武汉市大气污染变化趋势预测

以2014年武汉市大气污染源排放清单为基准,结合《武汉市城市空气质量达标规划（2013-2027年）》研究工作,预测了其实施后在控制"两高"行业新增产能、污染源综合治理、淘汰落后产能、控制机动车保有量等方面对武汉市SO₂、NO_x、PM₁₀、PM_2.5的减排量.利用嵌套网格空气质量预报模式系统（NAQPMS）,模拟分析了《达标规划》大气污染治理重点工程实施的空气质量改善效果.结果表明：《达标规划》实施后,2020年武汉市SO₂、NO₂、PM₁₀和PM_2.5排放量将分别比2014年削减22%~66%、6%~37%、14%~40%和17%~46%;武汉市空气质量有所改善,但NO₂和颗粒物年均浓度仍不能达到环境空气质量二级标准.     

盱眙县空气污染特征及其气象条件分析

采用盱眙县环境监测站 2014年1—12月PM₁₀、PM_2.5、SO₂、NO₂、O₃逐日质量浓度资料及盱眙国家基本气象站同期气象资料,分析不同气象条件下盱眙县空气质量变化.结果表明:盱眙县主要污染物是PM_2.5、PM₁₀,污染较轻的是SO₂、NO₂、O₃;盱眙县空气质量变化趋势为春冬季污染严重,夏秋季污染较轻.气象条件中的降水因子对改善空气质量、清除颗粒物具有明显作用;当温度在0 ℃以下或30 ℃以上时空气质量相对较好,0～20 ℃时空气污染情况较为严重;偏东风时大气环境质量较差,偏北风时大气环境质量较好.     

基于CMAQ-MCM模型的长三角地区夏季臭氧和大气氧化性影响的研究

传统的空气质量模型多使用简化的光化学反应机制来模拟大气污染物的形成.这些机制主要基于烟雾箱实验拟合的反应速率和产物来模拟二次产物（如臭氧（O₃））前体物的氧化反应,具有一定的不确定性,导致模拟结果产生偏差.针对该问题,本研究将详细的大气化学机理（MCMv3.3.1）与美国国家环境保护局研制的第三代空气质量预报和评估系统CMAQ相结合（CMAQ-MCM）,模拟研究长三角地区2015年8月27—9月5日臭氧高发时段的空气质量.CMAQ-MCM模型可以较好地模拟长三角地区6个代表城市O₃和其前体物随时间的变化趋势.对模拟的O₃日最大8 h平均浓度的统计分析表明,徐州表现最好（标准平均误差=-0.15,标准平均偏差=0.23）.在长三角地区,居民源对挥发性有机物（VOCs）的贡献最大,占39.08%,其次是交通运输（33.25%）和工业（25.56%）.能源对总VOCs的贡献最小,约为2.11%.对活性氧化氮（NO_y）的分析表明,其主要组分是NO_x（80%）,其次是硝酸（HNO₃）（<10%）.O₃的空间分布与NO_y和NO_x非常相似.HCHO等其他氧化产物的分布与NO_x相似,这很可能是由于在高NO_x条件下VOCs氧化产生的产物.甲基乙烯基酮（MVK）和甲基丙烯醛（MACR）的空间分布与自然源VOCs （BVOCs）非常相似,表明长三角地区MVK和MACR主要由BVOCs氧化生成.长三角地区受到人为源和自然源排放相互作用的影响.     

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.     

三亚地区冬春季大气污染特征观测研究

为了研究海南省三亚地区冬春季大气污染状况,于2011年12月—2013年4月的冬春季节在三亚鹿回头村(监测点位于三亚市郊,三面临海,周围没有工业污染源)开展了大气主要污染物(NO_x、O₃、PM_2.5)的连续监测,利用观测数据对三亚地区冬春季大气污染变化特征进行分析.结果表明:三亚地区大气污染物浓度均低于国家一级标准的浓度值,NO、NO₂、NO_x、O₃、PM_2.5质量浓度的日平均值(平均值±标准差)分别为(2.1±2.2)、(5.2±3.4)、(7.3±3.8)、(59.8±28.4)和(17.5±14.3)μg·m^-3.在污染物的日变化方面,NO_x、PM_2.5呈现典型的双峰型,其峰值分别出现在08:00和17:00,峰谷在13:00;O₃的日变化为单峰型,峰值出现在13:00.通过后向轨迹分析发现,三亚地区大气污染物受局地源排放和外源输送的共同影响,来自陆地的气流易造成污染物的积累,而来自海上的气流则有利于污染物的清除.     

关中5市环境空气质量特征与成因分析

基于国家生态环境部发布的环境空气质量监测数据等资料,采取调查研究与量化分析相结合的方法,对关中地区西安、渭南、咸阳、铜川、宝鸡5市空气质量的总体特征和空间差异进行研究.结果表明：颗粒污染物普遍严重超标,其中PM_2.5和PM₁₀分别超标91%和77%;空气污染具有明显的季节性,冬季的首要污染物是PM_2.5和PM₁₀,夏季的主要污染物是O₃;关中空气污染受地形、气象条件和工业排放、采暖、施工、道路扬尘、汽车尾气等人类活动综合影响,大气污染具有相似性,同时表现出一定的差异性.     

武汉市冬季灰霾期PM2.5中水溶性无机离子的特征

2018年1月,利用颗粒物采样器采集武汉市大气PM_2.5样品并进行水溶性无机离子（F^-、Cl^-、NO₃^-、SO₄^2-、Na⁺、NH₄⁺、K⁺、Mg²⁺、Ca²⁺）的分析.结果表明,NO₃^-、SO₄^2-、NH₄⁺是PM_2.5中最主要的3种水溶性无机离子,除Mg²⁺与Ca²⁺外,PM_2.5与WSⅡs （水溶性无机离子）之间的相关性显著,且移动源贡献占主导地位.阴阳离子平衡表明武汉市冬季灰霾期PM_2.5呈中性或弱酸性.通过混合单粒子拉格朗日综合轨迹模式模拟并采用分层聚类得出了4种主要的后向气流轨迹及相应的PM_2.5和水溶性离子浓度,结果表明区域传输对此次灰霾期影响较大.     

南京地区大气颗粒物影响近地面臭氧的个例研究

通过对2008年4月2～7日南京地区地面气象观测数据以及两个站点空气质量(O₃、NO_x、PM10)监测资料的分析, 发现O₃和PM10之间存在一定程度的反相关。利用一个光化学箱模式对该个例中大气颗粒物影响近地面臭氧的过程进行模拟, 结果发现大气颗粒物浓度的升高使得气溶胶光学厚度增加20％～40%, 导致NO₂和O₃近地面光解率下降20%～30%, OH和HO₂自由基浓度分别减少20%～50%, 造成O₃净生成率下降30%～40%。研究表明, 颗粒物对光化学过程的抑制造成了大气氧化能力的降低, 是近地面臭氧浓度减少的可能原因。     

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。     

湖北省PM2.5质量浓度遥感估算与时空分布特征

针对地面站点稀疏不足以提供高空间覆盖、高空间分辨率的面域PM_2.5数据支撑区域细颗粒物污染防治的问题,以湖北地区2015-2017年的MODIS卫星遥感气溶胶光学厚度（AOD）产品数据为主预测量,结合温度、湿度、风速、压强等气象参数和植被指数数据等辅助预测量,建立了AOD-PM_2.5关系逐日变化的线性混合效应（LME）模型,用于估算湖北地区的PM_2.5浓度水平.利用十折交叉验证方法进行了模型精度评估.结果表明：1）2015-2017年的交叉验证R²分别达到0.89、0.85和0.88,利用MODIS AOD数据反演近地面PM_2.5质量浓度的线性混合效应模型能很好地用于区域细颗粒物遥感监测;2）省内PM_2.5质量浓度空间差异显著,鄂东、鄂南和鄂北高,鄂西北和鄂东南低;3）全省PM_2.5估算时空数据年均值呈下降态势,分别为65.6±39.8、57.1±34.1和48.1±28.3 μg/m³,各市除随州、咸宁2016、2017年年均值持平外,都呈下降趋势.     

An evaluation of air quality modeling over the Pearl River Delta during November 2006

In this paper, we evaluate the performance of several air quality models using the Pearl River Delta (PRD) region, including the Nested Air Quality Prediction Modeling System (NAQPMS), the Community Multiscale Air Quality (CMAQ) model, and the Comprehensive Air Quality Model with extensions (CAMx). All three model runs are based on the same meteorological fields generated by the Fifth-Generation Pennsylvania State University/National Center for Atmospheric Research (PSU/NCAR) Mesoscale Model (MM5) and the same emission inventories. The emission data are processed by the Sparse Matrix Operator Kernel Emissions (SMOKE) model, with the inventories generated from the Transport and Chemical Evolution over the Pacific/Intercontinental Chemical Transport Experiment Phase B (TRACE-P/INTEX-B) and local emission inventory data. The results show that: (1) the meteorological simulation of the MM5 model is reasonable compared with the observations at the regional background and urban stations. (2) The models have different advantages at different stations. The CAMx model has the best performance for SO₂ simulation, with the lowest mean normalized bias (MNB) and mean normalized error (MNE) at most of the Guangzhou stations, while the CMAQ model has the lowest normalized mean square error (NMSE) value for SO₂ simulation at most of the other PRD urban stations. The NAQPMS model has the best performance in the NO₂ simulation at most of the Guangzhou stations. (3) The model performance at the Guangzhou stations is better than that at the other stations, and the emissions may be underestimated in the other PRD cities. (4) The PM₁₀ simulation has the best model measures of FAC2 (fraction of predictions within a factor of two of the observations) (average 53–56%) and NMSE (0.904–1.015), while the SO₂ simulation has the best concentration distribution compared with the observations, according to the quantile–quantile (Q–Q) plots.     

Determination of the uncertainties of the German emission inventories for particulate matter and aerosol precursors using Monte-Carlo analysis

This paper presents the application of a Monte-Carlo simulation for assessing the uncertainties of German 2005 emissions of particulate matter (PM₁₀ & PM_2.5) and aerosol precursors (SO₂, NO_x, NH₃ and NMVOC) carried out in the PAREST (PArticle REduction STrategies) research project. For the uncertainty analysis the German Federal Environment Agency’s emission inventory was amended and integrated with a model on the disaggregation of energy balance data. A series of algorithms was developed in order to make efficient and pragmatic use of available literature and expert judgement data for uncertainties of emission model input data. The inventories for PM₁₀ (95 %-confidence interval: ?16 %/+23 %), PM_2.5 (?15 %/+19 %) and NO_x (?10 %/+23 %) appear most uncertain, while the inventories for SO₂ (?9 %/+9 %), NMVOC (?10 %/+12 %) and NH₃ (?13 %/+13 %) show a higher accuracy. The source categories adding the most relevant contributions to overall uncertainty vary across the pollutants and comprise agriculture, mobile machinery in agriculture and forestry, construction sites, small businesses/carpentries, cigarette smoke and fireworks, road traffic, solvent use and stationary combustion. The PAREST results on relative uncertainties have been quoted in the German Informative Inventory Reports since 2012. A comparison shows that the PAREST results for Germany are within the range of (for NH₃: close below) other European countries’ results on air pollutant inventory uncertainties as reported in the 2013 Informative Inventory Reports.     

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...     

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.     

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。     

Impacts of 21st century climate change on global air pollution-related premature mortality

Climate change modulates surface concentrations of fine particulate matter (PM_2.5) and ozone (O₃), indirectly affecting premature mortality attributed to air pollution. We estimate the change in global premature mortality and years of life lost (YLL) associated with changes in surface O₃ and PM_2.5 over the 21st century as a result of climate change. We use a global coupled chemistry-climate model to simulate current and future climate and the effect of changing climate on air quality. Epidemiological concentration-response relationships are applied to estimate resulting changes in premature mortality and YLL. The effect of climate change on air quality is isolated by holding emissions of air pollutants constant while allowing climate to evolve over the 21st century according to a moderate projection of greenhouse gas emissions (A1B scenario). Resulting changes in 21st century climate alone lead to an increase in simulated PM_2.5 concentrations globally, and to higher (lower) O₃ concentrations over populated (remote) regions. Global annual premature mortality associated with chronic exposure to PM_2.5 increases by approximately 100 thousand deaths (95 % confidence interval, CI, of 66–130 thousand) with corresponding YLL increasing by nearly 900 thousand (95 % CI, 576–1,128 thousand) years. The annual premature mortality due to respiratory disease associated with chronic O₃ exposure increases by +6,300 deaths (95 % CI, 1,600–10,400). This climate penalty indicates that stronger emission controls will be needed in the future to meet current air quality standards and to avoid higher health risks associated with climate change induced worsening of air quality over populated regions.     

基于极端随机树方法的WRF-CMAQ-MOS模型研究

随着城市化、工业化的快速发展,空气污染已经成为了公众最关注的问题之一。为了提高空气质量预报的准确度,以多尺度空气质量模型（Community Multi-Scale Air Quality,CMAQ）为工具,结合中尺度WRF（Weather Research and Forecast Model）气象预报数据、气象观测数据、污染物浓度观测数据,基于极端随机树方法建立了WRF-CMAQ-MOS（Weather Research and Forecast Model-Community Multi-Scale Air Quality-Model Output Statistics）统计修正模型。结果表明,结合WRF气象预报的CMAQ-MOS方法明显修正了由于模型非客观性产生的模式预报偏差,提高了预报效果。使用线性回归方法不能获得较好的优化效果,选取极端随机树方法和梯度提升回归树方法对模型进行改进和比较,发现极端随机树方法对结合WRF气象要素的CMAQ-MOS模型有较大的提升。针对徐州地区空气质量预报,进一步使用基于极端随机树方法的WRF-CMAQ-MOS模型对2016年1、2、3月的空气质量指数（AQI）及PM_2.5、PM₁₀、NO₂、SO₂、O₃、CO六种污染物优化试验进行验证,发现优化效果最为明显的两种污染物分别是NO₂及O₃,2016年1、2、3月整体相关系数NO₂由0.35升至0.63,O₃由0.39升至0.79,均方根误差NO₂由0.0346减至0.0243 mg/m3,O₃由0.0447减至0.0367 mg/m3。文中发展的WRF-CMAQ-MOS统计修正模型可以有效提升预报精度,在空气质量预报中具有很好的应用前景。      

2020年新冠肺炎疫情管控期江苏省大气污染物浓度变化特征

利用江苏省大气环境监测站点的大气污染物监测数据,分析了2020年初新冠肺炎疫情管控期间(2—3月)主要大气污染物浓度的变化特征。结果显示,相比于2019、2020年疫情管控期间PM_2.5、PM₁₀、NO₂、SO₂、CO浓度的全省平均降幅分别为37.5%、36.9%、31.9%、28.2%和21.2%。严格管控期的2月和生产恢复期的3月,江苏省十三市PM_2.5、PM₁₀浓度同比降幅大致相当,呈现出较好的时间连续性和空间均匀性。但各市臭氧浓度同比变化呈现出较大的时空差异。空间上,沿江以南城市南京、无锡、常州、苏州和镇江五市臭氧浓度明显上升,而其他城市臭氧浓度以下降为主;时间上,2月南京等九市臭氧浓度上升,3月徐州等八市臭氧浓度持平或者下降。假设未发生新冠肺炎疫情以及未采取为阻断疫情蔓延而实施的种种举措,在仅考虑近年来大气污染防治政策持续实施的情况下,与预期降幅相比,疫情管控对NO₂实况浓度降幅的影响最大,其次是PM_2.5