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Remote Sensing of Environment 174 (2016) 109–121

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Remote Sensing of Environment

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Spatiotemporal patterns of remotely sensed PM2.5 concentration in Chinafrom 1999 to 2011

Jian Peng a,⁎, Sha Chen b, Huiling Lü b, Yanxu Liu a, Jiansheng Wu b

a Laboratory for Earth Surface Processes, Ministry of Education, College of Urban and Environmental Sciences, Peking University, Beijing 100871, Chinab Key Laboratory for Environmental and Urban Sciences, School of Urban Planning and Design, Shenzhen Graduate School, Peking University, Shenzhen 518055, China

⁎ Corresponding author.

http://dx.doi.org/10.1016/j.rse.2015.12.0080034-4257/© 2015 Elsevier Inc. All rights reserved.

a b s t r a c t
a r t i c l e i n f o
Article history:Received 18 May 2015Received in revised form 2 December 2015Accepted 9 December 2015Available online 17 December 2015

Air pollution in the form of fine particulatematter, or PM2.5, can decrease human life expectancy and increase theoverall mortality rate. Based on a time series of remotely sensed PM2.5 concentrations, this study analyzed thespatiotemporal patterns of this crucial pollutant in China from 1999 to 2011 using trend analysis and standarddeviation ellipse analysis, and carried out a health risk assessment of human exposure to PM2.5. The resultsshowed that PM2.5 concentrations increased significantly from 1999 to 2011 in China, especially in the centraland eastern parts of the country. The proportion of areas with PM2.5 concentrations higher than 35 μg/m3 in-creased year by year, and the areas with PM2.5 concentrations lower than the annual primary standard of15 μg/m3 decreased continuously. The areas most polluted by PM2.5 were south of Hebei, north of Henan andwest of Shandong provinces, with changes in the overall spatial distribution of the pollutant occurring fasteralong a south–north axis than along an east–west axis, and also faster along an east–south axis than along awest–north axis. Based on the PM2.5 concentrations in China from 1999 to 2011, a two-tier standard (level-Iand level-II) was proposed for delineated areas to assist in nationwide air pollution control. It was also foundthat the proportion of the population exposed to PM2.5 concentrations greater than 35 μg/m3 increased year byyear, and increased faster than the proportion of population exposed to PM2.5 concentrations in the range15–35 μg/m3. The health risk in the central and eastern areas of the country was the highest. Based on these re-sults, PM2.5 pollution poses an increasingly serious risk to human health across China and there is an immediateneed to implement its regional control. In addition,more attention should be paid at the national scale in terms ofpollution risk, rather than focusing narrowly on a city scale.

© 2015 Elsevier Inc. All rights reserved.

Keywords:PM2.5 concentrationSpatiotemporal patternsStandard deviation ellipse analysisZoning controlHealth risk exposure

1. Introduction

Clean air is a basic requirement for human health and survival. How-ever, with the acceleration of urbanization and industrialization, air pol-lution continues to threaten human health (Lin et al., 2014). A WorldHealth Organization (WHO) study showed that the number of deathsworldwide caused each year by air pollution had reached more thantwo million by the start of the 21st century (WHO, 2002). Particulateair pollutants, a kind of deadly air pollution, are common condensationnuclei forming aerosol particles. Aerosol particles with an effective di-ameter smaller than 10 μm can enter the bronchi, while aerosols parti-cles with an effective diameter smaller than 2.5 μm can enter as far asthe gas exchange region in the lungs. Accordingly, ‘PM2.5’ refers to anyaerosol particles smaller than 2.5 μm in diameter that are suspendedin the air. Given their small size and the fact that they dissolve easilyin fluids and generate a chemical reaction, PM2.5 can easily enter thehuman body, causing respiratory and cardiopulmonary diseases. It hasbeen shown that a 100 μg/m3 increase in the concentration of respirable

particulate matter would result in a 3-year reduction in average life ex-pectancy and a 14% increase in overall mortality (Chen, Ebenstein,Greenstone, & Li, 2013). Therefore, aerosols and PM2.5 have becomefocal points of international air pollution research in a series of researchprograms including the Monitoring Atmospheric Composition and Cli-mate, International Global Atmospheric Chemistry, Infusing SatelliteData into Environmental Applications, AErosol RObotic NETwork,European Study of Cohorts for Air Pollution Effects, and Climate andClean Air Coalition.

With the launch of satellites and the continuous improvements indata retrieval technology, studies have begun to map the dynamicchanges in PM2.5 concentration across China through the inversion ofremote sensing data (Zhang & Li, 2015). Compared with ground-basedsite monitoring, the advantage of remote sensing inversion lies in theability to quickly obtain large-scale regional data of aerosol concentra-tions. This enables an effective way to study the regional distributionand variation of different kinds of aerosols using satellite-derived aero-sol optical depth (AOD) data (Tian & Chen, 2010). However, becausesatellite-derived AOD data reflect the aerosol optical properties of thetotal atmospheric column, whereas the PM2.5 concentration is usuallymeasured at ground level (Wang et al., 2010b), the relationship

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between remote sensing retrieved AOD and PM2.5 has been actively in-vestigated using different methods (Hidy et al., 2009). Currently,Moderate-resolution imaging spectroradiometer (MODIS) Collection 5AOD products have been validated at a global scale and are suitablefor predicting PM2.5 concentrations (Emili et al., 2010; Hu et al., 2014;Lin et al., 2015; Remer et al., 2005). Two other products, namelyMulti-angle imaging spectroradiometer (MISR) AOD and Sea-viewingWide Field-of-view Sensor (SeaWiFS) AOD, are also widely used inPM2.5 prediction (Hoff & Christopher, 2009). Because the high spatio-temporal resolution and the high inversion precision of satellite-retrieved AOD data usually cannot be achieved simultaneously, differ-ent design characteristics between satellite instruments and their dataretrieval capabilities can benefit particular applications (vanDonkelaar, Martin, Brauer, & Boys, 2015). Thus a combined predictionof PM2.5 concentration using several AOD products is recommended.

For the past few years, studies have examined the spatiotemporalpatterns of PM2.5 on different scales, from global, continent and country,to regional levels. On the global scale, van Donkelaar et al. (2010) firstpredicted the global distribution of PM2.5 concentrations by using an at-mospheric chemical transport model (GEOS-Chem) with the combina-tion of both MODIS AOD and MISR AOD products. Using the averageconcentrations across the years 2001–2006 to reduce the uncertainty,they found that the highest threat of PM2.5 concentrations was locatedin Eastern China and Northern India. At the continent scale, Kurokawaet al. (2013) reported similar results in a study across Asia using datafrom 2000 to 2008, and found the highest average concentration ofPM2.5 to be in China, followed by India. In Europe, over the same time-scale, the highest concentrations of PM2.5 were in northern Italy, TheNetherlands, Eastern Europe, and France; the highest concentration ofPM2.5 in Italy was 36 μg/m3, which was slightly higher than the WHOstandard (Lecœur & Seigneur, 2013). On a national scale, the PM2.5 con-centrations in suburban areas were higher than in urban areas in theIndo-Gangetic basin of India (Dey et al., 2012). African dust storms,drought, atmospheric inversion and other weather conditions resultedin the highest PM2.5 concentrations during the summer over Spain(Pay et al., 2012). On a regional scale, the highest concentration ofPM2.5 over Las Vegas also occurred during the summer, while concen-trations were relatively low during the winter (Xian, 2007), which isconsistent with the results for southeastern cities of Spain (Galindo,Varea, Gil-Moltó, Yubero, & Nicolás, 2011), but opposite to those inChina and Japan (Khan, Shirasuna, Hirano, & Masunaga, 2010; Yuet al., 2013). The main reason for this difference is the different sourcesof pollution. Most studies of the spatiotemporal patterns of PM2.5 on aregional scale are conducted on a short time scale with high temporaland spatial resolution, whereas those on a large regional or globalscale are at a lower resolution, usually using inter-annual data andlong time series data sets with 25- or 50-km spatial resolution.

In recent years, air pollution in China has become particularly prom-inent (Liu, Li, & Chen, 2015). In 2013, the average number of fog andhaze days was the highest since records began in China 52 years previ-ously, several being over 500 μg/m3 and occurring in particular in earlyand late January (Chen et al., 2013; Gao et al., 2015). The extent of airpollution over the area north of the Qinhuai heating cable is higherthan in southern China because of the former area's reliance on coal-fired power stations; the average life expectancy in that areamay be re-duced by as much as 5 years as a result (Chen et al., 2013). Thus, stategovernments have begun to attach great importance to the introductionof the largest-ever air pollution control program in China, with fundingof ¥5 billion for the most-affected areas around Beijing, Tianjin, Hebeiand the surrounding region. It is expected that an additional ¥1 trillioninvestment will bemade. As a result, a nationwidemonitoring networkcomprising multiple sites was established in January 2013 (Huang,Zhang, Tang, & Liu, 2015).

Accordingly, the threat of PM2.5 pollution in China has attractedmuch attention recently. On a national scale, evidence of the increasingeffects of anthropogenic activities on PM2.5 pollution has been found

(Han et al., 2015a, 2015b). A significant positive correlation was identi-fied between urbanization indicators and urban PM2.5 concentrations(Han, Zhou, Li, & Li, 2014; Han et al., 2015c). On a regional scale, alarge number of monitoring stations observed evidence of atmosphericpollution in various cities. The average total mortality due to PM2.5 hasbeen estimated to be about 5100 individuals per year for the period2001–2012 in the central area of Beijing (Zheng, Pozzer, Cao, &Lelieveld, 2015); and the air pollution, low temperature and high tem-perature may increase the risk of out-of-hospital coronary deaths inShanghai (Dai et al., 2015). Wind and humidity are important condi-tions for forming PM2.5. The top and bottom 10% PM2.5 concentrationswere observed in the presence of westerly–easterly synoptic wind inShanghai, while the top and bottom 10% PM2.5 concentrations occurredin the presence of northerly–southerly synoptic wind in Guangzhou(Zhang et al., 2015b). The daily variation of PM2.5 concentrations in Bei-jing also tracked the pattern of relative humidity (Cheng et al., 2015). InChengdu, the annualmean concentrations of PM2.5were 5.7 times of theWHOguidelines (Qiao, Jaffe, Tang, Bresnahan, & Song, 2015). InWuhan,vehicle exhaust emission and coal burning contributed to the seriousPM2.5 concentrations (Zhang et al., 2015a). In Xi'an, emissions from fos-sil fuel burning were the most important source for PM2.5 (Wang et al.,2015).

Despite the numerous observations of air pollution in different cities,there has been a lack of research on PM2.5 concentration in China at highspatial resolution using more than a decade of data. As the most popu-lous country in the world, a substantial proportion of Chinese peoplecould have their health affected by PM2.5 pollution, although the annualchanges in PM2.5 pollution are yet unclear (Guo et al., 2014). Therefore,this study has three research objections. The first objective is to revealthe dynamic characteristics of PM2.5 concentrations in China usingtrend analysis and standard deviation ellipse analysis. The secondobjec-tive is to propose a zoning control scheme to facilitate air pollutionman-agement. The third objective is to explore the potential health risks ofPM2.5 to the Chinese population.

2. Data and methodology

2.1. Data sources and pre-processing

Currently, there is a lack of publically available global remote-sensing data relating to PM2.5. In their 2010 study, van Donkelaar et al.used the GEOS-Chem atmospheric chemical transport model based onMODIS aerosol data to successfully inverse thefirst web spatial distribu-tion map of PM2.5 concentrations (van Donkelaar et al., 2010), with aspatial resolution of 10 km. The authors applied the same approach tomonitor the particulate emissions from fires in Moscow (vanDonkelaar et al., 2011). The GEOS-Chem model combines weather andother factors into an atmospheric chemical transport equation; howev-er, the input parameters and calculations are complex and it is difficultto horizontally compare the results with those of other methods. Thetheoretical basis of the model is relevant to PM2.5 and AOD, which rep-resent complex functions of aerosol vertical structure, aerosol types andmeteorological parameters. van Donkelaar et al. (2011) continued toimprove their algorithms, further refining the inversion accuracy, andeventually adopted optimal estimation to obtain inversion of a long-term worldwide sequence coverage of PM2.5 remote-sensing data sets.The accuracy of this revised approach has been verified and approved(van Donkelaar et al., 2015). The global 10-km resolution of the PM2.5

data set from 1999 to 2011 inversed by van Donkelaar et al. (2011) isby far the most accurate PM2.5 remote-sensing data set with the largestcoverage and longest time span that is available; it has been validatedand can be effectively applied on a national scale (de Sherbinin, Levy,Zell, Weber, & Jaiteh, 2014; Lee et al., 2012).

For the uncertainty of abnormal value related to PM2.5 remote sens-ing inversion, existing studies often adopted an average taken acrossmany years. Therefore, preprocessing of PM2.5 remote sensing data

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is needed. In this study, we adopted the statistical method of using a 3-year average as the annual average, a practice used for US National Am-bient Air Quality Standards (NAAQS, http://www.epa.gov/air/criteria.html). This method helped to eliminate or reduce the impact of abnor-mal values, and was consistent with US Air Quality Standards statisticalrequirements. The latest primary standard for PM2.5 concentrationlimits, issued by theUSA in 2012, was 12 μg/m3; the secondary standardwas 15 μg/m3. The method was also consistent with the statistical ap-proach used to determine inhalable particulate (PM10) concentration.However, the data set was not complete for China because the sensingcan be affected by perennial cloud, snow-coveredmountains and sensormalfunctions; therefore, values of PM2.5 concentrations were some-times missing from the data set for locations in the vicinity of theHimalayas in southwestern China. Thus, a focal statistic method wasused to spatially estimate the missing values by calculating the averageof the values of neighboring observations in 5 × 5 rectangular slidingwindows, and repeating this process until the data coverage was com-plete. This process was particularly applied to the Tibetan Plateau.

The data used in this study included: (i) the 10-km resolution rasterdata sets of PM2.5 concentration in China from 1999 to 2011 as themaindata source, provided by the Atmospheric Physics Institute of theDalhousie University in Canada; and (ii) national 1-km grid demo-graphic data provided by the Earth System Science Data Sharing Net-work, which were mainly used for health risk assessment. In the datapreprocessing, the raw raster data with Tagged Image File Format(TIFF) wasmultiplied by 0.001 to get the true value of PM2.5 concentra-tion, and data values less than zero were identified as missing valuesand subsequently estimated using the focal statistic method.

2.2. Methods

2.2.1. Trend analysis of changes in PM2.5 concentrationTrend analysis is commonly used in temporal dynamic analysis to

explore inter-annual variation characteristics. In this study long se-quence PM2.5 change trends were quantitatively analyzed based ontwo trend analysis methods: unsupervised trend clustering and unarylinear regression. Unsupervised trend clustering is the extended appli-cation of traditional unsupervised classification, which uses temporalimages instead of spectral band data to operate unsupervised classifica-tion (Cao, Liang, Bai, Zhao, & Dang, 2010; Zhong, Zhang, & Gong, 2011).Unsupervised classification is commonly used in image segmentationand category extraction, and is based on the spectral characteristics ofeach band instead of on a priori knowledge of the image features. There-fore, the image stack for each year was used as a ‘spectral band’ to con-duct inter-annual variability similarity clustering. The unsupervisedclassification method of the Iterative Self-Organizing Data AnalysisTechnique Algorithm (ISODATA) was adopted, which repeatedly calcu-lated the uniformly distributed class average by self-organizing data(Jahng, Hong, Seo, & Choi, 2000). The method is also completely basedon pixel variation characteristics, avoiding the introduction of artificialsubjective influence. The maximum category was set as 20 times, withthe maximum number of iterations as 5, the minimum distance be-tween the mean of the categories as 5, and the largest classificationstandard deviation as 1. These settings resulted in nine category-change curves, which then were combined if they showed similarchanges, finally resulting in five categories of independent changes:(i) fast growth; (ii) slow growth; (iii) stable after reduction; (iv) stabletype I; and (v) stable type II.

To discriminate characteristics of the categories, a linear slope anal-ysiswas used to assist thedetermination. In detail, a unary linear regres-sion on the PM2.5 time series data was conducted with time as thehorizontal axis, resulting in the slope representing the change trend inPM2.5 concentration. Thus, the slope represented the slope of the regres-sion equation: if the slopewas positive, it indicated that therewas an in-creasing change trend in PM2.5 concentration, and vice versa. If the slopewas near zero, the categorywas a stable type. A slope thatwas less than

zero characterized a reducing category. The slope analysis also could beused to detect the changing trends of health risks from exposure toPM2.5. The tendency of PM2.5 variation could be determined usingEq. (1):

slope ¼n �

Xn

i¼1i � PM2:5i

−Xn

i¼1i

� � Xn

i¼1PM2:5i

� �

n �Xn

i¼1i2−

Xn

i¼1i

� �2 ð1Þ

in which PM2.5 is the grid unit PM2.5 concentration, n is the timespan, and i is the time unit.

2.2.2. Standard deviation ellipse analysis on the spatial pattern of PM2.5

concentrationThe standard deviation ellipse, also known as directional distribu-

tion analysis, is based on the average center of a set of discrete points,and the calculation of the standard distance of other points away fromthe average center. This calculation results in an ellipse that contains el-ements, the distribution ofwhich is defined by the standard deviation intwo directions. Therefore, the standard deviation ellipse can express themain distribution direction of a set of points and the degree of disper-sion in every direction; these two features are usually used to describethe overall characteristics of a geospatial distribution. This approachwas used when calculating the average center and standard deviationellipses, not only to consider the location of the point, but also to inte-grate the influence of PM2.5 concentration values. Therefore, the annualmoving trace of national PM2.5 concentrations could be demonstrated inan abstract view, whereas this trace cannot be revealed on pixel level.

By drawing the elements on amap, one can intuitively see the direc-tionality of the elements, and the standard deviation ellipse can makethis trend clearer. The output from this calculation procedure coversproperties of spatial center, spatial extent, spatial orientation, spatialshape and other aspects, with the specific indicators represented bythe average center, standard deviation ellipse, major and minor axisand azimuth. Among these parameters, the average center indicatesthe relative position of the spatial distribution of geographical features,the standard deviation ellipse represents elements in themain distribu-tion area, the major axis represents the dispersion degree of geograph-ical features in the main trend direction, the minor axis corresponds tothe dispersion degree of geographical features in the secondary direc-tion, and the azimuth reflects the main trend direction. By comparingchanges in standard deviation ellipses across a time series, it is possibleto characterize the overall spatial dynamic process. The moving of theaverage center can reveal the overall evolutionary track of elements;changes in the dimensions of themajor and minor axes of an ellipse in-dicates an expansion or contraction of a specific spatial direction; andchanges in the azimuth reflect spatial rotation, characterizing thechanges of overall elements in a particular spatial direction.

3. Results

3.1. Spatial-temporal changes of PM2.5 concentration

Fig. 1 shows the annual spatial distribution of PM2.5 concentrationover China from1999 to 2011 and the 13-year average. The national av-erage PM2.5 concentration increased significantly during this time spanfrom 31.7 to 43.7 μg/m3 at an average growth rate of 57%. In particular,this increase was experienced in southern Hebei and northern Henan.There was a rapid increase in high PM2.5 concentrations from 1999 to2007, with peaks during 2008 over the heavily polluted regions ofsouthern Hebei, northern Henan and western Shandong, followed by areduction in concentration until 2009, when concentrations began toincrease again in 2010. It is believed that this reduction was related tothe air pollution control policies that were put in place for the 2008Beijing Olympic Games, such as closing heavily polluting industry and

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Fig. 1. Spatial distribution of average PM2.5 concentration in China from 1999 to 2011.


transferring other large industries elsewhere. The changes in PM2.5 con-centrations indicated that thesemeasureswere effective to a certain ex-tent, which was consistent with the monitoring results for other

pollutants, including ozone and nitrogen oxides (Xin et al., 2010). Else-where in China, the concentration of PM2.5 continued to increase eachyear of the study period and very few regions showed a reduction in


PM2.5 concentration. In termsof inter-annual variability, the tendency ofhigh PM2.5 concentrations was to remain high, showing little tendencyto decrease after they had risen. This tendency suggested that highPM2.5 pollution in China is ever-increasing.

Based on the spatial distribution of PM2.5 concentrations, the region-al differences in the current situation and the temporal difference ofPM2.5 pollution were large. According to the latest version of China'sambient air quality standard (GB 3095-2012) (hereinafter referred toas “the Standard”) announced on February 29, 2012, the annual averageprimary standard of PM2.5 concentrationwas established at 15 μg/m3, inline with the target level of the third phase of theWHO transition peri-od. The secondary standard was set at 35 μg/m3, which was consistentwith the first stage of the WHO transition period; long-term exposureto this level of PM2.5 can lead to a 15% increased risk of death (Ostro,2004). From 1999 to 2011, areas of China that exceeded this secondarystandard of 35 μg/m3 continued to expand from the central eastern re-gion, and eventually connected with high concentration areas in the Si-chuan Basin, resulting in the entire eastern half of China having PM2.5

concentrations above the secondary standard. In addition, PM2.5 con-centrations over the western Tarim Basin were also higher, but thechange over the 13-year study period was not significant. Areasthat had a PM2.5 concentration lower than the primary standard of15 μg/m3 were mainly distributed in the northern Inner Mongolian Pla-teau, northeastern plains and the southwestern Tibetan Plateau. ThePM2.5 concentration over the Tibetan Plateau changed little over thestudy years, whereas in the northern Inner Mongolian Plateau andnortheastern plains, PM2.5 concentrations increased rapidly. In thenortheastern plains, almost the entire area experienced an increase inPM2.5 concentrations from less than 15 μg/m3 in 1999 to greater than35 μg/m3 by 2011. The highest PM2.5 concentrations (greater than100 μg/m3) often occurred in south of Hebei, north of Henan and eastof Sichuan.

Fig. 1 shows that PM2.5 concentration increased significantly from1999 to 2011 over most of China; the highest concentrations were ob-served in 2006 and 2007, and the spatial distribution in these twoyears was more widespread than in other years. By 2011, two-thirdsof China had PM2.5 concentrations that exceeded the standard of

Fig. 2. Spatial changes in the average center and error ellips

15 μg/m3. The concentrationswere particularly high in central and east-ern China, and themost severewere in southern Hebei, northern Henanand western Shandong.

Fig. 2 shows the overall spatial pattern changes in PM2.5 concentra-tion across China year by year. The center of the national PM2.5 concen-tration (average center) made a clear but gradual shift to the southeastfrom 1999 to 2011, changing in longitude from 104.7° to 106.9°, and inlatitude from 35.9° to 35.4°. The high concentration of PM2.5 in HebeiProvince led to the movement of the average center to the east. Thehigh concentration of PM2.5 that formed in Southern China, namelyGuangdong and Guangxi, triggered themovement of the average centerto the south. Therefore, the PM2.5 concentration in the southeast in-creased more quickly than in the northwest.

Themajor andminor axes of the ellipse characterized the spatial dis-persion tendency. In the study period, the major axis of the ellipse de-creased from 1690 km to 1662 km, whereas the minor axis slightlyincreased from 935 km to 992 km. Shorten of the major axis, increaseof the minor axis and shrink of the ellipse illustrated uneven spatialchanges in PM2.5 concentration over time. The spatial contraction ofthe ellipse suggested that PM2.5 grew faster in the internal error ellipsethan in the external error ellipse, which meant that the central regionexperienced larger increases in PM2.5 compared with areas along theouter edge. The shortening of the major axis and lengthening of theminor axis indicated that the PM2.5 concentration in the south–north di-rection increased faster than in east–west direction. The change tenden-cy in spatial direction can be analyzed by the azimuth of the major axisof the ellipse. The azimuth was reduced from 96.1° to 93.3°, whichmeant that the major axis rotated counterclockwise, indicating thegrowing influence of the northeast of China on the distribution and ori-entation of PM2.5 concentration changes.

3.2. Grade variation in PM2.5 concentration

The annual PM2.5 concentrations were categorized sequentially intofour grades (1–4) for which the upper concentrations were 15, 25, 35and 100 μg/m3, respectively; a fifth grade (5) consisted of concentra-tions greater than 100 μg/m3. Fig. 3 shows the inter-annual variability

e of PM2.5 concentrations in China from 1999 to 2011.

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Fig. 3. Area proportion of China under different PM2.5 concentration grades from 1999 to2011: Grades 1, 2, 3, 4, and 5 correspond to less than 15 μg/m3, 15–25 μg/m3, 25–35 μg/m3,35–100 μg/m3 and greater than100 μg/m3, respectively.


in PM2.5 concentration in all grades and the corresponding proportion ofarea inwhich each concentration rangewas found. During the study pe-riod, the proportion of area experiencing low PM2.5 concentrations inGrade 1 declined significantly, from44.9% to 34.2%,whereas the propor-tion of landunder a high concentration increased annually. For example,the proportion of land in Grade 5 increased from 0.3% in 1999 to 1.4% in2011, while the proportion of land in Grade 4 increased from 24.2% to29.9%. Similarly, the proportion of area undermid-range concentrationsalso increased, with Grade 3 area increasing from 13.3% to 15.1%, duringthe study period, and Grade 2 area increasing from 17.4% to 19.3%. Theareal extent of PM2.5 concentrations over the secondary standard of35 μg/m3 increased from 24.5% in 1999 to 31.3%, close to one-third ofthe land area, in 2011. Similarly, the proportion of area experiencingPM2.5 concentrations that were between the primary and secondarystandard increased from 30.7% to 34.4%, representing slightly morethan one-third of the national land area. Among the concentrationgrades, the biggest area increase occurred in Grade 4 (35–100 μg/m3),and this increase occurred at the fastest rate. The inter-annual undula-tion in area proportion was the largest for that comprising Grade 2and Grade 5, with both of these areas decreasing after initially increas-ing, but rapidly increasing thereafter. The largest area proportion (2%)of Grade 5 with PM2.5 concentrations greater than 100 μg/m3 occurredin 2006 and 2007, but this was not statistically different from the arearatio of 1.4% in 2011. The largest area proportion under Grade 2 withPM2.5 concentrations of 15–25 μg/m3 occurred in 2003, with an arearatio of 20.5%; this proportion declined to 17.7% in 2007, but increasedto 20.4% in 2011.

The spatial distribution illustrated in Fig. 4 shows themaximum andminimum values of PM2.5 concentrations over the study period. Themaximum values occurred in heavily polluted areas, mainly in the

Fig. 4. Spatial distribution of the minimum and maximum va

central eastern plains and the Sichuan Basin. Areaswith a PM2.5 concen-tration greater than 100 μg/m3 occurred in southern Hebei, northernHenan, western Shandong, southern Shaanxi, central Shaanxi and east-ern Sichuan. Although the minimum values in these areas did not ex-ceed 100 μg/m3, they were the highest in comparison withconcentrations in other regions, which were in the 70–100 μg/m3

range. Based on the maximum PM2.5 value in the 13-year study period,the proportion of area in each of concentration Grades 1–5 was 28.5%(less than 15 μg/m3), 30.0% (15–35 μg/m3), 30.6% (35–70 μg/m3), 8.0%(70–100 μg/m3), and 2.9% (greater than 100 μg/m3), respectively. Bycontrast, the proportion of area under the five concentration gradesusing the minimum values was 49.745%, 32.759%, 16.387%, 1.106%,and 0.003%, respectively. Therefore, there were diverse change ofPM2.5 concentrations, and the high-concentration areas in maximumvalues showed the most substantial change over the study period.

3.3. Trend clustering of PM2.5 concentration variation

In the categories from unsupervised classification, the Stable type Iwas consistently below the primary standard concentration limit of15 μg/m3 during the 13-year study period. The annual mean concentra-tion ranged from 8.99 to 11.70 μg/m3, which was close to the WHOPM2.5 guideline value of 10 μg/m3 (and is associatedwith the lowest im-pact on human health). The PM2.5 concentration of Stable type II washigher than that for Stable type I, and the annual mean value fluctuatedfrom 23.79 to 26.96 μg/m3. The overall PM2.5 concentration of the Fast-growth category increased the fastest, at a rate of 2.21 μg/m3/yr. In2011, the mean PM2.5 concentration of the Fast-growth categoryreached 87.34 μg/m3. The rate of concentration change in the Slow-growth category was 1.10 μg/m3/yr., and the mean concentrationreached 44.93 μg/m3 in 2011. The PM2.5 concentration in the Stable-after-reduction category declined significantly from a mean of50.56 μg/m3 in 1999 to 28.84 μg/m3 in 2004; in subsequent years themean concentration fluctuated around 30 μg/m3. The mid-easterncoastal areas of China were shown to be centers of rapid increases inPM2.5 concentration, while concentrations declined along an arc to in-land areas with the roof of the arc towards the southwest.

Fig. 5 illustrates that the Fast-growth category of concentrationchanges occurred in the most polluted areas of China, namely themid-east region and eastern Sichuan, and was sporadically distributedin the west, including Beijing, Hebei, Shandong, Jiangsu, Shanghai,Anhui, Henan, Shanxi, Shaanxi, Hubei, Jiangxi, Hunan, Chongqing, Si-chuan and Xinjiang provinces. Each province had different percentagesof fast-growth coverage, with Henan, Shandong, Jiangsu, Hebei, andAnhui havingmore than 10% of the total area in the Fast-growth catego-ry of concentration changes. Of these provinces, Henan had the largest

lues of PM2.5 concentrations in China from 1999 to 2011.

Fig. 5. Spatial distribution of PM2.5 concentration change trend in China from 1999 to 2011.


share of Fast-growth category in its total area (17.36%), followed byShandong (14.67%), Jiangsu (11.65%), Hebei (11.16%), Anhui (10.95%),Sichuan (8.49%) and Hubei (7.98%). The remaining provinces all hadless than 4% of their total area in the Fast-growth category.

Fig. 5 also shows that the Slow-growth category of concentrationchanges covered a larger area than did the Fast-growth category. Of allthe provinces, Xinjiang had the largest area percentage (19.01%) inthe Slow-growth category, mainly because of its large administrativeareas. The remaining provinces had less than 8% of land area in theSlow-growth category, and the mainly affected provinces wereGuangxi, Hunan, Jiangxi, Guizhou, Guangdong, Shanxi, Inner Mongolia,Shaanxi, Liaoning, and Hubei. The Stable-after-reduction category ofPM2.5 concentration changes occurredmainly in thewestern and north-ern parts of China. Xinjiang had the highest proportion (23.83%) of landcovered by this category, followed by Gansu (21.92%), Inner Mongolia(21.34%), Qinghai (10.09%), Shaanxi (9.36%), and Ningxia (9.18%).

Fig. 5 shows that the Stable type I category of PM2.5 concentrationchanges covered the largest area in China, and was mainly distributedin sparsely populated, or mountainous forested areas. The province ofTibet had the highest proportion (23.88%) of land area in the Stabletype I category, followed by Xinjiang (16.30%), Inner Mongolia(15.58%), Qinghai (11.96%), Heilongjiang (9.66%), Yunnan (6.87%), Si-chuan (5.90%), Gansu (3.08%), and Jilin (2.65%). Areas in the Stabletype II category of concentration changes were mainly distributed out-side of the areas in Stable-after-reduction category, namely InnerMongolia (27.32%), Xinjiang (27.08%) and Gansu (11.35%).

3.4. Zoning control for PM2.5 concentration

Given the vast territory of China, the spatiotemporal patterns ofPM2.5 concentration showed significant differentiation, as did the relat-ed human health hazards of PM2.5 exposure. Therefore, national-scalezoning controls based on PM2.5 concentration grades are necessary tocombat this type of air pollution. To facilitate these zoning controls, con-tours defining the spatial extent of designated primary and secondaryair quality standards for PM2.5 concentration, based on PM2.5 concentra-tion density grades and their changes over the 13-year study period,

were developed (Fig. 6). Those areas in which the PM2.5 concentrationalways exceeded the secondary standard of 35 μg/m3 over the study pe-riod were designated as “strict control areas”, and were delineated bythe national secondary standard contour as their outer boundary.Areas in which the PM2.5 concentration remained below the primarystandard of 15 μg/m3 were designated as “maintain optimal areas”,with the national primary standard contour delineating their outerboundary. The areas in which PM2.5 concentrations were between theprimary and secondary air quality standards were designated as “resil-ient regulatory areas”.

Fig. 6 illustrates that the “maintain optimal areas” were mainly dis-tributed in zones that contain few anthropogenic activities, includingthe northeastern Great Xing'an Mountains and the southwestern Qing-hai–Tibet Plateau, as well as part of the Altai Mountains in the north-west. Emissions of anthropogenic pollutants were limited in theseareas and, although the natural environment is relatively sensitive todisturbances, it is of high quality; therefore, PM2.5 concentrationsremained in the past and should be maintained in the future at a rela-tively low level. However, based on the changes in PM2.5 concentrationsdepicted in Fig. 1, it is clear that the extent of “maintain optimal areas” isconstantly decreasing. Therefore, there is a need to protect such areas,and the priority should be on pollution prevention rather than treat-ment. “Resilient regulatory areas” are zones in which the PM2.5 concen-tration varied from 15 μg/m3 to 35 μg/m3, and these covered strips ofterritory in the northern, central and southern regions of China that to-gether form a “Y” shape. Due to the large changes in PM2.5 concentrationin these areas, appropriate governance should be introduced dependingon the pollution status in specific locations. Moreover, the changetrends and rate of the pollution should remain as key points in theregulation.

“Strict control areas”mainly included Beijing, southern Hebei, Tian-jin, Shandong, Jiangsu, Shanghai, southern Shanxi, Henan, Anhui, south-ern Shaanxi, central and eastern Hubei, northern and central Hunan,northern Jiangxi, northern Zhejiang, eastern Sichuan, western Chong-qing, and the Xinjiang Tarim Basin. These areas are exposed to seriouslevels of PM2.5 pollution that are harmful to human health; if theselevels remain unchecked, the mortality rate is likely to increase

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Fig. 6. Delineation of air quality control zones based on contours of air quality standards for PM2.5 concentrations in China.


significantly. Therefore, strict control of PM2.5 concentrations is re-quired. Control measures will need to vary in these areas because ofthe different dominant factors causing the pollution and relevant localconditions. Anthropogenic activity in the west of China is significantlylower than in the east, with little, if any, distribution of anthropogenicPM2.5 emissions in the west (Kurokawa et al., 2013). However, one“strict control area” in the west is the Taklamakan Desert, in whichthe PM2.5 constituents are significantly different from those in the east(Yang et al., 2011) and the source of PM2.5 is mainly dust. In addition,the PM2.5 concentrations in this region are more significantly affectedby climate and other natural factors compared with regions in whichanthropogenic activity produces the PM2.5 pollution.

4. Discussion

4.1. Precision assessment of remotely sensed PM2.5 concentrations

The data set used in this study covered theChina region andwas partof a global data set provided by van Donkelaar et al. (2015). The dataprovided for the test had a deviation of global PM2.5 inversion in 2005of ±(3.0 μg/m3 + 35%). The 1145 sample points from North Americahad the highest test precision, with a correlation coefficient of 0.82, aslope of 0.89, and deviation of ±(1.0 μg/m3 + 27%), which were im-proved compared with those of a 2010 study. However, consideringthe regional differences, there was still the need to verify the precisionof data for the specific area of China that was included in the currentstudy.

Prior to January 2013, when China established several national PM2.5

network monitoring sites, there were no continuous observational dataavailable with which to verify remotely sensed air quality data (Wuet al., 2015). Therefore, actual measured values for different sites at dif-ferent times had to be obtained from published studies. As a result, 45sample points were extracted from 23 relevant studies (Table. 1). Thespatial distribution of these sample points and the corresponding mea-sured PM2.5 concentrations are shown in Fig. 7.

The corresponding spatial–temporal remote-sensing inversionof PM2.5 concentration data was calculated according to the spatialdistribution of the measured points, using “PM2.5In-situ” and“PM2.5Retrieved” to represent the measured values and remote-sensing inversion values, respectively. The results are shown in Fig. 8.Linear regression of PM2.5Retrieved against PM2.5In-situ had a high cor-relation coefficient of 0.79, and the distribution of the residuals' cumu-lative probability was random, which indicated that the overallregression results were reliable. In fact, the inversion accuracy washigher than that for inversion of European PM2.5 data, which had a cor-relation coefficient of 0.5–0.7 (Lecœur & Seigneur, 2013). As a whole,the inversion values were lower than the observation values, whichwas consistent with results of similar analyses in Europe and India(Dey et al., 2012; Lecœur & Seigneur, 2013; Pay et al., 2012), mainly be-cause of the differences between the PM2.5 measurementmethods usedin remote sensing and site monitoring. The difference was also relatedto inconsistencies in the time spans over which the site monitoringdata were obtained; for example, some sites were monitored only inJanuary and July.

4.2. Evaluation of health risk from exposure to PM2.5

The regional inequality in China is reflected by not only its spatialeconomic hierarchy, but also healthcare and mortality inequalities.Studies have shown that the mortality rate from chronic coronaryheart disease increased as PM2.5 concentration increased (Hu, 2009),and the mortality rate from exposure to PM2.5 was found to be15 times higher than that from exposure to O3 (Tagaris et al., 2009).Therefore, long-term exposure to high concentrations of PM2.5 has a sig-nificant negative impact on human health. Launching an assessment ofhealth risk due to PM2.5 exposure is of great significance for publichealth protection in China, and is also a good way to raise public aware-ness of the effect of environmental issues in the country (Huang, 2014).In the present study, following the risk assessment paradigm of ‘proba-bility-loss’, the health risks arising from exposure to PM2.5 were definedas the product function of the population size and PM2.5 concentrations.

Table 1Measured values of PM2.5 concentrations in China for different sampling periods as reported in 23 relevant studies.

Number Sites Actual values(μg/m3) Latitude Longitude Sampling period Reference

1 Qingdao 49.36 36.11 120.44 1998–2000 Hu et al. (2002)2 Taiwan 24.75 24.18 120.60 1998.6–8 Fang et al. (2002)3 Taiwan 45.55 24.22 120.58 1998.8–1999.11 Fang et al. (2002)4 Shanghai 66.1 31.20 121.50 1999.5–2000.3 Ye et al. (2003)5 Ling'an 90 30.28 119.75 1999.10–11 Xu et al. (2002)6 Hong Kong 42.8 22.30 114.20 2000.11–2001.2, 2001.6–8 Ho et al. (2006)7 Hong Kong 56.7 22.32 114.10 2000.11–2001.10 Louie et al. (2005)8 Hong Kong 33.5 22.38 114.10 2000.11–2001.10 Louie et al. (2005)9 Hong Kong 24.1 22.21 114.26 2000.11–2001.10 Louie et al. (2005)10 Beijing 154.26 39.54 116.28 2001–2003 Wang et al. (2005)11 Beijing 121.85 39.90 116.40 2003.1, 7 Cao et al. (2007)12 Changchun 100.05 43.90 125.30 2003.1, 7 Cao et al. (2007)13 Jinchang 83.1 38.30 101.10 2003.1, 7 Cao et al. (2007)14 Qingdao 79 36.00 120.30 2003.1, 7 Cao et al. (2007)15 Tianjin 141.3 39.10 117.20 2003.1, 7 Cao et al. (2007)16 Xi'an 253 34.20 108.90 2003.1, 7 Cao et al. (2007)17 Yulin 100.55 38.30 109.80 2003.1, 7 Cao et al. (2007)18 Chongqing 214.05 29.50 106.50 2003.1, 7 Cao et al. (2007)19 Guangzhou 102.55 23.10 113.20 2003.1, 7 Cao et al. (2007)20 Hangzhou 129.6 30.20 120.10 2003.1, 7 Cao et al. (2007)21 Shanghai 101.65 31.20 121.40 2003.1, 7 Cao et al. (2007)22 Wuhan 118.7 30.50 114.20 2003.1, 7 Cao et al. (2007)23 Xiamen 47.7 24.40 118.10 2003.1, 7 Cao et al. (2007)24 Beijing 77.8 40.00 116.34 2003 Wang et al. (2010a)25 Xi'an 183.8 34.16 108.54 2003.9–2007.7 Liu et al. (2009)26 Xi'an 189.1 34.16 108.54 2003.9–12 Liu et al. (2009)27 Shanghai 94.64 31.30 121.50 2003.9–2005.1 Wang et al. (2006)28 Guangzhou 102.9 23.13 113.26 2004.10.5–11.5 Andreae et al. (2008)29 Xi'an 167.4 34.16 108.54 2004 Liu et al. (2009)30 Xi'an 183.8 34.16 108.54 2005 Liu et al. (2009)31 Beijing 87.7 39.90 116.30 2005–2007 Zhao et al. (2009)32 Beijing 54.2 40.60 117.10 2005–2007 Zhao et al. (2009)33 Shanghai 92.9 31.30 121.30 2005.10–2006.7 Feng et al. (2009)34 Xi'an 193.1 34.16 108.54 2006 Liu et al. (2009)35 Xi'an 182.2 34.23 108.88 2006.1–2008.12 Cao et al. (2012)36 Xi'an 194.1 34.23 108.88 2006.3–2007.3 Zhang et al. (2011)37 Xi'an 192.8 34.16 108.54 2007.1–7 Liu et al. (2009)38 Fuzhou 44.33 26.11 119.30 2007.4–2008.1 Xu et al. (2012)39 Fuzhou 44.33 26.08 119.32 2007.4–2008.1 Xu et al. (2012)40 Guangzhou 113 22.70 113.53 2007.10.23–11.24 Ding et al. (2011)41 Guangzhou 76.8 23.12 113.35 2009.4–2010.1 Tao et al. (2012)42 Chengdu 165.1 30.66 104.02 2009.4–2010.1 Tao et al. (2013)43 Xiamen 86.16 24.61 118.06 2009.6–2010.5 Zhang et al. (2012)44 Beijing 33.3 39.97 115.43 2009.9–2011.9 Xin et al. (2014)45 Guangzhou 70.8 22.70 113.53 2010.11.02–12.26 Wang et al. (2012)


That is, the higher the PM2.5 concentration and the higher the propor-tion of the population exposed to that concentration, the higherwould be the proportion of unwell people, the greater would be themortality rate and the higher would be the health risk. Thus, populationdensity was multiplied by PM2.5 concentrations to evaluate health risk.

Based on population raster data statistics, the proportion of the pop-ulation exposed to various PM2.5 concentrations each year is shown inFig. 9, which illustrates that the proportion of the Chinese populationthat was exposed to PM2.5 pollution increased in each subsequent yearof the 13-year study period. In summary, the proportion of the popula-tion exposed to PM2.5 concentrations greater than 100 μg/m3 increasedfrom 0.6% in 1999 to 9.0% in 2011, while the proportion exposed togreater than 50 μg/m3 increased from 39.7% to 62.0%. Likewise, the pro-portion of the population exposed to PM2.5 concentrations greater thanthe first WHO target concentration of 35 μg/m3 increased from 60.0% in1999 to 78.2% in 2011, while the proportion exposed to concentrationsgreater than the second WHO target concentration of 25 μg/m3 in-creased from 73.4% to 89.4%, and the proportion exposed to PM2.5

concentrations greater than the third WHO target concentration of15 μg/m3 increased from 89.2% in 1999 to 97.7% in 2011. In fact, almostnone of the population was spared from exposure to PM2.5 pollution;the proportion of the population exposed to PM2.5 concentrations great-er than the WHO reference value of 10 μg/m3 increased from 96.3% in

1999 to 99.2% 2011. Thus, the greater increases in the proportion ofthe exposed populationwere for those exposed to the higher PM2.5 con-centrations. The largest change in the proportion of the exposed popu-lation occurred from exposure to a PM2.5 concentration of 70 μg/m3;the proportion increased fivefold from 8.4% in 1999 to 41.3% in 2011.

Fig. 10 shows the spatial distribution of health risks arising from ex-posure to PM2.5 in 1999 and 2011 in China. The unchanged category in-cludes Taiwan and other areas where there is a lack of demographicdata. Health risks increased from 1999 to 2011, and high-risk areasalso expanded in size. In 1999, the high-risk and ultra-high risk areas in-cluded eastern Sichuan, central Shaanxi, Beijing, southern Hebei, north-ern Henan, western Shandong, Anhui, and northern Jiangsu. By 2011,there was little change in health risk in eastern Sichuan, but there wasa significant increase in this risk in mid-east China and further to theeast, such as in Guangdong Province. However, in some areas, such asXinjiang, although therewas a high PM2.5 concentration, the populationdensity was low; thus, the health risk related to PM2.5 exposurewas nothigh.

Fig. 11 shows the classification of the trend-change of health risks inresponse to PM2.5 exposure during the study period. The slope of therisk change across the 13 years was mainly positive, with the only neg-ative ones appearing in parts of Shaanxi, Gansu and Ningxia. Accordingto the sign and magnitude of the slope, the slope of change can be

Fig. 7. Spatial distribution of PM2.5 monitoring sample sites in China.

Fig. 8. Linear correlation between PM2.5 measured values and remote-sensing values.

Fig. 9. Changes in the proportion of the population exposed to different concentrations of PM2.5 in China from 1999 to 2011.


Fig. 10.Health risks arising from PM2.5 exposure in China in 1999 and 2011. Risk categories were Low risk, Middle Risk, High Risk and Ultra-high risk, corresponding to less than 1.5 × 106,1.5 × 106–3.5 × 106, 3.5 × 106–7.0 × 106, and greater than 7.0 × 106 μg·person/m3, respectively.


divided into five grades based on a quantile method: (i) rapid decline;(ii) general decline; (iii) essentially constant; (iv) general rise;and (v) rapid rise. The negative threshold between rapid declineand general decline was −1.03; the positive threshold betweenrapid rise and general rise was 1.32; and the unit of the slope was1 × 105 μg·person/m3 yr. A zero of slope value was classified as essen-tially constant. Among these five grades, the general rise grade wasthe most widely distributed, accounting for 80.7% of all the areas theslope value not equal to zero. This was followed by the rapid risegrade, accounting for 9.9% of all the areas with positive or negativeslope values, which were mainly distributed in Beijing, southernHebei, eastern Henan, western Shandong, and southeastern Sichuan.The percentage distributions of the rapid decline and the general de-cline grades were small, accounting for 0.1% and 9.3%, respectively.Overall, there was a higher health risk in mid-east China, with

Fig. 11. Classification of the changing trend of health risks arising from exposure to PM

significantly increasing of health risk as PM2.5 concentrations increased.The high-risk areas continuously expanded in size while the low-riskareas decreased. The health risks in Henan, Shandong, Hebei, Jiangsuand Sichuan were the highest and should be a priority for air pollutionmanagement.

In future studies, higher precision PM2.5 inversionmapping of main-land China should be undertaken through better parameter calibration.In addition, the effects of natural and human factors should be separatedto quantitatively identify the causes of PM2.5 spatiotemporal variation atdifferent scales. It is also important to clarify thedifferent types of healthrisk response resulting from demographic change, and to evaluate theindividual vulnerability to health risk exposed to PM2.5. Furthermore,the Chinese air quality standard for PM2.5 needs to be adjusted becausethe areas that have PM2.5 concentrations below the primary standardare mainly on nature reserves, whereas large areas having high

2.5 pollution in China. The five grades were classified using the quantile method.


population density are exposed to PM2.5 concentrations that exceed theupper secondary standard. There is also a need to focus on a nationallevel of PM2.5 control in China, rather than limiting intervention to indi-vidual urban areas.

5. Conclusions

This paper analyzed the spatiotemporal patterns of PM2.5 concentra-tion in China from 1999 to 2011 based on a long sequence of PM2.5 con-centration remote-sensing spatial data. The exposure of the Chinesepopulation to PM2.5 air pollution across the study years was also exam-ined. The study led to the following conclusions.

The concentration of PM2.5 increased in most areas of China from1999 to 2011 and exceeded the WHO average annual limit of primaryPM2.5 standards. The center of the average national PM2.5 concentrationgradually shifted to the southeast of the country during the study peri-od. The proportion of the Chinese population that was exposed to highconcentrations of PM2.5 increased year by year, and the growth rate inthe proportion of the population that was exposed to high PM2.5 con-centrations was significantly faster than the growth rate at which pop-ulation exposed to low PM2.5 concentrations. The extent of high-riskareas in China is continuously expanding, and the provinces of Henan,Shandong, Hebei, Jiangsu and Sichuan are in particular need of pollutioncontrol. Strict control of PM2.5 concentrations is required, and this con-trol can be facilitated using zoningmaps produced in this study. The re-quirement for further health risk assessments and air pollution controlin China should be highlighted, and the regional differences in the fac-tors that influence this risk should be examined.

Acknowledgments

The authors thank Dr. Aaron van Donkelaar of the AtmosphericPhysics Institute of the Dalhousie University in Canada who offeredthe data used in this research. This research was financially supportedby the National Natural Science Foundation of China (Grant Number:41322004).

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