中国典型区域2017―2020年期间大气重污染的时空特征与演变

褚旸晰; 殷丽娜; 邵盼阳; 胡京南

doi:10.16462/j.cnki.zhjbkz.2021.10.004

中国典型区域2017―2020年期间大气重污染的时空特征与演变

doi: 10.16462/j.cnki.zhjbkz.2021.10.004

100012 北京，中国环境科学研究院大气环境研究所

基金项目:

国家自然科学基金 41905122

O₃和PM_2.5复合污染协同防控科技攻关预研课题 DQGG202001

详细信息

通讯作者:
胡京南，E-mail：hujn@craes.org.cn

中图分类号: X51;R181
计量
- 文章访问数: 343
- HTML全文浏览量: 186
- PDF下载量: 64
- 被引次数: 0
出版历程
- 收稿日期: 2021-05-31
- 修回日期: 2021-08-31
- 网络出版日期: 2021-11-17
- 刊出日期: 2021-10-10

The spatio-temporal variation of heavy air pollution in typical regions of China from 2017 to 2020

Institute of Atmospheric Environment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China

Funds:

National Science Foundation of China 41905122

National Research Program for Key Issues in Air Pollution Control DQGG202001

More Information

Corresponding author: HU Jing-nan, E-mail: hujn@craes.org.cn

摘要

摘要: 目的大气重污染(heavy air pollution, HAP)对人体健康存在明显危害。基于2017―2020年重污染的特征，归纳重污染成因，为“十四五”规划期间大气污染治理政策的制定提供科学依据。方法收集整理2017―2020年全国空气质量监测数据，分析重污染的时空分布特征与演变规律。结果从空间上看，2017―2020年，全国54.7%的重污染天数分布在重点区域，其中京津冀及周边地区、汾渭平原和长三角地区分别占36.3%、11.1%和7.3%；非重点区域中，乌昌石城市群重污染天数较多，占比为8.7%。从时间变化上看，2017―2020年重点区域重污染天数减少49.4%~61.5%，乌昌石城市群重污染天数减少4.6%，较重点区域降幅偏小；细颗粒物(fine particulate matter, PM_2.5)重污染多发于11月至次年2月，可吸入颗粒物(inhalable particles, PM₁₀)重污染集中在3―5月和10―11月的季节交替期，臭氧(ozone, O₃)重污染发生于5―7月。从污染物上看，各区域PM_2.5重污染占全部重污染天数的87.0%~96.0%，其次为PM₁₀和O₃，占比分别为3.0%~12.0%和＜3.0%。结论 2017―2020年重污染天数总体减少，但部分地区和城市重污染形势仍比较严峻。应将持续削减秋冬季PM_2.5重污染作为大气污染治理重点，加大京津冀及周边地区、汾渭平原、乌昌石城市群治理力度，切实削减污染排放，提升公众健康效益。
- 重污染 /
- 细颗粒物 /
- 可吸入颗粒物 /
- 臭氧 /
- 时间变化 /
- 空间分布
Abstract: Objective Heavy air pollution (HAP) is harmful to human health. Based on the characteristics of HAP from 2017 to 2020, we aimed to summarize the cause of HAP and provide a scientific basis for the formulation of air pollution prevention and control policies in "the Fourteenth Five-Year Plan" period. Methods The national air quality monitoring data during 2017-2020 were collected to analyze the spatio-temporal HAP distribution patterns. Results Spatially, 54.7% of HAP days in China occurred in key regions, including Beijing-Tianjin-Hebei (BTH) and surrounding region, Fen-Wei River Plain and Yangtze River Delta, which accounted for 36.3%, 11.1% and 7.3%, respectively. Among non-key regions, the Urumqi-Changji-Shihezi City cluster (UCS) accounted for 8.7% of nationwide HAP days. Temporally, HAP days in key regions decreased by 49.4%-61.5% from 2017 to 2020, while those in UCS only decreased by 4.6%, much smaller than in key regions. Fine particulate matter (PM_2.5) HAP days normally occur from November to February, those due to inhalable particles (PM₁₀) frequently happen from April to May and from October to November, and those due to ozone (O₃) mostly take place from May to July. PM_2.5 accounted for 87.0%-96.0% of HAP days in the studied regions, followed by PM₁₀ and O₃, waking up 3.0%-12.0% and < 3.0%, respectively. Conclusions The number of HAP days from 2017 to 2020 generally decreased. Still, the HAP situation in some regions and cities remains challenging. Continuous reduction of PM_2.5 HAP days in autumn and winter should be the focus of air pollution prevention and control, with enhanced measures in BTH and surrounding region, Fen-Wei River Plain and UCS to effectively reduce air pollutant emissions and improve the public health benefits.
- Heavy air pollution /
- Fine particulate matter /
- Inhalable particles /
- Ozone /
- Temporal variation /
- Spatial distribution

HTML全文

图 1 2017―2020年各区域重污染天数时间分布

注：(a)表示京津冀及周边地区，(b)表示汾渭平原，(c)表示长三角地区，(d)表示乌昌石城市群；每月和全年(1―12月)从左至右的四条柱依次表示2017―2020年相应时段。

Figure 1. The temporal distribution of HAP days in different regions from 2017 to 2020

下载: 全尺寸图片幻灯片

图 2 2017―2020年重点区域重污染天数空间分布

注：(a)表示PM_2.5重污染天数，(b)表示PM₁₀重污染天数，(c)表示O₃重污染天数，(d)表示全部重污染天数，(e)表示重污染天数变化幅度的空间分布。

Figure 2. The spatial distribution of HAP days in key regions from 2017 to 2020

下载: 全尺寸图片幻灯片

图 3 2017―2020年乌昌石城市群重污染天数空间分布

注：(a)表示PM_2.5重污染天数，(b)表示PM₁₀重污染天数，(c)表示O₃重污染天数，(d)表示全部重污染天数，(e)表示重污染天数变化幅度的空间分布。

Figure 3. The spatial distribution of HAP days in Urumqi-Changji-Shihezi City cluster from 2017 to 2020

下载: 全尺寸图片幻灯片

参考文献(11)

[1]	谢元博, 陈娟, 李巍. 雾霾重污染期间北京居民对高浓度PM_2.5持续暴露的健康风险及其损害价值评估[J]. 环境科学, 2014, 35(1): 1-8. DOI: 10.13227/j.hjkx.2014.01.057. Xie YB, Chen J, Li W. An assessment of PM_2.5 related health risks and impaired values of Beijing residents in a consecutive high-level exposure during heavy haze days[J]. Environ Sci, 2014, 35(1): 1-8. DOI: 10.13227/j.hjkx.2014.01.057.
[2]	王金玉, 李盛, 王式功, 等. 沙尘污染对人体健康的影响及其机制研究进展[J]. 中国沙漠, 2013, 33(4): 1160-1165. DOI: 10.7522/j.issn.1000-694x.2013.00164. Wang JY, Li S, Wang SG, et al. A review on effect of dust pollution on human health and its mechanism[J]. J Desert Res, 2013, 33(4): 1160-1165. DOI: 10.7522/j.issn.1000-694x.2013.00164.
[3]	董继元, 刘兴荣, 张本忠, 等. 我国臭氧短期暴露与人群死亡风险的Meta分析[J]. 环境科学学报, 2016, 36(4): 1477-1485. DOI: 10.13671/j.hjkxxb.2015.0555. Dong JY, Liu XR, Zhang BZ, et al. Meta-analysis of association between short-term ozone exposure and population mortality in China[J]. Acta Scientiae Circumstantiae, 2016, 36(4): 1477-1485. DOI: 10.13671/j.hjkxxb.2015.0555.
[4]	Avnery S, Mauzerall DL, Liu JF, et al. Global crop yield reductions due to surface ozone exposure: 2. Year 2030 potential crop production losses and economic damage under two scenarios of O₃ pollution[J]. Atmos Environ, 2011, 45(13), 2297-2309. DOI: 10.1016/j.atmosenv.2011.01.002.
[5]	中国环境监测总站. 国家环境空气质量监测网[EB/OL]. (2017-11-08)[2021-08-26]. http://www.cnemc.cn/zzjj/jcwl/dqjcwl/201711/t20171108_645109.shtml. China National Environmental Monitoring Centre. National ambient air quality monitoring network[EB/OL]. (2017-11-08)[2021-08-26]. http://www.cnemc.cn/zzjj/jcwl/dqjcwl/201711/t20171108_645109.shtml.
[6]	中华人民共和国环境保护部. HJ 633-2012环境空气质量指数(AQI)技术规定(试行)[S]. 北京: 中国环境科学出版社, 2012. Ministry of Environmental Protection of the People's Republic of China. Technical regulation on ambient air quality index (on trial) HJ633-2012[S]. Beijing: China Environmental Press, 2012.
[7]	武卫玲, 薛文博, 王燕丽, 等. 《大气污染防治行动计划》实施的环境健康效果评估[J]. 环境科学, 2019, 40(7): 2961-2966. DOI: 10.13227/j.hjkx.201811110. Wu WL, Xue WB, Wang YL, et al. Health benefit evaluation for air pollution prevention and control action plan in China[J]. Environ Sci, 2019, 40(7): 2961-2966. DOI: 10.13227/j.hjkx.201811110.
[8]	陈小龙. 中国城市统计年鉴—2020[M]. 北京: 中国统计出版社, 2021: 13-19. Chen XL. China City Statistical Yearbook-2020[M]. Beijing: China Statistics Press, 2021: 13-19.
[9]	胡京南, 柴发合, 段菁春, 等. 京津冀及周边地区秋冬季PM_2.5爆发式增长成因与应急管控对策[J]. 环境科学研究, 2019, 32(10): 1704-1712. DOI: 10.13198/j.issn.1001-6929.2019.09.11. Hu JN, Chai FH, Duan JC, et al. Explosive growth of PM_2.5 during the autumn and winter seasons in the Jing-Jin-Ji and surrounding area and its control measures with emergency response[J]. Res Environ Sci, 2019, 32(10): 1704-1712. DOI: 10.13198/j.issn.1001-6929.2019.09.11.
[10]	程良晓, 范萌, 陈良富, 等. 秋季秸秆焚烧对京津冀地区霾污染过程的影响分析[J]. 中国环境科学, 2017, 37(8): 2801-2812. DOI: 10.3969/j.issn.1000-6923.2017.08.001. Cheng LX, Fan M, Chen LF, et al. Effects on the haze pollution from autumn crop residue burning over the Jing-Jin-Ji Region[J]. China Environ Sci, 2017, 37(8): 2801-2812. DOI: 10.3969/j.issn.1000-6923.2017.08.001.
[11]	孙婷婷, 张天舒, 项衍, 等. 2018年春节期间京津冀地区污染过程分析[J]. 中国环境科学, 2020, 40(4): 1393-1402. DOI: 10.19674/j.cnki.issn1000-6923.2020.0154. Sun TT, Zhang TS, Xiang Y, et al. Analysis of the pollution process in the Beijing-Tianjin-Hebei region during the Spring Festival of 2018[J]. China Environ Sci, 2020, 40(4): 1393-1402. DOI: 10.19674/j.cnki.issn1000-6923.2020.0154.