共查询到19条相似文献，搜索用时 171 毫秒
1.
利用北京宝联站及北京上甸子大气本底站2006—2008年的7—9月PM2.5连续观测资料以及北京市观象台的探空数据、海淀气象站的风廓线雷达和降水量等资料,对北京地区夏末秋初PM2.5的质量浓度特征及其与气象要素的关系进行了统计分析。结果表明:城区站各月平均PM2.5质量浓度明显高于郊区站,高空偏南气流的输送是造成城区及本底地区出现细颗粒物污染的主要原因。从地面风速来看,城区当北风和南风分别达到2 m·s-1和3.5 m·s-1以上时能起到扩散作用;郊区在低风速的北风条件下也能起到扩散和稀释作用,而南风基本上对郊区的颗粒物无扩散作用。PM2.5质量浓度在降水前后的清除量与降水量、初始质量浓度均呈正相关关系,城区及郊区的云下清除过程更多取决于降水前污染物的浓度,降水量作用较弱。当混合层高度突破1500 m时,垂直扩散对污染物的稀释扩散效果明显。 相似文献
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《干旱气象》2016,(2)
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。 相似文献
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为深入了解晋城市颗粒物浓度时空分布特征,对晋城市2017年12月至2018年5月国控点、小型站和微型站PM2.5及PM10小时浓度数据进行收集整理,并进行空间插值分析和时间变化趋势分析及与气象监测数据的相关分析。结果表明:颗粒物浓度在冬、春季节具有明显差异,冬季PM10与PM2.5高值区主要位于东北部及东南小部分区域,春季PM10高值区位于城区南部区域,PM2.5高值区主要集中于城区。晋城市城区和郊区PM10与PM2.5月均浓度整体呈单峰型变化,PM10在4月份最高(157.54±5.67μg·m^-3),PM2.5在1月份最高(94.08±2.25μg·m^-3)。冬季PM2.5/PM10平均为0.57,春季平均为0.45。颗粒物小时浓度的变化呈现单峰单谷的型式,冬季PM10与PM2.5小时平均浓度最高值均出现在10时,春季均出现在09时。监测期间晋城市PM10与PM2.5的小时浓度值与相对湿度有较高的正相关性(p<0.01),与风速、风向有较高的负相关性(p<0.01),与温度和气压的相关性较低。冬季,东北至正南风向时,PM10与PM2.5的浓度普遍高于西北风向时的浓度,对晋城冬、春季国控点颗粒物浓度贡献率最高的风向风速为东南偏南风向,风速在1 m/s以内。 相似文献
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威海PM2.5浓度与气象要素的相关分析 总被引：1，自引：0，他引：1
分析2013年10月—2014年1月威海监测站逐日、逐时的PM2.5平均值与气象要素的相关性。结果表明,风速和风向与PM2.5的浓度均有一定的相关关系。当风速小于3m/s时,PM2.5值缓慢增大或一直持续;当北风风速连续大于3 m/s时,PM2.5值将迅速下降;南风增强对PM2.5的浓度没有明显的改变。PM2.5浓度增大的多数形势为高空为平直的西风环流或高空槽前。PM2.5浓度最大值往往出现在地面冷锋过境后北风风力较弱的时间段,北风风速大于3m/s后且持续,PM2.5的浓度将迅速下降。 相似文献
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天津城区秋季PM2.5质量浓度垂直分布特征研究 总被引：8，自引：2，他引：6
为研究天津大气颗粒物的污染水平和时空分布特征,利用天津大气边界层观测铁塔(255m),分别在40m、120m、220m处设立监测点,通过监测到的PM2.5的质量浓度,结合PM10、能见度等资料来分析污染物的时空分布规律和分布特征.结果表明,天津城区PM2.5污染水平相当严重,日均质量浓度远高于美国1997年制定的65μg*m-3的排放标准.混合层厚度和稳定度的变化对PM2.5浓度变化有一定的影响,随混合层厚度的变化,不同高度PM2.5质量浓度值有所不同.23时至11时,120m浓度明显高于其它各层,11-18时,由于大气扩散能力的增强,三层污染物质量浓度开始下降,而到了18-23时,低层污染物浓度较高,各层浓度总体趋势为120m＞40m＞220m.PM2.5质量浓度的日变化与稳定度的变化较一致.气象条件和早晚出行高峰期的影响导致PM2.5的质量浓度出现峰值.PM10与PM2.5的总体变化趋势基本一致,说明污染物来源基本相同.能见度水平和细粒子污染水平呈现较好的负相关,细粒子质量浓度的高低是决定能见度好坏的主要因子.降水过程是颗粒物从大气中清除的重要机制. 相似文献
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城市近郊常受到城区污染物扩散和输送的影响,2010年7月21日至8月6日利用β射线颗粒物连续监测仪和黑碳仪对北京西北郊区PM2.5和黑碳气溶胶(BC)进行了连续观测。结果表明,北京西北郊区夏季PM2.5和BC的质量浓度分别是(133.16±81.64)、(2.89±1.62)μg/m3。受明显的山谷风的影响,来自观测点东南方的城区的气流使PM2.5和BC浓度升高,来自观测点西北方向的风则使PM2.5和BC浓度降低。受局地排放、区域输送和气象条件的共同影响,郊区的PM2.5和BC浓度表现出明显日变化特征,二者浓度在上午、傍晚和夜间显著上升。 相似文献
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为了探明苏州地区大气污染物的时空分布特征,收集2012年苏州、昆山和太仓三个大气环境监测站的PM2.5、PM10等大气污染物观测资料及三站全自动气象观测数据,分析三站的PM2.5和PM10的时空分布特征;探讨气象条件对PM2.5和PM10的影响。结果表明:（1） 苏州市区PM2.5和PM10的年平均值分别是42.5和85.5 μg/m3,周边地区的年平均值是62.0和111.5 μg/m3;一年中苏州地区PM2.5和PM10的最大值出现在春季,最小值出现在夏季。（2） 一天中,苏州地区PM2.5和PM10的最大值出现在上午的8—9点。（3） 降水、气温、风速、气压等气象条件对PM2.5和PM10高浓度污染变化有重要影响。降水对PM2.5和PM10具有明显的清除作用,风则有较好的稀释扩散效应;PM2.5和PM10的浓度随气温的上升而升高;在高压状态下,PM2.5和PM10的浓度上升。（4） 苏州站PM2.5/PM10的变化范围和平均值都低于昆山站和太仓站,且PM2.5/PM10日变化存在明显的季节差异。 相似文献
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利用2006—2014年4—9月常规高空、地面观测资料以及同期南岳高山气象站(简称南岳站)逐时风场资料和湖南省96个常规观测站逐时雨量资料,对同期117例强降水天气过程进行天气影响系统分型,并分析5类南岳站风场型对湖南不同天气型强降水预报的指示作用。结果表明:南岳站风场在6种天气型强降水天气过程中主要有5种不同表现型,其中4类逐时风场演变特征对湖南强降水带移动和强度变化有较好的指示作用。该站南风超过10 m·s-1的时刻较湘北和湘西北地区开始出现强降水通常有2~6 h的预报提前量,达到急流标准(12 m·s-1)的时刻较湘中以北地区降水达到最强提前2~5 h,南风剧减至8 m·s-1以下或转为北风的时刻较湘南地区降水加强有2~4 h的预报提前量。当南岳站主导风为南风且风速在12~14 m·s-1之间时,湘西北和湘北降水强度最大,但当该站南风超过14 m·s-1时,雨区将北抬至长江以北。当该站主导风为北风且风速在1~3 m·s-1之间时,湘中和湘西南降水较强;当其在3~5 m·s-1之间时,湘中、湘西南、湘东南地区均有降水;当其超过5 m·s-1时,湘中和湘西南降水减弱或停止,湘东南降水则加强。台风暴雨期间,该站出现明显的南北风交替且风速基本在5 m·s-1以下时,强降水区一般位于湘东南。 相似文献
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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。 相似文献
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在“双碳”目标背景下，从国家层面到地方层面，区域、城市、行业企业都在制定和实施双碳目标行动计划。CO2模拟因其客观性和高时空分辨率等优势，在城市碳排放研究中深受重视。本研究以京津冀地区为研究区域，采用Picarro仪器高精度观测的2019—2020年CO2数据，利用WRF模式进行CO2传输模拟，分析了CO2浓度变化的季节特征，评估了模式在城区中心、城郊及背景3个观测站点的模拟效果，并对边界层高度及化石燃料碳排放等可能影响CO2浓度的因素进行了研究。3个观测站点分别为北京中国科学院大气物理研究所325 m气象塔观测站（北京站）、河北香河观测站（香河站）和上甸子区域本底观测站（上甸子站）。模拟结果表明：上甸子站优于香河站，香河站优于北京站，在冬季尤其明显；CO2浓度的高值区主要分布在城区、电厂和工业区，尤其是唐山、石家庄和邯郸地区，大量交通、工业排放导致CO2浓度明显上升，且高值区的范围在冬季最大；就日平均变化和日变化而言，边界层高度与CO2浓度存在相反变化趋势；3个站点的化石燃料碳排放（FFECO2）与近地面总CO2浓度存在正相关关系，冬春季的相关性高于夏秋季，且FFECO2的占比从大到小依次为北京站、香河站、上甸子站；CO2传输模拟的不确定性存在空间差异和季节变化。 相似文献
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Denghui JI Zhaoze DENG Xiaoyu SUN Liang RAN Xiangao XIA Disong FU Zijue SONG Pucai WANG Yunfei WU Ping TIAN Mengyu HUANG 《大气科学进展》2020,37(7):671-678
Aerosols in the atmosphere not only degrade visibility, but are also detrimental to human health and transportation. In order to develop a method to estimate PM_(2.5) mass concentration from the widely measured visibility, a field campaign was conducted in Southwest China in January 2019. Visibility, ambient relative humidity(RH), PM_(2.5) mass concentrations and scattering coefficients of dry particles were measured. During the campaign, two pollution episodes, i.e., from 4-9 January and from 10-16 January, were encountered. Each of the two episodes could be divided into two periods. High aerosol hygroscopicity was found during the first period, when RH was higher than 80% at most of the time, and sometimes even approached 100%. The second period experienced a relatively dry but more polluted condition and aerosol hygroscopicity was lower than that during the first period. An empirical relationship between PM_(2.5) mass concentration and visibility(ambient aerosol extinction) under different RH conditions could thus be established. Based on the empirical relationship,PM_(2.5) mass concentration could be well estimated from visibility and RH. This method will be useful for remote sensing of PM_(2.5) mass concentration. 相似文献
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基于1980~2014年上甸子国家级地面气象台站人工观测的大气水平能见度数据和大气成分站资料,采用Mann-Kendall趋势分析及突变检验法对大气能见度进行分析,并结合气象和污染要素进行相关性检验,以了解华北背景地区大气能见度的变化趋势及其影响因素。结果表明:上甸子地区年均能见度呈下降趋势,能见度最大和最小变率出现在夏季和春季,分别为3.4 km（10 a）-1和1.7 km（10 a）-1;冬季能见度（38.1 km）最高,秋季（36.2 km）次之,春季（32.8 km）和夏季（31.4 km）较低;突变分析表明上甸子地区的年均能见度未出现明显突变。能见度受各类气象因子的综合影响。根据Person相关和偏相关的统计结果,能见度与相对湿度和风速均呈明显负相关;与气压呈明显的正相关;而与气温的相关系数时正时负,表明气温对能见度的影响具有两面性。能见度下降的主要原因为大气污染,能见度随着大气细颗粒物增加呈幂指数降低（决定系数R2=0.98,显著性水平p < 0.01）;能见度为10 km时对应的细颗粒物（PM2.5）的边界浓度为74 μg/m3;在现行的国家环境空气质量标准二级标准（75μg/m3）下,可以使华北背景地区保持较高的大气能见度（≥ 10 km）。 相似文献
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Meteorological conditions, particularly the vertical wind field structure, have a direct influence on the PM2.5 concentrations over the Pearl River Delta (PRD). In October 2012, an exceptional air pollution event occurred in the PRD, and a high concentration of PM2.5 was registered at some stations. During days with PM2.5 air pollution, the wind speed was less than 3 m s-1 at the surface, and the vertical wind field featured a weak wind layer (WWL) with a thickness of approximately 1000 m. The mean atmospheric boundary layer height was less than 500 m during pollution days, but it was greater than 1400 m during non-pollution days. A strong negative correlation was detected between the PM2.5 concentration and the ventilation index (VI). The VI was less than 2000 m2 s?1 during PM2.5 air pollution days. Because of the weak wind, sea–land breezes occurred frequently, the recirculation factor (RF) values were small at a height of 800 m during pollution days, and the zones with the lowest RF values always occurred between the heights of 300 and 600 m. The RF values during PM2.5 pollution days were approximately 0.4 to 0.6 below a height of 800 m, reducing the transportation capacity of the wind field to only 40% to 60%. The RF and wind profile characteristics indicated that sea–land breezes were highly important in the accumulation of PM2.5 air pollution in the PRD. The sea breezes may transport pollutants back inland and may result in the peak PM2.5 concentrations at night. 相似文献
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In recent years,several studies pointed out that anthropogenic emission sources in China which significantly contribute to the PM2.5mass burden was an important cause of particulate pollution in South Korea.However,most studies generally focused upon a single pollution event.It is rare to see comprehensive research that captures those features prone to interannual variations concerning the transboundary pollutant contribution in South Korea using a unified method.In this paper,we establish the emission inventories covering East Asia in 2010,2015,and 2017,and then conduct the source apportionment by applying a coupled regional air quality model called the Integrated Source Apportionment Module(ISAM).Comparison of simulated and observed PM2.5mass concentration at 165 CNEMC(China National Environmental Monitoring Center)sites suggests that the PM2.5concentrations are well represented by the modeling system.The model is used to quantitatively investigate the contribution from emission sources in China to PM2.5concentrations over South Korea and those features found to be prone to interannual variations are then discussed.The results show that the average annual contribution of PM2.5has dropped significantly from 28.0%in 2010 to 15.7%in 2017,which strongly suggests that China has achieved remarkable results in the treatment of atmospheric particulates. 相似文献
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2013年1—3月北京及周边地区雾、霾高发,气候特征异于常年。利用2013年1—3月北京及周边地区6个地面观测站观测资料,研究PM2.5和黑碳 (BC) 的质量浓度、区域分布特征及气象要素的影响情况。结果表明:北京及周边地区PM2.5污染呈区域性高值、污染局地积累以及由南向北输送的特征。北京上甸子站在雾、霾与清洁期间BC与PM2.5质量浓度的比值分别为7.1%和10.3%,雾、霾期间低于清洁期间;而河北固城站在雾、霾与清洁期间BC与PM2.5质量浓度的比值分别为17.5%和11.9%,雾、霾期间明显高于清洁期间。二者相反的比值特征反映在清洁的下游地区雾、霾过程中二次生成的气溶胶所占比例较污染的上游地区偏高。 相似文献
17.
北京上甸子秋冬季大气气溶胶的散射特征 总被引：6，自引：3，他引：6
分析了北京上甸子秋冬季气溶胶散射系数的变化特征、散射系数与PM2.5质量浓度的关系, 结合气象资料分析了风场对气溶胶散射系数变化的影响。通过研究得出, 上甸子秋冬季气溶胶散射系数平均值和标准差分别为179.7 Mm-1和253.2 Mm-1；阴天条件下的散射系数明显高于晴天；散射系数与PM2.5质量浓度之间有较好的相关性, 其相关系数为0.93；此外, 由于上甸子特殊的地理位置, 风场对气溶胶散射系数的影响显著, 不同风向条件下气溶胶散射系数差别很大。 相似文献
18.
A better knowledge of aerosol properties is of great significance for elucidating the complex mechanisms behind frequently occurring haze pollution events. In this study, we examine the temporal and spatial variations in both PM_1 and its major chemical constituents using three-year field measurements that were collected in six representative regions in China between 2012 and 2014. Our results show that both PM_1 and its chemical compositions varied significantly in space and time, with high PM_1 loadings mainly observed in the winter. By comparing chemical constituents between clean and polluted episodes, we find that the elevated PM_1 mass concentration during pollution events should be largely attributable to significant increases in organic matter(OM) and inorganic aerosols like sulfate, nitrate, and ammonium(SNA),indicative of the critical role of primary emissions and secondary aerosols in elevating PM_1 pollution levels. The ratios of PM_1/PM_(2.5) are found to be generally high in Shanghai and Guangzhou, while relatively low ratios are seen in Xi'an and Chengdu, indicating anthropogenic emissions were more likely to accumulate in forms of finer particles. With respect to the relative importance of chemical components and meteorological factors quantified via statistical modeling practices, we find that primary emissions and secondary aerosols were the two leading factors contributing to PM_1 variations, though meteorological factors also played important roles in regulating the dispersion of atmospheric PM. 相似文献
19.
The planetary boundary layer height (PBLH) was calculated using the radiosonde sounding data, including 120 L-band operational sites and 8 GPS sites in China. The diurnal and seasonal variations of PBLH were analyzed using radiosonde sounding (OBS-PBLH) and ERA data (ERA-PBLH). Based on comparison and error analyses, we discussed the main error sources in these data. The frequency distributions of PBLH variations under different regimes (the convective boundary layer, the neutral residual layer, and the stable boundary layer) can be well fitted by a Gamma distribution and the shape parameter k and scale parameter s values were obtained for different regions of China. The variation characteristics of PBLH were found in summer under these three regimes for different regions. The relationships between PBLH and PM2.5 concentration generally follow a power law under very low or no precipitation conditions in the region of Beijing, Tianjin and Hebei in summer. The results usually deviated from this power distribution only under strong precipitation or high relative humidity conditions because of the effects of hygroscopic growth of aerosols or wet deposition. The OBS-PBLH provided a reasonable spatial distribution relative to ERA-PBLH. This indicates that OBS-PBLH has the potential for identifying the variation of PM2.5 concentration. 相似文献