Assessment of Air Quality Before and After the COVID-19 Pandemic in Indonesia

The emergence of COVID-19 pandemic has forced many countries implement social restrictions, including Indonesia. There has been a growing interest in understanding the impact of the pandemic on air quality. This research analyses the air pollution before and after the COVID-19 pandemic in Jakarta and Banjarmasin, Indonesia, with a detailed analysis. It compared the results with previous years to determine the significant improvement in air quality and related weather factors obtained from Landsat 8 and 9 imagery. OMI and MERRA-2 were analysed for PM2.5, NO2, SO2, O3 and WRF-Chem model result especially for PM2.5 against the COVID-19 pandemic. As a result, there was a decrease in PM2.5 during the pandemic year in Jakarta, although it was not as good as in 2016 conditions. In Jakarta and Banjarmasin, PM2.5, NO2 and SO2 decreased in 2021 from 2020, which were in line with the high incidence of COVID-19 in 2021. This shows that more air quality increased in the form of healthy days in DKI Jakarta in 2020 than in 2019. In other words, there was an increase in air quality during the implementation Large-Scale Social Restriction (PSBB) policy in 2020 compared to 2019 before the COVID-19 pandemic. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Impact of Anthropogenic Emission Reduction during COVID-19 on Air Quality in Nanjing, China

To avoid the spread of COVID-19, China has implemented strict lockdown policies and control measures, resulting in a dramatic decrease in air pollution and improved air quality. In this study, the air quality model WRF-Chem and the latest MEIC2019 and MEIC2020 anthropogenic emission inventories were used to simulate the air quality during the COVID-19 lockdown in 2020 and the same period in 2019. By designing different emission scenarios, this study explored the impact of the COVID-19 lockdown on the concentration of air pollutants emitted by different sectors (industrial sector and transportation sector) in Nanjing for the first time. The results indicate that influenced by the COVID-19 lockdown policies, compared with the same period in 2019, the concentrations of PM2.5, PM10, and NO2 in Nanjing decreased by 15%, 17.1%, and 20.3%, respectively, while the concentration of O3 increased by 45.1% in comparison;the concentrations of PM2.5, PM10 and NO2 emitted by industrial sector decreased by 30.7%, 30.8% and 14.0% respectively;the concentrations of PM2.5, PM10 and NO2 emitted by transportation sector decreased by 15.6%, 15.7% and 26.2% respectively. The COVID-19 lockdown has a greater impact on the concentrations of PM2.5 and PM10 emitted by the industrial sector, while the impact on air pollutants emitted by the transportation sector is more reflected in the concentration of NO2. This study provides some theoretical basis for the treatment of air pollutants in different departments in Nanjing.

Causes of the unexpected slowness in reducing winter PM2.5 for 2014-2018 in Henan Province.

Toughest-ever clean air actions in China have been implemented nationwide to improve air quality. However, it was unexpected that from 2014 to 2018, the observed wintertime PM2.5 (particulate matter with an aerodynamic diameter of less than 2.5 µm) concentrations showed an insignificant decrease in Henan Province (HNP), a region in the west of the North China Plain. Emission controls seem to have failed to improve winter air quality in HNP, which has caused great confusion in formulating the next air improvement strategy. We employed a deweathering technique to decouple the impact of meteorological conditions. The results showed that the deweathered PM2.5 trend was -3.3%/yr in winter from 2014 to 2018, which had a larger decrease than the observed concentrations (-0.9%/yr), demonstrating that emission reduction was effective at improving air quality. However, compared with the other two megacity clusters, Beijing-Tianjin-Hebei (BTH) (-8.4%/yr) and Yangtze River Delta (YRD) (-7.4%/yr), the deweathered decreasing trend of PM2.5 for HNP remained slow. The underlying mechanism driving the changes in PM2.5 and its chemical components was further explored, using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). Model simulations indicated that nitrate dominated the increase of PM2.5 components in HNP and the proportions of nitrate to total PM2.5 increased from 22.4% in January 2015 to 39.7% in January 2019. There are two primary reasons for this phenomenon. One is the limited control of nitrogen oxide emissions, which facilitates the conversion of nitric acid to particulate nitrate by ammonia. The other is unfavourable meteorological conditions, particularly increasing humidity, further enhancing nitrate formation through multiphase reactions. This study highly emphasizes the importance of reducing nitrogen oxide emissions owing to their impact on the formation of particulate nitrate in China, especially in the HNP region.

Urban Air Pollution Reduction During the COVID Pandemia

Severe measures concerting the mobility of people have been implemented worldwide to contain the spread of COVID-19. This exceptional situation during COVID-19 can serve as a chemical experiment that expands our understanding of photochemical processes in the atmosphere. In this study, we analyze changes in the urban air quality during COVID lockdown restrictions, especially changes in O3 concentrations due to drastic NO2 reductions, in the Metropolitan Area of Barcelona. Model results are evaluated against ground monitoring stations available in this area. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Impact of coronavirus-driven reduction in aerosols on precipitation in the western United States.

Among the many impacts of COVID-19, the pandemic led to improved air quality conditions in the countries under quarantine due to the shutdown of industries, drastically reduced traffic, and lockdowns. Meanwhile, the western United States, particularly the coastal areas from Washington to California, received much less precipitation than normal during early 2020. Is it possible that this reduction in precipitation was driven by the reduced aerosols due to the coronavirus? Here we show that the reduction in aerosols resulted in higher temperatures (up to ∼0.5 °C) and generally lower snow amounts but cannot explain the observed low precipitation amounts over this region. In addition to an assessment of the effects of the coronavirus-related reduction in aerosols on precipitation across the western United States, our findings also provide basic information on the potential impacts different mitigation efforts aimed at reducing anthropogenic aerosols would have on the regional climate.

SOURCE APPORTIONMENT ANALYSIS IN PM AND O3 CONCENTRATIONS DURING COVID-19 LOCKDOWN PERIOD IN MADRID (SPAIN)

In a first analysis of the impacts of the reduction of anthropogenic emissions during COVID-19 lockdown over Madrid (Spain) area, we found an important NOx level reduction but the O3 and PM concentrations were increased. In this work the causes of the increments are studied using Source Apportionment Technology (SAT) included in the Comprehensive Air Quality Model with Extensions (CAMx) model. CAMx is driven by the Weather Research and Forecasting model (WRF). Two simulations are run: one simulation considers the emission reductions during the lockdown (COVID simulation) and a second simulation,” business as usual” (BAU simulation) with an emissions scenario without restrictions. Source apportionment techniques are used to identify and quantify the contributions from main pollution sources with the purpose to provide understanding on what measures should be taken to address them and this work shows the potential of these technique. SAT was used to estimate the contributions of multiple sources, and pollutant types (NOx and VOC) to ozone and particle formation in a single model run. Differences in SAT results under baseline (BAU) and COVID scenarios are used to quantify the contributions of O3 and PM2.5 reductions associated with emissions reduction in individual sectors due to the lowered human activities with a high spatial resolution (1 km). Road transport is the main emission source reduced by the lockdown and reduction in NOx emissions (59%) is higher that VOC reduction (14%). This study helps to elucidate the complex and nonlinear response of O3 and PM concentrations after a reduction of emissions mainly from the transport sector, during the COVID-19 lockdown period, that must be taken into account in the control strategies to mitigate haze pollution. The results show that despite extreme reductions in primary emissions, current air pollution cannot be fully tackled. Further consideration needs to be given to the reorganisation of energy and industrial strategy together with trans-regional joint monitoring for a comprehensive long-term air pollution plan. Source apportionment studies can support of authorities responsible to develop air quality plans. © British Crown Copyright (2022)

Weather-Climate Anomalies and Regional Transport Contribute to Air Pollution in Northern China During the COVID-19 Lockdown.

Two persistent and heavy haze episodes during the COVID-19 lockdown (from 20 Jan to 22 Feb 2020) still occur in northern China, when anthropogenic emissions, particularly from transportation sources, are greatly reduced. To investigate the underlying cause, this study comprehensively uses in-situ measurements for ambient surface pollutants, reanalysis meteorological data and the WRF-Chem model to calculate the contribution of NOx emission change and weather-climate change to the "unexpectedly heavy" haze. Results show that a substantial NOx reduction has slightly decreased PM2.5 concentration. By contrast, the weakest East Asian winter monsoon (EAWM) in the 2019-2020 winter relative to the past decade is particularly important for haze occurrence. A warmer and moister climate is also favorable. Model results suggest that climate anomalies lead to a 25-50 µg m-3 increase of PM2.5 concentration, and atmospheric transport is also an important contributor to two haze episodes. The first haze is closely related to the atmospheric transport of pollutants from NEC to the south, and fireworks emissions in NEC are a possible amplifying factor that warrants future studies. The second one is caused by the convergence of a southerly wind and a mountain wind, resulting in an intra-regional transport within BTH, with a maximal PM2.5 increment of 50-100 µg m-3. These results suggest that climate change and regional transport are of great importance to haze occurrence in China, even with significant emission reductions of pollutants.

Impact of COVID-19 lockdown on the ambient air-pollutants over the Arabian Peninsula

Lockdowns imposed across the world to combat the spread of the COVID-19 pandemic also reduced the anthropogenic emissions. This study investigates the changes in the anthropogenic and natural pollution levels during the lockdown over the Arabian Peninsula (AP), a region where natural pollutants (mineral dust) dominate. In-situ and satellite observations, reanalysis products, and Weather Research and Forecasting model (WRF) coupled with Chemistry module (WRF-Chem) simulations were analyzed to investigate the influence of COVID-19 lockdown on the aerosols (PM2.5, PM10, and AOD) and trace gases (NO2 and SO2). WRF-Chem reasonably reproduced the satellite and in-situ measurements during the study period, with correlation coefficients varying between 0.6-0.8 (0.3-0.8) for PM10 (NO2 and SO2) at 95% confidence levels. During the lockdown, WRF-Chem simulations indicate a significant reduction (50-60%) in the trace gas concentrations over the entire AP compared to the pre-lockdown period. This is shown to be mostly due to a significant reduction in the emissions and an increase in the boundary layer height. An increase in the aerosol concentrations over the central and northern parts of the AP, and a decrease over the north-west AP, Red Sea, and Gulf of Aden regions are noticeable during the lockdown. WRF-Chem simulations suggest that the increase in particulate concentrations over the central and northern AP during the lockdown is mainly due to an increase in dust concentrations, manifested by the stronger convergence and upliftment of winds and warmer surface temperatures (15-25%) over the desert regions. The restricted anthropogenic activities drastically reduced the trace gas concentrations, however, the reduction in particulate concentration levels is offset by the increase in the natural processes (dust emissions).

Changes of air pollutants and simulation of a heavy pollution process during COVID-19 in Shenyang.

During COVID-19, Shenyang implemented strict household isolation measures, resulting in a sharp reduction in anthropogenic emission sources, providing an opportunity to explore the impact of human activities on air pollution. The period from January to April of 2020 was divided into normal period, blockade period and resumption period. Combined with meteorological and pollutant data, mathematical statistics and spatial analysis methods were used to compare with the same period of 2015-2019. The results showed that PM2.5, PM10, NO2 and O3 increased by 32.6%, 13.2%, 4.65% and 22.7% in the normal period, among which the western area changed significantly. During the blockade period, the concentration of pollutants decreased by 35.79%, 35.87%, 32.45% and -4.84%, of which the central area changed significantly. During the resumption period, the concentration of pollutants increased by 21.8%, 8.7%, 5.7% and -6.3%, and the area with the largest change was located in the western. During the blockade period, a heavy pollution occurred with PM2.5 as the main pollutant. The WRF-Chem model and the HYSPLIT model were used to reproduce the pollution occurrence process. The result showed that winds circulated as zonal winds during the pollution process at high altitudes. These winds were controlled by straight westerly and weak northwesterly airflows in front of the high pressure, and the ground was located behind the warm low pressure. Weather conditions were relatively stable. Thus, high temperatures (average > 10 ℃), high humidity (40%-60%) and slow wind (2 m/s) conditions prevailed for a long time in the Shenyang area. The unfavorable meteorological conditions lead to the occurrence of pollution. The backward trajectory showed that the potential source areas were concentrated in the urban agglomeration around Shenyang, and sporadic contributions came from North Korea.

Changes in a Bottom-Up Vehicular Emissions Inventory and Its Impact on Air Pollution During COVID-19 Lockdown in São Paulo, Brazil

Due to coronavirus disease 2019 (COVID-19), many cities implemented strict lockdown to stop the spread of this new disease. Consequently, it was reported lower levels of air pollution due to less human activity outdoors. The changes were registered using surface monitoring stations or satellite observations. However, modeling those environmental changes has remained a challenge because of our limitations in the emissions estimation and also, for the numerical modeling itself. In this study, the vehicular emissions were estimated for March 2020 in the megacity of São Paulo using the Vehicular Emissions INventory model (VEIN). The emissions estimation showed an increment of VOC/NO2 downtown, due to the decrease in circulation of urban transportation and light vehicles. Then, a set of Weather Research and Forecasting models with chemistry (WRF-Chem) simulations were performed with different chemical mechanisms and initial conditions. The modeled diurnal cycles represent the variations observed in March 2020 for the periods pre-lockdown, transition, and lockdown. However, it is imperative to include other sources than vehicular to have a local and comprehensive emissions inventory. Copyright © 2022 Ibarra-Espinosa, Rehbein, Freitas, Martins, Andrade and Landulfo.

Nationwide CoViD-19 lockdown impact on air quality in India

This paper presents the comparative results of surface and satellite measurements made during the Phase 1 (25 March to 14 April), Phase 2 (15 April to 3 May) and Phase 3 (3 May to 17 May) of Covid-19 imposed lockdown periods of 2020 and those of the same locations and periods during 2019 over India. These comparative analyses are performed for Indian states and Tier 1 megacities where economic activities have been severely affected with the nationwide lockdown. The focus is on changes in the surface concentration of sulfur dioxide (SO2), carbon monoxide (CO), PM2.5 and PM10, Ozone (O-3), Nitrogen dioxide (NO2) and retrieved columnar NO2 from TROPOMI and Aerosol Optical Depth (AOD) from MODIS satellite. Surface concentrations of PM2.5 were reduced by 30.59%, 31.64% and 37.06%, PM10 by 40.64%, 44.95% and 46.58%, SO2 by 16.73%, 12.13% and 6.71%, columnar NO2 by 46.34%, 45.82% and 39.58% and CO by 45.08%, 41.51% and 60.45% during lockdown periods of Phase 1, Phase 2 and Phase 3 respectively as compared to those of 2019 periods over India. During 1st phase of lockdown, model simulated PM2.5 shows overestimations to those of observed PM2.5 mass concentrations. The model underestimates the PM2.5 to those of without reduction before lockdown and 1st phase of lockdown periods. The reduction in emissions of PM2.5, PM10, CO and columnar NO2 are discussed with the surface transportation mobility maps during the study periods. Reduction in the emissions based on the observed reduction in the surface mobility data, the model showed excellent skills in capturing the observed PM2.5 concentrations. Nevertheless, during the 1st & 3rd phases of lockdown periods AOD reduced by 5 to 40%. Surface O-3 was increased by 1.52% and 5.91% during 1st and 3rd Phases of lockdown periods respectively, while decreased by-8.29% during 2nd Phase of lockdown period.

[Impacts of Changes in Meteorological Conditions During COVID-19 Lockdown on PM2.5 Concentrations over the Jing-Jin-Ji Region].

The Chinese government triggered the immediate implementation of a lockdown policy in China following the outbreak of the COVID-19 pandemic, leading to drastic decreases in air pollutant emissions. However, concentrations of PM2.5 and other pollutants increased during the COVID-19 lockdown over the Jing-Jin-Ji region compared with those averaged over 2015-2019, and two PM2.5 pollution events occurred during the lockdown. Using the ERA5 reanalysis data, we found that the Jing-Jin-Ji region during the COVID-19 lockdown was characterized by higher relative humidity, lower planetary boundary layer height, and anomalous updraft. These conditions were favorable for condensation and the secondary formation of aerosols and prevented turbulent diffusion of pollutants. Furthermore, we conducted sensitivity tests using the WRF-Chem model and found that ρ(PM2.5) increased by 20-55 µg·m-3(60%-170%) over the middle region of Jing-Jin-Ji during the COVID-19 lockdown due to changes in meteorological conditions. Furthermore, the enhanced aerosol chemistry and unfavorable diffusion conditions were identified as the key factors driving increases in PM2.5 concentrations during the lockdown. Planetary boundary layer height and relative humidity may become the important factors in forecasting PM2.5 pollution events over the Jing-Jin-Ji region under the background of emission reduction.

Process-based diagnostics of extreme pollution trail using numerical modelling during fatal second COVID-19 wave in the Indian capital.

The world's worst outbreak, the second COVID-19 wave, not only unleashed unprecedented devastation of human life, but also made an impact of lockdown in the Indian capital, New Delhi, in particulate matter (PM: PM2.5 and PM10) virtually ineffective during April to May 2021. The air quality remained not only unabated but also was marred by some unusual extreme pollution events. SAFAR-framework model simulations with different sensitivity experiments were conducted using the newly developed lockdown emission inventory to understand various processes responsible for these anomalies in PM. Model results well captured the magnitude and variations of the observed PM before and after the lockdown but significantly underestimated their levels in the initial period of lockdown followed by the first high pollution event when the mortality counts were at their peak (∼400 deaths/day). It is believed that an unaccounted emission source was playing a leading role after balancing off the impact of curtailed lockdown emissions. The model suggests that the unprecedented surge in PM10 (690 µg/m3) on May 23, 2021, though Delhi was still under lockdown, was associated with large-scale dust transport originating from the north west part of India combined with the thunderstorm. The rainfall and local dust lifting played decisive roles in other unusual events. Obtained results and the proposed interpretation are likely to enhance our understanding and envisaged to help policymakers to frame suitable strategies in such kinds of emergencies in the future.

Improving PM2.5 predictions during COVID-19 lockdown by assimilating multi-source observations and adjusting emissions.

The Coronavirus Disease 2019 (COVID-19) outbreak caused a suspension of almost all non-essential human activities, leading to a significant reduction of anthropogenic emissions. However, the emission inventory of the chemistry transport model cannot be updated in time, resulting in large uncertainty in PM2.5 predictions. This study adopted a three-dimensional variational approach to assimilate multi-source PM2.5 data from satellite and ground observations and jointly adjusted emissions to improve PM2.5 predictions of the WRF-Chem model. Experiments were conducted to verify the method over Hubei Province, China, during the COVID-19 epidemic from Jan 21st to Mar 20th, 2020. The results showed that PM2.5 predictions were improved at almost all the validation sites, and the benefit of data assimilation (DA) can last for 48 h. However, the benefits of DA diminished quickly with the increase of the forecast time. By adjusting emissions, the PM2.5 predictions showed a much slower error accumulation along forecast time. At 48Z, the RMSE still has an 8.85 µg/m3 (19.49%) improvement, suggesting the effectiveness of emissions adjustment based on the improved initial conditions via DA.

Impacts of COVID-19 on air quality in mid-eastern China: An insight into meteorology and emissions.

The coronavirus disease (COVID-19) spread rapidly worldwide in the first half of 2020. Stringent national lockdown policies imposed by China to prevent the spread of the virus reduced anthropogenic emissions and improved air quality. A weather research and forecasting model coupled with chemistry was applied to evaluate the impact of meteorology and emissions on air quality during the COVID-19 outbreak (from January 23 to February 29, 2020) in mid-eastern China. The results show that air pollution episodes still occurred on polluted days and accounted for 31.6%-60.5% of the total number of outbreak days in mid-eastern China from January 23 to February 29, 2020. However, anthropogenic emissions decreased significantly, indicating that anthropogenic emission reduction cannot completely offset the impact of unfavorable meteorological conditions on air quality. Favorable meteorological conditions in 2019 improved the overall air quality for a COVID-19 outbreak in 2019 instead of 2020. PM2.5 concentrations decreased by 4.2%-29.2% in Beijing, Tianjin, Shijiazhuang, and Taiyuan, and increased by 6.1%-11.5% in Jinan and Zhengzhou. PM2.5 concentrations increased by 10.9%-20.5% without the COVID-19 outbreak of 2020 in mid-eastern China, and the frequency of polluted days increased by 5.3%-18.4%. Source apportionment of PM2.5 during the COVID-19 outbreak showed that industry and residential emissions were the dominant PM2.5 contributors (32.7%-49.6% and 26.0%-44.5%, respectively) followed by agriculture (18.7%-24.0%), transportation (7.7%-15.5%), and power (4.1%-5.9%). In Beijing, industrial and residential contributions to PM2.5 concentrations were lower (32.7%) and higher (44.5%), respectively, than in other cities (38.7%-49.6% for industry and 26.0%-36.2% for residential). Therefore, enhancing regional cooperation and implementing a united air pollution control are effective emission mitigation measures for future air quality improvement, especially the development of new technologies for industrial and cooking fumes.

Mitigating NOX emissions does not help alleviate wintertime particulate pollution in Beijing-Tianjin-Hebei, China.

Stringent mitigation measures have reduced wintertime fine particulate matter (PM2.5) concentrations by 42.2% from 2013 to 2018 in the Beijing-Tianjin-Hebei (BTH) region, but severe PM pollution still frequently engulfs the region. The observed nitrate aerosols have not exhibited a significant decreasing trend and constituted a major fraction (about 20%) of the total PM2.5, although the surface-measured NO2 concentration has decreased by over 20%. The contributions of nitrogen oxides (NOX) emissions mitigation to the nitrate and PM2.5 concentrations and how to alleviate nitrate aerosols efficiently under the current situation still remains elusive. The WRF-Chem model simulations of a persistent and heavy PM pollution episode in January 2019 in the BTH reveal that NOX emissions mitigation does not help lower wintertime nitrate and PM2.5 concentrations under current conditions in the BTH. A 50% reduction in NOX emissions only decreases nitrate mass by 10.3% but increases PM2.5 concentrations by 3.2%, because the substantial O3 increase induced by NOX mitigation offsets the HNO3 loss and enhances sulfate and secondary organic aerosols formation. Our results are further consolidated by the occurrence of severe PM pollution in the BTH during the COVID-19 outbreak, with a significant reduction in NO2 concentration. Mitigation of NH3 emissions constitutes the priority measure to effectively lower the nitrate and PM2.5 concentrations in the BTH under current conditions, with 35.5% and 12.7% decrease, respectively, when NH3 emissions are reduced by 50%.

On modelling growing menace of household emissions under COVID-19 in Indian metros.

While local anthropogenic emission sources contribute largely to deteriorate metro air quality, long range transport can also play a significant role in influencing levels of pollutants, particularly carbon monoxide (CO) that has a relatively long life span. A nationwide lockdown of two months imposed across India amid COVID-19 led to a dramatic decline in major sources of emissions except for household, mainly from cooking. This initially led to declined levels of CO in two of the largest megacities of India, Delhi and Mumbai under stable weather conditions, followed by a distinctly different variability under the influence of prevailing mesoscale circulation. We hereby trace the sources of CO from local emissions to transport pathways and interpret the observed variability in CO using the interactive WRF-Chem model and back trajectory analysis. For this purpose, COVID-19 emission inventory of CO has been estimated. Model results indicate a significant contribution from externally generated CO in Delhi from surrounding regions and an unusual peak on 17th May amid lockdown due to long range transport from the source region of biofuel emissions in central India. However, the oceanic winds played a larger role in keeping CO levels in check in a coastal megacity Mumbai which otherwise has high CO emissions from household sources due to a larger share of urban slums. Keeping track of evolving carbon-intensive pathways can help inform government responses to the COVID-19 pandemic to prioritize controls of emissions sources.