The Impact of COVID-19 Lockdown on Ambient Air Quality in Shanghai, 2022

The COVID-19 lockdown contributes to the improvement of air quality. Most previous studies have attributed this to the reduction of human activity while ignoring the meteorological changes, this may lead to an overestimation or underestimation of the impact of COVID-19 lockdown measures on air pollution levels. To investigate this issue, we propose an XGBoost-based model to predict the concentrations of PM2.5 and PM10 during the COVID-19 lockdown period in 2022, Shanghai, and thus explore the limits of anthropogenic emission on air pollution levels by comprehensively employing the meteorological factors and the concentrations of other air pollutants. Results demonstrate that actual observations of PM2.5 and PM10 during the COVID-19 lockdown period were reduced by 60.81% and 43.12% compared with the predicted values (regarded as the period without the lockdown measures). In addition, by comparing with the time series prediction results without considering meteorological factors, the actual observations of PM2.5 and PM10 during the lockdown period were reduced by 50.20% and 19.06%, respectively, against the predicted values during the non-lockdown period. The analysis results indicate that ignoring meteorological factors will underestimate the positive impact of COVID-19 lockdown measures on air quality. © 2023 by the authors.

Characteristics and sources of carbonaceous aerosols in a semi-arid city: Quantifying anthropogenic and meteorological impacts.

Carbonaceous aerosols have great adverse impacts on air quality, human health, and climate. However, there is a limited understanding of carbonaceous aerosols in semi-arid areas. The correlation between carbonaceous aerosols and control measures is still unclear owing to the insufficient information regarding meteorological contribution. To reveal the complex relationship between control measures and carbonaceous aerosols, offline and online observations of carbonaceous aerosols were conducted from October 8, 2019 to October 7, 2020 in Hohhot, a semi-arid city. The characteristics and sources of carbonaceous aerosols and impacts of anthropogenic emissions and meteorological conditions were studied. The annual mean concentrations (± standard deviation) of fine particulate matter (PM2.5), organic carbon (OC), and elemental carbon (EC) were 42.81 (±40.13), 7.57 (±6.43), and 2.25 (±1.39) µg m-3, respectively. The highest PM2.5 and carbonaceous aerosol concentrations were observed in winter, whereas the lowest was observed in summer. The result indicated that coal combustion for heating had a critical role in air quality degradation in Hohhot. A boost regression tree model was applied to quantify the impacts of anthropogenic emissions and meteorological conditions on carbonaceous aerosols. The results suggested that the anthropogenic contributions of PM2.5, OC, and EC during the COVID-19 lockdown period were 53.0, 15.0, and 2.36 µg m-3, respectively, while the meteorological contributions were 5.38, 2.49, and -0.62 µg m-3, respectively. Secondary formation caused by unfavorable meteorological conditions offset the emission reduction during the COVID-19 lockdown period. Coal combustion (46.4% for OC and 35.4% for EC) and vehicular emissions (32.0% for OC and 50.4% for EC) were the predominant contributors of carbonaceous aerosols. The result indicated that Hohhot must regulate coal use and vehicle emissions to reduce carbonaceous aerosol pollution. This study provides new insights and a comprehensive understanding of the complex relationships between control strategies, meteorological conditions, and air quality.

Response of PM2.5 pollution to meteorological and anthropogenic emissions changes during COVID-19 lockdown in Hunan Province based on WRF-Chem model.

In December 2019, the New Crown Pneumonia (the COVID-19) outbroke around the globe, and China imposed a nationwide lockdown starting as early as January 23, 2020. This decision has significantly impacted China's air quality, especially the sharp decrease in PM2.5 (aerodynamic equivalent diameter of particulate matter less than or equal to 2.5 µm) pollution. Hunan Province is located in the central and eastern part of China, with a "horseshoe basin" topography. The reduction rate of PM2.5 concentrations in Hunan province during the COVID-19 (24.8%) was significantly higher than the national average (20.3%). Through the analysis of the changing character and pollution sources of haze pollution events in Hunan Province, more scientific countermeasures can be provided for the government. We use the Weather Research and Forecasting with Chemistry (WRF-Chem, V4.0) model to predict and simulate the PM2.5 concentrations under seven scenarios before the lockdown (2020.1.1-2020.1.22) and during the lockdown (2020.1.23-2020.2.14). Then, the PM2.5 concentrations under different conditions is compared to differentiate the contribution of meteorological conditions and local human activities to PM2.5 pollution. The results indicate the most important cause of PM2.5 pollution reduction is anthropogenic emissions from the residential sector, followed by the industrial sector, while the influence of meteorological factors contribute only 0.5% to PM2.5. The explanation is that emission reductions from the residential sector contribute the most to the reduction of seven primary contaminants. Finally, we trace the source and transport path of the air mass in Hunan Province through the Concentration Weight Trajectory Analysis (CWT). We found that the external input of PM2.5 in Hunan Province is mainly from the air mass transported from the northeast, accounting for 28.6%-30.0%. To improve future air quality, there is an urgent need to burn clean energy, improve the industrial structure, rationalize energy use, and strengthen cross-regional air pollution synergy control.

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.

CCN activation of ultrafine biogenic-WSOC under restricted anthropogenic emissions: A study over eastern Himalaya in India

The present study was conducted to investigate the potential of water soluble organic carbon (WSOC) in CCN activation under restricted anthropogenic emissions over a high altitude station, Darjeeling (27.01° N and 88.15° E,∼2200 amsl and covered with huge coniferous forests) in the eastern parts of Himalaya in India. We measured CN, CCN, and ultrafine WSOC (WSOC0.1) during April–May 2020 (COVID-19 lockdown) and compared with the normal period (April–May 2019) to investigate the relative dominance of biogenic over anthropogenic emissions to the aerosol-CCN activation. Though an expected significant decline (53%) in CN concentration was observed, CCN exhibited ∼17% increase during the lockdown period. The activation ratio (AR: CCN/CN) jumped from 0.30 during normal to 0.72 during the lockdown period. The aerosol solubility was also found to be increased during the lockdown period (∼27% decrease in the k- parameter (k)). Lockdown-WSOC was higher (1.62 μg m−3) than the normal-WSOC (1.13 μg m−3) and exhibited better regression with CCN in absence of anthropogenic emissions (Lockdown: R2 = 0.83, p < 0.05;Normal: R2 = 0.40, p < 0.05). Here we hypothesize that under restricted fossil fuel emissions during lockdown (57% decline in NOx), surface ozone was increased by 31%, that in turn favored the photochemical oxidation of biogenic VOCs emitted only from coniferous forest cover to produce huge amount of SOC. The ultrafine "biogenic-only” WSOC (under restricted anthropogenic WSOC during lockdown) participated in CCN activation actively and with higher proficiency compared to the normal period. The study bears immense importance of the role of biogenic emissions in cloud droplet formation over this part of the Himalaya under restricted anthropogenic emissions. The present hypothesis could open a new route of aerosol formation and their CCN activation under high deficiency of anthropogenic emissions. © 2023 Elsevier B.V.

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)

Molecular Tracer Characterization during COVID-19 Pandemic in Shanghai: Changes in the Aerosol Aqueous Environment and Implications for Secondary Organic Aerosol Formation

The COVID-19 lockdown has opened a unique window for investigating aerosol formation and evolution with controlled anthropogenic emissions in urban areas. Here, variations of PM2.5 chemical compositions, gaseous pollutants, meteorological conditions, and secondary organic aerosol (SOA) molecular tracers were monitored during three stages at an urban site (Pudong) and a suburban site (Qingpu) in Shanghai, which were defined as pre-COVID lockdown (PL), during COVID lockdown (DL), and after COVID lockdown (AL) in 2020. Abundances of pollutants during the same periods back in 2019 were also analyzed for a more comprehensive intercomparison and evaluation of the impact of the 2020 COVID-19 lockdown on regional air quality. With the sudden cessation of anthropogenic activities during the lockdown, significant reductions in PM2.5 were observed compared to both PL in 2020 (32% in Pudong and 36% in Qingpu) and the DL period back in 2019 (31% in Pudong and 35% in Qingpu), which was accompanied by the significantly reduced PM2.5 components (29-44% and 14-44% reductions in sulfate, nitrate, ammonium, organic carbon, and elemental carbon for Pudong and Qingpu, respectively). In particular, with the reduced secondary inorganic aerosol (SIA), the time series of SOA molecular tracers also underwent significant reduction that was characteristic to the lockdown. Amid the uncontrolled biogenic emissions and even slightly enhanced atmospheric oxidation capacity during the 2020 DL period, controlling anthropogenic emissions exhibits synergistic effects on the reduction of SIA and SOA, which could be further attributed to the changes in the aerosol aqueous-phase environment, such as aerosol liquid water content (ALWC), ionic strength, sulfate content, and particulate NH4+. Based on thermodynamic modeling, greatly reduced ALWC was observed during 2020 DL, which can prevent the partitioning of oxygenated organics into the condensed phase as well as the aqueous-phase formation of SOA. Higher ionic strength in 2020 DL may have a "salting-out" effect on gas- particle partitioning of oxygenated organics. The reduced SOA during 2020 DL at both sites can generally be reflected by the predicted heterogeneous reaction kinetics (gamma) of the isoprene SOA formation pathway. Overall, our study showed a synergistic effect in suppressing SIA and SOA formation upon the reduction of anthropogenic emissions during the COVID-19 lockdown, which shed light on the importance of controlling anthropogenic emissions in regulating secondary aerosol formation in typical urban areas of East China.

Ambient fine particulate matter and ozone pollution in China: synergy in anthropogenic emissions and atmospheric processes

Since 2013, China has taken a series of actions to relieve serious PM2.5 pollution. As a result, the annual PM2.5 concentration decreased by more than 50% from 2013 to 2021. However, ozone pollution has become more pronounced, especially in the North China Plain. Here, we review the impacts of anthropogenic emissions, meteorology, and atmospheric processes on ambient PM2.5 loading and components and O3 pollution in China. The reported influence of interannual meteorological changes on PM2.5 and O3 pollution during 2013–2019 ranged from 10%–20% and 20%–40%, respectively. During the same period, the anthropogenic emissions of NOx, SO2, primary PM2.5, NMVOC and NH3 are estimated to decrease by 38%, 51%, 35%, 11% and 17%, respectively. Such emission reduction is the main cause for the decrease in PM2.5 concentration across China. However, the imbalanced reductions in various precursors also result in the variation in nitrate gas-particle partitioning and hence an increase in the nitrate fraction in PM2.5. The increase of ozone concentration and the enhancement of atmospheric oxidation capacity can also have substantial impact on the secondary components of PM2.5, which partly explained the growth of organic aerosols during haze events and the COVID-19 shutdown period. The uneven reduction in NOx and NMVOC is suggested to be the most important reason for the rapid O3 increase after 2013. In addition, the decrease in PM2.5 may also have affected O3 formation via radiation effects and heterogeneous reactions. Moreover, climate change is expected to influence both anthropogenic emissions and atmospheric processes. However, the extent and pathways of the PM2.5-O3 interplay and how it will be impacted by the changing emission and atmospheric conditions making the synergetic control of PM2.5 and O3 difficult. Further research on the interaction of PM2.5 and O3 is needed to provide basis for a scientifically-grounded and effective co-control strategy.

Quantitative analysis of the impact of anthropogenic emissions and meteorological factors on air quality: Cases during the epidemic in Xingtai City

Meteorological and human factors during the specific epidemic are critical for effectively evaluating the causes of air quality changes in different areas. This study selected Xingtai City, Hebei Province as the research object, took 2020 epidemic situation as an experimental scenario of extreme emission reduction under the extreme control measures, and 2021 epidemic situation as an experimental analysis scenario of future normalized epidemic prevention and control. Compared with the period prior to the epidemic, the ozone concentration during the two epidemics increased, and the particle concentration during the 2021 epidemic also increased. The concentration of other pollutants during the 2020 epidemic decreased to varying degrees. Compared with the same period in 2019, the ozone concentration during the two epidemics also increased. In addition, the pollutant concentration during the 2021 epidemic declined more. Using LSTM algorithm and WRF-CMAQ model to quantify impacts of meteorological factors on the changes in pollutant concentration during the two epidemic periods. The human-induced changes in different pollutant concentrations were deduced as indicated by the results from the air quality simulation. The simulation of LSTM algorithm during the two outbreaks shows that human being had a negative impact on pollutants (reducing their concentration) and accounted for a high proportion in the total change, while the influence of meteorological factors simulated with CMAQ model was much higher than that with LSTM algorithm. Anthropogenic influences dominated during the 2020 epidemic period, while compared to that during the 2020 epidemic period, the impact of anthropogenic activities on pollutants (except NO2) was positive (promoting an increase in pollutant concentration) during the 2021 epidemic period. © 2022 Chinese Society for Environmental Sciences. All rights reserved.

Abrupt emission reduction during COVID-19 intensified the spring 2020 rainfall over India

The high level of aerosol pollution in South Asia has a measurable impact on clouds, radiation, and precipitation. Here, exploring multiple observational data sets and simulations of the state-of-the-art ECHAM6-HAMMOZ chemistry-climate model, we report that the reduction in anthropogenic emissions during the COVID-19 lockdown period has enhanced precipitation by 5-25% over India. This precipitation enhancement is the result of the combined effect of an enhancement in cloud cover, a reduction in aerosol induced cloud invigoration and dynamical changes. We observed that the increase in cloud cover was associated with a reduction in cloud base height and an increase in the effective radius of cloud particles which led to an increase in cloud water content. In response to sudden emission reduction, an anomalous northward moisture transport was observed adding convection and precipitation over the Indian region. Importantly, we show that there is an advantage of anthropogenic pollution reduction for water availability in addition to benefits of air quality, human health, and crop yield.

Elucidating the impacts of COVID-19 lockdown on air quality and ozone chemical characteristics in India

India implemented a stay-at-home order (i.e. lockdown) on 24 March 2020 to decrease the spread of novel COVID-19, which reduced air pollutant emissions in different sectors. The Weather Research and Forecasting model with Chemistry (WRF-Chem) was used to better understand the processes controlling the changes in PM2.5 and ozone in northern India during the lockdown period, including (1) the contributions of inter-annual variability in meteorology and emissions (dust, biogenic, and biomass burning) and lockdown emissions to changes in PM2.5 and ozone in northern India and (2) to analyze changes in ozone production regimes due to the lockdown. We found that both meteorology and lockdown emissions contributed to daytime PM2.5 (-12% and -12%, respectively) and ozone (-8% and -5%, respectively) reduction averaged in April 2020 in the Indo-Gangetic Plain, and in smaller magnitudes in northern India. However, the ozone concentration response to reductions in its precursors (i.e. NO2 and VOCs) due to the lockdown emissions was not constant over the domain. While the ozone concentration decreased in most parts of the domain, it occasionally increased in major cities like Delhi and in regions with many power plants. We utilized the reaction rate information in WRF-Chem to study the ozone chemistry. We found carbon monoxide, formaldehyde, isoprene, acetaldehyde, and ethylene as the major VOCs that contribute to the ozone formation in India. We used the ratio of radical termination from radical-radical interactions to radical-NOx interactions, and its corresponding formaldehyde to NO2 ratio (FNR) to find the ozone chemical regimes. We showed that the FNR transition range in a region depends on whether it is an urban, rural, or power plant region. Using the FNR information, we found that most parts of India are within the NOx-limited regime. We also found that large emission reduction during the lockdown period shifted the chemical regimes toward NOx-limited although it did not necessarily change the chemical regime in many VOC-limited regions. The results of this study highlight the fact that reducing the exposure to both PM2.5 and ozone requires air pollution management strategies that consider both NOx and VOC emission reductions, and that take into account regional characteristics.

Modeling Surface Air Pollution with Reduced Emissions during the COVID-19 Pandemic Using CHIMERE and COSMO-ART Chemical Transport Models

The results of numerical modeling of air pollution using CHIMERE and COSMO-ART chemical transport models are presented. The modeling was performed according to the scenarios of the 50–60% reduction of emissions from anthropogenic sources in the Moscow region during the period of March–July 2020. Scenario calculations of pollutant concentrations were compared with baseline simulations using regionally adapted inventory of anthropogenic pollutant emissions to the atmosphere. The most significant decrease in the concentrations of NO2 and CO was reproduced by the models when emissions from two sectoral sources (vehicles and nonindustrial plants) were reduced. The PM10 drop was mostly influenced by the reduction of emissions from industrial combustion. With the total reduction of emissions from anthropogenic sources as compared to the baseline calculations, the pollutant concentration decreased by 44–54% for NO2, by 38–44% for CO, and by 26–39% for PM10. This generally coincides with the quantitative estimates of the pollution level drop obtained by other authors. The greatest effect of reducing pollutant emissions into the atmosphere was found during the episodes of adverse weather conditions for air purification, when the simulated and observed pollution level increases by 3–5 times as compared to the conditions of intense pollutant dispersion.

Temporal variation of gaseous elemental mercury in a northern coastal city in China: Monsoon and COVID-19 lockdown effects

Continuous measurements of gaseous elemental mercury (GEM) were conducted in Qingdao from March 2020 to March 2021. The average concentration of GEM was (2.39 ± 1.07 ng/m3) with a variation range of 0.27–10.78 ng/m3. GEM exhibited a clear pattern of daily variation, with daily peaks occurring between 11:00–13:00. GEM concentrations were higher in winter (2.80 ± 1.28 ng/m3) than that in summer (2.18 ± 1.05 ng/m3). The high winter concentrations were related to coal-fired heating and the increased frequency of polluted weather in northern China. Principal component analysis showed that the main factors affecting GEM concentration were fossil fuel combustion, natural source release and atmospheric diffusion conditions. The anthropogenic emission sources were the main source of GEM in spring and winter, and natural sources of GEM was large in summer. The potential source contribution function suggested that North and Central China were the main potential sources of GEM, and there were large differences in the potential sources of GEM in different seasons. Comparing the GEM in the same time periods in 2018, 2020, and 2021, government policies, temporary lockdown measures for the COVID-19 epidemic, and urban village renovation led to a decreasing trend of GEM concentrations. This study contributes to a better understanding of the effects of long-range transport of air masses and anthropogenic emissions on atmospheric mercury in eastern coastal cities and offshore areas.

Estimating the effect of the COVID-19 pandemic on pollutant emissions in Europe

Changes in primary emissions due to the COVID-19 lockdowns in Europe for the year 2020 have been estimated by considering fully open-access and near-real-time measured activity data from a wide range of information sources and with simple computational techniques. The estimates consist on a dataset of reduction factors that are both time- and country-dependent and provided for the following source categories: energy industry (power plants), manufacturing industry, road traffic, aviation, shipping and other stationary combustion activities such as residential and commercial-institutional activities. Inspired in other authors’ estimates for COVID reductions, the advantage of this methodology is that there is no use of machine learning, making this procedure more accessible to the general scientific community. We have followed a fast methodology that takes advantage of observed relationships between variables (e.g. temperature and energy demand) without needing special algorithms for finding those relationships. The comparison of our estimates with others from other authors indicate a reasonable agreement and pointing out that emissions dropped by a 17% on average in Europe, with large differences between sectors of activities and spatial heterogeneity. The most affected sector was aviation, with a spatial-averaged variation of −63% in emissions since the implementation of first restrictions with respect business-as-usual values. 2020 emission changes with respect to business-as-usual values in countries ranges from a −13% in Norway and Poland to a more than −20% in several Mediterranean countries as well as the United Kingdom. Two main periods of emission reductions have been identified. © 2022 Turkish National Committee for Air Pollution Research and Control

Unexpected Increases of Severe Haze Pollution During the Post COVID-19 Period: Effects of Emissions, Meteorology, and Secondary Production

Unexpectedly frequent severe haze episodes were observed in Beijing during February-March in 2021 after two phases of clean air action plan (2013-2020), yet the causes remained unclear. Here, we conducted real-time fine particle (PM2.5) composition measurements during January-March in 2021 using a time-of-flight aerosol chemical speciation monitor and an aethalometer and compared with those during the coronavirus disease (COVID-19) period in 2020. Our results showed ubiquitously elevated concentrations of chloride, black carbon (BC), and primary organic aerosol (POA) in 2021, suggesting increased primary emissions during the post-COVID-19 period. By using the machine learning-based random forest (RF) algorithm, we found largely different responses of aerosol changes to meteorology in different months. After decoupling the effects of meteorology, the PM2.5 changes from 2020 to 2021 were reduced from -35.6% to -29.0% in January, -24.1% to -4.5% in February, and +92.6% to +34.2% in March, respectively. Our results demonstrate the dominant roles of stagnant meteorology and secondary production in the formation of severe haze episodes in March 2021. In particular, we found that the compositions of observed and deweathered PM2.5 were fairly similar between 2020 and 2021, and the ratios of secondary OA to secondary inorganic aerosols were close. Our study indicates that decoupling the influence of meteorological conditions is of great importance for better evaluation of mitigating strategies of air pollution due to the large impact of meteorology on the changes in PM2.5 species particularly in a short period.

Decarbonizing the oil refining industry: A systematic review of sociotechnical systems, technological innovations, and policy options

The oil refining industry, which was established in the mid-19th century, has become a foundation of modern society. While the refining of crude oil to produce transportation fuels, petrochemical feedstocks and a variety of other products has brought manifold benefits, it has also led to the global proliferation of greenhouse gas emissions as well as local air pollution from the combustion of fossil fuels. The industry is therefore confronted with a growing need to decarbonize its operations, as well as to support decarbonization of the end use sectors that it directly enables. This paper provides a systematic and critical literature review to uncover the means by which the oil refining industry can decarbonize and evolve as part of an increasingly carbon constrained future. A sociotechnical perspective is used to understand the full range of industrial and economic activities where a decarbonized oil refining industry is expected to remain important and to provide the framework to assess key technical, economic, social and political factors that will likely impact the evolution of the oil refining industry. We highlight key opportunities for this industry to decarbonize while also exposing gaps in the existing literature concerning its decarbonization. The insights provided are expected to support policy makers, researchers and practitioners with the tools needed advance a low-carbon transition of the oil refining industry.

Revealing Drivers of Haze Pollution by Explainable Machine Learning

Many places on earth still suffer from a high level of atmospheric fine particulate matter (PM2.5) pollution. Formation of a particulate pollution event or haze episode (HE) involves many factors, including meteorology, emissions, and chemistry. Understanding the direct causes of and key drivers behind the HE is thus essential. Traditionally, this is done via chemical transport models. However, substantial uncertainties are introduced into the model estimation when there are significant changes in the emissions inventory due to interventions (e.g., the COVID-19 lockdown). Here we applied a Random Forest model coupled with a Shapley additive explanation algorithm, a post hoc explanation technique, to investigate the roles of major meteorological factors, primary emissions, and chemistry in five severe HEs that occurred before or during the COVID-19 lockdown in China. We discovered that, in addition to the high level of primary emissions, PM2.5 in these haze episodes was largely driven by meteorological effects (with average contributions of 30-65 mu g m(-3) for the five HEs), followed by chemistry (similar to 15-30 mu g m(-3)). Photochemistry was likely the major pathway of formation of nitrate, while air humidity was the predominant factor in forming sulfate. Our results highlight that the machine learning driven by data has the potential to be a complementary tool in predicting and interpreting air pollution.

Chances in satellite retrievals of atmospheric composition over eastern China during the 2020 COVID-19 lockdowns

We examined daily level-3 satellite retrievals of Atmospheric Infrared Sounder (AIRS) CO, Ozone Monitoring Instrument (OMI) SO2 and NO2, and Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) over eastern China to understand how COVID-19 lockdowns affected atmospheric composition. Changes in 2020 were strongly dependent on the choice of background period since 2005 and whether trends in atmospheric composition were accounted for. Over central east China during the 23 January-8 April lockdown window, CO in 2020 was between 3 % and 12 % lower than average depending on the background period. The 2020 CO was not consistently less than expected from trends beginning between 2005 and 2016 and ending in 2019 but was 3 %-4 % lower than the background mean during the 2017-2019 period when CO changes had flattened Similarly for AOD, 2020 was between 14 % and 30 % lower than averages beginning in 2005 and 14 %-17 % lower compared to different background means beginning in 2016. NO2 in 2020 was between 30 % and 43 % lower than the mean over different background periods and between 17 % and 33 % lower than what would be expected for trends beginning later than 2011. Relative to the 2016-2019 period when NO2 had flattened, 2020 was 30 %-33 % lower. Over southern China, 2020 NO2 was between 23 % and 27 % lower than different background means beginning in 2013, the beginning of a period of persistently lower NO2. CO over southern China was significantly higher in 2020 than what would be expected, which we suggest was partly because of an active fire season in neighboring countries. Over central east and southern China, 2020 SO2 was higher than expected, but this depended strongly on how daily regional values were calculated from individual retrievals and reflects background values approaching the retrieval detection limit. Future work over China, or other regions, needs to take into account the sensitivity of differences in 2020 to different background periods and trends in order to separate the effects of COVID-19 on air quality from previously occurring changes or from variability in other sources.

Untangling the contributions of meteorological conditions and human mobility to tropospheric NO2 in Chinese mainland during the COVID-19 pandemic in early 2020.

In early 2020, unprecedented lockdowns and travel bans were implemented in Chinese mainland to fight COVID-19, which led to a large reduction in anthropogenic emissions. This provided a unique opportunity to isolate the effects from emission and meteorology on tropospheric nitrogen dioxide (NO2). Comparing the atmospheric NO2 in 2020 with that in 2017, we found the changes of emission have led to a 49.3 ± 23.5% reduction, which was ∼12% more than satellite-observed reduction of 37.8 ± 16.3%. The discrepancy was mainly a result of changes of meteorology, which have contributed to an 8.1 ± 14.2% increase of NO2. We also revealed that the emission-induced reduction of NO2 has significantly negative correlations to human mobility, particularly that inside the city. The intra-city migration index derived from Baidu Location-Based-Service can explain 40.4% ± 17.7% variance of the emission-induced reduction of NO2 in 29 megacities, each of which has a population of over 8 million in Chinese mainland.

Frequency distribution of pollutant concentrations over Indian megacities impacted by the COVID-19 lockdown.

The megacities experience poor air quality frequently due to stronger anthropogenic emissions. India had one of the longest lockdowns in 2020 to curb the spread of COVID-19, leading to reductions in the emissions from anthropogenic activities. In this article, the frequency distributions of different pollutants have been analysed over two densely populated megacities: Delhi (28.70° N; 77.10° E) and Kolkata (22.57° N; 88.36° E). In Delhi, the percentage of days with PM2.5 levels exceeding the National Ambient Air Quality Standards (NAAQS) between 25 March and 17 June dropped from 98% in 2019 to 61% in 2020. The lockdown phase 1 brought down the PM10 (particulate matter having an aerodynamic diameter ≤ 10 µm) levels below the daily NAAQS limit over Delhi and Kolkata. However, PM10 exceeded the limit of 100 µgm-3 during phases 2-5 of lockdown over Delhi due to lower temperature, weaker winds, increased relative humidity and commencement of limited traffic movement. The PM2.5 levels exhibit a regressive trend in the highest range from the year 2019 to 2020 in Delhi. The daily mean value for PM2.5 concentrations dropped from 85-90 µgm-3 to 40-45 µgm-3 bin, whereas the PM10 levels witnessed a reduction from 160-180 µgm-3 to 100-120 µgm-3 bin due to the lockdown. Kolkata also experienced a shift in the peak of PM10 distribution from 80-100 µgm-3 in 2019 to 20-40 µgm-3 during the lockdown. The PM2.5 levels in peak frequency distribution were recorded in the 35-40 µgm-3 bin in 2019 which dropped to 15-20 µgm-3 in 2020. In line with particulate matter, other primary gaseous pollutants (NOx, CO, SO2, NH3) also showed decline. However, changes in O3 showed mixed trends with enhancements in some of the phases and reductions in other phases. In contrast to daily mean O3, 8-h maximum O3 showed a reduction over Delhi during lockdown phases except for phase 3. Interestingly, the time of daily maximum was observed to be delayed by ~ 2 h over Delhi (from 1300 to 1500 h) and ~ 1 h over Kolkata (from 1300 to 1400 h) almost coinciding with the time of maximum temperature, highlighting the role of meteorology versus precursors. Emission reductions weakened the chemical sink of O3 leading to enhancement (120%; 11 ppbv) in night-time O3 over Delhi during phases 1-3.