公园绿地植物配置对大气PM_(2.5)浓度的消减作用及影响因子

城市公园绿地具有消减大气细颗粒物浓度的功能,通过对北京4家公园内典型植物配置群落全年大气中细颗粒物(PM_(2.5))的测定,定量研究了不同植物配置模式对大气PM_(2.5)浓度的消减作用,分析了植物配置模式的各表征因子对大气PM_(2.5)消减率的影响。并分析了气象因子对大气PM_(2.5)浓度变化的影响。研究结果表明:1)不同配置模式绿地对细颗粒物的消减效果略有差异,但差异不显著。纯林绿地或乔草配置型绿地消减PM_(2.5)能力最佳,纯草坪绿地对PM_(2.5)消减率最低;2)植物配置模式的表征因子中,斑块面积会显著影响绿地对大气PM_(2.5)浓度的消减能力(P0.01),斑块面积越大,绿地对消减pM_(2.5)浓度的效果越明显。而与乔木层郁闭度、乔木层高度、草坪盖度等结构指标相关性不显著;3)各观测点PM_(2.5)浓度显示与群落内的相对湿度呈显著正相关,与大气压无关。     

园林绿地秋季温湿度与细颗粒物(PM_(2.5))浓度的关系研究——以北京玉渊潭公园为例

近年来,空气质量越来越成为社会所关注的主要环境问题之一,尤其是以PM_(2.5)为代表的空气颗粒物,对居民生活产生了巨大的影响。园林绿地是城市重要的组成部分,具有降低空气污染物等生态功能。以北京玉渊潭公园为研究地点,采用多点同步连续监测的方式,对公园内不同区域的温、湿度及细颗粒物(PM_(2.5))浓度进行监测。通过一个完整秋季的数据,分析园林绿地中温、湿度和PM_(2.5)浓度在秋季的变化情况,温、湿度和PM_(2.5)浓度之间的相关性,以及不同站点间温、湿度与PM_(2.5)浓度之间的关系。     

公园绿地和广场空气颗粒物浓度对比及影响因素研究

大气污染是中国城市环境面临的严峻问题之一,场地尺度的污染防治面临巨大压力。一些研究认为城市绿地对改善空气质量具有重要作用,然而也有部分研究提出了质疑,认为绿地植被加剧了空气污染。鉴于结论的不一致性,选择西安地区公园绿地丹枫园和紧邻的开敞广场作为研究对象,开展对比分析,采用更高的时间分辨率,对距道路不同水平距离的颗粒物浓度和气象因子进行为期一周的连续监测,分析其时空变化规律和影响因素。结果表明：（1）温度、气压、湿度和风速对PM2.5和PM10浓度均有显著影响,湿度和风速对颗粒物的影响存在阈值,湿度阈值为70%,风速阈值为1.8 m·s-1,颗粒物浓度随着湿度和风速的增大而升高,湿度和风速超过阈值时,颗粒物浓度逐渐降低,但广场风速超过阈值时,广场PM10浓度继续升高。（2）颗粒物浓度日变化呈“峰谷型”,6点至8点,19点至24点颗粒物浓度较高,样地内颗粒物浓度随水平距离增加而升高,绿地内颗粒物浓度低于广场。10点至18点颗粒物浓度较低,颗粒物浓度随距离增加而降低,绿地内颗粒物浓度高于广场。（3）对比硬质广场,绿地能有效削弱颗粒物浓度峰值和PM10浓度。结果表明绿地主要通过影响绿地风速和湿...     

基于公共健康视角的城市公园颗粒物 时空分布及影响因素研究

城市公园是主要的室外公共空间,其大气颗粒物污染的时空分布与居民健康密切相关。在秋季晴朗微风天气,在郑州人民公园5类园林空间(露天广场、草坪、半室内空间、林下空间、滨水空间),监测不同空间中颗粒物(PM_1、PM_(2.5)、PM_(10))浓度和环境因子(风速、温度、湿度、天空可视因子、叶面积指数),探究城市公园中不同空间颗粒物浓度时空分布差异及与环境因子的关系。结果表明,草坪在8:00—14:00与其他空间PM_1、PM_(2.5)浓度有显著差异(P0.05);不同空间颗粒物浓度日均变化趋势相同,部分时段间存在显著差异(P 0.05),峰谷均在14:00—16:00,因而在颗粒物浓度较低的14:00—16:00更合适户外活动;相同空间3种颗粒物浓度均与空气温度显著负相关,与相对湿度显著正相关;空间内下垫面类型、人群的活动方式、周边植物群落结构等对颗粒物浓度均有影响。研究结果为公共健康视角下公园规划设计及居民对公园的使用提供依据和建议。     

西安市空气质量与气象因素的典型相关分析

目的通过对2013年11月1日—2014年10月31日西安市空气污染指标,以及气象因素指标的分析,揭示西安市气象因素对空气污染物浓度的影响规律。方法对研究期间AQI指数、PM_(2.5)、PM_(10)、SO_2、NO_2、CO空气污染物水平进行统计描述;采用简单相关和典型相关分析,探讨空气主要污染物与气温、气湿、风速、气压、降雨量等气象因素之间的关系。结果根据《环境空气质量标准》(GB 3095-2012)年均值二级标准,研究期间,(1)西安市PM_(2.5)和PM_(10)年均值均超标,CO和SO_2达到二级标准,而NO_2略超过二级标准;(2)西安市气压变化平稳,月均湿度在70%上下波动,秋季略高,风速全年较平稳,冬春季略低,降雨量表现为冬春季偏低,夏季略高,秋季明显增多;(3)简单相关分析表明,气温同AQI、PM_(2.5)、PM_(10)、CO、NO_2和SO_2均有显著的相关关系,相关系数均大于0.5;气湿与SO_2的相关系数较大;风速与NO_2的相关系数较大,接近0.5;气压与CO、NO_2和SO_2的相关系数较大,均大于0.5;降雨量与AQI、PM_(2.5)、PM_(10)、CO、NO_2和SO_2均有显著的相关关系,相关系数较小。(4)典型相关分析表明,气象因素中气温主要影响气态污染物的浓度,湿度主要影响PM_(2.5)的浓度,而风速主要对NO_2浓度产生较大影响,降雨量则主要影响的是颗粒态污染物的浓度。结论在本研究期内,西安市空气质量与气象因素间有相关性。     

城市公园大气颗粒物浓度的节假日特征

以杭州西湖风景区花港观鱼公园为研究对象,对比分析节假日与非节假日公园内大气颗粒物(PM_(2.5)、PM_(10))浓度与游客量、气象因子、植物群落类型的相关性,并从宏观—中观—微观3个层次探究花港观鱼公园大气颗粒物浓度的节假日特征,为城市公园的科学建设提供参考和依据。研究发现:大气颗粒物浓度有明显的节假日效应。宏观方面,杭州主城区元旦节期间大气污染物主要是PM_(2.5)和PM_(10),节日期间的平均浓度分别为102±41.51μg.m~(-3)、155±64.86μg.m~(-3),分别是非节日的1.6倍、1.7倍。杭州主城各区受节日影响较大的是余杭区、萧山区和下城区。中观方面,花港观鱼公园节日期间PM_(2.5)和PM_(10)有波峰现象且振幅剧烈呈锯齿状;非节日期间午后出现波谷,上下振幅较小,其影响因子特征表现为:节日期间游客量与大气颗粒物浓度相关性较高,游客游览观光活动对PM_(10)的贡献率更大;气象因子对PM_(10)的影响比PM_(2.5)显著。微观方面,公园内3种不同植物群落类型绿地的PM_(2.5)浓度表现为:密林纯林草坪。     

哈尔滨市采暖期颗粒物数浓度变化特征

研究了采暖期大气颗粒物数浓度的变化特征,并在气象监测网上获得相对湿度、温度,风级等气象因子以及大气能见度。分析结果显示,2015年10、11、12月的能见度低于10 km的天数分别为3、11、9 d,PM_(2.5)浓度高于100μg/m~3分别有1、9、9 d;颗粒物数浓度平均值分别为17 775、36 345、34 640个/cm~3,能见度的平均值分别为23.6、8.5、9.7 km,说明11和12月主要是由于采暖燃煤量增加导致数浓度增大。3个月中,12月3—16日的数浓度变化范围波动较大,这是因为降雪的影响,一部分颗粒物吸附在雪中,随之降落到地面,使空气中悬浮的颗粒物减少。颗粒物数浓度与PM_(2.5)浓度、能见度、相对湿度显著相关,特别是大气中PM_(2.5)数浓度越高,能见度就相对越低。     

滇东地区城市空气污染变化特征及其与气象条件的关系

基于滇东城市曲靖2014-2018年2个国控空气质量监测点的逐日空气质量指数和6种空气污染物(SO_2、NO_2、PM_(10)、PM_(2.5)、CO和O_3)逐小时浓度资料以及同期气象要素数据,统计分析了曲靖主城区空气污染变化特征及气象因子对污染物浓度分布的影响.结果表明:①2014至2018年,曲靖主城区空气质量优良率为97%-99.7%,污染日数呈逐年减少趋势,首要污染物以PM_(10)、PM_(2.5)和O_3为主.②曲靖主城区空气质量呈现出夏秋季节较好、冬春季节较差的季节性特征.③6种污染物浓度各自表现出不同的季节性变化和日变化特征.气象条件影响着曲靖主城区污染物的扩散、迁移和转变.④风速与SO_2、NO_2、CO和PM_(2.5)浓度具有较好的负相关关系;与O_3浓度呈正相关关系;风速对PM_(10)影响较复杂,当风速小于2 m/s时有利于PM_(10)扩散,当风速超过2 m/s时反而导致PM_(10)浓度增加.⑤地面盛行西北风和东南风时,SO_2、NO_2、CO、PM_(10)和PM_(2.5)浓度较高;地面盛行西南风时,O_3浓度达到最高值.⑥降水对6种污染物具有显著冲刷清洁作用.⑦温度与O_3浓度呈显著性正相关关系,与NO_2、CO、PM_(10)和PM_(2.5)浓度呈显著性负相关关系;与SO_2浓度关系不显著.⑧相对湿度与O_3、PM_(10)和PM_(2.5) 3种首要污染物浓度呈显著性负相关关系;与SO_2、NO_2和CO 3种非首要污染物浓度的关系不显著.     

上海市大气中微小颗粒物的污染状况

1 前言 大气中的微小颗粒物是悬浮在大气中的尺度为几十埃至几百微米的固体或液体粒子。其中大气环境科学中最关注的颗粒物有3种——TSP、PM_(10)和PM_(2.5),分别指空气动力学当量直径小于或等于100μm、10μm和2.5μm的悬浮颗粒物。TSP中粒径较大的粒子由于重力作用,会较快沉降下来(重力沉降速度为0.01～1m/s);粒径大于10μm的颗粒     

香烟产生不同粒径颗粒物的浓度分布及散发特征

本文在温湿度可调的洁净环境舱内对3类6种香烟(烤烟型A、雪茄型A、混合型A、B、C、D)燃烧产生的颗粒物PM_(1.0)、PM_(2.5)和PM_(10)的浓度变化进行了逐时测试,并确定了不同种香烟颗粒物的散发特征,同时分析了温度、湿度、焦油含量对香烟颗粒物散发特征的影响。结果表明:1)在固定温度为23℃、相对湿度为40%时香烟燃烧产生的颗粒物中的98%以上为细微颗粒物PM_(1.0),PM_(1.0)、PM_(2.5)和PM_(10)的散发因子的规律均为烤烟型混合型雪茄型,且散发因子范围在7.61～16.54mg/g范围内; 2)保持40%的湿度不变,随着温度从15℃增加到23℃和35℃,混合型A香烟释放颗粒物的质量浓度、散发特征无明显变化; 3)保持温度为23℃不变,随着湿度从40%增加到55%和70%,烤烟型A、雪茄型A和混合型A香烟释放颗粒物PM_(1.0)、PM_(2.5)和PM_(10)的散发因子和散发速率均出现了下降的趋势; 4)香烟中焦油含量越高,散发颗粒物的质量浓度和散发因子越高,对人体健康影响越大。研究结果可为了解香烟散发颗粒物的特征及控制香烟污染提供基础数据。     

Factors influencing mass concentration and chemical composition of fine aerosols during a PM high pollution episode

Results obtained during a winter field campaign for the fine fractions of particulate matter are presented. A high pollution episode together with an analysis of the main factors, which influence accumulation of pollutants is described. The measurement campaigns were carried out simultaneously at two sites in Northern Italy, Milan and Erba, during the winter of 2000. The daily variability in the mass concentration values and PM2.5/PM10 ratios appeared to be strongly dependent upon meteorological and atmospheric stability conditions and, in particular, wind regimes. During the intensive field campaign a high-pollution episode occurred that led to TSP and fine fraction concentrations well above the attention and alarm thresholds, reaching values of up to 200-250 microg m(-3). The elemental concentrations were determined by ED-XRF analysis. The elemental composition of the particulate matter indicated that crustal matter oxides (soil dust) were the main component in particles with aerodynamic diameter d(ae) > 10 microm. They were an important part also in particles with 2.5 < d(ae) < 10 microm, but strongly decreased in particles with d(ae) < 2.5 microm. In the finer fraction sulphates nitrogen and carbon compounds played a major role. The temporal patterns of mass and elemental concentrations, as well as the main components of PM were very similar at the two sites. The high-pollution episode was recorded at many locations in the Po plain, highlighting the role of meteorology and thermodynamic atmospheric conditions on pollution build-up on a large area.     

Isoconcentration mapping of particulate matter in Hamedan intercity bus stations

To accomplish this study, the total concentration of suspended particles, PM₁₀ and PM_2.5, was mapped at intercity bus stations in the central square of Hamedan. To measure the particulate matter (PM), portable air sampling systems that collect integrated filter samples were used. The PM concentration was collected at various time intervals and measured gravimetrically. The results were then analysed using the ArcView GIS 3.3 software to map the particulate dispersion patterns. The mean concentrations of the total suspended particles (TSP), PM₁₀ and PM_2.5 were 1220.94 ± 1418.5, 524.7 ± 217.5 and 386 ± 193.6 μg/m³, which were 16, 7.72 and 4.7 times greater than the World Health Organization (WHO) air quality standard, respectively. The PM concentration was not correlated with wind velocity or air temperature, but was correlated with humidity (P = 0.01). Overall, the results of this pilot study indicate that people at bus stations are exposed to respirable particulate matter (RPM) at levels high enough to pose a serious health risk.     

A combined analysis of backward trajectories and aerosol chemistry to characterise long-range transport episodes of particulate matter: the Madrid air basin, a case study

This study has investigated the influence of synoptic weather patterns and long-range transport episodes on the concentration levels of airborne particulate matter (TSP, PM10 and PM2.5) and some major ions (SO(4)(2-), NO(3)(-) and NH(4)(+)) at a background rural station in central Spain. Air mass back-trajectories arriving at the site in 1999-2005 have been analysed by statistical methods. First, cluster analysis was used to group trajectories into 8 clusters depending on their direction and speed. Meteorological scenarios associated to each cluster have been obtained and interpreted. Then, the incidence of different air mass transport patterns on particle concentrations and composition recorded at this station was evaluated. This evaluation included PM10 and PM2.5 concentrations and chemical composition data, obtained at three representative sites of the Madrid air basin during sampling campaigns carried out in the course of the 1999-2005 period. Finally, a residence time analysis of trajectories was also performed to detect remote sources and transport pathways. Significantly elevated concentrations of TSP and PM10 were observed for Northern African flows as a consequence of the transport of mineral dust. Significant inter-cluster differences were also observed for PM2.5 and secondary inorganic compounds, with the highest concentrations associated with low baric gradient situations and Southern European flows. The residence time analysis confirmed that current TSP and PM10 concentrations in central Spain are likely to be influenced significantly by long-range transport of desert dust from different desert regions in North Africa. Furthermore, emissions from continental Europe with a high time of residence in the western and central areas of the Mediterranean basin, seem to significantly influence PM2.5 and secondary inorganic aerosol concentrations in this region.     

PM_(10)浓度及微观特征季节分布分析——以乌鲁木齐天山区2004年为例

利用2004年乌鲁木齐城区(以天山区为例)PM10日平均浓度和气象要素观测资料,对不同季节PM10浓度变化特征、不同级别污染日数进行统计分析．同时,结合环境扫描电镜／X射线能谱(ESEM-EDX)对不同季节的颗粒物的形貌及来源进行了初步探讨。结果表明2004年PM10浓度变化为:冬季>秋季>春季>夏季;冬季出现4级以上污染日数最多,占39．5％;夏季最为洁净,好于2级的日数占到76．1％．PM10和气象因子的相关分析表明浓度与风速成正比,与降水成反比,与温度,相对湿度和逆温层厚度相关比较复杂,有时成正相关,有时呈负相关。颗粒物的形貌在不同季节特征明显,冬季颗粒物多呈圆球形,春季形貌不规则,夏季既有圆球形又有不规则形貌的颗粒,而秋季颗粒物多呈链状．     

Particulate matter exposure along designated bicycle routes in Vancouver, British Columbia

An instrumented bicycle was used to elucidate particulate matter exposures along bicycle routes passing through a variety of land uses over 14 days during summer and fall in a mid-latitude traffic dominated urban setting. Overall, exposures were low or comparable to those found in studies elsewhere (mean PM(2.5) and PM(10) concentrations over each daily bicycle traverse varied between 7-34 microg m(-3) and 26-77 microg m(-3) respectively). Meteorological factors were responsible for significant day-to-day variability with PM(2.5) positively correlated with air temperature, PM(10) negatively correlated with precipitation, and ultrafine particles negatively correlated with both air temperature and wind speed. On individual days, land use and proximity to traffic were factors significantly affecting exposure along designated bicycle routes. While concentrations of PM(2.5) were found to be relatively spatially uniform over the length of the study route, PM(10) showed a more heterogeneous spatial distribution. Specifically, construction sites and areas susceptible to the suspension of road dust have higher concentrations of coarse particles. Ultrafine particles were also heterogeneously distributed in space, with areas with heavy traffic volumes having the highest concentrations. Observations show qualitative agreement in terms of spatial patterns with a land-use regression (LUR) model for annual PM(2.5) concentrations.     

Temporal variations and spatial distribution of ambient PM2.2 and PM10 concentrations in Dhaka, Bangladesh

Concentrations and characteristics of airborne particulate matter (PM(10), PM(2.2) and BC) on air quality have been studied at two air quality-monitoring stations in Dhaka, the capital of Bangladesh. One site is at the Farm Gate area, a hot spot with very high pollutant concentrations because of its proximity to major roadways. The other site is at a semi-residential area located at the Atomic Energy Centre, Dhaka Campus, (AECD) with relatively less traffic. The samples were collected using a 'Gent' stacked filter unit in two fractions of 0-2.2 mum and 2.2-10 mum sizes. Samples of fine (PM(2.2)) and coarse (PM(2.2-10)) airborne particulate matter fractions collected from 2000 to 2003 were studied. It has been observed that fine particulate matter has a decreasing trend, from prior year measurements, because of Government policy interventions like phase-wise plans to take two-stroke three-wheelers off the roads in Dhaka and finally banned from January 1, 2003. Other policy interventions were banning of old buses and trucks to ply on Dhaka city promotion of the using compressed natural gas (CNG), introducing air pollution control devices in vehicles, etc. It was found that both local (mostly from vehicular emissions) and possibly some regional emission sources are responsible for high PM(2.2) and BC concentrations in Dhaka. PM(2.2), PM(2.2-10) and black carbon concentration levels depend on the season, wind direction and wind speed. Transport related emissions are the major source of BC and long-range transportation from fossil fuel related sources and biomass burning could be another substantial source of BC.     

Estimating the frequency distributions of PM10 and PM2.5 by the statistics of wind speed at Sha-Lu,Taiwan

The frequency distribution of air pollutant concentration varies with the meteorological conditions and pollutant emission level. There exists a simple relation between the frequency distribution of wind speed and frequency distribution of air pollutant concentration. The concentration of air pollutant, C, at cumulative probability, p, is inversely proportional to the wind speeds, u, at probability of (100 - p) when the distributional types and shape factors of both data are the same. The relationship is shown as K=Cp u(100 - p), where K is constant. In this study, three theoretical distributions (log-normal, Weibull and type V Pearson distributions) are selected to fit the measured data of PM10, PM2.5 and wind speed. The frequency distributions of air pollutants can be estimated from the simple relationship of air pollutant concentration and wind speed. The results show that the log-normal distribution is the best one to represent the data of PM10, PM2.5 and wind speed. The K values of PM10 and PM2.5 are nearly constant from the 30-80th percentiles. It was also found that the distributions of PM10 and PM2.5 can be successfully estimated from the distribution of wind speed. The Kolmogorov-Smirnov (K-S) test shows that there is no significant discrepancy between the estimated and measured distribution of PM10 and PM2.5 at the 95% confidence level. Therefore, the distribution of air pollutants is easily estimated when the wind speed data are known.     

Characterization of chemical species in PM2.5 and PM10 aerosols in suburban and rural sites of central Taiwan.

Aerosol samples for PM2.5 (particulate matter with aerodynamic diameters less than 2.5 microns), PM2.5-10 (particulate matter with aerodynamic diameters between 2.5 and 10 microns) and TSP were collected from June to September 1998 at THU (suburban) and HKIT (rural) sites in central Taiwan. The ratios of PM2.5/PM10 averaged 0.70 for the daytime and 0.63 for the nighttime at THU, respectively. At HKIT, the PM2.5/PM10 ratios averaged 0.56 for the daytime and 0.72 in the nighttime, respectively. These results indicated that the PM2.5 concentrations contribute the majority of the PM10 concentration and PM10 concentrations contribute the majority of the TSP at both sites. The averaged PM2.5 concentrations at THU are higher than those measured at HKIT during the daytime period. However, the average PM2.5-10 concentrations in THU are lower than those measured at HKIT during nighttime. The samples collected were also analyzed by atomic absorption spectrophotometry for the elemental analysis of Ca, Fe, Pb, Zn, Cu, Mn and Cr. Meanwhile ion chromatography was used to analyze for the water-soluble ions: sulphate, nitrate and chloride in the Universal samples. The concentrations of heavy metals in PM10 during daytime were all higher than nighttime at THU. However, the averaged concentrations of metal elements in PM10 during day and night period were distributed irregularly at HKIT. The results indicated that for metal elements collected at HKIT have different emission sources. The concentrations of metal elements during daytime in PM10 at THU were generally higher than HKIT. The phenomena owing to the averaged PM2.5 particle concentrations at THU (suburban) were higher than those measured at HKIT (rural) and PM2.5 occupied the major portions of PM10 for both sites during the day period. For anion species, there are no significant differences between day and night period in PM10 concentrations at both suburban and rural sites.     

The characteristic study of TSP, PM2.5-10 and PM2.5 in the rural site of central Taiwan.

The total suspended particle (TSP), PM2.5-10 (aerodynamic diameter less than 10 microns) and PM2.5 concentration (aerodynamic diameter less than 2.5 microns) concentrations were sampled by PS-1 and Universal sampler on the roof (25 m) of the Medical and Engineering Building in the campus of Hungkuang Institute of Technology (HKIT) which is located at a height of 500 m on Da Du Mountain. The results indicated that average TSP, PM2.5-10 and PM2.5 concentrations are 0.42, 0.34 and 0.019 mg/m3 in the day time, respectively and are 0.32, 0.26 and 0.017 mg/m3 in the night time, respectively. The ratios of PM2.5-10/TSP were from 76% to 85% and from 50% to 91% for day and night period, respectively. It indicated that the major composition in the total suspended particles was PM2.5-10 in the rural site. The relationship between TSP and PM2.5-10 is TSP = 1.16PM2.5-10 + 0.027 and TSP = 1.01 PM2.5-10 + 0.058 in the day and night time, respectively. The correlation coefficient (R2) is 0.98 and 0.97 for day and night period, respectively. The relationship between PM2.5-10 and PM2.5 is PM2.5 = 0.0005PM2.5-10 + 0.019 and PM2.5 = 0.037PM2.5-10 + 0.0076 in the day and night period, respectively. The correlation coefficient (R2) is 3E-5 and 0.67 for day and night period, respectively. The relationships between TSP, PM2.5-10, PM2.5 particle concentrations and wind speed (R2) in the day time are 0.71, 0.64, 0.43, respectively and are 0.83, 0.79, 0.57, respectively in the night time. The proposed reasons are that there are more activities caused by people (students) and natural living animals which absorbed some of the particles during the day time. Thus, the correlation coefficients for the night time are better than those of day time. The particle size distributions are both bimodel in the day and night time. The major peaks in the day time appear in the particle diameter between 0.031-0.056 micron and 3.16-5.62 microns in the day period and appear between 0.017-0.031 micron and 1.78-3.16 microns in the night period. The results indicate that the particle size distribution in the day time tends to be of larger particle size mode than the night time.