污染源减排对长三角区域冬季细颗粒物污染的敏感性研究

为了进一步精准有效地降低细颗粒物浓度,针对长三角区域细颗粒物PM2.5浓度,选取8个省级区域的5种污染物为减排目标,设定5个基准排放情景,采用CMAQ-DDM敏感性技术分别进行敏感性分析。结果表明,冬季长三角区域PM2.5污染受区域内的4个省级区域一次PM2.5排放影响最大,区域外的排放影响主要来自河南省和山东省的氨气和一次PM2.5。分别削减本地60%一次PM2.5的排放,安徽省PM2.5平均质量浓度下降了23. 24μg/m^3,江苏省下降了18. 32μg/m^3,上海市下降了15. 17μg/m^3,浙江省下降了9. 07μg/m3。综合各省(市)浓度响应曲线,最大排放因子均为本地一次PM2.5,削减20%左右存在敏感性最大值,削减60%之后浓度曲线趋于平缓,其他因子削减40%以后PM2.5浓度下降逐渐明显,对最后一位排放因子的响应则比较平缓。     

广州冬季霾天气大气PM2.5污染特征分析

收集了2005年12月至2006年2月的PM2.5浓度观测数据及同步气象数据,分析了冬季PM2.5质量浓度日变化趋势以及霾日期间PM2.5质量浓度日变化和小时变化趋势.结果发现,观测期间PM2.5日均值浓度为69μg/m3,霾日期间PM2.5日均值浓度为72μg/m3.冬季霾天气的发生频率为45%,霾天气过程最短持续2天,最长持续9天.较高的PM2.5浓度和较高的相对湿度及较小的风速是导致霾天气形成的主要原因.霾日期间PM2.5小时浓度变化趋势与人类活动周期和气象条件密切相关.     

西安市区大气中PM2.5和PM10质量浓度污染特征

2013年3月—2014年2月期间,设置1个监测点位,采集了西安市区大气环境中PM10和PM2.5样品,采用重量法测定了PM2.5和PM10质量浓度。结果表明,西安市区PM2.5质量浓度为16~558μg/m3,平均值为128μg/m3,超标率69.1%;PM10质量浓度范围为32~887μg/m3,平均值为249μg/m3,超标率71.8%。虽然PM2.5和PM10质量浓度的逐日变化幅度比较大,但是整体变化趋势非常相似,存在显著的正相关关系(r=0.831 9)。PM2.5和PM10质量浓度存在明显的季节变化,均为冬季最高,春季次之,秋季较低,夏季最低。ρ(PM2.5)/ρ(PM10)为0.245~0.822,平均值为0.510,说明PM2.5在PM10中所占比例大于PM2.5~10;此外,该比值呈现一定的季节变化规律,冬季、夏季较高,秋季次之,春季最低。霾天气发生时,该比值和PM2.5质量浓度明显高于无霾天气。     

一次连续在线观测分析天津市细颗粒物污染特征

根据2005年的5月17日—5月23日GR IMM(1.109#)谱分析仪在线观测结果考察天津市细颗粒物浓度和质量浓度特征。观测期间,天津市颗粒物数浓度平均值为1 124 cm-3,粒径分布为0.25μm~0.60μm,98.5%粒子的粒径0.65μm。同期PM10日均质量浓度值为204μg/m3,ρ(PM2.5)为104μg/m3,ρ(PM1.0)为82.9μg/m3。ρ(PM1.0)/ρ(PM2.5)超过80%,粒径1μm超细颗粒物为天津城市大气颗粒物的主要成分。     

上海市秋季典型PM2.5污染过程数值预报分析

基于2012年10月上海出现的一次典型PM2.5污染案例,验证评估上海市空气质量数值预报系统Model-3/CMAQ预报性能,采用过程分析技术,定量评估不同大气物理化学过程对上海代表性点位PM2.5浓度变化的作用规律。结果表明:Model-3/CMAQ模式系统能较好地反映PM2.5的浓度变化趋势与特点。对于上海市区点位(徐汇上师大)和东南部点位(奉贤海湾和浦东惠南),PM2.5浓度上升主要受本地源排放影响,其贡献比例超过40%,其次是区域大气传输作用的影响。对于西北部点位(崇明监测站和青浦淀山湖),区域大气传输是PM2.5浓度上升的主要原因,贡献比例超过70%,其次是源排放。各点位PM2.5浓度的主要去除途径均为大气传输,贡献比例均超过70%,其次是干沉降。气溶胶过程对PM2.5主要起二次颗粒物生成的作用,特别是市区及东南部点位,贡献比例较西北部点位更高。     

北京地区气象条件与区域传输对PM2.5浓度影响研究

通过采用KZ（Kolmogorov Zurbenko）滤波统计方法,结合WRF/CMAQ数值模型情景分析技术,定量分析气象条件和区域传输对北京市PM2.5浓度的影响。结果显示: 2018年7月—2019年6月,北京市PM2.5平均质量浓度为46.0 μg/m3,气象条件同比偏差6.9%,外来传输平均为43.7%。从日变化上看,外来传输在早晚高峰期间明显下降,体现出本地机动车排放贡献明显上升的特点,气象条件对PM2.5浓度的影响主要表现为白天整体有利于污染物扩散,夜间气象条件转为偏不利的特征。2018年10月—2019年3月秋冬季期间,气象条件同比偏差3.3%,在气象条件较为不利和区域同比反弹的情况下,北京市PM2.5浓度持续走低,主要为源减排的效益。     

烟花燃放对空气中PM2.5及水溶性离子的影响研究

于2013年2月9日—2月16日在南京城区连续观测PM10、PM2.5、PM1的质量浓度、能见度、PM2.5中水溶性离子浓度等参数,探讨了因春节期间烟花爆竹的燃放导致大气中颗粒物浓度出现短时峰值,同时能见度急剧降低,空气质量下降的原因。研究发现:因烟花爆竹的燃放,PM2.5局地短时间浓度可达863μg/m3,能见度仅为1.2km;PM2.5中Cl-、K+与SO2-4浓度短时间上升,这与烟花爆竹中氧化剂、还原剂等组分的燃烧释放有关。由春节期间观测结果统计发现,因烟花爆竹燃放对PM2.5中水溶性离子的贡献约占50%。     

内蒙古干草原冬、春季大气气溶胶的若干特征观测研究

内蒙古半干旱草原区大气气溶胶浓度以及散射等特性对生态环境、气候变化与预测研究有重要意义,文利用2009年1～4月在锡林浩特观象台草原站的观测资料,分析了冬、春季背景大气气溶胶质量浓度、黑碳质量浓度、散射系数的分布特征。研究发现,背景天气下,PM10、PM2.5、PM1.0浓度值都较低,平均值分别为22.7、9.5、6.1μg/m3,3种PM浓度值间的相关性不同;黑碳浓度平均值为0.59μg/m3,小粒子中的含量较高,其日分布规律受人类活动影响较大,与各PM浓度分布有较大不同;散射系数平均值为31.2Mm-1,与PM10、PM2.5、PM1.0、黑碳质量浓度都显著相关。三种PM中,PM2.5对散射和吸收的影响最大。风速、相对湿度对不同粒径的PM以及黑碳浓度、散射系数的影响有所不同。     

冬季大气中PM_(10)和PM_(2.5)污染特征及形貌分析

2008年冬季采集大气中PM10和PM2.5样品,利用SPSS软件进行分析。结果表明,PM10质量浓度在92.87～384.7μg/m3之间,平均值为201.09μg/m3,超标率71.43%。PM2.5浓度跨度为57.27～230.21μg/m3,平均值为133.82μg/m3,超标率89.47%。PM10和PM2.5空间分布略有差异。PM2.5/PM10在29.10%～94.76%之间,均值为66.55%。PM2.5与PM10质量浓度之间有显著相关性,相关方程:PM2.5=0.7993×PM10-55.984(R2=0.9524,置信度为95%)。通过颗粒物形貌分析,初步判定冬季大气主要污染源为燃煤和机动车尾气排放。     

2017年厦门金砖会晤期间PM2.5的化学特征及来源

为了探讨厦门金砖会晤期间的排放控制措施以及天气形势对大气颗粒物污染特征的影响,于2017年8月10日至9月10日对厦门气态污染物、细颗粒物(PM2.5)中的水溶性离子以及有机碳(OC)、元素碳(EC)等主要化学成分开展了高时间分辨率的在线监测。根据空气质量管控措施和天气形势将研究期分为6个阶段。管控前、管控期Ⅰ(非台风)和管控期Ⅱ(非台风) PM2.5质量浓度分别为(33. 12±9. 48)、(30. 30±17. 00)、(16. 01±4. 71)μg/m^3。管控期Ⅰ(台风)和管控期Ⅱ(台风) PM2.5质量浓度分别为(12. 40±3. 73)、(12. 45±3. 28)μg/m^3。结果表明:管控期Ⅰ(非台风)阶段受静稳天气的影响,管控效果削弱,PM2.5质量浓度下降幅度小;台风对颗粒物质量浓度下降的影响比管控更显著。管控初期,PM2.5中二次无机离子的质量浓度下降明显;台风对碳质组分质量浓度的影响不如无机组分显著。PMF源解析结果表明,二次无机源是PM2.5主要来源,随着管控措施的实行,扬尘源的贡献从21%降低到6%,而机动车源的贡献降幅不明显。台风期间SO4^2-、NO3^-、SO2、NO2以及硫酸盐氧化比值(SOR)均明显低于非台风期间,氮氧化比值(NOR)反而升高。台风和非台风期间NOR的日变化特征一致,NOR与阳离子的相关性分析结果表明,台风或高风速海风期间NOR与Na^+呈现很强的正相关性,说明海盐粒子可促进NO2非均相反应生成NO3-。     

Study of size and mass distribution of particulate matter due to crop residue burning with seasonal variation in rural area of Punjab, India

Emission from field burning of agricultural crop residue is a common environmental hazard observed in northern India. It has a significant potential health risk for the rural population due to respirable suspended particulate matter (RSPM). A study on eight stage size segregated mass distribution of RSPM was done for 2 wheat and 3 rice crop seasons. The study was undertaken at rural and agricultural sites of Patiala (India) where the RSPM levels remained close to the National Ambient Air quality standards (NAAQS). Fine particulate matter (PM(2.5)) contributed almost 55% to 64% of the RSPM, showing that, in general, the smaller particles dominated during the whole study period with more contribution during the rice crop as compared to that of wheat crop residue burning. Fine particulate matter content in the total RSPM increased with decrease in temperature. Concentration levels of PM(10) and PM(2.5) were higher during the winter months as compared to that in the summer months. Background concentration levels of PM(10), PM(2.5) and PM(10-2.5) were found to be around 97 ± 21, 57 ± 15 and 40 ± 6 μg m(-3), respectively. The levels increased up to 66, 78 and 71% during rice season and 51, 43 and 61% during wheat crop residue burning, respectively. Extensive statistical analysis of the data was done by using pair t-test. Overall results show that the concentration levels of different size particulate matter are greatly affected by agricultural crop residue burning but the total distribution of the particulate matter remains almost constant.     

成渝地区一次区域重污染过程来源解析及减排效果研究

基于嵌套网格空气质量预报模式(NAQPMS)及耦合的污染来源追踪模块模拟2017年12月16日至2018年1月3日成渝地区一次区域重污染过程,定量解析成渝地区主要城市PM2.5来源,评估过程中应急减排的成效。结果表明,天气静稳和风向辐合是造成此次重污染过程的重要因素,污染峰值阶段,成渝地区多个城市PM2.5日均质量浓度超过150μg/m3,达到重度污染级别。污染过程中,成都市PM2.5本地排放的贡献率为42%,眉山和德阳贡献率将近30%;重庆市PM2.5本地排放的贡献率为60%,外来输送以湖北、湖南和其他地区为主,贡献率为24%,成都和重庆市的工业源和交通源的贡献最大。区域联防联控应急减排对成渝各城市空气质量改善效果显著,成渝地区PM2.5浓度降低率为5%~11%,对于未实施应急预警方案的地区(如眉山市)受周边城市减排影响,浓度降低可达6%。     

Characteristics of aerosol types during large-scale transport of air pollution over the Yellow Sea region and at Cheongwon,Korea, in 2008

Episodes of large-scale transport of airborne dust and anthropogenic pollutant particles from different sources in the East Asian continent in 2008 were identified by National Oceanic and Atmospheric Administration satellite RGB (red, green, and blue)-composite images and the mass concentrations of ground level particulate matter. These particles were divided into dust, sea salt, smoke plume, and sulfate by an aerosol classification algorithm. To analyze the aerosol size distribution during large-scale transport of atmospheric aerosols, aerosol optical depth (AOD) and fine aerosol weighting (FW) of moderate imaging spectroradiometer aerosol products were used over the East Asian region. Six episodes of massive airborne dust particles, originating from sandstorms in northern China, Mongolia, and the Loess Plateau of China, were observed at Cheongwon. Classified dust aerosol types were distributed on a large-scale over the Yellow Sea region. The average PM10 and PM2.5 ratio to the total mass concentration TSP were 70% and 15%, respectively. However, the mass concentration of PM2.5 among TSP increased to as high as 23% in an episode where dust traveled in by way of an industrial area in eastern China. In the other five episodes of anthropogenic pollutant particles that flowed into the Korean Peninsula from eastern China, the anthropogenic pollutant particles were largely detected in the form of smoke over the Yellow Sea region. The average PM10 and PM2.5 ratios to TSP were 82% and 65%, respectively. The ratio of PM2.5 mass concentrations among TSP varied significantly depending on the origin and pathway of the airborne dust particles. The average AOD for the large-scale transport of anthropogenic pollutant particles in the East Asian region was measured to be 0.42 ± 0.17, which is higher in terms of the rate against atmospheric aerosols as compared with the AOD (0.36 ± 0.13) for airborne dust particles with sandstorms. In particular, the region ranging from eastern China, the Yellow Sea, and the Korean Peninsula to the Korea East Sea was characterized by high AOD distributions. In the episode of anthropogenic polluted aerosols, FW averaged 0.63 ± 0.16, a value higher than that in the episode of airborne dust particles (0.52 ± 0.13) with sandstorms, showing that fine anthropogenic pollutant particles contribute greatly to atmospheric aerosols in East Asia.     

无锡市MODIS气溶胶光学厚度与PM_(2.5)质量浓度的相关性分析

将MODIS数据反演得出的气溶胶光学厚度与无锡市区实测得到的PM2.5质量浓度进行相关性分析,结果两者的直接相关性较低,相关系数为0.283 4。气溶胶光学厚度经垂直分布和湿度修正后,两者相关性显著提高,相关系数为0.565 9。虽然修正过程存在误差,相关性未达预期程度,但该方法得到的气溶胶光学厚度可作为PM2.5监测的有效补充。     

Aerosol size distribution and mass concentration measurements in various cities of Pakistan

During March and April 2010 aerosol inventories from four large cities in Pakistan were assessed in terms of particle size distributions (N), mass (M) concentrations, and particulate matter (PM) concentrations. These M and PM concentrations were obtained for Karachi, Lahore, Rawalpindi, and Peshawar from N concentrations using a native algorithm based on the Grimm model 1.109 dust monitor. The results have confirmed high N, M and PM concentrations in all four cities. They also revealed major contributions to the aerosol concentrations from the re-suspension of road dust, from sea salt aerosols, and from vehicular and industrial emissions. During the study period the 24 hour average PM(10) concentrations for three sites in Karachi were found to be 461 μg m(-3), 270 μg m(-3), and 88 μg m(-3), while the average values for Lahore, Rawalpindi and Peshawar were 198 μg m(-3), 448 μg m(-3), and 540 μg m(-3), respectively. The corresponding 24 hour average PM(2.5) concentrations were 185 μg m(-3), 151 μg m(-3), and 60 μg m(-3) for the three sites in Karachi, and 91 μg m(-3), 140 μg m(-3), and 160 μg m(-3) for Lahore, Rawalpindi and Peshawar, respectively. The low PM(2.5)/PM(10) ratios revealed a high proportion of coarser particles, which are likely to have originated from (a) traffic, (b) other combustion sources, and (c) the re-suspension of road dust. Our calculated 24 hour averaged PM(10) and PM(2.5) concentrations at all sampling points were between 2 and 10 times higher than the maximum PM concentrations recommended by the WHO guidelines. The aerosol samples collected were analyzed for crustal elements (Al, Fe, Si, Mg, Ca) and trace elements (B, Ba, Cr, Cu, K, Na, Mn, Ni, P, Pb, S, Sr, Cd, Ti, Zn and Zr). The averaged concentrations for crustal elements ranged from 1.02 ± 0.76 μg m(-3) for Si at the Sea View location in Karachi to 74.96 ± 7.39 μg m(-3) for Ca in Rawalpindi, and averaged concentrations for trace elements varied from 7.0 ± 0.75 ng m(-3) for B from the SUPARCO location in Karachi to 17.84 ± 0.30 μg m(-3) for Na at the M. A. Jinnah Road location, also in Karachi.     

乌鲁木齐市可吸入颗粒物污染水平及其与气象因素的相关性分析

为了解可吸入颗粒物污染水平与气象因素之间的关系,从2008年9月—2010年2月采集乌鲁木齐市可吸入颗粒物样品,并对其随时间的变化特征及其与气象因素之间的相关性进行了统计分析。结果表明,采样时间内可吸入颗粒物中PM2.5和PM2.5-10的质量浓度的范围分别为38.2~468.7μg/m3和20.8~243.1μg/m3,平均浓度分别为134.2μg/m3和69.2μg/m3。可吸入颗粒物同时受几种气象因素的影响,其浓度与温度、能见度、风速呈负相关,与湿度呈正相关。     

Evaluation and optimization of an urban PM2.5 monitoring network

The objective of this study was to evaluate the PM(2.5) monitoring network established in the Greater Cincinnati and Northern Kentucky metropolitan area for measuring the 24 h integrated PM(2.5) concentration, as well as-at selected sites-hourly PM(2.5) concentration and 24 h integrated PM(2.5) speciation. The data collected during three years at 13 measurement sites were analyzed for spatial and temporal variations. It was found that both daily and hourly concentrations of PM(2.5) have low spatial variation due to a regional influence of secondary ammonium sulfate. In contrast, the trace element concentrations had high spatial variation. Seasonal variation accounted for most of the total temporal variation (60%), while yearly, monthly, weekly and daily variations were lower. The variance components and cluster analyses were applied to optimize the number of sites for measuring the 24 h PM(2.5) concentration. It was found that the 13-site network may be optimized by reducing the number of sites to 8, which would result in a relative precision reduction of 9% and a relative cost reduction of 36%. At the same time, the data suggest that the spatial resolution of speciation monitors and real-time PM(2.5) mass monitors should be increased to better represent spatial and temporal variations of the markers of local air pollution sources.