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Frontiers of Environmental Science & Engineering

Front. Environ. Sci. Eng.    2020, Vol. 14 Issue (2) : 23     https://doi.org/10.1007/s11783-019-1202-8
REVIEW ARTICLE
PM2.5 over North China based on MODIS AOD and effect of meteorological elements during 2003‒2015
Youfang Chen1,2, Yimin Zhou1,2, Xinyi Zhao1,2()
1. College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
2. Key Laboratory for Earth Surface Processes of the Ministry of Education, Peking University, Beijing 100871, China
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Abstract

• The Taihang Mountains was the boundary between high and low pollution areas.

• There were one high value center for PM2.5 pollution and two low value centers.

• In 2004, 2009 and after 2013, PM2.5 concentration was relatively low.

Over the past 40 years, PM2.5 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 PM2.5 exposure levels, further making it unfavorable for the prediction and prevention of PM2.5 pollutions. This paper therefore uses the mixed effect model to estimate daily PM2.5 concentrations of North China between 2003 and 2015 with ground observation data and MODIS AOD satellite data. The tempo-spatial characteristics of PM2.5 and the influence of meteorological elements on PM2.5 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, PM2.5 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 PM2.5 pollution in North China are consistent with the local emission level, unfavorable meteorological conditions and topographic changes.

Keywords Aerosol optical depth      PM2.5      MODIS      Mixed effect model      Canonical correlation analysis     
Corresponding Author(s): Xinyi Zhao   
Issue Date: 19 December 2019
 Cite this article:   
Youfang Chen,Yimin Zhou,Xinyi Zhao. PM2.5 over North China based on MODIS AOD and effect of meteorological elements during 2003‒2015[J]. Front. Environ. Sci. Eng., 2020, 14(2): 23.
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http://journal.hep.com.cn/fese/EN/10.1007/s11783-019-1202-8
http://journal.hep.com.cn/fese/EN/Y2020/V14/I2/23
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Fig.1  Study area location and spatial distribution of cities involved with PM2.5 monitoring sites in this study. In total there are 215 monitoring sites in 41 cities. Red points represent the cities, where altitude is represented by the gray color bar.
Parameters Time range Data sources Resolution
MODIS AOD 2003?2015 MYD04_3 km data set Daily, 3 km × 3 km
PM2.5 measurements Apr. 2014 ? Mar. 2015 MEP of PRC Hourly, cities
Meteorological elements 2003?2015 NCAR/NCEP reanalysis (R1), NCEP/DOE Reanalysis 2 (R2) Every 6 h, 1° × 1°
Tab.1  Data introduction
Fig.2  Plots of fitting performance of retrieved PM2.5 and observed PM2.5 during Apr. ? Dec. 2014. The gray line denotes the fitted linear regression line.
Fig.3  (a) Conventional first mode of EOF (EOF1) of PM2.5 over North China based on quarterly pollution day ratio for the period of 2003–2015. Percentages of explained variance are printed at the upper left on the EOF maps. (b) Daily mean PM2.5 concentrations during 2003?2015, the unit for the legend is μg/cm3.
Fig.4  (a) Conventional first principal component (PC1) of PM2.5 over North China based on quarterly pollution day ratio for the period of 2003–2015. Percentages of explained variance are printed at the upper left on the PC maps. (b) Pollution day ratio during study period, red line represented the pollution day ratio of the pollution core areas and gray line indicated across the study area.
Meteorological elements Pressure layer(hpa) First CCC Second CCC Third CCC Fourth CCC Fifth CCC Sixth CCC
RHa) Surface 0.64 0.51 0.46 0.45 0.44 0.43
1000 0.67 0.52 0.46 0.44 0.43 0.42
975 0.65 0.52 0.47 0.44 0.44 0.42
950 0.64 0.50 0.46 0.44 0.42 0.42
925 0.63 0.48 0.44 0.43 0.41 0.41
900 0.63 0.47 0.43 0.42 0.41 0.40
850 0.64 0.45 0.42 0.41 0.41 0.40
800 0.60 0.46 0.42 0.41 0.41 0.40
750 0.57 0.47 0.43 0.43 0.42 0.41
700 0.54 0.45 0.43 0.43 0.42 0.41
Uwindb) Surface 0.63 0.50 0.44 0.44 0.43 0.42
1000 0.59 0.47 0.45 0.44 0.43 0.42
975 0.59 0.47 0.45 0.44 0.44 0.43
950 0.58 0.47 0.44 0.44 0.43 0.43
925 0.57 0.47 0.44 0.44 0.43 0.43
900 0.56 0.47 0.44 0.44 0.44 0.43
850 0.56 0.46 0.44 0.44 0.43 0.42
800 0.58 0.46 0.45 0.45 0.43 0.43
750 0.57 0.46 0.45 0.44 0.43 0.42
700 0.56 0.47 0.45 0.44 0.44 0.43
Vwindc) surface 0.62 0.48 0.44 0.43 0.43 0.42
1000 0.61 0.46 0.45 0.45 0.44 0.43
975 0.61 0.47 0.45 0.46 0.44 0.44
950 0.59 0.48 0.46 0.45 0.44 0.43
925 0.58 0.46 0.48 0.45 0.44 0.43
900 0.57 0.48 0.45 0.44 0.44 0.43
850 0.55 0.47 0.46 0.45 0.44 0.43
800 0.54 0.48 0.46 0.45 0.44 0.43
750 0.51 0.47 0.45 0.44 0.43 0.43
700 0.48 0.46 0.46 0.44 0.44 0.43
PBLHd) surface 0.64 0.51 0.46 0.45 0.44 0.43
Tab.2  Canonical correlation coefficient (CCC) between PM2.5 concentration and meteorological elements at different vertical height
Fig.5  Spatial distribution of the average PM2.5 concentrations in different relative humidity intervals during study period, the unit for the color bar is μg/cm3.
Fig.6  Spatial distribution of the correlation coefficient of RH with its (a) first and second (b) typical variable and PM2.5 with its (c) first and second (d) typical variable. Through these relationships and the correlation coefficient of each pair of typical variables, the correlation between the corresponding PM2.5 and meteorological elements can be known.
Fig.7  Spatial distribution of the correlation coefficient of Uwind with its (a) first typical variable and (b) PM2.5 with its first typical variable.
Fig.8  Spatial distribution of the average PM2.5 concentrations in different height of planetary boundary layer intervals during study period, the unit for the color bar is μg/cm3.
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