Non-negligible impacts of clean air regulations on the reduction of tropospheric NO2 over East China during the COVID-19 pandemic observed by OMI and TROPOMI

Impact of COVID-19 Lockdown on NO2 Pollution and the Associated Health Burden in China: A Comparison of Different Approaches

So far, a large number of studies have quantified the effect of COVID-19 lockdown measures on air quality in different countries worldwide. However, few studies have compared the influence of different approaches on the estimation results. The present study aimed to utilize a random forest machine learning approach as well as a difference-to-difference approach to explore the effect of lockdown policy on nitrogen dioxide (NO2) concentration during COVID-19 outbreak period in mainland China. Datasets from 2017 to 2019 were adopted to establish the random forest models, which were then applied to predict the NO2 concentrations in 2020, representing a scenario without the lockdown effect. The results showed that random forest models achieved remarkable predictive accuracy for predicting NO2 concentrations, with index of agreement values ranging between 0.34 and 0.76. Compared with the modelled NO2 concentrations, on average, the observed NO2 concentrations decreased by approximately 16 µg/m3 in the lockdown period in 2020. The difference-to-difference approach tended to underestimate the influence of COVID-19 lockdown measures. Due to the improvement of NO2 pollution, around 3722 non-accidental premature deaths were avoided in the studied population. The presented machine learning modelling framework has a great potential to be transferred to other short-term events with abrupt pollutant emission changes.

Impacts of emissions and meteorological conditions in three different phases of aerosol pollution during 2013-2022 in Anhui, China

The PM2.5 concentrations in Anhui, which links the Yangtze River Delta region, China's fastest growing economy area, with the Beijing-Tianjin-Hebei (BTH) region, China's most polluted region, are influenced not only by emissions, but also by variation of meteorological conditions. A comprehensive understanding of the relative impacts of meteorology and emissions on heavy pollution in Anhui at three phases (i.e., phase1: from 2013 to 2017; phase2: from 2018 to 2020; phase 3: from 2021 to 2022) from 2013 to 2022, which can provide suggestions for pollution prevention and control in the future. The decrease in pollutant concentrations from 2013 to 2022 is mainly attributed to the continued reduction in emissions, while the year-to-year fluctuations in pollutant concentrations are largely influenced by meteorological conditions. Although emissions are decreasing, the proportions of residential biofuel combustion and cement are increasing. In addition to the effects of prevailing northeasterly and northwesterly winds (i.e., Type1 and Type2), there is also concern about the influences of static weather and neighboring regional transport (i.e., Type5 and Type6), especially in 2016. The contribution of emissions is greater in phase 2 and phase 3, with a 17 % increase compared to phase 1. Overall, approximately 57 % of explosive growth in PM2.5 concentration during the cumulative stage (CS) can be regarded as the feedback effect of the deteriorating meteorological conditions. Therefore, statistical analyses show that limiting PM2.5 concentrations below about 73 μg m-3 would weaken the feedback effects, which in turn would avoid most of the explosive growth processes in the CS of the 60 heavy pollution processes, which can provide a reference for the government to set a target for sustained emission reduction.

Modeling NO2 air pollution variation during and after COVID-19-regulation using principal component analysis of satellite imagery

By implementing Principal Component Analysis (PCA) of multitemporal satellite data, this paper presents modeling solutions for air pollutant variation in three scenarios related to COVID-19 lockdown: pre, during, and after lockdown. Tropospheric NO2 satellite data from Sentinel-5P was used. Two novel PCA-models were developed: Weighted Principal Component Analysis (WPCA) and Rescaled Principal Component Analysis (RPCA). Model results were tested for goodness-of-fit to empirical NO2 data. The models were used to predict actual near-surface NO2 concentrations. Model-predicted NO2 concentrations were validated with NO2 data acquired at ground monitoring stations. Besides, meteorological bias affecting NO2 was assessed. It was found that the weather component had substantial impact on NO2 built-ups, propitiating air pollutant decrease during lockdown and increase after. WPCA and RPCA models well fitted to observed NO2. Both models accurately estimated near-surface NO2 concentrations. Modeled NO2 variation results evidenced the prolongated effect of the total lockdown (up to half a year). Model-predicted NO2 concentrations were found to highly correlate with monitoring station NO2 data collected on the ground. It is concluded that PCA is reliable in identifying and predicting air pollution variation patterns. The implementation of PCA is recommended when analyzing other pollutant gases.

The Variation in Chemical Composition and Source Apportionment of PM2.5 before, during, and after COVID-19 Restrictions in Zhengzhou, China

Despite significant improvements in air quality during and after COVID-19 restrictions, haze continued to occur in Zhengzhou afterwards. This paper compares ionic compositions and sources of PM2.5 before (2019), during (2020), and after (2021) the restrictions to explore the reasons for the haze. The average concentration of PM2.5 decreased by 28.5% in 2020 and 27.9% in 2021, respectively, from 102.49 μg m-3 in 2019. The concentration of secondary inorganic aerosols (SIAs) was 51.87 μg m-3 in 2019, which decreased by 3.1% in 2020 and 12.8% in 2021. In contrast, the contributions of SIAs to PM2.5 increased from 50.61% (2019) to 68.6% (2020) and 61.2% (2021). SIAs contributed significantly to PM2.5 levels in 2020-2021. Despite a 22~62% decline in NOx levels in 2020-2021, the increased O3 caused a similar NO3- concentration (20.69~23.00 μg m-3) in 2020-2021 to that (22.93 μg m-3) in 2019, hindering PM2.5 reduction in Zhengzhou. Six PM2.5 sources, including secondary inorganic aerosols, industrial emissions, coal combustion, biomass burning, soil dust, and traffic emissions, were identified by the positive matrix factorization model in 2019-2021. Compared to 2019, the reduction in PM2.5 from the secondary aerosol source in 2020 and 2021 was small, and the contribution of secondary aerosol to PM2.5 increased by 13.32% in 2020 and 12.94% in 2021. In comparison, the primary emissions, including biomass burning, traffic, and dust, were reduced by 29.71% in 2020 and 27.7% in 2021. The results indicated that the secondary production did not significantly contribute to the PM2.5 decrease during and after the COVID-19 restrictions. Therefore, it is essential to understand the formation of secondary aerosols under high O3 and low precursor gases to mitigate air pollution in the future.

Tracking air quality trends and vehicle traffic dynamics at urban scale using satellite and ground data before and after the COVID-19 outbreak

The implications of the COVID-19 outbreak are subjected to an increasing number of studies. So far, air quality trends related to the lockdown due to the pandemic have been analysed in large cities or entire regions. In this work, the region studied is the metropolitan area of Cagliari, which is the main city on the island of Sardinia (Italy) and can be representative of a coastal city that includes industrial settlements. The purpose of the study is to evaluate the effect of restrictions related to the COVID-19 outbreak on air quality levels and the traffic dynamics in this type of urban area. Nitrogen Dioxide (NO₂) levels before, during and after COVID-19 lockdown have been investigated using data acquired from the Sentinel-5P/TROPOMI satellite combined with on-site measurements. Both TROPOMI detected and ground-based data have revealed higher levels of NO₂ before and after the lockdown, compared to those during the period of COVID-related restrictions, in particular in the urban area of Cagliari. On the other hand, NO2 registered in the oil refinery area did not show significant differences associated with lockdown. The correlation of TROPOMI NO₂ tropospheric column with ground data (surface NO2) on a monthly mean basis showed different values based on the background and the highest Pearson's coefficient was of about 0.78 near to the city centre, where traffic can be considered a significant source of emission. In addition, a comparison of the air pollution level with the dynamics of vehicle traffic was investigated. The study highlighted a remarkable correlation between the reduction of the number of vehicles and the corresponding tropospheric NO₂ values that decreased on a weekly mean basis.

Impact of mobility restrictions on NO2 concentrations in key Latin American cities during the first wave of the COVID-19 pandemic

Between March and June 2020, activity in the major cities of Latin America declined due to containment efforts implemented by local governments to avoid the rapid spread of COVID-19. Our study compared 2020 with the previous year and demonstrated a considerable drop in tropospheric NO₂ levels obtained by the SENTINEL 5P satellite in major Latin American cities. Lima (47.5%), Santiago (36.1%), São Paulo (27%), Rio de Janeiro (23%), Quito (18.6%), Bogota (17.5%), Buenos Aires (16.6%), Guayaquil (15.3%), Medellin (14.2%), La Paz (9.5%), Belo Horizonte (7.8%), Mexico (7.6%) and Brasilia (5.9%) registered statistically significant decreases in NO₂ concentrations during the study period. In addition, we analyzed mobility data from Google and Apple reports as well as meteorological information from atmospheric reanalysis data along with satellite fields between 2011 and 2020, and performed a refined multivariate analysis (non-negative matrix approximation) to show that this decrease was associated with a reduction in population mobility rather than meteorological factors. Our findings corroborate the argument that confinement scenarios may indicate how air pollutant concentrations can be effectively reduced and managed.

Increased diurnal difference of NO2 concentrations and its impact on recent ozone pollution in eastern China in summer

Nitrogen dioxide (NO₂) is a key tropospheric O₃ precursor. Since 2013, efforts to decrease air pollution in China have driven substantial declines in annual NO₂ concentrations, whereas ozone (O₃) concentrations have increased. Based on nationwide NO₂ observations and a regional air quality model (WRF-CMAQ), we analyzed trends in the diurnal difference (DD, the difference between nighttime and daytime concentrations) of NO₂ concentrations across eastern China and in five national urban agglomerations (UAs) from 2014 to 2021, and explored the factors underlying such changes and the potential impacts on O₃ pollution. We found that the observed DD of NO₂ has increased in most cities and UAs, and that this trend can be primarily attributed to changes in anthropogenic emissions, based on comparison with DDs simulated with fixed anthropogenic emissions, which generally showed much weaker trends and little interannual variation. A sensitivity analysis using the WRF-CMAQ model was conducted to investigate the impact of a modified diurnal cycle of nitrogen oxides (NO_x) emissions on O₃ concentrations. The result revealed that enhancing the DD of NO₂ would increase O₃ concentrations in the morning and the daily maximum 8-h O₃ concentrations in the cities with high NO_x concentrations, as well as downwind areas of cities, indicating that greater DDs in NO₂ is one of the reasons that have led to the enhanced China's O₃ pollution in recent years.

Environmental policies for the treatment of waste generated by COVID-19: Text mining review

The rapid transmission of COVID-19 has meant that all economic and human efforts have been focused on confronting it, ignoring environmental aspects whose consequences are causing adverse situations all over the planet. The saturation of the sanitary system and confinement measures have multiplied the waste generated, which implies the need to adapt environmental policies to this new situation caused by the pandemic. It is a review article whose objective was to identify the environmental policies that would facilitate an adequate treatment of the waste generated by the pandemic. It was proposed to analyse the current lines of research developed on this paradigm, applying the text mining methodology. A systematic review of 111 studies published in environmental journals indexed in the Web of Science was carried out. The results identified three areas of interest: knowledge of transmission routes, management of the massive generation of plastics and appropriate treatment of solid waste in extreme situations. Leaders are called upon to implement the contingency plans to sustainably alleviate the enormous waste burden caused by society's adaptation to the restrictions imposed by the pandemic. Specifically, innovation aimed at achieving the reuse of medical products, the promotion of the circular economy and educational campaigns to promote clean environments should be encouraged.

Haze caused by NOx oxidation under restricted residential and industrial activities in a mega city in the south of North China Plain

The formation of secondary aerosol species, including nitrate and sulfate, induces severe haze in the North China Plain. However, despite substantial reductions in anthropogenic pollutants due to severe restriction of residential and industrial activities in 2020 to stop the spread of COVID-19, haze still formed in Zhengzhou. We compared ionic compositions of PM_2.5 during the period of the restriction with that immediately before the restriction and in the comparison period in 2019 to investigate the processes that caused the haze. The average concentration of PM_2.5 was 83.9 μg m^-3 in the restriction period, 241.8 μg m^-3 before the restriction, and 94.0 μg m^-3 in 2019. Nitrate was the largest contributor to the PM_2.5 in all periods, with an average mass fraction of 24%-30%. The average molar concentration of total nitrogen compounds (NO_x + nitrate) was 0.89 μmol m^-3 in the restriction period, which was much lower than that in the non-restriction periods (1.85-2.74 μmol m^-3). In contrast, the concentration of sulfur compounds (SO₂ + sulfate) was 0.34-0.39 μmol m^-3 in all periods. The conversion rate of NO_x to nitrate (NOR) was 0.35 in the restriction period, significantly higher than that before the restriction (0.26) and in 2019 (0.25). NOR was higher with relative humidity in 40-80% in the restriction period than in the other two periods, whereas the conversion rate of SO₂ to sulfate did not, indicating nitrate formation was more efficient during the restriction. When O₃ occupied more than half of the oxidants (O_x = O₃ + NO₂), NOR increased rapidly with the ratio of O₃ to O_x and was much higher in the daytime than nighttime. Therefore, haze in the restriction period was caused by increased NO_x-to-nitrate conversion driven by photochemical reactions.

New sights on the impact of spatial composition of production factors for socioeconomic recovery in the post-epidemic era: a case study of cities in central and eastern China

The COVID-19 pandemic led to a sharp economic contraction. A comprehensive understanding of the relationship between the spatial composition of production factor (SCPF) and socioeconomic recovery is still missing. Here, we applied the contrasting status of nitrogen dioxide (NO₂) concentrations in cities in central and eastern China as natural laboratories. From the perspective of the spatial composition of land (SCL) and the dependence on the inflow population (DIP), four quantifiable indicators (resilience, impact, sensitivity, recovery speed) were used to analyze the adaptability of SCPF to the epidemic lockdown. The results indicate that appropriate SCPF is a prerequisite for a complete "land-population-industry" nexus. The built-up area proportion is below 74.38%, with higher adaptability to epidemic shocks. The range of rural built-up proportion conducive to economic recovery is 10.18%-15.18%. The proportions of various land types inside the city's defense unit should also be constrained. Similarly, DIP is advocated to be maintained below 17.5%. For urban-rural fringe areas, the response to epidemic prevention and socioeconomic recovery are rapid. This observation-driven study indicated that COVID-19 is a shocking reminder for policymakers, to improve the socioeconomic recovery ability from the spatial composition of production factor perspective in the post-COVID-19 era.

NO2 retrievals from NOAA-20 OMPS: Algorithm, evaluation, and observations of drastic changes during COVID-19

We present the first NO2 measurements from the Nadir Mapper of Ozone Mapping and Profiler Suite (OMPS) instrument aboard the NOAA-20 satellite. NOAA-20 OMPS was launched in November 2017, with a nadir resolution of 17 × 13 km2 similar to the Ozone Monitoring Instrument (OMI). The retrieval of NOAA-20 NO2 vertical columns were achieved through the Direct Vertical Column Fitting (DVCF) algorithm, which was uniquely designed and successfully used to retrieve NO2 from OMPS aboard Suomi National Polar-orbiting Partnership (SNPP) spacecraft, predecessor to NOAA-20. Observations from NOAA-20 reveal a 20-40% decline in regional tropospheric NO2 in January-April 2020 due to COVID-19 lockdown, consistent with the findings from other satellite observations. The NO2 retrievals are preliminarily validated against ground-based Pandora spectrometer measurements over the New York City area as well as other U.S. Pandora locations. It shows OMPS total columns tend to be lower in polluted urban regions and higher in clean areas/episodes associated with relatively small NO2 total columns, but generally the agreement is within ±2.5 × 1015 molecules/cm2. Comparisons of stratospheric NO2 columns exhibit the excellent agreement between OMPS and OMI, validating OMPS capability in capturing the stratospheric background accurately. These results demonstrate the high sensitivity of OMPS to tropospheric NO2 and highlight its potential use for extending the long-term global NO2 record.

Quantifying changes in ambient NOx, O3 and PM10 concentrations in Austria during the COVID-19 related lockdown in spring 2020

During spring 2020, unprecedented changes in local and regional emissions have occurred around the globe due to governmental restrictions associated with COVID-19. Many European countries including Austria issued partial curfews or stay-at-home order policies, which have impacted ambient air quality through reductions in non-essential transportation and energy consumption of industrial sites and work places. Here, we analyse the effect of these measures on ambient concentrations of nitrogen oxides (NO_x), ozone (O₃) and particulate matter (PM₁₀) during the first nationwide lockdown in Austria (16.03.2020 to 14.04.2020). To ensure a robust analysis, the Austrian domain is divided into four individual subsectors contingent on regional climate. For air quality analysis a novel method is applied for filtering days with comparable weather conditions during the 2020 lockdown and spring 2017 to 2019. In general, our analysis shows decreasing pollutant concentrations, although in magnitude dependent on pollutant and regional subdomain. Largest reductions are found for NO_x reaching up to −68% at traffic sites reflecting the substantial decrease in non-essential transport. Changes in the O₃ concentrations at background sites show a rather weak response to NO_x declines varying between roughly −18 to +8% for both the median and the upper tail of the distribution. Occasional site level increases in O₃ concentrations can be attributed to comparably weak titration during night-time. PM₁₀ concentrations show the smallest response among air pollutants, attributable to manifold precursor sources not affected by the lockdown measures. However, our analysis indicates also a shift of PM₁₀ distributions at traffic sites closer to distributions observed at background sites.

The regional impact of the COVID-19 lockdown on the air quality in Ji'nan, China

A number of strict lockdown measures were implemented in the areas most affected by COVID-19 in China, including Ji'nan city, from 24 January to 7 February 2020. Due to these forced restrictions, the pollution levels in cities across the country drastically decreased within just a few days. Since traffic pollution and industrial emissions are important factors affecting regional air quality, congestion has a significant impact on the environment. Therefore, using the aid of air quality data for six pollutants (PM10, PM2.5, SO2, NO2, CO and O3) from 11 monitoring stations (located in urban, suburban and urban-industrial regions) across Ji'nan, we employed the air quality index (AQI) to investigate the spatial pattern of air quality in the pre-COVID-19 (pre-COVID) and COVID-19-related lockdown (COVID lockdown) periods. The results showed that air quality significantly improved during the COVID lockdown period. Among the selected pollutants, compared to the corresponding pre-COVID levels, the greatest reduction was observed for the concentration of NO2 (54.02%), while the smallest reduction was observed for the concentration of SO2 (27.92%). The PM2.5 (38.73%), PM10 (44.92%) and CO (30.60%) levels also decreased during the COVID lockdown period; only the O3 concentration increased (37.42%) during this period. Overall, air quality improved by approximate improvements of 37.33% during the COVID lockdown period. Approximately 35.48%, 37.01% and 43.43% in the AQI were observed in urban, suburban and urban-industrial regions, respectively. Therefore, the AQI exhibited remarkable regional differences in Ji'nan. This study demonstrates the contributions of the transportation sector and local emissions to improving air quality in typical urban areas, and these research results can provide guidance for the further monitoring of air pollution in northern Chinese cities.

Disentangling drivers of air pollutant and health risk changes during the COVID-19 lockdown in China

The COVID-19 restrictions in 2020 have led to distinct variations in NO2 and O3 concentrations in China. Here, the different drivers of anthropogenic emission changes, including the effects of the Chinese New Year (CNY), China's 2018-2020 Clean Air Plan (CAP), and the COVID-19 lockdown and their impact on NO2 and O3 are isolated by using a combined model-measurement approach. In addition, the contribution of prevailing meteorological conditions to the concentration changes was evaluated by applying a machine-learning method. The resulting impact on the multi-pollutant Health-based Air Quality Index (HAQI) is quantified. The results show that the CNY reduces NO2 concentrations on average by 26.7% each year, while the COVID-lockdown measures have led to an additional 11.6% reduction in 2020, and the CAP over 2018-2020 to a reduction in NO2 by 15.7%. On the other hand, meteorological conditions from 23 January to March 7, 2020 led to increase in NO2 of 7.8%. Neglecting the CAP and meteorological drivers thus leads to an overestimate and underestimate of the effect of the COVID-lockdown on NO2 reductions, respectively. For O3 the opposite behavior is found, with changes of +23.3%, +21.0%, +4.9%, and -0.9% for CNY, COVID-lockdown, CAP, and meteorology effects, respectively. The total effects of these drivers show a drastic reduction in multi-air pollutant-related health risk across China, with meteorology affecting particularly the Northeast of China adversely. Importantly, the CAP's contribution highlights the effectiveness of the Chinese government's air-quality regulations on NO2 reduction.

Returning long-range PM2.5 transport into the leeward of East Asia in 2021 after Chinese economic recovery from the COVID-19 pandemic

Changes in the aerosol composition of sulfate (SO₄²⁻) and nitrate (NO₃⁻) from 2012 to 2019 have been captured as a paradigm shift in the region downwind of China. Specifically, SO₄²⁻ dramatically decreased and NO₃⁻ dramatically increased over downwind locations such as western Japan due to the faster reduction of SO₂ emissions than NO_x emissions and the almost constant trend of NH₃ emissions from China. Emissions from China sharply decreased during COVID-19 lockdowns in February–March 2020, after which China’s economic situation seemed to recover going into 2021. Given this substantial change in Chinese emissions, it is necessary to clarify the impact of long-range PM_2.5 transport into the leeward of East Asia. In this study, ground-based aerosol compositions observed at three sites in western Japan were analysed. The concentrations of PM_2.5, SO₄²⁻ and NO₃⁻ decreased in 2020 (during COVID-19) compared with 2018–2019 (before COVID-19). In 2021 (after COVID-19), PM_2.5 and NO₃⁻ increased and SO₄²⁻ was unchanged. This suggests the returning long-range PM_2.5 transport in 2021. From numerical simulations, the status of Chinese emissions during COVID-19 did not explain this returning impact in 2021. This study shows that the status of Chinese emissions in 2021 recovered to that before COVID-19.

The short-term impact of the COVID-19 epidemic on socioeconomic activities in China based on the OMI-NO2 data

As an air pollutant closely related to urban traffic and heavy industrial capacity, the variation of NO2 (nitrogen dioxide) concentration can directly reflect the strength of socioeconomic activities. Using the weekly average results of daily product synthesis of tropospheric NO2 column concentrations from OMI (Ozone Monitoring Instrument) satellite inversion, a weekly-scale variation series of standardized socioeconomic activity index during the Spring Festival period of 2019-2021 is constructed. The results show that the OMI-NO2 satellite data are in good consistency with ground-based monitoring data; the Spring Festival holiday also suppresses socioeconomic activity in normal years, but the coronavirus disease 2019 (COVID-19) epidemic leads to an extended period of 2-3 weeks of weakened socioeconomic activity in China after the holiday, while the minimum value of socioeconomic activity intensity decreases by 0.12. Although socioeconomic activity is significantly suppressed in the short term, the intensity of socioeconomic activity rises steadily with the gradual resumption of work and production everywhere from the third week after the Chinese Spring Festival and has reached 60.91% of the highest level before the holiday in the seventh week after the holiday. OMI-NO2 satellite data can be used for a rapid assessment of the intensity of air pollution emissions and the level of socioeconomic activity in different regions.

Atmospheric Organic Nitrogen Deposition in Strategic Water Sources of China after COVID-19 Lockdown

Atmospheric nitrogen deposition (AND) may lead to water acidification and eutrophication. In the five months after December 2019, China took strict isolation and COVID-19 prevention measures, thereby causing lockdowns for approximately 1.4 billion people. The Danjiangkou Reservoir refers to the water source in the middle route of South-to-North Water Diversion Project in China, where the AND has increased significantly; thus, the human activities during the COVID-19 period is a unique case to study the influence of AND to water quality. This work monitored the AND distribution around the Danjiangkou Reservoir, including agricultural, urban, traffic, yard, and forest areas. After lockdown, the DTN, DON, and Urea-N were 1.99 kg · hm⁻² · month⁻¹, 0.80 kg · hm⁻² · month⁻¹, and 0.15 kg · hm⁻² · month⁻¹, respectively. The detected values for DTN, DON, and Urea-N in the lockdown period decreased by 9.6%, 30.4%, and 28.97%, respectively, compared to 2019. The reduction in human activities is the reason for the decrease. The urban travel intensity in Nanyang city reduced from 6 to 1 during the lockdown period; the 3 million population which should normally travel out from city were in isolation at home before May. The fertilization action to wheat and orange were also delayed.

Long-Term Impacts of COVID-19 Lockdown on the NO2 Concentrations and Urban Thermal Environment: Evidence from the Five Largest Urban Agglomerations in China

Under the threat of COVID-19, many regions around the world implemented lockdown policies to control the spread of the virus. This restriction on both social and economic activities has improved the quality of the environment in certain aspects. However, most previous studies have only focused on the short-term impact of lockdown policies on the urban environment. The long-term effects of lockdown require a more focused exploration and analysis. Thus, five major urban agglomerations in China were selected as the research area; changes in the numerical and spatial distribution of NO2 concentration and surface temperature during four different lockdown stages in 2019, 2020, and 2021 were investigated to analyze the long-term effects of lockdown policies on the urban environment. The results indicated that the impact of shorter lockdowns was short-term and unsustainable; the NO2 concentrations increased again with the resumption of production. Compared with air pollutants, thermal environmental problems are more complex. The effect of the lockdown policy was not reflected in the decrease in the area proportions of the high- and sub-high-temperature regions but rather in the spatial distribution of the high-temperature area, which was manifested as a fragmentation and dispersion of heat source patches. In addition to the severity of the lockdown, the impact of the lockdown policy was also closely related to the level of development and industrial structure of each city. Among the urban environments of the five agglomerations, the most affected were the Yangtze River Delta and Yangtze River Middle-Reach urban agglomerations, which had the largest decline in NO2 concentrations and the most notable fragmentation of heat source patches.

COVID-19 pandemic: What can we learn for better air quality and human health?

The COVID-19 lockdown resulted in improved air quality in many cities across the world. With the objective of what could be the new learning from the COVID-19 pandemic and subsequent lockdowns for better air quality and human health, a critical synthesis of the available evidence concerning air pollution reduction, the population at risk and natural versus anthropogenic emissions was conducted. Can the new societal norms adopted during pandemics, such as the use of face cover, awareness regarding respiratory hand hygiene, and physical distancing, help in reducing disease burden in the future? The use of masks will be more socially acceptable during the high air pollution episodes in lower and middle-income countries, which could help to reduce air pollution exposure. Although post-pandemic, some air pollution reduction strategies may be affected, such as car-pooling and the use of mass transit systems for commuting to avoid exposure to airborne infections like coronavirus. However, promoting non-motorized modes of transportation such as cycling and walking within cities as currently being enabled in Europe and other countries could overshadow such losses. This demand focus on increasing walkability in a town for all ages and populations, including for a differently-abled community. The study highlighted that for better health and sustainability there. is also a need to promote other measures such as work-from-home, technological infrastructure, the extension of smart cities, and the use of information technology.

Variations of Urban NO2 Pollution during the COVID-19 Outbreak and Post-Epidemic Era in China: A Synthesis of Remote Sensing and In Situ Measurements

Since the COVID-19 outbreak in 2020, China’s air pollution has been significantly affected by control measures on industrial production and human activities. In this study, we analyzed the temporal variations of NO2 concentrations during the COVID-19 lockdown and post-epidemic era in 11 Chinese megacities by using satellite and ground-based remote sensing as well as in situ measurements. The average satellite tropospheric vertical column density (TVCD) of NO2 by TROPOMI decreased by 39.2–71.93% during the 15 days after Chinese New Year when the lockdown was at its most rigorous compared to that of 2019, while the in situ NO2 concentration measured by China National Environmental Monitoring Centre (CNEMC) decreased by 42.53–69.81% for these cities. Such differences between both measurements were further investigated by using ground-based multi-axis differential optical absorption spectroscopy (MAX-DOAS) remote sensing of NO2 vertical profiles. For instance, in Beijing, MAX-DOAS NO2 showed a decrease of 14.19% (versus 18.63% by in situ) at the ground surface, and 36.24% (versus 36.25% by satellite) for the total tropospheric column. Thus, vertical discrepancies of atmospheric NO2 can largely explain the differences between satellite and in situ NO2 variations. In the post-epidemic era of 2021, satellite NO2 TVCD and in situ NO2 concentrations decreased by 10.42–64.96% and 1.05–34.99% compared to 2019, respectively, possibly related to the reduction of the transportation industry. This study reveals the changes of China’s urban NO2 pollution in the post-epidemic era and indicates that COVID-19 had a profound impact on human social activities and industrial production.

Identification of NOx hotspots from oversampled TROPOMI NO2 column based on image segmentation method

Satellite-based measures of NO₂ have become increasingly available for resolving the limitation on insufficient spatial and temporal coverage of ground-level monitoring networks. Oversampled NO₂ column density can obtain more detailed features of NO₂ column with a spatial resolution as high as 2 km × 2 km, while it is still challenging to identify hotspots of NO_x pollution plume in city-scale due to background interference. In this study, we proposed a method for detecting the NO_x hotspot grids from oversampled satellite NO₂ column based on the image segmentation method, and identifying major source types using Term frequency-inverse document frequency (TF-IDF). A fractal model was used to evaluate and eliminate the background portion of the NO₂ column and an adaptive threshold method was adopted to identify the region of interest (ROI) of local hotspot NO₂ column. Hot-grid index, counting the frequency of NO₂ hotspot ROI in each grid, was conducted to identify the hotspot grids. TF-IDF was used to semantically analyze the major source types of NO₂ hotspot grids. Taking Central and Eastern China as the studied domain, the hotspot grids of NO₂ and the relevant major source types were identified based on the proposed method. The major non-road mobile sources (such as Beijing Capital International Airport), industrial areas (such as Caofeidian Industrial Park) and urban areas were clearly distinguished. The power plant, Coke and Iron and Steel were identified as major source types in the whole year in the corresponding NO_x hotspot grids. Notably, the identification of hotspot grids indicated a higher probability of a local high-intensity NO_x pollution plume rather than a quantitative NO_x emission; the key source types were the semantic keywords in hotspot grids, which does not mean there were no other exiting emission sources. This proposed method has strong implications on rapidly identifying the NO_x hotspot grids based on oversampled TROPOMI NO₂ column and the list of industrial enterprises.

Assessment of the Performance of TROPOMI NO2 and SO2 Data Products in the North China Plain: Comparison, Correction and Application

The TROPOspheric Monitoring Instrument (TROPOMI) aboard the Sentinel-5 Precursor satellite has been used to detect the atmospheric environment since 2017, and it is of great significance to investigate the accuracy of its products. In this work, we present comparisons between TROPOMI tropospheric NO2 and total SO2 products against ground-based MAX-DOAS at a single site (Xianghe) and OMI products over a seriously polluted region (North China Plain, NCP) in China. The results show that both NO2 and SO2 data from three datasets exhibit a similar tendency and seasonality. In addition, TROPOMI tropospheric NO2 columns are generally underestimated compared with collocated MAX-DOAS and OMI data by about 30–60%. In contrast to NO2, the monthly average SO2 retrieved from TROPOMI is larger than MAX-DOAS and OMI, with a mean bias of 2.41 (153.8%) and 2.17 × 1016 molec cm−2 (120.7%), respectively. All the results demonstrated that the TROPOMI NO2 as well as the SO2 algorithms need to be further improved. Thus, to ensure reliable analysis in NCP area, a correction method has been proposed and applied to TROPOMI Level 3 data. The revised datasets agree reasonably well with OMI observations (R > 0.95 for NO2, and R > 0.85 for SO2) over the NCP region and have smaller mean biases with MAX-DOAS. In the application during COVID-19 pandemic, it showed that the NO2 column in January-April 2020 decreased by almost 25–45% compared to the same period in 2019 due to the lockdown for COVID-19, and there was an apparent rebound of nearly 15–50% during 2021. In contrast, a marginal change of the corresponding SO2 is revealed in the NCP region. It signifies that short-term control measures are expected to have more effects on NO2 reduction than SO2; conversely, we need to recognize that although the COVID-19 lockdown measures improved air quality in the short term, the pollution status will rebound to its previous level once industrial and human activities return to normal.

Analyzing the Contribution of Human Mobility to Changes in Air Pollutants: Insights from the COVID-19 Lockdown in Wuhan

During the COVID-19 lockdown in Wuhan, transportation, industrial production and other human activities declined significantly, as did the NO2 concentration. In order to assess the relative contributions of different factors to reductions in air pollutants, we implemented sensitivity experiments by Random Forest (RF) models, with the comparison of the contributions of meteorological conditions, human mobility, and emissions from industry and households between different periods. In addition, we conducted scenario analyses to suggest an appropriate limit for control of human mobility. Different mechanisms for air pollutants were shown in the pre-pandemic, pre-lockdown, lockdown, and post-pandemic periods. Wind speed and the Within-city Migration index, representing intra-city mobility intensity, were excluded from stepwise multiple linear models in the pre-lockdown and lockdown periods. The results of sensitivity experiments show that, in the COVID-19 lockdown period, 73.3% of the reduction can be attributed to decreased human mobility. In the post-pandemic period, meteorological conditions control about 42.2% of the decrease, and emissions from industry and households control 40.0%, while human mobility only contributes 17.8%. The results of the scenario analysis suggest that the priority of restriction should be given to human mobility within the city than other kinds of human mobility. The reduction in the NO2 concentration tends to be smaller when human mobility within the city decreases by more than 70%. A limit of less than 40% on the control of the human mobility can achieve a better effect, especially in cities with severe traffic pollution.

Estimation of surface-level NO2 and O3 concentrations using TROPOMI data and machine learning over East Asia

In East Asia, air quality has been recognized as an important public health problem. In particular, the surface concentrations of air pollutants are closely related to human life. This study aims to develop models for estimating high spatial resolution surface concentrations of NO₂ and O₃ from TROPOspheric Monitoring Instrument (TROPOMI) data in East Asia. The machine learning was adopted by fusion of various satellite-based variables, numerical model-based meteorological variables, and land-use variables. Four machine learning approaches-Support Vector Regression (SVR), Random Forest (RF), Extreme Gradient Boost (XGB), and Light Gradient Boosting Machine (LGBM)-were evaluated and compared with Multiple Linear Regression (MLR) as a base statistical method. This study also modeled the NO₂ and O₃ concentrations over the ocean surface (i.e., land model for scheme 1 and ocean model for scheme 2). The estimated surface concentrations were validated through three cross-validation approaches (i.e., random, temporal, and spatial). The results showed that the NO₂ model produced R² of 0.63-0.70 and normalized root-mean-square-error (nRMSE) of 38.3-42.2% and the O₃ model resulted in R² of 0.65-0.78 and nRMSE of 19.6-24.7% for scheme 1. The indirect validation based on the stations near the coastline for scheme 2 showed slight decrease (~0.3-2.4%) in nRMSE when compared to scheme 1. The contributions of input variables to the models were analyzed based on SHapely Additive exPlanations (SHAP) values. The NO₂ vertical column density among the TROPOMI-derived variables showed the largest contribution in both the NO₂ and O₃ models.

Nitrogen dioxide decline and rebound observed by GOME-2 and TROPOMI during COVID-19 pandemic

Since its first confirmed case in December 2019, coronavirus disease 2019 (COVID-19) has become a worldwide pandemic with more than 90 million confirmed cases by January 2021. Countries around the world have enforced lockdown measures to prevent the spread of the virus, introducing a temporal change of air pollutants such as nitrogen dioxide (NO2) that are strongly related to transportation, industry, and energy. In this study, NO2 variations over regions with strong responses to COVID-19 are analysed using datasets from the Global Ozone Monitoring Experiment-2 (GOME-2) sensor aboard the EUMETSAT Metop satellites and TROPOspheric Monitoring Instrument (TROPOMI) aboard the EU/ESA Sentinel-5 Precursor satellite. The global GOME-2 and TROPOMI NO2 datasets are generated at the German Aerospace Center (DLR) using harmonized retrieval algorithms; potential influences of the long-term trend and seasonal cycle, as well as the short-term meteorological variation, are taken into account statistically. We present the application of the GOME-2 data to analyze the lockdown-related NO2 variations for morning conditions. Consistent NO2 variations are observed for the GOME-2 measurements and the early afternoon TROPOMI data: regions with strong social responses to COVID-19 in Asia, Europe, North America, and South America show strong NO2 reductions of ∼ 30-50% on average due to restriction of social and economic activities, followed by a gradual rebound with lifted restriction measures.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11869-021-01046-2.

Atmospheric Impacts of COVID-19 on NOx and VOC Levels over China Based on TROPOMI and IASI Satellite Data and Modeling

China was the first country to undergo large-scale lockdowns in response to the pandemic in early 2020 and a progressive return to normalization after April 2020. Spaceborne observations of atmospheric nitrogen dioxide (NO2) and oxygenated volatile organic compounds (OVOCs), including formaldehyde (HCHO), glyoxal (CHOCHO), and peroxyacetyl nitrate (PAN), reveal important changes over China in 2020, relative to 2019, in response to the pandemic-induced shutdown and the subsequent drop in pollutant emissions. In February, at the peak of the shutdown, the observed declines in OVOC levels were generally weaker (less than 20%) compared to the observed NO2 reductions (−40%). In May 2020, the observations reveal moderate decreases in NO2 (−15%) and PAN (−21%), small changes in CHOCHO (−3%) and HCHO (6%). Model simulations using the regional model MAGRITTEv1.1 with anthropogenic emissions accounting for the reductions due to the pandemic explain to a large extent the observed changes in lockdown-affected regions. The model results suggest that meteorological variability accounts for a minor but non-negligible part (~−5%) of the observed changes for NO2, whereas it is negligible for CHOCHO but plays a more substantial role for HCHO and PAN, especially in May. The interannual variability of biogenic and biomass burning emissions also contribute to the observed variations, explaining e.g., the important column increases of NO2 and OVOCs in February 2020, relative to 2019. These changes are well captured by the model simulations.

The Impact of Large-Scale Social Restriction Phases on the Air Quality Index in Jakarta

We reported the result of our study on the impact of Large-Scale Social Restriction (LSSR) phases due to the COVID-19 outbreak on the air quality in Jakarta. Specifically, this study covered the change of Air Quality Index (AQI) based on five pollutants, PM10, SO2, CO, O3, and NO2, contained in Jakarta’s air before and during LSSR. The AQI data were provided by the Ministry of Environment and Forestry, Indonesia, from January 2019 to December 2020 at five different locations in Jakarta, with missing data for March and September 2020 due to unknown reasons. These data were grouped into the period before the LSSR from January 2019 to February 2020 and the period during LSSR from April 2020 to December 2020. In order to measure the change in the air quality of Jakarta before and during LSSR, we ran a chi-squared test to the AQI for each location and LSSR phase as well as paired one-sided t-test for the seasonal trend. The result of this study showed that, in general, LSSR improved the air quality of Jakarta. The improvement was mainly contributed by reduced transportation activities that were induced by LSSR. Further analysis on the seasonal pollutants trend showed a variation of AQI improvement in each phase due to their unique characteristics.

How changes in human activities during the lockdown impacted air quality parameters: A review

The health emergency linked to the spread of COVID-19 has led to important reduction in industrial and logistics activities, as well as to a drastic changes in citizens' behaviors and habits. The restrictions on working activities, journeys and relationships imposed by the lockdown have had important consequences, including for environmental quality. This review aims to provide a structured and critical evaluation of the recent scientific bibliography that analyzed and described the impact of lockdown on human activities and on air quality. The results indicate an important effect of the lockdown during the first few months of 2020 on air pollution levels, compared to previous periods. The concentrations of particulate matter, nitrogen dioxide, sulfur dioxide and carbon monoxide have decreased. Tropospheric ozone, on the other hand, has significantly increased. These results are important indicators that can become decision drivers for future policies and strategies in industrial and logistics activities (including the mobility sector) aimed at their environmental sustainability. The scenario imposed by COVID-19 has supported the understanding of the link between the reduction of polluting emissions and the state of air quality and will be able to support strategic choices for the future sustainable growth of the industrial and logistics sector.

Comprehensive Insights Into O3 Changes During the COVID-19 From O3 Formation Regime and Atmospheric Oxidation Capacity

Economic activities and the associated emissions have significantly declined during the 2019 novel coronavirus (COVID-19) pandemic, which has created a natural experiment to assess the impact of the emitted precursor control policy on ozone (O3) pollution. In this study, we utilized comprehensive satellite, ground-level observations, and source-oriented chemical transport modeling to investigate the O3 variations during the COVID-19 pandemic in China. Here, we found that the significant elevated O3 in the North China Plain (40%) and Yangtze River Delta (35%) were mainly attributed to the enhanced atmospheric oxidation capacity (AOC) in these regions, associated with the meteorology and emission reduction during lockdown. Besides, O3 formation regimes shifted from VOC-limited regimes to NOx-limited and transition regimes with the decline of NOx during lockdown. We suggest that future O3 control policies should comprehensively consider the effects of AOC on the O3 elevation and coordinated regulations of the O3 precursor emissions.

Responses of decline in air pollution and recovery associated with COVID-19 lockdown in the Pearl River Delta

The Guangdong government implemented lockdown measures on January 23, 2020, to ease the spread of the coronavirus disease 2019 (COVID-19). These measures prohibit a series of human activities and lead to a great reduction in anthropogenic emissions. Starting on February 20, all companies resumed work and production, and emissions gradually recovered. To investigate the response of air pollutants in the Pearl River Delta (PRD) to the emission reduction and recovery related to COVID-19 lockdown, we used the Community Multi-scale Air Quality (CMAQ) model to estimate the changes in air pollutants, including three periods: Period I (January 10 to January 22, 2020), Period II (January 23 to February 19, 2020), Period III (February 20 to March 9, 2020). During Period II, under the concurrent influence of emissions and meteorology, air quality improved significantly with PM2.5, NO2, and SO2 decreased by 52%, 67%, and 25%, respectively. O3 had no obvious changes in most cities, which mainly due to the synergetic effects of emissions and meteorology. In Period III, with the recovery of emissions and the changes in meteorology, the increase of secondary components was faster than that of primary PM2.5 (PPM), which indicated that changes in PPM concentration were more sensitive to emissions reduction. O3 concentration increased as emission and temperature rising. Our findings elucidate that more effective emission control strategies should be implemented in PRD to alleviate the increasingly serious pollution situation.

Regional Scale Impact of the COVID-19 Lockdown on Air Quality: Gaseous Pollutants in the Po Valley, Northern Italy

The impact of the reduced atmospheric emissions due to the COVID-19 lockdown on ambient air quality in the Po Valley of Northern Italy was assessed for gaseous pollutants (NO2, benzene, ammonia) based on data collected at the monitoring stations distributed all over the area. Concentration data for each month of the first semester of 2020 were compared with those of the previous six years, on monthly, daily, and hourly bases, so that pre, during, and post-lockdown conditions of air quality could be separately analyzed. The results show that, as in many other areas worldwide, the Po Valley experienced better air quality during 2020 spring months for NO2 and benzene. In agreement with the reductions of nitrogen oxides and benzene emissions from road traffic, estimated to be −35% compared to the regional average, the monthly mean concentration levels for 2020 showed reductions in the −40% to −35% range compared with the previous years, but with higher reductions, close to −50%, at high-volume-traffic sites in urban areas. Conversely, NH3 ambient concentration levels, almost entirely due the emissions of the agricultural sector, did not show any relevant change, even at high-volume-traffic sites in urban areas. These results point out the important role of traffic emissions in NO2 and benzene ambient levels in the Po Valley, and confirm that this region is a rather homogeneous air basin with urban area hot-spots, the contributions of which add up to a relatively high regional background concentration level. Additionally, the relatively slow response of the air quality levels to the sudden decrease of the emissions due to the lockdown shows that this region is characterized by a weak exchange of the air masses that favors both the build-up of atmospheric pollutants and the development of secondary formation processes. Thus, air quality control strategies should aim for structural interventions intended to reduce traffic emissions at the regional scale and not only in the largest urban areas.

The Status of Air Quality in the United States During the COVID-19 Pandemic: A Remote Sensing Perspective

The recent COVID-19 pandemic has prompted global governments to take several measures to limit and contain the spread of the novel virus. In the United States (US), most states have imposed a partial to complete lockdown that has led to decreased traffic volumes and reduced vehicle emissions. In this study, we investigate the impacts of the pandemic-related lockdown on air quality in the US using remote sensing products for nitrogen dioxide tropospheric column (NO2), carbon monoxide atmospheric column (CO), tropospheric ozone column (O3), and aerosol optical depth (AOD). We focus on states with distinctive anomalies and high traffic volume, New York (NY), Illinois (IL), Florida (FL), Texas (TX), and California (CA). We evaluate the effectiveness of reduced traffic volume to improve air quality by comparing the significant reductions during the pandemic to the interannual variability (IAV) of a respective reference period for each pollutant. We also investigate and address the potential factors that might have contributed to changes in air quality during the pandemic. As a result of the lockdown and the significant reduction in traffic volume, there have been reductions in CO and NO2. These reductions were, in many instances, compensated by local emissions and, or affected by meteorological conditions. Ozone was reduced by varying magnitude in all cases related to the decrease or increase of NO2 concentrations, depending on ozone photochemical sensitivity. Regarding the policy impacts of this large-scale experiment, our results indicate that reduction of traffic volume during the pandemic was effective in improving air quality in regions where traffic is the main pollution source, such as in New York City and FL, while was not effective in reducing pollution events where other pollution sources dominate, such as in IL, TX and CA. Therefore, policies to reduce other emissions sources (e.g., industrial emissions) should also be considered, especially in places where the reduction in traffic volume was not effective in improving air quality (AQ).