Photochemical properties and source of pollutants during continuous pollution episodes in Beijing, October, 2011

Beijing sufered from serious air pollution in October, 2011 with the occurrence of three continuous episodes. Here we analyze the pollution status of particulate matter, the relationship between the gaseous pollutants, physical and chemical properties of single particles, and the profile of watersoluble ions in PM2.5during the three episodes. Regional and photochemically aged air masses, which were characterized as having high values of O3and SO2, were hypothesized to have played a dominant role in the first episode. After mixing local air masses with freshly-emitted primary pollutants, the concentration of NOx continued to increase and the size of SO4 2, NO3 and NH4 +in the particle population continued to become smaller. The amount of elemental carbon-rich and organic carbonrich particles in the scaled single particles(0.2–2 μm) and water-soluble K+in PM2.5also increased in the episodes. All the available information suggests that the biomass or fuel burning sources in or around Beijing may have had a huge impact on the last two episodes.     

Impact of emission control on regional air quality: An observational study of air pollutants before, during and after the Beijing Olympic Games

An observational study on trace gases and PM2.5 was conducted at three sites in and around Beijing, during the Olympic season from 2007 to 2009. Air quality improved significantly during the Olympic Games due to the special emission control measures. However, concentrations of the primary pollutants and PM were found to have risen significantly after the Games. Although the major O3 precursors （NOx and VOCs） were well controlled during the Olympic season, O3 was still found to be the highest in 2008, based on the data of ground-based observation. All this information suggests that while control of regional emissions for the Beijing Olympic Games did improved the air quality in Beijing, more efforts will be needed for the continuous improvement of regional air quality, especially for significant reductions of O3 and fine particulate pollution, and not only in Beijing, but also in the B eijing-Tianjin-Hebei region.     

Analysis of air quality characteristics of Beijing–Tianjin–Hebei and its surrounding air pollution transport channel cities in China

Beijing–Tianjin–Hebei(BTH) and its surrounding areas are very important to air pollution control in China.To analyze the characteristics of BTH and its surrounding areas of China,we collected 5,641,440 air quality data from 161 air monitoring stations and 37,123,000 continuous monitoring data from air polluting enterprises in BTH and surrounding cities to establish an indicator system for urban air quality portraits.The results showed that particulate matter with aerodynamic diameters of 2.5 μm(PM_(2.5)),particulate matter with aerodynamic diameters of 10 μm(PM_(10)) and SO_2 improved significantly in 31 cities from2015 to 2018,but ozone deteriorated.Air quality in BTH and the surrounding areas showed obvious seasonal characteristics,among which PM_(2.5),PM_(10),SO_2,and NO_2 showed a "U" type distribution from January to December,while O_3 had an "inverted U" distribution.The hourly changes in air quality revealed that peaks of PM_(2.5),PM_(10) and NO_2 appeared from 8:00 to 10:00,while those for O_3 appeared at 15:00–16:00.The exposure characteristics of the 31 cities showed that six districts in Beijing had the highest air quality population exposure,and that exposure levels in Zhengzhou,Puyang,Anyang,Jincheng were higher than the average of the 31 investigated cities.Additionally,multiple linear regression revealed a negative correlation between meteorological factors(especially wind and precipitation) and air quality,while a positive correlation existed between industrial pollution emissions and air quality in most of BTH and its surrounding cities.     

Chemical characteristics of PM2:5 during a typical haze episode in Guangzhou

The chemical characteristics (water-soluble ions and carbonaceous species) of PM2:5 in Guangzhou were measured during a typical haze episode. Most of the chemical species in PM2:5 showed significant di erence between normal and haze days. The highest contributors to PM2:5 were organic carbon (OC), nitrate, and sulfate in haze days and were OC, sulfate, and elemental carbon (EC) in normal days. The concentrations of secondary species such as, NO3??, SO4 2??, and NH4 + in haze days were 6.5, 3.9, and 5.3 times higher than those in normal days, respectively, while primary species (EC, Ca2+, K+) show similar increase from normal to haze days by a factor about 2.2–2.4. OC/EC ratio ranged from 2.8 to 6.2 with an average of 4.7 and the estimation on a minimum OC/EC ratio showed that SOC (secondary organic carbon) accounted more than 36.6% for the total organic carbon in haze days. The significantly increase in the secondary species (SOC, NO3??, SO4 2??, and NH4 +), especially in NO3??, caused the worst air quality in this region. Simultaneously, the result illustrated that the serious air pollution in haze episodes was strongly correlated with the meteorological conditions. During the sampling periods, air pollution and visibility had a good relationship with the air mass transport distance; the shorter air masses transport distance, the worse air quality and visibility in Guangzhou, indicating the strong domination of local sources contributing to haze formation. High concentration of the secondary aerosol in haze episodes was likely due to the higher oxidation rates of sulfur and nitrogen species.     

Urban air quality, meteorology and traffic linkages: Evidence from a sixteen-day particulate matter pollution event in December 2015, Beijing

A heavy 16-day pollution episode occurred in Beijing from December 19, 2015 to January 3,2016. The mean daily AQI and PM_(2.5) were 240.44 and 203.6 μg/m~3. We analyzed the spatiotemporal characteristics of air pollutants, meteorology and road space speed during this period, then extended to reveal the combined effects of traffic restrictions and meteorology on urban air quality with observational data and a multivariate mutual information model. Results of spatiotemporal analysis showed that five pollution stages were identified with remarkable variation patterns based on evolution of PM_(2.5) concentration and weather conditions. Southern sites(DX, YDM and DS) experienced heavier pollution than northern ones(DL, CP and WL). Stage P2 exhibited combined functions of meteorology and traffic restrictions which were delayed peak-clipping effects on PM_(2.5).Mutual information values of Air quality–Traffic–Meteorology(ATM–MI) revealed that additive functions of traffic restrictions, suitable relative humidity and temperature were more effective on the removal of fine particles and CO than NO_2.     

Air pollution characteristics and their relationship with emissions and meteorology in the Yangtze River Delta region during 2014–2016

With rapid economic growth and urbanization, the Yangtze River Delta(YRD) region in China has experienced serious air pollution challenges. In this study, we analyzed the air pollution characteristics and their relationship with emissions and meteorology in the YRD region during 2014–2016. In recent years, the concentrations of all air pollutants, except O_3,decreased. Spatially, the PM_(2.5), PM_(10), SO_2, and CO concentrations were higher in the northern YRD region, and NO_2 and O_3 were higher in the central YRD region. Based on the number of non-attainment days(i.e., days with air quality index greater than 100), PM_(2.5) was the largest contributor to air pollution in the YRD region, followed by O_3, PM_(10), and NO_2.However, particulate matter pollution has declined gradually, while O_3 pollution worsened.Meteorological conditions mainly influenced day-to-day variations in pollutant concentrations. PM_(2.5) concentration was inversely related to wind speed, while O_3 concentration was positively correlated with temperature and negatively correlated with relative humidity.The air quality improvement in recent years was mainly attributed to emission reductions.During 2014–2016, PM_(2.5), PM_(10), SO_2, NO_x, CO, NH_3, and volatile organic compound(VOC)emissions in the YRD region were reduced by 26.3%, 29.2%, 32.4%, 8.1%, 15.9%, 4.5%, and0.3%, respectively. Regional transport also contributed to the air pollution. During regional haze periods, pollutants from North China and East China aggravated the pollution in the YRD region. Our findings suggest that emission reduction and regional joint prevention and control helped to improve the air quality in the YRD region.     

Relationship between types of urban forest and PM2.5 capture at three growth stages of leaves

Particulate matter diameter ≤ 2.5 μm(PM2.5) causes direct harm to human health. Finding forms of urban forest systems that with the ability to reduce the amount of particulate matter in air effectively is the aim of this study. Five commonly cultivated kinds of urban forest types were studied in Beijing city at three stages of leaf growth. Results show that the urban forest system is capable of storing and capturing dust from the air. The types of shrubs and broadleaf trees that have the ability to capture PM2.5from the air are most effective when leaves have fully developed. In the leafless season, the conifer and mixed tree types are the most effective in removing dust from the air. For all kinds of forest types and stages of leaf growth, the PM2.5concentration is highest in the morning but lower in the afternoon and evening. Grassland cannot control particles suspended in the air,but can reduce dust pollution caused by dust from the ground blown by the wind back into the air.     

Haze formation in China: Importance of secondary aerosol

Air pollution is the world's largest single environmental hazard that causes more than a few million premature deaths in 2012 (World Health Organization, 2014), particularly in developing countries with rapid industrialization and urbanization. Rapid economic growth of China in the last three decades has resulted in serious air pollution problems on both local and regional scales. Megacities in China such as Beijing and Shanghai have suffered from haze episodes frequently with the daily mass concentrations of fine particulate matter (PM2.5, fine particulates with aerodynamic diameter less than 2.5 μm) over the Chinese air pollution standard of 75 μg/m3 (China National Environmental Monitoring Centre, 2013), which is three times higher than the air quality guideline of 25 μg/m3 recommended by the World Health Organization, highlighting the urgency of urban PM mitigation in China.     

Online monitoring of water-soluble ionic composition of PM10 during early summer over Lanzhou City

Lanzhou is one of the most aerosol-polluted cities in China. In this study, an online analyzer for Monitoring for AeRosols and GAses was deployed to measure major water-soluble inorganic ions in PM10 at 1-hour time resolution, and 923 samples were obtained from Apr 1 to May 24, 2011. During the field campaign, air pollution days were encountered with Air Quality Index more than 100 and daily average concentration of PM10 exceeding 150 μg/m3. Based on the variation of water-soluble ions and results of Positive Matrix Factorization 3.0 model execution, the air pollution days were classified as crustal species- or secondary aerosol-induced, and the different formation mechanisms of these two air pollution types were studied. During the crustal species pollution days, the content of Ca2+increased and was about 2.3 times higher than the average on clear days, and the air parcel back trajectory was used to analyze the sources of crustal species. Data on sulfate, trace gases and meteorological factors were used to reveal the formation mechanism of secondary aerosol pollution. The sulfur oxidation ratio(SOR) was derived from the 923 samples, and the SOR had high positive correlation with relative humidity in early summer in Lanzhou.     

An economic assessment of the health effects and crop yield losses caused by air pollution in mainland China

Air pollution is severe in China, and pollutants such as PM_(2.5) and surface O_3 may cause major damage to human health and crops, respectively. Few studies have considered the health effects of PM_(2.5) or the loss of crop yields due to surface O_3 using model-simulated air pollution data in China. We used gridded outputs from the WRF-Chem model, high resolution population data, and crop yield data to evaluate the effects on human health and crop yield in mainland China. Our results showed that outdoor PM_(2.5) pollution was responsible for 1.70–1.99 million cases of all-cause mortality in 2006. The economic costs of these health effects were estimated to be 151.1–176.9 billion USD, of which 90% were attributed to mortality. The estimated crop yield losses for wheat, rice, maize, and soybean were approximately 9, 4.6, 0.44, and 0.34 million tons, respectively, resulting in economic losses of 3.4 billion USD. The total economic losses due to ambient air pollution were estimated to be 154.5–180.3 billion USD, accounting for approximately 5.7%–6.6% of the total GDP of China in 2006. Our results show that both population health and staple crop yields in China have been significantly affected by exposure to air pollution. Measures should be taken to reduce emissions, improve air quality, and mitigate the economic loss.     

基于空间插值的西安市重空气污染期间主要污染物时空变化特征及相关性分析

西安市是我国承东启西、连接南北的战略性枢纽城市,但其长期受到重空气污染的影响.基于2018年11月24日-12月3日西安市及其周边7个地级市共38个环境质量监测站点的逐时数据,利用空间插值、趋势分析和相关性分析方法,研究了西安市一次重空气污染期间六大污染物（PM_2.5、PM₁₀、CO、NO₂、SO₂和O₃）的质量浓度时空变化及彼此间的相关关系.结果表明:①IDW（inverse distance weighting,反距加权插值法）和OKri（ordinary Kriging,普通克里格插值法）均能较好地获得西安市空气污染物的时空变化情况,但IDW的插值精度优于OKri,距离指数为7的IDW可以满足西安市空气污染物时空变化模拟的要求.②研究期间,西安市首要污染物为PM_2.5和PM₁₀,二者分别是中度-重度污染及严重-"爆表"污染天气的首要贡献因子.③ρ（PM_2.5）、ρ（PM₁₀）、ρ（CO）、ρ（NO₂）和ρ（SO₂）均呈中部高、两边低,北部高、南部低的空间分布特点,而ρ（O₃）则相反;PM_2.5、PM₁₀、O₃污染程度日趋严重,NO₂污染程度逐渐缓解.④ρ（PM_2.5）、ρ（NO₂）、ρ（CO）之间呈中等正相关,三者在时空变化上具有较高的一致性;ρ（SO₂）与ρ（PM_2.5）、ρ（NO₂）、ρ（CO）均呈弱正相关;ρ（O₃）与ρ（NO₂）、ρ（CO）均呈弱负相关.受扬尘天气和特殊风向及地形共同影响,西安市PM₁₀出现"爆表"现象,导致ρ（PM₁₀）与其他污染物质量浓度之间的相关性不明显.研究显示,距离指数为7的IDW适合西安市空气污染情况时空变化的模拟,重污染天气条件下,西安市ρ（PM_2.5）、ρ（NO₂）、ρ（CO）之间具有较高的同源性,但各污染物间时空变化和相关性关系较复杂.      

重污染天气应对“绩效分级、差异化管控”措施首次实践效果后评估

为提高重污染天气应对的科学性和精准度,2019年7月生态环境部制定重污染天气应对“绩效分级、差异化管控”措施.为应对9月底至10月初的重污染过程,京津冀及周边共68个城市启动重污染预警,该措施得以首次实践.通过时间序列断点回归方法对该措施效果进行评估发现,空气质量改善存在滞后的现象,SO₂、 NO₂和CO这3个气态污染物改善速度较快,对涉及二次生成的O₃和PM_2.5两个污染物见效速度相对较慢. 10月1日恰逢在北京举办庆祝中华人民共和国成立70周年阅兵式,对10月1日当天进行评估,发现与假如不采取措施的情形相比,重污染应急措施使北京市PM_2.5、 NO₂和CO日均浓度显著下降,下降幅度分别为54.1%、 62.4%和25.8%.如果不采取重污染应急措施,北京10月1日上午可能出现中重度污染,但实际上空气质量保持在良的水平.区域启动预警的68城市PM_2.5、 PM₁₀、 NO₂、 SO     

沈阳及周边城市大气细粒子的分布特征及其对空气质量的影响

利用沈阳、鞍山、抚顺和本溪4城市2007-2009年大气细粒子PM2.5及大气污染物PM10SO2、NO2的观测资料,分析了4城市大气细粒子的分布特征及其与空气质量的关系.结果表明:4城市大气细粒子PM2.5污染很重,年均浓度平均值超过美国大气细粒子PM2.5年均浓度标准4倍左右;4城市PM10、SO2的年均浓度呈下降...     

G20峰会期间杭州地区空气质量特征及气象条件分析

利用空气质量和气象要素的监测资料与再分析资料,分析了2016年G20峰会期间(2016年8月10日—9月20日)杭州及周边地区空气质量演变及区域特征,探讨了气象条件对G20峰会期间杭州空气质量的影响.结果表明:G20峰会管控期间,由于机动车排放大幅度降低,杭州NO_2浓度较管控前有所下降,对比周边城市降幅居于首位;而由于不利气象条件的影响,PM_(2.5)、PM_(10)、SO_2、CO和O_3浓度比管控前有不同程度的增长,但增幅相比周边城市较小,说明管控措施对杭州空气质量有一定的改善效果.9月7日管控措施结束后污染反弹现象明显.气象条件对杭州的空气质量有重要影响:在管控前,杭州晴热高温天气有利于O3的生成,偏东风相对洁净,污染传输较少;在管控期,杭州虽受到静稳天气和外来污染传输的影响,但得益于减排应急管控措施的有效实施,NO_2浓度下降幅度最大,其他污染物的增幅也较周边城市偏小;在管控后,气象条件不利于污染物的垂直扩散,受静稳天气和污染源恢复常态的影响,PM_(2.5)、PM_(10)、NO_2、SO_2和CO出现了整个研究时段的最大值,而台风"莫兰蒂"使得杭州PM_(2.5)、PM_(10)和O_3浓度出现了整个研究时段的最低值.     

厦门湾空气质量对新冠疫情管控的响应

通过对厦门湾城市群在COVID-19封锁前后6周内（2020-01-11~2020-02-21）的空气污染物浓度变化进行分析,以确定影响本区域空气质量的主要人为污染源.在春节假期与封锁叠加期间,SO₂、NO₂、CO、PM₁₀和PM_2.5浓度相比于节前1周的下降幅度分别为6%~22%、53%~70%、34%~48%、47%~64%和53%~60%,而O₃浓度变化没有一致的规律性;与2018~2019年历史同期相比,PM_2.5、PM₁₀、CO和NO₂的下降幅度更大,但SO₂的下降幅度相当;在复工复产后,NO₂的反弹幅度最大（38%~138%）,远高于SO₂（2%~42%）,显示交通源相对于固定源更易受到疫情管控的影响;春节后风速增大和降水增多也为SO₂、NO₂和PM的下降提供了正向影响.利用反距离插值权重法,得到管控前后厦门湾城市群不同污染物的空间分布变化特征,显示NO₂浓度高值区的变化与交通源高度相关,CO和SO₂空间分布特征保持稳定,复工后PM_2.5和PM₁₀在人口与路网密集区变化不明显,而在工地相对集中区域有明显上升,O₃空间分布的低值区与NO₂的高值区具有较好的空间匹配性,显示NO₂对O₃滴定作用明显,可为进一步O₃污染减排措施的制定提供参考.     

利用多元线性回归方法评估气象条件和控制措施对APEC期间北京空气质量的影响

气象条件对大气污染物的扩散和传输有重要影响,准确分离和定量气象因素对空气质量的影响是评估大气污染控制政策有效性的前提.本研究利用APEC会议期间及前后(2014-10-15～2014-11-30)北京城区朝阳观测站点SO_2、NO、NO_2、NO_x、CO、PM_(2.5)、PM_1和PM_(10)以及气象因素的观测数据,采用多元线性回归分析方法,定量评估了气象条件和空气污染控制措施对APEC期间北京空气质量的影响.在假定排放条件不变的情况下,基于气象因素参数建立的预测污染物浓度的多元线性回归模型模拟效果较为理想,决定系数R~2在0. 494～0. 783之间.控制措施使得APEC控制期SO_2、NO、NO_2、NO_x、CO、PM_(2.5)、PM_1和PM_(10)浓度分别降低48. 3%、53. 5%、18. 7%、40. 6%、3. 6%、34. 8%、28. 8%和40. 6%,气象因素使得APEC控制期SO_2、NO、NO_2、NO_x、CO、PM_(2.5)、PM_1和PM_(10)浓度分别降低1. 7%、-2. 8%、18. 7%、4. 5%、18. 6%、27. 5%、30. 6%和35. 6%.气象因素和控制措施共同作用使得APEC控制期北京空气质量得到了明显改善.控制措施对SO_2和氮氧化物浓度的下降起主导作用,气象因素对CO浓度的下降起主导作用,气象因素和控制措施对颗粒物浓度降低的贡献相当.本研究还利用相对权重方法研究了气象因素对污染物浓度影响的贡献,结果表明影响不同污染物浓度的决定性气象因素不同.     

2014~2016年京津冀沿山城市空气质量首要污染物特征分析

对北京、保定、石家庄、邢台和邯郸这5个京津冀太行山沿山城市2014~2016年空气质量首要污染物进行分析,探讨其空间分布特征和时间变化趋势.结果表明,北京首要污染物由主到次为PM_2.5、O₃-8h、NO₂和PM₁₀,其他4个城市首要污染物排序为PM_2.5、PM₁₀、O₃-8h、NO₂、SO₂和CO.在空间分布上,各城市PM_2.5首要污染物天数比例3 a均值相当（53.3%~58.1%）,但从北向南,5个城市PM₁₀天数比例基本呈上升趋势,而O₃-8h反之.除邯郸PM_2.5首要污染物天数比例逐年明显下降外,其他4个城市的天数比例年际变化幅度较小;2016年石家庄、邢台和邯郸O₃-8h天数比例均显著上升.各城市PM_2.5和O₃-8h首要污染物天数月变化曲线分别呈"W"型和"倒U"型,PM₁₀首要污染物天数在3~5月出现明显高值区.从良至严重污染,各城市PM_2.5和PM₁₀首要污染物天数比例之和随空气质量级别逐级递增,其中PM₁₀天数比例逐级下降,而PM_2.5表现相反;O₃-8h首要污染物天基本出现在良至中度污染级别,且总体上逐级下降;NO₂仅在良级天有较高的天数比例贡献.     

太原市2014年春节期间常规大气污染物浓度变化及聚类分析

为了探寻太原市春节期间不同监测站点各常规大气污染物的质量浓度变化规律及相互之间的关系,记录和收集了太原市上兰、南寨、涧河、尖草坪、桃园、坞城、小店、金胜、晋源9个监测点2014年农历小年至元宵节(2014-01-23—2014-02-14)期间的大气PM2.5、PM10、CO、NO2、O3、SO2小时浓度值以及相应的气温、气压、湿度、风级、能见度等气象数据,采用相关分析、小波分析、单因子污染指数评价和系统聚类等方法进行研究,发现:1该时段内就太原市总体而言,PM2.5超标倍数最大,其次是PM10、SO2、CO、NO2,O3污染最小.2农历小年、除夕、正月初八、元宵节大气PM2.5和PM10的浓度迅速增加,与自然气象因子基本无关,说明烟花爆竹的集中燃放对大气颗粒物尤其是细颗粒物产生较大影响.3SO2、NO2、CO与PM2.5和PM10浓度变化的波动趋势相似、主周期相同,反映了部分PM2.5和PM10与SO2、NO2、CO有共同的来源;O3的波动趋势及主周期与上述污染物完全不同,显示出它来源的特殊性.4按PM2.5聚类,南寨、涧河、尖草坪、桃园4个点聚为一类,小店和坞城2个点聚为一类,金胜和晋源聚为一类,位于太原市最北端作为清洁对照的上兰监测点自成一类,与它们的地理位置有较好的相符性,同时,聚类分析结果与各监测点的单因子污染指数评价结果相一致.本文提示小波分析与聚类分析相结合可以较好地反映城市大气污染物浓度变化的时间与空间分布规律.     

重庆市冬季PM_(2.5)及气态污染物的分布特征与来源

利用重庆市大气污染物监测站2013年冬季(2013年11月—2014年1月)的实测数据,分析PM2.5及相关气态污染物(SO2、NO2、O3)的时空特征,并采用轨迹聚类与PSCF(潜在源贡献因子)分析污染物来源.结果表明:除ρ(O3)以外,其他3种污染物质量浓度的月际变化趋势基本一致,均呈12月升高、1月降低的特征;污染物空间分布不均,其中ρ(PM2.5)和ρ(NO2)在工业区和人口密集区较高,ρ(SO2)南高北低,ρ(O3)城区低于郊区.ρ(SO2)、ρ(NO2)与ρ(PM2.5)均呈显著正相关,其中ρ(SO2)与ρ(PM2.5)的R(相关系数)在城、郊区分别为0.71、0.65,ρ(NO2)与ρ(PM2.5)在城、效区的R分别为0.73、0.56;而ρ(O3)与ρ(PM2.5)未表现出显著相关.ρ(SO2)、ρ(NO2)与ρ(PM2.5)的相关性高低可在一定程度上说明二次气溶胶的污染程度,ρ(O3)与ρ(PM2.5)的相关性受到PM2.5来源和污染程度的影响.轨迹分析结果显示,重庆市2013年冬季主要受东北方向气流影响;聚类分析表明,重庆市11月没有表现出明显的PM2.5外来输送特征,但12月和1月的PM2.5外来输送特征明显,并且不同方向的气流污染物浓度差异也较大.PSCF分析发现,重庆市冬季PM2.5、SO2、NO2、O3主要来源于本地和周围城市局地传输,同时还受南宁、贵阳、遵义、达州等地的影响.     

2010年广州亚运期间空气质量与污染气象条件分析

利用2010年11月4日～12月10日广州地区NO2、O3、SO2、PM、能见度实测资料,区域空气污染指数RAQI及大气输送扩散特征参数,分析广州亚运期间空气质量与气象条件变化特征.结果表明,亚运期间空气质量比亚运前后好,能见度比亚运前后大,PM1和PM2.5浓度比亚运前后小,能见度与PM1和PM2.5有较好的反相关;亚运期间NO2和SO2日均值和小时均值均达到国家一级标准,PM10日均值和O3小时均值均满足国家二级标准,污染物得到较好的控制;广州地区SO2受本地源和外地源远距离输送叠加影响,NO2受本地源影响较大;广州周边城市NO2、SO2和PM10有向广州输送的潜势,而广州O3有向其周边城市扩散的潜势;亚运期间污染气象条件比亚运前后有利,亚运期间污染物浓度降低得益于政府实施的减排措施及良好的气象条件.