广州市城区站点冬季PM2.5时间变化及碳氮同位素组成特征

于2018年冬季在广州城区磨碟沙站点开展细颗粒物(PM_2.5)样品采集,并获得PM_2.5中水溶性离子、含碳组分、稳定碳氮同位素的组成及时间变化特征,重点讨论了PM_2.5浓度升高时段的化学组成特征变化,进而利用稳定碳氮同位素变化特征探究了主要污染来源。结果表明：采样期间,研究站点PM_2.5平均质量浓度为22.1μg/m³,共出现两个PM_2.5浓度水平升高时段,所对应的平均质量浓度分别达46.0μg/m³和63.0μg/m³。风速降低、温度升高等不利气象条件是导致上述时段PM_2.5浓度上升的重要原因。在上述时段,伴随着PM_2.5浓度的升高,NO^-₃和NH⁺₄浓度均出现显著升高,NO^-₃与SO^2-₄的摩...     

基于KZ滤波法的江淮地区PM2.5浓度变化影响分析

基于2018—2020年合肥、芜湖和马鞍山3个城市国控站点的PM_2.5逐日监测数据和同期地面气象观测资料,利用Kolmogorov-Zurbenko(KZ)滤波对PM_2.5日浓度的原始时间序列进行分解,获取短期分量、季节分量和长期分量,并进行多元线性逐步回归构建各分量与气象因子的模型,最后依据短期分量和基线分量的回归模型和残差分析,对序列进行重建,获取消除气象条件影响的PM_2.5长期分量。KZ滤波分析结果表明：2018—2020年气象条件对江淮区域PM_2.5污染改善影响存在波动,在2018—2019年为负贡献,而在2020年秋冬季则变为正贡献;江淮地区3个城市2018年和2020年PM_2.5修正后的长期分量均值表明气象条件对各市PM_2.5改善影响存在差异较大,气象条件对合肥PM_2.5改善的贡献仅为1.0%,芜湖为7.8%,马鞍山为21.0%;NAQPMS数值模式情景分析结果显示,减排措施对江淮之间PM_2.5浓度改...     

苏州大气颗粒物PM2.5和PM10质量浓度异常“倒挂”现象的成因及影响分析

为深入研究PM_2.5和PM₁₀质量浓度异常“倒挂”现象的成因及影响,在苏州市相城区国控点开展比对监测分析,回顾性分析了2016—2020年苏州全部国控点颗粒物浓度数据。苏州市相城区国控点PM_2.5浓度的比对分析结果表明：该国控点频繁出现PM_2.5浓度高于其他国控点PM_2.5浓度和高于该站点PM₁₀浓度(“倒挂”率高达34%)的“双高”现象,PM_2.5平均浓度比其他9个国控点高12.5%～37.2%,比位于同一站点的备用监测仪器(“倒挂”率为0)高38.1%。2016—2020年,苏州全部国控点“倒挂”时间的总体趋势都是逐年递增,且集中发生在相对湿度较高的20:00至次日07:00。这5年间各国控点PM_2.5浓度异常偏高导致的异常“倒挂”现象对全市年均浓度产生的正误差分别为1.6%、2.8%、6.0%、6.2%和4.1%,基本呈现出逐年递增的趋势。上述结果表明：苏州PM_2.5浓度偏高是由动态加...     

基于不同时间尺度综合指数贡献率的攀枝花市大气污染特征分析

选取2016—2022年攀枝花市环境空气质量监测数据，基于年度、季度、月度、小时时间尺度评价环境空气质量综合指数贡献率。结果表明，攀枝花市污染类型已由传统的单一工业型污染转变为复合型污染，且污染物在不同时间尺度下的分布特征明显。从季度尺度来看，第一季度细颗粒物(PM_2.5)综合指数贡献率较大，第二和第三季度臭氧(O₃)综合指数贡献率较大，第四季度二氧化氮(NO₂)及PM_2.5综合指数贡献率较大。从月度尺度来看，O₃在3—8月的贡献较大，PM_2.5、NO₂、可吸入颗粒物(PM₁₀)、一氧化碳(CO)、二氧化硫(SO₂)在1—2月及11—12月的贡献较大。从小时尺度来看，O₃在13—19点贡献较大，PM₁₀、PM_2.5、CO在8—13点贡献较大，SO₂在9—13点贡献较大，NO₂在8—1...     

襄阳市秋冬季PM2.5污染特征及潜在源区分析

为了解襄阳市秋冬季PM_2.5的污染特征及来源，基于2020年11月至2021年1月在线监测数据，对PM_2.5质量浓度、气象因素、化学组分、来源及潜在源区进行了分析。结果表明，襄阳市秋冬季污染天首要污染物均为PM_2.5,且随污染程度加重，PM_2.5与PM₁₀质量浓度比呈上升趋势，二次颗粒物的形成对PM_2.5的贡献更高。在PM_2.5化学组分中，水溶性离子占比最大，随着污染程度加重，二次离子(SNA)快速增长，二次离子的生成转化是污染的重要成因。轻度、中度污染时，湿度高、风速小、气温低，有利于污染的积累，重度污染时湿度大、风速回升，有利于上游污染的输送与二次转化。PMF模型解析出襄阳市PM_2.5主要来源及贡献率为二次源58.0%、工业企业源22.6%、机动车源10.7%、扬尘源8.7%。襄阳市潜在源区主要分布在河南省中北部、河北省南部、山东省西部、安徽省北部、江汉平原东部及南部区域，极少量分布在襄阳区域，长距离区域传输...     

成都气象要素等级对大气污染的改善作用研究

为找到对成都空气质量有改善作用的气象要素等级，量化成都气象条件对大气污染物的影响，对2015—2018年成都环境监测站空气质量监测数据和温江国家气候观象台同时段的气象观测资料进行了研究分析。结果表明：(1)成都中部地区的空气质量污染较为严重，全年主要以PM_2.5和O₃污染为主，占比达75%,2015—2018年成都的PM_2.5污染呈下降趋势，而O₃污染虽有所波动但没有显著改善。(2)PM_2.5持续重污染过程中，过去12 h降水量和过去24 h降水量均大于1 mm,均为有效降水，大于2.5 mm对PM_2.5污染的改善作用显著增强；相对湿度低于70%对中度及以上PM_2.5污染的改善作用同步增加，低于40%对轻度PM_2.5污染的改善作用较为明显，优良天气的发生概率显著增加；风速大于1 m/s时PM_2.5浓度随风速的增大而减小，大于2 m/s对PM_2.5浓度的改善作用显著加强。...     

新冠疫情管控期间西安细颗粒物中碳组分浓度变化特征

于2019年1月27日—3月18日及2020年1月27日—3月18日对西安市细颗粒物(PM_2.5)的碳组分浓度进行了在线观测,对比分析了非疫情与疫情期间各常规污染因子、气象要素、PM_2.5中有机碳(OC)和元素碳(EC)的污染特征。结果表明：非疫情与疫情期间西安市的气象条件总体水平较为相近。疫情期间的二氧化硫(SO₂)、臭氧(O₃)浓度相对升高。重污染天气下,除PM_2.5外,其他污染物浓度均降低,说明疫情管制对重污染天气污染物浓度的削弱作用明显。疫情期间,PM_2.5中的OC组分浓度及占比有显著升高,与疫情期间的各类交通管制导致的机动车尾气排放量显著降低有关。另外,OC与EC的相关性较强,说明污染来源与人类日常生活有关。疫情期间西安市颗粒物中碳组分主要来自各类生物质燃烧,并且存在SOC污染,SOC在OC中的占比达到37.8%。疫情期间重污染天气下,SOC在OC中的占比达到87.5%,说明SOC对重污染天气OC的贡献较大。     

临汾市大气污染物的时间分布特征与源区分析

多年来,临汾市多次名列我国生态环境部公布的空气质量最差的重点城市之列,对其大气污染的时间分布特征和潜在源区进行分析对其环境管理与污染防治具有重要意义。利用2015—2019年临汾市5个国控空气环境质量监测站点的6种空气污染物(SO₂、NO₂、CO、O₃、PM_2.5和PM₁₀)浓度数据和气象观测数据,使用HYSPLIT模型研究了该市空气污染物的时间变化特征、轨迹输送特征和可能的来源。结果表明,PM_2.5和PM₁₀的年均浓度均超过了《环境空气质量标准》(GB 3095—2012)Ⅱ级标准,SO₂仅在2016—2017年超过该标准,其余3种污染物的年均浓度均低于该标准。6种污染物2015—2019年的月均浓度的变化特征表现为O₃浓度呈以6、7月为中心的近似正态分布,SO₂、NO₂和CO以及PM_2.5和PM₁₀浓...     

基于复合LSTM模型的PM2.5浓度预测

在对淄博市19个空气质量监测站点监测数据进行分析后,提出了一种基于机器学习的复合模型——灰色关联度分析(GRA)-改进的完备总体经验模态分解(ICEEMD)-长短期记忆网络(LSTM)模型。通过分析淄博市2019年大气污染物和气象数据,选用LSTM模型预测PM_2.5浓度。由于传统单一模块机器学习模型具有训练时间较长和预测精度较低的问题,提出了复合LSTM模型。该模型由3部分组成：GRA,用于PM_2.5浓度影响因素变量筛选;ICEEMD,用于PM_2.5分解、分量筛选和原始大气污染物及气象数据处理;LSTM,用于PM_2.5浓度预测。预测结果表明：淄博市中部丘陵地带PM_2.5浓度高于南部山区和北部平原,东部高于西部;淄博市逐月PM_2.5浓度呈“U”形分布,1月最高,8月最低;淄博市PM_2.5浓度受PM₁₀和CO影响较大,受湿度和温度影响较小。对比单一LSTM模型和GRA-LSTM模型,GRA-ICEEMD-LSTM模型...     

2019—2021年徐州市大气细颗粒物化学组成特征及来源解析

徐州市地处四省交界,大气污染物来源复杂,颗粒物污染在气象条件不利时较为显著。通过地面观测数据、颗粒物组分连续观测、源解析及轨迹溯源等方法,对徐州市2019—2021年颗粒物变化特征进行了全面分析,以定量解析各类污染源的贡献,识别对颗粒物贡献显著的化学成分。结果表明：PM_2.5各组分质量占比中二次无机盐和有机碳相对较高,其中二次无机盐SNA(SO■、NO^-₃、NH⁺₄)占比达到59.1%～62.7%;水溶性离子总浓度逐年降低,但Mg²⁺和Ca²⁺浓度2021年分别同比增加2%和12.5%,说明扬尘污染存在反弹。秋冬季水溶性离子明显较高,Cl^-浓度明显高于其他季节,表明徐州市秋冬季受移动源、燃煤源和二次有机气溶胶共同影响。PM_2.5中OC与EC质量浓度比为4.88～8.40,说明徐州市颗粒物受多源影响,柴油、汽油机动车尾气排放以及燃煤排放对颗粒物贡献较大。后向轨迹分析表明,污染严重的1月污染物主...     

石家庄市大气颗粒物元素组分特征分析

为研究石家庄市大气颗粒物的污染特征及其来源,于2013年4—5月在主城6区分别采集TSP、PM10和PM2.5颗粒物样品,利用ICP-MS分析其中的22种元素浓度。结果表明,石家庄市城区Ca、Fe元素在各粒径颗粒物中含量都较高,PM2.5中的S、K含量较高,PM10和TSP中Mg、Al的浓度相对较高。颗粒物的主要来源为燃煤尘、道路尘和建筑尘,TSP、PM10和PM2.5具有较好的统计相关性和同源性。     

Characterisation of PM10, PM2.5 and Benzene Soluble Organic Fraction of Particulate Matter in an Urban Area of Kolkata, India

In this study, the relationship between inhalable particulate (PM₁₀), fine particulate (PM_2.5), coarse particles (PM_{2.5 – 10}) and meteorological parameters such as temperature, relative humidity, solar radiation, wind speed were statistically analyzed and modelled for urban area of Kolkata during winter months of 2003–2004. Ambient air quality was monitored with a sampling frequency of twenty-four hours at three monitoring sites located near traffic intersections and in an industrial area. The monitoring sites were located 3–5 m above ground near highly trafficked and congested areas. The 24 h average PM₁₀ and PM_2.5 samples were collected using Thermo-Andersen high volume samplers and exposed filter papers were extracted and analysed for benzene soluble organic fraction. The ratios between PM_2.5 and PM₁₀ were found to be in the range of 0.6 to 0.92 and the highest ratio was found in the most polluted urban site. Statistical analysis has shown a strong positive correlation between PM₁₀ and PM_2.5 and inverse correlation was observed between particulate matter (PM₁₀ and PM_2.5) and wind speed. Statistical analysis of air quality data shows that PM₁₀ and PM_2.5 are showing poor correlation with temperature, relative humidity and solar radiation. Regression equations for PM₁₀ and PM_2.5 and meteorological parameters were developed. The organic fraction of particulate matter soluble in benzene is an indication of poly aromatic hydrocarbon (PAH) concentration present in particulate matter. The relationship between the benzene soluble organic fraction (BSOF) of inhalable particulate (PM₁₀) and fine particulate (PM_2.5) were analysed for urban area of Kolkata. Significant positive correlation was observed between benzene soluble organic fraction of PM₁₀ (BSM10) and benzene soluble organic fraction of PM_2.5 (BSM2.5). Regression equations for BSM10 and BSM2.5 were developed.     

Particulate matter concentration levels during intense haze event in an urban environment

This study assessed concentration levels of particulate matter (PM) in the ambient environment of Ilorin metropolis, Nigeria, during haze episodes. Meteorological data (wind speed and direction, rainfall data, sunshine data, relative humidity and temperature) were obtained. Aerocet 531S particle counter (MetOne Instruments, USA) was used to measure four mass concentration ranges of PM (PM_1.0, PM_2.5, PM₁₀ and the total suspended particles (TSP)) in 10 locations taking into consideration land use patterns. Surfer® version 8 (Golden Software LLC, USA) was used to model the spatial variation of particulate matter concentration levels using kriging interpolation griding method. Human exposure assessment was done using the total respiratory deposition dose (TRDD) estimates and statutory limit breach (SLB) approaches. The appearance of dominating weak southern atmospheric wind flow was observed as wind speed ranged from 0 to 6.811 m/s while solar radiation periods ranged from 0.3 to 3.5 h/day. The relative humidity of the metropolis ranged between 28 and 57%, while daily temperature was 15 to 36 °C. Highest concentration levels of PM measured were 73.4, 562.7, 7066.3 and 9907.8 μg/m³ for PM_1.0, PM_2.5, PM₁₀ and TSP, respectively. Very strong negative correlations existed between the PM concentration levels and microclimatic parameters. Spatial variation of the concentration level as modelled using Surfer® version 8 indicated that particulate concentration level increases from south to north. Concentration levels of PM for the 24-h averaging period were generally above the 24-h threshold limit value set by the regulatory agencies for all the locations.     

北京地区不同季节PM2.5和PM10浓度对地面气象因素的响应

利用2013年1月—2014年12月北京地区PM_(2.5)和PM_(10)监测数据和同期近地面气象观测数据,采用非参数分析法(Spearman秩相关系数)研究了北京地区PM_(2.5)和PM_(10)的浓度对不同季节地面气象因素的响应。结果表明:北京地区大气颗粒物浓度水平具有明显的季节特征,冬季大气颗粒物污染最严重,夏季最轻。不同季节影响颗粒物浓度水平的气象因素各不相同,其中风速和日照时数为主要影响因素。PM_(2.5)和PM_(10)质量浓度对气象因素变化的响应程度也有较大区别,PM_(2.5)/PM_(10)比值冬季最高,PM_(2.5)影响最大,春季最低,PM_(10)影响最大。这些结论可对制订科学有效的大气污染控制策略提供参考。     

基于空间计量模型的四川省大气污染特征及影响因素分析

四川省细颗粒物污染问题越来越受到重视,为有效识别四川省大气污染空间分布情况及影响因素,利用2015—2020年PM_2.5监测数据,综合分析了四川省大气污染时空分布特征,选取同期气象要素观测数据和社会经济数据,区分出全省及省内不同经济区大气污染的主要影响因素。结果表明:2015—2020年四川省的PM_2.5浓度逐年下降,日变化存在明显的双峰双谷趋势,且具有明显季节性特征,空间分布上具有明显的空间聚集现象;PM_2.5的排放与人口密度、经济水平和气温呈显著正相关,与城市绿化、风速呈显著负相关。该研究为经济增长方式优化、产业结构调整、绿化水平改善等提供了政策建议,可为污染防治、优化人居环境提供参考。     

Characterization and Identification of the Sources of Chromium, Zinc, Lead, Cadmium, Nickel, Manganese and Iron in Pm10 Particulates at the Two Sites of Kolkata, India

Monitoring of ambient PM₁₀ (particulate matter which passes through a size selective impactor inlet with a 50% efficiency cut-off at 10 μm aerodynamic diameter) has been done at residential (Kasba) and industrial (Cossipore) sites of an urban region of Kolkata during November 2003 to November 2004. These sites were selected depending on the dominant anthropogenic activities. Metal constituents of atmospheric PM₁₀ deposited on glass fibre filter paper were estimated using Inductively Coupled Plasma Atomic Emission Spectrometer (ICP-AES). Chromium (Cr), zinc (Zn), lead (Pb), cadmium (Cd), nickel (Ni), manganese (Mn) and iron (Fe) are the seven toxic trace metals quantified from the measured PM₁₀ concentrations. The 24 h average concentrations of Cr, Zn, Pb, Cd, Ni, Mn and Fe from ninety PM₁₀ particulate samples of Kolkata were found to be 6.9, 506.1, 79.1, 3.3, 7.4, 2.4 and 103.6 ng/m³, respectively. The 24 h average PM₁₀ concentration exceeded national ambient air quality standard (NAAQS) as specified by central pollution control board, India at both residential (Kasba) and industrial (Cossipore) areas with mean concentration of 140.1 and 196.6 μg/m³, respectively. A simultaneous meteorology study was performed to assess the influence of air masses by wind speed, wind direction, rainfall, relative humidity and temperature. The measured toxic trace metals generally showed inverse relationship with wind speed, relative humidity and temperature. Factor analysis, a receptor modeling technique has been used for identification of the possible sources contributing to the PM₁₀. Varimax rotated factor analysis identified four possible sources of measured trace metals comprising solid waste dumping, vehicular traffic with the influence of road dust, road dust and soil dust at residential site (Kasba), while vehicular traffic with the influence of soil dust, road dust, galvanizing and electroplating industry, and tanning industry at industrial site (Cossipore).     

沿海城市PM10和PM2.5自动监测过程中质量浓度“倒挂”现象分析与成因探讨

2020年2—3月,位于福建沿海地区中部的莆田市在环境空气质量自动监测过程中出现了严重的PM_(10)和PM_(2.5)质量浓度"倒挂"现象,小时值"倒挂"率为19.86%,日均值"倒挂"率为16.67%。在高相对湿度和低风速气象条件下,颗粒物会出现严重的"倒挂"现象,"倒挂"过程中常伴随着颗粒物和气态污染物(SO_2、NO_2和CO)质量浓度的增加。因此,于2020年2月16日—3月26日开展了颗粒物自动监测和手工监测比对,并结合气象参数、气态污染物质量浓度,以及PM_(10)和PM_(2.5)中水溶性离子和液态水的含量特征,进一步探讨了莆田市颗粒物质量浓度"倒挂"的主要成因。研究表明,PM_(10)和PM_(2.5)自动监测仪器检测原理的差异是导致颗粒物质量浓度"倒挂"的重要原因之一,而气象条件(相对湿度、气温和风速等)、颗粒物质量浓度、颗粒物中主要吸湿组分(NO_3~-、SO_4~(2-)和NH_4~+)和液态水的含量也是颗粒物质量浓度"倒挂"的主要影响因素。莆田市2020年2—3月出现高频率"倒挂"现象是多重因素共同作用的结果,解决该问题需要同时考虑监测仪器检测原理、气象参数、颗粒物质量浓度和吸湿组分等的影响。     

呼和浩特市空气颗粒物影响因素研究

选择呼和浩特市城东腾飞路中段为研究地,在内蒙古环境监测中心站实验大楼楼顶设置空气质量自动监测点,于2009年11月至2012年12月连续3年对该区域空气质量进行研究性监测。采用数理统计方法,对实测数据进行分析和研究,利用AQI评价的结果显示,颗粒物已经成为影响当地空气质量的主要污染物;PM2.5/PM10年平均值为0.47,随季节变化明显,春夏季比值偏小(春季为0.361,夏季为0.398),秋冬季比值偏大(秋季为0.499,冬季为0.635);受风速和湿度影响明显,比值与风速在0.01水平上呈现显著负相关,与湿度呈现显著正相关;利用3年污染物数据建立回归方程,经检验所建立的方程有效,说明其他污染物对PM2.5浓度变化有较显著影响。     

Influence of Meteorological Parameters on Particulates and Atmospheric Pollutants at Taichung Harbor Sampling Site

Atmospheric aerosol particles and metallic concentrations, ionic species were monitored at the Experimental harbor of Taichung sampling site in this study. This work attempted to characterize metallic elements and ionic species associated with meteorological conditions variation on atmospheric particulate matter in TSP, PM_2.5, PM_2.5–10. The concentration distribution trend between TSP, PM_2.5, PM_2.5–10 particle concentration at the TH (Taichung harbor) sampling site were also displayed in this study. Besides, the meteorological conditions variation of metallic elements (Fe, Mg, Cr, Cu, Zn, Mn and Pb) and ions species (Cl⁻, NO₃ ⁻, SO₄ ²⁻, NH₄ ⁺, Mg²⁺, Ca²⁺ and Na⁺) concentrations attached with those particulate were also analyzed in this study. On non-parametric (Spearman) correlation analysis, the results indicated that the meteorological conditions have high correlation at largest particulate concentrations for TSP at TH sampling site in this study. In addition, the temperature and relative humidity of meteorological conditions that played a key role to affect particulate matter (PM) and have higher correlations then other meteorological conditions such as wind speed and atmospheric pressure. The parameter temperature and relative humidity also have high correlations with atmospheric pollutants compared with those of the other meteorological variables (wind speed, atmospheric pressure and prevalent wind direction). In addition, relative statistical equations between pollutants and meteorological variables were also characterized in this study.