西安沙尘天气特征及其对空气质量的影响

利用1971-2003年西安市常规气象观测资料、2001-2003年西安市区主要污染物日均浓度资料和2002年3月一次重度污染事件逐时的PM₁₀浓度资料,分析了西安市沙尘天气的时间分布特征及其对主要空气污染物浓度的影响。结果表明:西安市沙尘天气近30多年来总体呈波动式明显减少的趋势,但2001-2002年有所增多,2003年又减少。沙尘天气主要出现在春季,4月最多,占全年天数的22.6%。沙尘天气对西安空气质量影响显著,可使3~4月PM₁₀月浓度平均提高12.1%,在分析的一次强沙尘暴个例中,一小时内PM₁₀浓度最高增加了0.585mg·m^-3,达到0.970mg·m^-3,从而造成严重的空气污染事件。     

2004年春季沙尘天气对和田市空气质量的影响

利用沙尘暴监测网数据、常规气象资料以及MICAPS系统环流形势资料,对和田市2004年春季沙尘天气产生的污染进行了详细的对比分析。研究结果表明：和田市春季污染相当严重,主要以沙尘污染为主。整个春季79％时间API（环境污染指数）指数均达到500,且45％时间可吸入颗粒物PM10在TSP巾的浓度比例达50％。不同天气系统对和田市空气的污染贡献不同。从东路影响和田市的天气系统、高压底部型地面系统和浮尘天气所造成空气污染相当严重。其余天气系统和天气现象所产生的污染较小。     

2018年春季中国北方大范围沙尘天气对城市空气质量的影响及其天气学分析

利用多源卫星数据、空气质量数据和再分析资料ERA-Interim分析了2018年春季中国北方一次沙尘天气过程中沙尘气溶胶的光学特性及垂直结构、沙尘天气对颗粒物浓度及空气质量的影响及其机制。结果表明：（1）2018年3月27-30日中国北方地区气溶胶光学厚度、吸收性气溶胶光学厚度和气溶胶指数分别大于0.4、0.035和1.0。沙尘气溶胶主要分布在地面至8 km的范围内,且体积退偏比大部分在0.06~0.4,色比0.6~1.2。（2）各城市颗粒物浓度在一定程度上均受到沙尘天气的影响。3月27-28日,PM_2.5和PM₁₀的平均值分别是国家一级标准的4.1倍和4.3倍。29-30日,PM_2.5和PM₁₀的平均值分别是国家一级标准的3.5倍和3.4倍。28日河北张家口PM₁₀/PM_2.5达4 d之最,最大比值为10.9。（3）低层贝加尔湖高压南压对内蒙古西南部及河西走廊地区的起沙有重要影响,河套地区地面气旋和低层切变是造成华北西部起沙的关键因素,鄂海地区高压脊和东北地区地面的低压系统有利于东北地区的起沙。     

沙尘天气过程对中国北方城市空气质量的影响

2014年4月23-25日中国北方地区经历了一次大范围的强沙尘天气过程.利用13个城市的空气质量和气象数据结合后向轨迹模式和MODIS deep-blue气溶胶光学厚度数据,研究沙尘天气过程对中国北方城市空气质量的影响,特别是对PM_2.5质量浓度的贡献。结果表明:来源于塔克拉玛干沙漠及其周边地区的沙尘途经甘肃西北部和内蒙古中西部的沙漠区进而影响下游城市。MODIS deep-blue气溶胶光学厚度的空间分布能够较好地反映出沙尘的水平运动特征。沙尘天气过程可以使中国北方城市PM_2.5和PM₁₀质量浓度分别增加7.5%～1 141.2%和344.0%～2 562.1%,可以有效地降低SO₂、NO₂和CO气体污染物的浓度,而对O₃的浓度影响较小。银川、大同和库尔勒沙尘天气过程期间平均SO₂、NO₂和CO浓度较非沙尘天气过程期间降低最明显,分别降低约80.0%、78.7%和62.1%。     

沙尘天气对榆林市环境空气质量的影响

基于2013-2018年榆林市沙尘天气和环境空气质量逐日数据,定量分析了沙尘天气对榆林市空气质量指数(AQI)、可吸入颗粒物(PM10)和细颗粒物(PM2.5)的绝对贡献和相对贡献率.结果表明:沙尘天气对榆林市AQI的贡献率为0.7％-11.1％(平均值±标准差6.4％±3.8％);对PM10的绝对贡献为 1.0-13...     

中国北方典型沙尘天气特征研究

 根据API、风速、风向及相关气象数据初步研究了中国北方两次典型沙尘天气的天气特征。两次沙尘天气过程中极大风速大于7.2 m·s-1的气象站占88%。2005年4月27日极大风速超过17.2 m·s-1的气象站有31个,出现频率最高的风向为西西北,28日达到81个,风向为北风,极大风速高值区由内蒙古中东部向东北方向迅速扩大。2007年3月30日极大风速超过17.2 m·s-1的气象站有57个,31日达到68个,风向均为西西北,极大风速高值区分布较为稳定。受沙尘暴影响的地区API显著升高。2005年4月28日呼和浩特、大同、北京3个城市的API分别为418、500、500。2007年3月31日呼和浩特、赤峰、大同3个城市的API分别为500、500、423。对PM10与气象因子的相关性进行分析得出,沙尘暴期间,大气中可吸入颗粒物的浓度与风速存在显著的正相关关系,风速越高的地区,可吸入颗粒物的浓度越大。     

2007—2008年库姆塔格沙漠沙尘天气特征与天气分型

 利用2007年库姆塔格沙漠科学考察时自动气象站所获取的数据,分析了沙尘暴过境时库姆塔格沙漠气象要素的变化特征。以库姆塔格沙漠周边气象站点沙尘天气出现时间为依据,选取2008年所有沙尘天气过程,解读相应时间段内Micaps系统中各个层次的资料,客观分析了驱动库姆塔格沙漠沙尘天气爆发的动力成因,将天气形势分为4种类型:冷空气翻山型; 冷空气东灌型; 锋前热低压发展型; 局地对流型。冷空气翻山型与东灌型居多,共7次,锋前热低压发展型与局地对流型较少,仅为2次。利用库姆塔格沙漠北部1号气象站以及南部2号气象站资料,结合库姆塔格沙漠沙尘粒度分析资料,初步讨论了风对库姆塔格沙漠沙丘形态的影响。     

1960-2020年辽宁沙尘强度特征

基于1960-2020年辽宁56个气象站地面观测资料,计算沙尘强度指数,应用回归方程、M-K非参数检验和相关分析等得到辽宁沙尘天气的年际分布特征.结果表明:影响辽宁的主要沙尘天气是扬沙,其次是浮尘、沙尘暴.影响辽宁东南部和南部的主要沙尘天气是浮尘,影响其他地区的主要沙尘天气是扬沙.辽宁沙尘强度呈显著减弱趋势,2010年...     

中国沙尘天气的区域特征

利用筛选的1954~2000年中国338个站沙尘天气资料及相关气候资料,从沙尘天气区划方面着重分析研究了我国沙尘天气的区域特征。结果表明： 1) 我国沙尘天气多发区分别位于以民丰至和田为中心的南疆盆地和以民勤至吉兰泰为中心的河西地区。不同类型沙尘天气的空间分布范围不尽相同,其中沙尘暴主要发生在与北方沙漠及沙漠化土地相联系的极干旱、干旱和半干旱区内。扬沙和浮尘天气除了在沙尘暴发生区的绝大部分地区出现外,还向其它邻近地区扩展,如扬沙可向东北地区和东南的黄淮海平原及以南地区扩展;而浮尘天气则主要向东南方向扩展,可涉及整个黄淮海平原和长江中下游地区。相比之下,上风方向的中高纬地区,如北疆和东北北部地区,浮尘天气发生甚少。2) 全国沙尘暴天气易发区可划分为北疆、南疆、河西、柴达木盆地、河套、东北和青藏等7个亚区。沙尘暴和浮尘在南疆区发生日数最多,而扬沙在河西区发生日数最多。     

沙尘天气等对西安市空气污染影响的研究

通过对西安市1981—2000年TSP、SO2和NOx年平均浓度资料,1998—2000年周报和日报环境监测资料以及相应的地面、高空常规气象观测资料的统计分析,研究了该市空气污染的时间变化特点以及沙尘天气等几种气象条件对其浓度变化的影响。结果表明:(1)颗粒污染物(TSP和PM10)是西安市的首要污染物,其次是SO2。1981—2000年期间,TSP年平均浓度降低了75%,SO2年平均浓度降低了77%,NOx年平均浓度总体上变化不大;这三种污染物月平均浓度的年变化都呈单周期型,冬季1月份最高,夏季最低(TSP是7月份最低,SO2和NOx是8月份最低)。(2)2001年春季3～4月份沙尘天气的频繁发生,使西安市空气污染日出现全年的第二个多发期(23d·月-1),这有别于正常年份仅在冬季1月份出现一个浓度峰值的特点;强沙尘暴天气过程会使西安市PM10浓度在非常短的时间内提高3倍左右,造成严重的颗粒物污染。(3)西安市冬半年出现轻度污染以上级别的几率明显大于夏半年。影响西安市的地面天气系统可归纳为12类,当受不同天气系统控制时,其污染状况会有较大差异。(4)西安市一年四季都有逆温存在,100m平均逆温强度为0.90℃;全年以低层逆温出现日数最多,但冬季贴地逆温出现日数最多,厚度最厚,强度最大,是造成西安市冬季空气污染严重的最重要气象因素之一。(5)西安     

Regional characteristics of dust events in China

The regional characteristics of dust events in China has been mainly studied by using the data of dust storm, wind-blown sand and floating dust from 338 observation stations through China from 1954 to 2000. The results of this study are as follows: (1) In China, there are two high frequent areas of dust events, one is located in the area of Minfeng and Hotan in the South Xinjiang Basin, the other is situated in the area of Minqin and Jilantai in the Hexi Region. Furthermore, the spatial distributions of the various types of dust events are different. The dust storms mainly occur in the arid and semiarid areas covering the deserts and the areas undergoing desertification in northern China. Wind-blown sand and floating-dust not only occur in the areas where dust storms occur, but also extend to the neighboring areas. The range of wind-blown sand extends northeastward and southeastward, but floating-dust mainly extends southeastward to the low-latitude region such as the East China Plain and the area of the middle and lower reaches of the Yangtze River. Compared with wind-blown sand, the floating-dust seldom occurs in the high latitude areas such as North Xinjiang and Northeast China. (2) The affected areas of dust storms can be divided into seven sub-regions, that is, North Xinjiang Region, South Xinjiang Region, Hexi Region, Qaidam Basin Region, Hetao Region, Northeastern China Region and Qinghai-Xizang (Tibet) Region. The area of the most frequent occurrence of dust storms and floating-dust is in South Xinjiang Region, and of wind-blown sand in the Hexi Region. In general, the frequency of dust events in all the seven regions shows a decreasing tendency from 1954 to 2000, but there are certain differences between various dust events in different regions. The maximum interannual change and variance of dust events during this time happened in South Xinjiang Region and Hexi Region. The dust events generally occur most frequently in April in most parts of China. The spring occurred days of dust events occupied 60-70% of the whole year in Hetao Region and Northeastern China Region. However, in South Xinjiang Region and North Xinjiang Region, which was less affected by monsoon climate, dust events may occur at any time of the year, less than 50% of the events in this region occur during spring. In the remaining three regions 50-60% of the dust events occur in spring of a year.     

中国沙尘事件的区域特征

AsevereduststormoccurredoverEastAsiainthemiddletendaysofApril1998.Afteroneweek,itsdustplumeaffectedthecontinentofNorthAmerica.Thisattractedtheattentionofmanyscientistsintherelatedcountriestoduststorms.InrecentyearsthenumberofduststormshasbeenincreasinginChina.Itisnotonlyanimportantsymptomofdesertificationfromslowerchangetofasterchange,butalsoareflectionofthesynthesisofenvironmentalstates,includingclimate,ecosystemandsoil,whicharealldeterioratingtosomedegrees.AccordingtothedefinitionofMeteo…     

近几年沙尘天气对中国北方环保重点城市可吸入颗粒物污染的影响

 利用2000—2008年中国北方17个环保重点城市API指数资料和同期气象资料,评估沙尘天气对中国北方城市大气环境的整体影响,对比分析不同城市空气质量受春季沙尘影响的程度,并以一次强沙尘暴过程为例,探讨其影响中国北方城市空气质量的范围、程度和过程。结果表明,2000—2008年北方环保重点城市大气的首要污染物是可吸入颗粒物,污染状况严重。春季颗粒物高污染状况主要与沙尘天气有关。不同城市受沙尘影响的程度也不同,影响程度自西向东降低,其中对银川、兰州和西宁影响最大。沙尘天气容易造成严重颗粒物污染事件,颗粒物浓度变化可以清楚地反映沙尘的输送、移动及强度变化。即使在并未出现沙尘天气的地区,在上游沙尘输送作用下,颗粒物浓度也会受较大影响。     

沙尘事件对兰州河谷大气环境PM10的影响

利用1999—2001年兰州河谷大气中空气污染物的监测数据,结合河西地区沙尘事件频次,分析了兰州河谷大气环境PM10含量的季节变化特征及其成因。结果表明,源于河西地区的沙尘暴对兰州市空气中PM10含量有重要影响,PM10浓度在冬春季较高,在夏季较低。1985—2001年16 a来河西走廊中东部沙尘事件年均约为37.6 d,兰州河谷年均沙尘事件约为24.4 d,两者表现出显著的相关性（R=0.67,P=0.003）。2001年兰州河谷1 h平均沙尘PM10浓度统计分析显示:PM10含量大于1.0 mg·m-3 的峰值,主要由河西地区沙尘暴过程引起,而PM10含量0.5~1.0 mg·m-3之间的峰值,主要由工业污染物SO2、NOx及颗粒物的排放引起。要长期有效地改善兰州市大气环境质量,不仅要科学地制定本地污染物的减排措施,还要考虑周围地区生态环境的建设。     

沙尘对兰州市大气环境质量的影响

利用1951-2010年甘肃省气象局地面观测站沙尘气象资料和2006-2010年兰州环保局PM₁₀、SO₂、NO₂质量浓度资料,分析了兰州市沙尘天气时间变化特征及其对SO₂、NO₂、PM₁₀等大气污染物浓度的影响。结果表明:近60年来兰州市沙尘天气总体上呈现振荡性减少的趋势,沙尘暴、扬沙和浮尘年变化倾向率分别为-0.15 d、-0.72 d和-0.97 d,这与上游沙尘源区沙尘天气振荡性减少有关。沙尘对兰州地区SO₂和NO₂质量浓度影响不大,对PM_2.5质量浓度和PM_1.0数浓度的变化有一定的贡献,但对PM₁₀质量浓度影响非常大,沙尘对春季PM₁₀质量浓度的贡献率在18.4%～43.1%。对2007年5月10日一次强沙尘天气过程研究发现,随着沙尘天气的侵入,兰州市不同粒径气溶胶的浓度陡然上升,然后达到较高的浓度水平,之后由于沙尘天气的减弱、消退或离境,不同粒径气溶胶浓度也逐渐降低并缓慢恢复到正常水平。     

陕西袁家村跨地方的乡村性生产与呈现

以陕西袁家村“进城开店”这一典型跨地方扩张发展案例为研究对象,基于对袁家村关中印象体验店内日常生活的参与式观察和对店铺经营者、消费者、管理人员的深度访谈,探究在城市化和现代化转型过程中袁家村乡村性的建构和演变。研究发现：① 袁家村的乡村性嵌在乡村的发展过程中,并呈现历时性变化,也即乡村性并非是固化的结果,而是动态演变的过程;② 袁家村跨地方生产过程是乡村发展升级助推下,乡村物质、人、制度等因素超越地域边界的流动过程,此过程也是乡村与城市之间亲缘、业缘、地缘多重关系的整合;③ 袁家村跨地方的过程以饮食生产扩大呈现了袁家村的乡村性,具体包括乡村生活、乡村生产和乡村社会关系等内容,而乡村性在城市空间成功延续依靠权力和资本的扩张、标准化生产的转移和符号化空间的营造实现;④ 乡村性是社会建构的,强调不同群体对乡村的意义创造。袁家村“进城开店”超越了乡村地域边界,却与农业、土地、村集体呈现更深刻的强关联,有力回击了“村落的终结”“乡村性终结”等论断。研究认为,从多元、动态、融合的视角理解乡村性,有助于在复杂的乡村振兴发展过程中把握更原真的乡村性,为乡村的理论研究和现实探索提供新思考。     

陕西省耕地质量分等省级成果汇总的多属性集簇方法

为了将1∶10000高精度县级耕地分等数据汇总形成1∶500000省级耕地分等数据,基于ArcGIS软件和Access软件,采用多属性集簇的方法对乡代码、地类代码、自然质量等、利用等及经济等属性控制条件进行集簇,采用移动、分割、增加等三种策略进行簇点视觉汇总。研究结果表明多属性集簇的方法能够满足耕地分等省级数据汇总的精度要求,提高工作效率,探索形成了科学可行的省级数据汇总方法体系,采用ArcGIS软件准确快速地构建了省、县两级耕地分等单元之间的可追溯查询关系,取得了良好的应用效果。该研究方法为实现耕地分等省级数据汇总提供理论依据和技术支撑,对耕地分等省级成果汇总和国家级成果汇总具有借鉴和参考价值。     

Sensitivity and areal differentiation of vegetation responses to hydrothermal dynamics on the northern and southern slopes of the Qinling Mountains in Shaanxi province

The Qinling Mountains, located at the junction of warm temperate and subtropical zones, serve as the boundary between north and south China. Exploring the sensitivity of the response of vegetation there to hydrothermal dynamics elucidates the dynamics and mechanisms of the main vegetation types in the context of changes in temperature and moisture. Importance should be attached to changes in vegetation in different climate zones. To reveal the sensitivity and areal differentiation of vegetation responses to hydrothermal dynamics, the spatio-temporal variation characteristics of the normalized vegetation index(NDVI) and the standardized precipitation evapotranspiration index(SPEI) on the northern and southern slopes of the Qinling Mountains from 2000 to 2018 are explored using the meteorological data of 32 meteorological stations and the MODIS NDVI datasets. The results show that: 1) The overall vegetation coverage of the Qinling Mountains improved significantly from 2000 to 2018. The NDVI rise rate and area ratio on the southern slope were higher than those on the northern slope, and the vegetation on the southern slope improved more than that on the northern slope. The Qinling Mountains showed an insignificant humidification trend. The humidification rate and humidification area of the northern slope were greater than those on the southern slope. 2) Vegetation on the northern slope of the Qinling Mountains was more sensitive to hydrothermal dynamics than that on the southern slope. Vegetation was most sensitive to hydrothermal dynamics from March to June on the northern slope, and from March to May(spring) on the southern slope. The vegetation on the northern and southern slopes was mainly affected by hydrothermal dynamics on a scale of 3–7 months, responding weakly to hydrothermal dynamics on a scale of 11–12 months. 3) Some 90.34% of NDVI and SPEI was positively correlated in the Qinling Mountains. Spring humidification in most parts of the study area promoted the growth of vegetation all the year round. The sensitivity of vegetation responses to hydrothermal dynamics with increasing altitude increased first and then decreased. Elevations of 800 to 1200 m were the most sensitive range for vegetation response to hydrothermal dynamics. The sensitivity of the vegetation response at elevations of 1200–3000 m decreased with increasing altitude. As regards to vegetation type, grass was most sensitive to hydrothermal dynamics on both the northern and southern slopes of the Qinling Mountains; but most other vegetation types on the northern slope were more sensitive to hydrothermal dynamics than those on the southern slope.     

Seasonal variation in dust events and the causes of the variation in the Tarim Basin, China

We analyzed dust event occurrence and its seasonal distribution at 16 sites in the Tarim Basin,China.Although the overall frequency of dust events was the highest in spring in this region,its variation in other seasons could be classified into three patterns:(1) frequency of dust events in autumn > that in summer > that in winter(at the Kashi and Kuche sites);(2) frequency in summer > that in winter > that in autumn(at the Ruoqiang site);and(3) frequency in summer > that in autumn > that in winter(at all other areas of the Tarim Basin).The frequency of dust events and their seasonal variations in the Tarim Basin were mainly controlled by wind speed and locally available dust sources;the former was the key control when dust sources did not differ significantly.The seasonal variation in evaporation had a smaller,but still significant effect on the frequency of dust events.