Multidimensional Observation Analysis of the Air Quality Changes in Wuhan during the Coronavirus Disease-2019 Pandemic
-
摘要: 基于国控站点和武汉本地加密站点，利用2020年1月1日至2月13日6种常规污染物及细颗粒物（PM2.5）组分的逐小时监测数据，评估了新冠疫情管控前后武汉地区大气污染物的时空变化特征及管控措施对不同类型站点的影响区别。结果显示：在实施疫情管控措施后，武汉市颗粒物浓度大幅降低，基于两种观测网（89个站点）计算得出PM2.5、PM10浓度变化率分别为−23.44%、−32.95%，但O3浓度显著增加，变化率为55.22%，比国控站点变化率高10.6%；在空间分布上，颗粒物浓度呈现北高南低，但武汉偏南区域站点的浓度下降幅度更大，与偏南风频次增高导致管控措施更多影响下风方向区域有关。偏南区域站点NO2浓度降低幅度更大，同时O3浓度升高，其原因是NO浓度大幅降低，其滴定反应减弱，并且气象条件也有利于O3持续累积，维持较高浓度。从不同类别站点浓度变化差异来看，疫情管控措施对二次污染物的交通站点和工业园区站点影响最大，两者基于两种观测网计算所得的浓度变化率均超过国控站点的浓度变化率，PM2.5和O3的国控站点变化率分别比交通站点变化率低6%和18%。对于一次污染物而言，SO2、CO浓度变化小，浓度变化率分别为−6.10%和−5.61%，与疫情期间重点排放源没有停工有关；在6种常规污染物中，NO2浓度变化幅度最大，其浓度下降了−55.26 %，与交通管控直接相关。对比分析2019年和2020年同期气溶胶组分浓度发现，2020年疫情管控期间
$ {\mathrm{N}\mathrm{O}}_{3}^{-} $ /$ {\mathrm{S}\mathrm{O}}_{4}^{2-} $ 比值降低，更接近1，有机碳（OC）与元素碳（EC）比值增大到6.07，说明汽车尾气等移动源的比重减小，燃煤燃烧影响比重增大。Abstract: Based on the state-controlled sites and local-controlled sites, using the hourly monitoring data of six conventional pollutants and PM2.5 component data from 1 January to 13 February 2020, the temporal and spatial variation characteristics of air pollutants in the Wuhan area before and after the coronavirus disease 2019 (COVID-19) controls and the impact of control measures at different types of sites were evaluated. The results showed that after implementing epidemic control measures, the particulate matter concentration in Wuhan decreased significantly. Based on the calculation of two observation networks (89 stations), the change rates of PM2.5 and PM10 concentrations were −23.44% and −32.95%, respectively, but the O3 concentration increased significantly, at a rate of 55.22%, 10.6% higher than that of the state-controlled sites. In terms of the spatial distribution, the particulate matter concentration is higher in the north and lower in the south but decreases more at stations in south Wuhan, which is related to the increased frequency of southerly wind, resulting in more control measures affecting the downwind area. The decrease in NO2 concentration at the stations in the south is greater, while the O3 concentration increases. The reason is that as the NO concentration decreases greatly, the titration reaction weakens, and the meteorological conditions are also conducive to continuously accumulating O3 and maintaining a high concentration of O3. From the difference in concentration change among different types of stations, the epidemic control measures have the greatest impact on secondary pollutants at traffic and industrial stations. The concentration change rates calculated based on the two observation networks exceed those of state-controlled stations. The change rates of PM2.5 and O3 are 6% and 18% lower at state-controlled stations compared to traffic stations, respectively. The primary pollutants, SO2 and CO, show small concentration changes at rates of −6.10% and −5.61%, respectively, possibly because key emission sources were not shut down during the epidemic. Among the six conventional pollutants, the NO2 concentration changed the most, decreasing by −55.26%, which is directly related to traffic control. Comparing and analyzing the aerosol component concentrations in the same periods in 2019 and 2020 reveals that during the epidemic control period in 2020, the$ {\mathrm{N}\mathrm{O}}_{3}^{-} $ /$ {\mathrm{S}\mathrm{O}}_{4}^{2-} $ ratio decreased closer to 1, and the OC/EC ratio increased to 6.07, indicating a decrease in the proportion of mobile sources, such as automobile exhaust, and an increase in the impact of coal combustion.-
Key words:
- Air quality /
- Wuhan /
- PM2.5 /
- Ozone
-
图 2 2020年1月1日至2月13日武汉地区一组国控站点（蓝线）和加密站点（红线）的6种污染物浓度序列的对比。R为相关系数，RMSE为均方根误差，M1为国控站点均值，M2为加密站点均值，VD为有效数据比
Figure 2. Comparisons of the hourly observations of the six pollutants by the state-controlled sites (blue line) and the local-controlled sites (red line) in Wuhan from 1 Jan to 13 Feb 2020. R is the correlation coefficient, RMSE is the root-mean-square error, M1 is the mean value of the state-controlled sites, M2 is the mean value of the local-controlled sites, and VD is the effective data ratio
图 3 武汉地区2020年（a1−c1）第一阶段（1月1～22日）、（a2−c2）第二阶段（1月23日至2月13日）PM2.5（左）、PM10（中）、O3（右）浓度及（a3−c3）两阶段的浓度差异
Figure 3. PM2.5 (left), PM10 (middle), and O3 (right) concentrations in the (a1−c1) first stage (1−22 Jan) and (a2−c2) second stage (23 Jan−13 Feb), and (a3−c3) the concentration differences of the two stages in Wuhan
图 4 武汉地区2020年（a）第一、（b）第二阶段风向、风速玫瑰图
Figure 4. Rose chart of wind direction and speed in (a) the first and (b) the second stages in Wuhan in 2020
图 5 武汉地区2020年（a）PM2.5和（b）O3第一、二阶段站点浓度频数分布
Figure 5. (a) PM2.5 and (b) O3 concentration frequency distributions in the first and second stages in Wuhan 2020
图 6 武汉地区SO2（左）、CO（中）和NO2（右）（a1−c1）第一阶段、（a2−c2）第二阶段、（a3−c3）两阶段的浓度差异
Figure 6. SO2 (left), CO (middle), and NO2 (right) concentrations in the (a1−c1) first stage and (a2−c2) second stage, and (a3−c3) concentration diffidences between the two stages
图 7 武汉地区2020年1月1日至2月13日O3和NO2浓度时间序列
Figure 7. Time series of O3 and NO2 concentrations in Wuhan from 1 Jan to 13 Feb 2020
图 8 武汉地区2020年O3浓度（阴影）及（a）温度、（b）相对湿度、（c）风向和风速、（d）风速
Figure 8. Changes of O3 concentration (shadings) and (a) temperature, (b) relative humidity, (c) wind direction and wind speed, and (d) wind speed in Wuhan 2020
图 9 2019（左列）、2020年（右列）（a、b）PM2.5组分及其气态前体物的浓度均值和（c、d）第二阶段基于第一阶段的变化率
Figure 9. (a, b) Mean concentrations of PM2.5 components and their gaseous precursors and (c, d) the rate of change in the second stage based on the first stage in 2019 (left) and 2020 (right)
图 10 2020年武汉地区第一阶段与第二阶段（a）PM2.5、（b）SO2、（c）PM10、（d）CO、（e）O3、（f）NO2的浓度均值
Figure 10. Mean concentrations of (a) PM2.5, (b) SO2, (c) PM10, (d) CO, (e) O3, and (f) NO2 at different stations in the first phase and the second phase in Wuhan 2020
表 1 2020年武汉地区国控站点和加密站点PM2.5浓度观测值差异的统计参数对比
Table 1. Comparison of statistical parameters of observed PM2.5 concentration between the state-controlled sites and local-controlled sites in Wuhan 2020
国控站点 加密站点 站点间距离/km 相关系数（R） 浓度均方根误差（RMSE）/μg m−3 汉口花桥 大气复合污染监测实验室 1.752 0.99 5.5 沌口新区 神龙大道70号 1.58 0.92 12.9 汉阳月湖 汉阳环保局 1.84 0.92 16.7 武昌紫阳 洪山区烽火集团 2.847 0.94 14.2 东湖梨园 武昌水果湖中学 2.591 0.95 16.8 青山钢花 武昌铁四院中学 2.284 0.96 21.4 汉口江滩 大气复合污染监测实验室 3.222 0.92 16.1 吴家山 临空港大道金北一路 2.581 0.94 12.8 民族大道 洪山光谷职业学院 2.261 0.93 13.1 
下载: 导出CSV
表 2 2020年武汉地区国控站点和加密站点PM2.5、PM10和O3的浓度均值及变化率
Table 2. Mean concentrations and change rates of PM2.5, PM10, and O3 at the state-controlled sites and local-controlled sites in Wuhan 2020
污染物 观测站点 浓度均值 变化率 第一阶段/μg m−3 第二阶段/μg m−3 PM2.5 国控站点 61 48 −21.31% 加密站点 64 49 −23.44% 全部 64 49 −23.44% PM10 国控站点 76 55 −27.63% 加密站点 90 60 −33.33% 全部 88 59 −32.95% O3 国控站点 65 94 44.62% 加密站点 67 105 56.72% 全部 67 104 55.22%
下载: 导出CSV
表 3 2020年武汉地区国控站点和加密站点SO2、CO和NO2的浓度均值及变化率
Table 3. Mean concentrations and change rates of SO2, CO, and NO2 at the state-controlled sites and local-controlled sites in Wuhan 2020
污染物 观测站点 浓度均值 变化率 第一阶段/μg m−3 第二阶段/μg m−3 SO2 国控站点 6.6 6.3 −4.55% 加密站点 8.4 7.9 −5.95% 全部 8.2 7.7 −6.10% CO 国控站点 1.08×103 0.96×103 −11.11% 加密站点 1.07×103 1.01×103 −5.61% 全部 1.07×103 1.01×103 −5.61% NO2 国控站点 42 21 −50.00% 加密站点 37 16 −56.76% 全部 38 17 −55.26%
下载: 导出CSV
表 4 2019年和2020年武汉地区
$ {\mathbf{N}\mathbf{O}}_{3}^{-} $ 与$ {\mathbf{S}\mathbf{O}}_{4}^{2-} $ 、OC与EC在第一、二阶段中的浓度比值Table 4. Ratios of
$ {\mathbf{N}\mathbf{O}}_{3}^{-} $ to$ {\mathbf{S}\mathbf{O}}_{4}^{2-} $ and OC to EC in the first and second stages in Wuhan 2019 and 2020年份 浓度比值 时段 2020年 2019年 $ {\mathrm{N}\mathrm{O}}_{3}^{-} $/$ {\mathrm{S}\mathrm{O}}_{4}^{2-} $ 1月1～22日 2.52 2.13 1月23日至2月13日 1.31 1.80 OC/EC 1月1～22日 4.69 4.05 1月23日至2月13日 6.07 4.47
下载: 导出CSV
表 5 2020年武汉地区第二阶段相较于第一阶段PM2.5、PM10、O3、SO2、CO、NO2的浓度变化率
Table 5. Concentration change rate of PM2.5, PM10, O3, SO2, CO, and NO2 at different stations in the second phase compared with the first phase in Wuhan 2020
各污染物浓度变化率 站点 PM2.5 PM10 O3 SO2 CO NO2 工业园区 −27.14% −36.46% 64.64% −12.00% −12.82% −52.50% 交通点 −27.54% −36.56% 61.57% −25.97% −18.02% −53.85% 网格化 −20.97% −32.58% 53.88% 0 −1.90% −54.29% 省控点 −14.04% −25.32% 43.92% −6.76% 16.49% −55.56% 国控点 −21.31% −27.63% 45.83% −4.55% −11.11% −50.00%
下载: 导出CSV
-
[1] Bao R, Zhang A C. 2020. Does lockdown reduce air pollution? Evidence from 44 cities in northern China [J]. Sci. Total Environ., 731: 139052. doi: 10.1016/j.scitotenv.2020.139052 [2] Bauwens M, Compernolle S, Stavrakou T, et al. 2020. Impact of coronavirus outbreak on NO2 pollution assessed using TROPOMI and OMI observations [J]. Geophys. Res. Lett., 47(11): e2020GL087978. doi: 10.1029/2020GL087978 [3] Chu B W, Zhang S P, Liu J, et al. 2021. Significant concurrent decrease in PM2.5 and NO2 concentrations in China during COVID-19 epidemic [J]. J. Environ. Sci., 99: 346−353. doi: 10.1016/j.jes.2020.06.031 [4] Chu S H. 2005. Stable estimate of primary OC/EC ratios in the EC tracer method [J]. Atmos. Environ., 39(8): 1383−1392. doi: 10.1016/j.atmosenv.2004.11.038 [5] Fu S H, Gu Y Z. 2017. Highway toll and air pollution: Evidence from Chinese cities [J]. J. Environ. Econom. Manag., 83: 32−49. doi: 10.1016/j.jeem.2016.11.007 [6] Gao L, Yue X, Meng X Y, et al. 2020. Comparison of ozone and PM2.5 concentrations over urban, suburban, and background sites in China [J]. Adv. Atmos. Sci., 37(12): 1297−1309. doi: 10.1007/s00376-020-0054-2 [7] Ge S S, Wang G H, Zhang S, et al. 2019. Abundant NH3 in China enhances atmospheric HONO production by promoting the heterogeneous reaction of SO2 with NO2 [J]. Environ. Sci. Technol., 53(24): 14339−14347. doi: 10.1021/acs.est.9b04196 [8] Geng F H, Tie X, Xu J M, et al. 2008. Characterizations of ozone, NOx, and VOCs measured in Shanghai, China [J]. Atmos. Environ., 42(29): 6873−6883. doi: 10.1016/j.atmosenv.2008.05.045 [9] Huang X, Ding A J, Gao J, et al. 2021. Enhanced secondary pollution offset reduction of primary emissions during COVID-19 lockdown in China [J]. Natl. Sci. Rev., 8(2): nwaa137. doi: 10.1093/nsr/nwaa137 [10] Hui L R, Liu X G, Tan Q W, et al. 2018. Characteristics, source apportionment and contribution of VOCs to ozone formation in Wuhan, Central China [J]. Atmos. Environ., 192: 55−71. doi: 10.1016/j.atmosenv.2018.08.042 [11] Hui L R, Liu X G, Tan Q W, et al. 2019. VOC characteristics, sources and contributions to SOA formation during haze events in Wuhan, Central China [J]. Sci. Total Environ., 650: 2624−2639. doi: 10.1016/j.scitotenv.2018.10.029 [12] Le T H, Wang Y, Liu L, et al. 2020. Unexpected air pollution with marked emission reductions during the COVID-19 outbreak in China [J]. Science, 369(6504): 702−706. doi: 10.1126/science.abb7431 [13] 乐旭, 雷亚栋, 周浩, 等. 2020. 新冠肺炎疫情期间中国人为碳排放和大气污染物的变化 [J]. 大气科学学报, 43(2): 265−274. doi: 10.13878/j.cnki.dqkxxb.20200408010Yue Xu, Lei Yadong, Zhou Hao, et al. 2020. Changes of anthropogenic carbon emissions and air pollutants during the COVID-19 epidemic in China [J]. Trans. Atmos. Sci. (in Chinese), 43(2): 265−274. doi: 10.13878/j.cnki.dqkxxb.20200408010 [14] Li K, Jacob D J, Liao H, et al. 2019. Anthropogenic drivers of 2013–2017 trends in summer surface ozone in China [J]. Proc. Natl. Acad. Sci. USA, 116(2): 422−427. doi: 10.1073/pnas.1812168116 [15] Lian X B, Huang J P, Huang R J, et al. 2020. Impact of city lockdown on the air quality of COVID-19-hit of Wuhan city [J]. Sci. Total Environ., 742: 140556. doi: 10.1016/j.scitotenv.2020.140556 [16] Lu M M, Tang X, Wang Z F, et al. 2017. Source tagging modeling study of heavy haze episodes under complex regional transport processes over Wuhan megacity, Central China [J]. Environ. Pollut., 231: 612−621. doi: 10.1016/j.envpol.2017.08.046 [17] Sicard P, De Marco A, Agathokleous E, et al. 2020. Amplified ozone pollution in cities during the COVID-19 lockdown [J]. Sci. Total Environ., 735: 139542. doi: 10.1016/j.scitotenv.2020.139542 [18] Shi X Q, Brasseur G P. 2020. The response in air quality to the reduction of Chinese economic activities during the COVID-19 outbreak [J]. Geophys. Res. Lett., 47(11): e2020GL088070. doi: 10.1029/2020GL088070 [19] Sillman S, Logan J A, Wofsy S C. 1990. The sensitivity of ozone to nitrogen oxides and hydrocarbons in regional ozone episodes [J]. J. Geophys. Res. Atmos., 95(D2): 1837−1851. doi: 10.1029/JD095iD02p01837 [20] Tian H Y, Liu Y H, Li Y D, et al. 2020. An investigation of transmission control measures during the first 50 days of the COVID-19 epidemic in China [J]. Science, 368(6491): 638−642. doi: 10.1126/science.abb6105 [21] Wang C, Horby P W, Hayden F G, et al. 2020. A novel coronavirus outbreak of global health concern [J]. Lancet, 395(10223): 470−473. doi: 10.1016/S0140-6736(20)30185-9 [22] Wang P F, Chen K Y, Zhu S Q, et al. 2020. Severe air pollution events not avoided by reduced anthropogenic activities during COVID-19 outbreak [J]. Resour. Conserv. Recycl., 158: 104814. doi: 10.1016/j.resconrec.2020.104814 [23] Wang Q, Su M. 2020. A preliminary assessment of the impact of COVID-19 on environment—A case study of China [J]. Sci. Total Environ., 728: 138915. doi: 10.1016/j.scitotenv.2020.138915 [24] Wang T, Xue L K, Brimblecombe P, et al. 2017. Ozone pollution in China: A review of concentrations, meteorological influences, chemical precursors, and effects [J]. Sci. Total Environ., 575: 1582−1596. doi: 10.1016/j.scitotenv.2016.10.081 [25] Wang Y G, Ying Q, Hu J L, et al. 2014. Spatial and temporal variations of six criteria air pollutants in 31 provincial capital cities in China during 2013–2014 [J]. Environ. Int., 73: 413−422. doi: 10.1016/j.envint.2014.08.016 [26] Xiong Y, Zhou J B, Schauer J J, et al. 2017. Seasonal and spatial differences in source contributions to PM2.5 in Wuhan, China [J]. Sci. Total Environ., 577: 155−165. doi: 10.1016/j.scitotenv.2016.10.150 [27] Xu K J, Cui K P, Young L H, et al. 2020. Impact of the COVID-19 event on air quality in central China [J]. Aerosol Air Qual. Res., 20(5): 915−929. doi: 10.4209/aaqr.2020.04.0150 [28] Yang F, Tan J, Zhao Q, et al. 2011. Characteristics of PM2.5 speciation in representative megacities and across China [J]. Atmos. Chem. Phys., 11(11): 5207−5219. doi: 10.5194/acp-11-5207-2011 [29] Zeng P, Lyu X P, Guo H, et al. 2018. Causes of ozone pollution in summer in Wuhan, Central China [J]. Environ. Pollut., 241: 852−861. doi: 10.1016/j.envpol.2018.05.042 -
点击查看大图
计量
- 文章访问数: 212
- HTML全文浏览量: 19
- PDF下载量: 26
- 被引次数: 0
