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 10874_2008_9097_Article_IEq1.gif" alt="$${\text{NO}}_{\text{3}} ^ - $$" align="middle" border="0"> 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.     

1951—2007年辽宁省农业界限温度变化及其成因探讨

对1951-2007年辽宁省稳定通过0℃、10℃积温和持续日数变化进行了分析.结果表明:(1)近57年来辽宁省稳定通过0℃和10℃积温和持续日数呈递增趋势,其中稳定通过10℃积温和持续日数递增趋势更明显;(2)各标准气候期稳定通过0℃和10℃积温变化趋势不同,第1个和第2个30年标准气候期呈现递增趋势较弱,最近30年标准气候期呈显著递增趋势;各标准气候期稳定通过0℃和10℃持续日数均为递增趋势,而且后期递增较前期更为显著;(3)中部和沿海城市稳定通过0℃、10℃积温和持续日数递增趋势强于北部和西部;(4)城市化引起的热岛效应是观测到的稳定通过0℃、10℃积温和持续日数递增的重要原因,而区域气候变暖则进一步加强了递增趋势.     

地球系统10~(-1)年变化原因概述

我们曾论述了地球系统100～108年变化的原因[1], 唯对10-1年（月一年）的地球系统变化未指出其变化原因。经过近年的研究, 现在可以明确地指出, 地球内部有两类流体： 地外核和岩石圈裂隙中地下流体（地气）的活动是引发10-1年地球系统变化的原因。外核的上升运动会使其上部岩石圈产生上抬和压缩, 在地表层就出现3.2 m地温升高和降水减少的“干热异常”, 经过“孕震三步曲”最终引发构造地震。外核的下降运动会使其上部岩石圈产生下沉和拉张作用, 地表层表现为3.2 m地温降低, 同时降水增多的“湿冷异常”, 最终可导致发生陷落地震。外核的脉冲运动是引发岩石圈中形成地热（冷）涡的“源”。地气环流也是旋转地球上的一种流体运动, 其特征速度（地下风速）约为0.2 m/s, 据此可推得“自然气候周期”约为8个月。地气环流是使地气系统得以“流”的动力源。地热（冷）涡的“源”、 “流”相结合是使短期气候呈现纷繁复杂变化的原因。     

北京城区夏季污染建立、维持、消退的天气过程

利用北京城区污染观测站2006~2013年夏季可吸入颗粒物PM10逐日浓度检测资料,挑选所有PM10浓度大于150μg/m~3的个例,合成分析华北及北京地区风场变化情况,发现风速在污染当天变化不明显,南风与PM10的相关性普遍为正,污染当天各区南风增加较大,太行山一带甚至增长了5倍。南风异常可能会使河北、山东等地污染物向北京输送,造成北京大气污染。同时我们分析北京夏季空气污染时大气环流特征。在500 h Pa与200 h Pa,北京和内蒙古上空有显著的高压异常。在850 h Pa,环流场表现为东正西负的高度场异常,其中北京在正负异常分界线上。低层气压梯度异常会造成北京和以南地区南风异常。同时,我们发现北京污染天气伴随的高空环流异常具有准定常特征。在污染前4天,蒙古上空存在一个显著的高层高压异常。该高压异常增强并向南延伸,在污染当天控制北京和内蒙古。在污染消退期,该异常也逐渐消退。但在消退后第四天,北京和内蒙古上空依然受高压异常控制。这表明北京夏季污染和高空准定常环流异常有关。     

沈阳冬夏季可吸入颗粒物浓度及尺度谱分布特征

利用沈阳大气成分监测站颗粒物监测仪 (Grimm 180) 连续测得的夏季 (2006年8月)、冬季 (2006年12月和2007年1月) 可吸入颗粒物的数浓度和质量浓度数据, 分析了沈阳市可吸入颗粒物浓度日变化、谱分布及污染特征, 在此基础上结合沈阳市常规气象资料, 分析了气象要素和颗粒物污染之间的关系。结果表明:沈阳市冬、夏季部分时段可吸入颗粒物浓度存在明显的日变化和日际变化; 谱分布较好地符合Junge分布; 沈阳冬季PM₁₀超标日数占冬季观测总天数的77%, PM_2.5超标日数 (按美国EPA日均标准) 占冬季观测总天数的87%, PM₁₀平均数浓度为6668.7个/cm3, 平均质量浓度达252.8μg/m3, 分别是夏季的3.0和2.4倍; 冬、夏季PM_2.5/PM₁₀平均质量分数分别为0.647和0.603, PM_2.5占可吸入颗粒物总数量的99%以上; 浓度变化在很大程度上受到各种气象要素的影响, 与温度、风速负相关, 与湿度正相关, 降雨、降雪过程使得颗粒物浓度明显降低, 近地层逆温和雾是颗粒物增多的一个重要因素。颗粒物污染对城市能见度影响较大。     

离岸型背景风和海陆风对珠江三角洲地区灰霾天气的影响

不同类型的背景场和海陆风对珠江三角洲地区大气灰霾有不同的影响。作者主要利用空气质量模式系统Models-3 (MM5/SMOKE/CMAQ)研究离岸型背景风和海陆风对珠江三角洲一次灰霾天气的影响, 结果表明, 中尺度气象模式MM5对珠江三角洲地区这次灰霾天气的气象模拟, 较好地模拟了风速和风向的变化。多尺度空气质量模式CMAQ模拟出了PM10浓度的变化, 与观测值比较一致。在这次灰霾天气过程中, 由于离岸型背景风与陆风风向一致, 在陆风维持的情况下, 内陆源区的PM10被输送到沿海地区, 导致沿海城市和海面上PM10浓度比较高; 而在海风维持的情况下, 海风与离岸型背景风方向相反, 造成海风较小, 致使整个珠江三角洲地区灰霾天气都比较严重。敏感性试验结果表明离岸背景风和海陆风的相互作用对灰霾天气的生成与分布有重大的影响。