基于光学遥测技术的合肥市气溶胶参数观测

为了解合肥市气溶胶光学特性参数,采用太阳光度计CE318对雾霾期间气溶胶进行监测并分析了气溶胶光学厚度(AOD)、Angstorm波长指数(α)、体积谱函数等气溶胶光学特性参数。同时采用多轴差分吸收光谱技术(MAX-DOAS)反演了雾霾期间二次气溶胶前体物NO2柱浓度并和固定点测量的颗粒物(PM)浓度进行了对比。分析表明,雾霾期间的气溶胶光学厚度比晴天高,且随波长的增加而减少。Angstorm波长指数在雾霾天气时平均值较高,表明合肥雾霾天气期间气溶胶粒子以细粒子为主。气溶胶前体物NO2浓度变化与雾霾天气空气中颗粒物含量(PM10、PM2.5等)变化一致性较好,表明二次气溶胶可能对气溶胶颗粒浓度有一定影响。     

基于GIS的石家庄市PM10和PM2.5时空分布研究

利用2018年261个乡镇环境空气自动监测站监测数据,结合GIS空间分析技术,对石家庄市PM10和PM2.5的时空污染特征进行了研究。结果表明,石家庄地区PM10和PM2.5污染的空间分布整体表现为西北部山区好于东南部的平原地区,主城区好于周边县(市、区)的特征。采暖期PM10和PM2.5的污染程度明显重于非采暖期。PM2.5稳定性差于PM10,PM10和PM2.5的稳定性与污染程度具有一定的负相关性,表现出污染越轻的区域稳定性越差。两者的日均值浓度变化在时间序列上呈极强正相关,且污染越重的区域时间相关性越强。与日均值相关性不同,污染程度越轻的区域PM10和PM2.5年均值的线性相关性越强。     

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。     

广州地区大气能见度与颗粒物关系的初探

采用番禺大气成分站2007—2013年的能见度、颗粒物(PM1、PM2.5、PM10)及番禺气象局站的相对湿度(RH)资料,对颗粒物7年来的变化状况进行了分析。以RH为标准,将能见度和颗粒物数据分为RH≥90%、80%RH90%和RH≤80%3个部分,并以相关系数(R2)为判断标准,分别对其之间的相关性进行了分析。结果表明,PM10、PM2.5、PM1多年日平均值分别为56.6、43.0、38.5μg/m3,颗粒物值旱季高于雨季。当RH≤80%时,颗粒物与能见度的相关性最好,R2大小顺序为:PM10(0.47)PM2.5(0.57)PM1(0.58);当80%RH90%时,颗粒物与能见度的相关性次之,分别为PM10(0.4)PM2.5(0.46)PM1(0.49);当RH≥90%时,颗粒物与能见度的相关性较差。     

以长春为例研究环境空气中TSP、PM_(10)和PM_(2.5)的相关性

选取长春市解放大路与人民大街的交叉口为研究地点,分别进行TSP、PM10和PM2.5的采样和分析.然后利用相关系敷法和t检验对测定结果进行相关性分析,得到备元素的含量在三种污染物中的相关系敖:在TSP与PM10中为0.9349;在PM2.5与PM10中为0.8797;在TSP与PM2.5中为0.7824.得到各元素含量在三种污染物中的T检验统计值,在TSP与PM10中为0.90103;在PM2.5与PM10中为0.04745;在TSP与PM2.5中为0.047986.从分析结果可以看出,各元素含量在TSP与PM10中的相关性最好,在PM2.5与PM10中次之,研究结果为相关环境管理提供科学依据.     

珠三角秋冬季节长时间灰霾污染特性与成因

利用珠三角大气超级站2012年10月与2013年1月能见度、不同粒径颗粒物与BC质量浓度、气溶胶光散射系数、O3、相对湿度等在线监测数据,分析秋冬季节2次持续时间超过10 d的长时间灰霾过程污染特性与成因。结果表明,冬季灰霾过程中气溶胶吸光系数和光散射系数对大气总消光系数的贡献分别为13%和67%;PM2.5、PM1占PM10质量浓度分别为66%和39%;较高的PM2.5与BC日均浓度相关系数(R2=0.88)体现了一次排放对颗粒物质量浓度及能见度的显著影响。秋季灰霾过程中气溶胶吸光系数和光散射系数对大气总消光系数的贡献分别为11%和69%,由BC导致的吸光效应较冬季下降了约20%;PM2.5和PM1占PM10质量浓度比例分别为68%和45%,均高于冬季;O3浓度日最大小时值的平均值接近冬季的2倍;二次来源对PM2.5浓度升高和能见度下降起主导作用。来自不同方向的2种气团在珠三角僵持,大气扩散条件差是导致这2次灰霾过程的重要外在条件,应成为灰霾预报预警的重点关注对象。     

大中型商场PM10、PM2.5污染水平与来源分析

利用便携式气溶胶监测仪,对平顶山市区的中原商场、商业大楼、食品城总店三家大型商场不同楼层空气PM10和PM2.5进行了现场测定。结果显示,平顶山市大中型商场可吸入颗粒物污染严重,PM10、PM2.5污染平均超标率分别为13.7%和48.0%;PM10、PM2.5的质量浓度在时间和空间分布上存在很大差异;PM10中PM2.5所占比例为83%。     

南京市区PM_(2.5)扩散与气象条件的关系

使用2013年PM2.5监测数据和南京气候基准站的气象资料,分析PM2.5扩散与气象条件的关系。结果表明:PM2.5质量浓度与降水量有良好的负相关关系;较大混合层厚度和不稳定的大气层结有利于PM2.5质量浓度的降低;在南京地区,PM2.5质量浓度在东北风向和西南风下相对较低,而且与风速也有较好的负相关性;较高的湿度不利于PM2.5质量浓度的降低,并会影响能见度,60%~70%的湿度区间是PM2.5污染加重的转折点。     

北京地区PM_(2.5)与空气污染物的关系模型

为了研究北京地区PM2.5与空气污染物的质量浓度关系。从PM2.5监测网收集2013-04-01~2014-05-15期间PM2.5、PM10、SO2、NO2、CO、O3等主要空气污染物数据,用多元线性回归模型建立PM2.5与空气污染物的质量浓度关系。结果表明:北京地区PM2.5与空气污染物PM10、SO2、NO2、CO、O3的质量浓度相关系数分别为0.9172、0.6332、0.7683、0.8166和-0.1797,优化的拟合方程为:[PM2.5]=-22.5925+0.569109×[PM10]+23.94913×[CO]+0.113025×[BPM2.5],模型的估算值与观测值相关系数为0.9426,此方程能较好地模拟北京地区的PM2.5质量浓度。     

新版《环境空气质量标准》热点污染物PM2.5监控策略的思考与建议

随着工业化和城市化进程的加速,大气气溶胶污染日趋严重,由气溶胶细粒子PM2.5污染造成的能见度恶化事件越来越多,中国东部地区灰霾天气迅速增加.灰霾天气的本质是细粒子气溶胶污染,与光化学污染相关联,形成灰霾天气的气溶胶组成非常复杂.近年来由于灰霾天气日趋严重引发的环境效应问题,以及气溶胶辐射强迫引发的气候效应问题,已引起科学界、政府部门和社会公众的广泛关注,成为热门话题.在此背景下,国家出台了新版《环境空气质量标准》(GB 3095-2012),增设PM2.5浓度限值,对环境监测、环境管理和环境评价提出了新的要求.通过分析中国大气污染背景、国际组织和其他国家的PM2.5标准,及近期热点问题,提出在环境监测、环境管理和环境评价过程中实施新标准,监控PM2.5的策略.     

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.     

Satellite monitoring for carbon monoxide and particulate matter during forest fire episodes in Northern Thailand

This study explored the use of satellite data to monitor carbon monoxide (CO) and particulate matter (PM) in Northern Thailand during the dry season when forest fires are known to be an important cause of air pollution. Satellite data, including Measurement of Pollution in the Troposphere (MOPITT) CO, Moderate Resolution Imaging Spectroradiometer aerosol optical depth (MODIS AOD), and MODIS fire hotspots, were analyzed with air pollution data measured at nine automatic air quality monitoring stations in the study area for February–April months of 2008–2010. The correlation analysis showed that daily CO and PM with size below 10 μm (PM₁₀) were associated with the forest fire hotspot counts, especially in the rural areas with the maximum correlation coefficient (R) of 0.59 for CO and 0.65 for PM₁₀. The correlations between MODIS AOD and PM₁₀, between MOPITT CO and CO, and between MODIS AOD and MOPITT CO were also analyzed, confirming the association between these variables. Two forest fire episodes were selected, and the dispersion of pollution plumes was studied using the MOPITT CO total column and MODIS AOD data, together with the surface wind vectors. The results showed consistency between the plume dispersion, locations of dense hotspots, ground monitoring data, and prevalent winds. The satellite data were shown to be useful in monitoring the regional transport of forest fire plumes.     

Analysis of spatial and seasonal distributions of MODIS aerosol optical properties and ground-based measurements of mass concentrations in the Yellow Sea region in 2009

Satellite-retrieved data on aerosol optical depth (AOD) and Ångström exponent (AE) using a moderate resolution imaging spectrometer (MODIS) were used to analyze large-scale distributions of atmospheric aerosols in East Asia. AOD was relatively high in March (0.44?±?0.25) and low in September (0.24?±?0.21) in the East Asian region in 2009. Sandstorms originating from the deserts and dry areas in northern China and Mongolia were transported on a massive scale during the springtime, thus contributing to the high AOD in East Asia. However, whereas PM10 with diameters ≤10 μm was the highest in February at Anmyon, Cheongwon, and Ulleung, located leeward about halfway through the Korean Peninsula, AOD rose to its highest in May. The growth of hygroscopic aerosols attendant on increases in relative humidity prior to the Asian monsoon season contributed to a high AOD level in May. AE typically appears at high levels (1.30?±?0.37) in August due to anthropogenic aerosols originating from the industrial areas in eastern China, while AOD stays low in summer due to the removal process caused by rainfall. The linear correlation coefficients of the MODIS AOD and ground-based mass concentrations of PM10 at Anmyon, Cheongwon, and Ulleung were measured at 0.4~0.6. Four cases (6 days) of mineral dustfall from sandstorms and six cases (12 days) of anthropogenically polluted particles were observed in the central area of the Korean Peninsula in 2009. PM10 mass concentrations increased at both Anmyon and Cheongwon in the cases of mineral dustfall and anthropogenically polluted particles. Cases of dustfall from sandstorms and anthropogenic polluted particles, with increasing PM10 mass concentrations, showed higher AOD values in the Yellow Sea region.     

基于遥感数据的京津冀地区PM2.5时空分布特征

基于MODIS AOD遥感数据,采用多元线性回归模型对PM2.5地面监测数据进行模拟估算,同时加入降水量、相对湿度等气象因子以提高模型精度,结合GIS空间分析技术,得到2015—2016年京津冀地区空间连续的PM2.5浓度分布。结果表明:利用多元线性回归模型反演PM2.5浓度效果较好,R 2均在0.59~0.84之间。在时间上,京津冀地区PM2.5浓度呈现出夏季最低、秋季稍高、冬春两季最高的变化趋势;在空间上,2015年和2016年京津冀地区PM2.5浓度有明显的区域差异,均呈现出西北低、东南高的分布格局,大致与燕山山脉和太行山脉走向一致。     

睢宁地区大气PM_(2.5)及气态污染物季节性变化特征

根据2014年全年实时在线观测数据,分析了徐州睢宁地区大气细颗粒物(PM_(2.5))和气态污染物(包括SO_2、CO、NO_x、O_3)质量浓度的季节性变化特征。结合后向轨迹模型,分析不同气团对该地区大气污染浓度的影响。PM_(2.5)与O_3值在夏季最低,呈显著相关,表明夏季PM_(2.5)主要受控于本地大气光化学活性。在冬季,除O_3外,PM_(2.5)、SO_2、CO、NO_x值最高,且大气颗粒物主要以细粒子为主。O_3在春季最高,并与远程传输的气团且经过我国东部污染源密集地区相对应。高浓度的PM_(2.5)主要与冬季缓慢移动的气团相对应,这可能将PM_(2.5)及其气态前体物传输至该地区进而加重大气污染程度。     

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。     

南京北郊气溶胶散射特性观测研究

南京北郊是重工业区,空气污染严重。该文利用浊度仪对大气气溶胶散射系数进行了四季观测,研究了该区气溶胶散射系数、后向散射比、质量散射系数及散射系数与能见度的关系,结果表明,观测期间散射系数波动较大,平均值为344.84 Mm^-1,标准差为215.99 Mm^-1,散射系数季节差异明显,主要受气象条件和外界污染排放的影响;后向散射比平均值为0.139,春季变化剧烈,夏、秋、冬季在波动中缓慢上升,大气中细粒子污染严重且含量有增加的趋势;散射系数与PM_2.5质量浓度相关系数达到0.91,质量散射系数平均值为1.87m²/g;散射系数与能见度之间存在-0.66的相关性,两者呈负幂函数关系。     

乌鲁木齐市2011年冬季PM2.5/PM10浓度特征分析

利用乌鲁木齐市PM2.5//PM10自动监测数据,分析PM2.5与PM10的浓度分布特征和时间变化规律。结果表明,按照《环境空气质量标准》(二次征求意见稿)的标准限值,乌鲁木齐市冬季PM2.5污染重于PM10。PM2.5浓度为0.164mg/m3,超过二级年标准限值的3.7倍,超标率为73.9%。PM2.5浓度日变化曲线昼高夜低,呈单峰型,峰值出现在13:00~14:00(北京时间)。PM10中PM2.5所占比例较高,PM2.5/PM10为0.79,相关分析和检验显示PM2.5与PM10的线性相关显著,相关系数为0.92。     

2000—2019年常州市MODIS气溶胶光学厚度时空变化特征研究

利用2000—2019年TERRA和AQUA相结合的气溶胶光学厚度(AOD)产品数据，从时间和空间角度分析了常州市AOD的变化特征。结果显示：(1)2012—2019年常州市PM_2.5与AOD年均值的相关系数为0.898,表明AOD产品适用于常州市气溶胶污染年际变化研究。(2)2000—2019年常州市AOD年均值范围为0.463～0.688,平均值为0.627。其中，2000—2007年常州市AOD年均值整体呈上升趋势，2011—2019年呈下降趋势。常州市AOD的月变化趋势呈倒“U”形，特征最高值出现在6月，最低值出现在12月。常州市AOD有明显的季节变化特征，夏季最高，冬季最低。(3)常州市AOD高值主要分布在西部的溧阳市金坛区，北部的新北区也存在少量高值分布。(4)通过Moran指数发现，常州市Moran指数均大于零，表明各年份AOD均呈集聚状态。2000—2010年常州市AOD的空间集聚程度较高，2010年以后的空间集聚效应逐渐减弱。空间热点分析表明，2011—2019年常州市AOD高值集聚区域相比2000—2010年有所减少，冷点集聚区域有所增加，AO...     

Characteristics of particulate matter pollution in the Pearl River Delta region, China: an observational-based analysis of two monitoring sites

Two-year monitoring data (2006 and 2009), collected at the sub-urban site (WQS) and the background site (TH), were used to study the characteristics of Particulate Matter (PM) pollution in the Pearl River Delta region, China. Similar levels of PM(2.5) concentration measured at both sites seem to confirm that the fine particles have emerged as a major regional pollution issue. The seasonal variation of PM(2.5) concentration is associated with the regional monsoon circulations while the diurnal variation is related to land-sea breeze, traffic emissions and boundary layer development. Negative correlation was found in PM(2.5)-wind speed and PM(2.5)-humidity. Analysis of radiation, temperature and ozone suggests the existence of secondary aerosol formation. Transport effect may be another contributing factor to high PM pollution in the region, such as occasional long-distance dust intrusion and trans-boundary effects from upwind areas.