兰州市主要大气污染物浓度季节变化时空特征分析

应用统计方法分析了兰州市大气污染物SO2、NOx、TSP浓度时空分布的季节变化特征。使用GIS空间叠加分析技术,利用污染源、人口、绿地覆盖等空间数据,对上述污染物时空分布特征的成因进行了探讨。研究表明,兰州市大气环境质量状态存在鲜明的冬春高、夏秋低的季节差异。空间上,经济活跃、人口密集城区污染程度更高。TSP污染物是造成兰州市大气环境质量下降的主要污染源,但其他两种污染物对兰州市区大气环境质量的影响也不能忽视。相关分析表明,社会经济因素对兰州市空气质量的时空分布有一定影响。GIS空间分析功能是分析城市空气环境质量时空变化特征的一个有效工具。     

乌鲁木齐市大气污染时空分布规律研究

为掌握乌鲁木齐市大气污染时空分布规律，利用近年乌鲁木齐市大气污染物的浓度最新资料，详尽分析了其空气质量的年际变化和空间分布特征。统计了2008年各污染物日、月变化规律。结果表明，近年乌鲁木齐市城区大气污染物质量浓度具有明显时空分布规律，即大气污染物质量浓度冬春季大于夏秋季，PM10和SO2浓度夜间大于白天。在空间分布上，PM10和SO2南部区域最高，中部次之，市区北部最轻，NO2则呈现出由北向南逐渐升高的分布特征。     

浅谈监测数据审核及异常数据的分析与判断

１异常数据的判断与分析发现和判别异常数据,首先要用污染时空分布及其变化来进行判断,如排污口污染物浓度低于远离污染源地区的浓度,污染物浓度明显超越常年水平,浓度时空分布出现反常现象等。因此,采样时要对采样点周围环境状况、气象条件、样品感观及样品采集等情...     

基于环境监测数据的APEC会议空气质量保障环境改善效果评估

在简要回顾APEC期间的空气质量情况的基础上,从多方面较系统的分析了APEC期间北京市空气质量的变化特征,包括各项污染物浓度水平的同比分析、不同区域不同类别站点小时浓度的百分数分布及变化情况分析、污染物日变化规律变化特征分析、空气质量改善效果的空间分布特征分析、颗粒物组分变化特征分析、污染来源解析模型、数值污染模型等方法,力求从多个方面深入了解APEC控制措施对北京市污染水平、污染特征造成的影响,并利用组分、模型等方法定性定量的评估主要空气质量影响因素、不同的污染控制措施对APEC期间空气质量改善的作用及贡献。结果表明,APEC期间,北京市空气质量得到明显改善,空气质量基本处于优良级别,各项污染物浓度大幅下降,APEC污染控制期各项污染物的百分位数浓度与无控制期出现明显分离特征,污染物的日变化低浓度持续时间更长且增长更缓慢。     

洞庭湖区一次重度空气污染过程成因分析

利用AQI和PM_(2. 5)质量浓度、地面气象要素、NCEP、ERSST_V3、GBL等资料,对2016年12月29日至2017年1月5日洞庭湖区一次重度空气污染过程成因进行了分析。结果表明,静稳天气形势下的累积效应和本地持续升温、降压、增湿、小风导致污染物浓度不断增加。本地风速与雨量对污染物浓度产生显著影响。降温前风速明显加大,有利于污染物快速扩散。湿度增加有利于污染物吸湿性增长,但高湿易引起降水有利于污染物的湿清除。此次重度空气污染过程中大气稳定度为中性或稳定,14:00混合层高度逐渐降低且重度空气污染日降至100 m以下。污染物空间分布与主导风向和污染通道密切相关。气流后向轨迹分析表明,洞庭湖区各地气流来源和影响路径差异明显,且存在大范围区域性同步污染现象。北方外来污染源是洞庭湖区重要的污染面源,本地工业污染排放点源和地理条件也是洞庭湖区空气污染物空间分布差异的重要因素。     

广州主要工业源排放的气态污染物扩散模拟研究

以广州省控工业污染源排放的气态污染物(SO₂、NO_x为主要研究对象,通过中尺度气象模式MM5与空气质量模式CALPUFF耦合,模拟11月典型气象条件下, SO₂和NO_x的扩散传输过程,研究其时空分布特征,并分析省控工业污染源排放对特定区域(主要针对2010年亚运场馆)空气质量的影响。结果表明,主要受典型风速的影响,SO₂和NO_x浓度具有明显的时空分布不均匀性。浓度高峰值主要出现在晚间至凌晨时段,而浓度低峰值主要出现在白天至中午时段。受污染源分布、排放高度和风向的影响,荔湾区和越秀区污染物浓度较高,且在广州西南部形成较明显的污染带;且这些省控污染源对南沙体育馆空气质量有较大影响。 研究结果对广州空气污染来源分析具有一定参考意义。     

中国中东部一次大范围重污染过程特征分析

采用嵌套网格空气质量预报模式(NAQPMS)模拟与气象、污染物观测资料相结合的方式,分析了2016年12月影响中国中东部地区的一次重污染过程中PM_(2.5)时空分布特征及来源成因。结果表明,重污染过程中PM_(2.5)具有较明显的时空变化规律,污染呈现一定程度的区域性分布特点,不同地理位置条件下,污染物浓度的累积和传输方式表现出不同的特征,细颗粒物快速二次生成及不利扩散条件下的持续积累可能是此次污染过程的主要原因,不利于污染物扩散的高低空天气形势的配合抑制了污染物的快速消散,为大气污染的形成及维持提供了稳定的大气环境背景,形成了此次污染过程污染浓度高、影响范围大的态势。     

广东省城市环境空气质量时空特征及经济影响因素分析

选取2014—2018年广东省21个城市的空气质量指数(AQI)以及PM_(2.5)、PM_(10)、CO、NO_2、O_3、SO_2的浓度数据,利用重心模型和空间自相关模型,对广东省的空气质量和各污染物的浓度水平进行时空特征分析,同时,利用空间计量分析模型分析社会经济特征变量对环境空气质量空间特征的影响。结果表明:2014—2018年广东省的大气污染重心一直徘徊在广州市和东莞市的交界地带。全省的重心由东北向西南迁移,6种污染物的迁移路径各有特点。6种污染物中,PM_(2.5)、PM_(10)、NO_2、O_3的重心整体向西南方向迁移,CO的污染重心整体向东北方向迁移,而SO_2污染重心整体向南迁移。6种污染的污染重心每年迁移的距离非常微小。广东省春夏的污染重心偏东北方向,分布较松散;秋冬季的污染重心偏向西南方向,分布较集中。2014—2018年,广东省存在大气污染的"热点"和"冷点"区域,呈现出高污染区域聚集和低污染区域聚集的态势。珠江入海口地区、清远市周边的空气质量较差;湛江沿海地区、汕尾地区的空气质量相对较好。2014—2018年,广东省人均地区生产总值(GDP)、第三产业占GDP比重与城市AQI呈现负相关关系,工业产值占GDP比重、人均可支配收入、研究与试验发展(RD)经费支出与城市AQI呈现正相关关系,外商直接投资和大气环境之间的关系不确定。     

对比分析法在环境空气质量预报业务中的应用

通过分析京津冀及周边区域环境空气质量预报的实际案例,从实时监测、污染源、大气条件以及数值预报模拟4个方面阐述了对比分析法在空气质量预报业务中的应用,帮助预报员比较类似污染源排放条件下的大气条件变化或者类似大气条件下的污染源变化,以便进一步开展量化分析。研究结果显示:利用时间同比、空间比较和大气条件对比的分析方法,能够判断在类似污染源排放和相似大气条件下,主要污染物的浓度水平、重污染发生、影响范围、持续时间、严重程度等污染特征以及污染物的水平传输影响等;采用污染源变化对比分析法,能够获得在类似大气条件下,污染源排放的减少或剧增对主要污染物浓度水平的影响程度;通过数值预报模式结果对比分析,能够获得在类似的污染源排放条件下,大气环流形势的稳定程度和变化情况,从而判断其对污染物浓度水平的影响。对比分析法是开展京津冀及周边区域环境空气质量预报业务中的重要环节,有利于持续提高空气质量预报的准确率,供全国空气质量预报员开展辖区空气质量预报时参考。     

河西走廊PM10污染的气象特征

选用敦煌、酒泉、河西走廊气象站2005年可吸人颗粒物PM10逐时浓度监测资料，较为系统地统计分析了河西走廊地区主要空气污染物-PM10的时空分布特征，其中包括PM10平均浓度和各等级出现频率的逐月变化．揭示了河西走廊站PM10污染年变化趋势，并分析了PM如浓度与地面常规气象要素的相关性。     

Temporal and spatial variations of the quality of ambient air in the Kingdom of Bahrain during 2007

The main objective of the present investigation is to study the temporal and spatial variations of the quality of ambient air in the Kingdom of Bahrain. The non-parametric Kruskal-Wallis (KW) test showed significant spatial variations and interactions of spatial-temporal among five mobile monitoring stations for 11 air pollutants. The Mann Whitney (MW) test demonstrated the seasonality of spring over winter for the PM(10), PM(2.5), NO(2), CO and p-xylene, the seasonality of winter over spring for O(3), and no significant seasonal variation for NH(3), benzene, SO(2), toluene and H(2)S. It is concluded that emissions from automobile exhaust, industrial and developmental projects are responsible for the spatial air pollution, and that air temperature is the controlling factor for the seasonal variations.     

Spatio-temporal variations in water quality of Nullah Aik-tributary of the river Chenab, Pakistan

This study reports the spatio-temporal changes in water quality of Nullah Aik, tributary of the Chenab River, Pakistan. Stream water samples were collected at seven sampling sites on seasonal basis from September 2004 to April 2006 and were analyzed for 24 water quality parameters. Most significant parameters which contributed in spatio-temporal variations were assessed by statistical techniques such as Hierarchical Agglomerative Cluster Analysis (HACA), Factor Analysis/Principal Components Analysis (FA/PCA), and Discriminant Function Analysis (DFA). HACA identified three different classes of sites: Relatively Unimpaired, Impaired and Less Impaired Regions on the basis of similarity among different physicochemical characteristics and pollutant level between the sampling sites. DFA produced the best results for identification of main variables for temporal and spatial analysis and separated eight parameters (DO, hardness, sulphides, K, Fe, Pb, Cr and Zn) that accounted 89.7% of total variations of spatial analysis. Temporal analysis using DFA separated six parameters (E.C., TDS, salinity, hardness, chlorides and Pb) that showed more than 84.6% of total temporal variation. FA/PCA identified six significant factors (sources) which were responsible for major variations in water quality dataset of Nullah Aik. The results signify that parameters identified by statistical analyses were responsible for water quality change and suggest the possibility of industrial, municipal and agricultural runoff, parent rock material contamination. The results suggest dire need for proper management measures to restore the water quality of this tributary for a healthy and promising aquatic ecosystem and also highlights its importance for objective ecological policy and decision making process.     

安徽省臭氧污染特征及气象影响因素分析

基于2016—2018年安徽省68个国控环境空气质量自动监测站点的臭氧(O_3)监测数据,研究分析了安徽省O_3污染特征及其与气象因子的相关性。结果表明:安徽省O_3污染程度呈现逐年加重趋势,并有显著的季节和月度变化特征。2016—2018年,各年度单月O_3日最大8小时滑动平均质量浓度第90百分位数的最大值分别出现在9月、5月、6月。O_3日变化趋势为典型的单峰形,各年度最低值出现在晨间07:00左右,最高值则是在15:00—16:00。全省O_3浓度总体上呈现出北高南低的空间特征。温度、相对湿度与O_3浓度分别呈现显著正相关、负相关,但在不同季节存在一定差异,其中,春秋季温度与O_3浓度的相关性好于夏冬季,夏季相对湿度与O_3浓度的相关性最为显著。O_3浓度在平均风速为2.1～2.2 m/s时更易出现超标。中部和北部城市在东南风的作用下易出现O_3超标并达到O_3浓度高值,而南部地区在风向为西风时更容易出现O_3超标。     

Spatio-Temporal Variations of Sulphur Dioxide Patterns with Wind Conditions in Central Taiwan

Sulphur dioxide (SO₂) is one of the main atmospheric pollutants in central Taiwan. This article analyses the SO₂ concentration seasonal variations and spatial distribution using data obtained from ten air quality monitoring stations and the Taiwan Weather Bureau. It reveals that SO₂ concentration is high in winter and low in summer and that high concentration centers are located south of the Taichung coal-fired power plant, the main source of SO₂ emissions in the region.The location of high concentration centers changeswith different prevailing winds. SO₂ variations due towind direction are not unique. During short periods,when meteorological conditions are constant, variationin the pollution sources cause variations in thespatial distribution. This has been deduced byappreciation of Intervention analysis to time seriesof hourly data.     

基于ＧＩＳ的乌鲁木齐市ＮＯ２ 时空分布特征

以乌鲁木齐市2008-2012年7个空气自动监测点位小时浓度数据为基础数据，利用ArcGIS 技术，分析了其NO2年变化、月变化、日变化、空间分布等污染特征。结果表明，NO2年均值为0．065～0．068 mg/m3，基本保持稳定；NO2呈明显的季节变化，冬季污染较重，春节、秋季次之，夏季空气质量相对较好；NO2呈现“单峰型”的日变化特征，夜间NO2明显高于白天；不同季节 NO2的空间分布特征不同，与交通、供暖、人口密度、地理位置密切相关；NO2分布与风速相关关系明显，而与气温、湿度的关系为非线性。     

上海市路边环境空气黑碳气溶胶污染特征及影响因素研究

为研究上海市路边环境空气黑碳(BC)的污染特征,采用连续监测方法对2016年1月至2018年12月上海市路边环境空气BC浓度进行了监测,并同步监测了气象因子,分析了BC的时间变化特征,探讨了气象因素对BC的影响以及不同空气质量等级下BC浓度水平。结果表明:2016、2017、2018年上海市路边环境空气BC年均质量浓度分别为(2 908±2 189)、(2 959±2 224)、(2 824±2 002) ng/m3,呈现出下降趋势;2016、2017、2018年BC与PM2.5年均质量浓度比分别为9. 30%、9. 20%、9. 50%;BC季节变化特征明显,整体表现为春夏高、秋冬低的特点;昼夜变化特征均呈现出双峰分布,第一个峰值均出现在06:00,第二个峰值均出现在16:00-19:00,且第一个峰值高于第二个峰值。气象因素对BC有一定影响,在降水、相对湿度低以及非静风条件下BC浓度较低。随着上海市空气质量由好转差,上海市路边环境空气BC浓度均呈现上升趋势,空气质量为良、轻度污染、中度污染、重度污染时路边环境空气BC平均浓度分别较空气质量为优时增加了0. 38、0. 96、1. 61、1. 96倍。     

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。     

An Analysis of the Annual and Seasonal Trends of Air Quality Index of Delhi

The Air Quality Index (AQI) is an index for reporting daily air quality. A study on the annual and seasonal variations of Air Quality Index over a period of 9 years (1996-2004) based on daily averaged concentration data of criteria air pollutants has been conducted for Delhi. An attempt has been made to quantify the changes in the AQI on annual and seasonal (winter, summer, monsoon and post monsoon) basis for 9 years. Measurements for the seven monitoring sites (Nizamuddin, Ashok Vihar, Shahzada Baug, Shahadara, Janakpuri, Sirifort and ITO) in Delhi were analysed and trends were also compared amongst these sites. Maximum Operator Function method was used to compute the Air Quality Index of the above areas and percentage variations in different severity class is discussed which provides in depth analysis of the trends. The best air quality was depicted by Shahzada Baug followed by Shahdara, both of these were classified as industrial areas indicating that policy measures relating to the industries in the city during past years have helped in improving the air quality. The air quality in other areas have improved slightly in the span of nine years but still remains critical indicating continued rigorous efforts in this direction. Increased traffic density seems to have resulted into the worst air quality at ITO in the city amongst all the monitoring stations. There is a shift for the worst AQI in the city from winter to summer season in a time span of these nine years. Change of season for worst AQI from Winter to Summer may also be likely due to increased photochemical reactions playing major role with change in the nature of emissions imposed due to different control measures such as CNG implementation, significant shift to LPG in domestic sector etc. calling for a detailed study, those which started after the year 2000. After the year 2000, there is a significant increase in the Nitrogen-dioxide (NO(2)) concentration at all stations. ITO which has shown continuous exponential increase in pollution levels has first time showed a declining AQI trend in the year 2004 and one of the contributing factors could have been the Delhi metro (initiated in 2002) passing through congested neighbouring areas causing traffic decongestion here. In general, the areas which are farthest from metro route viz., Siri-fort, Nizamuddin, Janakpuri etc. did not record declining AQI in 2003 onwards as happened with stations closer to Metro route such as Ashok Vihar and ITO. An attempt has been made to quantify the reasons that lead to the changes in the values of the AQI.     

Seasonal Variation of Toxic Benzene Emissions in Petroleum Refinery

Petroleum refineries are largest chemical industries that are responsible for the emission of several pollutants into the atmosphere. Benzene is among the most important air pollutants that are emitted by petroleum refineries, since they are involved in almost every refinery process. Volatile organic compounds (VOCs) are a major group of air pollutants, which play a critical role in atmospheric chemistry. These contribute to toxic oxidants, which are harmful to ecosystem, human health and atmosphere. The variability of pollutants is an important factor in determining human exposure to these chemicals. The ambient air concentrations of benzene were measured in several sites around the Digboi petroleum refinery, near the city of Gowahati in northeast India, during winter and summer 2004. The seasonal and spatial variations of the ambient air concentrations of this benzene were investigated and analyzed. An estimation of the contribution of the refinery to the measured atmospheric levels of benzene was also performed. The ambient air mixing ratios of benzene in a large area outside the refinery was generally low, in ppbv range, much lower than the ambient air quality standards. This article presents the temporal and spatial variation of air pollution in and around petroleum refinery and showed that no health risk due to benzene is present in the areas adjacent to the refinery.