2013~2017年中国PM2.5污染的快速改善及其健康效益

大气细颗粒物(PM2.5)污染对公众健康造成严重危害. 2013年,中国发布了《大气污染防治行动计划》,开始实施严格的污染控制措施,空气质量随之迅速改善.在此背景下,本研究评估了2013~2017年中国地区PM2.5暴露及其健康影响的变化情况.首先结合地面观测数据、卫星遥感数据和大气化学传输模型模拟,构建了2013~2017年中国高时空分辨率PM2.5浓度数据集,基于该数据集评估了PM2.5暴露的时空变化,并结合PM2.5暴露的长期和短期健康效应模型评估了中国PM2.5暴露导致的超额死亡人数的变化情况.研究显示, 2013~2017年间中国人口加权的PM2.5年均浓度从67.4μg m-3降至45.5μg m-3,下降幅度达到32%.在此期间, PM2.5浓度的快速降低使得与PM2.5长期暴露相关的超额死亡人数下降了14%,从2013年的120万人/年(95%置信区间:100, 130;占总死亡人数的13%)降至2017年的100万人/年(95%置信区间:90, 120;占总死亡人数的10%).目前中国大多数地区的PM2.5暴露依然处于较高水平,由于在高浓度区间PM2.5暴露水平下降带来的健康效益改善幅度要小于暴露下降幅度,虽然2013~2017年间PM2.5浓度迅速下降,但带来的健康效益却相对有限.研究还发现由于重污染天数迅速减少,PM2.5急性暴露导致的超额死亡人数在2013~2017年间降低了61%.本研究表明中国的清洁空气政策有效缓解了当前空气污染所导致的健康危害,但未来仍需要继续大幅减少大气污染物排放,以进一步保护公众健康.     

实施《大气污染防治行动计划》对中国东部地区PM2.5化学成分的影响

为应对严重的大气细颗粒物(PM2.5)污染,中国于2013年发布了《大气污染防治行动计划》(以下简称"大气十条"),制定了严格的污染控制措施.大气中PM2.5化学成分的浓度变化与其前体物排放的变化直接相关,因此,分析"大气十条"实施期间中国PM2.5化学成分的时空变化有助于评估控制措施的效果,并可为未来减排政策的制订提供参考.然而目前中国尚未开展PM2.5化学成分的常规监测,对区域尺度PM2.5化学成分的时空变化特征尚不清楚.本研究融合卫星遥感数据和空气质量模型模拟,构建了中国东部地区2013~2017年时空覆盖完整的PM2.5化学成分浓度数据集,并据此分析了中国东部地区大气PM2.5化学成分的时空变化特征.结果表明, 2013~2017年间,中国东部地区PM2.5各种成分的浓度均有所下降,硫酸盐、硝酸盐、铵盐、有机碳、黑碳和其他组分的人口加权平均浓度分别从2013年的11.1、13.8、7.4、9.9、4.6和12.9μg m–3下降至2017年的6.7、13.1、5.8、8.4、3.8和9.6μg m–3.其中硫酸盐的下降幅度最大, 2017年的浓度相较于2013年下降了40%,而硝酸盐下降幅度最小,仅为5%.由此导致PM2.5中硝酸盐比例升高,硫酸盐比例下降.在区域层面,京津冀地区PM2.5及其化学成分的下降幅度最大.硫酸盐浓度的下降幅度与其前体物SO2排放的下降幅度相当,而SO2排放下降主要由工业部门减排主导.硝酸盐浓度的下降幅度较小,这主要是由于大气富氨条件下硫酸盐浓度降低,促进了大气中硝酸向硝酸盐的生成,从而部分抵消了NOx减排带来的成效.为更有效地控制PM2.5污染,未来应加强对氨的减排工作.     

2013~2017年气象条件变化对中国重点地区PM2.5质量浓度下降的影响

2013~2017年中国出台《大气污染防治行动计划》(简称"大气十条"),实施了系列污染减排措施,重点地区PM2.5质量浓度下降明显,这其中气象条件变化起到了多大作用,是政府和公众特别关心的问题.文章主要基于各类气象要素观测、诊断、结合污染-气象条件指数等对PM2.5污染影响的深入分析,发现"大气十条"实施后的2014~2015年中国重点地区气象条件相较2013年变差, 2016和2017年气象条件相较转好.但在京津冀地区2017年相较2013年PM2.5质量浓度下降的39.6%中,仅有~5%(约占总PM2.5降幅的13%)是来自气象条件转好的贡献;在长三角地区下降的34.3%中,有~7%(约占总PM2.5降幅的20%)是来自气象条件转好的贡献,由于气象条件改善程度明显低于此区域观测到的PM2.5降幅,显示出"大气十条"实施五年减排仍然发挥了PM2.5污染改善的主导作用,天气和气候变化因素虽有影响但没有起到控制性作用(文章是用PLAM指数来量化气象条件变好或变差的).在珠三角地区,气象条件对2017年相较2013年的年均PM2.5浓度下降影响较弱,下降成效也主要来自减排的贡献. 2017年冬季气象条件在京津冀和长三角区域相较2013年分别转好约20%和30%,在两区域冬季PM2.5分别约40.2%和38.2%的降幅中起到了明显的"助推"作用.京津冀区域2016年冬季气象条件好于2017年冬季约14%,但2017年冬季PM2.5降幅仍大于2016年,显示出2017年更大力度的减排措施发挥了重要作用;在北京冬季持续性重污染期间选择气象条件相同的过程对比,也发现因减排导致的PM2.5下降幅度逐年增加,特别是2016和2017年下降的PM2.5浓度幅度更为明显,表明"大气十条"实施5年后空气质量改善的根本原因还是在于各项控制措施取得了实质性进展,特别是2017年冬季污染物排放量得到了有效削减.中国大气PM2.5持续性重污染主要发生在冬季,冬季京津冀地区仅因气象条件不利就会导致PM2.5浓度较其他季节上升约40~100%,这与冬季到达地面的太阳辐射下降有关,与中国华北冬季受青藏高原大地形"背风坡"效应所导致的下沉气流和"弱风效应"有关,与气候变暖导致的区域边界层结构日趋稳定有关.重污染形成是因为区域出现停滞-静稳的形势,高空环流型主要可分为平直西风和高压脊型,污染形成后不断累积的PM2.5污染还会进一步导致边界层气象条件转差、转差气象条件的反馈作用控制了PM2.5的"爆发性增长"现象,形成显著的不利气象条件与PM2.5累积之间的双向反馈.这些表明在中国现今大气气溶胶污染程度仍然居高的情况下,不利气象条件是持续性重污染形成、累积的必要外部条件.在重污染形成初期大幅降低区域污染排放,是消除和减少持续性重污染事件的关键手段.即使在有利气象条件下,也不宜无限制地允许排放,因为当污染累积到一定程度后会显著改变边界层气象条件、会"关闭"污染扩散的"气象通道".     

Impact of clean air action on PM2.5 pollution in China

<正>China suffers from severe air pollution in the past decades,characterized by high-levels of fine particulate matter(PM_(2.5)) concentrations. To mitigate PM_(2.5) pollution, the Chinese government issued the Air Pollution Prevention and Control Action Plan (referred to as the Clean Air Action hereinafter) in 2013, which requires the three key regions,     

Source apportionment and secondary organic aerosol estimation of PM2.5 in an urban atmosphere in China

PM2.5 is the key pollutant in atmospheric pollution in China.With new national air quality standards taking effect,PM2.5 has become a major issue for future pollution control.To effectively prevent and control PM2.5,its emission sources must be precisely and thoroughly understood.However,there are few publications reporting comprehensive and systematic results of PM2.5 source apportionment in the country.Based on PM2.5 sampling during 2009 in Shenzhen and follow-up investigation,positive matrix factorization(PMF)analysis has been carried out to understand the major sources and their temporal and spatial variations.The results show that in urban Shenzhen(University Town site),annual mean PM2.5 concentration was 42.2μg m?3,with secondary sulfate,vehicular emission,biomass burning and secondary nitrate as major sources;these contributed30.0%,26.9%,9.8%and 9.3%to total PM2.5,respectively.Other sources included high chloride,heavy oil combustion,sea salt,dust and the metallurgical industry,with contributions between 2%–4%.Spatiotemporal variations of various sources show that vehicular emission was mainly a local source,whereas secondary sulfate and biomass burning were mostly regional.Secondary nitrate had both local and regional sources.Identification of secondary organic aerosol(SOA)has always been difficult in aerosol source apportionment.In this study,the PMF model and organic carbon/elemental carbon(OC/EC)ratio method were combined to estimate SOA in PM2.5.The results show that in urban Shenzhen,annual SOA mass concentration was 7.5μg m?3,accounting for 57%of total organic matter,with precursors emitted from vehicles as the major source.This work can serve as a case study for further in-depth research on PM2.5 pollution and source apportionment in China.     

The impact of meteorological changes from 2013 to 2017 on PM2.5 mass reduction in key regions in China

Science China Earth Sciences - In 2013, China issued the “Action Plan for the Prevention and Control of Air Pollution” (“Ten Statements of Atmosphere”) and implemented a...     

Prediction of PM10 and SO2 exceedances to control air pollution in the Bay of Algeciras,Spain

In this paper, the authors apply different classification techniques in order to provide 24 h advance forecasts of the daily peaks of SO₂ and PM10 concentrations in the Bay of Algeciras. K-nearest-neighbours, multilayer neural network with backpropagation and support vector machines (SVMs) are the classification methods used. The aim of this research is to obtain a suitable prediction model that would enable us to predict the peaks of pollutant concentrations in critical meteorological situations caused by the widespread existing industry and population in the area. A resampling strategy with twofold crossvalidation has been applied, using different quality indexes to evaluate the performance of the prediction models. SVM models achieved better true positive rate and accuracy (ACC) quality indexes. Results of ACC index value of 0.795 for PM10 and 0.755 for SO₂ showed the ability of the model to predict peaks and non-peaks correctly.     

Average mass concentrations of TSP, PM10 and PM2.5 in Erzurum urban atmosphere, Turkey

In this study, particulate matters (TSP, PM₁₀, PM_2.5 and PM_10–2.5) which are hazardous for environment and human health were investigated in Erzurum urban atmosphere at a sampling point from February 2005 to February 2006. During sampling, two low volume samplers were used and each sampling period lasted approximately 24 h. In order for detection of representative sampling region and point of Erzurum, Kriging method was applied to the black smoke concentration data for winter seasons. Mass concentrations of TSP, PM₁₀ and PM_2.5 of Erzurum urban atmosphere were measured on average, as 129, 31 and 13 μg/m³, respectively, in the sampling period. Meteorological factors, such as temperature, wind speed, wind direction and rainfall were typically found to be affecting PMs, especially PM_2.5. Air temperature did not seem to be significantly affecting TSP and PM₁₀ mass concentrations, but had a considerably negative induction on PM_2.5 mass concentrations. However, combustion sourced PM_2.5 was usually diluted from the urban atmosphere by the speed of wind, soil sourced coarse mode particle concentrations (TSP, PM₁₀) were slightly affected by the speed of wind. Rainfall was found to be decreasing concentrations to 48% in all fractions (TSP, PM₁₀, PM_10–2.5, PM_2.5) and played an important role on dilution of the atmosphere. Fine mode fraction of PM (PM_2.5) showed significant daily and seasonal variations on mass concentrations. On the other hand, coarse mode fractions (TSP, PM₁₀ and PM_10–2.5) revealed more steady variations. It was observed that fine mode fraction variations were affected by the heating in residences during winter seasons.     

The effect of ambient fine particulate matter (PM2.5) on respiratory diseases in China: a systematic review and meta-analysis

Stochastic Environmental Research and Risk Assessment - Only a few recent systematic reviews and meta-analysis studies have quantitatively assessed the effect of short-term exposure to ambient fine...     

Vertical structures of PM10 and PM 2.5 and their dynamical character in low atmosphere in Beijing urban areas

The vertical structures and their dynamical character of PM2.5 and PM10 over Beijing urban areas are revealed using the 1 min mean continuous mass concentration data of PM2.5 and PM10 at 8, 100, and 320 m heights of the meteorological observation tower of 325 m at Institute of Atmospheric Physics, Chinese Academy of Sciences (IAP CAS tower hereafter) on 10―26 August, 2003, as well as the daily mean mass concentration data of PM2.5 and PM10 and the continuous data of CO and NO2 at 8, 100 (low layer), 200 (middle layer), and 320 m (high layer) heights, in combination with the same period meteorological field observation data of the meteorological tower. The vertical distributions of aerosols observed on IAP CAS tower in Beijing can be roughly divided into two patterns: gradually and rapidly decreasing patterns, I.e. The vertical distribution of aerosols in calm weather or on pollution day belongs to the gradually decreasing pattern, while one on clean day or weak cold air day belongs to the rapidly decreasing pattern. The vertical distributive characters of aerosols were closely related with the dynamical/thermal structure and turbulence character of the atmosphere boundary layer. On the clean day, the low layer PM2.5 and PM10 concentrations were close to those at 8 m height, while the concentrations rapidly decreased at the high layer, and their values were only one half of those at 8 m, especially, the concentration of PM2.5 dropped even more. On the clean day, there existed stronger turbulence below 150 m, aerosols were well mixed, but blocked by the more stronger inversion layer aloft, and meanwhile, at various heights, especially in the high layer, the horizontal wind speed was larger, resulting in the rapid decrease of aerosol concentration, I.e. Resulting in the obvious vertical difference of aerosol concentrations between the low and high layers. On the pollution day, the concentrations of PM2.5 and PM10 at the low, middle, and high layers dropped successively by, on average, about 10% for each layer in comparison with those at 8 m height. On pollution days, in company with the low wind speed, there existed two shallow inversion layers in the boundary layer, but aerosols might be, to some extent, mixed below the inversion layer, therefore, on the pollution day the concentrations of PM2.5 and PM10 dropped with height slowly; and the observational results also show that the concentrations at 320 m height were obviously high under SW and SE winds, but at other heights, the concentrations were not correlated with wind directions. The computational results of footprint analysis suggest that this was due to the fact that the 320 m height was impacted by the pollutants transfer of southerly flow from the southern peripheral heavier polluted areas, such as Baoding, and Shijiazhuang of Hebei Province, Tianjin, and Shandong Province, etc., while the low layer was only affected by Beijing's local pollution source. The computational results of power spectra and periods preliminarily reveal that under the condition of calm weather, the periods of PM10 concentration at various heights of the tower were on the order of minutes, while in cases of larger wind speed, the concentrations of PM2.5 and PM10 at 320 m height not only had the short periods of minute-order, but also the longer periods of hour order. Consistent with the conclusion previously drawn by Ding et al., that air pollutants at different heights and at different sites in Beijing had the character of "in-phase" variation, was also observed for the diurnal variation and mean diurnal variation of PM2.5 and PM10 at various heights of the tower in this experiment, again confirming the "in-phase" temporal/spatial distributive character of air pollutants in the urban canopy of Beijing. The gentle double-peak character of the mean diurnal variation of PM2.5 and PM10 was closely related with the evident/similar diurnal variation of turbulent momentum fluxes, sensible heat fluxes, and turbulent kinetic energy at various heights in the urban canopy. Besides, under the condition of calm weather, the concentration of PM2.5 and PM10 declined with height slowly, it was 90% of 8 m concentration at the low layer, a little lesser than 90% at the middle layer, and 80% at the high layer, respectively. Under the condition of weak cold air weather, the concentration remarkably dropped with height, it was 70% of 8 m concentration at the low layer, and 20%―30% at the middle and high layers, especially the concentration of PM2.5 was even lower.