河北沧州市臭氧质量浓度与气象因子的关系分析

利用2014—2020年河北沧州逐小时气象与环境监测数据,对沧州市臭氧(O_(3))污染加剧现状及其与气象因子的关系进行分析。结果表明:(1)沧州地区O_(3)污染呈加剧态势,且O_(3)已上升为该地区首要污染物;O_(3)污染集中出现在5—9月,O_(3)质量浓度日变化呈单峰单谷型,最大浓度出现在16:00前后;(2)5—9月O_(3)日最大8 h平均质量浓度(简称“O_(3)-8 h”)所处时段,平均气温、最高气温、相对湿度、总辐射辐照度与O_(3)质量浓度的相关性较好,本站气压、水汽压和平均风速与O_(3)质量浓度的相关性未通过显著性检验;(3)5—9月O_(3)-8 h时段,当同时满足8 h平均气温高于30.9℃、最高气温高于32.7℃、平均相对湿度低于42.1%、平均总辐射辐照度高于505.8 W·m^(-2)时,出现O_(3)污染的概率达84%;(4)气象因子不是O_(3)小时质量浓度快速增长的充分条件。     

西宁近地面臭氧特征及其影响因素

利用青藏高原东北部城市西宁2015—2017年O_3质量浓度和各气象要素数据(紫外辐射、最高气温等),分析近地面O_3变化特征及其影响因素,结果表明:该地区臭氧平均质量浓度呈现单峰型日变化规律。每年6—8月O_3质量浓度最大,12月至翌年2月最小。依据环境空气质量指数AQI统计分析,6—8月污染天气O_3占首要污染物总天数的72%。O_3与NO_2、CO呈极显著负相关,臭氧日最大1 h平均质量浓度与紫外辐射、日最高气温呈极显著正相关,与日平均气压、日最高气压、日最低气压呈极显著负相关,与日平均相对湿度相关性不显著。不同季节不同高度风速大小和风向频率对O_3质量浓度影响不同,500 h Pa盛行风向以WNW为主时有利于扩散。2017年青海省大部地区O_3月平均质量浓度总体呈先增加后减小变化趋势。纬度越低,海拔越高的地区,O_3质量浓度升高越早。降水量的差异对O_3质量浓度影响较小。     

2017年陕西气象条件对大气环境质量影响分析

利用2017年1月1日—7月31日陕西省十地市空气质量资料和气象站地面观测资料,分析了2017年1—7月陕西省空气质量时间变化特征及影响大气环境质量的气象条件。结果表明：全省城市空气质量与2016年同期相比较差,1—3月全省首要污染物为颗粒物（PM25和PM10）,5—7月为臭氧。1—3月各市平均风速均在30 m/s以下且小风频率较高;全省冷空气活动较上年同期减少3次且强度偏弱;全省平均混合层高度与上年同期相比降低22 m。与上年同期相比,平均风速小,小风日数增多,冷空气活动次数减少且强度偏弱,混合层高度偏低,是颗粒物污染过程增多的主要因素。5—7月臭氧质量浓度与高温显著正相关,当日平均气温≥30 ℃或日最高气温≥35 ℃时,臭氧显著超标;臭氧质量浓度随日照时数增加而升高,日照时数≥6 h时,各市臭氧平均质量浓度均较高,日照时数≥10 h时臭氧超标率最高;臭氧质量浓度随日平均相对湿度的升高而降低,当相对湿度<600%时,臭氧平均质量浓度超过140 μg/m3,当相对湿度≥700%时,臭氧超标率明显降低。与上年同期相比,气温偏高,日照充足,湿度减小是造成臭氧超标日增多的主要因素。     

中山市2015—2019年臭氧超标特征及其与气象条件的关系

利用广东省中山市2015—2019年的地面臭氧浓度及气象观测数据,分析了中山市近年来臭氧超标与气象条件的关系。结果表明,中山市2015—2019年臭氧超标天数从22天增加至66天,臭氧年评价值增长36%,中度污染以上天数占超标天数比例从9.1%增长至36.4%。臭氧超标主要集中在8—11月,其中9月超标天数最多。夏秋季节臭氧超标主要发生在气温高、湿度低、太阳辐射强、日间10—14时无明显降水、吹北风的气象条件下,臭氧的污染潜在源区主要位于中山西部到北部的城市。风向和气温是臭氧超标最重要的指标,夏、秋季日间吹北风且日最高气温在33 ℃或以上时超标率分别达到89.1%和78.6%。2017年和2019年在相同的最高温、相对湿度、太阳辐射强度、降水和风速条件下的臭氧超标率均远高于2015年。当臭氧起始浓度在10 μg/m3以下、11~30 μg/m3及30 μg/m3以上时,夏(秋)季从起始浓度达到超标分别用时7.1(6.9) h、6.2(6.2) h和5.8(5.9) h,相应气温上升7.2(7.1) ℃、5.8(5.8) ℃和4.7(5.1)℃,起始浓度增大时,超标耗时和气温变化均呈减小趋势。      

惠州市春季臭氧污染和气象条件的关系

利用广东省惠州市2013—2018年逐日、逐时的环境和气象资料,研究了惠州市春季(3—5月)臭氧污染天气特征,并对2013年3月5—9日的一次臭氧污染过程进行了分析。结果表明:(1)惠州市春季臭氧质量浓度和臭氧污染日2015年起呈上升趋势,O_3-8 h平均质量浓度为92.0μg/m^3,年平均出现日数为4 d。(2)春季臭氧污染日出现在天气晴朗干燥、气温较高、日照充足且云量较少的情况下,臭氧污染日偏西风出现频率为22.4%,与无污染日相比偏高了13.1%;东南风出现频率为39.6%,与无污染日相比基本一致。(3)2013年3月5—9日臭氧污染期间,冷空气影响后惠州市出现晴朗干燥天气,有利于臭氧生成;地面到850 hPa均吹偏西风,惠州处于珠三角东侧,吹偏西风时处于城市群下风向,存在区域污染输送的可能。     

南宁市臭氧质量浓度特征及其与气象因素的关系

利用2015—2020年南宁市近地面臭氧监测数据及气象观测数据,分析南宁市近地面臭氧质量浓度时间变化特征及超标情况,并采用相关分析等统计方法研究臭氧与气象因素的关系。结果表明,南宁市第90百分位O₃-8 h质量浓度呈逐年增高趋势。O₃月平均质量浓度呈现双峰型变化,峰值出现在4—5月和8—10月。南宁市O₃质量浓度与日照时数、最高气温、相对湿度的相关性高。南宁市O₃超标以轻度污染为主。O₃超标时日平均气温为12.3～31.4℃,最高气温为20.9～37.7℃,相对湿度为46%～88%,日平均风速为0.7～3.4 m·s^-1,气压为980.3～1011.7 hPa。     

中国典型城市臭氧变化特征及其与气象条件的关系

利用不同气候背景代表城市北京、沈阳、银川、成都、南京和广州6个城市2014-2016年臭氧质量浓度和同期气象要素数据,对典型城市臭氧(O_3)浓度变化特征及其与气象条件的关系进行研究。结果表明:2014-2016年臭氧年平均浓度由高到低的顺序为南京沈阳北京银川成都广州,3年间广州臭氧浓度呈下降趋势,沈阳变化不大,其他城市总体呈上升趋势,其中,银川增幅最大,北京增幅最小;臭氧浓度月变化特征受纬度影响较大,随纬度增高单峰结构越明显,且各月郊区臭氧普遍高于市区;各城市臭氧日最大值出现在15:00(北京时,下同)-16:00,最小值出现在07:00-08:00,但其峰值、谷值及日变幅有明显差异,广州全天郊区臭氧都显著高于市区,其他城市则不同,11:00-17:00间两者差别较小,成都、南京、银川郊区峰值浓度甚至略低于市区,其余时段郊区高于市区;6个城市影响臭氧变化最主要的气象要素均是气温和日照时数,其次是相对湿度,再次是风速,气温高、日照长、湿度低有利于臭氧生成,相对而言,对于日照时间较长的北京、银川和沈阳,臭氧对气温的变化较其他城市更敏感,且与风速呈弱的正相关,而对于气温、湿度较高的广州、南京和成都,臭氧与日照时数和相对湿度的相关性较其他3个城市强,且与风速呈弱的负相关;城区臭氧与气象要素相关性普遍较郊区好。     

一次连续臭氧污染过程的气象条件分析

利用邢台市生态环境局的大气污染物监测数据和同期气象观测资料,对邢台市2018年6月10—24日的一次臭氧污染过程进行了分析。结果表明:(1)污染过程中邢台市4个监测点臭氧质量浓度变化趋势基本一致,邢师高专臭氧质量浓度最高,市环保局最低;臭氧质量浓度日变化呈单峰型,05:00—06:00最低,15:00最高,邢师高专臭氧质量浓度昼夜差最大,市环保局昼夜差最小。(2)晴天、阴天、雨天臭氧质量浓度变化趋势大致相同,日变化也呈单峰型,晴天臭氧质量浓度日变化剧烈,雨天则变化平缓。(3)臭氧质量浓度与平均气温、最高气温、最低气温、太阳辐射、平均风速均呈显著的正相关关系,其中与最高气温相关系数最高;臭氧质量浓度与NO_2、PM_(10)、CO、PM_(2.5)污染物之间呈显著负相关关系。(4)经过较强太阳辐射照射后,当最高气温在29℃及以上,相对湿度在30%～60%之间,风向为偏南风时,臭氧质量浓度在12:00—19:00时段易超标。     

气象因素对常州市区PM2.5浓度影响

利用2016—2018年常州市区环境空气细颗粒物数据,结合同期地面气象资料,分析了常州市区PM_2.5以及气象因素的变化特征,并统计分析气象因素对PM_2.5浓度的影响。结果表明：常州市区PM_2.5、降水量、相对湿度和气温等具有明显季节性,呈夏季较高冬季较低,而气压夏季较低冬季较高的特征。相对湿度与PM_2.5呈正相关,即随着相对湿度的增加PM_2.5超标率和平均浓度均增加;降水对PM_2.5具有一定的清除作用,清除率与降水前PM_2.5浓度、降水量、降水强度有关,降水量、降水强度越大,则降水清除效果越好,而降水前PM_2.5浓度较小,则清除率不明显;常州市区偏西风时PM_2.5的超标率和平均浓度较其他风向较高;风速对常州市区PM_2.5的影响呈负相关,即风速越大PM_2.5超标率和平均浓度均减小;常州市区地面天气形势可以分为两种类型,第一种类型表现为气压较低气温较高,PM_2.5超标率以及平均浓度相对较低,而第二种类型表现为气压较高气温较低,PM_2.5超标率以及平均浓度相对较高。     

长三角地区城市O_3和PM_(2.5)污染特征及影响因素分析

O_3和PM_(2.5)是影响长三角地区空气质量的主要污染物。利用2016年33个城市大气环境监测站6项污染物的小时浓度及4个省会城市的气象数据进行统计分析,研究了该地区O_3和PM_(2.5)浓度的时空分布特征及其影响因素。结果表明:长三角地区O_3年平均浓度为50～73μg·m~(-3),平均为61μg·m~(-3);除芜湖和宣城外,其余31城市均存在不同程度的超标状况,超标率为0.34%～18.86%,平均为5.68%。O_3在5月和9月达到浓度高值;四季O_3日变化均呈单峰型,峰值出现在15∶00,夏季O_3峰值浓度最高值为157μg·m~(-3)。O_3浓度沿海城市整体高于内陆城市;夏季宿迁—淮安—滁州片区O_3污染较重。O_3与NO_2、CO显著负相关,且与NO_2相关性较强;O_3与气温、日照时数显著正相关,与相对湿度、降水呈负相关。PM_(2.5)年平均浓度在25～62μg·m~(-3)范围内,平均为49μg·m~(-3);各城市均出现PM_(2.5)超标,滁州PM_(2.5)超标率最大,为23.91%。PM_(2.5)在3月和12、1月达到浓度峰值;其日变化呈双峰型,09∶00—10∶00和22∶00—23∶00达到峰值。冬季徐州PM_(2.5)浓度最高,为102μg·m~(-3)。PM_(2.5)与NO_2、CO、SO_2、PM_(10)显著正相关,与气温、风速、降水负相关。     

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.     

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.     

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.     

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     

全球贸易隐含碳变化及其影响分析

基于最新的GTAP8 (Global Trade Analysis Project）数据库,使用投入产出法,分析了2004年到2007年全球贸易变化下南北集团贸易隐含碳变化及对全球碳排放的影响。结果显示,随着发展中国家进出口规模扩张,全球贸易隐含碳流向的重心逐渐向发展中国家转移。2004年到2007年,发达国家高端设备制造业和服务业出口以及发展中国家资源、能源密集型行业及中低端制造业出口的趋势加强,该过程的生产转移导致全球碳排放增长4.15亿t,占研究时段全球贸易隐含碳增量的63%。未来发展中国家的出口隐含碳比重还将进一步提高。贸易变化带来的南北集团隐含碳流动变化对全球应对气候变化行动的影响日益突出,发达国家对此负有重要责任。     

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.     

Retrieval of outgoing longwave radiation from COMS narrowband infrared imagery

Hourly outgoing longwave radiation(OLR) from the geostationary satellite Communication Oceanography Meteorological Satellite(COMS) has been retrieved since June 2010. The COMS OLR retrieval algorithms are based on regression analyses of radiative transfer simulations for spectral functions of COMS infrared channels. This study documents the accuracies of OLRs for future climate applications by making an intercomparison of four OLRs from one single-channel algorithm(OLR12.0using the 12.0 μm channel) and three multiple-channel algorithms(OLR10.8+12.0using the 10.8 and 12.0 μm channels; OLR6.7+10.8using the 6.7 and 10.8 μm channels; and OLR All using the 6.7, 10.8, and 12.0 μm channels). The COMS OLRs from these algorithms were validated with direct measurements of OLR from a broadband radiometer of the Clouds and Earth's Radiant Energy System(CERES) over the full COMS field of view [roughly(50°S–50°N, 70°–170°E)] during April 2011.Validation results show that the root-mean-square errors of COMS OLRs are 5–7 W m-2, which indicates good agreement with CERES OLR over the vast domain. OLR6.7+10.8and OLR All have much smaller errors(～ 6 W m-2) than OLR12.0and OLR10.8+12.0(～ 8 W m-2). Moreover, the small errors of OLR6.7+10.8and OLR All are systematic and can be readily reduced through additional mean bias correction and/or radiance calibration. These results indicate a noteworthy role of the6.7 μm water vapor absorption channel in improving the accuracy of the OLRs. The dependence of the accuracy of COMS OLRs on various surface, atmospheric, and observational conditions is also discussed.