Spatiotemporal variations of ambient air pollutants and meteorological influences over typical urban agglomerations in China during the COVID-19 lockdown
“…Wang et al [15] observed a reduction of 36-53% in the concentration of Nitrogen Dioxide (NO 2 ) over six megacities of China. Fang et al [16] observed a reduction of 18-45%, 17-53%, 47-64%, 9-34%, and 16-52%, respectively for particulate matters 2.5 (PM 2.5 ), particulate matters 10 (PM 10 ), NO 2 , Sulfur Dioxide (SO 2 ) and Carbon Monoxide (CO), over urban agglomerations in China, during lockdown period relative to pre-lockdown period. Mendez-Espinosa et al [17] reported a reduction of 60%, 44%, and 40% respectively in the concentration of NO 2 , PM 10 , and PM 2.5 over South America during the strict lockdown amid COVID-19.…”
COVID-19 driven lockdown has affected air quality worldwide. Changes in air pollutants concentration, Air Quality Index (AQI), and associated Excess Health Risk (ER%) were assessed using satellite data of before (2019), and during (2020) COVID-19 periods in the industrially, agriculturally developed and highly populated area of Haryana in the northern region of Indo-Gangetic Plains. Parameters such as Aerosol Optical Depth (AOD), Particulate matters (PM), Sulphur Di-Oxide (SO 2 ), Nitrogen Di-Oxide (NO 2 ), Carbon Mono-oxide (CO), and Methane (CH 4 ) were derived using satellite data and validated using ground-based observations (n = 23). The coefficient of correlation (r) 0.91, 0.90, 0.95, 0.73, 0.81 and 0.80 were established with AOD, PM 2.5 , PM 10 , SO 2 , NO 2 and CO, respectively. Significant reduction (p \ 0.005) in the concentration of air pollutants, viz. 38% in AOD, 55% in PM 2.5 , 61% in PM 10 , 31% in SO 2 , 10% in NO 2 , 5% in CO and 1% in CH 4 were observed during lockdown. Significant (p \ 0.00) improvement in air quality was observed due to a 44% reduction in pollution level, which led to the reduction in ER% by 71%, which is quite significant. AQI and ER% from satellite and ground showed a high r 2 i.e. 0.88 and 0.99 respectively, suggesting the potential application of satellite data for periodic AQI and ER% assessment.
“…Wang et al [15] observed a reduction of 36-53% in the concentration of Nitrogen Dioxide (NO 2 ) over six megacities of China. Fang et al [16] observed a reduction of 18-45%, 17-53%, 47-64%, 9-34%, and 16-52%, respectively for particulate matters 2.5 (PM 2.5 ), particulate matters 10 (PM 10 ), NO 2 , Sulfur Dioxide (SO 2 ) and Carbon Monoxide (CO), over urban agglomerations in China, during lockdown period relative to pre-lockdown period. Mendez-Espinosa et al [17] reported a reduction of 60%, 44%, and 40% respectively in the concentration of NO 2 , PM 10 , and PM 2.5 over South America during the strict lockdown amid COVID-19.…”
COVID-19 driven lockdown has affected air quality worldwide. Changes in air pollutants concentration, Air Quality Index (AQI), and associated Excess Health Risk (ER%) were assessed using satellite data of before (2019), and during (2020) COVID-19 periods in the industrially, agriculturally developed and highly populated area of Haryana in the northern region of Indo-Gangetic Plains. Parameters such as Aerosol Optical Depth (AOD), Particulate matters (PM), Sulphur Di-Oxide (SO 2 ), Nitrogen Di-Oxide (NO 2 ), Carbon Mono-oxide (CO), and Methane (CH 4 ) were derived using satellite data and validated using ground-based observations (n = 23). The coefficient of correlation (r) 0.91, 0.90, 0.95, 0.73, 0.81 and 0.80 were established with AOD, PM 2.5 , PM 10 , SO 2 , NO 2 and CO, respectively. Significant reduction (p \ 0.005) in the concentration of air pollutants, viz. 38% in AOD, 55% in PM 2.5 , 61% in PM 10 , 31% in SO 2 , 10% in NO 2 , 5% in CO and 1% in CH 4 were observed during lockdown. Significant (p \ 0.00) improvement in air quality was observed due to a 44% reduction in pollution level, which led to the reduction in ER% by 71%, which is quite significant. AQI and ER% from satellite and ground showed a high r 2 i.e. 0.88 and 0.99 respectively, suggesting the potential application of satellite data for periodic AQI and ER% assessment.
“…Many studies, especially the first published ones, did not take into account the weather influence, and it is well known that it plays a crucial role in the formation, transport, deposition and transformation of air pollutants [ 33 , 34 ]. Thus, unfavourable meteorological conditions can lead to days of heavy pollution, even if the total emission is reduced [ 35 , 36 , 37 , 38 ]. If meteorology is not controlled for, the fluctuations observed in pollutant concentrations may be masked by meteorological variation rather than emission reductions [ 39 ], which can lead to wrong conclusions regarding the assessment of the environmental impact of an unusual event [ 40 , 41 ].…”
The global COVID-19 pandemic that began in late December 2019 led to unprecedented lockdowns worldwide, providing a unique opportunity to investigate in detail the impacts of restricted anthropogenic emissions on air quality. A wide range of strategies and approaches exist to achieve this. In this paper, we use the “deweather” R package, based on Boosted Regression Tree (BRT) models, first to remove the influences of meteorology and emission trend patterns from NO, NO2, PM10 and O3 data series, and then to calculate the relative changes in air pollutant levels in 2020 with respect to the previous seven years (2013–2019). Data from a northern Spanish region, Cantabria, with all types of monitoring stations (traffic, urban background, industrial and rural) were used, dividing the calendar year into eight periods according to the intensity of government restrictions. The results showed mean reductions in the lockdown period above −50% for NOx, around −10% for PM10 and below −5% for O3. Small differences were found between the relative changes obtained from normalised data with respect to those from observations. These results highlight the importance of developing an integrated policy to reduce anthropogenic emissions and the need to move towards sustainable mobility to ensure safer air quality levels, as pre-existing concentrations in some cases exceed the safe threshold.
“…3 a). It may be attributed not only to the poor air movement in mountainous, hill and other complex terrain but also to ineffective influences of the extreme lock-down strategies on transportation and industry during this period ( Fan et al, 2021 ). Fig.…”
Section: The Changes and Significance Of No
2
/Somentioning
confidence: 99%
“…were largely affected by traffic, industries and meteorological factors ( Shang et al, 2020 ). The concentrations of PMs negatively correlated with WS indicating the predominant role in the dispersion of PM 2.5 and PM 10 ( Figure S2 ) ( Fan et al, 2021 ). While RH was one of the necessary conditions for the formations of PM 2.5 corresponding to its positive correlation with PMs ( Figure S2 ) ( Cheng et al, 2015 ).…”
Section: The Changes and Significance Of Pm
25
/Pm
10mentioning
To prevent the Corona Virus Disease 2019 (COVID-19) spreading, Chinese government takes a series of corresponding measures to restrict human mobility, including transportation lock-down and industries suspension, which significantly influenced the ambient air quality and provided vary rare time windows to assess the impacts of anthropological activities on air pollution. In this work, we divided the studied timeframe (2019/12/24–2020/2/24) into four periods and selected 88 cities from 31 representative urban agglomerations. The indicators of PM
2.5
/PM
10
and NO
2
/SO
2
were applied, for the first time, to analyze the changes in stoichiometric characteristics of ambient air pollutants pre-to post-COVID-19 in China. The results indicated that the ratios of NO
2
/SO
2
presented a responding decline, especially in YRD (−5.01), YH (−3.87), and MYR (−3.84), with the sharp reduction of traffic in post-COVID-19 periods (P3–P4: 2.34 ± 0.94 m/m) comparing with pre-COVID-19 periods (P1–P2: 4.49 ± 2.03 m/m). Whereas the ratios of PM
2.5
/PM
10
increased in P1–P3, then decreased in P4 with relatively higher levels (>0.5) in almost all urban agglomerations. Furthermore, NO
2
presented a stronger association with PM
2.5
/PM
10
variation than CO; and PM
2.5
with NO
2
/SO
2
variation than PM
10
. In summary, the economic structure, lockdown measures and meteorological conditions could explain the noteworthy variations in different urban agglomerations. These results would be in great help for improving air quality in the post-epidemic periods.
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