To investigate the "South-High North-Low" (SHNL) spatial distribution of aerosol pollution in the Beijing area, this study analyzes the spatial and temporal characteristics of aerosol and influencing mechanisms using hourly PM2.5 concentrations (diameter <= 2.5 mu m) data collected from 33 air quality stations in Beijing from 2015 to 2019. PM2.5 concentrations in the Beijing plain generally decreased, but the SHNL pattern remained prominent, with PM2.5 emission sources in Beijing and its surroundings showing a "south-strong, north-weak" distribution as an essential prerequisite. Between southern and northern regions, the maximum annual aerosol concentration difference (ACD) was 16.0 mu g m- 3, and the maximum seasonal ACD occurred in winter (24.1 mu g m-3) when the SHNL pattern was most prevalent. Higher Aerosol Concentration in Southern region (HACIS) is the dominant pollution type. Two distinct types of pollution events were analyzed. The HACIS type pollution event originated from synergistic effect of weak southerly and strong northerly winds. The HACIN (Higher Aerosol Concentration in Northern region) event originated from synergistic effect of stronger southerly and weaker northerly winds. The Regional Air-pollutant Distribution Regulator (RADR), defined as a local endogenous wind system (including mountain-plain circulation, urban heat island circulation, and sea-land breezes) and their interactions, plays a key regulatory role in shaping pollution distribution. This study aims to lay a preliminary foundation for understanding Beijing's aerosol pollution dynamics and may be of some relevance for developing targeted pollution control strategies.

Airborne pollen is considered to be one of the air pollutants that can cause allergic reactions in humans, leading to the occurrence or aggravation of a series of allergic diseases. The latest study showed that the positive rate of pollen allergens in allergic rhinitis patients in urban areas of Beijing exceeded 80%. Accurate prediction of pollen content could provide more effective assistance to susceptible populations. Based on the measured data from multiple stations in the urban area of Beijing during the pollen season from 2021 to 2022, the spatiotemporal distribution characteristics of pollen content were analyzed. The results showed that the main meteorological factors affecting spring pollen content in the urban area of Beijing were daily average wind speed, 3-day average temperature, water vapor pressure, daily average, temperature, and accumulated temperature. The main meteorological factors affecting autumn pollen content were 3-day average temperature, water vapor pressure, minimum surface temperature, and daily average temperature. In addition, it was found that there was a consistent spatial correlation between the current air pollen content and meteorological elements in the urban area of Beijing, but this correlation had significant seasonal differences. Furthermore, the Granger causality test method was applied to select the main meteorological factors that affected airborne pollen content in the urban area of Beijing, and two prediction models for air pollen content in the Beijing urban area for different seasons were established based on the support vector machine method （SVM） and multiple linear regression theory. The test of the prediction results for 2023 showed that both the SVM model considering seasonal differences and the multiple linear regression model could predict the daily distribution trend of pollen content well. The overall correlation coefficients between the predicted pollen content and the measured values were 0.693 and 0.636 （P &lt;0.01）, respectively. Additionally, both models had good predictive ability for several severe content pollen pollution events within the year. In the spring of 2023, the prediction accuracy of the SVM model and linear model were 61.2% and 60.1%, respectively. During autumn, the prediction accuracy was 68.1% and 66.7%, respectively. The performance was better than that of existing business models, especially in the cross-level error improvement of heavy pollution event prediction. The research results provide reference value for further improving the prediction technology of airborne pollen content in the Beijing area.

A number of simulation studies show that the sea-breeze fronts (SBFs) that form at the Bohai Bay coast can penetrate far inland through the Beijing-Tianjin-Hebei urban agglomeration (BTH). However, there has been little observational evidence for this claim. Based on dense surface observation networks, we observed a SBF that penetrated 170 km inland from Bohai Bay, interacting with cities in the BTH. The SBF was retarded in urban areas compared to surrounding areas, leading to the front line bending in urban areas. The nocturnal surface air temperature of both urban and rural stations increased temporarily as the SBF passed. Urban roughness effects caused strong updrafts during the passage. Ceilometers observed that the strong updraft of the SBF lifted near-surface aerosols to form sea-breeze heads (SBHs), leading to higher SBHs in urban areas than in rural areas. The uplifted aerosols gradually formed thin aerosol plumes, and produced clouds about four hours after the SBF passed. Our observations will help to improve understanding of the interactions between inland SBFs and cities, and provide a basis for further research on the physical mechanisms of how the inland SBF affects aerosols, clouds, and precipitation in the BTH and elsewhere.

基于环境、气象、探空、AERONET、云高仪和卫星遥感多数据源对北京春季一次重污染过程的热、动力机制和粒子微物理属性展开研究.结果表明:①多源数据协同观测显示本次污染起源于中国西部,影响范围广泛,对北京构成了达6级的重度污染,PM2.5、PM10质量浓度高达600 μg/m3和1 000 μg/m3.②MCD19A2 MAIAC AOD能很好地揭示本次污染起源、强度、输送和影响区域,MISR AOD显示本次污染粒子微物理属性具有大模态、非球形、非吸收型的特征.③CALIPSO显示,在1～4 km高度,退偏在0.1～0.5,色比在0.5～1.3均有分布,有"小退偏和大色比"现象.④AERONET数据显示,污染前、中、后AOD 550 nm值分别为0.9、2.0、0.2,AE 440-675 nm值分别为1.30、0.4、1.0,说明本次污染为PM2.5和PM10混合型污染.⑤探空显示本次污染大气层结非常稳定,各阶段有显著的热动力特征.污染爆发前(5月3日),上层大气潮湿且温度递减率小,低层大气干燥上升,构成对流空间.污染爆发期(5月4日),出现多层逆温,干燥大气下沉运动,受上层西风下层偏南风的动力配合.污染末期(5月5日),低层逆温消失,上层逆温层高度持续抬升,且空气干燥,深厚西风区风速持续加大,终结污染过程.⑥云高仪监测和EC模式结果表明5月4日大气稳定,边界层在空间上分布均匀,08:00大气边界层高度约1200 m,20:00约300 m.

This study aimed to promote the coordinated development of regional social economy and ecological environment, build a better living environment, accurately prevent and control pollution, and carry out in-depth surveys and general surveys of air pollution in Beijing, Tianjin, and Hebei. Based on 6 years (June 2014 to December 2019) of ground environmental observation data and satellite data from 2000 to 2019, the distribution characteristics and evolution trend of air pollution in different time and spatial scales were analyzed. The results showed that:① according to the daily average concentration of PM2.5 at the sites, the pollution in the Beijing-Tianjin-Hebei region showed the characteristics of more days, heavy levels, and overall improvement. Pollution mainly occurred from October to April of the following year, accounting for nearly half a year. The pollution level of PM2.5 was the best at Zhangjiakou, followed by Qinhuangdao. ② Based on the 20-year average PM2.5 annual average concentration data retrieved from satellites, the PM2.5 concentration presented a spatial distribution characteristic in which that in the plains was higher than that in mountain area, and PM2.5 concentration in the city was higher than that in the suburbs. PM2.5 concentration changed with time, showing a four-stage bimodal structure of "M"-type evolution characteristics, which gradually increased starting in 2000; the first peak appeared in 2006 and gradually decreased from 2007 to 2012. It rose sharply to the second peak in 2013 and then decreased yearly until 2017. ③ The monthly average AOT data based on satellites every 10 years indicated that the value of AOT in the first time period (2000-2009) was larger than that in the same month of the second time period (2010-2019). The maximum value was in July, and the minimum value was in December. The monthly average AOT in Zhangjiakou and Chengde changed slightly over the past 20 years, and the seasonal and spatial differences were significant in the plain area. ④ Judging from the daily average value of O3-8h observed at the stations, good levels of O3-8h concentrations in the Beijing-Tianjin-Hebei area occurred frequently and widely from March to October. There were at least seven instances of light pollution levels, and the moderate pollution levels and above were not observed. ⑤ The daily average value of SO2 observed on the ground showed that there was no light pollution or above; the good pollution level occurred in winter, and most appeared in the form of pollution for several consecutive days. ⑥ The analysis of AQI data revealed that from 2015 to 2019, the proportion of AQI excellent grades in Beijing increased from 27% to 38%, and the proportion of Tianjin AQI good grades increased from 44% to 64%. The highest proportion of Handan AQI superior grades appeared in 2016, accounting for only 9%. ⑦ The 20-year monthly average concentration of SO2 data based on satellites showed that high-value areas were in Handan, Xingtai, and Shijiazhuang, and low-value areas were in Zhangjiakou and Chengde. The 20-year average NO2 data showed that the high-value centers were in Beijing, Tianjin, Tangshan, Handan, Xingtai, and Shijiazhuang.

The distribution and diurnal variation of short-duration heavy rainfall (SDHR) and the influence of a complex underlying surface were studied by using fine-scale hourly precipitation data in the Beijing–Tianjin–Hebei (BTH) region during the summers of 2014–2020. Areas prone to SDHR are located mainly in the southern foothills of the Yanshan Mountains, the foothills area, and the trumpet-shaped topographic entrance area north of Beijing, areas inland of the west coast of Bohai Bay, and the northern Beijing urban area. Owing to the influence of topography and the geographical location, the distribution and diurnal variation SDHR is significantly different in the western and northern mountainous areas, the foothills, and the plains. Compared to the underlying urban surface, the topography and the land–sea interface have considerable effects on the distribution of SDHR. A key finding is that the foothills of northern of Beijing, eastern slope and piedmont area of the Taihang Mountains, and the land–sea interface of Bohai Bay play important roles in the formation and propagation of SDHR.

Despite frequent foehns in the Beijing–Tianjin–Hebei (BTH) region, there are only a few studies of their effects on air pollution in this region, or elsewhere. Here, we discuss a foehn-induced haze front (HF) event using observational data to document its structure and evolution. Using a dense network of comprehensive measurements in the BTH region, our analyses indicate that the foehn played an important role in the formation of the HF with significant impacts on air pollution. Northerly warm–dry foehn winds, with low particulate concentration in the northern area, collided with a cold–wet polluted air mass to the south and formed an HF in the urban area. The HF, which is associated with a surface wind convergence line and distinct contrasts of temperature, humidity and pollutant concentrations, resulted in an explosive growth of particulate concentration. As the plain–mountain wind circulation was overpowered by the foehn, a weak pressure gradient due to the different air densities between air masses was the main factor forcing advances of the polluted air mass into the clean air mass, resulting in severe air pollution over the main urban areas. Our results show that the foehn can affect air pollution through two effects: direct wind transport of air pollutants, and altering the air mass properties to inhibit boundary layer growth and thus indirectly aggravating air pollution. This study highlights the need to further investigate the foehn and its impacts on air pollution in the BTH region.

Studying precipitation diurnal variation characteristics is crucial to better understand their formation mechanism. Moreover, it is fundamental in assessing regional climate variability and to validate the effectiveness of cloud and precipitation parameterization schemes in weather and climate models. Based on observational data from 2008 to 2017, this manuscript objective is to assess the aerosol effects on summer precipitation on an hourly-scale diurnal variation basis in the Beijing metropolitan area. The results put in evidence that the precipitation frequency and duration on polluted days were 25.0% and 14.8% lower with respect to clean days. No significant differences were observed in total daily accumulated rainfall between clear and polluted days. While heavy precipitation mean intensity on polluted days increased by 13.5%, which is potentially linked with aerosol microphysical effects. Note that precipitation occurs earlier on polluted days, with peak time of 1 ∼ 2-hour in advance compared with clean days over the urban areas of Beijing, which may be primarily ascribed to the influence of advanced turning local circulation of mountain-valley breezes and urban heat island circulation due to aerosol-radiation effects.

为了弄清蒙古气旋外围出现的霾和沙尘复合污染特征及其形成的关键气象条件,本研究利用多种遥感设备(增强型云高仪、风廓线雷达和微波辐射计等)垂直加密观测数据,结合大气主要污染物(PM10 、PM2.5、SO2、NO2)监测数据、加密自动气象站观测数据,以及常规地面和高空气象观测数据、NCEP再分析数据等,分析发生在北京春季的2次霾和沙尘重污染过程.结果表明,2017年5月4-5日为一次PM10和PM2.5混合污染过程,与上游地区强烈发展的蒙古气旋后部风沙区的输送有关.上游地区因受中-低空西来槽影响上升气流加强,使沙尘细颗粒物(粒径≤10 μm)悬浮于空中,由中-低空偏西风输送至下游地区,被北京及附近的弱下沉气流带至地面造成严重的PM10、PM2.5混合污染.其中,地面偏西风对上游地区的PM10、PM2.5的水平输送作用明显;2018年3月27-28日凌晨是受蒙古气旋底部低压区辐合作用和偏南气流输送作用形成的积累型霾(PM2.5)污染.28日凌晨2:00开始蒙古气旋后部沙尘区随东-西向冷高压南压而向南扩散.随后冷高压不断东移形成回流偏东风,偏东风使北京及西北部地区的低层大气产生辐合上升运动,导致本地尘土扬起,造成PM2.5重污染和PM10极严重污染;浮尘天气引发的大气污染具有突发性特征,且持续时间较长.边界层高度低、低层大气存在逆温层(或等温层)并长时间维持是霾和沙尘复合污染形成和持续的重要条件.霾和沙尘复合重污染的形成是人为污染物、沙尘细颗粒物水平和垂直输送,以及大气层结稳定共同作用的结果.

利用2002、2010、2015年北京地区空间分辨率为1 km×1 km的人口密度和统计年鉴的总人口、车辆数、道路面积、12类能耗数据,获取能耗分时系数,优化由交通产生的人为热计算方法,计算北京地区冬、夏季节人为热,并研究其时空分布特征.结果表明,人为热主要由交通产生,最低占比40％,高峰期可达80％.电力、能耗产生的人为热各占约15％,冬季供热占比约15％,人体代谢约3％.交通产生的人为热呈早晚双峰结构,分别发生在08:00和18:00(北京时,下同);电力产生的人为热冬季为双峰结构,峰值在09:00和20:00,夏季为弧形结构,弧顶发生在15:00;能耗产生的人为热冬季为主、次峰形态,主峰出现在09:00,次峰出现在18:00,夏季为单峰形态,18:00为峰值.一天中01:00—11:00,电力和能耗产生的人为热冬季大于夏季,其余时间相反;夏季00:00—07:00,电力产生的人为热大于能耗,其余时间能耗大于电力;冬季所有时刻,能耗产生的人为热大于电力.2002—2015年人为热逐年增大,冬季峰值从130 W·m-2增至330 W·m-2,夏季从120 W·m-2增至300 W·m-2.交通产生的人为热逐年大幅增大;电力产生的人为热2015年最大,约为20 W·m-2,2010和2015年接近,但比2002年都有明显增加;能耗产生的人为热呈现出与电力完全相反的特点.人为热空间分布与人口密度分布有很强的相关性,人为热高值区主要在人口密集的城市核心区,2002年冬、夏人为热最大值分别为300、240 W·m-2,2010年分别为660、431 W·m-2,2015年分别为666、423 W·m-2.

The temporal and spatial distribution characteristics, evolution trend and potential climatic effects of air pollution in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) were analyzed on different time scales and spatial spaces, based on ground environment observation data from June 2014 to December 2018 and satellite remote sensing inversion products from 2000 to 2018. The results show that:① From the in-situ observed daily average concentration of PM2.5, good or mild to moderate pollution occurred in January, February, October, November, and December every year, and the rest of the time was excellent. ② Based on the annual average PM2.5 concentration obtained by satellite for the past 20 years, the spatial characteristics showed that the external radiation is centered on Guangzhou and Foshan. The time evolution shows the characteristics of an Ω shape, which increases gradually from 2000 to 2009, is highest in 2008, and then gradually decreases. ③ The monthly average aerosol optical thickness (AOT) value from the Multi-angle Imaging Spectro Radiometer satellite reversion every 10 years for a period (2000-2009 for a period, 2010-2018 for a period) was used to see the monthly variation. The monthly average AOT value in the first period was larger than that in the second period of the same month, the maximum value was in March and April, and the minimum value was in November and December. It is envisaged to draw a line along the north-south direction of the Pearl River Port, which basically shows that the AOT value in the west is greater than that in the east. ④ According to the observed daily average concentration data of O3-8h, the main concentration level of O3-8h in the GBA is excellent. The cities with good ozone concentration were most numerous in 2014, with five cities, and least in 2018, with only one city. The highest ozone concentration was in September, followed by June and November, and then May and July. In the past 20 years, the spatial distribution of the average concentration of O3 monitored by satellite remote sensing showed a characteristic Ω shape, increasing initially and then decreasing. The maximum value was in May, and the north-south boundary line appeared in space. ⑤ There is a good linear relationship between the interannual variation of monthly mean temperature and radiation, whereas the relationship between AOT and radiation cannot be described by a simple linear relationship.

Heat wave is serious natural disaster that can harm human health and affect social economy, transportation and ecological environment. This paper investigates the long term trends of high temperature events in three major cities (Beijing, Tianjin and Shijiazhuang) of northern China during 1970–2019, and quantifies the contributions of main influencing factors to the variability of high temperature days. High temperature events in Beijing–Tianjin–Shijiazhuang cities mainly occur from June to July and account for 60–65% of the annual total, showing a significant upward trend in the interannual change. There is a trend of high temperature events starting early and ending late in recent 50 years, and this trend is intensified by the increasing urbanization. Due to geographical location and city scale, the frequency and intensity of high temperature events show difference among Beijing, Tianjin and Shijiazhuang cities. Variance analysis shows that climate warming contributes 20.2–25.5% to the variability of high temperature days, while urban heat island accounts for 14.7–24.2%, equal to the sum of the contributions from atmospheric circulation and solar activity.

To reveal the effect of Mountain Valley Breeze (MVB) and Sea Land Breeze (SLB) in winter on the spatial-temporal distribution of air pollutants in the Beijing-Tianjin-Hebei region, hourly data from Automatic Weather Stations (AWS) and hourly air pollutant concentration data in December 2016 from the China National Environmental Monitoring Center were used to calculate the average wind vector fields and PM2.5 concentration fields. The change rule of MVB and SLB and its influence on the distribution of PM2.5 concentration were analyzed. The prevailing factor for the MVB days was the southerly wind (valley wind) in the Beijing-Tianjin-Hebei region from noon to afternoon, this valley wind transports air pollutants from the eastern areas of the Taihang Mountains and southwestern areas of Beijing northward. In the evening, "herringbone" convergence lines formed between the emerging mountain breeze in the western and northern parts of Beijing, as well as in the piedmont areas of the Taihang Mountains, and the southerly wind. The PM2.5 concentration increased in Beijing, Langfang, Baoding, Shijiazhuang, and Xingtai according to the concentration of the convergence lines. For the SLB days, the PM2.5 concentration increased in the piedmont areas of the Taihang Mountains due to the influence of valley wind from noon to afternoon. For the MVB days, from noon until midnight, the sea breeze appeared in the eastern coastal areas and reached the southeastern part of Tianjin, the PM2.5 concentration increased towards the front of the sea breeze. The influence of MVB and SLB on the distribution of air pollutants in the heavy pollutions process were surveyed by analyzing the temporal variation relationship between the vertical distribution of wind over 0-325 m tower (at the Institute of Atmospheric Physics) and PM2.5 concentration of urban area, and by using the Cressman method to interpolate the 10 m wind data and PM2.5 concentration data to 2D grid field. From noon to afternoon, the air pollutants were blown to Beijing by valley wind. In the evening, the air pollutants converged near the convergence lines, which were formed by the mountain breeze and southerly wind. The severe pollution zone formed in the plains of Beijing and south of Beijing. From midnight to early morning, the air pollutants in Beijing were gradually blown away by the mountain breeze and stayed south of Beijing and northwest of Tianjin. In the winter, the effect of MVB on the recycling and accumulation of air pollutants plays an important role in severe atmospheric pollution incidents in Beijing, south of Beijing, and the eastern areas of the Taihang Mountains.

From May 3 to 5, 2017, a special heavy pollution event occurred in Beijing. The meteorological conditions associated with the heavy pollution were relatively special, so the pollution forms and causes were studied. The general characteristics of this pollution event were obtained based on data from 35 environmental monitoring stations in Beijing. Matching characteristics of PM10 and PM2.5 concentrations with ground wind field data from automatic weather stations closest to the environmental monitoring stations were analyzed. By using MODIS and CALIPSO data, the spatial distribution in the horizontal and vertical directions was obtained, and the transport paths and pollutant categories of the pollution were elucidated. The causes of the pollution were analyzed by using ECMWF ERA-Interim data and Wind Profiler radar data. It was hoped that the special morphological characteristics and influencing factors of the pollution could be obtained by means of ground-space monitoring technology combined with meteorological conditions. The results showed that pollution characteristics and constraints could be better reflected by stereo observations and comprehensive analyses based on the above multi-source data. The pollution started abruptly and dropped sharply, and the pollution process lasted for about 30 hours. The whole process was divided into the following three stages:the first half, intermittent period, and second half. The concentrations of PM10 and PM2.5 were high throughout the whole process, reaching to 600-1000 μg·m-3 and 200-700 μg·m-3, respectively. The causes of pollution in the first half and second half and the resulting PM10 and PM2.5 concentrations were different in terms of the spatial distribution. In the first half, the dominant wind direction was northwest wind, and the wind speed was small. The spatial difference of PM10 concentrations was also small, with concentrations more than 800 μg·m-3; meanwhile, the spatial difference of PM2.5 concentrations was great. The concentration of PM2.5 was high in the south and urban areas, reaching to 600-700 μg·m-3, and it was low in other places, reaching to 350-500 μg·m-3. During the intermission, the wind direction in the lower layer shifted from northwest wind to south wind, and the upper layer maintained northwest wind. The concentration of PM10 in the south and urban area decreased obviously to 650 μg·m-3, and the concentration of PM10 in the north remained at 800 μg·m-3. At this time, the concentration of PM2.5 in the north even dropped to 200 μg·m-3. The dominant wind returned to northwest wind in the latter half, and the wind speed increased sharply. At this time, the spatial difference of PM2.5 concentrations was small and the concentration of PM2.5 at the same station was less than that in the former half, ranging from 250 to 500 μg·m-3. The PM10 concentrations returned to the level of 800 μg·m-3. The pollution process involved mixed pollution consisting of haze and sand. Under the influence of westerly winds, the main contribution to Beijing pollution was dust-type PM10, while under southerly flows, the contribution to Beijing pollution was not only dust, but also PM2.5. Heavy pollution was accompanied by high wind speeds. The vertical motion of the atmosphere converged at an altitude of about 2-3 km, which resulted in the accumulation of pollutants at this altitude.

This study analyzes the statistical characteristics of the major pollutant distribution in 7 cities in the circum-Beijing region based on the daily PM2.5 concentration indices over the past 3 years, and Granger causality test is used to discuss the PM2.5 spillover effect within the region. The results indicate that significant regional differences exist in the PM2.5 concentration distribution in the circum-Beijing cities, with higher values in the southern cities than in the northern cities. Moreover, the PM2.5 concentrations in the different cities are the highest in the winter and at its lowest in summer, with a bimodal structure in the monthly distribution. The PM2.5 indices of the cities do not follow a normal distribution but present a skewed distribution with a sharp peak and heavy tail. Furthermore, strong inter-correlation and autocorrelation are found among the PM2.5 indices of the cities, indicating the regional consistency and strong continuity of the haze pollution in this region. A PM2.5 spillover effect exists in the circum-Beijing cities and present a seasonal difference. The spillover effect is most significant with a lag of 2–3 days and becomes weaker with increasing confidence levels. Additionally, cities close to each other easily formed a PM2.5 spillover in a short time period but additional accumulation time is required for distant cities to form a spillover effect.

This paper uses the temperature product of Modis by the Terra satellite. Isograms, anomaly value and indirect method are applied to analyze the daily variation, seasonal variation and annual variation of Beijing heat field. The results show that isograms are tidier in the evening compared with the day. And the temperature gradient is diminishing from the city center to the edge of urban areas. The heat island effect is the strongest during the day in the summer of all seasons during the day and night. The heat island in the evening of the four seasons is as obvious as the day in the spring and fall. The increasing temperature, the enhancing intensity and the expanding area of the heat island are the current situation of Beijing which are the result of the multiplying of the population. Because of the limited data, further studies are needed to study the seasonal variation and annual variation of Beijing.

In order to improve the skill of weather forecast and provide better meteorological service for Olympic Sailing Competition,high resolution numerical model system for Qingdao Olympic Sailing Competition(including forecast model and interpretation models) is developed. Based on Weather Research & Forecast(WRF) Model Version 3.0,a forecast model is set up with gird of 60×50×38 and horizontal resolution of 500 m.It takes about 1 hour and 20 minutes to produce 15-hour forecast on an IBM computer with 8 threads,which meets the requirement of operational forecast. Dynamic interpretations to the forecast results are carried out with the aid of a high-resolution Planetary Boundary Layer Model(PBLM) and an Urban Neighborhood Scale Model(UNSM)(with the horizontal resolution of 100 m and 10 m respectively). This model system runs continuously during the summer of 2008,and the model products are used in Qingdao Branch of Beijing Olympic Meteorological Service Center.Results show that,the model system is robust and practical,and performs quite well on the simulation of urban heat island and local circulations (e.g.,sea-land breeze and terrain/building effects).Analyses of numerical cases indicate that urbanization leads to urban heat island,increases sea-land temperature difference,and strengthens sea-breeze.Meanwhile the drag effect of urban building decreases the wind speed,and slows down the advance of seabreeze. The introducing of fine underlying surface data is critical to high resolution numerical simulation of local circulations(e.g.,sea-breeze). The dynamic interpretation to the forecast results with the aid of a high resolution PBLM with the horizontal resolution of 100 m indicates that,generally PBLM could simulate the wind field and the effects of surrounding terrain very well.The PBLM results are consistent with the observations from buoys and automatic weather stations,and have similar characteristics with Lidar observations.Further fine-scale dynamic interpretation in terms of UNSM with the horizontal resolution of 10 meters shows that UNSM could well simulate the wind in urban blocks.So the dynamic interpretations based on the terrain-following coordinate PBLM and the build-aware UNSM are effective to simulate the effect of local terrain and buildings on the wind in interest venues.

The first band (0.45-0.52 μm) of two landsat7/ETM high resolution images are used in an effort to assess spatial distribution of AOT (Aerosol Optical Thickness) by improved DTA (Different Texture Analysis) method. Atmospheric correction process for reference image and cloud mask process for two images were added to original DTA method. Retrieved AOT result is compared with MOD4_L2 products provided by NASA. Results showed that the concentration spatial distribution of AOT in Beijing can be preferably described quantitatively by improved DTA method. In the end, Limitations and improvements of the DTA method need to be done were discussed. © 2008 IEEE.

The first band of two landsat5/TM high resolution images are used in an effort to assess spatial distribution of aerosol by structure function method based on the contrast reduction method, and spatial distribution of the same day air visibility was calculated and analyzed in the greater Beijing area in the paper. In addition, a new judgment method of 'optimal distance index' was proposed. Results show that the concentration spatial distribution of aerosols in Beijing can be described semi-quantitatively by the structure function methods. In the end, the causations of the pollution were analyzed and the limitations and improvements need to be done were discussed.