冬季沈阳市典型源排放PM10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。     

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.     

Evaluation of PM10 forecasting based on the artificial neural network model and intake fraction in an urban area: A case study in Taiyuan City,China

Primary fine particulate matters with a diameter of less than 10 µm (PM₁₀) are important air emissions causing human health damage. PM₁₀ concentration forecast is important and necessary to perform in order to assess the impact of air on the health of living beings. To better understand the PM₁₀ pollution health risk in Taiyuan City, China, this paper forecasted the temporal and spatial distribution of PM₁₀ yearly average concentration, using Back Propagation Artificial Neural Network (BPANN) model with various air quality parameters. The predicted results of the models were consistent with the observations with a correlation coefficient of 0.72. The PM₁₀ yearly average concentrations combined with the population data from 2002 to 2008 were given into the Intake Fraction (IF) model to calculate the IFs, which are defined as the integrated incremental intake of a pollutant released from a source category or a region over all exposed individuals. The results in this study are only for main stationary sources of the research area, and the traffic sources have not been included. The computed IFs results are therefore under-estimations. The IFs of PM₁₀ from Taiyuan with a mean of 8.5 per million were relatively high compared with other IFs of the United States, Northern Europe and other cities in China. The results of this study indicate that the artificial neural network is an effective method for PM₁₀ pollution modeling, and the Intake Fraction model provides a rapid population risk estimate for pollutant emission reduction strategies and policies.

Implications The PM₁₀ (particulate matter with an aerodynamic diameter ≤10 μm) yearly average concentration of Taiyuan, with a mean of 0.176 mg/m³, was higher than the 65 μg/m³ recommended by the U.S. Environmental Protection Agency (EPA). The spatial distribution of PM₁₀ yearly average concentrations showed that wind direction and wind speed played an important role, whereas temperature and humidity had a lower effect than expected. Intake fraction estimates of Taiyuan were relatively high compared with those observed in other cities. Population density was the major factor influencing PM₁₀ spatial distribution. The results indicated that the artificial neural network was an effective method for PM₁₀ pollution modeling.     

机动车单车扬尘浓度分布规律的模拟

为了掌握汽车扬尘中PM10污染分布特征,采用数值模拟的方法对汽车扬尘PM10浓度分布规律及其影响因素进行了研究,分析了车速、积尘负荷对汽车扬尘PM10浓度的影响.结果表明,车速与扬尘运移速率成正比,且车速在20～60 km/h范围内与扬尘高度近似成线性分布;积尘负荷与汽车扬尘PM10平均浓度同比例变化.     

Integrated chemical species analysis with source-receptor modeling results to characterize the effects of terrain and monsoon on ambient aerosols in a basin

This study integrated estimated oxidation ratio of sulfur (SOR) and oxidation ratio of nitrogen (NOR) with source-receptor modeling results to identify the effects of terrain and monsoons on ambient aerosols in an urban area (north basin) and a rural area (south basin) of the Taichung Basin. The estimated results indicate that the conversion of sulfur mainly occurs in fine particles (PM_2.5), whereas the conversion of nitrogen occurs in approximately equal quantities of PM_2.5 and coarse particles (PM_2.5–10). The results show a direct relationship for PM_2.5 between the modeling results with SOR and NOR. The high PM_2.5 SOR, NOR, and secondary aerosol values all occurred in the upwind area during both monsoons; this shows that the photochemical reaction and the terrain effect on the pollutant transmission were significant in the basin. Additionally, the urban heat island effect on the urban area and the valley effect on the rural area were significant. The results show that secondary aerosol in PM_2.5–10 contributed approximately 10 % during both monsoons, and the difference in the contribution from secondary aerosol between both areas was small. Vehicle exhaust emissions and wind-borne dust were two crucial PM_2.5–10 contributors during both monsoons; their average contributions in both areas were higher than 34 and 32 %, respectively.     

Seasonal and diurnal variations of ambient PM2.5 concentration in urban and rural environments in Beijing

Three years of measurement of PM_2.5 with 5-min time resolution was conducted from 2005 to 2007 in urban and rural environments in Beijing to study the seasonal and diurnal variations in PM_2.5 concentration. Pronounced seasonal variation was observed in the urban area, with the highest concentrations typically observed in the winter and the lowest concentrations generally found in the summer. In the rural area, the maximum in PM_2.5 concentration usually appeared during the spring, followed by a second maximum in the summer, while the minimum generally occurred in the winter. Significant diurnal variations in PM_2.5 concentration were observed in both urban and rural areas. In the urban area, the PM_2.5 concentration displays a bimodal pattern, with peaks between 7:00 and 8:00 a.m. and between 7:00 and 11:00 p.m. The minimum generally appears around noon. The morning peak is attributed to enhanced anthropogenic activity during rush hours. The decreases of boundary layer height and wind speed in the afternoon companying with increased source activity during the afternoon rush hour result in the highest PM_2.5 concentration during evening hours. In the rural area, the PM_2.5 concentration shows a unimodal pattern with a significant peak between 5:00 and 11:00 p.m.The seasonal and diurnal variations in PM_2.5 concentration in the urban area are mostly dominated by the seasonal and diurnal variability of boundary layer and source emissions. The year-to-year variability of rainfall also has an important influence on the seasonal variation of PM_2.5 in the urban area. The seasonal and diurnal wind patterns are more important factors for PM_2.5 variation in the rural area. Southerly winds carry pollutants emitted in southern urban areas northward and significantly enhance the PM_2.5 concentration level in the rural area.     

PM10 concentration levels at an urban and background site in Cyprus: The impact of urban sources and dust storms

Air quality in Cyprus is influenced by both local and transported pollution, including desert dust storms. We examined PM₁₀ concentration data collected in Nicosia (urban representative) from April 1, 1993, through December 11, 2008, and in Ayia Marina (rural background representative) from January 1, 1999, through December 31, 2008. Measurements were conducted using a Tapered Element Oscillating Micro-balance (TEOM). PM₁₀ concentrations, meteorological records, and satellite data were used to identify dust storm days. We investigated long-term trends using a Generalized Additive Model (GAM) after controlling for day of week, month, temperature, wind speed, and relative humidity. In Nicosia, annual PM₁₀ concentrations ranged from 50.4 to 63.8 μg/m³ and exceeded the EU annual standard limit enacted in 2005 of 40 μg/m³ every year. A large, statistically significant impact of urban sources (defined as the difference between urban and background levels) was seen in Nicosia over the period 2000–2008, and was highest during traffic hours, weekdays, cold months, and low wind conditions. Our estimate of the mean (standard error) contribution of urban sources to the daily ambient PM₁₀ was 24.0 (0.4) μg/m³. The study of yearly trends showed that PM₁₀ levels in Nicosia decreased from 59.4 μg/m³ in 1993 to 49.0 μg/m³ in 2008, probably in part as a result of traffic emission control policies in Cyprus. In Ayia Marina, annual concentrations ranged from 27.3 to 35.6 μg/m³, and no obvious time trends were observed. The levels measured at the Cyprus background site are comparable to background concentrations reported in other Eastern Mediterranean countries. Average daily PM₁₀ concentrations during desert dust storms were around 100 μg/m³ since 2000 and much higher in earlier years. Despite the large impact of dust storms and their increasing frequency over time, dust storms were responsible for a small fraction of the exceedances of the daily PM₁₀ limit.

ImplicationsThis paper examines PM₁₀ concentrations in Nicosia, Cyprus, from 1993 to 2008. The decrease in PM₁₀ levels in Nicosia suggests that the implementation of traffic emission control policies in Cyprus has been effective. However, particle levels still exceeded the European Union annual standard, and dust storms were responsible for a small fraction of the daily PM₁₀ limit exceedances. Other natural particles that are not assessed in this study, such as resuspended soil and sea salt, may be responsible in part for the high particle levels.     

PAH air pollution at a Portuguese urban area: carcinogenic risks and sources identification

This study aimed to characterize air pollution and the associated carcinogenic risks of polycyclic aromatic hydrocarbon (PAHs) at an urban site, to identify possible emission sources of PAHs using several statistical methodologies, and to analyze the influence of other air pollutants and meteorological variables on PAH concentrations.The air quality and meteorological data were collected in Oporto, the second largest city of Portugal. Eighteen PAHs (the 16 PAHs considered by United States Environment Protection Agency (USEPA) as priority pollutants, dibenzo[a,l]pyrene, and benzo[j]fluoranthene) were collected daily for 24 h in air (gas phase and in particles) during 40 consecutive days in November and December 2008 by constant low-flow samplers and using polytetrafluoroethylene (PTFE) membrane filters for particulate (PM₁₀ and PM_2.5 bound) PAHs and pre-cleaned polyurethane foam plugs for gaseous compounds. The other monitored air pollutants were SO₂, PM₁₀, NO₂, CO, and O₃; the meteorological variables were temperature, relative humidity, wind speed, total precipitation, and solar radiation. Benzo[a]pyrene reached a mean concentration of 2.02 ng?m^?3, surpassing the EU annual limit value. The target carcinogenic risks were equal than the health-based guideline level set by USEPA (10^?6) at the studied site, with the cancer risks of eight PAHs reaching senior levels of 9.98?×?10^?7 in PM₁₀ and 1.06?×?10^?6 in air. The applied statistical methods, correlation matrix, cluster analysis, and principal component analysis, were in agreement in the grouping of the PAHs. The groups were formed according to their chemical structure (number of rings), phase distribution, and emission sources. PAH diagnostic ratios were also calculated to evaluate the main emission sources. Diesel vehicular emissions were the major source of PAHs at the studied site. Besides that source, emissions from residential heating and oil refinery were identified to contribute to PAH levels at the respective area. Additionally, principal component regression indicated that SO₂, NO₂, PM₁₀, CO, and solar radiation had positive correlation with PAHs concentrations, while O₃, temperature, relative humidity, and wind speed were negatively correlated.     

Assessment of traffic-related air pollution in the urban streets before and during the 2008 Beijing Olympic Games traffic control period

In order to investigate the air quality and the abatement of traffic-related pollution during the 2008 Olympic Games, we select 12 avenues in the urban area of Beijing to calculate the concentrations of PM₁₀, CO, NO₂ and O₃ before and during the Olympic traffic controlling days, with the OSPM model.Through comparing the modeled results with the measurement results on a representative street, the OSPM model is validated as sufficient to predict the average concentrations of these pollutants at street level, and also reflects their daily variations well, i.e. CO presents the similar double peaks as the traffic flow, PM₁₀ concentration is influenced by other sources. Meanwhile, the model predicts O₃ to stay less during the daytime and ascend in the night, just opposite to NO₂, which reveals the impact of photochemical reactions. In addition, the predicted concentrations on the windward side often exceed the leeward side, indicating the impact of the special street shape, as well as the wind.The comparison between the predicted street concentrations before and during the Olympic traffic control period shows that the overall on-road air quality was improved effectively, due to the 32.3% traffic flow reduction. The concentrations of PM₁₀, CO and NO₂ have reduced from 142.6 μg m⁻³, 3.02 mg m⁻³ and 118.7 μg m⁻³ to 102.0 μg m⁻³, 2.43 mg m⁻³ and 104.1 μg m⁻³. However, the different pollutants show diverse changes after the traffic control. PM₁₀ decreases most, and the reduction effect focusing on the first half-day even clears the morning peak, whereas CO and NO₂ have even reductions to minify the daily fluctuations on the whole. Opposite to the other pollutants, ozone shows an increase of concentration. The average reduction rate of PM₁₀, CO, NO₂ and O₃ are respectively 28%, 19.3%, 12.3% and −25.2%. Furthermore, the streets in east, west, south and north areas present different air quality improvements, probably induced by the varied background pollution in different regions around Beijing, along with the impact of wind force. This finding suggests the pollution control in the surrounding regions, not only in the urban area.     

Assessment of ambient air quality in the port of Naples

Two experimental monitoring campaigns were carried out in 2012 to investigate the air quality in the port of Naples, the most important in southern Italy for traffic of passengers and one of the most important for goods. Therefore, it represents an important air pollution source located close to the city of Naples. The concentrations of sulfur dioxide (SO₂), nitrogen dioxide (NO₂), and BTEX (benzene, toluene, ethylbenzene, and xylenes) in the air were measured at 15 points inside the Naples port area through the use of passive samplers. In addition, a mobile laboratory was positioned in a fixed point inside the port area to measure continuous concentration of pollutants together with particulate matter, ambient parameters, and wind direction and intensity. The pollution levels monitored were compared with those observed in the urban area of Naples and in other Mediterranean ports. Even though the observation time was limited, measured concentrations were also compared with limit values established by European legislation. All the measured pollutants were below the limits with the exception of nitrogen dioxide: its average concentration during the exposition time exceeded the yearly limit value. A spatial analysis of data, according to the measured wind direction and intensity, provided information about the effects that ship emissions have on ambient air quality in the port area. The main evidence indicates that ship emissions influence sulfur dioxide concentration more than any other pollutants analyzed.

Implications: Two monitoring campaigns were carried out to measure BTEX, SO₂, NO₂, and PM₁₀ (particulate matter with an aerodynamic diameter <10 μm) air concentrations in the port of Naples. NO₂ hourly average and PM₁₀ daily average comply with European legislative standards. Spatial variation of pollutants long the axis corresponding to the prevailing wind direction seems to indicate a certain influence of ship emissions for SO₂. For NO₂ and PM₁₀, a correlation between concentrations in the harbor and those measured by the air quality monitoring stations sited in the urban area of Naples was observed, indicating a possible contribution of the near road traffic to the air pollution in the port of Naples.