气象条件对石家庄PM_(2.5)浓度的影响分析

利用2013~2014年石家庄逐小时PM2.5监测浓度与地面及探空等气象观测资料,从大气的垂直扩散、水平扩散和地面局地环流等方面,探讨气象条件对PM2.5浓度的定量影响关系。结果表明:(1)石家庄PM2.5浓度具有明显的日、月和季节变化特征,早晨08时前后PM2.5浓度最高,下午16时前后浓度最低;冬季PM2.5浓度最高,夏季最低;(2)2 a共出现485 d逆温,其中10~12月出现频率最多,达82.8%~86.2%,逆温致使低层大气垂直运动受阻,不利于污染物扩散;(3)大气混合层高度与PM2.5浓度呈反相关,PM2.5浓度75μg/m3(空气质量优良),对应大气混合层高度平均为1 448 m,而PM2.5浓度≥150μg/m3(空气重污染)的混合层高度降到878 m;(4)受地形影响,石家庄地面风与边界层附近风对污染物的影响明显不同:925 h Pa西南风、地面偏东风不利于污染物扩散;925 h Pa西北风、地面偏西风有利于污染物浓度降低。925 h Pa风速4 m/s、地面偏西风风速2 m/s、地面偏东风风速3 m/s,有利于污染物扩散;(5)降水对污染物有湿清除作用,清除量不仅与降水量有关,还与前期PM2.5浓度有关,且冬季降雪过程对PM2.5的清除作用是降雨的4倍。     

广州市一次霾污染过程及其气象成因分析

利用空气质量监测资料、地面气象观测及微波辐射计数据,对2017年1月1—8日广州市出现的一次灰霾污染过程进行了分析。结果表明:(1)1月1—8日逐日灰霾时出现6~17个,共出现74个,主要是轻微和轻度级别,占全部灰霾时的95.9%,其中5日出现了3个时次的中度灰霾;(2)灰霾污染期间颗粒物PM2.5和PM10均超标,5日颗粒物PM2.5和PM10质量浓度14:00—17:00 4个时次达到重度污染级别,广雅中学站5日14:00 PM2.5质量浓度最大值达292μg/m3(严重污染),超标3.89倍,颗粒物PM10最高质量浓度达238μg/m3,超标1.59倍;(3)受地面均压场控制,近地层平均风速较小,4和5日平均风速1.5 m/s左右;4和5日多次出现逆温,4日02:00出现贴地逆温,09:00逆温出现在850~1 000 m,5日02:00和09:00均为贴地逆温;贴地逆温高度均为100 m左右。霾污染期间空气相对湿度较大,有88.1%时次达90%以上。     

广州城区和城郊大气颗粒物污染现状及来源分析

为更好地表征当前广州城区和城郊大气颗粒物污染现状与差异及其与气象要素的关系,对2017年城郊站黄埔和城区站番禺的大气颗粒物(PM2.5和PM10)和气象观测数据进行了分析。结果表明:全年来看,城区和城郊PM2.5和PM10具有相似的质量浓度频率、月际变化和日变化;城区站的平均PM2.5质量浓度为39.3μg/m3,高于城郊站(35.3μg/m3);城区站PM10质量浓度为58.5μg/m3,低于城郊站(62.9μg/m3);ρ(PM2.5)/ρ(PM10)显示,相比城郊,城区的2次污染更严重,细粒子占比更高。风玫瑰图分析发现,静风或小风状态下,城区番禺的颗粒物污染程度要高于城区黄埔,本地排放对城区站的颗粒物污染,特别是细颗粒物污染的影响更为显著;城郊站颗粒物污染的形成也同时受本地排放和外源输送影响,主要的输送来源于西北上风向肇庆、佛山一带的污染排放,其对PM2.5的影响更为显著。     

天津冬季重霾污染过程及气象和边界层特征分析

京津冀大气灰霾污染严重,天津市作为其核心组成之一其污染形势亦严峻。选取2013年2月20~28日天津重霾污染时段7站PM2.5(空气动力学当量直径小于等于2.5μm的颗粒物,即细颗粒物)和气态污染物数据,结合北京污染数据、地面气象要素、能见度、边界层温湿和风廓线、后向轨迹,深入分析重霾污染过程特征及气象和边界层成因。结果显示,研究时段天津PM2.5、SO2、NO2、CO和O3浓度均值为150、87、56、2.4和22μg m-3,气态污染物各站差异显著,但仅有SO2全面超过国家空气质量一级标准(50μg m-3),而PM2.5具有区域同步变化特征,且严重超标,是一级标准(35μg m-3)的2~8倍,最高小时均值高达364μg m-3;高浓度PM2.5是导致低能见度的主因,能见度小于10 km对应PM2.5阈值为50μg m-3。弱风和高湿度导致局地排放累积,PM2.5始增,在高湿度条件下,持续偏南风促使其稳步增加,配合弱北风和弱东风PM2.5震荡上扬,污染高值阶段,南北气流短时迅速切换,区域污染传输叠加污染的循环累积,PM2.5浓度峰值达到最高;除因边界层强东风导致的平流逆温外,高浓度PM2.5与平流逆温密切相关;高污染时段高湿主要集中在500 m以下,且随高度递减幅度较大;位于200~600 m的低空急流一定程度抑制污染上升,尤其持续强东风使PM2.5浓度稳步降低到二级水平,污染迅速有效清除最终依赖整层的强西北风。北京、环绕天津的河北中部和西南部地区对天津重污染有显著贡献。     

广州地区旱季一次典型灰霾过程的特征及成因分析

通过研究2009年11月广州市气溶胶颗粒物质量浓度(PM10、PM2.5、PM1)、黑碳浓度、散射系数(Scatter)等大气成分要素,以及微波辐射计、激光雷达及风廓线雷达所探测的风、温、湿等边界层结构,统计分析广州旱季一次典型灰霾过程(2009年11月23—29日)中气溶胶颗粒物及其光学特性的时空变化特征,并配合天气形势背景、边界层结构对其形成原因进行详细分析。在典型灰霾过程中,黑碳浓度高达58.7μg/m3,散射系数高达1 902.7 Mm-1,PM10浓度高达423.5μg/m3,PM2.5浓度高达355.7μg/m3,PM1浓度高达286.5μg/m3。通过对同期的气象条件分析表明在广州地区旱季,区域性污染过程,特别是灰霾天气的形成具有以下三种气象条件:大气边界层高度较低;高压变性出海的天气形势与之密切相关;在偏东和偏南气流带来的高湿度环境下,气溶胶吸湿增长效应显著,导致出现严重灰霾天气。     

2015年11月沈阳地区一次PM2.5重污染过程综合分析

利用多源观测资料综合分析了2015年11月沈阳地区一次PM2.5 重污染天气的气象条件、垂直风场演变、大气边界层特征以及污染物的来源。结果表明:本次重污染过程中,沈阳市区PM2.5浓度长达81h超过250μg · m^-3 ,其中峰值浓度达到1287μg · m^-3 ,重污染期间PM2.5 /PM10 的比例最高为90%。受地面倒槽和黄淮气旋影响,近地面层持续存在的逆温层、高相对湿度和弱偏北风为颗粒物吸湿增长和长时间聚集提供有利的天气条件。风廓线雷达风场资料显示在重污染期间,近地面层存在弱风速区、凌乱风场和弱下沉气流。利用风廓线雷达资料计算了边界层通风量(Ventilation Index,VI)和局地环流指数(Recirculation,R),边界层通风量VI和PM2.5 存在明显的负相关,非污染日VI是重污染日的2倍,局地环流指数R在重污染天气前大于0.9,而在污染期间部分空间R小于0.8。通过后向轨迹模式和火点监测资料分析发现,沈阳上空300m高度气团来自于生物质燃烧区域,而且沈阳地区NO2和CO浓度的变化与PM2.5一致,说明本次重污染过程也可能和生物质燃烧有关。     

长江三角洲西部地区PM_(2.5)输送轨迹分类研究

长三角4个省会(直辖市)城市(上海、南京、合肥、杭州)中,合肥与南京的PM_(2.5)浓度演变有较高的一致性。应用聚类分析的方法对2013—2015年合肥非降水日(日降水量低于10 mm)100 m高度(代表近地层)和1000 m高度(代表边界层中上部)的72 h后向轨迹进行分类,结合合肥2013—2015年PM_(2.5)日均浓度资料,探讨近地层和边界层中上部输送轨迹与长三角西部PM_(2.5)浓度的关系。近地层和边界层中上部分别得到7组和6组不同的后向轨迹;不同输送轨迹对应的PM_(2.5)浓度、重污染(重度以上污染,PM_(2.5)日均浓度大于150μg/m3)天数、能见度、地面风速、相对湿度等都有显著不同,尤其是在近地层。100 m高度,平均长度最短、来向偏东的轨迹组对应的PM_(2.5)浓度均值最高(约是组内均值最低值的2倍)、重污染天数最多,且占比最高(30%),重污染日对应的气流在过去72 h下降高度均值仅28 m,明显低于其他PM_(2.5)污染等级日;来向偏西北、长度较短的轨迹组,PM_(2.5)浓度均值和重污染天数为第2高,这一类轨迹占比14%,气流到达本地前存在明显的下沉运动,反映了远距离输送加剧本地PM_(2.5)重污染的特征。这两类轨迹常对应PM_(2.5)日均浓度的上升。PM_(2.5)平均浓度最低的2个轨迹组分别是来自东北和西南的较长轨迹组,所占比例分别为6.4%和10.3%,这2类轨迹往往对应着PM_(2.5)日均浓度下降。1000 m高度的结果与100 m高度结果类似,但PM_(2.5)平均浓度的组间差异不及100 m高度,与2001—2005年PM10浓度与输送轨迹的关系不同。对3 a中84个重污染日两个高度的后向轨迹进行聚类,近地层和边界层中上部各得到7类和6类PM_(2.5)重污染日的天气形势。近地层92%的重污染日对应的海平面气压形势场上,从华北到华东属于均压区,气压梯度小,轨迹来向以偏东到偏北方向为主,垂直方向延伸高度在950 hPa以下。1000 m高度,77%的重污染日属于相对较短的轨迹组,对应的850 hPa高度场特征为从中国西北(新疆)到东南受高压控制,长三角或位于高压底部,或位于两高压之间的均压区。这对PM_(2.5)浓度预报有较好的指示意义。     

北京两次沙尘污染过程中PM2.5浓度变化特征

利用北京市空气质量监测数据和气象资料,对2013年2月28日和3月9日两次沙尘污染过程PM2.5(空气动力学当量直径小于等于2.5μm的颗粒物,即细颗粒物)、PM10(空气动力学当量直径小于等于10μm的颗粒物,即可吸入颗粒物)浓度及PM2.5浓度/PM10浓度比值的变化特征进行了分析,研究结果表明:(1)沙尘开始影响北京时,PM2.5与PM10浓度表现出反位相变化,PM10浓度在两次沙尘过程中2 h内分别上升50.8%与202.4%,最高达800μg m-3以上;PM2.5浓度分别下降58.3%与50.9%,直至下降至35μg m-3以下,PM2.5有明显改善现象。(2)虽然PM2.5浓度在沙尘到达前有缓升的迹象,但沙尘抵达后,PM2.5浓度持续快速下降,PM2.5浓度/PM10浓度比值由沙尘影响前的0.75以上降至0.25以下。沙尘影响前,PM2.5日均值均超过150μg m-3,北京地区处于重度污染水平。这说明沙尘来临前以人为污染为主,主要由细粒子"贡献",沙尘来临后的空气污染,主要由巨、大粒子的沙尘"贡献"。     

荆门市冬季PM2.5传输轨迹及潜在源分析

对2015年3月至2018年2月共36个月荆门市PM2.5浓度值按月和季节作特征分析，利用HYSPLIT轨迹模型对污染最为严重的冬季进行后向48h气团轨迹模拟。结果表明：PM2.5月均浓度表现为1月最高，达到107μg/m3，7月最低，为30μg/m3，冬季平均值为92μg/m3，显著高于其它季节，并且冬季高浓度PM2.5主要与本地地面5—11m/s的偏北（N、NNE）大风伴随出现；气团轨迹分为西南、东北、西北三个路径，近地面传输的东北路径和高空传输的西南路径气团均引起PM2.5浓度升高，而西北路径气团整体上对污染物具有一定清除作用；东北路径方向的河南以及靠近荆门市的西北、西南向地区为48h的潜在源贡献大值区。在通过气象条件定性判断荆门未来的PM2.5浓度变化时，因东北路径近地面传输的特性，应关注上游潜在源区内地面站点PM2.5的浓度值；对于高空传输的西南路径，应关注高空水汽的输送情况，以及轨迹高度下降地区即水汽的沉降区是否在潜在源区；西北路径为干冷空气的高空传输，在较接近荆门时轨迹高度才开始明显下降，应关注西北方向近距离潜在源区的地面站点PM2.5的浓度值。     

汕头市PM10和PM2.5时间分布特征分析

采用β射线法大气颗粒物监测仪连续观测了汕头市PM10和PM2.5浓度,分析2015年11月至2017年10月的PM10和PM2.5的浓度水平、时间变化规律等。结果表明,PM10的年均日浓度为67.3μg/m~3,PM2.5的年均日浓度为35.9μg/m~3,其质量浓度日变化特征与人类活动和气象条件变化密切相关。PM10和PM2.5的月平均质量浓度变化趋势全年保持基本一致,谷值出现在6月,峰值出现在3月和12月。PM2.5/PM10比值为0.533,相关系数为0.75,存在显著的线性关系。     

COMPARATIVE ANALYSIS OF VARIOUS DATA OF AIR–SEA TEMPERATURE DIFFERENCE AND ITS VARIATION ACROSS SOUTH CHINA SEA IN THE PAST 35 YEARS

Using the International Comprehensive Ocean-Atmosphere Data Set(ICOADS) and ERA-Interim data, spatial distributions of air-sea temperature difference(ASTD) in the South China Sea(SCS) for the past 35 years are compared,and variations of spatial and temporal distributions of ASTD in this region are addressed using empirical orthogonal function decomposition and wavelet analysis methods. The results indicate that both ICOADS and ERA-Interim data can reflect actual distribution characteristics of ASTD in the SCS, but values of ASTD from the ERA-Interim data are smaller than those of the ICOADS data in the same region. In addition, the ASTD characteristics from the ERA-Interim data are not obvious inshore. A seesaw-type, north-south distribution of ASTD is dominant in the SCS; i.e., a positive peak in the south is associated with a negative peak in the north in November, and a negative peak in the south is accompanied by a positive peak in the north during April and May. Interannual ASTD variations in summer or autumn are decreasing. There is a seesaw-type distribution of ASTD between Beibu Bay and most of the SCS in summer, and the center of large values is in the Nansha Islands area in autumn. The ASTD in the SCS has a strong quasi-3a oscillation period in all seasons, and a quasi-11 a period in winter and spring. The ASTD is positively correlated with the Nio3.4 index in summer and autumn but negatively correlated in spring and winter.     

CHARACTERISTICS AND TRENDS OF CLIMATIC EXTREMES IN CHINA DURING 1959–2014

The spatial and temporal variations of daily maximum temperature(Tmax), daily minimum temperature(Tmin), daily maximum precipitation(Pmax) and daily maximum wind speed(WSmax) were examined in China using Mann-Kendall test and linear regression method. The results indicated that for China as a whole, Tmax, Tmin and Pmax had significant increasing trends at rates of 0.15℃ per decade, 0.45℃ per decade and 0.58 mm per decade,respectively, while WSmax had decreased significantly at 1.18 m·s~(-1) per decade during 1959—2014. In all regions of China, Tmin increased and WSmax decreased significantly. Spatially, Tmax increased significantly at most of the stations in South China(SC), northwestern North China(NC), northeastern Northeast China(NEC), eastern Northwest China(NWC) and eastern Southwest China(SWC), and the increasing trends were significant in NC, SC, NWC and SWC on the regional average. Tmin increased significantly at most of the stations in China, with notable increase in NEC, northern and southeastern NC and northwestern and eastern NWC. Pmax showed no significant trend at most of the stations in China, and on the regional average it decreased significantly in NC but increased in SC, NWC and the mid-lower Yangtze River valley(YR). WSmax decreased significantly at the vast majority of stations in China, with remarkable decrease in northern NC, northern and central YR, central and southern SC and in parts of central NEC and western NWC. With global climate change and rapidly economic development, China has become more vulnerable to climatic extremes and meteorological disasters, so more strategies of mitigation and/or adaptation of climatic extremes,such as environmentally-friendly and low-cost energy production systems and the enhancement of engineering defense measures are necessary for government and social publics.     

IMPACT OF ATMOSPHERIC LOW-FREQUENCY OSCILLATION ON THE IMMIGRATION OF NILAPARVATA LUGENS(STL) IN HUNAN AND JIANGXI PROVINCES

Various features of the atmospheric environment affect the number of migratory insects, besides their initial population. However, little is known about the impact of atmospheric low-frequency oscillation(10 to 90 days) on insect migration. A case study was conducted to ascertain the influence of low-frequency atmospheric oscillation on the immigration of brown planthopper, Nilaparvata lugens(Stl), in Hunan and Jiangxi provinces. The results showed the following:(1) The number of immigrating N. lugens from April to June of 2007 through 2016 mainly exhibited a periodic oscillation of 10 to 20 days.(2) The 10-20 d low-frequency number of immigrating N. lugens was significantly correlated with a low-frequency wind field and a geopotential height field at 850 h Pa.(3) During the peak phase of immigration, southwest or south winds served as a driving force and carried N. lugens populations northward, and when in the back of the trough and the front of the ridge, the downward airflow created a favorable condition for N. lugens to land in the study area. In conclusion, the northward migration of N. lugens was influenced by a low-frequency atmospheric circulation based on the analysis of dynamics. This study was the first research connecting atmospheric low-frequency oscillation to insect migration.     

A COMPARATIVE ANALYSIS OF ATMOSPHERIC AND OCEANIC CONDITIONS BEFORE THE OCCURRENCE OF TWO TYPES OF EL NIO EVENTS

The atmospheric and oceanic conditions before the onset of EP El Ni?o and CP El Ni?o in nearly 30 years are compared and analyzed by using 850 hPa wind, 20℃ isotherm depth, sea surface temperature and the Wheeler and Hendon index. The results are as follows: In the western equatorial Pacific, the occurrence of the anomalously strong westerly winds of the EP El Ni?o is earlier than that of the CP El Ni?o. Its intensity is far stronger than that of the CP El Ni?o. Two months before the El Ni?o, the anomaly westerly winds of the EP El Ni?o have extended to the eastern Pacific region, while the westerly wind anomaly of the CP El Ni?o can only extend to the west of the dateline three months before the El Ni?o and later stay there. Unlike the EP El Ni?o, the CP El Ni?o is always associated with easterly wind anomaly in the eastern equatorial Pacific before its onset. The thermocline depth anomaly of the EP El Ni?o can significantly move eastward and deepen. In addition, we also find that the evolution of thermocline is ahead of the development of the sea surface temperature for the EP El Ni?o. The strong MJO activity of the EP El Ni?o in the western and central Pacific is earlier than that of the CP El Ni?o. Measured by the standard deviation of the zonal wind square, the intensity of MJO activity of the EP El Ni?o is significantly greater than that of the CP El Ni?o before the onset of El Ni?o.     

ANALYSIS OF “BIMODAL PATTERN” STORM ACTIVITY CHARACTERISTICS OF THE BAY OF BENGAL

Storms that occur at the Bay of Bengal (BoB) are of a bimodal pattern, which is different from that of the other sea areas. By using the NCEP, SST and JTWC data, the causes of the bimodal pattern storm activity of the BoB are diagnosed and analyzed in this paper. The result shows that the seasonal variation of general atmosphere circulation in East Asia has a regulating and controlling impact on the BoB storm activity, and the “bimodal period” of the storm activity corresponds exactly to the seasonal conversion period of atmospheric circulation. The minor wind speed of shear spring and autumn contributed to the storm, which was a crucial factor for the generation and occurrence of the “bimodal pattern” storm activity in the BoB. The analysis on sea surface temperature (SST) shows that the SSTs of all the year around in the BoB area meet the conditions required for the generation of tropical cyclones (TCs). However, the SSTs in the central area of the bay are higher than that of the surrounding areas in spring and autumn, which facilitates the occurrence of a “two-peak” storm activity pattern. The genesis potential index (GPI) quantifies and reflects the environmental conditions for the generation of the BoB storms. For GPI, the intense low-level vortex disturbance in the troposphere and high-humidity atmosphere are the sufficient conditions for storms, while large maximum wind velocity of the ground vortex radius and small vertical wind shear are the necessary conditions of storms.     

LOW-FREQUENCY CIRCULATION AND EXTENDED-RANGE FORECAST IN CONNECTION WITH AUTUMN EXTREME HEAVY RAINFALL PROCESS IN HAINAN

Observed daily precipitation data from the National Meteorological Observatory in Hainan province and daily data from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis-2 dataset from 1981 to 2014 are used to analyze the relationship between Hainan extreme heavy rainfall processes in autumn (referred to as EHRPs) and 10–30 d low-frequency circulation. Based on the key low-frequency signals and the NCEP Climate Forecast System Version 2 (CFSv2) model forecasting products, a dynamical-statistical method is established for the extended-range forecast of EHRPs. The results suggest that EHRPs have a close relationship with the 10–30 d low-frequency oscillation of 850 hPa zonal wind over Hainan Island and to its north, and that they basically occur during the trough phase of the low-frequency oscillation of zonal wind. The latitudinal propagation of the low-frequency wave train in the middle-high latitudes and the meridional propagation of the low-frequency wave train along the coast of East Asia contribute to the ‘north high (cold), south low (warm)’ pattern near Hainan Island, which results in the zonal wind over Hainan Island and to its north reaching its trough, consequently leading to EHRPs. Considering the link between low-frequency circulation and EHRPs, a low-frequency wave train index (LWTI) is defined and adopted to forecast EHRPs by using NCEP CFSv2 forecasting products. EHRPs are predicted to occur during peak phases of LWTI with value larger than 1 for three or more consecutive forecast days. Hindcast experiments for EHRPs in 2015–2016 indicate that EHRPs can be predicted 8–24 d in advance, with an average period of validity of 16.7 d.     

AN ATTRIBUTION ANALYSIS OF CHANGES IN POTENTIAL EVAPOTRANSPIRATION IN THE BEIJING–TIANJIN–HEBEI REGION UNDER CLIMATE CHANGE

Based on the measurements obtained at 64 national meteorological stations in the Beijing–Tianjin–Hebei (BTH) region between 1970 and 2013, the potential evapotranspiration (ET0) in this region was estimated using the Penman–Monteith equation and its sensitivity to maximum temperature (Tmax), minimum temperature (Tmin), wind speed (Vw), net radiation (Rn) and water vapor pressure (Pwv) was analyzed, respectively. The results are shown as follows. (1) The climatic elements in the BTH region underwent significant changes in the study period. Vw and Rn decreased significantly, whereas Tmin, Tmax and Pwv increased considerably. (2) In the BTH region, ET0 also exhibited a significant decreasing trend, and the sensitivity of ET0 to the climatic elements exhibited seasonal characteristics. Of all the climatic elements, ET0 was most sensitive to Pwv in the fall and winter and Rn in the spring and summer. On the annual scale, ET0 was most sensitive to Pwv, followed by Rn, Vw, Tmax and Tmin. In addition, the sensitivity coefficient of ET0 with respect to Pwv had a negative value for all the areas, indicating that increases in Pwv can prevent ET0 from increasing. (3) The sensitivity of ET0 to Tmin and Tmax was significantly lower than its sensitivity to other climatic elements. However, increases in temperature can lead to changes in Pwv and Rn. The temperature should be considered the key intrinsic climatic element that has caused the "evaporation paradox" phenomenon in the BTH region.     

Could the Recent Taal Volcano Eruption Trigger an El Ni?o and Lead to Eurasian Warming?

正The Taal Volcano in Luzon is one of the most active and dangerous volcanoes of the Philippines. A recent eruption occurred on 12 January 2020(Fig. 1a), and this volcano is still active with the occurrence of volcanic earthquakes. The eruption has become a deep concern worldwide, not only for its damage on local society, but also for potential hazardous consequences on the Earth's climate and environment.     

VARIABILITY OF INTER-ANNUAL RELATIONSHIP BETWEEN INDIAN OCEAN DIPOLE AND HUAXI REGION’S AUTUMN PRECIPITATION

The moving-window correlation analysis was applied to investigate the relationship between autumn Indian Ocean Dipole (IOD) events and the synchronous autumn precipitation in Huaxi region, based on the daily precipitation, sea surface temperature (SST) and atmospheric circulation data from 1960 to 2012. The correlation curves of IOD and the early modulation of Huaxi region’s autumn precipitation indicated a mutational site appeared in the 1970s. During 1960 to 1979, when the IOD was in positive phase in autumn, the circulations changed from a “W” shape to an ”M” shape at 500 hPa in Asia middle-high latitude region. Cold flux got into the Sichuan province with Northwest flow, the positive anomaly of the water vapor flux transported from Western Pacific to Huaxi region strengthened, caused precipitation increase in east Huaxi region. During 1980 to 1999, when the IOD in autumn was positive phase, the atmospheric circulation presented a “W” shape at 500 hPa, the positive anomaly of the water vapor flux transported from Bay of Bengal to Huaxi region strengthened, caused precipitation ascend in west Huaxi region. In summary, the Indian Ocean changed from cold phase to warm phase since the 1970s, caused the instability of the inter-annual relationship between the IOD and the autumn rainfall in Huaxi region.     

Revisiting the Concentration Observations and Source Apportionment of Atmospheric Ammonia

正While China’s Air Pollution Prevention and Control Action Plan on particulate matter since 2013 has reduced sulfate significantly, aerosol ammonium nitrate remains high in East China. As the high nitrate abundances are strongly linked with ammonia, reducing ammonia emissions is becoming increasingly important to improve the air quality of China. Although satellite data provide evidence of substantial increases in atmospheric ammonia concentrations over major agricultural regions, long-term surface observation of ammonia concentrations are sparse. In addition, there is still no consensus on