2007和2008年夏季北京奥运馆大气PM10与PM2.5质量浓度变化特征

为了监测北京奥运主场馆附近大气颗粒物的污染状况以及评估奥运污染源减排措施对北京大气颗粒物质量浓度变化的影响,利用颗粒物在线监测仪器TEOM于2007年和2008年夏季,在奥运主场馆附近的中国科学院遥感应用研究所办公楼楼顶对大气颗粒物PM₁₀和PM_2.5进行了连续同步观测。结果表明,2007年夏季监测点附近大气PM₁₀与PM_2.5质量浓度的平均值分别为153.9和71.2μg·m^-3,而2008年夏季PM₁₀与PM_2.5质量浓度的平均值分别为85.2和52.8μg·m^-3。与奥运前一年同时段相比,奥运时段大气PM₁₀和PM_2.5的质量浓度分别下降44.5%和25.1%。对比分析奥运前后的2次典型污染过程发现,空气相对湿度的增加和偏南气流输送的共同影响易造成大气颗粒物的累积增长,而降雨的湿清除作用和偏北气流则会使大气颗粒物浓度迅速降低。在相近的气象条件下,奥运前后的污染过程中,大气细粒子的日均增长速率分别为25.1和13.9μg·m^-3·d^-1,而大气粗粒子的日均增长速率分别为20.8和2.2μg·m^-3·d^-1,奥运时段污染累积过程中大气粗、细粒子的增长速率分别显著低于和略低于奥运前同时段污染过程中颗粒物的增长速率。污染源减排措施的实施是奥运期间大气颗粒物质量浓度降低的主要原因,从控制效果来看,奥运期间实施的污染源减排措施对大气粗粒子的控制效果明显好于大气细粒子。     

Origin and source regions of PM10 in the Eastern Mediterranean atmosphere

A set of daily PM₁₀ (n = 281) samples collected from April 2001 to April 2002 at a rural site (Erdemli), located on the coast of the Eastern Mediterranean, were analyzed applying Mass Closure (MC), absolute principal factor analysis (APFA) and Positive Matrix Factorization (PMF) to determine source contributions. The results from the three techniques were compared to identify the similarities and differences in the sources and source contributions. Source apportionment analysis indicated that PM₁₀ were mainly originated from natural sources (sea salt + crustal ≈ 60%) whilst secondary aerosols and residual oil burning accounted for approximately 20% and 10% of the total PM₁₀ mass, respectively. Calculations for sulfate showed that on average 8% and 12% of its total concentration were originated from sea salt and biogenic emissions, respectively. However, the contribution by biogenic emissions may reach up to a maximum of ~ 40% in the summer. Potential Source Contribution Function (PSCF) analysis for identification of source regions showed that the Saharan desert was the main source area for crustal components. For secondary aerosol components the analysis revealed one source region, (i.e. the south-Eastern Black Sea), whereas for residual oil, Western Europe and the western Balkans areas were found to be the main source regions.     

东胜区污染状况分析

文章利用东胜区主要污染源排放情况以及2005年9月1日-2007年8月31日每日SO2、NO2、PM10浓度监测值和2005-2007年SO2、NO2、PM10平均值,分析了东胜区主要污染类型、污染物来源以及东胜区近3年SO2、NO2、PM10监测值日、月、季、年分布特征和变化规律;提出了消减大气污染物排放的对策。     

大同基准站PM10监测项目简析

本文首先介绍了PM10的基本概念以及对人体的危害和国际社会对其的重视程度,简单阐述了大同基准站PM10监测设备的基本原理,详细分析了PM10监测扬沙和烟尘等视程障碍天气的能力,用数据曲线清晰的展示出了PM10值在沙尘天气下的变化。同时详细分析了PM10值变化与能见度值的相互关系,通过分析提出了在一定的天气下可以参考PM10的值来判断能见度的观点。本文总结了PM10值与沙尘天气和能见度的关系。     

Vertical Observations and Analysis of PM2.5, O3, and NOx at Beijing and Tianjin from Towers during Summer and Autumn 2006

During the period between 18 August and 22 September 2006, an ultraviolet photometric O3 analyzer, a NO-NO2-NOx chemiluminescence analyzer, and a quartz micro-oscillating-scale particle concentration analyzer were simultaneously used for monitoring at three different heights each at Beijing (325-m tower) and Tianjin (255-m tower). These towers belong to the Institute of Atmospheric Physics (IAP) of the Chinese Academy of Sciences (CAS) and to the Tianjin Municipal Meteorological Bureau, respectively. These measurements were used to continuously measure the atmospheric O3 and NOx volume-by-volume concentrations and the PM2.5 mass concentration within a vertical gradient. When combined with meteorological data and information on the variation of vertical characteristics of the various atmospheric pollutants in the two cities, analysis shows that these two cities were seriously polluted by both PM2.5 and O3 during summer and autumn. The highest daily-average concentrations of PM2.5 near the ground in Beijing and Tianjin reached 183 μg m-3 and 165 μg m-3, respectively, while the O3 concentrations reached 52 ppb and 77 ppb, and NOx concentrations reached 48 ppb and 62 ppb for these two cities, respectively. The variations in the daily-average concentrations of PM2.5 between Beijing and Tianjin were demonstrated to be consistent over time. The concentrations of PM2.5 measured in Beijing were found to be higher than those in Tianjin. However, the overall O3 concentrations near the ground in Tianjin were higher than in Beijing. NOx concentrations in Tianjin were consistently lower than in Beijing. It was also found that PM2.5 pollution in Beijings atmosphere may also be affected by the pollutants originating in and delivered from Tianjin, and that Ti     

脱硝技术与天然气应用情景下京津冀地区空气质量模拟评估

针对京津冀地区主要大气污染物NOx（氮氧化物）和PM2.5（大气中粒径小于或等于2.5μm的颗粒物）,应用柴油车尾气净化技术及中小锅炉烟气脱硝技术,并根据2015年和2030年我国能源规划,设计3种技术应用情景,采用WRF-CAMx耦合模式,对京津冀地区大气中NOx和PM2.5进行了应用情景模拟。结果表明,单独应用柴油车尾气净化技术后（方案1）,北京、天津地区大气中的NOx浓度降低幅度达20%,河北地区降低5%;PM2.5的浓度降低幅度约10%;应用柴油车尾气净化技术和2015年能源规划情景（方案2）,京津冀地区大气中NOx和PM2.5浓度的降低幅度均超过20%;应用柴油车尾气净化技术和2030年能源规划情景（方案3）,该地区NOx浓度降低幅度与之相当,PM2.5浓度降低幅度超过30%。可见脱硝技术和清洁能源利用的有效性依赖于其应用比例。二次气粒转化的化学过程形成的硝酸盐、硫酸盐和铵盐对该地区空气中PM2.5浓度的贡献很大,冬、春、秋季硝酸盐最大贡献高达60%,夏、秋季硫酸盐最大贡献超过70%,铵盐四季最大贡献约25%。这说明PM2.5的主要前体物NOx、SO2、NH3、VOCs (Volatile Organic Compounds)、CO等均大幅度削减才能有效降低该地区空气中PM2.5浓度。     

10～30 d延伸期预报研究进展和业务现状

10～30 d延伸期预报在气象业务发展和国民经济服务中具有重要的作用。本文回顾了关于延伸期预报的相关理论和技术研究进展,概要介绍了国内外开展延伸期预报业务现状;结合目前气象科技发展水平,进一步分析和阐述了现阶段我国开展延伸期预报业务亟待解决的关键技术问题,旨在共同探讨和推动延伸期预报业务的发展。     

Particle composition and size distributions in and around a deep-pit swine operation,Ames, IA

The contribution of emissions from agricultural facilities is rapidly becoming a major concern for local and regional air quality. Characterization of particle properties such as physical size distribution and chemical composition can be valuable in understanding the processes contributing to emissions and ultimate fate of particulate matter from agricultural facilities. A measurement campaign was conducted at an Iowa, deep-pit, three-barn swine finishing facility to characterize near-source ambient particulate matter. Size-specific mass concentrations were determined using minivol samplers, with additional size distribution information obtain using optical particle counters. Particulate composition was determined via ion chromatographic analysis of the collected filters. A thermal-CO₂ elemental/organic carbon analyzer measured particulate carbon. The chemical composition and size distribution of sub-micron particles were determined via real-time aerosol mass spectrometry. Primary particulate was not found to be a major emission from the examined facility, with filter-based impactor samples showing average near-source increases (~15–50 m) in ambient PM₁₀ of 5.8 ± 2.9 μg m⁻³ above background levels. PM_2.5 also showed contribution attributable to the facility (1.7 ± 1.1 μg m⁻³). Optical particle counter analysis of the numerical size distributions showed bimodal distributions for both the upwind and downwind conditions, with maximums around 2.5 μm and below the minimum quantified diameter of 0.3 μm. The distributions showed increased numbers of coarse particles (PM₁₀) during periods when wind transport came from the barns, but the differences were not statistically significant at the 95% confidence level. The PM₁₀ aerosols showed statistically increased concentrations of sulfate, nitrate, ammonium, calcium, organic carbon, and elemental carbon when the samplers were downwind from the pig barns. Organic carbon was the major constituent of the barn-impacted particulate matter in both sub-micron (54%) and coarse size (20%) ranges. The AMS PM₁ chemical speciation showed similar species increases, with the exception of and Ca⁺², the latter not quantified by the AMS.     

Satellite and ground observations for large-scale air pollution transport in the Yellow Sea region

Large-scale air pollution transport (LSAPT) in the Yellow Sea region and their inflow onto the Korean Peninsula were observed through satellite images and ground measurements. LSAPT includes regional continental air-masses saturated with pollutants originating from China and subsequently landing on or passing through the Korean Peninsula. It is also possible to identify the distribution and transport patterns of LSAPT over the Yellow Sea. The ground concentrations for PM10, PM2.5 and CO measured at Cheongwon, located in the centre of south Korea, were compared with NOAA satellite images. Notably, the episodes observed of the LSAPT show a PM2.5 to PM10 ratio of 74% of the daily maximum concentrations. However, cases of duststorms were clearly distinguished by much higher PM10 concentrations and a ratio of 30% of PM2.5 to PM10 for daily maximum concentrations. For the episode on January 27, 2006, the inflow of a regionally polluted continental air-mass into the central and southwestern regions of the Korean Peninsula was observed sequentially at various ground observatories as well as by satellite. The north airflow dissipated the clouds over Mt. Halla on Jeju Island and further downwind, reducing air pollution and creating a von Kármán vortex.     

Current status of ENSO prediction skill in coupled ocean–atmosphere models

The overall skill of ENSO prediction in retrospective forecasts made with ten different coupled GCMs is investigated. The coupled GCM datasets of the APCC/CliPAS and DEMETER projects are used for four seasons in the common 22 years from 1980 to 2001. As a baseline, a dynamic-statistical SST forecast and persistence are compared. Our study focuses on the tropical Pacific SST, especially by analyzing the NINO34 index. In coupled models, the accuracy of the simulated variability is related to the accuracy of the simulated mean state. Almost all models have problems in simulating the mean and mean annual cycle of SST, in spite of the positive influence of realistic initial conditions. As a result, the simulation of the interannual SST variability is also far from perfect in most coupled models. With increasing lead time, this discrepancy gets worse. As one measure of forecast skill, the tier-1 multi-model ensemble (MME) forecasts of NINO3.4 SST have an anomaly correlation coefficient of 0.86 at the month 6. This is higher than that of any individual model as well as both forecasts based on persistence and those made with the dynamic-statistical model. The forecast skill of individual models and the MME depends strongly on season, ENSO phase, and ENSO intensity. A stronger El Niño is better predicted. The growth phases of both the warm and cold events are better predicted than the corresponding decaying phases. ENSO-neutral periods are far worse predicted than warm or cold events. The skill of forecasts that start in February or May drops faster than that of forecasts that start in August or November. This behavior, often termed the spring predictability barrier, is in part because predictions starting from February or May contain more events in the decaying phase of ENSO.