Wang Xin, Gu Weihua, Wang Feng, Liu Li, Wang Yu, Han Xuemin, Xie Zhouqing
State Grid Anhui Electric Power Research Institute, Hefei, 230026 Anhui China.
Department of Environmental Science and Technology, University of Science and Technology of China, Hefei, 230026 Anhui China.
SN Appl Sci. 2022;4(6):164. doi: 10.1007/s42452-022-05045-5. Epub 2022 May 10.
Surface ozone pollution has attracted extensive attention with the decreasing of haze pollution, especially in China. However, it is still difficult to efficiently control the pollution in time despite numbers of reports on mechanism of ozone pollution. Here we report a method for implementing effective control of ozone pollution through power big data. Combining the observation of surface ozone, NO, meteorological parameters together with hourly electricity consumption data from volatile organic compounds (VOCs) emitting companies, a generalized additive model (GAM) is established for quantifying the influencing factors on the temporal and spatial distribution of surface ozone pollution from 2020 to 2021 in Anhui province, central China. The average R value for the modelling results of 16 cities is 0.82, indicating that the GAM model effectively captures the characteristics of ozone. The model quantifies the contribution of input variables to ozone, with both NO and industrial VOCs being the main contributors to ozone, contributing 33.72% and 21.12% to ozone formation respectively. Further analysis suggested the negative correlation between ozone and NO, revealing VOCs primarily control the increase in ozone. Under scenarios controlling for a 10% and 20% reduction in electricity use in VOC-electricity sensitive industries that can be identified by power big data, ozone concentrations decreased by 9.7% and 19.1% during the pollution period. This study suggests a huge potential for controlling ozone pollution through power big data and offers specific control pathways.
The online version contains supplementary material available at 10.1007/s42452-022-05045-5.
随着雾霾污染的减少，地表臭氧污染受到了广泛关注，尤其是在中国。然而，尽管有大量关于臭氧污染机制的报道，但仍难以及时有效地控制污染。在此，我们报告一种通过电力大数据实现有效控制臭氧污染的方法。结合地表臭氧、一氧化氮、气象参数的观测数据以及来自挥发性有机化合物（VOCs）排放企业的每小时用电量数据，建立了一个广义相加模型（GAM），用于量化2020年至2021年中国中部安徽省地表臭氧污染时空分布的影响因素。16个城市建模结果的平均R值为0.82，表明GAM模型有效地捕捉了臭氧的特征。该模型量化了输入变量对臭氧的贡献，一氧化氮和工业挥发性有机化合物都是臭氧的主要贡献者，分别对臭氧形成贡献了33.72%和21.12%。进一步分析表明臭氧与一氧化氮之间呈负相关，这表明挥发性有机化合物主要控制臭氧的增加。在通过电力大数据识别出的对电力敏感的挥发性有机化合物行业中，控制用电量分别减少10%和20%的情景下，污染期间臭氧浓度分别下降了9.7%和19.1%。本研究表明通过电力大数据控制臭氧污染具有巨大潜力，并提供了具体的控制途径。
在线版本包含可在10.1007/s42452-022-05045-5获取的补充材料。
