Global air quality change during COVID-19: a synthetic analysis of satellite, reanalysis and ground station data

Abstract

Coronavirus disease 2019 (COVID-19) pandemic has led to a rare reduction in human activities. In such a background, data from ground-based environmental stations, satellites, and reanalysis materials are utilized to conduct a comprehensive analysis of the global air quality changes during the COVID-19 outbreak. The results showed that under the impact of the COVID-19 outbreak, a significant decrease in particulate matter (PM x ) and nitrogen dioxide (NO2) occurred in more than 40% of the world’s land area, with NO2 (PM x ) decreasing by ∼30% (∼20%). The mobility, meteorological factors, and the response speed to COVID-19 outbreaks were examined. It was further found that in quick-response cities, lockdowns produced a sharp decline in mobility and had a dominant impact on air quality. In contrast, in slow-response cities, mobility dropped gradually since the confirmation of the first COVID-19 case (FCC) and he impact of the FCC, lockdowns, and meteorological factors were comparable.

Spatiotemporal impact of COVID-19 on Taiwan air quality in the absence of a lockdown: Influence of urban public transportation use and meteorological conditions

“…Consequently, a remarkable change in air pollutant concentrations was observed throughout the world, particularly in countries that imposed lockdown, creating a silver lining in the dark cloud of COVID-19 ( Jephcote et al, 2021 ; Nakada and Urban, 2020 ; Kanniah et al , 2020 ). A significant reduction in air pollutant concentration is observed worldwide, wherein the average concentrations of ground-level nitrogen dioxide (NO 2 ) and particulate matter with average aerodynamic diameters less than 10 μm and 2.5 μm (PM 10 and PM 2.5 ) declined by approximately 30% and 20%, respectively, compared to 2019 ( Yang et al , 2021 ). Since then, there has been growing attention to utilizing both high-resolution satellite images and/or ground-based monitoring data to quantify the impact of COVID-19 on the local atmospheric environment, particularly to compare the differences before and after lockdown implementation, for instance, in India ( Mahato et al, 2020 ), China ( Shen et al, 2021 ), Singapore ( Li and Tartarini, 2020 ), Malaysia ( Abdullah et al, 2020 ), Iran ( Broomandi et al, 2020 ), Bangladesh ( Rahman et al, 2021 ), Brazil ( Nakada and Urban, 2020 ), and Turkey ( Ghasempour et al, 2021 ).…”

Using a Low-Cost Sensor to Estimate Fine Particulate Matter: A Case Study in Samutprakarn, Thailand

“…In this study, a LCS (Plantower Laser Dust Sensor PMS7003) was tested at several monitoring sites, from December 2021 to April 2022. The Plantower Laser Dust Sensor PMS7003 is a low-cost, commercially available sensor that costs between THB 800-1100 (USD [25][26][27][28][29][30][31][32][33][34][35]. The advantages of this sensor are that it can be controlled remotely via a Raspberry Pi and is small enough to fit into a mobile or wearable enclosure, while previous research has shown that, compared with other sensors such as Plantower PMS5003 and Alphasense OPC-N2, PMS7003 tends to show a significant correlation with the reference instrument and good reproducibility [47,48,62].…”

“…During and after the COVID-19 lockdown, the concept was to reduce air quality (AQ) due to the strict control of the virus transmission, and this potentially led to an air quality change in high-risk areas [23][24][25]. In addition, an improvement in air quality resulted from policies that controlled and monitored ambient air (AA) monitoring [26][27][28][29]. Thailand has serious air quality problems in several areas in every dry season (haze episodes from December to February).…”

Spatiotemporal Change of Air-Quality Patterns in Hubei Province—A Pre- to Post-COVID-19 Analysis Using Path Analysis and Regression

“…The regression between PM 2.5 and AQI is strong every year (r 2 > 0.89 to r 2 < 0.96), however the lowest value recorded is in the post-COVID-19 period (r 2 = 0.89) (Figure 5). Since the COVID-19 pandemic, many scholars at home and abroad have discussed the characteristics of the changes in air quality under epidemic prevention and control, but relatively few studies have used statistical methods for their analysis [22,23]. In this study, a path analysis and a regression analysis were the main methods used to measure the relationships between the AQI value, the number of days of primary pollutant pollution, the pollutant concentration, and NO 2 /SO 2 , among other values, from January 2019 to Similarly, the regression between PM 10 and AQI is strong every year (r 2 > 0.87 to r 2 < 0.92), however the lowest value recorded is post-COVID-19 (r 2 = 0.87).…”

“…Since the COVID-19 pandemic, many scholars at home and abroad have discussed the characteristics of the changes in air quality under epidemic prevention and control, but relatively few studies have used statistical methods for their analysis [22,23]. In this study, a path analysis and a regression analysis were the main methods used to measure the relationships between the AQI value, the number of days of primary pollutant pollution, the pollutant concentration, and NO 2 /SO 2 , among other values, from January 2019 to August 2021 and the same period in 2017 and 2018.…”