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, NO2, 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 R2 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 NO2 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 NO2, 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. Article Highlights Surface ozone pollution in central China was investigated during the prevalence of the COVID-19 (2020.1–2021.5) NO2 and industrial VOCs contributing 33.72% and 21.12% to ozone formation Potential controlling pathway was proposed Supplementary Information The online version contains supplementary material available at 10.1007/s42452-022-05045-5.
ABSTRACT
Air pollution is becoming serious in developing country, and how to quantify the role of local emission and/or meteorological factors is very important for government to implement policy to control pollution. Here, we use a random forest model, a machine learning (ML) approach, combined with a de-weather method to analyze the PM2.5 level during the COVID-19 outbreak in Hubei Province. The results show that changes in anthropogenic emissions have reduced PM2.5 concentrations in February and March 2020 by about 33.3% compared to the same period in 2019, while changes in meteorological conditions have increased PM2.5 concentrations by about 8.8%. Moreover, the impact of meteorological conditions is more significant in the central region, which is likely to be related to regional transport. After excluding the contribution of meteorological conditions, the PM2.5 concentration in Hubei Province in February and March 2020 is lower than the secondary standard of China (35 µ g/m3). Our estimates also indicate that under similar meteorological conditions as in February and March 2019, an emission reduction intensity equivalent to about 48% of the emission reduction intensity during the lockdown may bring the annual average PM2.5 concentration to the standard (35 µ g/m3). Our study shows that machine learning is a powerful tool to quantify the influencing factors of PM2.5, and the results further emphasize the need for scientific emission reduction as well as joint regional control measures in future.