气象因素对长沙市PM_(2.5)周期性变化规律的影响分析

以长沙市10个城市环境空气自动监测站点2013年的历史监测数据为基础,分析了PM2.5质量浓度的周期性变化规律,并采用非参数分析(Pearson相关性)法,研究了气象因素对长沙市PM2.5质量浓度周期性变化的影响。结果表明,PM2.5日均质量浓度在不同季节的绝对值和变化周期都相差很大。总体上,PM2.5在冬季的浓度高于夏季;PM2.5质量浓度的变化周期在3~8d。在2013年4个典型月份内,温度和风速与PM2.5质量浓度负相关,而湿度和气压与PM2.5质量浓度正相关,相关系数分别为-0.573、-0.395、0.519和0.440。PM2.5周期性变化与区域内大气环境容量相关,而大风、降雨等强对流天气是终结PM2.5变化周期的主要环境因素。     

柳州市2009-2014年空气污染变化

近年来,城市空气污染日益严重,已成为公众广泛关注的环境问题之一。柳州是中国西部的工业重镇、广西有名的工业城市,位列国家划定的113个大气污染防治重点城市之中,是广西第一个开展PM2.5监测的城市。本研究于2009—2014年连续6年对柳州市大气主要污染物SO2、NO2、PM10和PM2.5的浓度进行在线观测,获得了污染物的长期时间和空间分布特征。结果显示,SO2浓度呈逐年下降趋势,并于2011年达标之后显著下降,2014年相比2009年下降了50.0%;NO2浓度一直在低于标准以下波动(24.6~35.1μg/m3);PM10浓度呈逐年增长趋势,并从2011年开始超标,2014年相对于2009年增长了69.3%。各污染物浓度都具有显著的季节变化:冬季秋季春季夏季。SO2、NO2、PM10和PM2.5的浓度冬季相比夏季分别提高82.9%、56.3%、66.9%和133.6%。冬季SO2和秋冬季PM10超标,PM2.5除7月外全线超标。PM2.5/PM10的比值冬季也高于夏季,表明冬季更易富集细颗粒。各污染物浓度也表现出不同的空间分布。九中各污染物的浓度都最高,可能与其离柳州钢铁公司距离较近有关。SO2除九中外,其他站点均达标。NO2全部达标。PM10市监测站和九中超标。PM2.5所有站点超标严重。本研究结果表明,柳州市煤烟型污染得到有效控制,但颗粒物污染,尤其是细颗粒物污染日益严重。     

济南市大气中一氧化碳污染特征研究

为研究城市近地面CO浓度变化特征,于2015年1~12月在山东大学开展了CO连续监测试验。结果表明,济南城区CO年均浓度为1.431 mg/m~3。CO表现出明显的双峰型日变化规律,峰值出现在8:00和21:00,谷值出现在15:00。不同季节的CO日变化幅度存在差异,夏季最小,冬季幅度最大。一周之中周末CO浓度要高于工作日时。CO存在明显的季节变化规律,浓度最高值出现在12月,最低值出现在7月份。     

巴中市城区PM_(2.5)污染特征及时空变化规律分析

利用巴中市城区一个自然年(2016年3月1日~2017年2月28日)的空气质量数据,分析了巴中市城区PM_(2.5)的污染特征和时空变化规律。结果表明,PM_(2.5)日均浓度对数值接近正态分布特征,PM_(2.5)与其他主要大气污染物都具有显著的相关关系。CO、NO_2是主要的相关因素,与PM_(2.5)的相关系数都高达0.7以上。PM_(2.5)浓度表现为冬季秋季春季夏季,这与首要污染物是PM_(2.5)的天数占比以及PM_(2.5)与PM_(10)相关系数的季节变化一致,反映了PM_(2.5)呈现出以冬季污染最重,春、秋季污染中等,夏季污染最小的季节特征。PM_(2.5)与PM10的浓度比值表现为冬季秋季夏季春季。各个站点的PM_(2.5)变化趋势一致,相互之间浓度差异小且比较均衡,巴中中学站点的PM_(2.5)浓度无论在任何季节都高于其他站点,苏山坪站点在冬季的PM_(2.5)浓度明显低于其他站点,表明PM_(2.5)污染具有明显的区域性特征,与人类活动强度相关的局地污染对PM_(2.5)污染具有一定影响。     

香港地区不同类型站点大气污染变化特征对比研究

选取香港环境保护署设立的5个代表性站点(可归类为路边站、一般站及背景站)2016年的监测结果,对获取的各类污染物(PM_(2.5)、PM_(10)、NO_2、NO_X、SO_2、O_3)浓度变化特征进行对比研究,结果表明:(1)三类站点颗粒物及SO_2浓度年均值差异较小,体现了其区域性的污染特性,NO_2与NO_X年均值均为路边站一般站背景站,而O_3则相反,路边站最低而背景站最高;(2)各类站点PM_(2.5)与PM_(10)季节差异表现为夏季最低而冬季最高,O_3最低值也出现在夏季;(3)三类站点大气污染物日变化特征差异显著,路边和一般站NO_2与NO_X日变化表现为"早晚双峰"型,背景站变化幅度较小;路边站、一般站颗粒物日变化表现为单峰型,峰值出现在晚间22∶00,背景站变化幅度较小;O_3在路边站和一般站呈现双峰型变化,而在背景站为单峰型,峰值出现在下午15∶00左右;(4)所有站点PM_(2.5)、NO_X及O_3"假日效应"明显,具体表现为:PM_(2.5)和O_3浓度在假日全天浓度高于工作日;路边站和一般站的NO_X浓度在假日0∶00~8∶00时段高于工作日,其余时间均低于工作日。     

长沙市城区典型交通路口PM_(10)和PM_(2.5)污染特征研究

利用车载环境空气质量监测系统对长沙市城区典型交通路口的近地面空气质量进行了实时监测。结果表明,在监测时段(14∶00~20∶00)内,该监测点环境空气中PM10的小时质量浓度范围在0.097~0.222mg/m3之间,平均值0.163mg/m3;PM2.5的小时质量浓度范围在0.050~0.158mg/m3之间,平均值0.103mg/m3。PM2.5/PM10比值在48.1%~76.6%之间,平均值62.4%。PM10与PM2.5质量浓度在星期一相对较低,星期二有所升高,星期三至周末总体上保持基本稳定。在监测时段PM10与PM2.5小时质量浓度呈现先降后升的变化规律,即14∶00~15∶00,PM10与PM2.5质量浓度相对较高,16∶00左右降至最低,从17∶00开始逐渐升高,20∶00达到峰值。PM10和PM2.5的质量浓度变化与车流量和车速密切相关,温度、相对湿度和风速等气象因素对PM10和PM2.5质量浓度的变化影响也较显著。     

上海市青浦区PM10污染状况及其与气象要素的关系

利用2006-2010年上海市青浦区PM10和同期地面气象要素的监测资料,定量分析PM10的季节变化规律以及PM10与降雨量、大气湿度和风速之间的关系。分析结果表明:PM10浓度在夏季处于低值,冬季处于高值;5mm/d以上的降雨对PM10有显著的清除作用,且春夏季降雨的清除作用大于秋冬季节。PM10浓度与大气湿度基本呈负相关关系。风速在一定范围内有利于PM10的扩散但不至造成扬尘,春夏季节的适宜风速是1.5～3.5m/s,冬季的适宜风速是1.5～2.5m/s。     

2017～2018年荆州城区空气质量分布及重污染天气特征分析

利用2017年1月至2018年12月荆州城区3个国家环境空气质量监测点的污染物监测数据和荆州国家气象站地面气象要素观测数据,采用统计学方法,对荆州城区空气质量分布及重污染过程天气特征进行了初步分析,结果表明:空气污染综合指数冬季最高,春秋季次之,夏季最低;PM2.5、PM10、 CO和NO2的浓度在上午和傍晚至上半夜其...     

2015-2018年衡阳市城区环境空气污染特征分析

本文利用2015~2018年衡阳市城区空气质量监测数据,分析环境空气污染特征及变化趋势。研究结果表明:2015~2018年衡阳市城区环境空气以PM2.5、PM10、O3污染为主,其中PM2.5和PM10年均值浓度呈下降趋势,O3年均值浓度呈不规则波动变化;年内时空变化上,O3呈现夏、秋季浓度高和冬、春季浓度低的特点,PM2.5和PM10呈现出夏季浓度低、冬季浓度高的特点。针对衡阳市空气污染物特征,提出了衡阳市城区空气质量改善的对策建议。     

乌鲁木齐市区PM2．5污染特征及其溯源与追踪分析

重点研究乌鲁木齐市大气中PM2.5的污染特征，分析其质量变化浓度与各种气象影响因素的相关性．利用美国空气资源实验室的HYSPLIT模型对颗粒物进行备季节代表月份的溯源和追踪分析，为正确认识乌鲁木齐市区大气PM2.5污染状况提供重要基础数据，为以后的对比研究和制定相应的污染控制措施提供参考依据。结果表明：（1）PM2.5质量浓度的最高值出现在1月，受采暖期的影响，冬季PM2.5质量浓度全年最高。（2）PM2.5的质量浓度与温度呈负相关性，与气压的呈正相关性；在无降水的前提下，PM2.5的质量浓度与相对湿度呈正相关。（3）春、夏、秋三季PM2.5来源主要是阿拉山口、阿勒泰北部及南疆，冬季则主要来自于南疆。到达乌鲁木齐的颗粒物主要向河西走廊及阿拉山口方向移动。     

The ionic compositions of fine and coarse particle fractions in the two urban areas of Korea

The ionic composition of PM2.5 and PM10 was investigated using PM samples collected from Seoul and Busan during the winter of 2002. Based on the measurement data, we attempted to investigate the relative roles of different source processes in the composition of airborne particles at the two distinct urban areas. According to our measurements, the major components of both PM fractions were clearly distinguished from each other at the two different sites. It was found that the ionic concentrations in coarse fractions were generally compatible with each other at the two sites (NH(4)(+), NO(3)(-), NSSS, etc.). However, differences in their concentration levels were significant in the fine fractions and their mass concentration levels in Seoul exhibit threefold enhancement relative to Busan. The results further indicated that anthropogenic signatures were generally more evident in the fine rather than coarse fractions at both sites. However, a comparison of the coarse fraction data indicated the dominance of natural (oceanic) processes at the Busan site and conservative relationships among the different ionic components. The results of the back trajectory analysis confirmed that air mass movement patterns influence the ionic compositions of PM across different particle fractions and between different study sites.     

环境空气背景值中PM2．5和PM10的相关性分析

四川省大气背景站颗粒物PM2．5和PM10实时监测结果表明,两个点位的PM2．5浓度占PM10浓度分别为80．15％、68．9％,两个点位的PM2．j浓度和PM10浓度均呈显著线性关系,点位1：y=0．8634x-0．2212,R^2=0．9823;点位2：y=0．7352x-0．2142,R^2=0．7767。     

Distribution of lead in relation to size of airborne particulate matter in Islamabad, Pakistan

Airborne particulate matter (PM) collected from two sampling stations in Islamabad, Pakistan, was analyzed for lead content and size gradation. A high volume air sampler was used to trap particulates on glass fiber filters for 8-12 h on a daily basis. Lead was estimated using a nitric acid digestion based AAS method on 44 samples from station 1 and 61 samples from station 2. Particle size fractions were categorized as <2.5, 2.5-10, 10-100 and >100 microm. The correlation between lead concentration and particle size was investigated. The results from two stations indicated average airborne lead concentrations of 0.505 and 0.185 microg/m3. Enhanced levels of lead were measured at a maximum of 4.075 microg/m3 at station 1 and 4.000 microg/m3 at station 2. PM < 2.5 and PM > 100 were found to constitute the local atmosphere in comparable proportions. A comparison of the lead levels is made with the existing permissible levels of this element laid down by different international agencies.     

长沙城区大气中二氧化碳浓度变化及与其它污染物相关性分析

利用长沙市城区2011年及2012年连续自动监测获得的CO2数据,两年的平均值为412．2×10^-6,高于世界本底站青海瓦里关5．6％,与临安、无锡相当,略高于乌鲁木齐,而低于北京、上海。冬季CO2浓度日小时变化呈现双峰形态,峰值出现在上午9时及晚上19时～21时。夏季日小时变化为单峰形态,峰值出现在上午8时。冬季CO2浓度日均值为420．3×10^-6,比夏季高3．4％。CO2除与O3呈负相关以外,与其它污染物均呈现显著性正相关,特别是与CO、NO、NO2、NOx、SO2的相关性最强,而与颗粒物（PM10、PM25）的相关性稍差。     

我国早晨与傍晚的大气边界层厚度研究

首先探讨了常规气象资料计算大气边界层厚度的方法,然后利用全国１２５个无线电探空站的观测资料,计算了我国早晨与傍晚时的大气边界层厚度ｈ,并分析了ｈ的地区分布与季节变化特征。结果表明,华南地区边界层厚度年平均值最大（ｈ＞５００ｍ）;陕甘宁地区最小（ｈ＝３１８ｍ）;多数地区的ｈ值在冬季最小而夏季或春季最大。     

Spatial variations in selected metal contents and particle size distribution in an urban and rural atmosphere of Islamabad, Pakistan

Spatial variations in total suspended particulate matter (TSP) were investigated for distribution of metals and particle size fractions in the urban and rural atmosphere of Islamabad, Pakistan. The metals Na, K, Fe, Mn, Pb, Cd, Cr, Zn, Ni and Co, and the particle fractions <2.5, 2.5-10, 10-100 and >100 microm were included in the study. TSP samples were trapped on glass fibre filters using high volume samplers and quantification of metals was done using Atomic Absorption Spectrometry employing HNO(3) based wet digestion. At the urban site, Na was dominant at 2.384 microg/m(3) followed by K, Fe and Zn with 0.778, 0.667 and 0.567 microg/m(3) as mean concentrations, respectively. The metal levels for the rural site ranged from 0.002 microg/m(3) for Cd to 1.077 microg/m(3) for Na. However, compared with the urban site, mean Pb concentration showed an almost two-fold enhancement, i.e., 0.163 Vs. 0.327 microg/m(3). Metals and particle size source identification was done using Principal Component Analysis and Cluster Analysis. Five sources were traced out for the urban site: industrial, soil, automobile emissions, metallurgical industries and excavation activities. For the rural site, four sources were recorded: agricultural, automotive emissions, excavation activities and metallurgical units. Collectively, for both the sites, PM(10-100) emerged as a major contributor to TSP, followed by PM(2.5-10), PM(<2.5) and PM(>100) in that order. The metals showed in general positive relationship with fine particulate fractions (PM(2.5-10), PM(<2.5)), and negative correlation with coarse fractions (PM(10-100), PM(>100)). Comparison with the corresponding data from various Asian sites revealed that the levels of Na, K, Fe, Mn, Co and Ni for the present study were lower than those reported for grossly polluted cities of the world.     

The direct influence of ship traffic on atmospheric PM2.5, PM10 and PAH in Venice

The direct influence of ship traffic on atmospheric levels of coarse and fine particulate matter (PM_2.5, PM₁₀) and fifteen polycyclic aromatic hydrocarbons (PAHs) has been estimated in the urban area of Venice. Data analysis has been performed on results collected at three sites over the summer, when ship traffic is at a maximum. Results indicate that monitoring of the PM daily concentrations is not sufficiently detailed for the evaluation of this contribution, even though it could be useful for specific markers such as PAHs. Therefore a new methodology, based on high temporal resolution measurements coupled with wind direction information and the database of ship passages of the Harbour Authority of Venice has been developed. The sampling sites were monitored with optical detectors (DustTrack^® and Mie pDR-1200) operating at a high temporal resolution (20s and 1s respectively) for PM_2.5 and PM₁₀. PAH in the particulate and gas phases were recovered from quartz fibre filters and polyurethane foam plugs using pressurised solvent extraction, the extracts were then analysed by gas chromatography- high-resolution mass spectrometry. Our results shows that the direct contribution of ships traffic to PAHs in the gas phase is 10% while the contribution to PM_2.5 and to PM₁₀ is from 1% up to 8%.