银川大气污染物浓度变化特征及其与气象条件的关系

利用2013年银川地区6个监测点污染物质量浓度和同期气象要素数据,对区域内污染物浓度变化特征及其与气象条件的关系进行分析。结果表明：银川市区PM10年均值超标0.7倍,PM_2.5年均值超标0.4倍,SO₂和NO₂也有一定程度超标,CO和O₃未超标|1、2、11月和12月为SO₂、NO₂、PM₁₀、PM_2.5、CO质量浓度较高月,O₃浓度最高月为5月,次高月为10月|9:00-12:00和21:00-00:00是SO₂、NO₂、PM₁₀、PM_2.5和CO质量浓度较高的两个时段,O₃浓度一般于15:00达到最大;6类污染物普遍表现出季节性的准7 d周期和全年性的准30 d周期|空气质量状况良的频率是56 %,轻度污染26 %,优仅为12%;首要污染物以PM₁₀、PM_2.5和SO₂为主|风速与SO₂、NO₂和CO具有良好的负相关关系,与O₃则呈显著正相关关系,风速对PM₁₀和PM_2.5影响较复杂,当风速小于某一值时,有利于PM₁₀和PM_2.5扩散,当风速达到一定程度后,又会导致PM₁₀和PM_2.5浓度的增加|降水对污染物有较好的冲刷作用,且对SO₂的清除作用最明显,对O₃的清洁作用最弱。     

彭州市大气污染物浓度变化特征研究

本文利用2017年彭州市大气污染物(SO₂、NO₂、O₃、CO、PM₁₀、PM_2.5)小时浓度数据并结合地面气象观测资料,统计分析了该地区大气污染物浓度的演变规律及影响因素。结果表明:该地区细粒子(PM₁₀和PM_2.5)污染较为严重,O₃次之,其它污染物浓度水平则低于国家新二级标准限值。观测期间,各污染物浓度表现出明显的日变化与季节变化,其中SO₂、O₃呈单峰型日变化,NO₂、CO和细粒子则呈双峰型日变化;污染物浓度的季节变化基本表现为冬高夏低、春秋次之的变化特征(O₃为夏高冬低),并且固态污染物(PM₁₀、PM_2.5)与气态污染物(NO₂、CO)之间有显著的相关性。在污染物浓度与气象要素相关性分析中表明,湿度对于污染物浓度的影响整体上要弱于温度和风速,除了O₃与温度、风速呈正相关外,其它污染物与两者均呈负相关。除此以外,风向对于当地各种大气污染物的积累与清除也有直接的影响。      

2018年冬季鲁西北大气污染特征及影响因子分析

利用2018年12月至2019年2月滨州、德州和聊城PM_2.5、PM₁₀、NO₂、SO₂、CO和O₃逐日质量浓度及其对应的气象资料,分析了鲁西北大气污染特征和影响因子。结果表明：2018年冬季鲁西北大气污染比较严重,聊城、德州和滨州轻度及以上污染天数分别占61%、60%和54%,重度以上染污天数分别占24%、11%和9%;首要污染物均为PM_2.5、PM₁₀和NO₂,其中PM_2.5占60%以上。PM_2.5、PM₁₀、SO₂、NO₂和CO日变化呈双峰双谷型,谷值分别出现在04-07时和15-17时,且下午比清晨更低,峰值出现在上午和下午交通高峰期后2-3 h,且峰值上午大于下午;O₃呈单峰型分布,09时出现极小值,18-19时出现极大值。PM_2.5是鲁西北主要的首要污染物,与PM₁₀、CO、NO₂均为显著正相关,并通过0.01水平显著性检验,与NO₂的相关性在低相对湿度(< 60%)时大于高相对湿度(≥ 60%),与CO的相关性在高相对湿度时大于低相对湿度;污染时段(PM_2.5>75 μg·m^-3)的平均相对湿度和平均温度明显大于清洁时段(PM_2.5 ≤ 75 μg·m^-3),清洁时段风速和气压比污染时段明显偏大。     

汾渭平原CUACE模式空气质量预报性能的检验订正及环境评估

利用2019年1—6月地面环境监测资料和PM_2.5气象条件评估指数，结合滚动偏差订正方法，对汾渭平原CUACE空气质量预报产品进行了检验订正，并对气象条件和污染减排影响进行了评估。结果表明：CUACE模式对空气质量指数(AQI)、PM_2.5和SO₂浓度预报值较接近观测值，PM₁₀、CO和NO₂预报值小于观测值，O₃预报值大于观测值；对首要污染物O₃和PM_2.5及重度和严重等级污染的预报的TS评分最高，漏报率和空报率最小，预报偏差最接近1；滚动偏差订正方法对改善CUACE空气质量预报效果较为明显，尤其是对PM₁₀、O₃和NO₂改善最为明显；汾渭平原2019年上半年气象条件变化使PM_2.5浓度较2018年同期和过去5年同期分别上升了18.26%和11.18%，减排措施使PM_2.5浓度较2018年同期和过去5年...     

京津冀典型工业城市沙河市大气污染特征及来源分析

近些年京津冀地区秋、冬季大气重污染事件频发,工业生产与居民燃煤是大气灰霾污染的重要原因。河北省沙河市是京津冀地区以玻璃制造和加工为主的典型工业城市,本研究选取该城市为研究对象,主要利用2017年1月至12月国控站点的大气环境监测和气象数据,采用扩散模型、潜在源分析等手段,分析了沙河市主要污染物的时空分布特征和污染来源。主要结论有:（1）沙河市首要污染物具有明显季节特征,春季、夏季、秋冬季分别以PM₁₀、O₃、PM_2.5污染为主,季节贡献率分别为43.3%、72.3%、61.5%。（2）受城市大气边界层和排放的共同影响,PM₁₀、PM_2.5、SO₂、NO₂和CO浓度均有剧烈的季节—日变化特征。（3）冬季东北风时PM_2.5、NO₂、SO₂均展现出高浓度和高相关性特征,表明站点可能受东北方向玻璃企业排放影响。同时,站点可能也受城中村散煤燃烧影响。（4）沙河市冬季PM_2.5浓度为143 μg m-3。冬季的一次重污染中硫氧化率SOR、氮氧化率NOR的最高值分别达0.67、0.39,气态污染物的二次转化剧烈,高湿度利于二次粒子的生成。重污染中C(NO₃-)/C(SO₄2-)均值为1.89,推测沙河市NO₂主要来自大型运输车辆和企业的共同排放。（5）本地源是沙河市PM_2.5的主要潜在源区,周边几个重工业城市也有一定贡献。因此本研究建议沙河市PM_2.5的治理除需加强本地污染源的削减和控制外,区域联防联控也十分重要。     

四川盆地最大混合层厚度与大气污染物浓度的关系

基于2015～2018年四川盆地温江站、宜宾站、达川站和沙坪坝站的探空和地面观测资料以及同期AQI、6种主要污染物(SO₂、NO₂、CO、O₃、PM_2.5、PM₁₀)质量浓度资料,使用逐步逼近法计算得到了四川盆地成都、宜宾、达州、重庆四城市的每日最大混合层厚度(Maximum mixing depth,MMD),并对其时间变化特征及其与各种污染物浓度之间的关系进行了分析。结果表明,四川盆地年平均MMD约1200m。季节变化明显,春夏高、秋冬低。9月至次年1月MMD相对较小。相关分析显示,剔除降水影响后,MMD与AQI、PM_2.5、PM₁₀、SO₂、NO₂、CO浓度均呈负相关,而与O₃浓度显著正相关。在污染最为严重的冬季,MMD明显低于春夏季节。MMD越小、颗粒物浓度越高。低MMD大大压缩了近地面污染物的扩散空间,污染物在有限的空间内不断累积、浓度增大。      

银川市主要污染物变化特征及其关系研究

利用2013-2019年银川市主要污染物浓度数据,分析了近年来银川市主要污染物浓度变化特征,并运用主成分分析法对主要污染物之间的关系进行研究。结果表明：近年来银川市主要污染物浓度除O₃逐年呈上升趋势外,其他均呈下降趋势;市区站O₃浓度较郊区背景站低,其他污染物市区较郊区背景站高;市区站PM₁₀和PM_2.5浓度超国家二级标准;除O₃浓度夏季高,冬季低外,其他污染物冬季高,夏季低;CO、NO₂、PM₁₀、PM_2.5浓度呈"双峰型"日变化特征,O₃和SO₂呈"单峰型"日变化特征。银川市主要污染物NO₂浓度与CO和O₃相关性显著,PM₁₀和PM_2.5之间相关性显著;污染物第一主成分是NO₂、CO和O₃,方差贡献率达到50%以上,加之银川市O₃浓度逐年呈升高趋势,表明近年来银川市大气光化学污染增加。     

南京北郊 O3、NO2和 SO2变化特征分析

利用 2008年1-12月南京北郊O₃、NO₂及SO₂质量浓度连续观测资料,分析了南京北郊气体污染物(O₃、NO₂、SO₂) 的质量浓度变化规律。结果表明：南京北郊O₃浓度夏季较高,日变化曲线呈单峰型,NO₂和SO₂浓度夏季较低,日变化曲线呈双峰型,NO₂与O₃的日变化呈现负相关关系,该地区SO₂浓度整体较高,夏季周末效应NO₂和SO₂较O₃更明显。     

成都市夏季臭氧污染特征及影响因素

利用2014年夏季成都市3个国控环境监测站（金泉两河,君平街和梁家巷）O₃、NO₂及PM_2.5逐时观测数据,结合国家基准站温江站的气温、湿度、风速、风向、太阳辐照度、降雨等地面气象要素观测资料,分析O₃的日、月变化及空间分布特征;探究前体物及气象因子对O₃浓度的影响。结果表明：成都市O₃-8 h平均浓度为104.4 μg·m^-3,O₃超标率为2.8%—15.3%。O₃浓度6月最高,8月最低;呈现明显的“单峰型”日变化特征,午后15：00达到峰值。O₃与NO₂呈现负相关,相关系数为-0.5;与PM_2.5无显著相关性。高温、低湿、强太阳辐射有利于O₃的形成;风速为2.5—3.0 m·s^-1,风向为南风时,O₃浓度相对较高。     

利用MODIS资料监测京津冀地区近地面PM2.5方法研究

为建立京津冀地区冬季近地面细颗粒物浓度监测方法模型,利用气象模式资料对2013年1-3月MODIS的AOD二级深蓝算法产品进行湿度和垂直订正,与同期观测的地面细颗粒物PM_2.5资料进行相关分析。结果表明：AQUA的MODIS深蓝算法AOD产品更适用于建立冬季AOD-PM2.5遥感监测模型,其R²为0.33;以气象模式资料中边界层高度代替气溶胶标高对MODIS的AOD进行垂直订正,并结合IMPROVE观测的气溶胶吸湿增长特征构建分区湿度订正方法,可以提高AOD-PM_2.5模型结果的精度,建立较为理想的京津冀地区冬季遥感反演综合模型,模型结果与地面监测结果R²达0.5以上。根据建立的模型计算了2013年1-3月的京津冀地区PM_2.5月平均浓度,京津冀地区1月的PM_2.5浓度较高,南部大部分地区空气质量已经达到重度污染水平。     

Investigating the Changes in Air Pollutant Emissions over the Beijing-Tianjin-Hebei Region in February from 2014 to 2019 through an Inverse Emission Method

In recent years, China has implemented several measures to improve air quality. The Beijing-Tianjin-Hebei(BTH)region is one area that has suffered from the most serious air pollution in China and has undergone huge changes in air quality in the past few years. How to scientifically assess these change processes remain the key issue in further improving the air quality over this region in the future. To evaluate the changes in major air pollutant emissions over this region, this paper employs ens...     

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.     

Relative influence of air pollutants and weather conditions on solar radiation – Part 1: Relationship of air pollutants with weather conditions

Summary Air pollution measurements from January 1999 to December 2003 were studied in 14 sites covering most of Egypt, with the aim of understanding the governing processes pollutants phase interaction. The nature of the contributing sources has been investigated, and some attempts have been made to indicate the role played by neighboring regions in determining the air quality at these sites. The seasonal variability of particulate matter (PM₁₀) and some gaseous pollutants (e.g., SO₂, NO₂, CO and O₃) were analyzed. Their relationships with meteorology were also examined. The results reveal that levels of major air pollutants were not significantly different at the rural and background sites during any season. On contrary the high atmospheric loading for PM₁₀, CO and SO₂ was frequently observed in winter at the urban sites. As expected, the prevailing winds were found to have an influence not only on air pollutants but also on the correlation between them.     

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.     

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.     

Spatiotemporal analysis of particulate matter, sulfur dioxide and carbon monoxide concentrations over the city of Rio de Janeiro, Brazil

Time series of pollutants and weather variables measured at four sites in the city of Rio de Janeiro, Brazil, between 2002 and 2004, were used to characterize temporal and spatial relationships of air pollution. Concentrations of particulate matter (PM₁₀), sulfur dioxide (SO₂) and carbon monoxide (CO) were compared to national and international standards. The annual median concentration of PM₁₀ was higher than the standard set by the World Health Organization (WHO) on all sites and the 24?h means exceeded the standards on several occasions on two sites. SO₂ and CO did not exceed the limits, but the daily maximum of CO in one of the stations was 27% higher on weekends compared to weekdays, due to increased activity in a nearby Convention Center. Air temperature and vapor pressure deficit have both presented the highest correlations with pollutant??s concentrations. The concentrations of SO₂ and CO were not correlated between sites, suggesting that local sources are more important to those pollutants compared to PM₁₀. The time series of pollutants and air temperature were decomposed in time and frequency by wavelet analysis. The results revealed that the common variability of air temperature and PM₁₀ is dominated by temporal scales of 1?C8?days, time scales that are associated with the passage of weather events, such as cold fronts.     

Effects of meteorology and land use on ambient measurements of primary pollutants in Córdoba city, Argentina

Summary The atmospheric concentrations of several primary species: NO, NO₂, NO_x, CO, SO₂, reactive hydrocarbons (ROG) and other 15 atmospheric and meteorological variables have been measured at several locations in Córdoba city, Argentina since June 1995. The measurements are carried out using two mobile stations to cover several important areas of Córdoba. The objective of this work is to estimate the effects of meteorology and urban structure on the air quality levels for this city using simple statistics. We analyze the correlation between primary pollutants (CO and NO_x) and site locations of the air quality monitoring stations (AQMS) during the whole 1995 field campaign. In this study we take the measured data for primary pollutants and group them by location and time of the year. The results of this work may be useful to forecast air pollution episodes. Also we can get indirect information about emissions and maybe identify source characteristics. Once the influences of topography, meteorology, and land use will be fully characterized, the existing monitoring data will be used to do air quality modeling analysis and to select monitoring locations. The use of mobile stations instead of stationary ones at this stage is justified because of limited funding. Therefore, it is a valid option to decide in the future the additional instrumentation required to characterize completely the atmospheric urban area.With 5 Figures     

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.     

北京秋季一次典型大气污染过程多站点分析

多站点多种大气污染物的同步在线观测对深入剖析大气污染的成因和演变机制有重要意义。以龙潭湖、北京325 m塔、双清路和阳坊4监测站点实时NO_x、SO₂、O₃、PM2.5和PM10浓度观测数据为基础,介绍了北京地区2010年10月3～11日发生的一次典型污染过程。不同污染物在污染过程中变化特征不一致,表现为NO_x、SO₂、O₃浓度有明显日变化,而PM浓度升高后一直维持在高值,日变化幅度很小。通过分析不同站点、相同污染物之间的相关性和变异系数发现,4站点间一次污染物NO和SO₂空间浓度差别大,变异系数分别为77%和70%,相关系数低于0.44;而二次污染物NO₂、PM2.5、O₃空间浓度差别较小,变异系数分别为34%、36%和29%,相关系数均超过0.54。结合中尺度气象模式研究发现,该污染过程中,850 hPa高空持续的西南暖平流造成华北地区显著平流逆温,与近地层辐射逆温共同作用,使北京地区混合层高度维持在1200 m以下。低混合层高度和低风速限制了大气垂直和水平扩散,造成北京地区近地层污染物累积,形成重度污染。     

武汉市大气污染源排放清单及分布特征研究

以武汉市为研究区域,基于实地调查获得典型行业污染源活动水平,以大气污染物排放清单编制技术指南为参考,利用排放因子法建立2014年武汉市大气污染源排放清单,并结合经纬度、人口密度分布、土地利用类型、道路长度等数据将排放清单进行了3 km×3 km网格化处理.结果表明,2014年武汉市SO₂、NO_x、PM₁₀、PM_2.5、CO、BC、OC、VOCs和NH₃排放量分别为10.3、17.0、16.3、7.1、63.1、0.6、0.4、19.8和1.6万t.固定燃烧源为SO₂排放的主要来源,其贡献率约64%;移动源为NO_x的主要来源,其贡献率约51%;颗粒物排放主要来源于扬尘源和工艺过程源;CO和VOCs主要来源于工艺过程源,BC和OC排放均以移动源和生物质燃烧源为主,NH₃排放主要来自农业源.污染物排放主要集中在青山区至新洲区一带.