冬季沈阳市典型源排放PM_(10)浓度分布模拟分析

选取沈阳市7个典型的大气污染源2006年12月~2007年2月的PM10排放浓度资料,利用CALPUFF对PM10浓度月平均分布做模拟分析。模拟结果分析表明:冬季月平均PM10浓度分布的范围与风场、地形有直接的关系。地势平坦、风速大时,污染物扩散范围大,污染物浓度小;地势不平、风速小时,污染物扩散范围小,污染物浓度大。1月份是沈阳市冬季月平均大气污染最严重的月份,污染物分布主要集中在市区的北部、东部和南部地区,东部地区大气污染最为严重。     

丽水市大气污染的时空分布特征及与气象要素的相关分析

利用2007-2010年丽水市逐日大气污染物浓度数据和地面气象观测资料,对PM10、SO2、NO23种大气污染物浓度进行了时空分布特征研究,进而探讨了气象要素对大气污染的影响.结果表明:2007-2010年,丽水市主要的3种大气污染物的负荷为PM10＞ NO2 ＞SO2,影响大气环境质量的污染物以PM10为主；总体来说,NO2的月均浓度基本达到《环境空气质量标准》(GB3095-1996)-级标准,冬半年(9-12月、1-2月)SO2的月均浓度仅达到二级标准,而夏半年(3-8月)月均浓度基本达到一级标准,PM10的月均浓度都达到二级标准(0.10 mg/m3)；在空间分布上,PM10、NO2的年均浓度都表现出东向西逐渐减少的特征,而SO2年均浓度主要体现为南向北递增的特征,3种大气污染物在空间上都表现为在东部缙云、青田等地的污染相对严重,而在西面的遂昌、龙泉等地的污染程度较轻;各种气象要素对大气污染的影响中,除了气压与3种大气污染物的浓度呈极显著正相关外,其他气象要素都表现为负相关,只是影响程度有所差异.气象要素对大气污染的影响不是单一作用的,而是通过多种气象要素相互配合、相互作用、综合反应来产生作用的.     

杭州城市可吸入颗粒物污染与气象条件关系的分级评价和BP神经网络评估

为了解杭州城市环境空气质量与气象条件之间的关系,利用杭州市区2003-2007年的可吸入颗粒物(PM10)浓度数据和气象资料,通过分级评价的方法和基于BP神经网络的污染物浓度评估模型,得到PM10浓度与气象条件的对应关系.结果表明,随着日降水量的增大,PM10浓度减小;风速与PM10浓度呈明显的负相关,随着风速的增大,PM10浓度明显减小;气象因素与PM10浓度之间呈非线性关系,大气能见度对PM10和相对湿度的变化极为敏感.随着PM10浓度的增大,大气能见度迅速降低,相对湿度越高,大气能见度则越低;近几年杭州市气象条件不利于大气污染物的扩散和清洗,是杭州城市环境空气质量上升缓慢的主要原因之一.     

夏季局地环流对京津冀区域大气污染影响

为研究局地环流对京津冀大气污染分布特征的影响,利用京津冀区域大气污染监测网7个站点的大气污染物浓度观测资料,结合WRF数值模式对气象场的模拟结果,对区域夏季局地环流对大气污染浓度水平和空间分布特征进行了研究。结果表明,2010年6月局地环流发生时,京津冀大气中PM10的平均浓度可高达156.4μg/m3,而在强天气系统过境时仅为89.1μg/m3。京津冀受区域局地环流控制时,大气中可吸入颗粒物比强天气系统过境时高75%;海-陆风回流携带的高浓度污染物,导致海滨区域夜间大气中PM10平均浓度从46.2μg/m3上升到64.7μg/m3;山地-平原风导致京津冀大气本底区域河北兴隆臭氧浓度峰值较北京城区滞后3 h。京津冀近年来强天气过程比例逐渐下降,目前仅占月20%,而以山地-平原风和海-陆风叠加的局地环流气象条件占比增加,造成京津冀区域大气污染易聚难散。     

西安市灰霾天气下PM_(2.5)浓度与气象条件分析

分析了2013年1—3月西安市12个空气监测子站监测的PM10、PM2.5以及相关气象参数;绘制了不同月的主城区浓度分布等值线图。运用单样本K-S非参数检验法检验表明,PM2.5浓度符合对数正态分布;各站点间的PM2.5浓度相关性非常高,变化趋势一致;PM10和PM2.5的变化规律呈现"W"型三峰分布;PM2.5日均值与能见度、净辐射量、平均气温、最高气温、最低气温均呈现显著负相关,且相关性较强;与平均湿度、最大湿度、最小湿度呈现显著正相关;与总辐射量、日照时数、气压、露点温度的相关性较弱;节日烟花燃放、沙尘天气容易造成严重大气污染,其中节日烟花燃放、沙尘天气对PM10的贡献量大于对PM2.5的贡献。     

湖北省大气颗粒物浓度时空变化特征

了解大气颗粒物浓度的时空变化格局对于大气污染防治、预警预报等具有重要理论和实践意义。根据2015年1月至2015年12月湖北省13个主要城市53个监测站点每小时发布的PM10和PM2.5浓度数据,研究了湖北省大气颗粒物浓度时空变化特征。结果表明,PM10和PM2.5浓度在空间上均呈现出鄂西部最低、中部最高、东部居中、南北向的均质性;时间上各城市均呈现颗粒物浓度随着月份变化先降低后升高,1月份最高,8月份最低,且呈现夏季浓度秋季浓度春季浓度冬季浓度的变化规律。分析表明,湖北省大气颗粒物浓度时空变化特征与降水量、气温等气象因子呈现出显著负相关关系,与风速关系不显著;与其来源中建筑施工面积、机动车保有量、货运量和客运量、人均GDP和人均第二产值关系紧密。     

不对称街谷内PM_(2.5)浓度垂直分布特征及成因

当前细颗粒物PM2.5已成为城市环境的主要污染物,研究城市不对称街谷内PM2.5浓度的垂直分布特征,对居民日常生活与健康出行有现实意义。实验选取2013年3个不同阶段对高度在1~35 m范围的街谷进行PM2.5浓度监测,同时引用街谷内流场模型与浓度场模型,对PM2.5浓度垂直分布特征及成因进行探究。结果表明,不对称街谷受大气对流、风速、风向影响,街谷内细颗粒物存在不均匀分布特点,在较高侧随着壁面高度的增加PM2.5浓度大体呈"S"型曲线变化。同时在同一阶段监测的4天中街谷内PM2.5浓度分布特征大体一致,而阶段之间差异明显;街谷内PM2.5浓度垂直分布的最高浓度差出现在阶段1,高达75μg/m3,阶段2与阶段3浓度差相对减弱,仅在20~30μg/m3之间。通过阶段2与阶段3对比可知,北京冬季供暖燃煤对大气细颗粒物的贡献较大,导致颗粒物浓度偏高;而非采暖期气温回升,大气对流作用较强,有助于大气颗粒物扩散,因而街谷内PM2.5污染程度相对较低。     

和田绿洲沙尘暴强度及其影响因子分析——以墨玉县为例

以和田绿洲西北部的墨玉县为研究区域,对该地区2016—2018年发生的沙尘暴天气资料以及气象因子(气温、风速、湿度、气压、水汽压、日照时数)和大气污染物(PM2.5、PM10、SO2、NO2、CO、O3)进行分析。结果表明,墨玉县沙尘暴天气主要发生在春夏季(3—8月),平均占全年发生频率的77.12%。墨玉县沙尘暴强度主要由气象因子决定,特别是气温、风速、日照时数和湿度。沙尘暴强度与大气颗粒物(PM10和PM2.5)存在着明显的间接关系,主要因为两者均受风速影响较大,沙尘暴强度越大,大气中PM10和PM2.5浓度越高。沙尘暴强度与SO2、NO2、CO、O3等大气污染物的关系非常微弱,但O3与沙尘暴的形成季节比较一致。     

成都市大气颗粒物污染特征及与气象因子的关联性分析

利用成都市2013年6月至2014年5月的PM10和PM2.5浓度监测数据,分析大气颗粒物污染特征,并探讨其与气温、相对湿度、降雨、风向、风速等气象因子的关联性。结果表明:成都市大气PM2.5污染较严重;PM10和PM2.5浓度及超标率均表现为冬季秋季春季夏季,秋季和冬季为大气颗粒物污染高发期;PM2.5对PM10贡献显著;气温超过10℃时,PM10和PM2.5最高浓度大体随气温升高而降低;相对湿度为40%~80%时,PM10和PM2.5浓度随相对湿度增加而升高;相对湿度超过80%时,易发生降雨,PM10和PM2.5浓度降低;降雨对PM10的清除量高于PM2.5,但降雨后PM10和PM2.5浓度较快回升;PM10和PM2.5浓度在偏西风下高于其他风向;PM10主要受局地源影响,而PM2.5主要受西北方向上的外来源影响。     

哈尔滨市大气污染与气象因素的相关性分析

为了研究哈尔滨市大气污染特征以及气象要素对大气污染的影响,对哈尔滨市2013年采暖期及非采暖期内4种大气污染物(二氧化硫SO_2、二氧化氮NO_2、可吸入颗粒物PM_(10)、细颗粒物PM_(2.5))日均浓度分布特征以及日均浓度与部分地面气象要素(风速、气温、气压、相对湿度)相关性进行研究。提出哈尔滨市4种大气污染物日均浓度均符合对数正态分布。采暖期和非采暖期内4种大气污染物浓度与地面气象要素的相关性存在显著差异。采暖期内,4种污染物浓度与风速显著负相关,与风速相关系数最高达-0.639;与气压和相对湿度正相关。非采暖期内,4种大气污染物均与相对湿度呈负相关,相关系数为-0.5左右,与其他3种气象要素相关性普遍不高。全年4种污染物中仅有SO_2与气温呈较好负相关,相关系数为-0.4。     

First assessment of the PM<Subscript>10</Subscript> and PM<Subscript>2.5</Subscript> particulate level in the ambient air of belgrade city

INTENTION, GOAL, SCOPE, BACKGROUND: As the strong negative health effect of exposure to the inhalable particulate matter PM10 in the urban environment has been confirmed, the study of the mass concentrations, physico-chemical characteristics, sources, as well as spatial and temporal variation of atmospheric aerosol particles becomes very important. OBJECTIVE: This work is a pilot study to assess the concentration level of ambient suspended particulate matter, with an aerodynamic diameter of less than 10 microm, in the Belgrade central urban area. Average daily concentrations of PM10 and PM2.5 have been measured at three representative points in the city between June 2002 and December 2002. The influence of meteorological parameters on PM10 and PM2.5 concentrations was analyzed, and possible pollution sources were identified. METHODS: Suspended particles were collected on Pure Teflon filters by using a Mini-Vol low-volume air sampler (Airmetrics Co., Inc.; 5 l min(-1) flow rate). Particle mass was determined gravimetrically after 48 h of conditioning in a desiccator, in a Class 100 clean room at the temperature T = 20 degrees C and at about 50% constant relative humidity (RH). RESULTS AND DISCUSSION: Analysis of the PM10 data indicated a marked difference between season without heating--(summer; mean value 56 microg m(-3)) and heating season--(winter; mean value 96 microg m3); 62% of samples exceeded the level of 50 microg m(-3). The impact of meteorological factors on PM concentrations was not immediately apparent, but there was a significant negative correlation with the wind speed. CONCLUSIONS: The PM10 and PM2.5 mass concentrations in the Belgrade urban area had high average values (77 microg m(-3) and 61 microg m(-3)) in comparison with other European cities. The main sources of particulate matter were traffic emission, road dust resuspension, and individual heating emissions. When the air masses are coming from the SW direction, the contribution from the Obrenovac power plants is evident. During days of exceptionally severe pollution, in both summer and winter periods, high production of secondary aerosols occurred, as can be seen from an increase in PM2.5 in respect to PM10 mass concentration. RECOMMENDATION AND OUTLOOK: The results obtained gave us the first impression of the concentration level of particulate matter, with an aerodynamic diameter of less than 10 microm, in the Belgrade ambient air. Due to measured high PM mass concentrations, it is obvious that it would be very difficult to meet the EU standards (EEC 1999) by 2010. It is necessary to continue with PM10 and PM2.5 sampling; and after comprehensive analysis which includes the results of chemical and physical characterization of particles, we will be able to recommend effective control measures in order to improve air quality in Belgrade.     

Spatial and seasonal variation of PM10 mass concentrations in Taiwan

Hourly data of PM10 concentration collected from an air quality-monitoring network has been analyzed over Taiwan from 1994 to 1999. Fourteen sites from 72 monitoring stations were selected to evaluate the spatial and seasonal variations in the regions of north, southwest, south, east and National Park. The selected monitoring sites are located in a suburban environment, except Nantz and Linyuan that are located in industrial areas. Moreover, Yangming and Hengchuen are located in National Park. Spatial and seasonal variations of PM10 concentrations are rather large over Taiwan. Annual average in south is approximately six times higher than in National parks. In northern sites, the highest concentration occurs in March–May, which is attributed to the occurrence of dust storms in arid regions of central Asia and the transport of dust by northeasterly monsoon. A marked seasonal variation of PM10 concentrations can be observed both in southwestern and southern regions. The pattern is characterized by high concentrations in winter and low in summer. Appearance of the highest monthly PM10 concentration in winter of south may be in part due to the lowest number of monthly precipitation days and low temperature, both of which occurred in winter. The frequency of PM10 daily mean concentration for exceeded 150 μg m⁻³ is 15% during winter in south, which reflects the serious pollution problem there. Monitoring sites in National Park are representatives of remote environments, but the PM10 concentrations are still affected by the dust storms and human activities.     

Respirable particulate matter at an urban and nearby industrial location: concentrations and variability and synoptic weather conditions during high pollution episodes

Continuous data of the concentration measurements of respirable suspended particulates (PM10, particles with aerodynamic diameter smaller than or equal to 10 pm) were analyzed. These measurements were carried out at an urban and nearby industrial location in northern Greece for the 5-year period 1996-2000. The time series concentration trend was examined, the seasonal and diurnal variations were identified, and the lognormality of the daily mean concentration data sets was tested. Over the 5-year data-gathering period, the days on which the U.S. Environmental Protection Agency (EPA) 24-hr PM10 standard was exceeded (episode days) were identified and their relation to prevailing synoptic-scale meteorological conditions was studied. The analysis led to useful information concerning the air quality levels, the contribution of the main pollution sources in this area, as well as some of the mechanisms that influence the PM10 concentrations. It also was proved that the measured PM10 concentrations are a result of a combination of processes including local anthropogenic sources, mesoscale transport, and resuspension. A complex system of sources and meteorological conditions modulate the heavy particulate pollution in the area of interest.     

Estimating the contribution of industrial facilities to annual PM10 concentrations at industrially influenced sites

If measures to reduce the industrial discharge of PM10 shall be planned with high accuracy, a first step must be to estimate the contribution of single industrial facilities to the overall PM10 burden as accurately as possible. In northern Duisburg as an example, an area where iron and steel producing industry is concentrated, PM10 was measured at 4 sampling sites very close to an industrial complex of blast furnaces, a sinter plant, oxygen steel works and a coke oven plant for 9 months in 2006. At two sites metals in PM10 were determined. The results, together with analytical data of urban background sites in the region and data of wind direction and wind speed were used for an estimation of the contribution of single plants to the PM10 burden. A careful analysis of the data showed, that the data of PM10, calcium, iron and zinc measured at two sites close to the industrial area and information about the urban background aerosol were sufficient to calculate the PM10 contribution of the main single plants. The data could be compared with those of modelling.     

Surface Air Pollutant Concentration Frequency Distributions: Implications for Urban Modeling

With the development of ambient air quality standards (AAQS), the need arises to describe the characteristics of regional surface air-pollutant concentration frequency distributions. In the evaluation of land use plans, numerous agencies will be concerned with evaluating the effectiveness of emission zoning and/or control actions. On a regional basis, one means of performing this assessment lies in determining the changes in the pollutant frequency distributions resulting from control actions.

This study presents new data concerning the surface air-pollutant concentration frequency distributions observed for area sources and continuous point sources, and compares these distributions with those of the pertinent meteorological variables describing the transport and diffusion of the pollutant. The observed surface air pollutant frequency distributions are compared to those corresponding to simple modeling concepts from either an urban area source or a continuous point source. For an urban source and a relatively inert pollutant like CO, we found that the observed frequency distribution for CO surface air concentration parallels the approximately log-normal frequency distribution of the reciprocal of the wind speed. We show that the constant relating these two well-correlated frequency distributions can be determined either experimentally or with a numerical simulation model of air pollution. The usefulness of numerical models in air pollution is discussed.     

A study on variations of concentrations of particulate matter with different sizes in Lanzhou,China

Lanzhou is one of the most air-polluted cities in China and in the world, and its primary air pollutant is particulate matter (PM). Different size particulate matter (TSP, PM₁₀, PM_2.5 and PM_1.0) have different sources and affect the environment and human health differently, so it is very important to study the pollutant characteristics of different particles in order to deeply understand the pollution situation of Lanzhou city and establish reasonable preventive countermeasures. TSP, PM₁₀, PM_2.5 and PM_1.0 concentrations were simultaneously measured in Lanzhou to detect the annual and diurnal variations of concentrations of PM with different sizes and possible causes. The main results are as follows: (1) The annual distribution of monthly average concentrations for coarse particles (TSP and PM₁₀) is bimodal with the highest peak in April, which is different from the situation in other cities not affected by sand-dust events. However, the annual distribution for fine particles (PM_2.5 and PM_1.0) is unimodal with the peak in December. This difference between coarse and fine particles indicates that sand-dust events in spring carry much more coarse than fine particles to Lanzhou. This result is supported by the correlation between springtime wind speed and concentrations of PM with different sizes. (2) Under normal conditions (without dust intrusions), the diurnal distribution of coarse particle concentration in Lanzhou is bimodal. However, the distribution is trimodal during dust intrusions in April, with an extra peak in the afternoon. (3) In general, the highest concentration peaks of the diurnal variations for TSP, PM₁₀, PM_2.5 and PM_1.0 occur at about the same time. However, there are obvious differences in the occurrence time of the minimum concentrations among different kinds of PM. The differences in the occurrence time of minima between coarse and fine particles are due to their different diffusion behaviors in the atmospheric boundary layer.     

Spatial variability of different fractions of particulate matter within an urban environment and between urban and rural sites

The spatial variability of different fractions of particulate matter (PM) was investigated in the city of Basel, Switzerland, based on measurements performed throughout 1997 with a mobile monitoring station at six sites and permanently recorded measurements from a fixed site. Additionally, PM10 measurements from the following year, which were concurrently recorded at two urban and two rural sites, were compared. Generally, the spatial variability of PM4, PM10, and total suspended particulates (TSP) within this Swiss urban environment (area = 36 km2) was rather limited. With the exception of one site in a street canyon next to a traffic light, traffic density had only a weak tendency to increase the levels of PM. Mean PM10 concentration at six sites with different traffic densities was in the range of less than +/- 10% of the mean urban PM10 level. However, comparing the mean PM levels on workdays to that on weekends indicated that the impact of human activities, including traffic, on ambient PM levels may be considerable. Differences in the daily PM10 concentrations between urban and more elevated rural sites were strongly influenced by the stability of the atmosphere. In summer, when no persistent surface inversions exist, differences between urban and rural sites were rather small. It can therefore be concluded that spatial variability of annual mean PM concentration between urban and rural sites in the Basel area may more likely be caused by varying altitude than by distance to the city center.     

Statistical characterization of atmospheric PM10 and PM2.5 concentrations at a non-impacted suburban site of Istanbul, Turkey

Inhalable particulate matter (PM10) has been monitored at several stations by Istanbul Municipality. On the other hand, information about fine fraction aerosols (PM2.5) in Istanbul atmosphere was not reported. In this study, 86 daily aerosol samples were collected between July 2002 and July 2003. The PM10 annual arithmetic mean value of 47.1 microg m(-3), was lower than the Turkish air quality standard of 60 microg m(-3). On the other hand, this value was found higher than the annual European Union air quality PM(10) standard of 40 microg m(-3). Furthermore, the annual mean concentration of PM2.5 20.8 microg m(-3) was found higher than The United States EPA standard of 15 microg m(-3). The statistics and relationships of fine, coarse, and inhalable particles were studied. Cyclic behavior of the monthly average concentrations of PM10 and PM2.5 data were investigated. Several frequency distribution functions were used to fit the measured data. According to Chi-squared and Kolmogorov-Smirnov tests, the frequency distributions of PM2.5 and PM10 data were found to fit Log-logistic functions.     

Patterns and concentrations of PM10 in a mountainous basin region

In this study, continuous data of PM10 (particles with aerodynamic diameter < 10 microns) concentration measurements for a 4-yr period were analyzed. These measurements have been carried out in the Eordea Basin, an industrial area in the northwestern mountainous region of Greece. The annual, monthly, and diurnal patterns are presented and investigated regarding the prevailing meteorological conditions and atmospheric processes that affect the ambient concentrations of PM10. The effect of wind on controlling PM10 concentration is also discussed. Based on the data analysis, an attempt is made to provide useful information about air quality levels, taking into account U.S. Environmental Protection Agency air quality standards.