贵州安顺市PM2.5气象输送条件及贡献源空间分布

利用贵州省安顺市2015—2019年大气污染物资料和气象资料,分析安顺市空气质量特征和主要大气污染物特征,通过TrajStat软件中HYSPLIT模型的后向轨迹模式,结合GDAS气象数据、PM_2.5浓度,分析不同季节输送途径及其污染轨迹,采用潜在源贡献作用和浓度权重轨迹分析方法,分析研究期内所有PM_2.5污染日（PM_2.5日浓度高于75 μg·m^-3）输送轨迹垂直与水平方向分布特征。结果表明: PM_2.5是安顺城区主要大气污染物,冬季输送污染轨迹占比较大,输送方向主要为贵州东北方向、偏南方向; 污染日PM_2.5输送路径以贵州东北方向近距离输送为主,该类轨迹基本分布在880—980 hPa高度; 潜在源高值区主要集中在贵阳整个地区、毕节织金县、黔西市、金沙县等,高贡献值区主要集中在安顺紫云县、镇宁县、毕节织金县、大方县等。     

基于微波辐射计和气溶胶激光雷达的边界层高度研究及应用

分别基于微波辐射计温湿度廓线资料的气块法、位温法和比湿法,地面气象资料的罗氏法及气溶胶激光雷达数据的梯度法,计算得出广州地区大气边界层高度,对比分析5种边界层高度结果及其与气象条件、空气质量之间的关系,结合典型大气污染过程分析边界层高度对PM_2.5、O₃浓度的影响。结果显示:（1）利用位温法、气块法、罗氏法、比湿法和梯度法计算得出广州地区平均边界层高度分别为2 207 m、1 239 m、901 m、717 m和660 m,位温法显著高估了广州地区的边界层高度;（2）利用气块法得出的混合层高度日变化能够较好地表征白天大气边界层演变特征,利用气块法和比湿法得出的白天混合层高度与近地面O₃浓度有显著的正相关关系,相关系数在0.5以上,在O₃污染防治中,应同时考虑边界层内垂直输送的影响;（3）利用梯度法得出的边界层高度在污染天气时与PM_2.5浓度的相关性较好,能较好地表现出大气污染情况,在PM_2.5污染天气过程分析中具有较好的应用价值。      

银川市大气边界层逆温影响因素及其与冬季PM2.5的关系

为了探究银川市大气边界层逆温特征和影响因素及其与冬季PM_2.5污染的关系,利用2015—2020年银川气象站探空、地面气象观测资料及银川市空气质量监测数据,在分析银川市大气边界层逆温及地面气象要素特征基础上,以冬季为研究时段,探讨逆温与地面气象要素对PM_2.5污染的影响。结果表明：(1)银川市清晨大气边界层较傍晚更易出现逆温,且逆温多为贴地逆温,贴地逆温较悬浮逆温强度大、厚度小;逆温频率和厚度冬季最大、夏季最小,逆温强度秋季最强、夏季最弱。(2)冬季晴天,地面平均风速1.0～1.5 m·s^-1、相对湿度30%～60%的气象条件下易出现逆温。(3)贴地逆温是影响冬季PM_2.5污染天气的主要气象因素之一,当逆温厚度超过596 m、强度超过1.4℃·(100 m)^-1时,易出现PM_2.5污染天气,且随着逆温厚度增大、强度增强,污染加重。(4)冬季PM_2.5污染天气下,清晨天空状况多为晴天,通常地面平均风速小于1.3 m·s^-1     

外来输送对江苏冬季PM2.5贡献的数值模拟研究

利用WRF-Chem模式对2016年12月中下旬的两次重污染过程进行模拟,定量研究外来污染输送对江苏省PM_2.5的污染贡献。15—17日和22—23日两次过程都存在明显的上游污染输送特征:宿迁、扬州、无锡(自西北向东南)的PM_2.5浓度先后出现峰值,峰值均出现在西北风场中,当风向转为偏北风时峰值逐渐减弱。第二次过程中地面风力更大,高空形势更有利于远距离输送,高值区范围强度明显强于第一次,重度污染层厚度达到900~1 500 m,且持续时间较长。第一次过程中江苏省内排放源贡献率为23%~79%(第二次为5%~32%),苏南仍以本地排放源污染为主,苏北外来输送贡献率超过50%。第二次过程中宿迁、扬州、无锡的PM_2.5外来输送贡献分别为105.9 μg/m3、83.1 μg/m3、64.8 μg/m3(第一次为40.2 μg/m3、20.9 μg/m3、11.1 μg/m3),山东省和京津冀地区排放源是主要污染输送来源,二者贡献之和在44%~70%。两次过程中,外来输送贡献均是自北向南递减,山东省贡献率高于京津冀地区,而其余周边省份的贡献率相对较低;安徽省对江苏西部城市的贡献率较高。      

2013-2015年合肥市PM2.5重污染特征研究

综合利用中国环境监测网公布的合肥市2013-2015年大气污染物浓度数据和合肥市气象站的常规气象资料,以及激光雷达探测资料、公益性行业（气象）专项（GYHY201206011）获得的气溶胶离子成分分析结果,分析了合肥市PM_2.5重污染（日均浓度>150 μg/m3）特征。结果表明:（1）2013-2015年,合肥市PM_2.5浓度和重污染天数空间分布差异明显,东北部多、西南部少,1月各站差异最大。除了低浓度日（日均浓度≤35 μg/m3）,PM_2.5浓度都存在明显的日变化,午后低、早晚高,且随着污染程度加重,早上峰值出现时间推后。（2）重污染日臭氧以外的气态污染物浓度都显著上升。（3）重污染日常伴随着霾和轻雾天气,以稳定、小风天气为主,重污染日白天相对湿度偏高、风速偏小,600 m以下的消光系数显著增大且峰值高度降低。（4）重污染日PM_2.5中水溶性无机离子含量增高,其中NO₃-含量的占比增加最多,超过了SO₄2-的占比。      

京津冀一次重度雾霾天气能见度及边界层关键气象要素的模拟研究

本文利用GRAPES_CUACE大气化学模式对京津冀地区2015年12月重度雾霾过程进行了模拟和评估。京津冀地区能见度和PM_2.5模拟值与观测值的对比表明:该模式能较好地模拟京津冀地区能见度和PM_2.5的逐日变化情况,但模式存在对伴随着重污染发生的低能见度模拟偏高的问题。以12月5~10日的重度雾霾过程为重点,针对地面风速、边界层高度、相对湿度、PM_2.5及其对能见度的影响进行了详细分析,研究结果表明:污染过程中大部分地区过程平均风速低于2 m s-1,边界层平均高度低于600 m,相对湿度较高。模式低能见度模拟偏高可能因为:（1）模式模拟重雾霾时段的PM_2.5极大值浓度偏低。（2）模拟相对湿度存在系统性偏低的误差,这一误差对能见度的影响表现为两方面,一是相对湿度会通过影响可溶性气溶胶的吸湿增长过程影响气溶胶质量浓度,导致气溶胶消光系数的计算偏低;二是目前模式中采用的能见度的参数化公式考虑了相对湿度对气溶胶吸湿增长的影响,没有考虑雾滴的直接消光作用。     

2010年长江三角洲临安本底站PM2.5理化特征

2010年在代表长三角区域背景地区的浙江省临安区域大气本底站开展了对大气细粒子PM_2.5为期1年的地面观测,并对细粒子中水溶性离子和碳组分的季节变化特征进行了分析。临安2010年大气中PM_2.5质量浓度平均为 (58.2±50.8) μg·m－3,PM_2.5质量浓度季节变化明显。利用HYSPLIT4模式计算了2010年临安72 h后向轨迹,根据轨迹计算与聚类结果,结合地面观测的PM_2.5数据进行了分析。研究表明:临安地区因受到长江三角洲区域及偏北气流引起的污染传输影响,呈现出高细粒子水平特征。PM_2.5中总水溶性离子年平均质量浓度为 (28.5±17.7) μg·m－3,占PM_2.5质量浓度的47%。其中,气溶胶组分SO₄2-,NO₃-和NH₄+所占比例最大,共占总水溶性离子的69%。PM_2.5中有机碳和元素碳的年平均质量浓度分别为 (10.1±6.7) μg·m－3和 (2.4±1.8) μg·m－3。有机碳和元素碳质量浓度显著相关,表明有机碳和元素碳主要来自相同的排放源。     

气象-气溶胶资料联合同化对秋季PM2.5浓度模拟的影响研究

相比冬季大范围静稳条件下的污染堆积过程,秋季气象条件更加复杂和局地化,气象条件模拟不确定性给秋季气溶胶模拟带来了更大难度,且目前研究较少考虑气象-气溶胶因素在线模拟和联合同化。使用WRF/Chem模式和格点统计差值（GSI）三维变分同化系统,2015年10月进行了为期1个月的气象-气溶胶资料联合同化及模拟试验,并基于此讨论了气象-气溶胶资料联合同化对秋季PM_2.5浓度模拟的影响。结果表明,WRF/Chem模式可以模拟出秋季污染天气过程,但对华北平原和中东部地区存在高估、西北部存在低估现象;同化地面PM_2.5浓度观测资料可以改进对PM_2.5浓度的模拟,上述两个地区的偏差均得到订正,6 h预报偏差均降低至6 μg/m3以内;重点针对华北地区的分析表明,秋季PM_2.5污染过程与特殊气象条件（湿度升高、风场辐合、区域输送）密切相关,因此在地面PM_2.5观测资料同化基础上增加常规气象资料同化,能进一步提高对华北平原气象-污染过程的表达,PM_2.5浓度预报相关系数从0.86提高至0.89。气象-气溶胶联合资料同化能更加准确地模拟秋季气溶胶污染过程,为更好地开展污染成因和在气象预报框架下开展气象-气溶胶相互影响研究提供了基础。      

2019年1月长春市一次霾污染过程成因分析

利用空气质量历史监测数据、地面气象要素及激光雷达探测资料,综合分析了2019年1月10—15日长春市一次霾污染过程,探讨了污染过程中污染物和气象要素的变化特征与影响机制。结果表明：此次霾污染过程中12—13日污染最重,PM_2.5和PM₁₀质量浓度均超过150 μg·m^-3,气溶胶消光最强,超过70%的PM_2.5/PM₁₀比值大于0.7,指出了细粒子对重污染事件的贡献;重污染期间近地面风速偏小、相对湿度增加、变压较小,同时低空风出现明显的风向转变,弱下沉运动与逆温以及较低的边界层共同削弱了大气的水平和垂直扩散能力,有利于污染物累积,导致霾污染。500 hPa天气形势表明长春市位于槽前脊后,850 hPa高度场为弱西风,相对湿度大;海平面气压场存在低压气旋及弱西南气流,该气流有利于将污染物输送至长春市,造成霾污染加剧;1月14—15日高空槽加深东移,850 hPa西北气流增强,近地面气压梯度力变大,污染物得到扩散,霾污染逐渐结束。     

肇庆市PM2.5重污染天气形势及冬季典型污染过程输送特征研究

为了揭示肇庆市颗粒物重污染过程的发生与发展规律,利用2013—2014年PM_2.5监测数据,分析该区域两年间的空气质量整体变化情况以及PM_2.5污染过程的季节变化规律,统计两年间所有颗粒物重污染过程,并根据污染过程的天气形势展开分析,运用空气质量数值模型（WRF-Chem）对冬季一次典型重污染过程进行模拟研究。结果表明,肇庆2013—2014年共发生27次PM_2.5重污染过程且主要出现在秋冬季,结合气象场的分布特征,总结出四种诱发重污染过程发生的天气形势,分别是高压出海型（48%）、热带低压型（22%）、锋面影响型（19%）及冷高压控制型（11%）。在四种天气形势的影响下,肇庆整体风向以东南风和南风为主,大气处于静稳状态,导致污染物的积累并诱发重污染过程。WRF-Chem模拟结果进一步发现,不利气象条件及本地排放源是造成肇庆冬季重污染过程发生的主要原因。结合四维通量模型对肇庆边界污染物输送情况进行定量分析后发现,肇庆PM_2.5以输出为主,其中硝酸盐与氨盐的输出通量较大。此外,模型还揭示了肇庆境内的主要污染物输送通道呈东南-西北走向,外地输入的污染物也通过此通道影响肇庆的空气质量      

Oscillation of Surface PM<Subscript>2.5</Subscript> Concentration Resulting from an Alternation of Easterly and Southerly Winds in Beijing: Mechanisms and Implications

We used simultaneous measurements of surface PM_2.5 concentration and vertical profiles of aerosol concentration, temperature, and humidity, together with regional air quality model simulations, to study an episode of aerosol pollution in Beijing from 15 to 19 November 2016. The potential effects of easterly and southerly winds on the surface concentrations and vertical profiles of the PM_2.5 pollution were investigated. Favorable easterly winds produced strong upward motion and were able to transport the PM_2.5 pollution at the surface to the upper levels of the atmosphere. The amount of surface PM_2.5 pollution transported by the easterly winds was determined by the strength and height of the upward motion produced by the easterly winds and the initial height of the upward wind. A greater amount of PM_2.5 pollution was transported to upper levels of the atmosphere by upward winds with a lower initial height. The pollutants were diluted by easterly winds from clean ocean air masses. The inversion layer was destroyed by the easterly winds and the surface pollutants and warm air masses were then lifted to the upper levels of the atmosphere, where they re-established a multi-layer inversion. This region of inversion was strengthened by the southerly winds, increasing the severity of pollution. A vortex was produced by southerly winds that led to the convergence of air along the Taihang Mountains. Pollutants were transported from southern–central Hebei Province to Beijing in the boundary layer. Warm advection associated with the southerly winds intensified the inversion produced by the easterly winds and a more stable boundary layer was formed. The layer with high PM_2.5 concentration became dee-per with persistent southerly winds of a certain depth. The polluted air masses then rose over the northern Taihang Mountains to the northern mountainous regions of Hebei Province.     

Relative contributions of boundary-layer meteorological factors to the explosive growth of PM<Subscript>2.5</Subscript> during the red-alert heavy pollution episodes in Beijing in December 2016

Based on observations of urban mass concentration of fine particulate matter smaller than 2.5 μm in diameter (PM_2.5), ground meteorological data, vertical measurements of winds, temperature, and relative humidity (RH), and ECMWF reanalysis data, the major changes in the vertical structures of meteorological factors in the boundary layer (BL) during the heavy aerosol pollution episodes (HPEs) that occurred in winter 2016 in the urban Beijing area were analyzed. The HPEs are divided into two stages: the transport of pollutants under prevailing southerly winds, known as the transport stage (TS), and the PM_2.5 explosive growth and pollution accumulation period characterized by a temperature inversion with low winds and high RH in the lower BL, known as the cumulative stage (CS). During the TS, a surface high lies south of Beijing, and pollutants are transported northwards. During the CS, a stable BL forms and is characterized by weak winds, temperature inversion, and moisture accumulation. Stable atmospheric stratification featured with light/calm winds and accumulated moisture (RH > 80%) below 250 m at the beginning of the CS is closely associated with the inversion, which is strengthened by the considerable decrease in near-surface air temperature due to the interaction between aerosols and radiation after the aerosol pollution occurs. A significant increase in the PLAM (Parameter Linking Aerosol Pollution and Meteorological Elements) index is found, which is linearly related to PM mass change. During the first 10 h of the CS, the more stable BL contributes approximately 84% of the explosive growth of PM_2.5 mass. Additional accumulated near-surface moisture caused by the ground temperature decrease, weak turbulent diffusion, low BL height, and inhibited vertical mixing of water vapor is conducive to the secondary aerosol formation through chemical reactions, including liquid phase and heterogeneous reactions, which further increases the PM_2.5 concentration levels. The contribution of these reaction mechanisms to the explosive growth of PM_2.5 mass during the early CS and subsequent pollution accumulation requires further investigation.     

南京北郊污染物来源及跨区域输送过程研究

利用全国空气质量指数(Air Quality Index,AQI)、PM_(2.5)地面观测数据、全球数据同化系统GDAS数据和FNL再分析气象资料,研究了2015/2016年冬季南京北郊空气质量变化特征以及环境输送条件和污染物源区。结果表明:以AQI为代表的冬季江淮地区污染程度存在3种典型的污染物跨区域输送路径—西北路径、北方路径和西南路径。西北路径通常发生在蒙古高压较强,且处于平均位置时刻,南京北郊上空有冷平流,不利于污染物扩散;北方路径对应蒙古高压弱,东北附近为弱高压控制,偏北气流将污染物带至南京北郊,如跨海洋,则污染减弱;西南路径对应南京北郊为边界层内反气旋式环流中心,下沉气流十分不利于污染物扩散。影响南京北郊污染的潜在源区主要分布在河北南部、山东西部、河南南部、安徽东部和湖北西部。河北省是重要的污染源区,河北南部和山东西部污染物通过北方路径输送至南京北郊,因此北方路径虽发生污染概率少于其他两种,却是形成南京北郊严重污染的重要路径。河南南部污染物通过西北路径输送。安徽和湖北污染物通过西南路径输送。定量分析表明,平流输送是南京北郊重度污染的重要原因,近地层风速对AQI的平流输送占AQI变化的贡献率超过70%,甚至可达85%。     

Evolution of Meteorological Conditions during a Heavy Air Pollution Event under the Influence of Shallow Foehn in Urumqi,China

The air pollution in Urumqi which is located on the northern slope of the Tianshan Mountains in northwestern China,is very serious in winter. Of particular importance is the influence of terrain-induced shallow foehn, known locally as elevated southeasterly gale(ESEG). It usually modulates atmospheric boundary layer structure and wind field patterns and produces favorable meteorological conditions conducive to hazardous air pollution. During 2013–17, Urumqi had an average of 50 d yr^–1...     

偏东小风在武汉市秋冬季重污染形成过程中的作用

综合运用了多元资料（环境空气质量监测资料、地面气象观测资料、L波段雷达探空资料、风廓线雷达探空资料和再分析资料）和多种方法（后向轨迹追踪、聚类分析、潜在源区贡献法和数值模拟）,研究了武汉地区特殊气象条件对重污染过程的影响,揭示了偏东小风所带来的外源污染物对形成严重污染日的贡献.主要研究结论如下：1）后向轨迹追踪分析表明,武汉地区严重污染日气流主要为来自安徽南部（47.5%）的偏东小风,模拟结果也显示偏东气流、偏北气流与局地环流共同作用,在武汉地区形成一个局地涡旋,成为反复污染带,加重了武汉地区的污染程度;2）利用潜在源区贡献法（PSCF）分析发现,武汉市秋冬季的潜在源区主要是安徽、江苏、山东、河南、湖南、江西以及武汉周边地区,因此在冬季大范围污染背景下,跨区域的联防联控（尤其是安徽南部地区）才能有效地减少武汉市秋冬季的重污染日.     

Investigating the Transport Mechanism of PM2.5 Pollution during January 2014 in Wuhan,Central China

Severe haze pollution that occurred in January 2014 in Wuhan was investigated. The factors leading to Wuhan’s PM_2.5 pollution and the characteristics and formation mechanism were found to be significantly different from other megacities, like Beijing. Both the growth rates and decline rates of PM_2.5 concentrations in Wuhan were lower than those in Beijing, but the monthly PM_2.5 value was approximately twice that in Beijing. Furthermore, the sharp increases of PM_2.5 concentrations were often accompanied by strong winds. A high-precision modeling system with an online source-tagged method was established to explore the formation mechanism of five haze episodes. The long-range transport of the polluted air masses from the North China Plain (NCP) was the main factor leading to the sharp increases of PM_2.5 concentrations in Wuhan, which contributed 53.4% of the monthly PM_2.5 concentrations and 38.5% of polluted days. Furthermore, the change in meteorological conditions such as weakened winds and stable weather conditions led to the accumulation of air pollutants in Wuhan after the long-range transport. The contribution from Wuhan and surrounding cities to the PM_2.5 concentrations was determined to be 67.4% during this period. Under the complex regional transport of pollutants from surrounding cities, the NCP, East China, and South China, the five episodes resulted in 30 haze days in Wuhan. The findings reveal important roles played by transregional and intercity transport in haze formation in Wuhan.     

Comparison of Two Air Pollution Episodes over Northeast China in Winter 2016/17 Using Ground-Based Lidar

This study analyzes and compares aerosol properties and meteorological conditions during two air pollution episodes in 19–22 (E1) and 25–26 (E2) December 2016 in Northeast China. The visibility, particulate matter (PM) mass concentration, and surface meteorological observations were examined, together with the planetary boundary layer (PBL) properties and vertical profiles of aerosol extinction coefficient and volume depolarization ratio that were measured by a ground-based lidar in Shenyang of Liaoning Province, China during December 2016–January 2017. Results suggest that the low PBL height led to poor pollution dilution in E1, while the high PBL accompanied by low visibility in E2 might have been due to cross-regional and vertical air transmission. The PM mass concentration decreased as the PBL height increased in E1 while these two variables were positively correlated in E2. The enhanced winds in E2 diffused the pollutants and contributed largely to the aerosol transport. Strong temperature inversion in E1 resulted in increased PM_2.5 and PM₁₀ concentrations, and the winds in E2 favoured the southwesterly transport of aerosols from the North China Plain into the region surrounding Shenyang. The large extinction coefficient was partially attributed to the local pollution under the low PBL with high ground-surface PM mass concentrations in E1, whereas the cross-regional transport of aerosols within a high PBL and the low PM mass concentration near the ground in E2 were associated with severe aerosol extinction at high altitudes. These results may facilitate better understanding of the vertical distribution of aerosol properties during winter pollution events in Northeast China.     

大气物理过程方案对PM2.5预报的敏感性试验

利用WRF-Chem大气化学模式,选择2015年12月中旬发生在我国的大范围空气污染过程,在采用同样化学方案条件下,针对模式中不同物理过程及其参数化方案开展了地面PM_(2.5)预报的敏感性试验。结果表明:该模式能较好展示此次PM_(2.5)污染的演变过程,与实况也较接近,但对青海经宁夏至内蒙的PM_(2.5)高值区出现了漏报现象,这可能是模式外边界未对污染物做更新所致。对地面PM_(2.5)的预报,各物理过程的敏感度不同,边界层(含近地面层)过程的影响要明显大于积云对流及微物理过程的影响,不同的参数化方案会造成不同的预报误差。边界层过程QNSE和与其配套的近地面层方案的组合是预报的较佳组合;而TEMF和与其配套的组合以及ACM2和Pleim-X的组合则不佳。合理的物理过程参数化方案有助于提高PM_(2.5)预报质量。模式预报对排放源也有适应过程,其Spin-up时间较气象要素长。     

2013年1月持续性霾天气中影响污染程度的气象条件分析

利用南京本站气象观测记录、环保局监测数据以及NCEP/NCAR再分析资料,分析2013年1月持续性污染天气过程的大气环流背景,并结合南京地区探空资料、风廓线雷达资料以及激光雷达资料,分析这次持续性污染过程中空气质量属良好、轻度污染、中度污染、重度污染典型个例的大气垂直特征和边界层内气象条件的差异。得到如下结论:2013年1月份北方冷空气活动较弱,南京地区大气层结稳定,近地层风速小,污染物气象扩散条件差。加之近地层以弱偏东风为主,水汽较多,有利于污染物颗粒直径增大。大气垂直结构以及边界层内水平风速均对大气污染程度起到一定影响。AQI与逆温层高度存在显著负相关关系;大气污染时,1000 m以下出现逆温结构,且逆温层越低、越厚,污染程度越大;重度污染时,近地层出现贴地逆温层,厚度为700m左右。逆温层高度下降,PM₁₀颗粒物高浓度区高度也明显下降,近地层污染物浓度对垂直方向上污染物浓度正响应的高度降低。在空气质量良好时, 150~1500m存在风速大值区,且风无空,湍流作用明显,有利于污染物和周围的洁净空气相混合而得到稀释,加速污染物的垂直扩散进程。当中度污染日和典型重度污染日时,150~1500 m之间并不存在大风速区。此外, PM₁₀的300μg·m^-3高浓度垂直高度延伸至300 m附近时,近地层PM_2.5明显上升至100μg·m^-3以上,高浓度区数值越大,近地层PM_2.5越大。