A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
The Impact of COVID-19 Control Measures on Air Quality in Guangdong Province
COVID-19 control measures had a significant social and economic impact in Guangdong Province, and provided a unique opportunity to assess the impact of human activities on air quality. Based on the monitoring data of PM2.5, PM10, NO2, and O3 concentrations from 101 air quality monitoring stations in Guangdong Province from October 2019 to April 2020, the PSCF (potential source contribution factor) analysis and LSTM (long short-term memory) neural network were applied to explore the impact of epidemic control measures on air quality in Guangdong Province. Results showed that during the lockdown, the average concentration of PM2.5, PM10, NO2, and O3 decreased by 37.84%, 51.56%, 58.82%, and 24.00%, respectively. The ranges of potential sources of pollutants were reduced, indicating that air quality in Guangdong Province improved significantly. The Pearl River Delta, characterized by a high population density, recorded the highest NO2 concentration values throughout the whole study period. Due to the lockdown, the areas with the highest concentrations of O3, PM2.5, and PM10 changed from the Pearl River Delta to the eastern and western Guangdong. Moreover, LSTM simulation results showed that the average concentration of PM2.5, PM10, NO2, and O3 decreased by 46.34%, 54.56%, 70.63%, and 26.76%, respectively, which was caused by human-made impacts. These findings reveal the remarkable impact of human activities on air quality and provide effective theoretical support for the prevention and control of air pollution in Guangdong Province.
The Impact of COVID-19 Control Measures on Air Quality in Guangdong Province
COVID-19 control measures had a significant social and economic impact in Guangdong Province, and provided a unique opportunity to assess the impact of human activities on air quality. Based on the monitoring data of PM2.5, PM10, NO2, and O3 concentrations from 101 air quality monitoring stations in Guangdong Province from October 2019 to April 2020, the PSCF (potential source contribution factor) analysis and LSTM (long short-term memory) neural network were applied to explore the impact of epidemic control measures on air quality in Guangdong Province. Results showed that during the lockdown, the average concentration of PM2.5, PM10, NO2, and O3 decreased by 37.84%, 51.56%, 58.82%, and 24.00%, respectively. The ranges of potential sources of pollutants were reduced, indicating that air quality in Guangdong Province improved significantly. The Pearl River Delta, characterized by a high population density, recorded the highest NO2 concentration values throughout the whole study period. Due to the lockdown, the areas with the highest concentrations of O3, PM2.5, and PM10 changed from the Pearl River Delta to the eastern and western Guangdong. Moreover, LSTM simulation results showed that the average concentration of PM2.5, PM10, NO2, and O3 decreased by 46.34%, 54.56%, 70.63%, and 26.76%, respectively, which was caused by human-made impacts. These findings reveal the remarkable impact of human activities on air quality and provide effective theoretical support for the prevention and control of air pollution in Guangdong Province.
The Impact of COVID-19 Control Measures on Air Quality in Guangdong Province
Lili Li (author) / Zhihui Mao (author) / Jianjun Du (author) / Tao Chen (author) / Lu Cheng (author) / Xiaocui Wen (author)
2022
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
Indoor Environmental Pollution State & Controlling Measures of Guangdong Province
| British Library Conference Proceedings | 2004
Disaster treatment in Guangdong province
British Library Conference Proceedings | 1998
Habitat Quality Assessment and Driving Factors Analysis of Guangdong Province, China
| DOAJ | 2023
Evolution of COVID-19 dynamics in Guangdong Province, China: an endemic-epidemic modeling study
| Springer Verlag | 2024