Spatial-temporal Distribution and Evolution Characteristics of Air Pollution in Beijing-Tianjin-Hebei Region Based on Long-term "Ground-Satellite" Data

被引:2
|
作者
Wang Y.-T. [1 ]
Yin Z.-P. [2 ]
Zheng Z.-F. [1 ]
Li J. [1 ]
Li Q.-C. [1 ]
Meng C.-L. [1 ]
Li W. [3 ]
机构
[1] Institute of Urban Meteorology, China Meteorological Administration, Beijing
[2] School of Remote Sensing and Information Engineering, Wuhan University, Wuhan
[3] National Climate Center, Beijing
来源
Huanjing Kexue/Environmental Science | 2022年 / 43卷 / 07期
关键词
Aerosol optical depth(AOD); Air pollution; Beijing-Tianjin-Hebei; Environmental investigation; Evolution; Spatio-temporal characteristics;
D O I
10.13227/j.hjkx.202109240
中图分类号
学科分类号
摘要
This study aimed to promote the coordinated development of regional social economy and ecological environment, build a better living environment, accurately prevent and control pollution, and carry out in-depth surveys and general surveys of air pollution in Beijing, Tianjin, and Hebei. Based on 6 years (June 2014 to December 2019) of ground environmental observation data and satellite data from 2000 to 2019, the distribution characteristics and evolution trend of air pollution in different time and spatial scales were analyzed. The results showed that: ① according to the daily average concentration of PM2.5 at the sites, the pollution in the Beijing-Tianjin-Hebei region showed the characteristics of more days, heavy levels, and overall improvement. Pollution mainly occurred from October to April of the following year, accounting for nearly half a year. The pollution level of PM2.5 was the best at Zhangjiakou, followed by Qinhuangdao. ② Based on the 20-year average PM2.5 annual average concentration data retrieved from satellites, the PM2.5 concentration presented a spatial distribution characteristic in which that in the plains was higher than that in mountain area, and PM2.5 concentration in the city was higher than that in the suburbs. PM2.5 concentration changed with time, showing a four-stage bimodal structure of "M"-type evolution characteristics, which gradually increased starting in 2000; the first peak appeared in 2006 and gradually decreased from 2007 to 2012. It rose sharply to the second peak in 2013 and then decreased yearly until 2017. ③ The monthly average AOT data based on satellites every 10 years indicated that the value of AOT in the first time period (2000-2009) was larger than that in the same month of the second time period (2010-2019). The maximum value was in July, and the minimum value was in December. The monthly average AOT in Zhangjiakou and Chengde changed slightly over the past 20 years, and the seasonal and spatial differences were significant in the plain area. ④ Judging from the daily average value of O3-8h observed at the stations, good levels of O3-8h concentrations in the Beijing-Tianjin-Hebei area occurred frequently and widely from March to October. There were at least seven instances of light pollution levels, and the moderate pollution levels and above were not observed. ⑤ The daily average value of SO2 observed on the ground showed that there was no light pollution or above; the good pollution level occurred in winter, and most appeared in the form of pollution for several consecutive days. ⑥ The analysis of AQI data revealed that from 2015 to 2019, the proportion of AQI excellent grades in Beijing increased from 27% to 38%, and the proportion of Tianjin AQI good grades increased from 44% to 64%. The highest proportion of Handan AQI superior grades appeared in 2016, accounting for only 9%. ⑦ The 20-year monthly average concentration of SO2 data based on satellites showed that high-value areas were in Handan, Xingtai, and Shijiazhuang, and low-value areas were in Zhangjiakou and Chengde. The 20-year average NO2 data showed that the high-value centers were in Beijing, Tianjin, Tangshan, Handan, Xingtai, and Shijiazhuang. © 2022, Science Press. All right reserved.
引用
收藏
页码:3508 / 3522
页数:14
相关论文
共 60 条
  • [1] Liu J, Han Y Q, Tang X, Et al., Estimating adult mortality attributable to PM<sub>2.5</sub> exposure in China with assimilated PM<sub>2.5</sub> concentrations based on a ground monitoring network, Science of the Total Environment, 568, pp. 1253-1262, (2016)
  • [2] Shi G Y, Wang B, Zhang H, Et al., The radiative and climatic effects of atmospheric aerosols, Chinese Journal of Atmospheric Sciences, 32, 4, pp. 826-840, (2008)
  • [3] Li X F, Zhang M J, Wang S J, Et al., Variation characteristics and influencing factors of air pollution index in China, Environmental Science, 33, 6, pp. 1936-1943, (2012)
  • [4] Jacobson M Z., Strong radiative heating due to the mixing state of black carbon in atmospheric aerosols, Nature, 409, 6821, pp. 695-697, (2001)
  • [5] Kaufman Y J, Tanre, Boucher O., A satellite view of aerosols in the climate system, Nature, 419, 6903, pp. 215-223, (2002)
  • [6] Keil A, Haywood J M., Solar radiative forcing by biomass burning aerosol particles during SAFARI 2000: A case study based on measured aerosol and cloud properties, Journal of Geophysical Research: Atmospheres, 108, D13, (2003)
  • [7] Che H Z, Zhang X Y, Li Y, Et al., Haze trends over the capital cities of 31 provinces in China, 1981-2005, Theoretical and Applied Climatology, 97, 3-4, pp. 235-242, (2009)
  • [8] Rohen G J, von Hoyningen-Huene W, Kokhanovsky A, Et al., Retrieval of aerosol mass load (PM<sub>10</sub>) from MERIS/Envisat top of atmosphere spectral reflectance measurements over Germany, Atmospheric Measurement Techniques, 4, 3, pp. 523-534, (2011)
  • [9] van Donkelaar A, Martin R V, Spurr R J D, Et al., Optimal estimation for global ground-level fine particulate matter concentrations, Journal of Geophysical Research: Atmospheres, 118, 11, pp. 5621-5636, (2013)
  • [10] Green M, Kondragunta S, Ciren P, Et al., Comparison of GOES and MODIS aerosol optical depth (AOD) to aerosol robotic network (AERONET) AOD and IMPROVE PM<sub>2.5</sub> mass at Bondville, Illinois, Journal of the Air & Waste Management Association, 59, 9, pp. 1082-1091, (2009)