近10年中国空气质量时空分布特征

空气污染是建设健康中国过程中亟待解决的难题。利用2005—2015年全国86个重点城市空气质量日报数据,综合运用全局自相关法、层次聚类法、空间插值法以及重心迁移模型,从年度、季节和月份3个时间尺度上探讨了近10年来中国空气质量的时空分布特征。结果表明,(1)全国空气质量表现出显著的时间变化规律。从年际变化上看,空气质量逐年好转趋势明显;从季节变化上看,夏季空气质量最好,春秋次之,冬季最差;从月份变化上看,空气质量呈现出显著的先降后升的"U"型变化规律。(2)全国空气质量呈现出显著的空间集聚和分异规律,表现为"北重南轻、东重西轻"的空间格局。其中,京津冀地区、西北地区以及山东、河南属于长期高污染区;以珠三角为核心的南部沿海地区、云贵高原和青藏高原地区属于常年优良区。近10年全国空气质量整体虽得到有效改善,但部分地区(河北、山东、河南和江苏)污染仍在加重,期间污染范围从整个华北地区、中部地区和西北地区向京津冀地区集中,空气污染分布模式从集中连片分布变成零星分布。(3)近10年全国空气质量重心以向东北方向移动为主,表明东部和北部地区部分省份的空气污染程度较全国其他省份严重。(4)研究结果揭示了近10年中国空气质量的时空分异规律,可为寻求污染治理办法提供理论参考。     

东北主要城市的空气质量长期变化特征

利用2001—2012年沈阳、大连、长春和哈尔滨市及2005—2012年齐齐哈尔市和牡丹江市城市空气质量日报资料对空气质量变化特征和影响因素进行了分析.结果表明,东北地区城市空气质量与国内北京、天津、石家庄等地区相比,空气污染相对较轻,6个城市空气质量都以Ⅰ级(优)与Ⅱ级(良)为主;6个城市首要污染物均为可吸入颗粒物,占样本总数80%以上.沈阳、长春、哈尔滨市空气污染指数(API)年平均下降趋势显著,通过P0.01的显著性检验.大连、齐齐哈尔空气污染指数(API)年平均下降趋势显著通过P0.05的显著性检验.地理因素、污染源及沙尘天气对东北主要城市API分布的区域性特征和季节变化具有重要的影响.     

土地利用差异与变化对区域热环境贡献研究——以京津冀城市群为例

人类活动改变土地利用、土地覆被,造成下垫面属性变化,直接引发区域热环境变化。采用2004—2006年、2014—2016年京津冀城市群MODIS地表温度产品,结合2005年和2015年土地利用数据,分别从行政区划和土地利用角度定量计算地级市和土地利用类型在不同季节、昼夜条件下对城市群热环境的贡献度指数(CI),归纳不同城市和土地利用类型对城市群热环境贡献的角色特征,度量不同土地利用类型对城市群和地级市热环境贡献度强度差异及变化。结果表明:(1)不同城市在白天和夜晚作为城市群热环境源汇角色不同,根据贡献度指数的昼夜差异可分为昼夜热源型城市(CI0)、昼汇夜源型城市(白天CI0,夜间CI0)和昼夜热汇型城市(CI0);(2)2005—2015年各土地利用类型对城市群热环境的贡献度指数绝对值增大,对城市群热环境的源汇作用强度增加;(3)耕地和林地分别为京津冀城市群热环境最主要的源汇景观。在春、秋、冬季夜间,耕地对城市群热环境贡献由源转为汇。(4)城市受内部土地利用空间配置和自身发展条件影响,不同土地利用类型对城市热环境贡献度存在显著的时空差异,如林地在春、夏、秋季夜间对不同城市热环境分别表现为源汇景观。区别源汇景观的依据在于判定其是否能够降低区域热环境,因此源汇景观在空间尺度或时间尺度上可能会发生角色转变。研究结果对于基于植被分区的城市设计所进行的热环境调控具有理论意义。     

2001—2012年哈尔滨市空气质量长期变化特征

根据2001—2012年环保部发布的哈尔滨市城市空气质量日报数据及同期气象资料分析了哈尔滨市空气污染指数(API)的变化特征.结果表明,哈尔滨市空气污染呈现明显的北方季节变化特征,API冬、春季高,夏、秋季低,供暖期高于非供暖期.哈尔滨市空气污染以可吸入颗粒物为首要污染物,空气质量状况以良居多,占总样本数的75.4%.从年际变化来看,空气质量有了一定的改善,12年间空气污染指数呈显著下降的趋势.哈尔滨市空气污染指数与风速呈显著正相关,与降水量、气温、能见度呈显著负相关.后向轨迹分析结果表明,哈尔滨市出现中度以上污染时,其气团来源体现了3种不同的特征.     

中国城市大气污染特征及社会经济影响分析

大气污染因影响人类健康、制约国家发展而成为全球最关注的环境问题之一。基于2016年中国361个城市空气质量数据,利用空间自相关和核密度法分析中国空气质量的时空演化特征,并运用空间计量经济模型从全国和区域两个尺度探讨空气污染的社会经济影响因素。结果表明,(1)从污染等级上看,全国及各区域空气质量以优、良和轻度污染天气居多。(2)从时间变化上看,全国及各区域空气质量呈现出"夏低冬高,春降秋升"的"U"型月变化规律。(3)从空间变化上看,全国空气质量整体呈现出由沿海到内陆、由南到北、由西到东、由非采暖区到采暖区、由欠发达到发达区逐渐加重的态势,其中,京津冀、山东半岛和黄河中游属于高污染热点集聚区,而南部沿海、青藏和云贵高原属于低污染冷点集聚区。(4)从全国层面上,人口集聚、工业化和能源消耗对大气污染的恶化具有推动作用;而经济发展、科技进步和城市绿化的提高则有助于改善城市空气质量。(5)从区域层面上看,绿地覆盖率对各地大气污染呈不同程度的负向改善效应;人口密度、能源消耗、第二产业占比和民用汽车拥有量对各地大气污染呈不同程度的正向加重效应;而科技支出占比和人均GDP对各地空气质量的影响具有双向性。该研究结果可为寻求污染治理办法提供理论参考,并为人与自然的和谐发展提供科学依据。     

京津冀地区近20年NDVI时空变化特征

健康稳定的自然生态系统是保障城市发展的重要基础.了解京津冀城镇快速发展过程中自然生态系统的变化,有助于该区域城镇绿色协调可持续发展.植被指数NDVI时空变化特征可反映地区自然生态系统状况及其演变规律.基于MOD13Q1和Landsat遥感影像数据,利用一元线性回归趋势分析法分析近20年(2000—2019年)京津冀地区...     

兰州—西宁城市群城乡建设用地开发强度空间格局变化及影响因素分析

明确土地开发强度空间格局特征和演变规律,对城市发展边界、生态安全红线和耕地保护红线划定具有重要的指导意义。以1995、2005、2015和2018年兰州—西宁(兰西)城市群39个县级单元为研究区,运用趋势面分析、空间自相关和地理探测器等方法,分析兰西城市群城乡建设用地开发强度空间格局特征及其影响因素。结果表明:(1)兰西城市群城乡建设用地开发强度均值总体呈增长态势,由1995年的2.74%增长到2018年的4.45%,在空间上呈现兰州—西宁2个中心高、外围地区依次降低的"核心—外围"空间分布格局。(2)东西方向和南北方向空间差异较大,且呈现出扁平的倒"U"型曲线分布态势。(3)城乡建设用地开发强度空间上存在显著正相关性且集聚特征明显,冷热点区的空间分布具有相对稳定性。(4)1995—2018年城乡建设用地开发强度受人口集聚水平、经济发展水平、产业结构水平和海拔等要素综合影响。研究结果可为兰西城市群以及同类型地区城乡建设用地管控和高质量发展提供决策参考。     

基于GEE平台的京津冀长时序水体时空格局及其影响因素

京津冀地区水资源严重短缺,已成为制约京津冀协同发展的瓶颈。明晰长时序地表水体时空变化特征及其影响因素,对该区域的水资源合理配置及构建协同发展生态安全格局具有重要意义。基于Google Earth Engine(GEE)云平台,综合利用多指数水体检测规则、线性斜率、多元线性回归和偏微分分解等方法,构建了京津冀地表水体高时空分辨率连续变化图谱,揭示了研究区及安固里淖、密云水库、白洋淀和北大港湿地4个典型区的地表水体时空分异规律,厘定了降水量、气温、潜在蒸散发、前一年水体面积、人类生产生活用水和和引水调水等因素对地表水体变化的影响量。结果表明,(1)1985-2021年京津冀地区水体面积整体呈先增加后减少趋势,永久性水体面积净增122.85 km²,季节性水体面积净增1 788.95km²。其中安固里淖和白洋淀水体面积呈减少趋势,密云水库和北大港湿地水体面积呈增加趋势。(2)京津冀地表水体空间分布特征整体表现为东部沿海和中部地区地表水资源较丰富,北部和南部地区相对匮乏,4个典型区水体转换幅度大,具有较强的空间异质性。(3)降水量、前一年水体面积、引...     

京津冀地区大气NO2污染特征研究

京津冀都市圈作为全国主要的重化工业基地,区域性大气污染问题成为关注的焦点。NO2作为二次颗粒物及光化学污染物的重要前体物,了解其在时空尺度的污染特征对于保护公众康健及大气污染综合治理具有重要意义。本研究主要基于OMI遥感反演数据并结合部分地面监测数据,研究了2005—2013年京津冀NO2区域污染特征。结果表明：京津冀NO2柱浓度总体呈现逐年升高的趋势,年平均增长速率可达5.69%。在空间格局上呈东南平原区高、西北山区低的特征,平原的年均柱浓度是山区的3倍多;平原区存在两大NO2高值区域,分别为北京-天津-唐山区域和石家庄-邢台-邯郸区域;9年内, NO2高值范围不断扩大,且呈现明显的连片趋势。各城市大气 NO2在9年内的增长趋势也表现出明显的空间差异性。其中石家庄、唐山、邢台等 NO2重度污染区域的增长速率最大,衡水、沧州、秦皇岛、廊坊等中度污染区域的增长速率次之,承德、张家口等轻度污染区域的增长速率最小。京津冀NO2柱浓度具有显著的季节变化特征,总体表现为秋冬高、春夏低,但山区与平原区差异较大。人口密度、能源消耗、机动车排放等人为因素与京津冀 NO2污染密切相关,不同城市的首要影响因素却不同。北京 NO2柱浓度变化主要受机动车排放影响,天津、唐山、石家庄、邯郸、邢台地区主要受工业燃煤的影响,其次为机动车排放。人为因素对平原区NO2柱浓度的影响作用始终占据主导地位,对山区的主导作用从2006年开始突显。此外,京津冀平原区NO2重污染中心的形成还受到特殊地形和不利的气象条件影响。2008奥运年,京津冀空气质量得到迅速且有效的改善,说明北京及周边省市联合开展大气污染治理及监管工作的有效性及必要性。     

京津冀地区大气NO_2污染特征研究

京津冀都市圈作为全国主要的重化工业基地,区域性大气污染问题成为关注的焦点。NO2作为二次颗粒物及光化学污染物的重要前体物,了解其在时空尺度的污染特征对于保护公众康健及大气污染综合治理具有重要意义。本研究主要基于OMI遥感反演数据并结合部分地面监测数据,研究了2005—2013年京津冀NO2区域污染特征。结果表明:京津冀NO2柱浓度总体呈现逐年升高的趋势,年平均增长速率可达5.69%。在空间格局上呈东南平原区高、西北山区低的特征,平原的年均柱浓度是山区的3倍多;平原区存在两大NO2高值区域,分别为北京-天津-唐山区域和石家庄-邢台-邯郸区域;9年内,NO2高值范围不断扩大,且呈现明显的连片趋势。各城市大气NO2在9年内的增长趋势也表现出明显的空间差异性。其中石家庄、唐山、邢台等NO2重度污染区域的增长速率最大,衡水、沧州、秦皇岛、廊坊等中度污染区域的增长速率次之,承德、张家口等轻度污染区域的增长速率最小。京津冀NO2柱浓度具有显著的季节变化特征,总体表现为秋冬高、春夏低,但山区与平原区差异较大。人口密度、能源消耗、机动车排放等人为因素与京津冀NO2污染密切相关,不同城市的首要影响因素却不同。北京NO2柱浓度变化主要受机动车排放影响,天津、唐山、石家庄、邯郸、邢台地区主要受工业燃煤的影响,其次为机动车排放。人为因素对平原区NO2柱浓度的影响作用始终占据主导地位,对山区的主导作用从2006年开始突显。此外,京津冀平原区NO2重污染中心的形成还受到特殊地形和不利的气象条件影响。2008奥运年,京津冀空气质量得到迅速且有效的改善,说明北京及周边省市联合开展大气污染治理及监管工作的有效性及必要性。     

Investigating the impact of air pollution on AMI and COPD hospital admissions in the coastal city of Qingdao,China

• The impact of air pollution on AMI/COPD hospital admissions were examined. • Significant connection was found between air pollutants and AMI/COPD in Qingdao. • Nonlinearity exists between air pollution and AMI/COPD hospital admissions. Air pollution has been widely associated with adverse effects on the respiratory and cardiovascular systems. We investigated the relationship between acute myocardial infarction (AMI), chronic obstructive pulmonary disease (COPD) and air pollution exposure in the coastal city of Qingdao, China. Air pollution in this region is characterized by inland and oceanic transportation sources in addition to local emission. We examined the influence of PM_2.5, PM₁₀, NO₂, SO₂, CO and O₃ concentrations on hospital admissions for AMI and COPD from October 1, 2014, to September 30, 2018, in Qingdao using a Poisson generalized additive model (GAM). We found that PM_2.5, PM₁₀, NO₂, SO₂ and CO exhibited a significant short-term (lag 1 day) association with AMI in the single-pollutant model among older adults (>65 years old) and females, especially during the cold season (October to March). In contrast, only NO₂ and SO₂ had clear cumulative lag associations with COPD admission for females and those over 65 years old at lag 01 and lag 03, respectively. In the two-pollutant model, the exposure-response relationship fitted by the two-pollutant model did not change significantly. Our findings indicated that there is an inflection point between the concentration of certain air pollutants and the hospital admissions of AMI and COPD even under the linear assumption, indicative of the benefits of reducing air pollution vary with pollution levels. This study has important implications for the development of policy for air pollution control in Qingdao and the public health benefits of reducing air pollution levels.     

Collaborative control of fine particles and ozone required in China for health benefit

● Increased DAAO offsets 3/4 of the decrease of DAAP in 2013–2020. ● DAAO increases are mainly due to O₃ concentration increase and population aging. ● Health benefit from PM_2.5 reduction after 2017 is larger than that before 2017. ● Reducing PM_2.5 concentration by 1% results in 0.6% reduction of DAAP. ● Reducing O₃ concentration by 1% results in 2% reduction of DAAO. PM_2.5 concentration declined significantly nationwide, while O₃ concentration increased in most regions in China in 2013–2020. Recent evidences proved that peak season O₃ is related to increased death risk from non-accidental and respiratory diseases. Based on these new evidences, we estimate excess deaths associated with long-term exposure to ambient PM_2.5 and O₃ in China following the counterfactual analytic framework from Global Burden Disease. Excess deaths from non-accidental diseases associated with long-term exposure to ambient O₃ in China reaches to 579 (95% confidential interval (CI): 93, 990) thousand in 2020, which has been significantly underestimated in previous studies. In addition, the increased excess deaths associated with long-term O₃ exposure (234 (95% CI: 177, 282) thousand) in 2013–2020 offset three quarters of the avoided excess deaths (302 (95% CI: 244, 366) thousand) mainly due to PM_2.5 exposure reduction. In key regions (the North China Plain, the Yangtze River Delta and the Fen-Wei Plain), the former is even larger than the latter, particularly in 2017–2020. Health benefit of PM_2.5 concentration reduction offsets the adverse effects of population growth and aging on excess deaths attributed to PM_2.5 exposure. Increase of excess deaths associated with O₃ exposure is mainly due to the strong increase of O₃ concentration, followed by population aging. Considering the faster population aging process in the future, collaborative control, and faster reduction of PM_2.5 and O₃ are needed to reduce the associated excess deaths.     

Mass concentrations and temporal profiles of PM10, PM2.5 and PM1 near major urban roads in Beijing

Mass concentrations of PM₁₀, PM_2.5 and PM₁ were measured near major roads in Beijing during six periods: summer and winter of 2001, winter of 2007, and periods before, during and after the 2008 Beijing Olympic Games. Since the control efforts for motor vehicles helped offset the increase of emissions from the rapid growth of vehicles, the averaged PM_2.5 concentrations at roadsides during the sampling period between 2001 and 2008 fluctuated over a relatively small range. With the implementation of temporary traffic control measures during the Olympics, a clear “V” shaped curve showing the concentrations of particulate matter and other gaseous air pollutants at roadsides over time was identified. The average concentrations of PM₁₀, PM_2.5, CO and NO decreased by 31.2%, 46.3%, 32.3% and 35.4%, respectively, from June to August; this was followed by a rebound of all air pollutants in December 2008. Daily PM₁₀ concentrations near major roads exceeded the National Ambient Air Quality Standard (Grade II) for 61.2% of the days in the non-Olympic periods, while only for 12.5% during the Olympics. The mean ratio of PM_2.5/PM₁₀ near major roads remained relatively stable at 0.55 (±0.108) on non-Olympic days. The ratio decreased to 0.48 (±0.099) during the Olympics due to a greater decline in fine particles than in coarse-mode PM. The ratios PM₁/PM_2.5 fluctuated over a wide range and were statistically different from each other during the sampling periods. The average ratios of PM₁/PM_2.5 on non-Olympic days were 0.71.     

Impact of anthropogenic heat emissions on meteorological parameters and air quality in Beijing using a high-resolution model simulation

• The Large scale Urban Consumption of energY model was updated and coupled with WRF. • Anthropogenic heat emissions altered the precipitation and its spatial distribution. • A reasonable AHE scheme could improve the performance of simulated PM_2.5. • AHE aggravated the O₃ pollution in urban areas. Anthropogenic heat emissions (AHE) play an important role in modulating the atmospheric thermodynamic and kinetic properties within the urban planetary boundary layer, particularly in densely populated megacities like Beijing. In this study, we estimate the AHE by using a Large-scale Urban Consumption of energY (LUCY) model and further couple LUCY with a high-resolution regional chemical transport model to evaluate the impact of AHE on atmospheric environment in Beijing. In areas with high AHE, the 2-m temperature (T₂) increased to varying degrees and showed distinct diurnal and seasonal variations with maxima in night and winter. The increase in 10-m wind speed (WS₁₀) and planetary boundary layer height (PBLH) exhibited slight diurnal variations but showed significant seasonal variations. Further, the systematic continuous precipitation increased by 2.1 mm due to the increase in PBLH and water vapor in upper air. In contrast, the precipitation in local thermal convective showers increased little because of the limited water vapor. Meanwhile, the PM_2.5 reduced in areas with high AHE because of the increase in WS₁₀ and PBLH and continued to reduce as the pollution levels increased. In contrast, in areas where prevailing wind direction was opposite to that of thermal circulation caused by AHE, the WS₁₀ reduced, leading to increased PM_2.5. The changes of PM_2.5 illustrated that a reasonable AHE scheme might be an effective means to improve the performance of PM_2.5 simulation. Besides, high AHE aggravated the O₃ pollution in urban areas due to the reduction in NO_x.     

New insights into the formation of ammonium nitrate from a physical and chemical level perspective

● Factor analysis of ammonium nitrate formation based on thermodynamic theory. ● Aerosol liquid water content has important role on the ammonium nitrate formation. ● Contribution of coal combustion and vehicle exhaust is significant in haze periods. High levels of fine particulate matter (PM_2.5) is linked to poor air quality and premature deaths, so haze pollution deserves the attention of the world. As abundant inorganic components in PM_2.5, ammonium nitrate (NH₄NO₃) formation includes two processes, the diffusion process (molecule of ammonia and nitric acid move from gas phase to liquid phase) and the ionization process (subsequent dissociation to form ions). In this study, we discuss the impact of meteorological factors, emission sources, and gaseous precursors on NH₄NO₃ formation based on thermodynamic theory, and identify the dominant factors during clean periods and haze periods. Results show that aerosol liquid water content has a more significant effect on ammonium nitrate formation regardless of the severity of pollution. The dust source is dominant emission source in clean periods; while a combination of coal combustion and vehicle exhaust sources is more important in haze periods. And the control of ammonia emission is more effective in reducing the formation of ammonium nitrate. The findings of this work inform the design of effective strategies to control particulate matter pollution.     

PM2.5 over North China based on MODIS AOD and effect of meteorological elements during 2003‒2015

• The Taihang Mountains was the boundary between high and low pollution areas. • There were one high value center for PM_2.5 pollution and two low value centers. • In 2004, 2009 and after 2013, PM_2.5 concentration was relatively low. Over the past 40 years, PM_2.5 pollution in North China has become increasingly serious and progressively exposes the densely populated areas to pollutants. However, due to limited ground data, it is challenging to estimate accurate PM_2.5 exposure levels, further making it unfavorable for the prediction and prevention of PM_2.5 pollutions. This paper therefore uses the mixed effect model to estimate daily PM_2.5 concentrations of North China between 2003 and 2015 with ground observation data and MODIS AOD satellite data. The tempo-spatial characteristics of PM_2.5 and the influence of meteorological elements on PM_2.5 is discussed with EOF and canonical correlation analysis respectively. Results show that overall R² is 0.36 and the root mean squared predicted error was 30.1 μg/m³ for the model prediction. Our time series analysis showed that, the Taihang Mountains acted as a boundary between the high and low pollution areas in North China; while the northern part of Henan Province, the southern part of Hebei Province and the western part of Shandong Province were the most polluted areas. Although, in 2004, 2009 and dates after 2013, PM_2.5 concentrations were relatively low. Meteorological/topography conditions, that include high surface humidity of area in the range of 34°‒40°N and 119°‒124°E, relatively low boundary layer heights, and southerly and easterly winds from the east and north area were common factors attributed to haze in the most polluted area. Overall, the spatial distribution of increasingly concentrated PM_2.5 pollution in North China are consistent with the local emission level, unfavorable meteorological conditions and topographic changes.     

嘉善冬季PM2.5化学组分特征及来源分析

为研究嘉兴地区嘉善冬季污染时段和清洁时段PM_2.5化学组分特征,结合气象数据对2019年1月嘉兴市嘉善县善西超级站在线自动监测PM_2.5及化学组分数据、气态污染物(NO₂和SO₂)进行了分析.结果表明,2019年1月嘉善善西超级站污染时段PM_2.5浓度(97.18μg·m^-3)为清洁时段(36.77μg·m^-3)的2.6倍.污染时段水溶性离子浓度(41.58μg·m^-3)较清洁时段(19.82μg·m^-3)高21.76μg·m^-3,但占比有所降低,含碳组分比例增加.OC;EC比值为3.93,可能受到燃煤及机动车排放的共同影响.低风速及高湿有利于NO₂和SO₂等气态污染物进行二次转化,污染时段硫转化率和氮转化率均比清洁时段高,分别增高7.93%和54.11%,说明NOx向硝酸盐二次转化较为明显,导致颗粒物浓度升高.聚类分析结果显示67.34%气流来自北方,且相应的气流轨迹上污染物浓度比周边高,说明污染物存在一定的长距离输送.结合风玫瑰图可以看出,污染主要为本地及其周边的输送,污染物的长距离输送在短时会使污染浓度突增.因此,在重点关注本地及周边污染的同时,偏北气流下的污染物区域输送不可忽视.     

How aerosol direct effects influence the source contributions to PM2.5 concentrations over Southern Hebei,China in severe winter haze episodes

The aerosol direct effects result in a 3%–9% increase in PM_2.5 concentrations over Southern Hebei. These impacts are substantially different under different PM_2.5 loadings. Industrial and domestic contributions will be underestimated if ignoring the feedbacks. Beijing-Tianjin-Hebei area is the most air polluted region in China and the three neighborhood southern Hebei cities, Shijiazhuang, Xingtai, and Handan, are listed in the top ten polluted cities with severe PM_2.5 pollution. The objective of this paper is to evaluate the impacts of aerosol direct effects on air quality over the southern Hebei cities, as well as the impacts when considering those effects on source apportionment using three dimensional air quality models. The WRF/Chem model was applied over the East Asia and northern China at 36 and 12 km horizontal grid resolutions, respectively, for the period of January 2013, with two sets of simulations with or without aerosol-meteorology feedbacks. The source contributions of power plants, industrial, domestic, transportation, and agriculture are evaluated using the Brute-Force Method (BFM) under the two simulation configurations. Our results indicate that, although the increases in PM_2.5 concentrations due to those effects over the three southern Hebei cities are only 3%–9% on montly average, they are much more significant under high PM_2.5 loadings (~50 μg·m⁻³ when PM_2.5 concentrations are higher than 400 μg m⁻³). When considering the aerosol feedbacks, the contributions of industrial and domestic sources assessed using the BFM will obviously increase (e.g., from 30%–34% to 32%–37% for industrial), especially under high PM_2.5 loadings (e.g., from 36%–44% to 43%–47% for domestic when PM_2.5>400 μg·m⁻³). Our results imply that the aerosol direct effects should not be ignored during severe pollution episodes, especially in short-term source apportionment using the BFM.     

Physical and chemical processes of wintertime secondary nitrate aerosol formation

The UCD/CIT model was modified to include a process analysis (PA) scheme for gas and particulate matter (PM) to study the formation of secondary nitrate aerosol during a stagnant wintertime air pollution episode during the California Regional PM_2.5/PM₁₀ Air Quality Study (CRPAQS) where detailed measurements of PM components are available at a few sites. Secondary nitrate is formed in the urban areas from near the ground to a few hundred meters above the surface during the day with a maximum modeled net increase rate of 4 μg·m^-3·d^-1 during the study episode. The secondary nitrate formation rate in rural areas is lower due to lower NO₂. In the afternoon hours, near-surface temperature can be high enough to evaporate the particulate nitrate. In the nighttime hours, both the gas phase N₂O₅ reactions with water vapor and the N₂O₅ heterogeneous reactions with particle-bound water are important for secondary nitrate formation. The N₂O₅ reactions are most import near the surface to a few hundred meters above surface with a maximum modeled net secondary nitrate increase rate of 1 μg·m^-3·d^-1 and are more significant in the rural areas where the O₃ concentrations are high at night. In general, vertical transport during the day moves the nitrate formed near the surface to higher elevations. During the stagnant days, process analysis indicates that the nitrate concentration in the upper air builds up and leads to a net downward flux of nitrate through vertical diffusion and a rapid increase of surface nitrate concentration.     

Seasonal variations of transport pathways and potential sources of PM2.5 in Chengdu,China (2012–2013)

PM_2.5 in Chengdu showed clear seasonal and diurnal variation. 5, 5, 5 and 3 mean clusters are generated in spring, summer, autumn, and winter. Short-distance air masses are important pathways in Chengdu. Emissions within the Sichuan Basin contribute significantly to PM_2.5 pollution. Long-range transport from Southern Xinjiang is a dust invasion path to Chengdu. Seasonal pattern of transport pathways and potential sources of PM_2.5 in Chengdu during 2012–2013 were investigated based on hourly PM_2.5 data, backward trajectories, clustering analysis, potential source contribution function (PSCF), and concentration-weighted trajectory (CWT) method. The annual hourly mean PM_2.5 concentration in Chengdu was 97.4 mg·m^–3. 5, 5, 5 and 3 mean clusters were generated in four seasons, respectively. Short-distance air masses, which travelled within the Sichuan Basin with no specific source direction and relatively high PM_2.5 loadings (>80 mg·m^–3) appeared as important pathways in all seasons. These short pathways indicated that emissions from both local and surrounding regions of Chengdu contributed significantly to PM_2.5 pollution. The cities in southern Chengdu were major potential sources with PSCF>0.6 and CWT>90 mg·m^–3. The northeastern pathway prevailed throughout the year with higher frequency in autumn and winter and lower frequency in spring and summer. In spring, long-range transport from southern Xinjiang was a representative dust invasion path to Chengdu, and the CWT values along the path were 30-60 mg·m^–3. Long-range transport was also observed in autumn from southeastern Xinjiang along a northwesterly pathway, and in winter from the Tibetan Plateau along a westerly pathway. In summer, the potential source regions of Chengdu were smaller than those in other seasons, and no long-range transport pathway was observed. Results of PSCF and CWT indicated that regions in Qinghai and Tibet contributed to PM_2.5 pollution in Chengdu as well, and their CWT values increased to above 30 mg·m^-3 in winter.