Smoke aerosols dispersion and transport from the 2013 New South Wales (Australia) bushfires

Environmental monitoring and modelling, especially in the regional context, has seen significant progress with the widely usage of satellite measurement in conjunction with local meteorological and air quality monitoring to understand the atmospheric dispersion and transport of air pollutants. This paper studies the application of these data and modelling tools to understand the environment effects of a major bushfire period in the state of New South Wales (NSW), Australia, in 2013. The bushfires have caused high pollution episodes at many sites in the greater Sydney metropolitan areas. The potential long-range transport of aerosols produced by bushfires to other region and states has been seen by regulators as a major concern. Using data and images collected from satellites, in addition to the results obtained from different simulations carried out using HYSPLIT trajectory model and a regional meteorological model called Conformal Cubic Atmospheric Model (CCAM), we were able to identify at least 2 days on which the smoke aerosols from bush fires in NSW has been transported at high altitude to the northern state of Queensland and the Coral Sea. As a result, widespread high particle concentration in South East Queensland including the Brisbane area, as measured by nearly all the air quality monitoring stations in this region, occurred on the day when the smoke aerosols intruded to lower altitude as indicated by the CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) Lidar measurements on the CALIPSO (Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation) satellite. The use of meteorological or air quality modelling to connect the ground-based measurements with satellite observations as shown in this study is useful to understand the pollutant transport due to bushfires and its impact on regional air quality.     

基于激光雷达分析2020年冬季镇江地区一次大气污染过程

利用气溶胶激光雷达观测结果,结合环境监测站污染物浓度数据、气象观测资料及HYSPLIT后向轨迹模式结果,综合分析2020年1月17—22日镇江市一次大气污染过程。结果显示,此次污染过程前期天气形势稳定,不利于污染物的清除及扩散,后期受偏北风影响,北方污染物向镇江输送,使得本地污染物持续累积,污染不断加重。特征雷达图分析表明此次污染为以PM2.5为主的二次污染。激光雷达显示污染日消光系数为0.0~0.9 km-1,消光系数垂直廓线日变化特征明显,气溶胶粒子主要堆积在0.6 km高度以下,并且很好地揭示了污染气团从高空逐渐下沉最终与本地污染叠加的过程,与HYSPLIT模式解析的污染气团来源结果基本一致。     

精细化预报在首届中国国际进口博览会空气质量保障工作中的应用

重大活动保障预报不同于业务预报,其核心是服务管控,并注重提前预报的时效性和精细化。该文总结了首届中国国际进口博览会空气质量保障预报工作的实践经验:在历次保障预报联合会商组织架构的基础上,完善联合会商机制,强调预报保障主体单位与支持单位的预报工作内容和职责的区分,通过数值模式的优化改进、针对性开展关键气象因素专题等多种技术手段不断提升预报精准度,并创新应用历史相似污染案例库挖掘和匹配分析,注重预报效果评估以及时总结预报经验,不断提升预报的准确性,为管理决策提供强有力的技术支撑。     

Using improved neural network model to analyze RSP,NOx and NO2 levels in urban air in Mong Kok,Hong Kong

As the health impact of air pollutants existing in ambient addresses much attention in recent years, forecasting of airpollutant parameters becomes an important and popular topic inenvironmental science. Airborne pollution is a serious, and willbe a major problem in Hong Kong within the next few years. InHong Kong, Respirable Suspended Particulate (RSP) and NitrogenOxides NO_x and NO₂ are major air pollutants due to thedominant diesel fuel usage by public transportation and heavyvehicles. Hence, the investigation and prediction of the influence and the tendency of these pollutants are ofsignificance to public and the city image. The multi-layerperceptron (MLP) neural network is regarded as a reliable andcost-effective method to achieve such tasks. The works presentedhere involve developing an improved neural network model, whichcombines the principal component analysis (PCA) technique and theradial basis function (RBF) network, and forecasting thepollutant levels and tendencies based in the recorded data. Inthe study, the PCA is firstly used to reduce and orthogonalizethe original input variables (data), these treated variables arethen used as new input vectors in RBF neural network modelestablished for forecasting the pollutant tendencies. Comparingwith the general neural network models, the proposed modelpossesses simpler network architecture, faster training speed,and more satisfactory predicting performance. This improvedmodel is evaluated by using hourly time series of RSP, NO_x and NO₂ concentrations collected at Mong Kok Roadside Gaseous Monitory Station in Hong Kong during the year 2000. By comparing the predicted RSP, NO_x and NO₂ concentrationswith the actual data of these pollutants recorded at the monitorystation, the effectiveness of the proposed model has been proven.Therefore, in authors' opinion, the model presented in the paper is a potential tool in forecasting air quality parameters and hasadvantages over the traditional neural network methods.     

基于OPAQ的城市空气质量预报系统研究

空气质量预测在国内的关注度日益提高,传统的空气质量预测系统通常运用数值化学传输模型,利用物理方程来计算污染物的扩散、沉降和化学反应。而化学传输模型的预测准确性很大程度上需要依赖详细的污染源排放信息和气象模型的输出结果。基于统计模型的OPAQ空气质量预报业务系统,采用人工神经网络算法,可预测各污染物的日均值或日最大值。并对北京空气质量预报的结果进行了评价,OPAQ空气质量预报业务系统对空气质量预测的准确性较高,能够利用较低的计算资源得到较为准确的预测结果。与数值预报相比,OPAQ空气质量预报业务系统不需要大量的基础数据作为输入,可弥补数值预报的不足,并成为数值预报的有力补充。     

洛阳市大气污染特征与相关气象要素24小时变化分析

通过对洛阳市环境空气监测资料的统计,归纳出洛阳市区日内24小时环境空气污染变化特征,并结合同步气象观测资料综合分析,找出了二者之间的联系特征。引入气温日较差这一便于观测、预报的气象要素表征空气污染物垂直扩散特征,为今后进行污染预报提供了新的思路。     

长株潭区域一次重度污染过程气象成因

利用PM_2.5质量浓度、地面气象要素、NCEP、ERSST＿V3、GBL等资料，研究了2021年12月7—11日长株潭地区一次重度空气污染过程的特征及成因。结果表明，高空平直环流、无明显槽脊影响，地面弱冷空气活跃是本次重度空气污染过程的主要环流形势特征；地面均压场、小风和升温增湿是此次重度空气污染过程的主要气象要素特征。污染物浓度变化与主导风向和污染通道密切相关，本地风速对混合层的高度、污染物水平扩散影响较大，600～700 hPa逆温层有利于污染物在主导风作用下近距离传输及在低层交换积累。我国中东部污染物积聚是长株潭区域重要的污染来源，长株潭地区存在区域性同步污染现象。低层流入长株潭区域气流轨迹差异及地理条件是长株潭污染物空间分布差异的重要因素。     

唐山市钢铁行业大气污染物排放清单建立

以唐山市钢铁企业为研究对象,在收集已有排放源数据的基础上,基于排放因子和活动水平数据采用排放因子法估算了唐山市钢铁企业多个大气污染物的排放量,得到了符合空气质量模型要求的污染源输入数据,建立钢铁行业和主要防控因子污染源数据库。结合唐山市各县区的环境空气质量状况,利用GIS技术,将不同污染因子的排放进行空间分布,最终形成准确完善的多尺度、高时空分辨率大气污染源排放清单。研究介绍了符合中国特色的区域高分辨率大气排放源清单建立的方法体系,为京津冀地区区域大气污染联防联控及2020年大气污染物区域削减计划工作提供数据支撑。     

秸秆焚烧对哈尔滨市灰霾天气的影响

2015年11月1—4日,哈尔滨市及周边地区发生了连续的灰霾天气,颗粒物浓度急剧升高。污染发生时,监测仪器均布设在哈尔滨市区上风向30 km处(哈尔滨市双城区)并开展了连续96 h的监测分析。综合利用气象观测资料,3D可视激光雷达监测资料及地面空气污染监测资料分析了灰霾天气发生的气象条件和污染边界层特征,根据哈尔滨市双城区大气污染物排放源谱库对主要成分进行来源解析,结合颗粒物质量浓度和气象条件研究了秸秆焚烧对灰霾天气的影响。结果表明,灰霾天气持续期间,夜间生物质燃烧源成为该地区颗粒物的第二大源;秸秆焚烧产生的大气污染物,由于地面长时间静风,污染边界层降低等原因,致使本地污染物累积、不易扩散,加剧了本次污染。     

“十三五”时期哈尔滨空气质量变化特征及影响因素分析

对"十三五"期间哈尔滨市空气质量状况、变化特征及影响因素进行分析,为哈尔滨市打赢蓝天保卫战提供科学参考。以哈尔滨市为研究区域,基于哈尔滨市12个国控监测点位监测结果,对"十三五"期间哈尔滨市环境空气质量优良天数及6项污染物进行分析,总结了环境空气质量变化特征及影响因素。结果表明:"十三五"期间,哈尔滨市环境空气质量呈现波动向好趋势,较"十二五"期间大幅改善,除细颗粒物外,其他指标已稳定达二级标准,臭氧成为仅次于细颗粒物的首要污染物,冬季燃煤污染是环境空气最主要的污染来源,秋季秸秆焚烧及春季清除秸秆根茬也会产生一定影响。     

北方沙尘对江苏环境空气中可吸入颗粒物浓度的影响

利用卫星遥感数据、气象观测资料和江苏省环境空气自动站的可吸入颗粒物的监测数据,对春季中国北方的沙尘暴过程对江苏环境空气中可吸入颗粒物浓度的影响分析发现,沙尘暴所含的沙尘粒子在冷空气的引导下可能进入江苏,对环境空气中可吸入颗粒物浓度将会产生较大的影响;北方沙尘暴所携带的沙尘粒子是春季江苏空气污染的重要来源之一,是预报空气质量异常偏高的重要依据之一。     

黑龙江省环境空气污染物和气象要素的典型相关分析

通过对黑龙江省4个自然年(2016年1月1日—2019年12月31日)环境空气污染物和气象要素的分析,揭示了黑龙江省气象条件对空气污染物浓度的影响规律与特征。对PM_(2.5)、PM_(10)、SO_2、NO_2、CO和O_3等6项污染物的描述性统计和简单的相关分析显示:黑龙江省环境空气质量呈现逐年变好的趋势,非采暖期环境空气质量好于采暖期,6项污染物中除O_3呈现夏季偏高以外,其余污染物采暖期浓度均高于非采暖期。运用典型相关分析法探究环境空气污染物与温度、降水量、相对湿度、风速和气压5项气象要素之间的关系,并进行统计学检验,结果表明:环境空气污染物与气象要素之间存在显著相关,温度、风速和相对湿度对污染物具有显著影响。非采暖期大气相对湿度对PM_(10)和O_3-8h的影响显著;而在采暖期,风速对PM_(10)和PM_(2.5)的影响显著。     

2001年～2008年及奥运会期间天津市大气污染特征分析

根据天津市大气质量监测数据,对2001年～2008年及奥运会期间天津市大气污染特征和主要大气污染物的变化规律进行了分析。结果表明,2001年～2008年天津市的PM10、SO2和NO2污染总体呈下降趋势,但质量浓度仍相对较高。2008年8月奥运会期间天津市PM10和SO2质量浓度达到国家空气质量二级标准,NO2质量浓度达到国家空气质量一级标准,空气质量良好。天津市PM10污染相对稳定,SO2和NO2的污染分布呈现明显的季节性,时间上表现为冬强夏弱。气象条件对污染物浓度影响明显,沙尘、大雾等天气可使污染物浓度急剧升高。     

Air quality assessment of Estarreja, an urban industrialized area, in a coastal region of Portugal

Despite the increasing concern given to air quality in urban and industrial areas in recent years, particular emphasis on regulation, control, and reduction of air pollutant emissions is still necessary to fully characterize the chain emissions–air quality–exposure–dose–health effects, for specific sources. The Estarreja region was selected as a case study because it has one of the largest chemical industrial complexes in Portugal that has been recently expanded, together with a growing urban area with an interesting location in the Portuguese coastland and crossed by important road traffic and rail national networks. This work presents the first air quality assessment for the region concerning pollutant emissions and meteorological and air quality monitoring data analysis, over the period 2000–2009. This assessment also includes a detailed investigation and characterization of past air pollution episodes for the most problematic pollutants: ozone and PM10. The contribution of different emission sources and meteorological conditions to these episodes is investigated. The stagnant meteorological conditions associated with local emissions, namely industrial activity and road traffic, are the major contributors to the air quality degradation over the study region. A set of measures to improve air quality—regarding ozone and PM10 levels—is proposed as an air quality management strategy for the study region.     

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。     

基于CFSv2的延伸期空气质量数值预报技术及效果评估

介绍了基于CFSv2产品开展空气质量数值模拟的技术方法以及基于该技术的成都市延伸期空气质量预报系统,评价了2019年9月25日至12月25日的预报结果,以2019年12月为例分析了系统对具体污染过程的预报能力。结果表明,系统AQI预报准确率为36.67%,等级预报准确率为68.93%,"时段1"的预报效果优于其余时段,但"时段2"和"时段3"在污染物浓度水平上仍然具有较高的参考价值。系统在PM2.5浓度、气温和气压的变化趋势上提供21 d左右的参考。后续工作中,将针对CFSv2产品开展气象模型参数方案优选,并尝试结合基于GFS产品驱动的常规业务数值预报产品为延伸期空气质量预报提供污染物浓度初始场,减少误差传递,从而提高延伸期空气质量预报产品的准确性和可用性。     

海口市臭氧污染特征

基于2013—2015年海口市4个空气质量自动监测站点数据,结合气象资料,分析了海口市O_3的污染特征。结果表明:海口市O_3总体优良,优良天数比例为99.4%,污染天数均为轻度污染;在良和污染天数中,O_3作为首要污染物的天数占40%,超过其他5项污染物占比。海口市10月O_3浓度最高。O_3月均浓度与温度呈负相关关系,同时与风向有密切关系:5—8月气温较高,以南风为主,O_3浓度较低;1月北风频率较高,易受外来污染传输作用,O_3浓度相对较高。O_3超标日以东北风为主,日变化并未呈现单峰型特征,12:00—22:00时段O_3浓度在10%范围内小幅变化。台风外围型和北方冷高压底部型是造成海口市O_3超标的2类典型天气形势。     

大气污染物指纹系统构建及在污染溯源中的应用

基于污染物检测特征信息结合区域产业结构、企业布局等信息构建了大气污染物指纹系统,通过污染物检测特征信息构建的第一级指纹数据库实现了对大气特征污染物的识别和污染物应急处置措施的推送;通过污染源行业及其排放的特征污染物信息构建的第二级指纹数据库实现了产排污行业的溯源;通过企业及其产排污信息,结合气象条件构建的第三级指纹数据库实现了对产排污企业的溯源。基于大气污染物指纹数据库的污染溯源技术为突发环境污染事故中污染物质的识别和污染源的追踪提供了新途径。实际应用验证表明,大气污染物指纹系统污染溯源响应快速、识别精准,实现了大气污染物从监测、溯源到应急处置的全过程无缝衔接。     

基于GIS的唐山地区大气污染物时空分布研究

应用数据统计和ArcGIS对北方重工业城市唐山地区2014年14个县(区)18个空气自动监测站的数据进行时空分布特征分析,监测的污染物为PM_(10)、SO_2、NO_2、PM_(2.5)、O_3、CO共6项。利用ArcGIS对各个自动监测站污染物数据建立网格模型,采用反距离权重法分别对年均、采暖期、非采暖期的环境空气质量综合指数和6项污染因子浓度的空间分布进行估算,直观比较了污染物在不同时期内的空间分布状况。结果表明,空气质量时间分布较为明显,非采暖期明显好于采暖期。同时,计算出每个网格单元污染指数的标准偏差,结合气象气候、地形地势、工业发展等情况,分析得出北部山区、市中心区附近区域空气质量波动较大。为区域大气污染有针对性的综合防治、联防联控及污染物区域削减计划打下数据基础。     

Forecasts using Box–Jenkins models for the ambient air quality data of Delhi City

The monthly maximum of the 24-h average time-series data of ambient air quality-sulphur dioxide (SO(2)), nitrogen dioxide (NO(2)) and suspended particulate matter (SPM) concentration monitored at the six National Ambient Air Quality Monitoring (NAAQM) stations in Delhi, was analysed using Box-Jenkins modelling approach (Box et al. 1994). Univariate linear stochastic models were developed to examine the degree of prediction possible for situations where only the past record of pollutant data are available. In all, 18 models were developed, three for each station for each of the respective pollutant. The model evaluation statistics suggest that considerably satisfactory real-time forecasts of pollution concentrations can be generated using the Box-Jenkins approach. The developed models can be used to provide short-term, real-time forecasts of extreme air pollution concentrations for the Air Quality Control Region (AQCR) of Delhi City, India.