Characteristics of Atmospheric Hydrogen Peroxide at an Urban Site in Beijing During Winter and Spring
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摘要: 大气过氧化氢(H2O2)是一种重要的光化学产物,也是硫酸盐气溶胶生成及降水酸化过程的关键氧化剂。然而,我国对H2O2的观测研究较少,尤其对雾霾期间H2O2浓度变化特征认识不足。该文介绍了冬春时段(2016年12月-2017年4月)在北京城区中国气象局的H2O2观测结果,并结合同期O3,PAN,NOX,PM2.5等污染物和气象要素观测数据,分析H2O2浓度变化特征与影响因素。观测结果表明:观测期间H2O2体积混合比(简称为浓度)为(0.65±0.59)×10-9,其中,春季浓度(0.83±0.67)×10-9高于冬季浓度(0.51±0.47)×10-9;H2O2平均日变化基本呈现单峰特征,峰值出现在18:00-21:00,比其他地区峰值出现稍晚,并滞后于O3峰值时间4~7 h;相对湿度对H2O2日峰值时间和浓度水平有影响,小于55%时日峰值出现于18:00-24:00,平均峰值浓度1.52×10-9;大于65%时日峰值出现于11:00-16:00,日峰值浓度均小于1×10-9。H2O2,O3和PAN虽然同属光化学产物,但在不同污染状况下浓度水平和变化趋势差异明显;H2O2清洁日峰值浓度高于污染日,但11:00-15:00污染日浓度略高于清洁日。Abstract: As a photochemical produced oxidant, gaseous hydrogen peroxide (H2O2) plays an important role in aerosols and acid rain production. However, not many measurements of gaseous H2O2 have been made in China in the past few years and further studies especially the level and trend of H2O2 in smog are needed. To make up for the lack of H2O2 data and provide support for air quality improvement in China, an observation experiment on gaseous H2O2 is carried out from 27 Dec 2016 to 28 Apr 2017 at an urban site in the northwest of mega-city Beijing, using a two-channel H2O2 monitor AL2021. The concentration level, variation and influence factors under different conditions are analyzed with several simultaneously observed pollutants (O3, PAN, NOX, PM2.5, etc.) and meteorological parameters. The mean mixing ratio of H2O2 for the entire period is (0.65±0.59)×10-9, with a higher mean of (0.83±0.67)×10-9 in spring and a lower mean of (0.51±0.47)×10-9 in winter. Day peaks with the value higher than 2×10-9 is also detected in winter indicating that high concentration of H2O2 can also happen under certain conditions. The concentration of H2O2 shows pronounced diurnal cycles with peaks in the period of 1800-2100 BT, occurring later than those reported for other sites in China or foreign countries and shows a delay of about 4 to 7 hours compared with the peaking time of O3. H2O2 level is found to be negatively correlated with relative humidity (RH), especially when only considering the maximum H2O2 level under RH over 55%. This is consistent with the uptake of gaseous H2O2 by water-containing aerosol particles under higher RH conditions. The H2O2 peaking time and peak level are closely related with RH as well as other factors, such as NOX. Under conditions of daily RH lower than 55%, H2O2 level can reach a mean peak value of 1.52×10-9 with peaking times during 1800-2400 BT, while it peaks lower (1×10-9) and earlier (before 1700 BT) with the daily mean RH higher than 65%. H2O2, O3 and PAN show different diurnal patterns and levels under different pollution conditions. H2O2 shows smaller average level differences for clean and hazy days, with a higher peak but a lower level during 1100-1500 BT under the clean condition. O3 shows a higher mixing ratio under clean condition than under hazy condition, while PAN reveals an opposite trend. Results also indicate that dynamical transport could be an important influencing factor of variations and levels of H2O2 and O3. The impact of photochemistry on haze formation in colder months in the urban environment of Beijing and its feedback warrant further studies, particularly the role of H2O2 in the formation of sulfate aerosol.
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图 4 峰值时段(11:00—24:00)与非峰值时段(01:00—10:00)在清洁和霾污染条件下,H2O2和相对湿度的小时平均值分布和线性拟合结果以及相对湿度在55%—100%区间内H2O2最大值与相对湿度的线性拟合结果
(填色代表对应PM2.5小时平均浓度)
Fig. 4 Relationships between H2O2 and relative humidity for peak period(1100-2400 BT) and non-peak period (0100-1000 BT) under hazy and clean conditions including overall correlations between H2O2 and relative humidity for different conditions and correlations between the maximum H2O2 and relative humidity within 55%-100%
(the shaded denotes the corresponding PM2.5 concentration)
表 1 不同观测时段H2O2浓度统计结果
Table 1 Statistics of H2O2 concentrations for different observation periods at CMA, Beijing
时段 H2O2浓度/10-9 样本量 平均值 最大值 中值 标准偏差 全时间序列 0.65 3.56 0.52 0.59 1489 高浓度时段(13:00—24:00) 0.89 3.56 0.77 0.67 715 低浓度时段(01:00—12:00) 0.44 1.92 0.33 0.39 774 冬季(2016年12月27日—2017年2月28日) 0.51 2.78 0.40 0.47 831 春季(2017年3月1日—4月28日) 0.83 3.56 0.70 0.67 658 -
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