Substantial Changes in Nitrate Oxide and Ozone after Excluding Meteorological Impacts during the COVID-19 Outbreak in Mainland China

Distinct responses of urban and rural O3 pollution with secondary particle changes to anthropogenic emission reductions: Insights from a case study over North China

Ozone (O3) pollution with excessive near-surface O3 levels has been an important environmental issue in China, although the anthropogenic emission reductions (AER) have improved air quality since 2013. In this study, we investigated the sensitivities of atmospheric chemical environment with the urban and rural changes to the AER targeting a typical O3 pollution episode over North China in summer 2019, by conducting two WRF-Chem simulation experiments under two scenarios of anthropogenic emission inventories of years 2012 and 2019 with the meteorological conditions in the 2019 summertime O3 pollution episode for excluding the meteorological impacts on O3 pollution. The results show that the unbalanced AER aroused more serious O3 pollution in urban and rural areas. The intense NO reduction was responsible for the significant increments of urban O3, while the falling NO2 and NO synergistically devoted to the slight O3 variations in rural areas. Induced by the recent-year AER, the urban O3 production was governed by VOCs-limited and transition regime, whereas the NOx-limited regime dominated over rural areas in North China. Also, the AER reinforced the atmospheric oxidation capacity with the elevations of atmospheric oxidants O3 and ROx radicals, strengthening the chemical conversions to secondary inorganic particles. In both urban and rural areas, the sharp drop in SO2 caused a decrease in sulfate fraction, while the enhanced AOC accelerated the transformation to nitrate even when NOx was reduced. The AER induced nitrate to occupy the principal position in secondary PM2.5 in urban and rural areas. The AER promoted daytime and suppressed nighttime the nitrate production in urban areas, and more vigorous conversion of secondary aerosols were found in rural areas with much lower AOC increments. This study provides insights from a case study over North China in distinct responses of urban and rural O3 pollution with secondary particle changes to AER in urban and rural atmospheric environment changes, with implications for an effective abatement strategy on O3 pollution.

Dynamics of atmospheric emissions and meteorological variables in Bangladesh from pre-to post-COVID-19 lockdown

Following the COVID-19 restrictions, there was a sharp decline in global air quality and related environmental metrics. Due to the limited availability of in situ atmospheric data in Bangladesh, this study collected data on various air pollutants (NO2, SO2, CO, and PM2.5), greenhouse gases (CO2, CH4, and O3), as well as meteorological variables like Land Surface Temperature (LST), Relative Humidity (RH), Precipitation, surface albedo and Aerosol Optical Depth (AOD) from different datasets by Google Earth Engine (GEE), the International Energy Agency (IEA), NASA Giovanni, and NASA Power Access Viewer, covering periods before (2019), during (2020), and after (2021-2023) the COVID-19 lockdown in Bangladesh. GIS-based assessment alongside Principal Component Analysis (PCA) has been performed to explore the patterns, trends and correlations among the observed variables. Results showed in 2020 compared to 2019, NO2, SO2, CO, PM2.5, and CO2 concentrations decreases by 1.94, 16.67, 1.95, 2.08, and 6 %, respectively, while CH4 and O3 continued to rise. Meteorological variables exhibited a 0.16 °C decreases in LST, 6.4 % increases in RH, a 6 % reduction in AOD, and 6.36 % declines in surface albedo. Post-lockdown in 2021, air pollutants surged (NO2, SO2, CO, and PM2.5 increases by 17.3, 23.6, 0.6, and 8.3 %, respectively), with CO2, LST, and AOD rising by 8.5 %, 0.13 °C, and 8.3 %, and a slight 0.46 % decrease in RH compared to 2019 due to resuming more economic activities, transportation and industrial production works. The years 2022-2023 saw slight improvements in most variables except CH4, though concentrations did not revert to those of 2019. The findings of correlation coefficients revealed that pollutants and GHG are highly correlated with the meteorological variables specially with RH. This study underscores the substantial shifts in atmospheric variables from pre-to post-lockdown periods, offering valuable insights for more effective management of the greenhouse effect and air pollution control strategies.

Spatial and temporal differentiation and its driving factors of air quality in the economic circle of Shandong Province during 2013-2020

As the negative repercussions of environmental devastation, such as air quality decline and air pollution, become more apparent, environmental consciousness is growing across the world, forcing nations to take steps to mitigate the damage. China pledged to achieve air quality improvement goal to combat global environment issue, yet the spatial-temporal differentiation and its driving factors of environment-meteorology-economic index for air quality are not fully analysed. To promote regional collaborative control of air pollution and achieve sustainable urban development, spatial and temporal different and its driving factors of air quality in Shandong Province during 2013-2020. Results revealed that concentrations of sulfur dioxide (SO2), nitrogen dioxide (NO2), particulate matter 2.5 (PM2.5), particulate matter 10 (PM10), and carbon monoxide (CO-95per) exhibited decreasing trend (SO2 concentrations decreasing 84 % and CO-95per concentrations decreasing 90 %). Air quality was improved from inland areas to coastal areas. Pollutant indicators of SO2, NO2, PM10, PM2.5, and CO-95per demonstrated significant positive correlation (P < 0.05). Air temperature and precipitation are significantly negatively correlated with concentrations of SO2, NO2, PM10, PM2.5, and CO-95per but significantly positively correlated with ozone (O3-8 h). SO2, NO2, PM2.5, PM10, CO-95per, and proportion of days with heavy pollution are strongly positively correlated with proportion of secondary industry but strongly negatively correlated with proportion of tertiary industry and volume of household waste. Except for O3-8 h, pollutant index of Provincial Capital Economic Circle (PCEC) and Southern Shandong Economic Circle (SSEC) has significant negative correlation (P < 0.05) with regional gross domestic product and investment in environmental protection; however, investment in environmental protection of Eastern Shandong Economic Circle (ESEC) has no significant correlation with air pollution index. There was significant negative correlation between vegetable sowing area and SSEC pollutant index. The relationship between pollution emission and investment in environmental protection has shifted from high pollution-low investment to low pollution-low investment in PCEC, ESEC and SSEC, and the inflection point was in 2020 for PCEC, 2019 for ESEC, and 2020 for SSEC. Those results provide empirical evidence and theoretical support for the improvement of regional air quality, aiming to achieve high-quality development. According to these findings, it has been found that meteorological elements, pollutant emission, socio-economic factors and agricultural data affect air quality. Those results could provide meaningful and significant supporting for synergistic regulation of diverse pollutants.

Convolutional Neural Networks Facilitate Process Understanding of Megacity Ozone Temporal Variability

Ozone pollution is profoundly modulated by meteorological features such as temperature, air pressure, wind, and humidity. While many studies have developed empirical models to elucidate the effects of meteorology on ozone variability, they predominantly focus on local weather conditions, overlooking the influences from high-altitude and broader regional meteorological patterns. Here, we employ convolutional neural networks (CNNs), a technique typically applied to image recognition, to investigate the influence of three-dimensional spatial variations in meteorological fields on the daily, seasonal, and interannual dynamics of ozone in Shenzhen, a major coastal urban center in China. Our optimized CNNs model, covering a 13° × 13° spatial domain, effectively explains over 70% of daily ozone variability, outperforming alternative empirical approaches by 7 to 62%. Model interpretations reveal the crucial roles of 2-m temperature and humidity as primary drivers, contributing 16% and 15% to daily ozone fluctuations, respectively. Regional wind fields account for up to 40% of ozone changes during the episodes. CNNs successfully replicate observed ozone temporal patterns, attributing -5-6 μg·m-3 of interannual ozone variability to weather anomalies. Our interpretable CNNs framework enables quantitative attribution of historical ozone fluctuations to nonlinear meteorological effects across spatiotemporal scales, offering vital process-based insights for managing megacity air quality amidst changing climate regimes.

The Impacts of COVID-19 Lockdowns on Road Transport Air Pollution in London: A State-Space Modelling Approach

The emergence of the COVID-19 pandemic in 2020 led to the implementation of legal restrictions on individual activities, significantly impacting traffic and air pollution levels in urban areas. This study employs a state-space intervention method to investigate the effects of three major COVID-19 lockdowns in March 2020, November 2020, and January 2021 on London's air quality. Data were collected from 20 monitoring stations across London (central, ultra-low emission zone, and greater London), with daily measurements of NOx, PM10, and PM2.5 for four years (January 2019-December 2022). Furthermore, the developed model was adjusted for seasonal effects, ambient temperature, and relative humidity. This study found significant reductions in the NOx levels during the first lockdown: 49% in central London, 33% in the ultra-low emission zone (ULEZ), and 37% in greater London. Although reductions in NOx were also observed during the second and third lockdowns, they were less than the first lockdown. In contrast, PM10 and PM2.5 increased by 12% and 1%, respectively, during the first lockdown, possibly due to higher residential energy consumption. However, during the second lockdown, PM10 and PM2.5 levels decreased by 11% and 13%, respectively, and remained unchanged during the third lockdown. These findings highlight the complex dynamics of urban air quality and underscore the need for targeted interventions to address specific pollution sources, particularly those related to road transport. The study provides valuable insights into the effectiveness of lockdown measures and informs future air quality management strategies.

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.

Radical chemistry and VOCs-NOx-O3-nitrate sensitivity in the polluted atmosphere of a suburban site in the North China Plain

In this study, the chemical mechanisms of O3 and nitrate formation as well as the control strategy were investigated based on extensive observations in Tai'an city in the NCP and an observation-constrained box model. The results showed that O3 pollution was severe with the maximum hourly O3 concentration reaching 150 ppb. Higher O3 concentration was typically accompanied by higher PM2.5 concentrations, which could be ascribed to the common precursors of VOCs and NOx. The modeled averaged peak concentrations of OH, HO2, and RO2 were relatively higher compared to previous observations, indicating strong atmospheric oxidation capacity in the study area. The ROx production rate increased from 2.8 ppb h-1 to 5 ppb h-1 from the clean case to the heavily polluted case and was dominated by HONO photolysis, followed by HCHO photolysis. The contribution of radical-self combination to radical termination gradually exceeded NO2 + OH from clean to polluted cases, indicating that O3 formation shifted to a more NOx-limited regime. The O3 production rate increased from 14 ppb h-1 to 22 ppb h-1 from clean to heavily polluted cases. The relative incremental reactivity (RIR) results showed that VOCs and NOx had comparable RIR values during most days, which suggested that decreasing VOCs or NOx was both effective in alleviating O3 pollution. In addition, HCHO, with the largest RIR value, made important contribution to O3 production. The Empirical Kinetic Modeling Approach (EKMA) revealed that synergistic control of O3 and nitrate can be achieved by decreasing both NOx and VOCs emissions (e.g., alkenes) with the ratio of 3:1. This study emphasized the importance of NOx abatement for the synergistic control of O3 and nitrate pollution in the Tai'an area as the sustained emissions control has shifted the O3 and nitrate formation to a more NOx-limited regime.

Characterization of air pollution and associated health risks in Gansu Province, China from 2015 to 2022

Air pollution poses a major threat to both the environment and public health. The air quality index (AQI), aggregate AQI, new health risk-based air quality index (NHAQI), and NHAQI-WHO were employed to quantitatively evaluate the characterization of air pollution and the associated health risk in Gansu Province before (P-I) and after (P-II) COVID-19 pandemic. The results indicated that AQI system undervalued the comprehensive health risk impact of the six criteria pollutants compared with the other three indices. The stringent lockdown measures contributed to a considerable reduction in SO2, CO, PM2.5, NO2 and PM10; these concentrations were 43.4%, 34.6%, 21.4%, 17.4%, and 14.2% lower in P-II than P-I, respectively. But the concentration of O3 had no obvious improvement. The higher sandstorm frequency in P-II led to no significant decrease in the ERtotal and even resulted in an increase in the average ERtotal in cities located in northwestern Gansu from 0.78% in P-I to 1.0% in P-II. The cumulative distribution of NHAQI-based population-weighted exposure revealed that 24% of the total population was still exposed to light pollution in spring during P-II, while the air quality in other three seasons had significant improvements and all people were under healthy air quality level.

Atmospheric Oxidation Capacity Elevated during 2020 Spring Lockdown in Chengdu, China: Lessons for Future Secondary Pollution Control

This study presents the measurement of photochemical precursors during the lockdown period from January 23, 2020, to March 14, 2020, in Chengdu in response to the coronavirus (COVID-19) pandemic. To derive the lockdown impact on air quality, the observations are compared to the equivalent periods in the last 2 years. An observation-based model is used to investigate the atmospheric oxidation capacity change during lockdown. OH, HO2, and RO2 concentrations are simulated, which are elevated by 42, 220, and 277%, respectively, during the lockdown period, mainly due to the reduction in nitrogen oxides (NOx). However, the radical turnover rates, i.e., OH oxidation rate L(OH) and local ozone production rate P(O3), which determine the secondary intermediates formation and O3 formation, only increase by 24 and 48%, respectively. Therefore, the oxidation capacity increases slightly during lockdown, which is partly attributed to unchanged alkene concentrations. During the lockdown, alkene ozonolysis seems to be a significant radical primary source due to the elevated O3 concentrations. This unique data set during the lockdown period highlights the importance of controlling alkene emission to mitigate secondary pollution formation in Chengdu and may also be applicable in other regions of China given an expected NOx reduction due to the rapid transformation to electrified fleets in the future.

Response of organic aerosol in Beijing to emission reductions during the XXIV Olympic Winter Games

Organic aerosol (OA) serves as a crucial component of fine particulate matter. However, the response of OA to changes in anthropogenic emissions remains unclear due to its complexity. The XXIV Olympic Winter Games (OWG) provided real atmospheric experimental conditions on studying the response of OA to substantial emission reductions in winter. Here, we explored the sources and variations of OA based on the observation of aerosol mass spectrometer (AMS) combined with positive matrix factorization (PMF) analysis in urban Beijing during the 2022 Olympic Winter Games. The influences of meteorological conditions on OA concentrations were corrected by CO and verified by deweathered model. The CO-normalized primary OA (POA) concentrations from traffic, cooking, coal and biomass burning during the OWG decreased by 39.8 %, 23.2 % and 65.0 %, respectively. Measures controlling coal and biomass burning were most effective in reducing POA during the OWG. For the CO-normalized concentration of secondary OA (SOA), aqueous-phase related oxygenated OA decreased by 51.8 % due to the lower relative humidity and emission reduction in precursors, while the less oxidized‑oxygenated OA even slightly increased as the enhanced atmospheric oxidation processes may partially offset the efficacy of emission control. Therefore, more targeted reduction of organic precursors shall be enhanced to lower atmospheric oxidation capacity and mitigate SOA pollution.

Control of fine particulate nitrate during severe winter haze in "2+26" cities

The "2+26" cities, suffering the most severe winter haze pollution, have been the key region for air quality improvement in China. Increasing prominent nitrate pollution is one of the most challenging environmental issues in this region, necessitating development of an effective control strategy. Herein, we use observations, and state-of-the-art model simulations with scenario analysis and process analysis to quantify the effectiveness of the future SO2-NOX-VOC-NH3 emission control on nitrate pollution mitigation in "2+26" cities. Focusing on a serious winter haze episode, we find that limited NOX emission reduction alone in the short-term period is a less effective choice than VOC or NH3 emission reduction alone to decrease nitrate concentrations, due to the accelerated NOX-HNO3 conversion by atmospheric oxidants and the enhanced HNO3 to NO3- partition by ammonia, although deep NOX emission reduction is essential in the long-term period. The synergistic NH3 and VOC emission control is strongly recommended, which can counteract the adverse effects of nonlinear photochemistry and aerosol chemical feedback to decrease nitrate more. Such extra benefits will be reduced if the synergistic NH3 and VOC reduction is delayed, and thus reducing emission of multiple precursors is urgently required for the effective control of increasingly severe winter nitrate pollution in "2+26" cities.

Elucidating the mechanisms of rapid O3 increase in North China Plain during COVID-19 lockdown period

Ozone (O3) levels in North China Plain (NCP) suffered from rapid increases during the COVID-19 period. Many previous studies have confirmed more rapid NOx reduction compared with VOCs might be responsible for the O3 increase during this period, while the comprehensive impacts of each VOC species and NOx on ambient O3 and their interactions with meteorology were not revealed clearly. To clarify the detailed reasons for the O3 increase, a continuous campaign was performed in a typical industrial city of NCP. Meanwhile, the machine-learning technique and the box model were employed to reveal the mechanisms of O3 increase from the perspective of meteorology and photochemical process, respectively. The result suggested that the ambient O3 level in Tangshan increased from 18.7 ± 4.63 to 45.6 ± 8.52 μg/m3 (143%) during COVID-19 lockdown, and the emission reduction and meteorology contributed to 54 % and 46 % of this increment, respectively. The lower wind speed (WS) coupled with regional transport played a significant role on O3 increase (30.8 kg/s). The O3 sensitivity verified that O3 production was highly volatile organic compounds (VOC)-sensitive (Relative incremental reactivity (RIR): 0.75), while the NOx showed the negative impact on O3 production in Tangshan (RIR: -0.59). It suggested that the control of VOCs rather than NOx might be more effective in reducing O3 level in Tangshan because it was located on the VOC-limited regime. Besides, both of ozone formation potential (OFP) analysis and observation-based model (OBM) demonstrated that the alkenes (36.3 ppb) and anthropogenic oxygenated volatile organic compounds (OVOCs) (15.2 ppb) showed the higher OFP compared with other species, and their reactions released a large number of HO2 and RO2 radicals. Moreover, the concentrations of these species did not experience marked decreases during COVID-19 lockdown, which were major contributors to O3 increase during this period. This study also underlined the necessity of priority controlling alkenes and OVOCs across the NCP.

LESO: A ten-year ensemble of satellite-derived intercontinental hourly surface ozone concentrations

This study presents a novel ensemble of surface ozone (O3) generated by the LEarning Surface Ozone (LESO) framework. The aim of this study is to investigate the spatial and temporal variation of surface O3. The LESO ensemble provides unique and accurate hourly (daily/monthly/yearly as needed) O3 surface concentrations on a fine spatial resolution of 0.1◦ × 0.1◦ across China, Europe, and the United States over a period of 10 years (2012-2021). The LESO ensemble was generated by establishing the relationship between surface O3 and satellite-derived O3 total columns together with high-resolution meteorological reanalysis data. This breakthrough overcomes the challenge of retrieving O3 in the lower atmosphere from satellite signals. A comprehensive validation indicated that the LESO datasets explained approximately 80% of the hourly variability of O3, with a root mean squared error of 19.63 μg/m3. The datasets convincingly captured the diurnal cycles, weekend effects, seasonality, and interannual variability, which can be valuable for research and applications related to atmospheric and climate sciences.

Assessment of meteorological and air quality drivers of elevated ambient ozone in Beijing via machine learning approach

Over the past few years, surface ozone (O3) pollution has dominated China's air pollution as particulate matter has decreased. In Beijing, the annual average concentrations of ground-level O3 from 2015 to 2020 regularly increased from 57.32 to 62.72 μg/m3, showing a change of almost 9.4%, with a 1.6% per year increase. The meteorological factors are the primary influencer of elevated O3 levels; however, their importance and heterogeneity of variables remain rarely understood. In this study, we used 13 meteorological factors and 6 air quality (AQ) parameters to estimate their influencing score using the random forest (RF) algorithm to explain and predict ambient O3. Among the meteorological variables and overall, both land surface temperature and temperature at 2 m from the surface emerged as the most influential factors, while NO2 stood out as the highest influencing factor from the AQ parameters. Indeed, it is crucial and imperative to reduce the temperature caused by climate change in order to effectively control ambient O3 levels in Beijing. Overall, meteorological factors alone exhibited a higher coefficient of determination (R2) value of 0.80, compared with AQ variables of 0.58, for the post-lockdown period. In addition, we calculated the number of days O3 concentration levels exceeded the WHO standard and newly proposed peak-season maximum daily 8-h average (MDA8) O3 guideline for Beijing. The exceedance number of days from the WHO standard of MDA8 ambient O3 was observed to be the highest in June, and each studied year crossed peak season guidelines by almost 2 times margin. This study demonstrates the contributions of meteorological variables and AQ parameters in surging ambient O3 and highlights the importance of future research toward devising an optimum strategy to combat growing O3 pollution in urban areas.

A machine learning-based study on the impact of COVID-19 on three kinds of pollution in Beijing-Tianjin-Hebei region

Large-scale restrictions on anthropogenic activities in China in 2020 due to the Corona Virus Disease 2019 (COVID-19) indirectly led to improvements in air quality. Previous studies have paid little attention to the changes in nitrogen dioxide (NO2), fine particulate matter (PM2.5) and ozone (O3) concentrations at different levels of anthropogenic activity limitation and their interactions. In this study, machine learning models were used to simulate the concentrations of three pollutants during periods of different levels of lockdown, and compare them with observations during the same period. The results show that the difference between the simulated and observed values of NO2 concentrations varies at different stages of the lockdown. Variation between simulated and observed O3 and PM2.5 concentrations were less distinct at different stages of lockdowns. During the most severe period of the lockdowns, NO2 concentrations decreased significantly with a maximum decrease of 65.28 %, and O3 concentrations increased with a maximum increase of 75.69 %. During the first two weeks of the lockdown, the titration reaction in the atmosphere was disrupted due to the rapid decrease in NO2 concentrations, leading to the redistribution of Ox (NO2 + O3) in the atmosphere and eventually to the production of O3 and secondary PM2.5. The effect of traffic restrictions on the reduction of NO2 concentrations is significant. However, it is also important to consider the increase in O3 due to the constant volatile organic compounds (VOCs) and the decrease in NOx (NO+NO2). Traffic restrictions had a limited effect on improving PM2.5 pollution, so other beneficial measures were needed to sustainably reduce particulate matter pollution. Research on COVID-19 could provide new insights into future clean air action.

A multifaceted approach to explain short- and long-term PM2.5 concentration changes in Northeast Asia in the month of January during 2016-2021

Changes in PM_2.5 concentrations are influenced by interwoven impacts of key drivers (e.g., meteorology, local emissions, and regional emissions). However, it is challenging to quantitatively disentangle their impacts individually at once. Therefore, we introduced a multifaceted approach (i.e., meteorology vs. emissions and self-contribution vs. long-range transport) to analyze the effects of major drivers for long- and short-term PM_2.5 concentration changes based on observation and simulation in the month of January during 2016-2021 in Northeast Asia. For the simulations, we conducted modeling with the WRF-CMAQ system. The observed PM_2.5 concentrations in China and South Korea in January 2021 decreased by 13.7 and 9.8 μg/m³, respectively, compared to those in January 2016. Emission change was the dominant factor to reduce PM_2.5 concentrations in China (-115%) and South Korea (-74%) for the 6 years. However, the short-term changes in PM_2.5 concentrations between January of 2020-2021 were mainly driven by meteorological conditions in China (-73%) and South Korea (-68%). At the same time, in South Korea located in downwind area, the impact of long-range transport from upwind area (LTI) decreased by 55% (9.6 μg/m³) over the 6 years whereas the impact of local emissions increased (+2.9 μg/m³/year) during 2016-2019 but decreased (-4.5 μg/m³/year) during 2019-2021. Additionally, PM_2.5 concentrations in the upwind area showed a positive relationship with LTIs. However, for the days when westerly winds became weak in the downwind area, high PM_2.5 concentrations in upwind area did not lead to high LTIs. These results imply that the decline of PM_2.5 concentrations in South Korea was significantly affected by a combination of emission reduction in upwind area and meteorological conditions that hinder long-range transport. The proposed multifaceted approach can identify the main drivers of PM_2.5 concentration change in a region by considering the regional characteristics.

Impact of the COVID-19 lockdown on typical ambient air pollutants: Cyclical response to anthropogenic emission reduction

Preliminary studies have confirmed that ambient air pollutant concentrations are significantly influenced by the COVID-19 lockdown measures, but little attention focus on the long term impacts of human countermeasures in cities all over the world during the period. Still, fewer have addressed their other essential properties, especially the cyclical response to concentration reduction. This paper aims to fill the gaps with combined methods of abrupt change test and wavelet analysis, research areas were made of five cities, Wuhan, Changchun, Shanghai, Shenzhen and Chengdu, in China. Abrupt changes in contaminant concentrations commonly occurred in the year prior to the outbreak. The lockdown has almost no effect on the short cycle below 30 d (days) for both pollutants, and a negligible impact on the cycle above 30 d. PM2.5 (fine particulate matter) has a stable short-cycle nature, which is greatly influenced by anthropogenic emissions. The analysis revealed that the sensitivity of PM2.5 to climate is increased along with the concentrations of PM2.5 were decreasing by the times when above the threshold (30-50 μg m-3), and which could lead to PM2.5 advancement relative to the ozone phase over a period of 60 d after the epidemic. These results suggest that the epidemic may have had an impact earlier than when it was known. And significant reductions in anthropogenic emissions have little impact on the cyclic nature of pollutants, but may alter the inter-pollutant phase differences during the study period.

Drivers of High Concentrations of Secondary Organic Aerosols in Northern China during the COVID-19 Lockdowns

During the COVID-19 lockdown in early 2020, observations in Beijing indicate that secondary organic aerosol (SOA) concentrations increased despite substantial emission reduction, but the reasons are not fully explained. Here, we integrate the two-dimensional volatility basis set into a state-of-the-art chemical transport model, which unprecedentedly reproduces organic aerosol (OA) components resolved by the positive matrix factorization based on aerosol mass spectrometer observations. The model shows that, for Beijing, the emission reduction during the lockdown lowered primary organic aerosol (POA)/SOA concentrations by 50%/18%, while deteriorated meteorological conditions increased them by 30%/119%, resulting in a net decrease in the POA concentration and a net increase in the SOA concentration. Emission reduction and meteorological changes both led to an increased OH concentration, which accounts for their distinct effects on POA and SOA. SOA from anthropogenic volatile organic compounds and organics with lower volatility contributed 28 and 62%, respectively, to the net SOA increase. Different from Beijing, the SOA concentration decreased in southern Hebei during the lockdown because of more favorable meteorology. Our findings confirm the effectiveness of organic emission reductions and meanwhile reveal the challenge in controlling SOA pollution that calls for large organic precursor emission reductions to rival the adverse impact of OH increase.

Why did air quality experience little improvement during the COVID-19 lockdown in megacities, northeast China?

To inhibit the COVID-19 (Coronavirus disease 2019) outbreak, unprecedented nationwide lockdowns were implemented in China in early 2020, resulting in a marked reduction of anthropogenic emissions. However, reasons for the insignificant improvement in air quality in megacities of northeast China, including Shenyang, Changchun, Jilin, Harbin, and Daqing, were scarcely reported. We assessed the influences of meteorological conditions and changes in emissions on air quality in the five megacities during the COVID-19 lockdown (February 2020) using the WRF-CMAQ model. Modeling results indicated that meteorology contributed a 14.7% increment in Air Quality Index (AQI) averaged over the five megacities, thus, the local unfavorable meteorology was one of the causes to yield little improved air quality. In terms of emission changes, the increase in residential emissions (+15%) accompanied by declining industry emissions (-15%) and transportation (-90%) emissions resulted in a slight AQI decrease of 3.1%, demonstrating the decrease in emissions associated with the lockdown were largely offset by the increment in residential emissions. Also, residential emissions contributed 42.3% to PM2.5 concentration on average based on the Integrated Source Apportionment tool. These results demonstrated the key role residential emissions played in determining air quality. The findings of this study provide a scenario that helps make appropriate emission mitigation measures for improving air quality in this part of China.

Tracking long-term population exposure risks to PM2.5 and ozone in urban agglomerations of China 2015-2021

China has experienced severe air pollution in the past decade, especially PM2.5 and emerging ozone pollution recently. In this study, we comprehensively analyzed long-term population exposure risks to PM2.5 and ozone in urban agglomerations of China during 2015-2021 regarding two-stage clean-air actions based on the Ministry of Ecology and the Environment (MEE) air monitoring network. Overall, the ratio of the population living in the regions exceeding the Chinese National Ambient Air Quality Standard (35 μg/m3) decreases by 29.9 % for PM2.5 from 2015 to 2021, driven by high proportions in the Middle Plain (MP, 42.3 %) and Lan-Xi (35.0 %) regions. However, this ratio almost remains unchanged for ozone and even increases by 1.5 % in the MP region. As expected, the improved air quality leads to 234.7 × 103 avoided premature mortality (ΔMort), mainly ascribed to the reduction in PM2.5 concentration. COVID-19 pandemic may influence the annual variation of PM2.5-related ΔMort as it affects the shape of the population exposure curve to become much steeper. Although all eleven urban agglomerations share stroke (43.6 %) and ischaemic heart disease (IHD, 30.1 %) as the two largest contributors to total ΔMort, cause-specific ΔMort is highly regional heterogeneous, in which ozone-related ΔMort is significantly higher (21 %) in the Tibet region than other urban agglomeration. Despite ozone-related ΔMort being one order of magnitude lower than PM2.5-related ΔMort from 2015 to 2021, ozone-related ΔMort is predicted to increase in major urban agglomerations initially along with a continuous decline for PM2.5-related ΔMort from 2020 to 2060, highlighting the importance of ozone control. Coordinated controls of PM2.5 and O3 are warranted for reducing health burdens in China during achieving carbon neutrality.

Staggered-peak production is a mixed blessing in the control of particulate matter pollution

Staggered-peak production (SP)-a measure to halt industrial production in the heating season-has been implemented in North China Plain to alleviate air pollution. We compared the variations of PM1 composition in Beijing during the SP period in the 2016 heating season (SPhs) with those in the normal production (NP) periods during the 2015 heating season (NPhs) and 2016 non-heating season (NPnhs) to investigate the effectiveness of SP. The PM1 mass concentration decreased from 70.0 ± 54.4 μg m-3 in NPhs to 53.0 ± 56.4 μg m-3 in SPhs, with prominent reductions in primary emissions. However, the fraction of nitrate during SPhs (20.2%) was roughly twice that during NPhs (12.7%) despite a large decrease of NOx, suggesting an efficient transformation of NOx to nitrate during the SP period. This is consistent with the increase of oxygenated organic aerosol (OOA), which almost doubled from NPhs (22.5%) to SPhs (43.0%) in the total organic aerosol (OA) fraction, highlighting efficient secondary formation during SP. The PM1 loading was similar between SPhs (53.0 ± 56.4 μg m-3) and NPnhs (50.7 ± 49.4 μg m-3), indicating a smaller difference in PM pollution between heating and non-heating seasons after the implementation of the SP measure. In addition, a machine learning technique was used to decouple the impact of meteorology on air pollutants. The deweathered results were comparable with the observed results, indicating that meteorological conditions did not have a large impact on the comparison results. Our study indicates that the SP policy is effective in reducing primary emissions but promotes the formation of secondary species.

The interaction between black carbon and planetary boundary layer in the Yangtze River Delta from 2015 to 2020: Why O3 didn't decline so significantly as PM2.5

Since the Air Pollution Prevention and Control Action Plan (air clean plan) issued in 2013, air quality has been in continuous improvement. The second stage of air clean plan since 2018 was focused on O₃ controlling, but it still didn't decline so significantly as PM_2.5. This study conducted a long-term observation on black carbon (BC) and utilized the observational data of other air pollutants (PM_2.5, PM₁₀, NO₂, SO₂, CO and O₃), the meteorological elements and the vertical sounding data of PBL in Nanjing. In the daytime (08:00-20:00), PM_2.5 kept decreasing from 2015 to 2020 at the rate of 4.8 μg⋅m^-3⋅a^-1, however, BC increased at the rate of 0.6 μg⋅m^-3⋅a^-1, which has led to the continuous growth of BC/PM_2.5 (0.9%⋅a^-1). However, during this period, O₃ was relatively stable and, in 2020, it returned below its value in 2015 after slight increases in 2017 and 2018. Meanwhile, the average surface temperature had increased by around 1.0 °C during 2015-2019 at the rate of 0.3 °C⋅a^-1. Also, the average height of the inversion layer had increased significantly by 494.0 and 176.7 m at 20:00 and 08:00, whose growth ratio was up to 57% and 25%, respectively. The above observation results have formed a set of chain reactions as follows. The growth of the surface BC caused the surface temperature to rise due to the increasing heating effect of BC. The continuous growth of the surface temperature made it easier for the PBL height to develop, which led to the lift of the inversion layer in the PBL and the larger atmospheric environment capacity. Ultimately, it is conducive to the diffusion of the near surface pollutants, thus helping reduce their concentrations, which offsets the increasing tendency of O₃ and add to the decreasing trend of PM_2.5. This phenomenon is the most remarkable in summer, with the fastest increasing rate of temperature (0.8 °C⋅a^-1) and O₃ (3.9 μg⋅m^-3⋅a^-1) during 2015-2019 (excluding 2020 to erase the great effect of COVID-19 lockdown on emissions).

Surface ozone changes during the COVID-19 outbreak in China: An insight into the pollution characteristics and formation regimes of ozone in the cold season

The countrywide lockdown in China during the COVID-19 pandemic provided a natural experiment to study the characteristics of surface ozone (O3). Based on statistical analysis of air quality across China before and during the lockdown, the tempo-spatial variations and site-specific formation regimes of wintertime O3 were analyzed. The results showed that the O3 pollution with concentrations higher than air quality standards could occur widely in winter, which had been aggravated by the emission reduction during the lockdown. On the national scale of China, with the significant decrease (54.03%) in NO2 level from pre-lockdown to COVID-19 lockdown, the maximum daily 8-h average concentration of O3 (MDA8h O3) increased by 39.43% from 49.05 to 64.22 μg/m3. This increase was comprehensively contributed by attenuated NOx suppression and favorable meteorological changes on O3 formation during the lockdown. As to the pollution states of different monitoring stations, surface O3 responded oppositely to the consistent decreased NO2 across China. The O3 levels were found to increase in the northern and central regions, but decrease in the southern region, where the changes in both meteorology (e.g. temperature drops) and precursors (reduced emissions) during the lockdown had diminished local O3 production. The spatial differences in NOx levels generally dictate the site-specific O3 formation regimes in winter, with NOx-titration/VOCs-sensitive regimes being dominant in northern and central China, while VOCs-sensitive/transition regimes being dominant in southern China. These findings highlight the influence of NOx saturation levels on winter O3 formation and the necessity of VOCs emission reductions on O3 pollution controls.

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.

Risk tradeoffs between nitrogen dioxide and ozone pollution during the COVID-19 lockdowns in the Greater Bay area of China

Photochemical regime for ozone (O3) formation is complicated in the sense that reducing emission of nitrogen oxides (NOx) may increase O3 concentration. The lockdown due to COVID-19 pandemic affords a unique opportunity to use real observations to explore the O3 formation regime and the effectiveness of NOx emission control strategies. In this study, observations from ground networks during the lockdowns were used to assess spatial disparity of the Ratio of Ozone Formation (ROF) for nitrogen dioxide (NO2) reduction in the Greater Bay Area (GBA) of China. The health risk model from Air Quality Health Index (AQHI) system in Hong Kong was adopted to evaluate the risk tradeoffs between NO2 and O3. Results show that the levels of O3 increase and NO2 reduction were comparable due to high ROF values in urban areas of central GBA. The ozone reactivity to NO2 reduction gradually declined outwards from central GBA. Despite the O3 increases, the NOx emission controls reduced the Integrated Health Risk (IHR) of NO2 and O3 in most regions of the GBA. When risk coefficients from the AQHI in Canada or the global review were adopted in the risk analyses, the results are extremely encouraging because the controls of NOx emission reduced the IHR of NO2 and O3 almost everywhere in the GBA. Our results underscore the importance of using a risk-based method to assess the effectiveness of emission control measures and the overall health benefit from NOx emission controls in the GBA.

Did unprecedented air pollution levels cause spike in Delhi's COVID cases during second wave?

The onset of the second wave of COVID-19 devastated many countries worldwide. Compared with the first wave, the second wave was more aggressive regarding infections and deaths. Numerous studies were conducted on the association of air pollutants and meteorological parameters during the first wave of COVID-19. However, little is known about their associations during the severe second wave of COVID-19. The present study is based on the air quality in Delhi during the second wave. Pollutant concentrations decreased during the lockdown period compared to pre-lockdown period (PM2.5: 67 µg m-3 (lockdown) versus 81 µg m-3 (pre-lockdown); PM10: 171 µg m-3 versus 235 µg m-3; CO: 0.9 mg m-3 versus 1.1 mg m-3) except ozone which increased during the lockdown period (57 µg m-3 versus 39 µg m-3). The variation in pollutant concentrations revealed that PM2.5, PM10 and CO were higher during the pre-COVID-19 period, followed by the second wave lockdown and the lowest in the first wave lockdown. These variations are corroborated by the spatiotemporal variability of the pollutants mapped using ArcGIS. During the lockdown period, the pollutants and meteorological variables explained 85% and 52% variability in COVID-19 confirmed cases and deaths (determined by General Linear Model). The results suggests that air pollution combined with meteorology acted as a driving force for the phenomenal growth of COVID-19 during the second wave. In addition to developing new drugs and vaccines, governments should focus on prediction models to better understand the effect of air pollution levels on COVID-19 cases. Policy and decision-makers can use the results from this study to implement the necessary guidelines for reducing air pollution. Also, the information presented here can help the public make informed decisions to improve the environment and human health significantly.

Impact of reduced anthropogenic emissions on chemical characteristics of urban aerosol by individual particle analysis

A single particle aerosol mass spectrometer was deployed in a heavily polluted area of China during a coronavirus lockdown to explore the impact of reduced anthropogenic emissions on the chemical composition, size distributions, mixing state, and secondary formation of urban aerosols. Ten particle groups were identified using an adaptive resonance network algorithm. Increased atmospheric oxidation during the lockdown period (LP) resulted in a 42.2%-54% increase in the major NaK-SN particle fraction relative to the normal period (NP). In contrast, EC-aged particles decreased from 31.5% (NP) to 23.7% (LP), possibly due to lower emissions from motor vehicles and coal combustion. The peak particle size diameter increased from 440 nm during the NP to 500 nm during LP due to secondary particle formation. High proportions of mixed 62NO3- indicate extensive particle aging. Correlations between secondary organic (43C2H3O+, oxalate) and secondary inorganic species (62NO3-, 97HSO4- and 18NH4+) versus oxidants (Ox = O3 + NO2) and relative humidity (RH) indicate that increased atmospheric oxidation promoted the generation of secondary species, while the effects of RH were more complex. Differences between the NP and LP show that reductions in primary emissions had a remarkable impact on the aerosol particles. This study provides new insights into the effects of pollution emissions on atmospheric reactions and the specific aerosol types in urban regions.

Analysis of the meteorological factors affecting the short-term increase in O3 concentrations in nine global cities during COVID-19

Surface ozone (O3) is a major air pollutant around the world. This study investigated O3 concentrations in nine cities during the Coronavirus disease 2019 (COVID-19) lockdown phases. A statistical model, named Generalized Additive Model (GAM), was also developed to assess different meteorological factors, estimate daily O3 release during COVID-19 lockdown and determine the relationship between the two. We found that: (1) Daily O3 significantly increased in all selected cities during the COVID-19 lockdown, presenting relative increases from -5.7% (in São Paulo) to 58.9% (in Guangzhou), with respect to the average value for the same period in the previous five years. (2) In the GAM model, the adjusted coefficient of determination (R2) ranged from 0.48 (Sao Paulo) to 0.84 (Rome), and it captured 51-85% of daily O3 variations. (3) Analyzing the expected O3 concentrations during the lockdown, using GAM fed by meteorological data, showed that O3 anomalies were dominantly controlled by meteorology. (4) The relevance of different meteorological variables depended on the cities. The positive O3 anomalies in Beijing, Wuhan, Guangzhou, and Delhi were mostly associated with low relative humidity and elevated maximum temperature. Low wind speed, elevated maximum temperature, and low relative humidity were the leading meteorological factors for O3 anomalies in London, Paris, and Rome. The two other cities had different leading factor combinations.

Statistical and machine learning methods for evaluating trends in air quality under changing meteorological conditions

Evaluating the influence of anthropogenic-emission changes on air quality requires accounting for the influence of meteorological variability. Statistical methods such as multiple linear regression (MLR) models with basic meteorological variables are often used to remove meteorological variability and estimate trends in measured pollutant concentrations attributable to emission changes. However, the ability of these widely used statistical approaches to correct for meteorological variability remains unknown, limiting their usefulness in the real-world policy evaluations. Here, we quantify the performance of MLR and other quantitative methods using simulations from a chemical transport model, GEOS-Chem, as a synthetic dataset. Focusing on the impacts of anthropogenic-emission changes in the US (2011 to 2017) and China (2013 to 2017) on PM2.5 and O3, we show that widely used regression methods do not perform well in correcting for meteorological variability and identifying long-term trends in ambient pollution related to changes in emissions. The estimation errors, characterized as the differences between meteorology-corrected trends and emission-driven trends under constant meteorology scenarios, can be reduced by 30%-42% using a random forest model that incorporates both local- and regional-scale meteorological features. We further design a correction method based on GEOS-Chem simulations with constant-emission input and quantify the degree to which anthropogenic emissions and meteorological influences are inseparable, due to their process-based interactions. We conclude by providing recommendations for evaluating the impacts of anthropogenic-emission changes on air quality using statistical approaches.

Impact Assessment of COVID-19 Lockdown on Vertical Distributions of NO2 and HCHO From MAX-DOAS Observations and Machine Learning Models

Responses to the COVID-19 pandemic led to major reductions on air pollutant emissions in modern history. To date, there has been no comprehensive assessment for the impact of lockdowns on the vertical distributions of nitrogen dioxide (NO2) and formaldehyde (HCHO). Based on profiles from 0 to 2 km retrieved by Multi-AXis-Differential Optical Absorption Spectroscopy observation and a large volume of real-time data at a suburb site in Shanghai, China, four types of machine learning models were developed and compared, including multiple linear regression, support vector machine, bagged trees (BT), and artificial neural network. Ultimately BT model was employed to reproduce NO2 and HCHO profiles with the best performance. Predictions with different meteorological and surface pollution scenarios were conducted from 2017 to 2019, for assessing the corresponding impacts on the changes of NO2 and HCHO profiles during COVID-19 lockdown. The simulations illustrate that the NO2 decreased in 2020 by 43.8%, 45.5%, and 44.6%, relative to 2017, 2018, and 2019, respectively. For HCHO, the lockdown-induced situation presented the declines of 28.6%, 32.1%, and 10.9%, respectively. In the comparisons of vertical distributions, NO2 maintained decreasing at all altitudes, while HCHO decreased at low altitudes and increased at high altitudes. During COVID-19 lockdown, the reduction of NO2 and HCHO from the variation of surface pollutants was dominated below 0.5 km, while the relevant meteorological factors played a more significant role above 0.5 km.

PM2.5 decadal data in cold vs. mild climate airports: COVID-19 era and a call for sustainable air quality policy

Airports are identified hotspots for air pollution, notably for fine particles (PM2.5) that are pivotal in aerosol-cloud interaction processes of climate change and human health. We herein studied the field observation and statistical analysis of 10-year data of PM2.5 and selected emitted co-pollutants (CO, NOx, and O3), in the vicinity of three major Canadian airports, with moderate to cold climates. The decadal data analysis indicated that in colder climate airports, pollutants like PM2.5 and CO accumulate disproportionally to their emissions in fall and winter, in comparison to airports in milder climates. Decadal daily averages and standard errors of PM2.5 concentrations were as follows: Vancouver, 5.31 ± 0.017; Toronto, 6.71 ± 0.199; and Montreal, 7.52 ± 0.023 μg/m3. The smallest and the coldest airport with the least flights/passengers had the highest PM2.5 concentration. QQQ-ICP-MS/MS and HR-S/TEM analysis of aerosols near Montreal Airport indicated a wide range of emerging contaminants (Cd, Mo, Co, As, Ni, Cr, and Pb) ranging from 0.90 to 622 μg/L, which were also observed in the atmosphere. During the lockdown, a pronounced decrease in the concentrations of PM2.5 and submicron particles, including nanoparticles, in residential areas close to airports was observed, conforming with the recommended workplace health thresholds (~ 2 × 104 cm-3), while before the lockdown, condensable particles were up to ~ 1 × 105 cm-3. Targeted reduction of PM2.5 emission is recommended for cold climate regions.

Local and transboundary impacts of PM2.5 sources identified in Seoul during the early stage of the COVID-19 outbreak

Countries in Northeast Asia have been regulating PM2.5 sources and studying their local and transboundary origins since PM2.5 causes severe impacts on public health and economic losses. However, the separation of local and transboundary impacts is not fully realized because it is impossible to change air pollutant emissions from multiple countries experimentally. Exceptionally, the early stage of the COVID-19 outbreak (January-March 2020) provided a cross-country experiment to separate each impact of PM2.5 sources identified in Seoul, a downwind area of China. We evaluated the contributions of PM2.5 sources compared to 2019 using dispersion normalized positive matrix factorization (DN-PMF) during three meteorological episodes. Episodes 1 and 2 revealed transboundary impacts and were related to reduced anthropogenic emissions and accumulated primary pollutants in Northeast China. Anthropogenic emissions, except for the residential sector, decreased, but primary air pollutants accumulated by residential coal combustion enhanced secondary aerosol formation. Thus, the contributions of sulfate and secondary nitrate increased in Seoul during episode 1 but then decreased maximally with other primary sources (biomass burning, district heating and incineration, industrial sources, and oil combustion) during episode 2 under meteorological conditions favorable to long-range transport. Local impact was demonstrated by atmospheric stagnation during episode 3. Meteorological condition unfavorable to local dispersion elevated the contributions of mobile and coal combustion and further contributed to PM2.5 high concentration events (HCE). Our study separates the local and transboundary impacts and highlights that cooperations in Northeast Asia on secondary aerosol formation and management of local sources are necessary.

Interactions of Nitric Oxide Molecules with Pure and Oxidized Silver ClustersAgn{plus minus}/AgnO{plus minus} (n=11-13). A Computational Study

In this work we studied, within DFT, the interaction of NO with pure and oxidized Agn, both anionic and cationic, composed from11 to 13 Ag atoms. In that size interval, shell closing effects are not expected, and structural and electronic odd-even effects will determine the strength of interaction. We obtained that species Agn{plus minus} and AgnO{plus minus} with odd number of electrons (n=12) adsorb NO with higher energy than their neighbours. This result agrees with the facts observed in recent mass spectroscopy measurements, which were performed at finite temperature. The adsorption energy is about twice for oxidized clusters compared to pure ones, and higher for anions than for cations. The adsorption of another NO molecule on AgnNO{plus minus} forms Agn(NO)2{plus minus}, with the dimer (NO)2 in cis configuration, and binding the two N atoms with two neigbour Ag atoms. The n=12 show the higher adsorption energy again. In absence of reaction barriers, Agn(NO)2{plus minus} dissociate spontaneously into AgnO{plus minus} and N2O, except the n= 12 anion. The máximum high barrier along the dissociation path of Ag13(NO)2- is about 0.7 eV. Further analysis of PDOS for Ag11-13 (NO)x{plus minus} (x=0,1,2) molecules shows that bonding between NO and Agn mainly occurs in the range between -3.0 eV and 3.0 eV. The overlap between 4 d of Ag and 2 p of N and O is larger for Ag12(NO)2{plus minus} than for neighbour sizes. For n=12, the d bands are close to the (NO)2 2π orbital, leading to extra back-donation charge from the 4 d of Ag to the closer 2π orbital of (NO)2.

Impact of the COVID-19 Pandemic on Air Quality in Metropolitan New Jersey

Improved air quality has been the silver lining of the pandemic since early 2020. The air quality in northern New Jersey (NJ) was continuously measured during the COVID-19 pandemic and through the three stages of recovery, i.e., the Stay-at-home stage, Reopening stage 1, and Reopening stage 2. A significant change in air quality was observed during the Stay-at-home stage (March 16 to May 16, 2020) as most people stayed home and industrial activity decreased 60%. Compared to 2019, carbon dioxide (CO2) decreased 17%, carbon monoxide (CO) decreased 7%, and nitrogen oxides (NOx) decreased 51% during the Stay-at-home stage in 2020. However, the ground-level ozone (O3) increased in 2020 because of the reduced NOx emission and the possibly increased levels of volatile organic compounds (VOCs) due to warmer weather. With the step-by-step reopening process, the difference in local CO2 levels between 2019 and 2020 was reduced, and the NOx concentration returned to its 2019 level. The CO2 concentrations were positively correlated with CO, and the NOx concentrations were negatively correlated with O3. Under the COVID-19 pandemic in 2020, NJ consumed 14% less natural gas and 21% less gasoline; therefore, the CO2, CO, and NOx emissions and concentration levels were reduced besides the effects of meteorology parameters on air quality in metropolitan New Jersey. Our findings support that replacing fossil fuels with electric or renewable energy in the transportation systems and industry could be beneficial for the concentration reduction of certain greenhouse gases.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11270-022-05764-w.

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.

NOx and O3 Trends at U.S. Non-Attainment Areas for 1995-2020: Influence of COVID-19 Reductions and Wildland Fires on Policy-Relevant Concentrations

We analyzed NO2 and O3 data from 32 U.S. non-attainment areas (NAAs) for 1995-2020. Since 1995, all regions have shown steady reductions in NO2 and the weekend-weekday pattern indicates that the O3 production regime in most NAAs has transitioned to a NOx-limited regime, while a few NAAs remain NOx-saturated. In the eastern U.S., all NAAs have made steady progress toward meeting the current (70 ppb) O3 standard, but this is less true in midwestern and western NAAs, with most showing little improvement in peak O3 concentrations since about 2010. Due to COVID-19 restrictions, NO2 concentrations were substantially reduced in 2020. In the eastern NAAs, we see significant reductions in both NO2 and peak O3 concentrations. In the midwestern U.S., results were more variable, with both higher and lower O3 values in 2020. In the western U.S. (WUS), we see variable reductions in NO2 but substantial increases in O3 at most sites, due to the influence from huge wildland fires. The recent pattern over the past decade shows that the large amount of wildland fires has a strong influence on the policy-relevant O3 metric in the WUS, and this is making it more difficult for these regions to meet the O3 standard.

Variations and Sources of Organic Aerosol in Winter Beijing under Markedly Reduced Anthropogenic Activities During COVID-2019

The COVID-19 outbreak provides a "controlled experiment" to investigate the response of aerosol pollution to the reduction of anthropogenic activities. Here we explore the chemical characteristics, variations, and emission sources of organic aerosol (OA) based on the observation of air pollutants and combination of aerosol mass spectrometer (AMS) and positive matrix factorization (PMF) analysis in Beijing in early 2020. By eliminating the impacts of atmospheric boundary layer and the Spring Festival, we found that the lockdown effectively reduced cooking-related OA (COA) but influenced fossil fuel combustion OA (FFOA) very little. In contrast, both secondary OA (SOA) and O3 formation was enhanced significantly after lockdown: less-oxidized oxygenated OA (LO-OOA, 37% in OA) was probably an aged product from fossil fuel and biomass burning emission with aqueous chemistry being an important formation pathway, while more-oxidized oxygenated OA (MO-OOA, 41% in OA) was affected by regional transport of air pollutants and related with both aqueous and photochemical processes. Combining FFOA and LO-OOA, more than 50% of OA pollution was attributed to combustion activities during the whole observation period. Our findings highlight that fossil fuel/biomass combustion are still the largest sources of OA pollution, and only controlling traffic and cooking emissions cannot efficiently eliminate the heavy air pollution in winter Beijing.

Evaluation of NOx emissions before, during, and after the COVID-19 lockdowns in China: A comparison of meteorological normalization methods

Meteorological normalization refers to the removal of meteorological effects on air pollutant concentrations for evaluating emission changes. There currently exist various meteorological normalization methods, yielding inconsistent results. This study aims to identify the state-of-the-art method of meteorological normalization for characterizing the spatiotemporal variation of NOx emissions caused by the COVID-19 pandemic in China. We obtained the hourly data of NO2 concentrations and meteorological conditions for 337 cities in China from January 1, 2019, to December 31, 2020. Three random-forest based meteorological normalization methods were compared, including (1) the method that only resamples meteorological variables, (2) the method that resamples meteorological and temporal variables, and (3) the method that does not need resampling, denoted as Resample-M, Resample-M&T, and Resample-None, respectively. The comparison results show that Resample-M&T considerably underestimated the emission reduction of NOx during the lockdowns, Resample-None generates widely fluctuating estimates that blur the emission recovery trend during work resumption, and Resample-M clearly delineates the emission changes over the entire period. Based on the Resample-M results, the maximum emission reduction occurred during January to February 2020, for most cities, with an average decrease of 19.1 ± 9.4% compared to 2019. During April of 2020 when work resumption initiated to the end of 2020, the emissions rapidly bounced back for most cities, with an average increase of 12.6 ± 15.8% relative to those during the strict lockdowns. Consequently, we recommend using Resample-M for meteorological normalization, and the normalized NO2 concentration dynamics for each city provide important implications for future emission reduction.

The impact of the COVID-19 pandemic on air pollution: A global assessment using machine learning techniques

In response to the COVID-19 pandemic, most countries implemented public health ordinances that resulted in restricted mobility and a resultant change in air quality. This has provided an opportunity to quantify the extent to which carbon-based transport and industrial activity affect air quality. However, quantification of these complex effects has proven to be difficult, depending on the stringency of restrictions, country-specific emission source profiles, long-term trends and meteorological effects on atmospheric chemistry, emission levels and in-flow from nearby countries. In this study, confounding factors were disentangled for a direct comparison of pandemic-related reductions in absolute pollutions levels, globally. The non-linear relationships between atmospheric processes and daily ground-level NO2 , PM10, PM2.5 and O3 measurements were captured in city- and pollutant-specific XGBoost models for over 700 cities, adjusting for weather, seasonality and trends. City-level modelling allowed adaptation to the distinct topography, urban morphology, climate and atmospheric conditions for each city, individually, as the weather variables that were most predictive varied across cities. Pollution forecasts for 2020 in absence of a pandemic were generated based on weather and formed an ensemble for country-level pollution reductions. Findings were robust to modelling assumptions and consistent with various published case studies. NO2 reduced most in China, Europe and India, following severe government restrictions as part of the initial lockdowns. Reductions were highly correlated with changes in mobility levels, especially trips to transit stations, workplaces, retail and recreation venues. Further, NO2 did not fully revert to pre-pandemic levels in 2020. Ambient PM2.5 pollution, which has severe adverse health consequences, reduced most in China and India. Since positive health effects could be offset to some extent by prolonged exposure to indoor pollution, alternative transport initiatives could prove to be an important pathway towards better health outcomes in these countries. Increased O3 levels during initial lockdowns have been documented widely. However, our analyses also found a subsequent reduction in O3 for many countries below what was expected based on meteorological conditions during summer months (e.g., China, United Kingdom, France, Germany, Poland, Turkey). The effects in periods with high O3 levels are especially important for the development of effective mitigation strategies to improve health outcomes.

Decisive role of ozone formation control in winter PM2.5 mitigation in Shenzhen, China

During the COVID-19 lockdown, atmospheric PM_2.5 in the Pearl River Delta (PRD) showed the highest reduction in China, but the reasons, being a critical question for future air quality policy design, are not yet clear. In this study, we analyzed the relationships among gaseous precursors, secondary aerosols and atmospheric oxidation capacity in Shenzhen, a megacity in the PRD, during the lockdown period in 2020 and the same period in 2021. The comprehensive observational datasets showed large lockdown declines in all primary and secondary pollutants (including O₃). We found that, however, the daytime concentrations of secondary aerosols during the lockdown period and normal period were rather similar when the corresponding odd oxygen (O_x≡O₃+NO₂, an indicator of photochemical processing avoiding the titration effect of O₃ by freshly emitted NO) were at similar levels. Therefore, reduced O_x, rather than the large reduction in precursors, was a direct driver to achieve the decline in secondary aerosols. Moreover, O_x was also found to determine the spatial distribution of intercity PM_2.5 levels in winter PRD. Thus, an effective strategy for winter PM_2.5 mitigation should emphasize on control of winter O₃ formation in the PRD and other regions with similar conditions.

Impacts of Covid-19 interventions on air quality: evidence from Brazilian metropolitan regions

The Covid-19 pandemic has negatively disrupted the way our economy and society functions. Nonetheless, there have also been some positive externalities of the pandemic on the environment. This paper aims to evaluate the concentration of nitrogen dioxide in Brazilian metropolitan regions after the policies adopted to confront Covid-19. In terms of methodological approach, the study employs cross-sectional quantitative analyses to compare the period of 36 days, i.e., 12 March to 16 April-before (in 2019) and after (in 2020) the pandemic declaration. The data were obtained from the Sentinel 5-P low-Earth polar satellite concerning Brazilian metropolitan regions (n = 24). Thorough spatial and statistical analyses were undertaken to identify the pre- and during pandemic nitrogen dioxide concentrations. Complementarily, Spearman's correlation test was performed with variables that impact air quality. The study results a fall in nitrogen dioxide concentration levels in 21 of the 24 metropolitan regions which was observed. The Spearman's correlation coefficient between the nitrogen dioxide variation and the vehicle density was 0.485, at a significance level of 0.05. With these findings in mind, the paper advocates that while the pandemic has a significant negative consequence on the health of population globally, a series of measures that result in a new social organization directly interfere in the reduction of air pollution that contributes to the quality of the air we breathe.

Investigation of PM2.5 pollution during COVID-19 pandemic in Guangzhou, China

The COVID-19 pandemic has raised awareness about various environmental issues, including PM2.5 pollution. Here, PM2.5 pollution during the COVID-19 lockdown was traced and analyzed to clarify the sources and factors influencing PM2.5 in Guangzhou, with an emphasis on heavy pollution. The lockdown led to large reductions in industrial and traffic emissions, which significantly reduced PM2.5 concentrations in Guangzhou. Interestingly, the trend of PM2.5 concentrations was not consistent with traffic and industrial emissions, as minimum concentrations were observed in the fourth period (3/01-3/31, 22.45 μg/m3) of the lockdown. However, the concentrations of other gaseous pollutants, e.g., SO2, NO2 and CO, were correlated with industrial and traffic emissions, and the lowest values were noticed in the second period (1/24-2/03) of the lockdown. Meteorological correlation analysis revealed that the decreased PM2.5 concentrations during COVID-19 can be mainly attributed to decreased industrial and traffic emissions rather than meteorological conditions. When meteorological factors were included in the PM2.5 composition and backward trajectory analyses, we found that long-distance transportation and secondary pollution offset the reduction of primary emissions in the second and third stages of the pandemic. Notably, industrial PM2.5 emissions from western, southern and southeastern Guangzhou play an important role in the formation of heavy pollution events. Our results not only verify the importance of controlling traffic and industrial emissions, but also provide targets for further improvements in PM2.5 pollution.

Exploring chemical changes of the haze pollution during a recent round of COVID-19 lockdown in a megacity in Northeast China

COVID-19 rebounded in China in January 2021, with Heilongjiang as one of the worst-affected provinces. This resulted in a new round of lockdown in Harbin, the capital city of Heilongjiang, from 20 January to 22 February of 2021. A field campaign was conducted to explore the responses of haze pollution in Harbin to the lockdown. Levoglucosan was used to reflect biomass burning emissions, while the molar ratio of sulfur (the sum of sulfur dioxide and sulfate) to nitrogen (the sum of nitrogen dioxide and nitrate), i.e., RS/N, was used as an indicator for the relative importance of coal combustion and vehicle emissions. Based on a synthesis of the levoglucosan and RS/N results, reference period was selected with minimal influences of non-lockdown-related emission variations. As indicated by the almost unchanged sulfur dioxide concentrations, coal combustion emissions were relatively stable throughout the lockdown and reference periods, presumably because the associated activities, e.g., heating supply, power generation, etc., were usually uninterruptible. On the other hand, as suggested by the increase of RS/N, vehicle emissions were considerably reduced during lockdown, likely due to the stay-at-home orders. Compared to results from the reference samples, the lockdown period exhibited higher levels of ozone and various indicators for secondary aerosol formation, pointing to an enhancement of secondary pollution. In addition, photochemistry-related reactions in aqueous phase appeared to be present during the lockdown period, which have not been reported in the frigid atmosphere over Northeast China.

Increased Shamal winds and dust activity over the Arabian Peninsula during the COVID-19 lockdown period in 2020

While anthropogenic pollutants have decreased during the lockdown imposed as an effort to contain the spread of the Coronavirus disease 2019 (COVID-19), changes in particulate matter (PM) do not necessarily exhibit the same tendency. This is the case for the eastern Arabian Peninsula, where in March-June 2020, and with respect to the same period in 2016-2019, a 30 % increase in PM concentration is observed. A stronger than normal nocturnal low-level jet and subtropical jet over parts of Saudi Arabia, in response to anomalous convection over the tropical Indian Ocean, promoted enhanced and more frequent episodes of Shamal winds over the Arabian Peninsula. Increased surface winds associated with the downward mixing of momentum to the surface fostered, in turn, dust lifting and increased PM concentrations. The stronger low-level winds also favoured long-range transport of aerosols, changing the PM values downstream. The competing effects of reduced anthropogenic and increased dust concentrations leave a small positive signal (<5 W m-2) in the net surface radiation flux (Rnet), with the former dominating during daytime and the latter at night. However, in parts of the Arabian Gulf, Sea of Oman and Iran Rnet increased by >20 W m-2 with respect to the baseline period, owing to a clearer environment and weaker winds. It is concluded that a reduction in anthropogenic emissions due to the lockdown does not necessarily go hand in hand with lower particulate matter concentrations. Therefore, emissions reduction strategies need to account for feedback effects in order to reach the planned long-term outcomes.

Machine learning-based estimation of ground-level NO2 concentrations over China

Most current scientific research on NO₂ remote sensing focuses on tropospheric NO₂ column concentrations rather than ground-level NO₂ concentrations; however, ground-level NO₂ concentrations are more related to anthropogenic emissions and human health. This study proposes a machine learning estimation method for retrieving the ground-level NO₂ concentrations throughout China based on the tropospheric NO₂ column concentrations from the TROPOspheric Monitoring Instrument (TROPOMI) and multisource geographic data from 2018 to 2020. This method adopts the XGBoost machine learning model characterized by a strong fitting ability and complex model structure, which can explain the complex nonlinear and high-order relationships between ground-measured NO₂ and its influencing factors. The R² values between the retrievals and the validation and test datasets are 0.67 and 0.73, respectively, which suggests that the proposed method can reliably retrieve the ground-level NO₂ concentrations across China. The distribution characteristics, seasonal variations and interannual differences in ground-level NO₂ concentrations are further analyzed based on the retrieval results, demonstrating that the ground-level NO₂ concentrations exhibit significant geographical and seasonal variations, with high concentrations in winter and low concentrations in summer, and the highly polluted regions are concentrated mainly in Beijing-Tianjin-Hebei (BTH), the Yangtze River Delta (YRD), the Pearl River Delta (PRD), Cheng-Yu District (CY) and other urban agglomerations. Finally, the interannual variation in the ground-level NO₂ concentrations indicates that pollution decreased continuously from 2018 to 2020.

A New Index Developed for Fast Diagnosis of Meteorological Roles in Ground-Level Ozone Variations

China experienced worsening ground-level ozone (O3) pollution from 2013 to 2019. In this study, meteorological parameters, including surface temperature (T 2 ), solar radiation (SW), and wind speed (WS), were classified into two aspects, (1) Photochemical Reaction Condition (PRC = T 2 × SW) and (2) Physical Dispersion Capacity (PDC = WS). In this way, a Meteorology Synthetic Index (MSI = PRC/PDC) was developed for the quantification of meteorology-induced ground-level O3 pollution. The positive linear relationship between the 90th percentile of MDA8 (maximum daily 8-h average) O3 concentration and MSI determined that the contribution of meteorological changes to ground-level O-3 varied on a latitudinal gradient, decreasing from ∼40% in southern China to 10%-20% in northern China. Favorable photochemical reaction conditions were more important for ground-level O3 pollution. This study proposes a universally applicable index for fast diagnosis of meteorological roles in ground-level O3 variability, which enables the assessment of the observed effects of precursor emissions reductions that can be used for designing future control policies.

ELECTRONIC SUPPLEMENTARY MATERIAL

Supplementary material is available in the online version of this article at 10.1007/s00376-021-1257-x.

Optimization of a NOx and VOC Cooperative Control Strategy Based on Clean Air Benefits

Serious ambient PM_2.5 and O₃ pollution is one of the most important environmental challenges of China, necessitating an urgent cost-effective cocontrol strategy. Herein, we introduced a novel integrated assessment system to optimize a NO_x and volatile organic compound (VOC) control strategy for the synergistic reduction of ambient PM_2.5 and O₃ pollution. Focusing on the Beijing-Tianjin-Hebei cities and their surrounding regions, which are experiencing the most serious PM_2.5 and O₃ pollution in China, we found that NO_x emission reduction (64-81%) is essential to attain the air quality standard no matter how much VOC emission is reduced. However, the synergistic VOC control is strongly recommended considering its substantially human health and crop production benefits, which are estimated up to 163 (PM_2.5-related) and 101 (O₃-related) billion CHY during the reduction of considerable emissions. Notably, such benefits will be greatly reduced if the synergistic VOC reduction is delayed. This study also highlights the necessity of simultaneous VOC and NO_x emission control in winter while enhancing the NO_x control in the summer, which is contrary to the current control strategy adopted in China. These findings point out the right pathways for future policy making on comitigating PM_2.5 and O₃ pollution in China and other countries.

Changes in tropospheric ozone concentration over Indo-Gangetic Plains: the role of meteorological parameters

This study seeks to understand and quantify the changes in tropospheric ozone (O3) in lower troposphere (LT), middle troposphere (MT) and upper middle troposphere (UMT) over the Indo-Gangetic Plains (IGPs), India during the COVID-19 lockdown 2020 with that of pre-lockdown 2019. The gridded datasets of ozone from the European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis product, ERA5 in combination with statistical interpolated (IDWs) surface NO2 observations, present a consistent picture and indicate a significant tropospheric ozone enhancement over IGP during COVID-19 lockdown restrictions in May 2020. The Paper also examines the influencing role of meteorological parameters on increasing ozone concentration. Over LT, an increase in O3 concentration (23%) is observed and in MT to UMT an enhancement of about 9–18% in O3 concentration have been seen during May 2020 with respect to May 2019. An investigation on causes of increasing  ozone concentration (35–85 ppbv) from MT to UMT during May 2020 reveals that there was significant rise (by 1–6%) in low cloud cover (LCC). Notably, higher LCC increases the backscattering of upward solar radiation from the top of the atmosphere. A positive difference of 5–25 W/m2 in upward solar radiation (USR) is observed across the entire study region. The result suggests that higher LCC significantly contributed to the enhanced USR. Thereby, resulting in higher photolysis rate that lead to an increase in mid tropospheric ozone concentration during May 2020. The results highlight the importance of LCC as an important pathway in ozone formation and aid in scientific understanding of it.

Statistical and machine learning methods for evaluating trends in air quality under changing meteorological conditions

Evaluating the influence of anthropogenic-emission changes on air quality requires accounting for the influence of meteorological variability. Statistical methods such as multiple linear regression (MLR) models with basic meteorological variables are often used to remove meteorological variability and estimate trends in measured pollutant concentrations attributable to emission changes. However, the ability of these widely used statistical approaches to correct for meteorological variability remains unknown, limiting their usefulness in the real-world policy evaluations. Here, we quantify the performance of MLR and other quantitative methods using simulations from a chemical transport model, GEOS-Chem, as a synthetic dataset. Focusing on the impacts of anthropogenic-emission changes in the US (2011 to 2017) and China (2013 to 2017) on PM_2.5 and O₃, we show that widely used regression methods do not perform well in correcting for meteorological variability and identifying long-term trends in ambient pollution related to changes in emissions. The estimation errors, characterized as the differences between meteorology-corrected trends and emission-driven trends under constant meteorology scenarios, can be reduced by 30%-42% using a random forest model that incorporates both local- and regional-scale meteorological features. We further design a correction method based on GEOS-Chem simulations with constant-emission input and quantify the degree to which anthropogenic emissions and meteorological influences are inseparable, due to their process-based interactions. We conclude by providing recommendations for evaluating the impacts of anthropogenic-emission changes on air quality using statistical approaches.

Meteorological Normalisation Using Boosted Regression Trees to Estimate the Impact of COVID-19 Restrictions on Air Quality Levels

The global COVID-19 pandemic that began in late December 2019 led to unprecedented lockdowns worldwide, providing a unique opportunity to investigate in detail the impacts of restricted anthropogenic emissions on air quality. A wide range of strategies and approaches exist to achieve this. In this paper, we use the "deweather" R package, based on Boosted Regression Tree (BRT) models, first to remove the influences of meteorology and emission trend patterns from NO, NO2, PM10 and O3 data series, and then to calculate the relative changes in air pollutant levels in 2020 with respect to the previous seven years (2013-2019). Data from a northern Spanish region, Cantabria, with all types of monitoring stations (traffic, urban background, industrial and rural) were used, dividing the calendar year into eight periods according to the intensity of government restrictions. The results showed mean reductions in the lockdown period above -50% for NOx, around -10% for PM10 and below -5% for O3. Small differences were found between the relative changes obtained from normalised data with respect to those from observations. These results highlight the importance of developing an integrated policy to reduce anthropogenic emissions and the need to move towards sustainable mobility to ensure safer air quality levels, as pre-existing concentrations in some cases exceed the safe threshold.