Regional Characteristics and Causes of Haze Events in Northeast China

Northeast China experiences severe atmospheric pollution, with an increasing occurrence of heavy haze episodes. However, the underlying forces driving haze formation during different seasons are poorly understood. In this study, we explored the spatio-temporal characteristics and causes of haze events in Northeast China by combining a range of data sources(i.e., ground monitoring, satellite-based products, and meteorological products). It was found that the ‘Shenyang-Changchun-Harbin(SCH)'city belt was the most polluted area in the region on an annual scale. The spatial distribution of air quality index(AQI) values had a clear seasonality, with the worst pollution occurring in winter, an approximately oval-shaped polluted area around western Jilin Province in spring, and the best air quality occurring in summer and most of the autumn. The three periods that typically experienced intense haze events were Period I from mid-October to mid-November(i.e., late autumn and early winter), Period II from late-December to February(i.e., the coldest time in winter), and Period III from April to mid-May(i.e., spring). During Period I, strong PM_(2.5) emissions from seasonal crop residue burning and coal burning for winter heating were the dominant reasons for the occurrence of extreme haze events(AQI 300). Period II had frequent heavy haze events(200 AQI 300) in the coldest months of January and February, which were due to high PM_(2.5) emissions from coal burning and vehicle fuel consumption, a lower atmospheric boundary layer, and stagnant atmospheric conditions. Haze events in Period III, with high PM_(10) concentrations, were primarily caused by the regional transportation of windblown dust from degraded grassland in central Inner Mongolia and bare soil in western Jilin Province. Local agricultural tilling could also release PM_(10) and enhance the levels of windblown dust from tilled soil. Better control of coal burning, fuel consumption, and crop residue burning in winter and autumn is urgently needed to address the haze problem in Northeast China.     

Characteristics and cause analysis of heavy haze in Changchun City in Northeast China

Northeast China has been reported as having serious air pollution in China with increasing occurrences of severe haze episodes. Changchun City, as the center of Northeast China, has longstanding industry and is an important agricultural base. Additionally, Changchun City has a long winter requiring heating of buildings emitting pollution into the air. These factors contribute to the complexity of haze pollution in this area. In order to analyze the causes of heavy haze, surface air quality has been monitored from 2013 to 2015. By using satellite and meteorological data, atmospheric pollution status, spatio-temporal variations and formation have been analyzed. Results indicated that the air quality in 88.9% of days exceeding air quality index(AQI) level-1 standard(AQI 50) according to the National Ambient Air Quality Standard(NAAQS) of China. Conversely, 33.7% of the days showed a higher level with AQI 100. Extreme haze events(AQI 300) occurred frequently during agricultural harvesting period(from October 10 to November 10), intensive winter heating period(from Late-December to February) and period of spring windblown dust(April and May). Most daily concentrations of gaseous pollutants, i.e., NO_2(43.8 μg/m~3), CO(0.9 mg/m~3), SO_2(37.9 μg/m~3), and O_3(74.9 μg/m~3) were evaluated within level-1 concentration limits of NAAQS standards. However, particulate matter(PM_(2.5) and PM10) concentrations(67.3 μg/m~3 and 115.2 μg/m~3, respectively) were significantly higher than their level-1 limits. Severe haze in spring was caused by offsite transported dust and windblown surface soil. Heavy haze periods during fall and winter were mainly formed by intensive emissions of atmospheric pollutants and steady weather conditions(i.e., low wind speed and inversion layer). The overlay emissions of widespread straw burning and coal combustion for heating were the dominant factors contributing to haze in autumn, while intensive coal burning during the coldest time was the primary component of total emissions. In addition, general emissions including automobile exhaust, road and construction dust, residential and industrial activities, have significantly increased in recent years, making heavy haze a more frequent occurrence. Therefore, both improved technological strategies and optimized pollution management on a regional scale are necessary to minimize emissions in specified seasons in Changchun City, as well as comprehensive control measures in Northeast China.     

Impact of Urban 3D Morphology on Particulate Matter 2.5(PM_(2.5)) Concentrations:Case Study of Beijing,China

Urban particulate matter 2.5(PM_(2.5)) pollution and public health are closely related, and concerns regarding PM_(2.5) are widespread. Of the underlying factors, the urban morphology is the most manageable. Therefore, investigations of the impact of urban three-dimensional(3D) morphology on PM_(2.5) concentration have important scientific significance. In this paper, 39 PM_(2.5) monitoring sites of Beijing in China were selected with PM_(2.5) automatic monitoring data that were collected in 2013. This data set was used to analyze the impacts of the meteorological condition and public transportation on PM_(2.5) concentrations. Based on the elimination of the meteorological conditions and public transportation factors, the relationships between urban 3D morphology and PM_(2.5) concentrations are highlighted. Ten urban 3D morphology indices were established to explore the spatial-temporal correlations between the indices and PM_(2.5) concentrations and analyze the impact of urban 3D morphology on the PM_(2.5) concentrations. Results demonstrated that road length density(RLD), road area density(RAD), construction area density(CAD), construction height density(CHD), construction volume density(CVD), construction otherness(CO), and vegetation area density(VAD) have positive impacts on the PM_(2.5) concentrations, whereas water area density(WAD), water fragmentation(WF), and vegetation fragmentation(VF)(except for the 500 m buffer) have negative impacts on the PM_(2.5) concentrations. Moreover, the correlations between the morphology indices and PM_(2.5) concentrations varied with the buffer scale. The findings could lay a foundation for the high-precision spatial-temporal modelling of PM_(2.5) concentrations and the scientific planning of urban 3D spaces by authorities responsible for controlling PM_(2.5) concentrations.     

中国近地面PM2.5浓度与排放的时空分布及其关联分析

PM_2.5是威胁人体健康的主要大气污染物之一。大量研究关注近地面PM_2.5浓度的监测及其时空分布,但目前针对PM_2.5排放及其与近地面浓度之间的关联研究较为缺乏。本文通过2000—2014年近地面PM_2.5浓度格网数据和PM_2.5排放格网数据,采用长时间序列分析法对PM_2.5浓度和PM_2.5排放从定性和定量两个角度进行时空变化趋势对比研究,并进一步结合标准差椭圆法和趋势分析法,分析了我国近地面PM_2.5浓度和PM_2.5排放的时空变化特征及其关联。结果表明,从总体时间序列趋势上,近地面PM_2.5浓度和PM_2.5排放之间在空间分布上基本呈现一致性,集中在胡焕庸线以东的人口密集区,但在时间上,PM_2.5浓度和排放之间存在动态变化时间差。且PM_2.5浓度的变化更为明显,2000—2007年高于35 μg/m³的国土面积占比增加了14.26%,2007—2014年减少了2.84%;从标准差椭圆分析来看,PM_2.5浓度椭圆和排放椭圆在覆盖面积和方位角上与人口和经济分布吻合,但前者面积更大,长轴更接近于东西方向,二者存在约17°差异,而两类椭圆的中心位置随时间变化呈现出较一致的轨迹特征并呈现出滞后特点;此外,受大气扩散、点源排放等因素影响,PM_2.5浓度变化趋势与排放变化趋势在胡焕庸线以东并不完全一致,部分区域排放呈降低趋势而浓度则反而呈升高趋势。因此,从全国层面来看,减排政策对浓度降低在时间上虽存在滞后,但边际效益显著,并已显露成效;而从局地来看,受地形、气象条件和大气化学过程等复杂影响,二者的变化在空间上仍会存在差异,有待进一步深入研究;从防控措施来看,在继续加强落实本地减排政策的同时,应考虑污染物的扩散迁移规律,加强联防联控,有效改善空气质量。     

融合GNSS气象参数的BP神经网络雾霾预测研究

结合IGS中心获取的BJFS站气象参数（气温（T）、气压（P）、大气可降水量（PWV））及同期PM_2.5数据,建立一种融合时序网络和回归网络的雾霾预测模型,对PM_2.5浓度进行预测。研究表明,引入GNSS气象参数的融合网络模型较单一网络模型适应性强、准确度高,在一定精度范围内可准确预测PM_2.5的变化,时效性达3 h。本文结论验证了卫星导航技术应用于雾霾天气监测及预报的可行性。     

海南岛气溶胶时空变化及来源追溯

海南岛独特的区域和国家战略位置,使得了解和掌握该地区气溶胶时空格局特征及其大气污染物传输来源是海南大气环境质量监测和污染防治中重要的关注问题。选取MODIS 3 km分辨率气溶胶光学厚度（AOD）产品MOD04_3K,对海南岛2003—2017年AOD时空变化特征进行分析;以海口市为起始点,采用HYSPLIT模式对8次典型大气污染事件在10、500、1000 m 3个相对地面高度上进行48 h后向轨迹计算,以探究该区域大气污染物来源。结果表明：① 海南岛AOD在空间上呈较为明显的三级阶梯状分布,从西北方向向东南递减,2003—2017年在不同阶梯的空间变化趋势差异十分显著;② 2003—2017年海南岛AOD年均值为0.095,各年均值整体呈轻微升高趋势;季节均值差异表现为“春季>秋季>冬季>夏季”,此差异除受区域天气系统与地方风向特征的影响外,本区旅游业发展造成的大气污染物排放也不可忽视;③ 海南岛（海口市）2013—2017年典型大气污染事件发生期间大气污染气流在3个高度上（10、500、1000 m）的后向轨迹基本保持一致,均主要来自于海南岛东北方向。据此,位于该方向毗邻的珠江三角洲地区陆源大气污染物在东北盛行风的影响下被输送至海南岛,在区域天气系统（如大陆冷高压）和局部天气系统（如海风辐合）共同作用下,是促成该地区大气污染事件发生的重要因素。研究结果对深入理解海南岛近十多年来大气环境状况的变化规律、完善该区域大气污染联防联控机制具有重要的意义。     

区域优化Peterson模型的气溶胶光学厚度估算分析

气溶胶光学厚度作为描述气溶胶光学特性的重要参数之一, 被广泛应用于空气质量监测及辐射传输模型的大气订正等研究中。卫星遥感可快速反演获取大范围气溶胶信息, 但其产品通常因云覆盖或暗目标算法等原因而存在空间覆盖率较低的问题, 且产品时相受限于卫星过境时间。水平能见度作为描述气溶胶光学特性的另一重要参数, 由分布广泛的气象台站一日8 次固定时间多次发布。建立水平能见度与气溶胶光学厚度的转换关系, 可实现对卫星反演气溶胶光学厚度的有益补充。本文利用2001-2009 年的MODIS气溶胶光学厚度产品与中国华东地区71 个气象台站的水平能见度数据, 对描述两者转换关系的Peterson 模型进行区域优化。采用分区域高斯曲线拟合的方法, 对影响转换精度的主要参数气溶胶标高随时间变化规律开展研究和模拟。利用2010 年数据对优化模型进行精度及区域适用性验证。结果表明, 优化后模型的气溶胶光学厚度估算均方根误差为0.31, 低于原模型误差;精度基本上与单站点优化模型一致, 但在实用性方面优于单站点优化模型。     

卫星红外遥感技术在我国环保领域中的应用与发展分析

本文较为系统地分析了卫星红外遥感技术在我国秸秆焚烧、沙尘、气溶胶、颗粒物、灰霾等大气环境遥感监测,水华、水质参数、水表温度、热污染、核电厂温排水等水环境遥感监测,以及土壤含水量、地表温度、干旱、城市热岛效应等生态环境遥感监测的应用。同时,指出目前国产卫星红外载荷业务化应用程度不高、辐射定标能力不足、应用反演算法原创性不强、地面观测和试验验证能力不足等问题;并提出需大力发展国产红外传感器、提高辐射定标能力、发展国内原创监测算法、建设大型环保应用综合试验场等建议,以促进红外遥感技术在环境保护领域的应用和发展。     

Spatiotemporal Variations and Influencing Factors Analysis of PM<Subscript>2.5</Subscript> Concentrations in Jilin Province,Northeast China

High PM_2.5 concentrations and frequent air pollution episodes during late autumn and winter in Jilin Province have attracted attention in recent years. To describe the spatial and temporal variations of PM_2.5 concentrations and identify the decisive influencing factors, a large amount of continuous daily PM_2.5 concentration data collected from 33 monitoring stations over 2-year period from 2015 to 2016 were analyzed. Meanwhile, the relationships were investigated between PM_2.5 concentrations and the land cover, socioeconomic and meteorological factors from the macroscopic perspective using multiple linear regressions (MLR) approach. PM_2.5 concentrations across Jilin Province averaged 49 μg/m³, nearly 1.5 times of the Chinese annual average standard, and exhibited seasonal patterns with generally higher levels during late autumn and over the long winter than the other seasons. Jilin Province could be divided into three kinds of sub-regions according to 2-year average PM_2.5 concentration of each city. Most of the spatial variation in PM_2.5 levels could be explained by forest land area, cultivated land area, urban greening rate, coal consumption and soot emissions of cement manufacturing. In addition, daily PM_2.5 concentrations had negative correlation with daily precipitation and positive correlation with air pressure for each city, and the spread and dilution effect of wind speed on PM_2.5 was more obvious at mountainous area in Jilin Province. These results indicated that coal consumption, cement manufacturing and straw burning were the most important emission sources for the high PM_2.5 levels, while afforestation and urban greening could mitigate particulate air pollution. Meanwhile, the individual meteorological factors such as precipitation, air pressure, wind speed and temperature could influence local PM_2.5 concentration indirectly.     

Monitoring Spatio-temporal Variance of an Extreme Heat Event Using Multiple-source Remote Sensing Data

Extreme heat events have serious effects on human daily life. Accurately capturing the dynamic variance of extreme high-temperature distributions in a timely manner is the basis for analyzing the potential impacts of extreme heat, thereby informing risk prevention strategies. This paper demonstrates the potential application of multiple source remote sensing data in mapping and monitoring the extreme heat events that occurred on Aug. 8, 2013 in Jiangsu Province, China. In combination with MODIS products, the thermal sharpening(Ts HARP) method and a binary linear model are compared to downscale the original daytime FengY un 2 F(FY-2 F) land surface temperature(LST) imagery, with a temporal resolution of 60 min, from 5 km to 1 km. Using the meteorological measurement data from Nanjing station as the reference, the research then estimates the instantaneous air temperature by using an iterative computation based on the Surface Energy Balance Algorithm for Land(SEBAL), which is used to analyze the spatio-temporal air temperature variance. The results show that the root mean square error(RMSE) of the LST downscaled from the binary linear model is 1.30℃ compared to the synchronous MODIS LST, and on this basis the estimated air temperature has the RMSE of 1.78℃. The spatial and temporal distribution of air temperature variance at each geographical location from 06:30 to 18:30 can be accurately determined, and indicates that the high temperature gradually increases and expands from the city center. For the spatial distribution, the air temperature and the defined scorching temperature proportion index increase from northern to middle, to southern part of Jiangsu, and are slightly lower in the eastern area near the Yellow Sea. In terms of temporal characteristics, the percentage of area with air temperature above 37℃ in each city increase with time after 10:30 and reach the peak value at 14:30 or 15:30. Then, they decrease gradually, and the rising and falling trends become smaller from the southern cities to the northern regions. Moreover, there is a distinct positive relationship between the percentage of area above 37℃ and the population density. The above results show that the spatio-temporal distributions of heat waves and their influencing factors can be determined by combining multiple sources of remotely sensed image data.     

Surface ozone in Jiuzhaigou National Park, eastern rim of the Qinghai-Tibet Plateau, China

Located in southwestern China, Jiuzhaigou National Park is one of the most popular tourism destinations in China, famous for its unique aquatic ecosystems and beautiful forests. However, plants in the park may be at high ozone risk as a result of the intensive use of diesel tour buses in the park. In addition, Jiuzhaigou is close to a region with relatively high regional anthropogenic NO x emissions. During the growing season, also the peak season of tourism, we measured ozone concentration at two sites within the Park and these were: Jiuzhaigou Bureau (JB) and Long Lake (LL). The results indicate that ozone concentrations were not high enough to cause foliar injury during the monitoring period, although the risk of ozone to plants was higher in spring than in summer and autumn. Diurnal ozone cycles at JB and LL displayed significantly higher ozone concentrations in the daytime than in the nighttime, suggesting photochemical production of ozone during the day and ozone deposition during the night as a result of the nocturnal boundary layer. In parallel with the seasonal change of background surface ozone in the Northern Hemisphere, maximum daily 8-h average ozone concentration (MDA8) and daily ozone concentration decreased from spring to autumn at the two sites. This temporal variation in Jiuzhaigou was most likely associated with the downward mixing of ozone-rich air from the free troposphere, because all the high-ozone events (MDA8 > 70.0 ppb) were observed in spring and ozone-rich air from the free troposphere was the dominant cause. In summary, our data suggest that ozone concentrations in Jiuzhaigou were more affected by the regional-scale of background pattern in air quality and meteorological conditions than by local tourist activities.     

中文文本蕴含气象灾害事件信息多模型融合抽取方法

随着气候变暖加剧,全球极端天气事件频发,重大气象灾害的发生频率与日俱增。研究气候变化与气象灾害发生频率的关系,对于气候变化背景下的防灾减灾具有重要意义。文献资料及泛在网络数据中蕴含了海量的气象灾害时空事件,为此,本文基于自然语言处理技术研发了文本气象灾害时空事件自动抽取方法。① 提出了基于专业文献的由粗到精的气象灾害标注语料训练库构建方法。首先针对不同文献资料存在的歧义和不兼容等问题,构建了面向文本事件统一的气象灾害知识体系。然后构建了基于章节结构的粗标注方法,分别针对长文本（现代文）和短文本（文言文）研发了基于Labeled LDA模型及基于TF-IDF和N-gram模型的精细标注语料筛选方法,解决了语料库的快速构建问题;② 基于BERT-CNN模型研发了融合上下文语义特征和多粒度的局部语义特征的、面向长短文本一体化处理的气象灾害时空事件自动分类方法;③ 利用该方法分别从文言文和泛在网络数据中自动抽取了灾害时空事件,其宏F1值分别达到89.09%和80.06%,主要气象灾害时空事件分布与专业统计数据相关性较高;④ 基于以上结果,重建了我国各历史时期灾害时空演变过程,发现各时期灾害数据量整体呈现出逐步上升趋势,暴雨灾害、洪涝灾害与干旱灾害是影响我国的主要灾种。本方法既可实现网络长文本事件的自动发现,也可实现文言文短文本事件的自动检测,为文本数据便捷应用于气象灾害研究和监测提供了新的技术方法。     

Characterization of air pollution in urban areas of Yangtze River Delta,China

The hallmark of development in the Yangtze River Delta (YRD) of East China has been sprawling urbanization. However, air pollution is a significant problem in these urban areas. In this paper, we investigated and analyzed the air pollution index (API) in four cities (Shanghai, Nanjing, Hangzhou and Ningbo) in the YRD from 2001 to 2012. We attempted to empirically examine the relationship between meteorological factors and air quality in the urban areas of the YRD. According to the monitoring data, the API in Shanghai, Nanjing, Hangzhou slightly declined and that in Ningbo increased over the study period. We analyzed the inter-annual, seasonal, and monthly variations of API, from which we found that the air quality had different temporal changes in the four cities. It was indicated that air quality was poor in winter and spring and best in summer. Furthermore, different weather conditions affected air quality level. The wind direction was considered as an important and influential factor to air pollution, which has an impact on the accumulating or cleaning processes of pollutants. The air quality was influenced by the different wind directions that varied with seasons and cities.     

多模态地理大数据时空分析方法

多模态地理大数据时空分析旨在融合地理大数据的多模态信息发现有价值的时空分布规律、异常表现、关联模式与变化趋势,是全空间信息系统的核心研究内容,并有望成为推进地理学人地关系研究的重要突破口。为应对地理大数据时代的新机遇与挑战,本文围绕4类核心的时空分析方法（时空聚类分析、时空异常分析、时空关联分析与时空预测分析）,系统归纳了国内外研究现状,探讨了时空分析中多尺度建模、多视角协同、多特征认知与多特性表达的研究难点。进而,介绍了多模态地理大数据时空聚类、异常、关联与预测分析模型,更加全面、客观、精准地认知与理解时空大数据中潜在的地理知识,并且能够在气象环境监测、公共安全管理、城市设施规划等多个应用领域发挥关键作用。     

基于静风期污染物的PM2.5排放清单空间精细化方法

有利气象条件之后的静风期,极大降低了PM_2.5跨区域传输的影响,能够揭示本地源的排放状况。本文尝试性引入了静风期污染物分布揭示本地源排放特征的概念,提出了一种基于遥感数据的PM_2.5排放清单空间精细化方法：首先,利用 MODIS MCD19A2反演的ChinaHighPM_2.5数据,构建高时空分辨率PM_2.5数据融合方法;然后,构建唐山市有利气象条件之后的静风期污染物遴选方法（合理风向和风速：有利气象条件为东风,地面10 m高度风速大于3 m/s,其他风向,持续的较大风力5~10 m/s;静风期风速小于1.5~2.0 m/s）;其次,基于遴选的静风期PM_2.5数据分配MEIC清单中的PM_2.5总排放量,同时对比传统插值方法：基于GDP、人口密度、路网、土地利用类型数据,实现清单各污染源PM_2.5的1 km×1 km空间分配;最后,利用WRF-CMAQ模拟数据和地面台站实测数据进行真实性检验。研究结果表明：① PM_2.5数据填补融合方法能够有效提高PM_2.5监测数据的时空分辨率,且与地面监测值显著相关（R²=0.94,RMSE=4.64 µg/m³,NMB=2%,NME=7%）;② 引入有利气象条件后的静风期概念,提出了静风期污染物的遴选方法,有效降低了PM_2.5跨区域传输的影响,更好地反映了本地源排放的空间分布特征;③ WRF-CMAQ模拟方法的精度验证结果表明,该方法较传统面积插值法NME降低7%,NMB降低10%,RMSE降低1.54 µg/m³,R²提高11%。该方法为排放清单的空间精细化提供了新的研究思路。     

上海夏季雾霾的影响因素研究

由于对流层中气象因素变化剧烈且蕴含大量的水蒸气和颗粒物,雾霾的发生也极易改变卫星信号的传播路径,从而导致对流层延迟发生变化,因此重点研究气象因素与雾霾之间的相关性问题。通过上海市2016年62 d(DOY 153～214)的大气中污染物含量资料与天顶对流层延迟比较分析发现,大气中每种污染物含量基本呈现相似的走势,天顶对流层延迟随PM2.5含量增加而增大,且在日值数据的比较中发现,上海夏季的PM2.5含量与气压、气温和相对湿度成正相关关系,与风速、降水量呈负相关关系。     

基于中国大气环境监测站点的2015—2019年大气质量状况时空变化分析

2013年以来几次严重的雾霾污染事件引起了公众的广泛关注,此后中国实施了一系列有关大气污染防治的政策、法规和措施来改善大气质量。为了分析近年来中国大气质量的时空变化特征,本文选取2015—2019年生态环境部国控站点监测的大气污染关键参数,对比分析了空气质量指数和6种大气污染物的季均、年均浓度变化结果,并利用组合指标分析法和相关分析法探讨了不同大气污染物之间的相关性。结果表明：① PM_2.5、PM₁₀、SO₂、CO和NO₂浓度和AQI均有明显下降,2019年均浓度较2015年均浓度分别下降4.5%、3.84%、7.86%、3.74%、0.95%,AQI下降了19.31%,同时,O₃浓度则上升了0.79%;② 从空间分布来看,中国北方地区PM₁₀、PM_2.5、O₃、NO₂、SO₂、CO年均质量浓度和AQI分别比南方地区高25.2%、18.73%、4.95%、17.6%,32.74%、16.17%、28.3%;③ 从季节性变化规律来看,除了O₃呈现出夏季浓度高,冬季浓度低外,其他5种污染物和AQI都呈现相反的季节变化规律;④ 总体而言,目前中国大气污染以PM_2.5和O₃为主,PM_2.5与NO₂、SO₂、CO之间有极显著的正相关关系（r>0.85,p<0.01）,而O₃与其前体物NO₂和CO之间存在显著的负相关关系（r>0.8,p<0.01）。     

Relationship between Antarctic sea ice and the climate in summer of China

The variations of sea ice are different in different regions in Antarctica, thus have different impacts on local atmospheric circulation and global climatic system. The relationships between the sea ice in Ross Sea and Weddell Sea regions and the synoptic climate in summer of China are investigated in this paper via diagnostic analysis methods by using global sea ice concentration gridded data covering Jan. 1968 through Dec. 2002 obtained from Hadley Center, combined with Geopotential Height on 500hPa and 100hPa over North Hemisphere and monthly precipitation and air temperatures data covering the corresponding period over 160 meteorological stations in China obtained from CMA （ China Meteorological Administration）. Results disclose that both these two regions are of indicative meanings to the climate in summer of China. The Ross Sea Region is the key sea ice region to the precipitation in Northeast China in summer. More sea ice in this region in September will result in less precipitation in Northeast China in the following June. Weddell Sea Region is the key sea ice region to the air temperature in Northeast China in summer. More sea ice in this region in September will contribute to lower air temperature in Northeast China in the following June.     

Preliminary comparison of trace metals in coastal aerosols between Qingdao and Liverpool

Aerosol samples were collected at two coastal suburban stations, Qingdao （China） in 1995-1996 and Liverpool （U.K.） in 1995, respectively. The samples were analyzed to determine the concentrations of trace metals （Cr, Zn, Cu, Co, Ni, Pb, V, and Cd） as well as A1, Fe and Mn. Data were examined to understand the difference of trace metals in aerosols between coastal zones downwind the developing area （near the Yellow Sea） and developed region （near the Irish Sea）. The results show that most elements at Qingdao have levels 4-5 times higher than those at Liverpool, particularly for the crust-dominated elements （e.g. Al, Fe and Mn）. Moreover, the aerosol composition at Qingdao is higher in spring than in summer, underlying the influence of westerlies and local emissions in combination, whereas seasonal change of aerosol composition is not significant at Liverpool. The enrichment factors for the crustal source elements （EFcrust） at Liverpool are much higher than those at Qingdao. The contributions from the pollutant source （Rp） for some trace metals like Cu, Pb, Zn and Cd are 〉90% at Qingdao and Liverpool, suggesting overwhelming anthropogenic contributions to these metals. The contributions from crustal source （Re） for trace metals tend to increase with higher aerosol levels and Al concentration at Qingdao, indicating a good correlation between the crust-dominated component and the air mass. At Liverpool, the Rc values for trace metals are positively correlated with Al concentrations instead of with aerosol mass, suggesting that Al in aerosols represents the crustal component even though the aerosols come from different sources.     

一种基于FY3D/MERSI2的AOD遥感反演方法

针对目前国产卫星对气溶胶遥感监测应用较少的情况,本研究综合暗象元和SARA算法优势,基于FY-3D MERSI2数据构建了一套气溶胶光学厚度(Aerial Optical Depth, AOD)快速遥感反演方法。首先,引入了传统暗象元算法中的地表反射率经验关系,然后利用AERONET长时间序列的地基观测数据,构建了红波段和蓝波段气溶胶光学厚度、不对称因子和单次散射率之间的关系,最后利用MOD04和AERONET的AOD产品对研究反演结果进行了验证和评估分析。结果发现：① 本研究反演的AOD不仅保持了与MODIS的气溶胶产品空间分布的一致性,而且合理地呈现了AOD的高值分布,改进了MOD04气溶胶产品在云和亮目标方面反演缺失问题;② 利用AERONET地基观测结果对本研究获取的MERSI2的AOD反演结果进行了对比分析,发现二者具有较高的线性相关性,蓝波段AOD线性相关系数超过0.85;③ 利用MERSI2数据完整地捕捉到了2018年3月9—14日京津冀及周边区域的一次重污染过程中气溶胶时空分布变化情况,这也说明了FY3D卫星具备良好的气溶胶遥感监测能力,为我国灰霾监测和预警提供参考依据。本研究对大力发展国产卫星在大气环境遥感的应用有重要参考意义。