贵阳市大气污染物浓度特征及降水的清除作用

本文利用2013年1月1日~2015年6月30日贵阳市9个环境监测站的6种主要大气污染物(SO2、NO2、O3、PM10、CO、PM2.5)监测数据,分析了贵阳市主要大气污染物的年变化、日变化特征及降水对首要污染物浓度变化的影响。发现SO2、NO2、PM10、CO、PM2.5浓度为单谷型年变化,夏季浓度最低,冬季浓度最高;O3浓度为双峰型年变化,4、10月分别有两个极大值、11~2月与7月分别为两个极小值;SO2、NO2、PM10、CO、PM2.5浓度日变化呈双峰型特征;O3浓度日变化为单峰型特征;郊区SO2、NO2、PM10、CO、PM2.5日平均浓度低于市区,而郊区O3日平均浓度高于市区。降水对O3的湿清除效果不好,对其余大气污染物的湿清除效果较好,尤其夜间降水对颗粒污染物(PM2.5、PM10)的清除效果优于白天降水,但会使O3浓度明显上升。     

关中地区大气污染特征分析

基于2014-2018年关中区域西安、咸阳、渭南、铜川、宝鸡等5个主要城市6种大气污染物(PM10、PM2.5、SO2、CO、NO2、O3)的逐日平均质量浓度和气象探空资料,分析了关中地区大气污染物时空分布特征、重污染过程持续性特征及逆温发生频率.结果表明:关中地区大气污染超标日达31％以上,西安最多为47％,其中颗粒...     

边界层臭氧浓度变化特征及相关因子分析

根据广州市城区麓湖、郊区花都测站的2004年污染物监测数据和气象资料,采用统计分析软件SPSS和Excel分析了广州市臭氧浓度的时间变化特征,包括臭氧浓度的年季变化、周变化及日变化特征,并分析了O3与污染物CO、NOx（NO和NO2）、SO2、PM10以及与气象条件之间的相关性。结果表明：广州市臭氧浓度一年出现2个峰值,分别为6月和10月并且郊区浓度大于城区;一周之中最大浓度出现在周末;O3日平均浓度与NOx、NO、CO、相对湿度负相关性较显著,与PM10和气温正相关性较显著;在气温较高、湿度较低的晴朗少云天气时,易造成广州市臭氧的高浓度。     

边界层臭氧浓度变化特征及相关因子分析

根据广州市城区麓湖、郊区花都测站的2004年污染物监测数据和气象资料,采用统计分析软件SPSS和Excel分析了广州市臭氧浓度的时间变化特征,包括臭氧浓度的年季变化、周变化及日变化特征,并分析了O3与污染物CO、NOx(NO和NO2)、SO2、PM10以及与气象条件之间的相关性。结果表明:广州市臭氧浓度一年出现2个峰值,分别为6月和10月并且郊区浓度大于城区;一周之中最大浓度出现在周末;O3日平均浓度与NOx、NO、CO、相对湿度负相关性较显著,与PM10和气温正相关性较显著;在气温较高、湿度较低的晴朗少云天气时,易造成广州市臭氧的高浓度。     

乌海市空气污染浓度的时间分布特征分析

利用2005—2007年乌海市区大气中SO2、NO2、PM10浓度的实测数据,分析了乌海市大气污染物的时间分布特征,分析结果表明:乌海市首要污染物是PM10,其次是SO2;从2005—2007年日平均值看:SO2、PM10呈下降趋势,NO2略呈上升趋势。SO2、NO2、PM10的浓度都有明显的季节变化。     

高陵区PM2.5污染特征及其与气象要素的关系研究

利用高陵区2018年1月1日—2020年12月31日 PM25质量浓度监测资料、空气质量指数,分析PM25的污染特征,结合气象观测资料, 通过线性相关分析定量分析不同季节PM25质量浓度与气温、相对湿度、风向风速、降水等气象要素之间相关性。结果表明：（1）近3 a来高陵区污染天气首要污染物为PM25的累计时间远超其他污染物为首要污染物的累计时间。（2）PM25平均质量浓度月变化呈明显的“U”型特征,1月最高,2月、12月次之;季节变化规律为冬春高、夏秋低,冬季最高,夏季最低。（3）PM25质量浓度日变化呈单峰单谷特征, 23时为最大峰值,17时左右为谷值,此变化趋势与气温、风速的日变化呈相反趋势,与相对湿度日变化趋势基本一致。（4）不同季节PM25质量浓度和气象要素的相关性存在差异,PM25质量浓度与风速及降水量在各个季节均呈显著负相关,与气温整体上呈负相关,与相对湿度整体呈正相关。（5）PM25质量浓度高值主导风向为偏西北风,其次是东北风,风向偏东和西南时PM25质量浓度值相对较小。     

东胜区污染状况分析

文章利用东胜区主要污染源排放情况以及2005年9月1日-2007年8月31日每日SO2、NO2、PM10浓度监测值和2005-2007年SO2、NO2、PM10平均值,分析了东胜区主要污染类型、污染物来源以及东胜区近3年SO2、NO2、PM10监测值日、月、季、年分布特征和变化规律;提出了消减大气污染物排放的对策。     

聊城市空气质量现状及污染原因分析

利用2013年10月至2014年9月山东省聊城市大气主要污染物监测数据,分析了各种污染物的时空分布特征及其对空气污染的贡献,探讨了聊城市大气污染的成因。结果表明:2013年10月至2014年9月聊城市轻度污染以上的空气质量日数所占比例高达70.0%,大气中SO2、NO2、CO、PM2.5和PM10浓度季节变化规律明显,即冬季各种污染物浓度远高于夏季。日首要污染物以PM2.5和PM10出现日数最多,其次为SO2作为首要污染物在冬季出现偏多,臭氧8 h作为首要污染物在夏季出现相对较多。聊城市5种污染物对空气污染的影响程度从大到小依次为PM2.5PM10NO2COSO2,其中PM2.5与PM10分担率大幅高于其他3种污染物,说明聊城市大气污染属于可吸入颗粒物与细颗粒物主导的类型。相关分析发现,PM2.5和PM10具有来自相同或相似污染源的可能性,扬尘与化石燃料使用是PM2.5和PM10污染的主要成因。     

辽宁中部城市群主要大气污染物时空分布特征的数值模拟

利用美国第三代空气质量模式系统Models-3对2002年1月17～18日辽宁中部城市群大气污染物SO2,NO2和PM10的浓度分布进行了数值模拟,并将模拟结果与监测结果进行了对比分析。结果表明:污染物的模拟值与观测值变化趋势具有一致性,模式反映了SO2,NO2和PM10浓度的时空分布特征和变化规律,再现了污染物浓度呈波峰波谷日变化的重要特征,可用于辽宁中部城市群区域大气污染物的研究。     

海口市空气污染特征分析及预报检验

利用海南省二次开发的CAPPS2.0模式,对2006年1月1日~2007年1月1日海口市逐日PM10、SO2、NO2污染浓度监测资料进行输出分析,得出海口市空气污染的变化特征。结果表明,污染物SO2和NO2的预报效果较好,而PM10预报效果较差。因此采用多元线性回归分析方法建立污染物浓度与气象要素的预报方程,并对PM10进行优化和校正,从而提高预报准确率。     

Estimation of transboundary transport contribution to the air pollution in the far east region using the chemistry transport model

The CHIMERE mesoscale chemistry transport model is used for the quantitative assessment of the contribution of transboundary transport of anthropogenic admixtures from China to the surface concentrations of major suspended pollutants, aerosol PM₁₀, ozone O₃, and nitrogen oxides NO_x in the Far Eastern region. Analyzed in detail are the time series of concentration of mentioned substances computed with the model taking account and not taking account of anthropogenic emissions in China. It is revealed that the transboundary transport of anthropogenic pollutants can cause the recurring episodes of manyfold increase in the concentration of PM₁₀ in the south of Khabarovsk region, as well as more rare variations of O₃ and NO_x concentration. The trajectory and synoptic analysis demonstrated that the episodes of the increase in the concentration of PM₁₀ and O₃ in the south of the region mainly depend on the carryover of air masses from northeastern China in the front part of continental cyclones.     

基于京津冀高密度地面观测网络的大气污染物浓度地面观测代表性误差估计

地面观测提供空间点的浓度信息,三维化学模式提供网格面的浓度信息,两者在进行对比验证或同化融合时会因为空间尺度不匹配引入误差,即观测代表性误差。本研究将大气污染地面国控监测站与区县监测站结合起来,获得了京津冀地区高密度地面观测数据,利用该数据首次对京津冀地区6项常规大气污染物（PM_2.5、PM₁₀、SO₂、NO₂、CO和O₃）的地面观测代表性误差进行了客观估计,并与Elbern et al.（2007）方法估计的代表性误差进行了对比。结果发现:两种方法对京津冀地区NO₂地面观测代表性误差估计非常接近,但Elbern et al.（2007）方法显著低估了SO₂、CO和O₃地面观测的代表性误差。在此基础上,我们对Elbern et al.（2007）方法及其误差特征参数进行了本地化修正,并增加了PM_2.5和PM₁₀的代表性误差特征参数,建立了京津冀大气污染地面观测代表性误差的客观估计方法。     

基于空气质量模拟的江苏省大气污染物排放清单比较研究

大气污染物排放清单是空气质量模拟和空气污染治理的重要依据.本研究比较分析了两套覆盖江苏省的2017年大气污染物排放清单，即分别由上海市环境科学研究院、江苏省环境科学研究院编制的"长三角清单"和"江苏省清单"，并结合区域空气质量模型CMAQ评估不同清单对长三角地区2017年1、4、7、10月的空气质量模拟的影响.清单比较结果表明，除二氧化硫（SO₂）以外，江苏省清单估算的各污染物排放量较长三角清单低.通过与观测数据比较，发现两套清单对SO₂、氮氧化物（NO_x）、臭氧（O₃）和细颗粒物（PM_2.5）的模型模拟性能均较好.江苏省清单与长三角清单两者的模拟结果空间分布接近，其中江苏省清单模拟的PM_2.5和O₃在长三角多数地区略低于长三角清单的模拟结果（1月O₃除外）.江苏省清单与长三角清单均能够用于空气质量模式模拟，可为江苏地区的细颗粒物和光化学烟雾污染的控制策略制定提供参考.     

基于极端随机树方法的WRF-CMAQ-MOS模型研究

随着城市化、工业化的快速发展,空气污染已经成为了公众最关注的问题之一。为了提高空气质量预报的准确度,以多尺度空气质量模型（Community Multi-Scale Air Quality,CMAQ）为工具,结合中尺度WRF（Weather Research and Forecast Model）气象预报数据、气象观测数据、污染物浓度观测数据,基于极端随机树方法建立了WRF-CMAQ-MOS（Weather Research and Forecast Model-Community Multi-Scale Air Quality-Model Output Statistics）统计修正模型。结果表明,结合WRF气象预报的CMAQ-MOS方法明显修正了由于模型非客观性产生的模式预报偏差,提高了预报效果。使用线性回归方法不能获得较好的优化效果,选取极端随机树方法和梯度提升回归树方法对模型进行改进和比较,发现极端随机树方法对结合WRF气象要素的CMAQ-MOS模型有较大的提升。针对徐州地区空气质量预报,进一步使用基于极端随机树方法的WRF-CMAQ-MOS模型对2016年1、2、3月的空气质量指数（AQI）及PM_2.5、PM₁₀、NO₂、SO₂、O₃、CO六种污染物优化试验进行验证,发现优化效果最为明显的两种污染物分别是NO₂及O₃,2016年1、2、3月整体相关系数NO₂由0.35升至0.63,O₃由0.39升至0.79,均方根误差NO₂由0.0346减至0.0243 mg/m3,O₃由0.0447减至0.0367 mg/m3。文中发展的WRF-CMAQ-MOS统计修正模型可以有效提升预报精度,在空气质量预报中具有很好的应用前景。      

Local and regional sources of fine and coarse particulate matter based on traffic and background monitoring

The aim of this study was to identify local and exogenous sources affecting particulate matter (PM) levels in five major cities of Northern Europe namely: London, Paris, Hamburg, Copenhagen and Stockholm. Besides local emissions, PM profile at urban and suburban areas of the European Union (EU) is also influenced by regional PM sources due to atmospheric transport, thus geographical city distribution is of a great importance. At each city, PM₁₀, PM_2.5, NO₂, SO₂, CO and O₃ air pollution data from two air pollution monitoring stations of the EU network were used. Different background characteristics of the selected two sampling sites at each city facilitated comparisons, providing a more exact analysis of PM sources. Four source apportionment methods: Pearson correlations among the levels of particulates and gaseous pollutants, characterisation of primal component analysis components, long-range transport analysis and extrapolation of PM size distribution ratios were applied. In general, fine (PM_2.5) and coarse (PM₁₀) particles were highly correlated, thus common sources are suggested. Combustion-originated gaseous pollutants (CO, NO₂, SO₂) were strongly associated to PM₁₀ and PM_2.5, primarily at areas severely affected by traffic. On the contrary, at background stations neighbouring important natural sources of particles or situated in suburban areas with rural background, natural emissions of aerosols were indicated. Series of daily PM_2.5/PM₁₀ ratios showed that minimum fraction values were detected during warm periods, due to higher volumes of airborne biogenic PM coarse, mainly at stations with important natural sources of particles in their vicinity. Hybrid single-particle Lagrangian integrated trajectory model was used, in order to extract 4-day backward air mass trajectories that arrived in the five cities which are under study during days with recorded PM₁₀ exceedances. At all five cities, a significantly large fraction of those trajectories were classified in short- and medium-range clusters, thus transportation of particulates along with slow moving air masses was identified. A finding that supports the assumption of long-range transport is that, at background stations, long-range transportation effects were stronger, in comparison to traffic stations, due to less local particle emissions. Short-range trajectories associated to PM transport in Stockholm, Copenhagen and Hamburg were mainly of a continental origin. All three cities were approached by slow moving air masses originated from Poland and the Czech Republic, whereas Copenhagen and Stockholm were also influenced by short-range trajectories from Germany and France and from Jutland Peninsula and Scandinavian Peninsula, respectively. London and Paris are located to the north-west part of Europe. Trajectories of short and medium length arrived to these two megacities mainly through France, Germany, UK and North Atlantic.     

Chemical characterization and multivariate analysis of atmospheric PM<Subscript>2.5</Subscript> particles

The new European Council Directive (PE-CONS 3696/07) frames the inhalable (PM₁₀) and fine particles (PM_2.5) on priority to chemically characterize these fractions in order to understand their possible relation with health effects. Considering this, PM_2.5 was collected during four different seasons to evaluate the relative abundance of bulk elements (Cl, S, Si, Al, Br, Cu, Fe, Ti, Ca, K, Pb, Zn, Ni, Mn, Cr and V) and water soluble ions (F⁻, Cl⁻, NO₂ ⁻, NO₃ ⁻, SO₄ ²⁻, Na⁺, NH₄ ⁺, Ca²⁺ and Mg²⁺) over Menen, a Belgian city near the French border. The air quality over Menen is influenced by industrialized regions on both sides of the border. The most abundant ionic species were NO₃ ⁻, SO₄ ²⁻ and NH₄ ⁺, and they showed distinct seasonal variation. The elevated levels of NO₃ ⁻ during spring and summer were found to be related to the larger availability of the NO_x precursor. The various elemental species analyzed were distinguished into crustal and anthropogenic source categories. The dominating elements were S and Cl in the PM_2.5 particles. The anthropogenic fraction (e.g. Zn, Pb, and Cu) shows a more scattered abundance. Furthermore, the ions and elemental data were also processed using principal component analysis and cluster analysis to identify their sources and chemistry. These approach identifies anthropogenic (traffic and industrial) emissions as a major source for fine particles. The variations in the natural/anthropogenic fractions of PM_2.5 were also found to be a function of meteorological conditions as well as of long-range transport of air masses from the industrialized regions of the continent. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Development and performance evaluation of statistical models correlating air pollutants and meteorological variables at Pantnagar,India

Ambient air quality in respect of SO₂, NO₂ and total suspended particulate matter (TSPM) was monitored at Pantnagar, India from May, 2008 to April, 2009 and statistically analyzed with meteorological variables such as relative humidity (RH), wind speed (WS), precipitation (P) and mean air temperature (T). TSPM was found to be the major air pollutant causing significant deterioration of air quality with annual mean concentrations of 280 μg/m³. Further, weekly mean air pollutant concentrations were statistically analyzed through stepwise multiple linear regression analysis in respect of independent meteorological variables to develop suitable statistical models. Both NO₂ and TSPM concentrations were found to have been influenced by meteorological variables with coefficient of determination (R²) of 82.21 and 92.84%, respectively. However, atmospheric SO₂ revealed only 22.87% of dependencies on meteorological variables. Partial correlation coefficients revealed that wind speed has the maximum influence (77.80 and 31.50%) on proposed equations for NO₂ and SO₂, closely followed by weekly mean temperature (73.60 and 24.30%). However, in case of TSPM, individual contribution of ambient temperature (94.40%) was found maximum, followed by relative humidity (86.50%). Model performances were evaluated through both quantitative data analysis techniques and statistical methods. Nearly 98 and 95% of potential error has been explained by the model developed for TSPM and NO₂, while in case of SO₂, it is found as only 61%. Therefore, performances of models (for TSPM and NO₂) to predict ambient weekly mean concentrations based on forecasted weather parameters were found to be excellent, however, performance of model developed for SO₂ was found only satisfactory.     

Particulate matter and gaseous pollutants during a tropical storm and air pollution episode in Southern Taiwan

Gaseous pollutants and PM_2.5 aerosol particles were investigated during a tropical storm and an air pollution episode in southern Taiwan. Field sampling and chemical analysis of particulate matter and gaseous pollutants were conducted in Daliao and Tzouying in the Kaohsiung area, using a denuder-filter pack system during the period of 22 October to 3 November 2004. Sulfate, nitrate and ammonium were the major ionic species in the PM_2.5, accounting for 46 and 39% of the PM_2.5 for Daliao and Tzouying, respectively. Higher PM_2.5, Cl^?, NO₃^? and NH₄⁺, HNO₂ and NH₃ concentrations were found at night in both stations, whereas higher HNO₃ was found during the day. In general, higher PM_2.5, HCl, NH₃, SO₂, Cl^?, NO₃^?, SO₄^2? and NH₄⁺ concentrations were found in Daliao. The synoptic weather during the experiment was first influenced by Typhoon NOCK-TEN, which resulted in the pollutant concentrations decreasing by about two-thirds. After the tropical thunderstorm system passed, the ambient air quality returned to the previous condition in 12 to 24 h. When there was a strong subsidence accompanied by a high-pressure system, a more stable environment with lower wind speed and mixing height resulted in higher PM_2.5, as well as HNO₂, NH₃, SO₄^2?, Cl^?, NO₃^?, NH₄⁺ and K⁺ concentrations during the episode days. The rainfall is mainly a scavenger of air pollutants in this study, and the stable atmospheric system and the high emission loading are the major reasons for high air pollutant concentrations.     

The fine and coarse particulate matter at four major Mediterranean cities: local and regional sources

Particulate air pollution is associated with adverse health effects to the population exposed. The aim of this paper is the identification of local and regional sources, affecting PM₁₀ and PM_2.5 levels in four large cities of southern Europe, namely: Lisbon, Madrid, Marseille, and Rome. Air pollution data from seven sampling sites of the European Union network were used. These stations were selected due to their ability of monitoring PM_2.5 concentrations and providing reliable series of data. Each station’s background was also taken into account. Pearson correlation coefficients and primal component analysis components were extracted separately for cold and warm periods in order to define the relationships among particle matters (PM_s) and gaseous pollutants (CO, NO₂, SO₂, and O₃) and evaluate the contributions of local sources. Possible seasonal variations of PM_2.5/PM₁₀ ratio daily values were also used as markers of PM sources, influencing particulate size distribution. Particle emissions were primarily attributed to traffic and secondarily to natural sources. Minimum daily values of PM_2.5/PM₁₀ ratio were observed during warm periods, particularly at suburban stations with rural background, due to dust resuspension and also due to the increase of biogenic coarse PM (pollen, dust, etc.). Hybrid Single-Particle Lagrangian Integrated Trajectory Model trajectory model was used in order to compute the 4-day backward trajectories of the air masses that affected the four cities which are under study during days with recorded PM₁₀ exceedances, within a 5-year period (2003–2007), at 300, 750, and 1,500 m above ground level (AGL). The trajectories were then divided to clusters with a K-means analysis. In all four cities, the influence of slow-moving air masses was associated with a large fraction of PM₁₀ exceedances and with high average and maximum daily mean PM₁₀ concentrations, principally at the 300 m AGL analysis. As far the issue of the increased PM₁₀ concentrations, the results were weaker in Marseille and particularly in Rome, probably due to their greater distance from Northwest Africa, in comparison to Madrid and Lisbon. Dust intrusions from the Sahara desert and transportation of Mediterranean/Atlantic sea spray, were characterized as primary regional sources of exogenous PM₁₀ in all four cities. Continental trajectories from the industrialized northern Italy affected PM₁₀ levels particularly in Marseille and Rome, due to their more eastern geographical position.     

Statistical analysis of PM10 measurements in Cordoba city, Argentina

Summary Daily sampling of atmospheric PM₁₀ particulate was carried out using a continuously weighing, Tapered Element Oscillating Microbalance (TEOM) equipped with a PM₁₀ size selective inlet. The TEOM collects PM₁₀ on a small filter interfaced with an inertial mass transducer, which allows near continuous weighing of the filter as the deposit accumulates. The sampler was sited at several urban and sub-urban places in Córdoba City, Argentina. With the complete data set of chemical and meteorological variables (CO, NO_x, O₃, wind speed, wind direction, ambient temperature, total and UV radiation, pressure and relative humidity, etc.) a stepwise regression was performed to select which variables have a major influence on the amount of PM₁₀ measured. Results are presented from the June 1995–May 1996 field campaign. Data for PM₁₀ values largely exceeded the one day standard average value of 150 g m^–3 during several days. The largest amount of particulate has been measured during the winter season. The primary aim of this work is to define the concentration characteristics of ambient PM₁₀ at each site where this pollutant has been measured and to examine the seasonal variation of PM₁₀.With 3 Figures