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1.
《气象与环境学报》2017,(4)
基于2001—2014年宁波市每日4个时次(02时、08时、14时、20时)的常规气象观测资料和同期宁波市环保局空气污染物(SO_2、NO_2、PM_(10))浓度的日监测数据,采用最小二乘曲线拟合法计算了2001—2014年宁波市大气混合层厚度,并分析了大气混合层厚度的时间变化特征及其与空气污染的关系。结果表明:2001—2014年宁波市年平均大气混合层厚度波动变化明显,大气混合层厚度极大值和极小值分别出现在2004年、2007年,分别为866.1 m和746.1 m。水平风速对宁波市大气混合层厚度的影响较大。春季和7月、8月宁波市大气混合层厚度较大,秋季和冬季大气混合层厚度较小,而6月大气混合层厚度最小。大气混合层厚度在中午达最大值,夜间达最小值,大气混合层位于500.0—1200.0 m高度的出现频率最高。随着大气混合层厚度增大,污染物浓度被稀释。夏季,大气混合层厚度对PM_(10)、SO_2和NO_2浓度的调节能力较强。由于输入性污染的影响,冬季PM_(10)与SO_2浓度的极大值明显高于夏季,同时大气混合层厚度的变化对PM_(10)和SO_2浓度的增减效应比夏季明显削弱,但对NO_2浓度的影响较小。另外,当大气混合层厚度位于500.0—1200.0 m高度时,在同一大气混合层厚度下,同一污染物浓度的变化范围较大。 相似文献
2.
《气象与环境学报》2018,(5)
利用2014—2016年宁波市镇海地区逐时气象观测资料和大气成分监测资料,对宁波地区霾天气的变化特征进行统计分析。结果表明:2014—2016年宁波地区霾天气小时出现频率为28.8%,湿霾出现频率为61.0%。近3 a宁波地区霾天气小时出现频率呈下降趋势,秋冬季(11月至翌年1月)霾天气小时出现频率较高,夏季(6—8月)霾天气小时出现频率较低;从日变化来看,霾天气小时出现频率峰值集中出现在上午09时和夜间20—23时。宁波地区重度霾的PM_(2.5)、PM_(10)颗粒物浓度为轻微霾的2.13倍和1.92倍,干霾颗粒物浓度高于湿霾,宁波地区霾天气的颗粒物组成较稳定,PM_(2.5)/PM_(10)比重为0.7左右。宁波地区颗粒物浓度与风速和降水量的相关性较好,春季和夏季风速与PM_(2.5)浓度的相关性较高,秋季和冬季风速与PM_(10)浓度的相关性较高;降水与PM_(10)浓度的相关性高于PM_(2.5)浓度。静稳天气时地面风速小易造成细颗粒物浓度的积累增长,冬季西北偏北风和东北风是影响宁波地区PM_(2.5)浓度变化的重要输送路径,当风向为西北风时,冬季和春季PM_(10)浓度增加明显。 相似文献
3.
徐州持续性霾过程的季节特征分析 总被引：3，自引：3，他引：0
利用常规气象观测资料、NCEP再分析资料及污染物浓度小时资料,从天气形势、地面气象要素特征、污染物浓度变化、霾形成及维持的机制等方面,分析了徐州2013年持续性霾过程的季节特征。结果表明：秋、冬季中高层为西到西北气流,低层暖脊,地面为高压后部或鞍型场;春、夏季在中高层西南风、低层高压后部偏南气流、地面风场不是很弱的情况下出现持续性霾。秋、冬季霾日夜间风速接近静风,白天风速较夜间略大,风向以偏北和偏东居多;春、夏风向、风速相对稳定,风速维持2~3 m·s-1,风向多为东到东南。秋、冬季出现霾时层结稳定,具有明显的贴地层逆温结构,逆温层顶较低,春季逆温层顶略高于秋、冬季,而夏季出现霾时可以是不稳定的层结,低层也不具备明显逆温特征。冬、夏季霾区上空多为微弱的上升运动,高度不高,其上为下沉气流;春、秋季夜间到早晨霾区上空多为下沉气流。 相似文献
4.
选取青岛2000—2015年逐日地面观测资料和环境监测中心站2011—2015年逐日主要大气污染物浓度资料,分析了青岛大气混合层高度和大气稳定度的变化特征,对青岛日最大混合层高度与空气污染物浓度进行相关分析。结果表明：近16a来,青岛大气混合层高度年际变化呈逐渐减小的趋势,日变化呈现单峰结构;大气稳定度以D类的出现频率最高,深秋到初冬大气较为稳定,4—8月A,B,C类稳定度较其他月份升高;清晨和傍晚大气以中性层结为主,中午弱不稳定发展,夜间稳定层结明显增强。空气污染物质量浓度与日最大混合层高度有明显的负相关关系,污染物质量浓度越大,日最大混合层高度越小;反之,污染物质量浓度越小,日最大混合层高度越大。 相似文献
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基于1961-2010年安徽省气象台站的定时观测资料,采用国标法计算安徽省近50年大气稳定度、混合层厚度和大气环境容量系数,并结合合肥市空气质量逐日观测数据初步分析了大气环境容量系数对空气质量的影响。结果表明：安徽省大气稳定度以中性类居多,稳定类其次;近50年来,中性类稳定度呈明显下降趋势,不稳定类和稳定类呈显著上升;不稳定类和稳定类有明显的季节差异,中性类不明显。年平均混合层厚度显著下降;春季混合层厚度在2000年左右发生转折,夏、秋、冬三季下降趋势显著;春、夏季混合层厚度高于秋、冬季,冬季最低,春季最高。安徽省大气环境容量系数以沿淮中部、大别山区南部和沿江中西部最大,淮北大部、大别山区北部和江南南部最小,各地均呈现一致的显著下降趋势,并具有明显的年代际变化特征。年内大气环境容量系数呈"双峰型"分布,秋、冬季为低值时段,大气对污染物容纳能力较差,不利于扩散和清除,空气质量较差。总的来看,1961-2010年安徽省大气稳定度显著增加,混合层厚度较明显下降、风速快速减弱是全省大气环境容量系数变小、大气自净能力减弱的最主要原因。 相似文献
6.
利用娄底市5个县(市)站的实况观测资料,分析霾日的空间与时间分布特征,结果表明:娄底市霾日西部多东部少,最多的新化站约是最少的双峰站的4倍;霾日的长期变化呈波动上升趋势,20世纪80和90年代中期出现2个峰值,2000年以后迅速上升;季节分布特点是冬季最多,占45.9%,夏季仅占7.7%,以12月最多,7月最少。利用娄底本站实况资料与NCEP/NCAR 1°×1°再分析资料,分析2008年1月5～10日重霾过程的气象条件表明,混合层高度、水平输送条件直接关系到霾天气的形成,逆温层下稳定天气形势有利于霾的维持,而双层逆温则更加强了天气形势的稳定性与持久性。 相似文献
7.
利用重庆沙坪坝气象站1989—2018年30 a地面观测资料,定量分析重庆主城区通风量以及混合层高度、大气稳定度和混合层平均风速的多时间尺度变化特征。结果表明:重庆主城区通风量阶段性变化明显,2003年以前位于一个较高水平,且减少趋势明显;2003年以后通风量较低,近10 a转为增加趋势。重庆主城区通风量具有2~3 a的振荡周期。通风量四季的年代际变化与年平均基本一致。在不同季节,通风量的年际振荡强度有所差异。夏季振荡幅度最大,秋季和冬季相对较小。通风量的季节差异明显,夏季最大,冬季最小。通风量的日变化与混合层高度、混合层平均风速的日变化基本一致,在午后达到最大,并能维持到傍晚。重庆主城区通风量与混合层高度、混合层平均风速显著相关。 相似文献
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9.
《广东气象》2020,(3)
利用WRF-Chem模式,模拟了2014年1月3—4日深圳市发生的一次冷锋前大陆高压脊影响下的重度霾污染天气过程的发生、发展及消散各阶段的温度场、风场、大气边界层以及污染物的三维结构特征,分析了PM_(2.5)时空变化特征及与气象环境场的关系,结果表明:(1)模式对该次重霾污染天气过程PM_(2.5)模拟值与实测值的相关性较好,能够较好地再现该次霾过程的污染物质量浓度场特征,但PM_(2.5)质量浓度整体略偏大;(2)PM_(2.5)质量浓度模拟结果表明,高质量浓度位于深圳中西部地区,中西部污染较东部严重,PM_(2.5)污染时段主要出现在20:00—02:00,与霾严重时段相吻合;(3)通过分析此次污染过程温度场、风场、大气边界层以及污染物的三维结构,首要污染物PM_(2.5)质量浓度的分布与大陆高压脊影响下造成的持续大范围弱北风、强下沉气流、较低的大气边界层以及逆温层有密切关系。持续弱北风和强下沉气流不利于污染物的水平和垂直扩散,较低大气边界层促进污染物在边界层内快速积累;逆温层的存在进一步抑制了大气垂直扩散能力,使得霾天气加剧。 相似文献
10.
我国中东部一次大范围霾天气的分析 总被引：27，自引：4，他引：23
利用空气污染资料、常规气象观测资料和NCEP再分析资料,对2007年2月5日我国中东部大范围霾天气进行了综合分析.结果表明:前倾槽结构和逆温层结为霾的形成提供了有利条件;800hPa以下层垂直速度、涡度和散度的绝对值较小.是霾维持的动力因子.大气混合层高度的变化对霾的生消有很好指示作用,霾天混合层高度北高南低分布,致使南方比北方更易出现严重霾天气.此外,要素对比结果表明,霾的相对湿度比雾低,温度露点差比雾大,逆温强度比雾弱,大气混合层高度比雾高,这些可为霾的预报提供参考依据. 相似文献
11.
利用苏州市2009年6月-2010年5月逐时的能见度、相对湿度、污染物(PM1o、PM2.5、黑碳)浓度和散射系数等资料进行灰霾的判识与统计分析,结果表明:苏州市灰霾日占全年天数的46.6％,雨日和“蓝天”分别占33.2％和21.9％.在苏州所有灰霾日中以轻微灰霾为主,占灰霾曰总数的70.6％,发生中度和重度灰霾的频率较小.灰霾出现频率的日变化规律表明白天出现灰霾的频率比夜间低,在5-8时灰霾出现的频率达到峰值,14-16时灰霾出现的频率最低.灰霾日的污染物浓度远大于非灰霾日,随着灰霾等级增大,黑碳浓度明显增大;除重度灰霾外,PM10和PM2.5浓度也明显增大;散射系数增大. 相似文献
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文中对比分析了2015年29个雾、霾及雾霾混合天气过程中,章丘探空站L波段探空雷达和山东省气象局院内德国14通道地基微波辐射计观测的温度资料。对观测数据实施了质量控制,检验了精度和可信度,统计分析了宏观物理参量特征和日变化规律。针对雾、霾及雾霾天气过程各选取了一个个例进行分析,分析了大气中PM2.5、PM10、SO2、NO2、O3、CO含量的变化情况,分析了相对湿度、液态水路径和综合水汽含量等的变化情况。结果表明:两种观测数据一致性较好,拟合优度高于0.97;贴地逆温层存在一定的季节变化,悬垂逆温层存在一定的差异,逆温层的变化、污染参量变化与雾霾的形成有密切关系;不同天气背景对大气物理参量有较大的影响,PM10、AQI（空气质量指数）和CO均在相同时间段出现峰值,有明显的起伏;CO峰值雾霾天气中尤为明显,由早到晚随时间峰值逐渐增大,雾天和霾天峰值较小,雾霾天气明显大于雾天或霾天。 相似文献
13.
Vertical distributions of aerosol optical properties during haze and floating dust weather in Shanghai 
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下载免费PDF全文 A comparative study on the vertical distributions of aerosol optical properties during haze and floating dust weather in Shanghai was conducted based on the data obtained from a micro pulse lidar.There was a distinct difference in layer thickness and extinction coefficient under the two types of weather conditions.Aerosols were concentrated below 1 km and the aerosol extinction coefficients ranged from 0.25 to 1.50km-1 on haze days.In contrast,aerosols with smaller extinction coefficients(0.20 0.35 km-1) accumulated mainly from the surface to 2 km on floating dust days.The seasonal variations of extinction and aerosol optical depth（AOD） for both haze and floating dust cases were similar greatest in winter,smaller in spring,and smallest in autumn.More than 85%of the aerosols appeared in the atmosphere below 1 km during severe haze and floating dust weather.The diurnal variation of the extinction coefficient of haze exhibited a bimodal shape with two peaks in the morning or at noon,and at nightfall,respectively.The aerosol extinction coefficient gradually increased throughout the day during floating dust weather.Case studies showed that haze aerosols were generated from the surface and then lifted up,but floating dust aerosols were transported vertically from higher altitude to the surface.The AOD during floating dust weather was higher than that during haze.The boundary layer was more stable during haze than during floating dust weather. 相似文献
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We present mobile vehicle lidar observations in Tianjin, China during the spring, summer, and winter of 2016. Mobile observations were carried out along the city border road of Tianjin to obtain the vertical distribution characteristics of PM2.5. Hygroscopic growth was not considered since relative humidity was less than 60% during the observation experiments. PM2.5 profile was obtained with the linear regression equation between the particle extinction coefficient and PM2.5 mass concentration. In spring, the vertical distribution of PM2.5 exhibited a hierarchical structure. In addition to a layer of particles that gathered near the ground, a portion of particles floated at 0.6–2.5-km height. In summer and winter, the fine particles basically gathered below 1 km near the ground. In spring and summer, the concentration of fine particles in the south was higher than that in the north because of the influence of south wind. In winter, the distribution of fine particles was opposite to that measured during spring and summer. High concentrations of PM2.5 were observed in the rural areas of North Tianjin with a maximum of 350 μg m–3 on 13 December 2016. It is shown that industrial and ship emissions in spring and summer and coal combustion in winter were the major sources of fine particles that polluted Tianjin. The results provide insights into the mechanisms of haze formation and the effects of meteorological conditions during haze–fog pollution episodes in the Tianjin area. 相似文献
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利用北京市道面自动气象站、国家级自动气象站等多种观测数据分析北京地区2007—2015年能见度及其主要影响因子, 并挑选两次典型低能见度事件过程进行详细分析。从空间分布看, 北京西北地区能见度明显高于中心城区和东南大部地区。从时间分布看, 北京地区平均能见度最大值出现在5月, 最小值出现在7月; 日间的最低值多出现在06:00(北京时, 下同)左右, 冬季略向后推迟; 最高值多出现在16:00前后, 冬季略有提前。整体而言, 2007—2015年北京地区发生低能见度事件的概率为62.14%, 且发生低能见度的事件集中于1~5 km, 霾事件中干霾、湿霾的发生频率分别为86.13%和13.87%。能见度的主要影响因子为相对湿度、风速和PM2.5浓度。其中, 能见度与风速呈正相关, 与相对湿度和PM2.5浓度呈反相关。需要指出的是, 当相对湿度增加至80%, 能见度受PM2.5浓度的影响程度在下降, 而主要受相对湿度的影响。基于所选个例, 当北京地区出现湿霾事件时, 能见度的恶化程度远高于干霾事件, 且PM2.5浓度需比干霾事件时下降得更低才能有效改善能见度。 相似文献
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Wavelet analysis was applied to lidar observations to retrieve the planetary boundary layer height(PBLH)over Guangzhou from September 2013 to November 2014 over Guangzhou. Impact of the boundary effect and the wavelet scale factor on the accuracy of the retrieved PBLH has been explored thoroughly. In addition, the PBLH diurnal variations and the relationship between PM_(2.5) concentration and PBLH during polluted and clean episodes were studied. Results indicate that the most steady retrieved PBLH can be obtained when scale factor is chosen between 300-390 m. The retrieved maximum and minimum PBLH in the annual mean diurnal cycle were ～1100 m and ～650 m, respectively. The PBLH was significantly lower in the dry season than in the wet season, with the average highest PBLH in the dry season and the wet season being ～1050 m and ～1200 m respectively. Compared to the wet season, the development of PBLH in the dry season was delayed by at least one hour due to the seasonal cycle of solar radiation. Episode analysis indicated that the PBLH was ～50% higher during clean episodes than during haze episodes. The average highest PBLH in the haze episodes and clean episodes were ～800 m and ～1300 m,respectively. A significant negative correlation between PBLH and PM_(2.5) concentration(r =-0.55**) is discovered.According to China"Ambient Air Quality Standard", the PBLH values in good and slightly polluted conditions were 1/6-1/3 lower than that in excellent conditions, while the corresponding PM_(2.5) concentration were ～2-2.5 times higher. 相似文献
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Miaomiao LU Xiao TANG Zifa WANG Lin WU Xueshun CHEN Shengwen LIANG Hui ZHOU Huangjian WU Ke HU Longjiao SHEN Jia YU Jiang ZHU 《大气科学进展》2019,36(11):1217-1234
Severe haze pollution that occurred in January 2014 in Wuhan was investigated. The factors leading to Wuhan’s PM2.5 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 PM2.5 concentrations in Wuhan were lower than those in Beijing, but the monthly PM2.5 value was approximately twice that in Beijing. Furthermore, the sharp increases of PM2.5 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 PM2.5 concentrations in Wuhan, which contributed 53.4% of the monthly PM2.5 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 PM2.5 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. 相似文献
18.
针对2013年1月江苏淮安地区发生的一次连续性雾霾天气过程,分析该天气过程中PM10和PM2.5的质量浓度演变特征、能见度与气象要素之间的关系、中低层环流特征以及污染物来源。结果表明:雾霾期间PM10和PM2.5质量浓度最低值出现在05:00至07:00(北京时间,下同)和13:00至17:00,最高值出现在21:00至23:00,PM10和PM2.5质量浓度并非同时达到极大值;持续变化较小的气压梯度、较低的风速、相对湿度的增大以及PM2.5和PM10质量浓度的增高是雾霾发生发展的必要条件;能见度与气压、相对湿度、PM2.5质量浓度的相关性较好,建立回归方程,对能见度的整体变化趋势拟合效果较好;高空环流形势平稳、中低层的暖平流、持续稳定少动的地面高压场分布为雾霾天气的持续发生发展提供了有利的形势背景;稳定的层结结构、中低层偏东及偏东北方向气团的输送、本地污染源以及严重的空气污染是此次过程中能见度偏低、霾天数较多的主要原因。 相似文献
19.
选取中国东部季风区长春、北京、武汉和广州市4个代表性城市,利用2012年9月至2014年8月和2016年1月至2017年12月共4年高密度自动站气温资料,比较了南北不同纬度带城市热岛效应（Urban Heat Island, UHI）强度的时空特征。结果表明:1）长春、北京、武汉和广州市建城区年平均UHII分别为0.96°C、1.06°C、0.91°C和0.78°C,北方城市秋、冬季多呈静稳和逆温天气,加上人为热释放量高于其他季节,UHII明显高于春夏季;而南方城市秋季“秋高气爽”,利于热岛的发生发展,冬季和夏季UHII次之,且季节间差异较小;2）4个城市热岛形成机制是一致的,均表现为夜间高于白天,清晨和午后UHII分别开始下降和上升;武汉市内水体较多,UHII日内波动幅度最大,广州市四季温和,UHII波动幅度最小;3）与南方城市比较,北方城市秋、冬季夜间高值时段长,白天低值时段短。本文揭示的南北不同气候带城市热岛强度差异,对于城市规划和城市运行管理具有实际意义。 相似文献
20.
Campaigns were conducted to measure Organic Carbon (OC) and Elemental Carbon (EC) in PM2.5 during winter and summer 2003 in Beijing. Modest differences of PM2.5 and PM10 mean concentrations were observed between the winter and summer campaigns. The mean PM2.5/PM10 ratio in both seasons was around 60%, indicating PM2.5 contributed significantly to PM10. The mean concentrations of OC and EC in PM2.5 were 11.2±7.5 and 6.0±5.0μg m-3 for the winter campaign, and 9.4±2.1 and 4.3±3.0 μg m-3 for the summer campaign, respectively. Diurnal concentrations of OC and EC in PM2.5 were found high at night and low during the daytime in winter, and characterized by an obvious minimum in the summer afternoon. The mean OC/EC ratio was 1.87±0.09 for winter and Z39±0.49 for summer. The higher OC/EC ratio in summer indicates some formation of Secondary Organic Carbon (SOC). The estimated SOC was 2.8 μg m-3 for winter and 4.2μg m-3 for summer. 相似文献