2013年苏州春季一次重污染天气的过程分析

研究了2013年3月在江苏范围内的一次重污染天气过程，重点分析苏州在此次污染过程中大气污染的变化特征。污染过程中，苏州市颗粒物浓度上升较为明显， PM10的小时质量浓度最高达548μg／m3， PM2．5质量浓度也达到197μg／m3，污染持续时间为2 d，3月8—9日当地空气质量均达到中度污染水平。根据后向轨迹模型、颗粒物离子浓度的分析，此次污染是由外来浮尘及苏州本地污染物排放所造成的区域霾污染影响所致。根据监测结果与实际污染特征，针对性地提出了对策和措施。     

哈尔滨市秸秆焚烧大气污染负荷估算方法研究

本文针对近些年哈尔滨秋末冬初大气污染程度增加的溯源问题,基于静风污染气象及降雪对秸秆焚烧的影响等基本假设,采用箱式模型和优化拟合的方法对秸秆焚烧产生污染物的源强及其负荷进行了估算.通过对2015年和2017年典型时段数据的优化拟合得到降雪前重污染天气下PM₁₀排放源强分别为20.16、21.83 μg·m^-2·s^-1,CO的排放源强分别为149.32、138.65 mg·m^-2·s^-1;降雪后重污染天气下PM₁₀排放源强分别为15.98、7.09 μg·m^-2·s^-1,CO的排放源强分别为122.91、89.21 mg·m^-2·s^-1.由降雪前后各污染物的源强差得到2015年和2017年秸秆焚烧产生的PM₁₀的排放源强分别为4.18、14.74 μg·m^-2·s^-1,负荷分别为20.73%、67.52%;CO的排放源强分别为26.41、49.44 mg·m^-2·s^-1,负荷分别为17.69%、35.66%.本文为相关清单的研究提供了一种客观的校核方法,具有重要的社会、环境及现实意义.     

南海浮游植物对沙尘和灰霾添加的响应

通过2016年5月~6月在南海3个站位开展的船基围隔培养实验,研究了沙尘和灰霾添加对南海浮游植物生长和群落结构变化的影响.结果发现,沙尘和灰霾添加由于提供了N、P等营养盐,整体上促进了浮游植物的生长,且促进程度与添加量密切相关.通过定量计算营养盐指数和叶绿素a累积浓度,发现培养期间叶绿素a累积浓度与沙尘添加浓度呈显著正相关关系（R²=0.87,P<0.01）;低浓度灰霾添加的作用与沙尘添加类似（R²=0.91,P<0.01）,但当灰霾浓度增大时,叶绿素a累积浓度的增加受到一定程度的抑制,这可能与灰霾中含有较高含量的毒性物质有关.各粒级浮游植物叶绿素a浓度的变化表明,沙尘和低浓度灰霾添加使浮游植物优势种群由超微型向小型和微型转变;在高浓度灰霾添加组,由于营养盐与毒性物质的综合作用,浮游植物粒级结构变化不明显.超微型浮游植物细胞丰度测定结果表明,沙尘对聚球藻、原绿球藻和超微型真核浮游植物均表现出促进作用,高浓度灰霾添加能够抑制聚球藻和超微型真核浮游植物的生长.     

天津大气稳定度和逆温特征对PM2.5污染的影响

利用气象铁塔资料分析了逆温频率和强度,采用温差-风速法计算了天津地区大气稳定度,探讨了其相互关系及对PM_2.5浓度月均值和超标日的影响,并对一次重污染过程中大气稳定度和逆温分布特征进行了分析.结果表明,2015年9月~2017年8月A,B,C,D,E和F类大气稳定度发生频率依次为6.7%,11.4%,22.4%,46.1%,11.1%和2.2%,秋冬季节稳定类天气（E,F类）较多,全年白天各时段均以不稳定类大气为主,夜间大气稳定度以中性为主,秋冬季夜间稳定类高达30%~40%.观测期内冬季逆温频率最高,5：00~8：00和21：00~23：00超过90%,冬季逆温强度也最高.随着稳定类大气层结日数的增多,PM_2.5月均值和污染日数均有所增大,同时逐月PM_2.5均值、污染日发生频率均与逆温发生频率呈正相关关系.2016年12月16~21日的一次重污染天气过程显示,PM_2.5受到大气稳定度和逆温发展的影响,霾形成、雾-霾交替和消散等阶段大气稳定度和逆温特征具有显著的不同.大气持续趋于稳定及逆温强度的逐渐增大,对污染生成和维持起了非常重要的作用,污染过程中大气稳定度和逆温特征的精细化分析有助于提升重污染天气预报预警水平.     

Seasonal characteristics of haze observed by continuous visibility monitoring in the urban atmosphere of Kwangju, Korea

Continuous visibility monitoring has been carried out inKwangju, Korea since May 1999. The total light extinctioncoefficient b _ext measured by a transmissometer andreveals seasonal trends in urban visual air quality,especially under hazy conditions with a visual range of lessthan 15 km. Seasonal atmospheric visibility under lowrelative humidity during the winter was observed to be betterthan during any other seasons. Summertime visibility wasseverely degraded due to highly increased light scattering byhygroscopic particles under high humidity atmosphericconditions. Visibility during spring and fall was alsomoderate. However, yellow sand in spring caused the lowestvisibility conditions over the measurement area for a fewdays. With continuous monitoring using the transmissometer,the daily average seasonal visual range was measured to be13.1, 9.2, 11.0, and 13.9 km in spring, summer, falland winter, respectively. Under the atmospheric humiditycondition less than 60%, visual range was observed tobe 16.1, 13.9, 15.1, and 16.6 km in spring, summer,fall, and winter, respectively. The mean light extinctionbudget by sulfate and nitrate aerosols was determined to bethe highest value of 63.71% during the summer and thelowest value of 27.08% during spring. During the `yellow sand dust' period, a mean light extinction budget by soil particles was estimated to be at an unusually high value of 44.22%.     

谈双鸭山市气象特征及地形特征对空气质量的影响

双鸭山市采暖期污染较重是因为采暖期燃煤量大、低矮分散的面源多的缘故,同时气象因素及地形特征对空气质量的影响也起着相当大的作用,采用2005年空气质量监测数据,进行双鸭山市气象特征及地形特征对空气质量影响分析,得出空气较稳定的取暖期和具有山风、谷风、局地环流的地形特征是形成双鸭山市空气污染的主要原因之一,并针对地形特征及气象特征对双鸭山市空气质量的影响提出空气污染治理措施.     

昆明空气污染物等级与天气背景的关系

通过样本分析得出：昆明空气污染物主要为SO2,为轻度污染;秋冬季出现污染日较多,可分为高压脊控制型和偏西气流型天气背景。夏季出现污染日较少,主要为高压脊控制型天气背景;污染物浓度在一天中的分布特征呈“W”型;污染物预报方程。     

阿拉伯半岛沙尘时空分布特征及气象条件

通过对阿拉伯半岛地面90个气象站20年沙尘天气、AI指数及风场的时空分布特征结合气象条件分析，确定该区域沙尘天气的发生规律及传输路径.结果表明：永久多尘地区是半岛扬沙发生最频繁的区域，从2月开始扩张，并在6月达到最大.浮尘的时空分布与扬沙类似，但还存在希贾兹山脉北部和波斯湾沿岸的高值中心，3月与永久多尘地区的高值区连成一片，6~7月达到最大，然后开始收缩并分裂成几个小中心.AI指数存在一个像倒箭头的相对高值中心，其变化具有明显的单峰分布，强度和范围在6月最大.10~4月和5~9月半岛分别盛行顺时针、逆时针旋转的风向，它们将沙尘远程传输到下游地区.天气学分析表明，冷锋入侵半岛使得来自北方的冷空气快速锲入暖空气之下，是导致沙尘天气出现的主要原因；后向轨迹聚类分析显示，半岛存在3类传输路径，其重要性依次为西北方向撒哈拉沙漠的远程传输、反气旋风场将来自伊拉克或伊朗的沙尘远程传输和本地的沙尘源传输.     

电力污闪事故的气象条件分析

对1980-2003年长治市发生的26次严重电力污闪事故进行了调研分析.找出了其中主要电力污闪事故发生的成因及其时空分布特征.对诱发污闪的主要气象条件(浓雾、小雪、雨夹雪、毛毛雨、小雨、冰融等恶劣天气)和气象要素(相对湿度、气温、风向、风速、结冰、积雪等)进行了综合分析,给出了污闪事故发生的各类气象条件综合性判别指标以及事故发生可能性的分级预测标准,并提出了预防电力污闪的一些安全措施.     

DRY AND WET WEATHER FLOW NUTRIENT LOADS FROM A LOS ANGELES WATERSHED1

Effective watershed management requires an accurate assessment of the pollutant loads from the associated point and nonpoint sources. The importance of wet weather flow (WWF) pollutant loads is well known, but in semi‐arid regions where urbanization is significant the pollutant load in dry weather flow (DWF) may also be important. This research compares the relative contributions of potential contaminants discharged in DWF and WWF from the Ballona Creek Watershed in Los Angeles, California. Models to predict DWF and WWF loads of total suspended solids, biochemical oxygen demand, nitrate‐nitrogen, nitrite‐nitrogen, ammonia‐nitrogen, total Kjeldahl nitrogen, and total phosphorus from the Ballona Creek Watershed for six water years dating from 1991 to 1996 were developed. The contaminants studied were selected based on data availability and their potential importance in the degradation of Ballona Creek and Santa Monica Bay beneficial uses. Wet weather flow was found to contribute approximately 75 percent to 90 percent of the total annual flow volume discharged by the Ballona Creek Watershed. Pollutant loads are also predominantly due to WWF, but during the dry season, DWF is a more significant contributor. Wet weather flow accounts for 67 to 98 percent of the annual load of the constituents studied. During the dry season, however, the portion attributable to DWF increases to greater than 40 percent for all constituents except biochemical oxygen demand and total suspended solids. When individual catchments within the watershed are considered, the DWF pollutant load from the largest catchment is similar to the WWF pollutant load in two other major catchments. This research indicates WWF is the most significant source of nonpoint source pollution load on an annual basis, but management of the effects of the nonpoint source pollutant load should consider the seasonal importance of DWF.