Discrepancies in ozone levels and temporal variations between urban and rural North China Plain
Discrepancies in ozone levels and temporal variations between urban and rural North China Plain
复制标题
华北平原城乡臭氧水平差异及时间变化
DOI：
10.1525/elementa.2022.00019
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Xiaobin Xu
中科院分区：
文献类型：
--
作者：
Xiaoyi Zhang;Wanyun Xu;Gen Zhang;Weili Lin;Huarong Zhao;Sanxue Ren;Guangsheng Zhou;Jianmin Chen;Xiaobin Xu
一些主要污染物的有效减排降低了气溶胶负荷，但导致次生污染物的相对重要性增加，中国城市和郊区温暖季节O3水平上升表明这一点近年来受到广泛关注，特别是在华北平原（NCP）。这引起了人们对其农业影响的严重关注，由于缺乏长期的农村观测，农业影响主要根据O3模型模拟或城市/郊区测量进行评估。在这项研究中，我们展示了 2013 年至 2019 年期间在 NCP 农村地点进行的非常有价值的连续 O3 观测。与附近城市/郊区的 O3 水平升高相比，农村观测结果显示 O3 摩尔分数降低。虽然从 2013 年到 2019 年，城市/郊区 NCP 地点的 O3 摩尔分数和 AOT40 大幅增加，但农村 NCP 地点 (GC) 的 O3 观测结果显示下降，特别是在夏季和秋季，AOT40 的比率更高。利用农村观测结果重新评估了 NCP 地区 O3 农业影响，导致小麦、玉米和大豆平均相对产量损失分别为 1 年 37 ± 14%、8 ± 4 % 和 30 ± 13%。正如先前基于城市/郊区 O3 数据的估计所表明的那样，O3 对作物产量的影响以及由此造成的经济损失并未增加。我们的分析表明存在严重高估（即小麦、玉米和大豆的估计作物产量损失的平均相对差异分别达到 53%、112% 和 75%）。尽管O3农业影响有所减轻，但河北省经济损失总额仍占河北省国内生产总值（3.47×1012美元）的0.89%。由于国家环境监测中心主要监测人口稠密地区的O3浓度，全国范围内缺乏具有代表性的农业区观测点。当前的研究强调了建立农村O3观测网络的迫切必要性，并鼓励对不同作物品种对O3的暴露-响应关系进行广泛的田间实验，以进行更准确的农业影响评估。此外，还应探讨城乡之间O3时间反向变化背后的潜在机制，为未来制定污染控制策略奠定基础。
Effective emission reductions of some primary pollutants have brought down aerosol loadings but led to increasing relative importance of secondary pollutants, as was indicated by the rising O3 levels during warm seasons within urban and suburban areas of China, which has received much attention in recent years, especially in the North China Plain (NCP). This has raised serious concerns on its agricultural impacts, which were mainly evaluated based upon O3 model simulations or urban/suburban measurements due to a lack in long-term rural observations. In this study, we present highly valuable continuous O3 observations at a rural NCP site during 2013–2019. Compared to nearby urban/suburban sites, which experienced increased O3 levels, rural observations exhibited decreasing O3 mole fractions. While O3 mole fractions and AOT40 widely increased at urban/suburban NCP sites from 2013 to 2019, O3 observations in the rural NCP site (GC) revealed decreases, especially during summer and autumn with greater rates for AOT40. A reassessment of O3 agricultural impacts in the NCP region was performed using rural observations, resulting in wheat, maize and soybean averaged relative yield losses of 37 ± 14, 8 ± 4 and 30 ± 13% yr–1, respectively. O3 impacts on crop yields and resulting economic losses did not increase as was suggested by previous estimations based on urban/suburban O3 data. Our analyses indicated high overestimations (i.e., average relative differences in estimated crop production loss reaching 53%, 112% and 75%, respectively, for wheat, maize, and soybean). Despite alleviated O3 agricultural impacts, the total economic cost loss in Hebei province still took up 0.89% of the gross domestic production (3.47 × 1012 USD) in Hebei province. Since the China National Environmental Monitoring Center mainly aims at monitoring O3 levels in populated areas, observation sites representative of agricultural regions are lacking across China. The current study highlights the urgent necessity for the establishment of rural O3 observation networks and encourages extensive field experiments on exposure–response relationships of different crops varieties to O3 for more accurate agricultural impact evaluations. Additionally, explorations into the underlying mechanisms behind the reversed O3 temporal variation between rural and urban areas should be conducted for future development of pollution control strategies.
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影响因子：
10.4
作者：
Tao Y;Huang W;Huang X;Zhong L;Lu SE;Li Y;Dai L;Zhang Y;Zhu T
通讯作者：
Zhu T
影响因子：
5
作者：
R. Dingenen;F. Dentener;F. Raes;M. Krol;L. Emberson;J. Cofala
通讯作者：
R. Dingenen;F. Dentener;F. Raes;M. Krol;L. Emberson;J. Cofala
影响因子：
8.9
作者：
Ruonan Wang;N. Bei;Jiarui Wu;Xia Li;Sui-xin Liu;Jiaoyang Yu;Qianqian Jiang;X. Tie;Guohui Li
通讯作者：
Ruonan Wang;N. Bei;Jiarui Wu;Xia Li;Sui-xin Liu;Jiaoyang Yu;Qianqian Jiang;X. Tie;Guohui Li
影响因子：
6.9
作者：
Jiang, Lijun;Feng, Zhaozhong;Paoletti, Elena
通讯作者：
Paoletti, Elena
影响因子：
8.9
作者：
Jinlong Peng;Bo Shang;Yansen Xu;Zhaozhong Feng;H. Pleijel;V. Calatayud
通讯作者：
Jinlong Peng;Bo Shang;Yansen Xu;Zhaozhong Feng;H. Pleijel;V. Calatayud