Variations of air pollutant response to COVID-19 lockdown in cities of the Tibetan Plateau

Chen, Xiyao; Zhang, Fan; Dian-guo, Zhang; Xu, Liang; Li, Rui; Teng, Xiaomi; Zhang, Xin; Wang, Shuo; Li, Weijun

doi:10.1039/d2ea00168c

Cited by 6 publications

(5 citation statements)

References 58 publications

(119 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The annual average maximum concentrations for PM 10 and PM 2.5 were 59 µg/m 3 in 2014 and 38 µg/m 3 in 2013, respectively, while the annual average minimum concentrations reached 38 µg/m 3 for PM 10 and 22 µg/m 3 for PM 2.5 in 2020 (Figure 2a). Over the last decade, both PM 10 and PM 2.5 have shown a consistent decreasing trend since the maximum in 2014 [28]. The PM 2.5 /PM 10 ratio decreased by about 11%, from about 68% in 2013 to about 57% in 2020 (see Figure 2a).…”

Section: Light Extinction Metricsmentioning

confidence: 83%

A Novel Approach to Assessing Light Extinction with Decade-Long Observations of Chemical and Optical Properties in Seoul, South Korea

Park,

Song

et al. 2024

Atmosphere

View full text Add to dashboard Cite

We performed continuous long-term measurements of PM2.5 mass, comprehensive chemical composition, and optical properties, including scattering and absorption coefficients, from March 2011 to December 2020 at the Metropolitan Air Quality Research Center in Seoul, South Korea. PM2.5 peaked at 38 μg/m3 in 2013 and has been declining steadily since then, reaching 22 μg/m3 in 2020. The extinction coefficients also decreased with the decline in PM2.5, but the correlation between the two factors was not as pronounced. This deviation was mainly attributed to the rapid changes in the chemical composition of PM2.5 over the same period. The mass contribution of sulphate to PM2.5 decreased from 33.9 to 24.1%, but the fraction of nitrate and organic carbon increased from 23.4 and 20.0 to 34.1 and 32.2%, respectively, indicating that sulphate has been replaced by nitrate and organic carbon over the past decade. To assess the effect of changing aerosol chemical compositions on light extinction, we compared the measured extinction coefficients with those estimated via the various existing light extinction approaches, including the revised IMPROVE algorithm. We found that the simplified linear regression model provided the best fit to our data, with a slope of 1.03 and R2 of 0.87, and that all non-linear methods, such as the IMPROVE algorithms, overestimated the observed long-term light extinction by 23 to 48%. This suggests that the simple linear regression scheme may be more appropriate for reflecting the varying aerosol conditions over long periods of time, especially for urban air. However, for conditions where the chemical composition does not change much, non-linear methods such as the IMPROVE scheme are likely to be more appropriate for reproducing light extinction.

show abstract

Section: Light Extinction Metricsmentioning

confidence: 83%

A Novel Approach to Assessing Light Extinction with Decade-Long Observations of Chemical and Optical Properties in Seoul, South Korea

Park,

Song

et al. 2024

Atmosphere

View full text Add to dashboard Cite

show abstract

“…The method, although simple, does not completely eliminate the meteorological effects. The second approach uses predictive machine learning models to isolate lockdown intervention on the air pollutant concentration [25][26][27][28][29][30][31]. In the present study, the second approach is applied by comparing the predicted results of 2020 with actual observations made during the lockdown period.…”

Section: Discussionmentioning

confidence: 99%

“…Hence, it would be difficult to determine from the observed data whether the changes in the concentrations (increase or decrease) are caused by weather conditions or by the traffic regulations implemented during the lockdown. By using machine learning models, one can subtract the weather component from the observation to obtain weather-normalized data that show the underlying causes of the change in the concentrations simulating a business-as-usual scenario (BAU) [18,[25][26][27][28][29][30][31][32]. Weather normalization can be achieved by using random forest (RF) regression models [41] via the 'randomForest' package in R [42].…”

Section: Machine Learning Modeling: Business As Usual Scenario Modelmentioning

confidence: 99%

“…Statistical and machine learning tools were used to analyze the air quality data to capture changes in the air quality, if any, due to the COVID-19 lockdown. Machine learning modeling was used to predict air pollutant concentrations during the lockdown period, by subtracting the influence of weather changes that might mask the true emission changes [18,[25][26][27][28][29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Impact of Regional Mobility on Air Quality during COVID-19 Lockdown in Mississippi, USA Using Machine Learning

Tuluri

Remata

Walters

et al. 2023

IJERPH

View full text Add to dashboard Cite

Social distancing measures and shelter-in-place orders to limit mobility and transportation were among the strategic measures taken to control the rapid spreading of COVID-19. In major metropolitan areas, there was an estimated decrease of 50 to 90 percent in transit use. The secondary effect of the COVID-19 lockdown was expected to improve air quality, leading to a decrease in respiratory diseases. The present study examines the impact of mobility on air quality during the COVID-19 lockdown in the state of Mississippi (MS), USA. The study region is selected because of its non-metropolitan and non-industrial settings. Concentrations of air pollutants—particulate matter 2.5 (PM2.5), particulate matter 10 (PM10), ozone (O3), nitrogen oxide (NO2), sulfur dioxide (SO2), and carbon monoxide (CO)—were collected from the Environmental Protection Agency, USA from 2011 to 2020. Because of limitations in the data availability, the air quality data of Jackson, MS were assumed to be representative of the entire region of the state. Weather data (temperature, humidity, pressure, precipitation, wind speed, and wind direction) were collected from the National Oceanic and Atmospheric Administration, USA. Traffic-related data (transit) were taken from Google for the year 2020. The statistical and machine learning tools of R Studio were used on the data to study the changes in air quality, if any, during the lockdown period. Weather-normalized machine learning modeling simulating business-as-scenario (BAU) predicted a significant difference in the means of the observed and predicted values for NO2, O3, and CO (p < 0.05). Due to the lockdown, the mean concentrations decreased for NO2 and CO by −4.1 ppb and −0.088 ppm, respectively, while it increased for O3 by 0.002 ppm. The observed and predicted air quality results agree with the observed decrease in transit by −50.5% as a percentage change of the baseline, and the observed decrease in the prevalence rate of asthma in MS during the lockdown. This study demonstrates the validity and use of simple, easy, and versatile analytical tools to assist policymakers with estimating changes in air quality in situations of a pandemic or natural hazards, and to take measures for mitigating if the deterioration of air quality is detected.

show abstract

“…It is a factor that can contribute to increased severity of COVID-19 and an increase in mortality associated with the disease [25]. In addition to health risks, air pollution contributes to climate change [26] and is harmful in uplands [27].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Impact of COVID-19 Restrictions on Air Pollution in Russia’s Largest Cities

et al. 2023

View full text Add to dashboard Cite

Governments around the world took unprecedented measures, such as social distancing and the minimization of public/industrial activity, in response to the COVID-19 pandemic in 2020. This provided a unique chance to assess the relationships between key air pollutant emissions and track the reductions in these emissions in various countries during the lockdown. This study considers atmospheric air pollution in the 78 largest Russian cities (with populations over 250,000) in March–June of 2019–2021. This is the first such study for the largest cities in Russia. The initial data were the TROPOMI measurements (Sentinel-5P satellite) of such pollutants as carbon monoxide (CO), formaldehyde (HCHO), nitrogen dioxide (NO2), and sulfur dioxide (SO2), which are the main anthropogenic pollutants. The data were downloaded from the Google Earth Engine’s cloud-based geospatial data platform. This provided L3-level information for subsequent analysis. The TROPOMI data indicated a decrease in the atmospheric content of the air pollutants in the largest Russian cities during the lockdown compared to the pre-pandemic and post-pandemic periods. The reduced economic activity due to the COVID-19 pandemic had the greatest impact on NO2 concentrations. The average reduction was −30.7%, while the maximum reduction was found within Moscow city limits that existed before 01.07.2012 (−41% with respect to the 2019 level). For sulfur dioxide, the average decrease was only 7%, with a further drop in 2021 (almost 20% relative to 2019). For formaldehyde and carbon monoxide, there were no reductions during the 2020 lockdown period (99.4% and 100.9%, respectively, with respect to 2019). The identified impacts of the COVID-19 lockdown on NO2, SO2, HCHO, and CO NO2 concentrations in major Russian cities generally followed the patterns observed in other industrialized cities in China, India, Turkey, and European countries. The COVID-19 pandemic had a local impact on NO2 concentration reductions in major Russian cities. The differences leveled off over time, and the baseline pollution level for each pollutant was restored.

show abstract

Variations of air pollutant response to COVID-19 lockdown in cities of the Tibetan Plateau

Abstract: Coronavirus Disease 2019 (COVID-19) accidently appeared in Tibet on August 7, 2022, and broke the 920 consecutive epidemic-free days. The cities in Tibet completely kept lockdown to restrict the public...

Cited by 6 publications

References 58 publications

A Novel Approach to Assessing Light Extinction with Decade-Long Observations of Chemical and Optical Properties in Seoul, South Korea

A Novel Approach to Assessing Light Extinction with Decade-Long Observations of Chemical and Optical Properties in Seoul, South Korea

Impact of Regional Mobility on Air Quality during COVID-19 Lockdown in Mississippi, USA Using Machine Learning

Evaluation of the Impact of COVID-19 Restrictions on Air Pollution in Russia’s Largest Cities

Contact Info

Product

Resources

About