华东森林及高山背景区域CO2和CH4浓度变化特征

对国家大气背景监测福建武夷山站2014—2018年的主要温室气体监测数据进行分析,探讨华东森林及高山背景区域大气中CO_2和CH_4浓度的变化特征。结果表明:华东森林及高山区域CO_2背景浓度为414.1×10~(-6)(摩尔分数,下同),5年间呈逐年上升趋势; CH_4背景浓度为1 977×10~(-9),2014—2016年呈逐年上升趋势,2016—2018年保持稳定;两者均具有较明显的季节和月变化特征,CO_2还具有较明显的日变化特征,但季节变化幅度、月平均浓度振幅和日变化幅度均较小,具有区域背景特征。     

南亚次大陆对中国大气传输影响及海螺沟背景站监测的指示效应

海螺沟背景站地处人烟稀少、远离工业带区域,颗粒物浓度水平与美国背景区域相当,通常情况下各项污染物浓度呈周期性缓慢变化,但通过实时自动监测发现,也有部分时段出现污染物浓度急剧升高的现象,对这种情况进行统计分析,2015年共有43 d因远距离传输导致背景站浓度急剧升高现象,其间PM_(2.5)平均质量浓度为19.4μg/m~3,比其年均质量浓度(8.3μg/m~3)高1倍多。通过对2015年背景站监测数据与年气象分析资料的联合分析,结合HSPLIT 4.8轨迹模式对污染物来源进行溯源,在海螺沟国家大气背景区域的200、3 700 m 2个高度都存在南亚次大陆向中国境内输送的气流路径。后向轨迹200 m高度聚类分析结果:海螺沟背景站PM_(2.5)监测值超"正常"浓度范围时段有84%的大气污染气团主要来自南亚次大陆方向,同时,常规6项其他监测项目的浓度水平也存在协同上升效应。     

基于分形求和模型的克拉玛依市空气质量评价

以克拉玛依市4个区2012年的大气自动监测数据为样本,基于分形求和模型,分析大气污染物的分布特征,利用分维数确定污染物浓度分布的随机程度,计算 SO2、NO2、PM10的大气环境背景值与标准值,确定适合于评价区域的ORAQI指数计算公式,并与 API指数作对比。ORAQI指数计算结果显示,克拉玛依市全年环境空气质量基本呈现“U”字形变化,春夏季大气质量好于秋冬季,全年空气质量有明显的季节变化,4个区中克拉玛依区空气质量相对较差,乌尔禾区空气质量最好。相对于 API指数的均匀分布结果,ORAQI指数具有更好的次要污染物体现能力,可以综合体现所评价的各项污染因子的贡献。     

川东北中心城市南充大气污染态势及其主要成因

基于南充市主城区6项大气污染物浓度数据,研究了2014-2020年南充市的空气质量指数、空气质量指数等级和首要污染物的时序分布。结果表明:随着大气污染防治的开展,南充市大气污染物浓度逐渐下降,出现首要污染物的天数逐年减少,空气质量逐步提高。受污染物节律性影响,空气质量呈现明显的季节差异,冬季空气质量最差,春季次之,夏季污染相对较轻,秋季最轻。首要污染物类型的季节分布特征表现为冬季出现首要污染物天数最多,春季和夏季次之,秋季最少。春、秋、冬季以PM_2.5污染为主,夏季以O₃污染为主。从全年来看,与O₃相比,PM_2.5对空气质量的影响更为突出。在持续控制大气污染物排放总量的同时,精细化协同管控细颗粒物、氮氧化物、挥发性有机物和二氧化硫排放将有助于现阶段的大气污染防治。     

1994—2010年东亚地区CO2浓度变化特征及成因分析

在东亚地区选取5个大气本底观测站1994年以来观测的 CO2监测资料,分析了各站大气 CO2的时空变化特征,以及 CO2主要人为源的变化及其影响。结果表明,5个本底站大气 CO2年均值均呈明显升高趋势,2010年较1994年增长幅度为8．4％～9．0％;在北半球国家,CO2月均值有明显的季节变化,高值多出现在冬春等寒冷季节,低值多出现在夏季。减少化石燃料消耗量、增加森林覆盖率及农业覆盖率将对大气中 CO2有削减作用。     

华东森林及高山背景区域SO2、NOx、CO本底特征

国家大气背景监测福建武夷山站是中国华东区域背景站点之一,可代表华东森林及高山区域背景状况。为了解该区域的大气背景状况,评估区域污染现状以及污染物输送在区域污染中的作用,选取福建武夷山背景站2011年3月至2012年2月主要气体污染物（SO₂、NO_x、CO）为期1年的监测数据,研究各污染物在不同时间尺度的浓度变化特征和相关关系,以及与气象因子的相关关系,并利用后向轨迹模式探讨区域输送对华东森林及高山背景区域各气体污染物质量浓度的影响。结果表明,武夷山背景点监测期间SO₂、NO_x、CO的平均质量浓度分别为3.9、5.1、409.8 μg/m³,且具有明显的季节变化特征,春、冬季明显高于夏、秋季;三者日变化幅度均很小,呈现出单谷型分布型态,说明武夷山背景点受人为活动的影响很小,主要受气象条件影响;相关性分析结果显示,SO₂与NO_x浓度相关性较好,与湿度有较好的负相关,与风速在冬季具有一定的正相关,NO_x与CO浓度在秋季和冬季的相关性较好,且二者与温度的负相关性较好。后向轨迹分析结果表明,SO₂全年最大浓度峰值主要来自北方采暖季燃煤排放的远距离输送影响,NO_x、CO全年最大值则源于生物质燃烧的远距离输送影响。     

上海虹桥机场大气污染特征分析

通过将上海虹桥机场2016年大气污染物监测数据与该市国控站点数据对比分析,结果表明:机场附近首要污染物为NO_2和PM_(2.5),随着污染级别加重,PM_(2.5)成为首要污染物的频次增加。虹桥机场NO_2浓度均值在各季节均高于各国控站点,日变化呈"双峰双谷"特征,峰值出现时间较其他站点早1 h。冬季PM_(2.5)浓度高于国控站点,其他三季相当。冬季PM_(2.5)日变化具有明显的"双峰"特征,上午峰值出现时间较其他站点早一两小时,夏季不明显。O_3日变化表现为上午其生成速率和NO_2的消耗速率都要高于其他站点。     

基于自动站资料的深圳灰霾特征对比分析

利用深圳自动气象站的气象要素和深圳大气成分监测系统采集的大气成分数据,分析了深圳城区和郊区灰霾季节变化、日变化差异和不同风向下污染物浓度差异,结果表明,城区由于人类活动频繁导致灰霾日比郊区多,以轻微灰霾偏多为主。秋、冬季城区冷空气活动频繁、能源消耗大,灰霾出现频率是郊区的1~2倍;春季冷空气和海上暖湿气流容易形成对峙,沿海颗粒物更容易吸湿增长,郊区灰霾频率反而比城区高25%;夏季对流强、降水频密,城郊差异最小。城区灰霾频率受早晚交通高峰期影响,日变化呈双峰型。而郊区受太阳辐射和光化学反应影响大,呈单峰型。偏北风条件下污染物浓度明显升高,偏南风带来的清洁空气使得颗粒物浓度降幅明显。     

南通市区大气污染特征分析及大气环境质量评价

通过对南通市区１９９０～１９９４年大气主要污染物（二氧化硫，总悬浮微粒）的年平均，季平均，日一次值浓度及其变化规律的分析研究，运用模糊综合评价法评价市区大气环境质量，并探讨污染变化特征及发展趋势。结果表明，近５年，南通市区大气中二氧化硫，总悬浮物微粒浓度呈下降趋势，大气环境质量有所改善，夏季的大气环境质量优于冬季。     

兰州市冬季冷锋前、后空气污染指数变化的个例分析

利用2002年12月4～13日兰州地区气象和大气污染物浓度资料，分析了此段时间内兰州市区大气污染现状和相应的大气环流形势，结果表明：当高空为暖高压脊控制、近地层逆温强度和厚度较大时，污染物浓度逐渐增高，并达到峰值；当有冷空气影响本地时，污染物浓度迅速降低．由于兰州城区冬季各大气污染源排放总量的日际变化是很小的，因此冷空气活动是造成污染浓度日际变化的重要因素，空气污染浓度的变化与气象条件有着显著的相关关系。     

CH4 continuous measurements in the upper Spanish plateau

Continuous methane, CH₄, concentrations were measured in a rural area of the upper Spanish plateau from June 2010 to May 2012 by cavity ring-down spectroscopy technique. The results obtained have proven the local impact of anthropogenic nearby sources on CH₄ concentrations, and evidence a significant influence on the overall mean, averaged daily and seasonal patterns recorded at the measuring site. The positive anomalies in CH₄ concentrations, statistically significant at 95 %, in the southeast sector, defined here as ESE, SE, SSE and S sectors, have been attributed to the contribution of the Valladolid urban plume and the urban landfill. Based on this finding, CH₄ background levels were associated to the concentrations recorded in the remaining un-disturbed sectors. CH₄ means of the overall data set, the southeast sector and background sectors yielded average means of 1,894.1, 1,927.9 and 1,887.1 ppb, respectively. The diurnal and seasonal patterns of the overall data set and background concentrations have shown that CH₄ concentrations are mainly dominated by its reaction with OH radicals. Maximum hourly concentrations were reached during night-time and early morning, 5–7 h, whereas minimum concentrations were recorded at 16 h. Maximum and minimum monthly means were recorded in January and July, respectively. The diurnal and seasonal amplitudes, namely, peak-to-peak means, of background concentrations were 25.1 and 48.1 ppb, respectively. These values were significantly lower than those obtained for the overall data set, 42.9 and 58.1 ppb, revealing the significant role of local influences on CH₄ concentrations despite the low frequency of southeast winds recorded at the measuring site, 16.9 %.     

Ground level volume mixing ratio of methane in a tropical coastal city

Urban regions are hotspots of greenhouse gas emissions which include CO₂, CH₄, N₂O, etc. Methane is a strong greenhouse gas which is produced from a number of sources including fossil fuel combustion, municipal waste, and sewage processing, etc. Ground level mixing ratio of methane in the tropical coastal city of Thiruvananthapuram in South India, during calm early morning period was measured. Measurements were done during both winter and summer seasons. Concentrations were significantly higher than global average value. Intra-city variation in ground level mixing ratio was also significant. Ground level methane concentration at Thiruvananthapuram urban area showed maximum value of 3.16 ppmV. Under stable atmospheric conditions in early morning, ground level mixing ratio of methane was 2.79 ppmV in winter and 2.54 ppmV during summer. The spatial distribution of methane concentration shows correlation with urban heat island.     

衡阳市区和衡山背景站臭氧浓度变化规律的对比分析

选取衡阳市区和衡山背景站臭氧自动监测数据,分析两地的臭氧污染特征。对空气质量的优良率情况、臭氧作为首要污染物的变化情况、臭氧浓度的日变化特征、典型时段的浓度变化特征、臭氧浓度的月际变化特征和臭氧与PM_(2.5)的关联情况等进行了分析。结果表明,多云及阴雨天气时,衡阳市区的臭氧浓度日变化幅度大于衡山背景站。夏季,衡阳市区和衡山背景站的臭氧浓度的日变化特征规律差异较大,臭氧浓度分布比较分散,前者为典型的单峰形,后者则波动平缓。冬季,日变化幅度不大,但衡阳市区的臭氧浓度明显低于衡山背景站。衡山背景站和衡阳市区的臭氧基本同步变化,但日均值高于衡阳市区。     

基于GOSAT卫星的中国CO2浓度时空特征分析

采用温室气体观测卫星(GOSAT) 傅里叶变换光谱仪(FTS)发布的CO₂柱浓度L₃级别数据集产品,利用TCCON地基站点的CO₂柱浓度数据对卫星遥感数据进行验证,分析中国CO₂柱浓度时空变化特征及其影响因素。研究结果表明,GOSAT卫星的CO₂柱浓度产品精度较高,线性回归的r²为0.99,线性方程斜率为0.98,平均偏差为0.11 mg/L。中国CO₂柱浓度呈现逐年增长的趋势,存在12个月的周期性季节性变化。2010、2020年区域年平均CO₂柱浓度分别约为389.30、412.62 mg/L,增长了23.32 mg/L,年平均增长率大约为0.58%。中国区域大气CO₂柱浓度的月变化存在明显的时空差异,最大值和最小值分别出现在4月和8月,2020年4月和8月的区域平均值分别为415.09、409.13 mg/L。中国区域CO₂柱浓度从东部沿海向西部逐级递减,且呈现明显的季节性变化,夏季高值主要集中在东南部沿海地区,冬季高值主要集中在华北地区。     

宜都市PM2.5与O3季节性分布特征及潜在源区分析

为了解宜都市PM_2.5与O₃的污染特征及潜在来源,利用宜都市2020年3月至2022年2月在线监测数据及气象数据,对宜都市PM_2.5与O₃质量浓度变化特征、气象影响因素及潜在源区进行了分析,结果表明:宜都市PM_2.5质量浓度冬高夏低,日变化呈双峰特征,O₃质量浓度夏高冬低,日变化呈单峰特征。高湿、静稳的气象条件以及较强偏北风作用下的区域污染传输对PM_2.5污染有重要影响,高温以及中湿度对O₃污染过程有重要作用。春、夏、秋季偏南方向气流轨迹占主导,且携带较高的污染物浓度,冬季来自湖北东北及西南方向的气流占比较高且携带的PM_2.5浓度较高;宜都市PM_2.5、O₃的潜在源区具有季节性差异,总体来看,主要分布在河南南部、湖北东部及湖南的北部区域。     

Inter-seasonal and spatial distribution of ground-level greenhouse gases (CO<Subscript>2</Subscript>, CH<Subscript>4</Subscript>, N<Subscript>2</Subscript>O) over Nagpur in India and their management roadmap

Ground-level concentrations of carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N₂O) were monitored over three seasons, i.e., post-monsoon (September–October), winter (January–February), and summer (May–June) for 1 year during 2013–2014 in Nagpur City in India. The selected gases had moderate to high variation both spatially (residential, commercial, traffic intersections, residential cum commercial sites) and temporally (at 7:00, 13:00, 18:00, and 23:00 hours in all three seasons). Concentrations of gases were randomly distributed diurnally over city in all seasons, and there was no specific increasing or decreasing trend with time in a day. Average CO₂ and N₂O concentrations in winter were higher over post-monsoon and summer while CH₄ had highest average concentration in summer. Observed concentrations of CO₂ were predominantly above global average of 400 ppmv while N₂O and CH₄ concentrations frequently dropped down below global average of 327 ppbv and 1.8 ppmv, respectively. Two-tailed Student’s t test indicated that post-monsoon CO₂ concentrations were statistically different from summer but not so from winter, while difference between summer and winter concentrations was statistically significant (P < 0.05). CH₄ concentrations in all seasons were statistically at par to each other. In case of N₂O, concentrations in post-monsoon were statistically different from summer but not so from winter, while difference between summer and winter concentrations was statistically significant (P < 0.05). Average ground-level concentrations of the gases calculated for three seasons together were higher in commercial areas. Environmental management priorities vis a vis greenhouse gas emissions in the city are also discussed.     

南京市臭氧、VOCs和PANs污染特征及变化趋势

对2013—2016年基于国家环境空气质量监测站以及省建大气多参数站所获取的南京市O_3、NO_2、CO、VOCs、PANs观测结果进行综合评价,结果表明:2016年南京市O_3第90百分位日最大8 h平均质量浓度比2013年上升33.3%,超标天数中O_3引起的超标占比增至32.0%。南京市区大气中非甲烷总烃冬季浓度高于夏季,含氧挥发性有机物则与之相反;在5—9月,含氧挥发性有机物组分在日变化过程中出现峰值的时间先后顺序依次为醚、醛、酮类,且O_3和过氧乙酰硝酸酯(PANs)生成存在有一定的线性关系。VOCs/NOx比值表明南京市处于VOCs控制区,因此对NO_2浓度下降不敏感,植物源挥发性有机物连续3年上升,夏季大气光化学反应活性未显著下降,这些现象是城市O_3浓度维持在较高水平的重要因素。     

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.