中国清洁空气行动对PM2.5污染的影响

随着经济快速发展,中国近十年来大气污染事件频发,严重危害居民公共健康.为应对严重的大气污染问题,切实改善空气质量,国务院于2013年颁布了《大气污染防治行动计划》(简称《大气十条》)(国务院, 2013),要求到2017年全国地级及以上城市可吸入颗粒物(PM10)年均浓度比2012年下降10%以上,优良天数逐年提高。     

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.     

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".     

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.     

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.     

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...     

Analytical trajectory model of air pollution

Summary The basic principles of the method of calculating air pollution in a complex terrain are presented. The method is based on a trajectory air pollution model. The formula for the distribution of pollutant concentration in a puff is obtained by solving a simple turbulent diffusion equation analytically. An example of the model's application is given.     

Monitoring air pollution in China from geostationary satellite: A synthetic study using simulated observations

We simulated geostationary satellite observations to assess the potential for high spatial-and temporal-resolution monitoring of air pollution in China with a focus on tropospheric ozone(O_3), nitrogen dioxide(NO_2), sulfur dioxide(SO_2), and formaldehyde(HCHO). Based on the capabilities and parameters of the payloads onboard sun-synchronous satellites, we simulated the observed spectrum based on a radiative transfer model using a geostationary satellite model. According to optimal estimation theory, we analyzed the sensitivities and retrieval uncertainties of the main parameters of the instrument for the target trace gases. Considering the retrieval error requirements of each trace gas, we determined the major instrument parameter values(e.g., observation channel, spectral resolution, and signal-to-noise ratio). To evaluate these values, retrieval simulation was performed based on the three-dimensional distribution of the atmospheric components over China using an atmospheric chemical transportation model. As many as 90% of the experiments met the retrieval requirements for all target gases. The retrieval precision of total-column and stratospheric O_3 was 2%. In addition, effective retrieval of all trace gases could be achieved at solar zenith angles larger than 70°. Therefore, the geostationary satellite observation and instrument parameters provided herein can be used in air pollution monitoring in China. This study offers a theoretical basis and simulation tool for improving the design of instruments onboard geostationary satellites.     

Singular value decomposition analysis of spatial relationships between monthly weather and air pollution index in China

Spatial patterns are important features for understanding regional air quality variability. Statistical analysis tools, such as empirical orthogonal function (EOF), have been extensively used to identify and classify spatial patterns. These tools, however, do not directly reveal the related weather conditions. This study used singular value decomposition (SVD) to identify spatial air pollution index (API) patterns related to meteorological conditions in China, one of world’s regions facing catastrophic air pollution. The monthly API and four meteorological variables (precipitation, surface air temperature, humidity, and wind speed) during 2001–2012 in 42 cities in China were used. The two leading SVD spatial patterns display the API anomalies with the same sign across China and opposite signs between northern and southern China, respectively. The meteorological variables have different relationships with these patterns. For the first pattern, wind speed is the most important. The key regions, where the correlations between the API field and the wind speed’s SVD time series are significant at the 99% confidence level, are found nationwide. Precipitation and air temperature are also important in the southern and northern portions of eastern China, respectively. For the second pattern, the key regions occur mainly in northern China for temperature and humidity and southern China for wind speed. Air humidity has the largest contribution to this pattern. The weather-API relationships characterized by these spatial patterns are useful for selecting factors for statistical air quality prediction models and determining the geographic regions with high prediction skills.     

Prediction of $$\hbox {PM}_{2.5}$$PM2.5 concentration based on weighted bagging and image contrast-sensitive features

Stochastic Environmental Research and Risk Assessment - In this paper, we propose an effective method of $$\hbox {PM}_{2.5}$$ prediction based on image contrast-sensitive features and weighted...     

Impacts of meteorology and emission variations on the heavy air pollution episode in North China around the 2020 Spring Festival

Based on the Weather Research and Forecasting model and the Models-3 community multi-scale air quality model(WRF-CMAQ),this study analyzes the impacts of meteorological conditions and changes in air pollutant emissions on the heavy air pollution episode occurred over North China around the 2020 Spring Festival(January to Februray 2020).Regional reductions in air pollutant emissions required to eliminate the PM2.5 heavy pollution episode are also quantified.Our results found that meteorological conditions for the Beijing-Tianjin-Hebei and surrounding"2+26"cities are the worst during the heavy pollution episode around the 2020 Spring Festival as compared with two other typical heavy pollution episodes that occurred after 2015.However,because of the substantial reductions in air pollutant emissions in the"2+26"cities in recent years,and the32%extra reduction in emissions during January to February 2020 compared with the baseline emission levels of the autumn and winter of 2019 to 2020,the maximum PM2.5 level during this heavy pollution episode around the 2020 Spring Festival was much lower than that in the other two typical episodes.Yet,these emission reductions are still not enough to eliminate regional heavy pollution episodes.Compared with the actual emission levels during January to February 2020,a 20%extra reduction in air pollutant emissions in the"2+26"cities(or a 45%extra reduction compared with baseline emission levels of the autumn and winter of 2019 to 2020)could help to generally eliminate regionwide severe pollution episodes,and avoid heavy pollution episodes that last three or more consecutive days in Beijing;a 40%extra reduction in emissions(or a 60%extra reduction compared with baseline emission levels of the autumn and winter of 2019 to 2020)could help to generally eliminate regionwide and continuous heavy pollution episodes.Our analysis finds that during the clean period after the heavy pollution episode around the 2020 Spring Festival,the regionwide heavy pollution episode would only occur with at least a 10-fold increase in air pollutant emissions.     

气象条件和排放变化对2020年春节前后华北地区重污染过程的影响

以2020年1～2月华北地区的大气重污染过程为对象,在WRF-CMAQ模型模拟的基础上,分析了气象条件和排放变化对此次重污染过程的影响,并量化分析了为消除PM2.5重度污染,区域主要大气污染物需要进一步减排的力度.研究结果显示,与2015年以来另外两次典型重污染过程相比,京津冀及周边“2+26”城市2020年1～2月期...     

Evaluation and potential improvements of WRF/CMAQ in simulating multi-levels air pollution in megacity Shanghai,China

The accuracy of atmospheric numerical model is important for the prediction of urban air pollution. This study investigated and quantified the uncertainties of meteorological and air quality model during multi-levels air pollution periods. We simulated the air quality of megacity Shanghai, China with WRF/CMAQ (Weather Research and Forecasting model and Community Multiscale Air Quality model) at both non-pollution and heavy-pollution episodes in 2012. The weather prediction model failed to reproduce the surface temperature and wind speed in condition of high aerosol loading. The accuracy of the air quality model showed a clear dropping tendency from good air quality conditions to heavily polluted episodes. The absolute model bias increased significantly from light air pollution to heavy air pollution for SO₂ (from 2 to 14%) and for PM₁₀ (from 1 to 33%) in both urban and suburban sites, for CO in urban sites (from 8 to 48%) and for NO₂ in suburban sites (from 1 to 58%). A test of applying the Urban Canopy Model scheme to the WRF model showed fairly good improvement on predicting the meteorology field, but less significant effect on the air pollutants (6% for SO₂ and 19% for NO₂ decease in model bias found only in urban sites). This study gave clear evidence to the sensitivities of the model performance on the air pollution levels. It is suggested to consider this impact as a source for model bias in the model assessment and make improvement in the model development in the future.     

Marine pollution research in China

Dr. Addison of the Marine Ecology Laboratory at the Bedford Institute of Oceanography, Dartmouth, Nova Scotia, recently spent 3 weeks in the People's Republic of China, as a member of a Canadian delegation touring marine research facilities. His impressions of Chinese activities in the field of marine pollution follow.     

顾及PM2.5浓度遥感生态指数模型的沧州市区生态环境质量动态监测分析

针对空气中颗粒物对城市生态环境存在影响,在遥感生态指数的基础上,加入反映颗粒物PM2.5浓度的差值指数(DI),采用主成分分析法耦合绿度、湿度、干度、热度和PM2.5浓度指标,构建新型遥感生态指数RSEInew.通过对比RSEI与RSEInew第一主成分贡献率和平均相关度,建立基于RSEInew模型的沧州市区生态评估模...