秸秆燃烧排放对北京及其周边地区PM2.5浓度影响的数值模拟

采用卫星监测的火点燃烧排放数据,利用区域化学传输模式WRF-Chem模拟分析了2017年5月华北地区细颗粒物(PM2.5)质量浓度分布,通过生物质燃烧排放源(华北区域以秸秆燃烧为主)开关的敏感性试验定量计算了燃烧排放对北京及其周边地区PM2.5质量浓度的影响。卫星监测结果显示,2017年5月华北地区有大量的秸秆焚烧现象,对该地区空气质量造成一定影响的燃烧天数为20 d,占全月总日数的65%左右。数值模拟结果表明:该地区秸秆燃烧排放导致PM2.5浓度升高的区域集中在华北平原农作物产区,其分布位置与卫星监测的火点分布吻合。秸秆燃烧导致这些地区PM2.5浓度月平均值上升幅度普遍超过3 μg/m~3,高值区超过了11 μg/m~3,上升比例可达10%以上;此外,来自华北平原及长三角地区的燃烧排放对北京(特别是东南部地区)污染物浓度的影响是不容忽视的,其中河南、山东、天津等地的秸秆燃烧在合适风场的作用下会严重影响北京,可导致丰台及通州等地PM2.5小时浓度上升超过17 μg/m~3,上升幅度超过40%。     

秸秆燃烧对北京秋季气溶胶浓度和短波辐射影响的模拟研究

利用地面细颗粒物（PM2.5）浓度和气象常规观测资料、地基 AERONET观测资料、GFED生物质燃烧排放清单和大气化学—天气耦合模式WRF-Chem,模拟研究了华北地区2014年10月气象要素和大气污染物的时空演变,重点关注北京10月7～11日的一次重霾事件及其天气形势、边界层气象特征、输送路径、PM2.5及其化学成分浓度变化等特征,以及秸秆燃烧对华北和北京地区细颗粒物浓度和地面短波辐射的影响。与观测资料的对比结果显示,模式可以很好地模拟北京地区地面气象要素和PM2.5质量浓度,考虑秸秆燃烧排放源可以明显改进北京PM2.5浓度模拟的准确性,但在重度污染情况下,模式总体上低估气溶胶光学厚度和高估地面短波辐射。10月7～11日北京地区重霾事件主要是不利气象条件下人为污染物累积和区域输送造成,也受到华北地区南部秸秆燃烧的影响。河南北部、河北南部和山东西部大面积秸秆燃烧释放的气态污染物和颗粒物在南风的作用下输送至北京,秸秆燃烧对北京地区地面PM2.5、有机碳（OC）、硝酸盐、铵盐、硫酸盐和黑碳（BC）的平均贡献率分别为24.6%、36.8%、23.2%、22.6%、7.1%和19.8%,秸秆燃烧产生的气溶胶可以导致北京地面平均短波辐射最大减小超过20 W m－2,约占总气溶胶导致地表短波辐射变化的24%。     

淮河流域秸秆焚烧关键期主要大气污染物浓度时空分布特征

基于2015年6月淮河流域卫星遥感监测火点信息、环境空气质量监测数据和常规气象观测资料,利用ANUSPLIN和ArcGISKriging方法对气象要素和主要大气污染物浓度空间栅格化,分析了秸秆焚烧关键期内AQI和主要污染物浓度的时空变化特征及其与气温、相对湿度、风速等气象要素的相关关系。结果表明:秸秆焚烧关键期内,淮河流域城市AQI、PM10与PM2.5浓度均明显升高,且与卫星监测火点具有一定时空响应关系。在时间变化上,AQI、PM10与PM2.5浓度6月上中旬呈波动上升,6月下旬趋于回落;在空间分布方面,AQI、PM10与PM2.5浓度三者分布形态相似,总体上呈现"南低北高、两高一低"分布特征;期间AQI、PM10与PM2.5浓度与气温呈显著正相关,与相对湿度呈显著负相关,与风速的相关性不显著。     

秸秆焚烧导致湖北中东部一次严重霾天气过程的分析

利用地面气象要素、火点信息及污染物资料,研究了2014年6月12~13日湖北省中东部地区一次重度霾天气的成因及污染特征。结果表明:导致此次霾天气的主要原因是安徽省北部大面积秸秆焚烧所形成污染气团受偏东北气流输送的影响,12日在湖北中东部形成了两条"带状"的能见度低值区,最低能见度仅为2.1 km。秸秆焚烧污染物输送气流由北向南影响湖北,主要作用于孝感—武汉—咸宁一带,3个地区细颗粒物(PM2.5)峰值浓度均超过了600μg/m3,且武汉和孝感的PM2.5与PM10质量浓度比值在12日增加到0.76和0.77,并出现了0.96和0.93的最大值,随着污染气团的传输,其中PM2.5所占比例会出现明显下降。SO2质量浓度的变化特征不显著,NO2质量浓度在污染物质量浓度达到峰值前1~3 h达到峰值,而CO是秸秆焚烧产生的主要污染气体,其质量浓度变化与PM2.5和PM10呈正相关关系,相关系数分别为0.66和0.67。风矢量和分析表明:6月12日湖北省中东部存在明显的东北来向气流输送,污染物的输送是该时段霾天气发生的主要影响因子,而6月13日湖北省东北边界处的输送气流已经明显减弱消失,东南部风矢量和异常偏小导致的污染物堆积是该地区污染持续的主要原因。     

京津冀周边秸秆燃烧对PM2.5无机组分影响

华北平原是我国主要农作物产区,田间秸秆焚烧现象普遍存在,选取秋收季节（2014年10月）分析了秸秆燃烧的排放特征,利用区域化学传输模型WRF-Chem模拟研究了燃烧排放对气态前体物及其氧化产物的影响,以及最终导致的PM_2.5中硫酸盐、硝酸盐和铵盐的变化。研究表明:2014年秋收季节,河南和山东等省份的秸秆燃烧排放会在东南风的输送作用下影响京津冀地区;秸秆燃烧排放大量挥发性有机物（VOCs）,导致火点源及周边地区大气中主要氧化剂浓度上升,提升了区域大气氧化能力;当携带大量VOCs的秸秆燃烧烟羽与以化石燃料排放为主的城市气团相混合时,大气氧化性增强会加速城市地区人为源排放的NO_x和SO₂等气态前体物的氧化过程,提高硫酸盐和硝酸盐的形成速率、促进二次无机气溶胶的生成。     

不同人为源排放清单对大气污染物浓度数值模拟的影响:以浙江省为例

应用大气化学模式WRF-Chem(Weather Research and Forecast-Chemistry),分别选用亚洲排放源清单INTEX-B(Intercontinental Chemical Transport Experiment-Phase B)、REASv2.1(Regional Emission inventory in Asia version 2.1)以及全球排放源清单HTAP_v2(Hemispheric Transport of Air Pollution version 2),对浙江省2013年12月进行模拟,分别记为IN、RE和HT试验,研究人为源排放清单对大气污染物浓度数值模拟的影响。结果表明,3组试验合理的反映出PM2.5(空气动力学当量直径小于等于2.5μm的颗粒物,即细颗粒物)、PM10(空气动力学当量直径小于等于10μm的颗粒物,即可吸入颗粒物)和NO_2近地面浓度的时空分布特征,相关系数为0.5~0.8,85%以上的模拟值落在观测值的0.5~2倍范围内,但对SO_2近地面浓度模拟较差。IN、RE、HT试验对PM2.5和PM10的模拟偏差均成递减趋势,约为30%、16%和6%,HT试验的模拟值更加接近观测。INTEX-B清单中PM2.5的一次排放与二次气溶胶前提物SO_2均高于REAS与HTAP清单,因此会导致更多的硫酸盐生成,从而进一步增加PM2.5浓度。HTAP_v2清单中较低的NH3排放会抑制硝酸盐的生成,从而有助于降低PM2.5浓度。3个清单的基准年与模拟年的差异对SO_2浓度模拟的准确性影响更大,INTEX-B清单中SO_2排放量明显高于REASv2.1与HTAP_v2清单,尤其在浙北和沿海工业发达地区,导致IN试验模拟的SO_2在这些地区存在明显高估。3组试验模拟的NO_2浓度偏差最小且更为接近(-8%~4%),主要原因是3个清单在浙江省的NOx排放十分一致。从3组试验结果之间的差异程度来看,浙江省范围内PM2.5、PM10、SO_2和NO_2逐日浓度模拟值之间的平均差异程度分别约为14%、15%、51%和16%,最大差异程度分别为69%、78%、137%和132%。月均浓度与逐日浓度的平均差异程度基本一致,但最大差异程度明显更低。总体来看3组试验模拟的PM2.5、PM10与NO_2的差异程度明显低于SO_2。     

邢台沙河市冬季大气污染特征与潜在源区分析

为了解邢台沙河市冬季大气污染特征,选取2017年12月至2018年2月沙河市区3个省控站点（司法局、市政府、宣传中心）的逐时监测数据,分析了沙河市主要污染物的时空分布特征和潜在源区。污染物浓度特征分析表明:整个冬季司法局、市政府和宣传中心站点的细颗粒物（PM2.5）平均浓度分别为118.0 μg/m3、121 μg/m3和135 μg/m3。在大气自然活动和人为污染排放的共同作用下,PM10、PM2.5、SO₂、NO₂和CO均有明显的日变化特征。整个冬季沙河市的ρ(PM2.5)/ρ(PM10)、ρ(SO₂)/ρ(NO₂)均值分别为0.57和1.05（ρ为各物质的浓度）。且随着污染加重,ρ(PM2.5)/ρ(PM10)、ρ(SO₂)/ρ(NO₂)均明显升高,表明燃煤贡献增加;污染物空间分布特征分析表明:位于3个站点东北处的玻璃企业产生的污染物可能对监测站点造成了一定影响。污染物空间差异分析表明,区域污染范围越大、强度越高,大气污染的空间差异性越小;潜在源分析表明:沙河市PM2.5的强潜在源区分布在其周边区域,随着PM2.5浓度增加,强潜在源区呈缩小趋势。沙河市东南部的本地源对PM2.5浓度有主要贡献,而此处正是玻璃企业的聚集地。     

广州城区和城郊大气颗粒物污染现状及来源分析

为更好地表征当前广州城区和城郊大气颗粒物污染现状与差异及其与气象要素的关系,对2017年城郊站黄埔和城区站番禺的大气颗粒物(PM2.5和PM10)和气象观测数据进行了分析。结果表明:全年来看,城区和城郊PM2.5和PM10具有相似的质量浓度频率、月际变化和日变化;城区站的平均PM2.5质量浓度为39.3μg/m3,高于城郊站(35.3μg/m3);城区站PM10质量浓度为58.5μg/m3,低于城郊站(62.9μg/m3);ρ(PM2.5)/ρ(PM10)显示,相比城郊,城区的2次污染更严重,细粒子占比更高。风玫瑰图分析发现,静风或小风状态下,城区番禺的颗粒物污染程度要高于城区黄埔,本地排放对城区站的颗粒物污染,特别是细颗粒物污染的影响更为显著;城郊站颗粒物污染的形成也同时受本地排放和外源输送影响,主要的输送来源于西北上风向肇庆、佛山一带的污染排放,其对PM2.5的影响更为显著。     

黄石市大气PM10和PM2.5质量浓度特征研究

利用2015年黄石市5个监测站点可吸入颗粒物(PM10)和细颗粒物(PM2.5)的在线监测数据和风向、风速、气温、气压等常规地面气象要素观测资料,分析了黄石市大气PM10和PM2.5的质量浓度水平分布特征及其与气象参数的关系。结果表明:2015年黄石市5个监测站点大气PM10和PM2.5年均浓度范围分别为95.8—108.6μg·m^-3和64.3—68.9μg·m^-3,均超过国家二级标准;季均质量浓度呈现显著的冬季高夏季低的变化规律,冬季PM10和PM2.5的质量浓度分别为(143.9±62.2)μg·m^-3和(95.5±44.5)μg·m^-3,夏季PM10和PM2.5的质量浓度分别为(75.2±24.0)μg·m^-3和(50.7±17.3)μg·m^-3。5个监测站中,下陆区、西塞山区和铁山区的PM10和PM2.5颗粒物污染较为严重;各站点大气PM10和PM2.5质量浓度显著相关。大气颗粒物浓度与气象因素的分析显示,黄石市大气颗粒物浓度与气温呈显著的负相关关系,与气压呈正相关关系,与风速和相对湿度的相关性不显著,受风向影响变化较大。     

晋城市冬、春季PM2.5和PM10污染水平时空分布及其与气象因子的关系

为深入了解晋城市颗粒物浓度时空分布特征,对晋城市2017年12月至2018年5月国控点、小型站和微型站PM2.5及PM10小时浓度数据进行收集整理,并进行空间插值分析和时间变化趋势分析及与气象监测数据的相关分析。结果表明:颗粒物浓度在冬、春季节具有明显差异,冬季PM10与PM2.5高值区主要位于东北部及东南小部分区域,春季PM10高值区位于城区南部区域,PM2.5高值区主要集中于城区。晋城市城区和郊区PM10与PM2.5月均浓度整体呈单峰型变化,PM10在4月份最高(157.54±5.67μg·m^-3),PM2.5在1月份最高(94.08±2.25μg·m^-3)。冬季PM2.5/PM10平均为0.57,春季平均为0.45。颗粒物小时浓度的变化呈现单峰单谷的型式,冬季PM10与PM2.5小时平均浓度最高值均出现在10时,春季均出现在09时。监测期间晋城市PM10与PM2.5的小时浓度值与相对湿度有较高的正相关性(p<0.01),与风速、风向有较高的负相关性(p<0.01),与温度和气压的相关性较低。冬季,东北至正南风向时,PM10与PM2.5的浓度普遍高于西北风向时的浓度,对晋城冬、春季国控点颗粒物浓度贡献率最高的风向风速为东南偏南风向,风速在1 m/s以内。     

An analysis on the concentration characteristics of PM2.5 in Seoul,Korea from 2005 to 2012

PM2.5 is a big issue as it is considerably more harmful than other sizes of particulate matter. World Health Organization (WHO) recommends 25 μg m^?3 as the daily average concentration, and 10 μg m^?3 per day as an annual average. To keep up with global trends, it is first necessary to understand the current status and characteristics of PM2.5 concentrations in Korea. Using the PM2.5 data measured by Seoul Metropolitan City from November 2005 to March 2012, the author analyzed its statistical characteristics and correlations with other air pollutants. For the time period from 2005 to 2012, the annual average concentration of PM2.5 was 27 μg m^?3, three times the WHO standard. Also, the daily average PM2.5 concentration of 215 days per year also exceeded the WHO standard. However, the number days exceeding the Korean daily average standard of 50 μg m^?3 to be enacted in 2014 was only three. PM2.5 concentration had a high correlation (r = 0.84) with PM10, and also showed high correlations with gaseous pollutants, such as SO₂, NO₂, and CO, but not O₃. This study suggests that the Korean government should strengthen their standard to match the criteria used by WHO.     

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.     

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.     

无锡梅雨期湿沉降综合分析

利用2008—2014年梅雨期间酸雨观测资料及2014年6月16—27日降水个例加密采样资料,结合大气污染物资料分析了近7 a无锡梅雨期酸雨特征,研究降水过程中空气污染物、p H值、电导率的变化及降水对污染物的清除作用。结果表明:无锡市梅雨期酸雨年平均p H值呈现逐年递增趋势。降水过程中,颗粒物质量浓度显著降低;气体浓度变化受其自身日变化及排放源影响大于雨水的清除作用;样品的p H值、K值每个过程变化并不一致,K值变化与颗粒物质量浓度变化大致保持一致。降水、风对颗粒物质量浓度影响大于对气体浓度的影响。长时间连续降水时,降水对颗粒污染物的清除存在极限。小时雨量在0~0.5 mm时,降水对颗粒物浓度做负清除,其值反而略有增加;小时雨量在0.6~5.0 mm时,降水对颗粒物质量浓度做正清除;小时雨量达到5.1 mm及以上时,对PM_(2.5)和PM_(2.5-10)做正清除,对PM_(10)做负清除。降水对SO_2有稀释清除作用;对NO_2的稀释作用取决于其开始浓度值;对CO、O_3的清除作用不显著。     

Intercomparison of Two Box Models of the Chemical Evolution in Biomass-Burning Smoke Plumes

Results from two independently developed biomass-burning smoke plume models are compared. Model results were obtained for the temporal evolution of two nascent smoke plumes originating from significantly different fire environments (an Alaskan boreal forest and an African savanna). The two smoke plume models differed by 1%–10% for [O₃], with similar differences for NO _x and formaldehyde (relative percent differences). Smaller intermodel differences were observed for the African savanna smoke plume as compared to the plume from the Alaskan boreal fire. Mechanistic differences between the models are heightened for the Alaskan smoke plume due to the higher VOC emission ratios as compared to the African savanna fire. The largest deviations result from the differences in oxidative photochemical mechanisms, with a smaller contribution attributable to the calculation of photolysis frequencies. The differences between the two smoke plume models are significantly smaller than the uncertainties of available photokinetic data or field measurements. Model accuracy depends most significantly on having the fullest possible VOC data, a requirement that is constrained by currently available instrumentation.     

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。     

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...     

Estimating emissions from forest fires in Thailand using MODIS active fire product and country specific data

Studies on air pollution and climate change have shown that forest fires constitute one of the major sources of atmospheric trace gases and particulate matter, especially during the dry season. However, these emissions remain difficult to quantify due to uncertainty on the extent of burned areas and deficient knowledge on the forest fire behaviours in each country. This study aims to estimate emissions from forest fires in Thailand by using the combination of the Moderate Resolution Imaging Spectroradiometer (MODIS) for active fire products and country-specific data based on prescribed burning experiments. The results indicate that 27817 fire hotspots (FHS) associated with forest fires were detected by the MODIS during 2005–2009. These FHS mainly occurred in the northern, western, and upper north-eastern parts of Thailand. Each year, the most significant fires were observed during January–May, with a peak in March. The majority of forest FHS were detected in the afternoon. According to the prescribed burning experiments, the average area of forest burned per fire event was found to fall within the range 1.09 to 12.47 ha, depending upon the terrain slope and weather conditions. The total burned area was computed at 159309 ha corresponding to the surface biomass fuel of 541515 tons dry matter. The forest fire emissions were computed at 855593 tons of CO₂, 56318 tons of CO, 3682 tons of CH₄, 108 tons of N₂O, 4928 tons of PM_2.5, 4603 tons of PM₁₀, 357 tons of BC and 2816 tons of OC.