| Title: | A potential controlling approach on surface ozone pollution based upon power big data |
| Author(s): | Wang X; Gu W; Wang F; Liu L; Wang Y; Han X; Xie Z; |
| Address: | "State Grid Anhui Electric Power Research Institute, Hefei, 230026 Anhui China. GRID: grid.433158.8. ISNI: 0000 0000 8891 7315 Department of Environmental Science and Technology, University of Science and Technology of China, Hefei, 230026 Anhui China. GRID: grid.59053.3a. ISNI: 0000000121679639 State Grid Anhui Electric Power CO. LTD, Hefei, 230026 Anhui China. GRID: grid.433158.8. ISNI: 0000 0000 8891 7315" |
| DOI: | 10.1007/s42452-022-05045-5 |
| ISSN/ISBN: | 2523-3971 (Electronic) 2523-3963 (Print) 2523-3963 (Linking) |
| Abstract: | "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(2), 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(2) 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(2) 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(2), 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s42452-022-05045-5" |
| Keywords: | Generalized additive models (GAMs) Power big data Surface ozone pollution Volatile organic compounds(VOCs); |
| Notes: | "PubMed-not-MEDLINEWang, Xin Gu, Weihua Wang, Feng Liu, Li Wang, Yu Han, Xuemin Xie, Zhouqing eng Switzerland 2022/05/17 SN Appl Sci. 2022; 4(6):164. doi: 10.1007/s42452-022-05045-5. Epub 2022 May 10" |
