的化学组成和来源的差异.结果表明,采样期间西安和蔺村的PM_2.5浓度分别为（49.7±22.8）和（62.6±14.2）μg/m³.西安PM_2.5中OC和EC的浓度[（6.5±2.5）μg/m³,（3.2±1.8）μg/m³]与蔺村[（6.8±1.8）μg/m³,（3.8±2.3）μg/m³]相当.西安OC/EC比值白天（2.6）高于夜晚（1.9）,蔺村反之（白天：1.6;夜晚：2.7）,主要是因为夜间城市地区重型卡车运输活动增强导致排放更多EC,而夜间农村地区人为活动较少导致EC排放显著降低.西安和蔺村无机离子总浓度分别为（20.2±14.6）和（30.1±10.5）μg/m³,占PM_2.5浓度的40.6%和47.6%.蔺村SO₄^2-的平均浓度高达19.0μg/m³,占PM_2.5浓度的30%以上,远高于西安（9.4μg/m³和18.9%）,主要与农村固体燃料（煤和生物质）使用有关.西安NO₃^-和Ca²⁺的浓度及其对PM_2.5的贡献、NO₃^-/SO₄^2-比值均明显大于蔺村,表明城市地区受机动车尾气和扬尘的影响更大.西安K⁺与Ca²⁺和Mg²⁺的相关性较强,而蔺村K⁺与EC的相关性显著强于西安,说明西安市区K⁺由粉尘源主导,而农村地区则主要来自生物质燃烧.     

沈阳市PM2.5离子成分对呼吸疾病门诊数影响研究

采用时间序列的半参数广义相加模型,在控制了长期趋势、"星期几效应"和气象因素等混杂因素的基础上,分析沈阳市大气污染物及PM_2.5中水溶性离子对呼吸系统疾病门诊就诊人数的影响,并按性别和年龄分层建模.结果表明：PM_2.5及其各离子成分与呼吸系统疾病门诊人数之间存在关联,并有明显的滞后效应.受冬季供暖燃煤排放影响,PM_2.5、NO₃^-和NH₄⁺呈显著关联,在滞后累积2d后风险最大.最佳滞后时间下,PM_2.5的浓度每增加10µg/m³,对应呼吸系统疾病日门诊就诊人数增加百分比（ER）为1.31%（95% CI：1.2%~1.43%）;离子成分SO₄^2-、NO₃^-、NH₄⁺、Cl^-、K⁺、Mg²⁺、Ca²⁺和Na⁺的浓度每增加1个4分位间距（IQR）,对应的呼吸系统疾病日门诊就诊人数增加百分比（ER）分别为3.22%（95% CI：2.81%~3.62%）、4.67%（95% CI：4.13%~5.22%）、5.41%（95% CI：4.49%~6.33%）、7.38%（95% CI：3.91%~10.96%）、0.14%（95% CI：-6.34%~7.07%）、7.64%（95% CI：-11.87%~31.47%）、3.57%（95% CI：-2.83%~10.39%）和0.46%（95% CI：-16.64%~21.06%）.PM_2.5、Cl^-、Mg²⁺、Ca²⁺和Na⁺对女性呼吸疾病门诊人数的影响比对男性的影响大.PM_2.5、SO₄^2-、Cl^-、Ca²⁺和Na⁺对≥65岁的老人门诊人数的影响比对15~65岁劳动年龄人群的影响大.表明不同性别、不同年龄由于生理结构和环境因素的不同而引起的差异不同.     

餐饮源有机颗粒物排放特征

餐饮油烟是大气有机颗粒物的重要来源之一.本研究在深圳市内选择了西餐、茶餐厅、职工食堂和韩式料理这4种类型的餐馆,通过对这4类餐厅的外场采样,分析各类型餐厅油烟中有机颗粒物的化学组成,筛选了餐饮油烟污染源的有机特征组分.结果表明,各餐馆排放的PM_(2.5)中,有机物占60%以上.在所有定量的有机组分之中,脂肪酸含量最高,其次是二元羧酸和正构烷烃,而多环芳烃、甾醇和单糖等有机组分的含量较低.颗粒物的有机组成特征受到菜系的影响,西餐厅和韩式料理排放脂肪酸、正构烷烃和多环芳烃等有机物含量较高,但却排放了低含量的甾醇和单糖,茶餐厅和职工食堂则相反.餐饮源颗粒物中Fla/(Fla+Pyr)和LG/(Gal+Man)的比值受菜系影响较小,也区别于其他污染源的特征比值,可以作为餐饮源潜在的示踪物.餐饮源为深圳市大气颗粒物贡献了大量的脂肪酸和二元羧酸.