Drivers for the poor air quality conditions in North China Plain during the COVID-19 outbreak

Impact of aerosol-radiation interaction and heterogeneous chemistry on the winter decreasing PM2.5 and increasing O3 in Eastern China 2014-2020

In the context of the prevalent winter air quality issues in China marked by declining PM2.5 and rising O3, this study employed a modified WRF-Chem model to examine the aerosol radiation interaction (ARI), heterogeneous chemistry (AHC), and their combined impact (ALL) on the variations in O3 and PM2.5 during the 2014-2020 in eastern China. Our analysis confirmed that ARI curtailed O3 while elevating PM2.5. AHC reduced O3 through heterogeneous absorption of NOx and hydroxides while notably fostering fine-grained sulfate, resulting in a PM2.5 increase. Emission reductions mitigated the inhibitory impact of ARI on meteorological fields and photolysis rates. Emission reduction individually without aerosol feedback led to a 5.43 ppb O3 increase and a 22.89 µg/m3 PM2.5 decrease. ARI and AHC amplified the emission-reduction-induced (ERI) O3 rise by 1.83 and 0.31 ppb, respectively. The response of ARI to emission diminution brought about a modest PM2.5 increase of 0.31 µg/m3. Conversely, AHC, acting as the primary contributor, caused a noteworthy PM2.5 decrease of 4.60 µg/m3. As efforts concentrate on reducing PM2.5, the promotion of ARI on PM2.5 counterbalanced the efficacy of emission reduction and the AHC-induced strengthening of PM2.5 decrease. The ALL magnified the ERI O3 increase by 38.9% and PM2.5 decrease by 18.7%. Sensitivity experiments with different degrees of emission reduction demonstrated a consistent linear relationship between the ALL-induced enhancement of O3 increase and PM2.5 decrease to the ERI PM2.5 decline. Our investigation revealed the complex connection between emissions and aerosol feedback in influencing air quality.

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.

Development and evaluation of an online monitoring single-particle optical particle counter with polarization detection

We developed a single-particle optical particle counter with polarization detection (SOPC) for the real-time measurement of the optical size and depolarization ratio (defined as the ratio of the vertical component to the parallel component of backward scattering) of atmospheric particles, the polarization ratio (DR) value can reflect the irregularity of the particles. The SOPC can detect aerosol particles with size larger than 500 nm and the maximum particle count rate reaches ∼1.8 × 105 particles per liter. The SOPC uses a modulated polarization laser to measure the optical size of particles according to forward scattering signal and the DR value of the particles by backward S and P signal components. The sampling rate of the SOPC was 106 #/(sec·channel), and all the raw data were processed online. The calibration curve was obtained by polystyrene latex spheres with sizes of 0.5-10 µm, and the average relative deviation of measurement was 3.96% for sub 3 µm particles. T-matrix method calculations showed that the DR value of backscatter light at 120° could describe the variations in the aspect ratio of particles in the above size range. We performed insitu observations for the evaluation of the SOPC, the mass concentration constructed by the SOPC showed good agreement with the PM2.5 measurements in a nearby state-controlled monitoring site. This instrument could provide useful data for source appointment and regulations against air pollution.

Establishing an emission inventory for ammonia, a key driver of haze formation in the southern North China plain during the COVID-19 pandemic

Despite the significant reduction in atmospheric pollutant levels during the COVID-19 lockdown, the presence of haze in the North China Plain remained a frequent occurrence owing to the enhanced formation of secondary inorganic aerosols under ammonia-rich conditions. Quantifying the increase or decrease in atmospheric ammonia (NH3) emissions is a key step in exploring the causes of the COVID-19 haze. Historic activity levels of anthropogenic NH3 emissions were collected through various yearbooks and studies, an anthropogenic NH3 emission inventory for Henan Province for 2020 was established, and the variations in NH3 emissions from different sources between COVID-19 and non-COVID-19 years were investigated. The validity of the NH3 emission inventory was further evaluated through comparison with previous studies and uncertainty analysis from Monte Carlo simulations. Results showed that the total NH3 emissions gradually increased from north-west to south-east, totalling 751.80 kt in 2020. Compared to the non-COVID-19 year of 2019, the total NH3 emissions were reduced by approximately 4 %, with traffic sources, waste disposal and biomass burning serving as the sources with the top three largest reductions, approximately 33 %, 9.97 % and 6.19 %, respectively. Emissions from humans and fuel combustion slightly increased. Meanwhile, livestock waste emissions decreased by only 3.72 %, and other agricultural emissions experienced insignificant change. Non-agricultural sources were more severely influenced by the COVID-19 lockdown than agricultural sources; nevertheless, agricultural activities contributed 84.35 % of the total NH3 emissions in 2020. These results show that haze treatment should be focused on reducing NH3, particularly controlling agricultural NH3 emissions.

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.

Black carbon over tropical Indian coast during the COVID-19 lockdown: inconspicuous role of coastal meteorology

Black carbon (BC) aerosols critically impact the climate and hydrological cycle. The impact of anthropogenic emissions and coastal meteorology on BC dynamics, however, remains unclear over tropical India, a globally identified hotspot. In this regard, we have performed in situ measurements of BC over a megacity (Chennai, 12° 59' 26.5″ N, 80° 13' 51.8″ E) on the eastern coast of India during January-June 2020, comprising the period of COVID-19-induced strict lockdown. Our measurements revealed an unprecedented reduction in BC concentration by an order of magnitude as reported by other studies for various other pollutants. This was despite having stronger precipitation during pre-lockdown and lesser precipitation washout during the lockdown. Our analyses, taking mesoscale dynamics into account, unravels stronger BC depletion in the continental air than marine air. Additionally, the BC source regime also shifted from a fossil-fuel dominance to a biomass burning dominance as a result of lockdown, indicating relative reduction in fossil fuel combustion. Considering the rarity of such a low concentration of BC in a tropical megacity environment, our observations and findings under near-natural or background levels of BC may be invaluable to validate model simulations dealing with BC dynamics and its climatic impacts in the Anthropocene.

Air pollution and its associated health risks before and after COVID-19 in Shaanxi Province, China

Air pollution is a serious environmental problem that damages public health. In the present study, we used the segmentation function to improve the health risk-based air quality index (HAQI) and named it new HAQI (NHAQI). To investigate the spatiotemporal distribution characteristics of air pollutants and the associated health risks in Shaanxi Province before (Period I, 2015-2019) and after (Period II, 2020-2021) COVID-19. The six criteria pollutants were analyzed between January 1, 2015, and December 31, 2021, using the air quality index (AQI), aggregate AQI (AAQI), and NHAQI. The results showed that compared with AAQI and NHAQI, AQI underestimated the combined effects of multiple pollutants. The average concentrations of the six criteria pollutants were lower in Period II than in Period I due to reductions in anthropogenic emissions, with the concentrations of PM_2.5 (particulate matter ≤2.5 μm diameter), PM₁₀ (PM ≤ 10 μm diameter) SO₂, NO₂, O₃, and CO decreased by 23.5%, 22.5%, 45.7%, 17.6%, 2.9%, and 41.6%, respectively. In Period II, the excess risk and the number of air pollution-related deaths decreased considerably by 46.5% and 49%, respectively. The cumulative population distribution estimated using the NHAQI revealed that 61% of the total number of individuals in Shaanxi Province were exposed to unhealthy air during Period I, whereas this proportion decreased to 16% during Period II. Although overall air quality exhibited substantial improvements, the associated health risks in winter remained high.

Analysis of Factors Influencing Air Quality in Different Periods during COVID-19: A Case Study of Tangshan, China

This study aimed to analyze the main factors influencing air quality in Tangshan during COVID-19, covering three different periods: the COVID-19 period, the Level I response period, and the Spring Festival period. Comparative analysis and the difference-in-differences (DID) method were used to explore differences in air quality between different stages of the epidemic and different years. During the COVID-19 period, the air quality index (AQI) and the concentrations of six conventional air pollutants (PM_2.5, PM₁₀, SO₂, NO₂, CO, and O₃-8h) decreased significantly compared to 2017-2019. For the Level I response period, the reduction in AQI caused by COVID-19 control measures were 29.07%, 31.43%, and 20.04% in February, March, and April of 2020, respectively. During the Spring Festival, the concentrations of the six pollutants were significantly higher than those in 2019 and 2021, which may be related to heavy pollution events caused by unfavorable meteorological conditions and regional transport. As for the further improvement in air quality, it is necessary to take strict measures to prevent and control air pollution while paying attention to meteorological factors.

Exploring the driving factors of haze events in Beijing during Chinese New Year holidays in 2020 and 2021 under the influence of COVID-19 pandemic

Unexpected outbreak of the 2019 novel coronavirus (COVID-19) has profoundly altered the way of human life and production activity, which posed visible impacts on PM_2.5 and its chemical species. The abruptly emergency reduction in human activities provided an opportunity to explore the synergetic impacts of multi-factors on shaping PM_2.5 pollution. Here, we conducted two comprehensive observation measurements of PM_2.5 and its chemical species from 1 January to 16 February in Beijing 2020 and the same lunar date in 2021, to investigate temporal variations and reveal the driving factors of haze before and after Chinese New Year (CNY). Results show that mean PM_2.5 concentrations during the whole observation were 63.83 and 66.86 μg/m³ in 2020 and 2021, respectively. Higher secondary inorganic species were observed after CNY, and K⁺, Cl^- showed three prominent peaks which associated closely with fireworks burnings from suburb Beijing and surroundings, verifying that they could be used as two representative tracers of fireworks. Further, we explored the impacts of meteorological conditions, regional transportation as well as chemical reactions on PM_2.5. We found that unfavorable meteorological conditions accounted for 11.0 % and 16.9 % of PM_2.5 during CNY holidays in 2020 and 2021, respectively. Regional transport from southwest and southeast (south) played an important role on PM_2.5 during the two observation periods. Higher ratio of NO₃^-/SO₄^2- were observed under high O_X and low RH conditions, suggesting the major pathway of NO₃^- and SO₄^2- formation could be photochemical process and aqueous-phase reaction. Additionally, nocturnal chemistry facilitated the formation of secondary components of both inorganic and organic. This study promotes understandings of PM_2.5 pollution in winter under the influence of COVID-19 pandemic and provides a well reference for haze and PM_2.5 control in future.

Variability of PM2.5 and O3 concentrations and their driving forces over Chinese megacities during 2018-2020

Recently, air pollution especially fine particulate matters (PM_2.5) and ozone (O₃) has become a severe issue in China. In this study, we first characterized the temporal trends of PM_2.5 and O₃ for Beijing, Guangzhou, Shanghai, and Wuhan respectively during 2018-2020. The annual mean PM_2.5 has decreased by 7.82%-33.92%, while O₃ concentration showed insignificant variations by -6.77%-4.65% during 2018-2020. The generalized additive models (GAMs) were implemented to quantify the contribution of individual meteorological factors and their gas precursors on PM_2.5 and O₃. On a short-term perspective, GAMs modeling shows that the daily variability of PM_2.5 concentration is largely related to the variation of precursor gases (R = 0.67-0.90), while meteorological conditions mainly affect the daily variability of O₃ concentration (R = 0.65-0.80) during 2018-2020. The impact of COVID-19 lockdown on PM_2.5 and O₃ concentrations were also quantified by using GAMs. During the 2020 lockdown, PM_2.5 decreased significantly for these megacities, yet the ozone concentration showed an increasing trend compared to 2019. The GAMs analysis indicated that the contribution of precursor gases to PM_2.5 and O₃ changes is 3-8 times higher than that of meteorological factors. In general, GAMs modeling on air quality is helpful to the understanding and control of PM_2.5 and O₃ pollution in China.

Important revelations of different degrees of COVID-19 lockdown on improving regional air quality: a case study of Shijiazhuang, China

To control the spread of COVID-19, Shijiazhuang implemented two lockdowns of different magnitudes in 2020 (lockdown I) and 2021 (lockdown II). We analyzed the changes in air quality index (AQI), PM_2.5, O₃, and VOCs during the two lockdowns and the same period in 2019 and quantified the effects of anthropogenic sources during the lockdowns. The results show that AQI decreased by 13.2% and 32.4%, and PM_2.5 concentrations decreased by 12.9% and 42.4% during lockdown I and lockdown II, respectively, due to the decrease in urban traffic mobility and industrial activity levels. However, the sudden and unreasonable emission reductions led to an increase in O₃ concentrations by 160.6% and 108.4%, respectively, during the lockdown period. To explore the causes of the O₃ surge, the major precursors NO_x and VOCs were studied separately, and the main VOCs species affecting ozone formation during the lockdown period and the source variation of VOCs were identified, and it is important to note that the relationship between diurnal variation characteristics of VOCs and cooking became apparent during the lockdown period. These findings suggest that regional air quality can be improved by limiting production, but attention should be paid to the surge of O₃ caused by unreasonable emission reductions, clarifying the control priorities for urban O₃ management.

Changes in Air Quality and Drivers for the Heavy PM2.5 Pollution on the North China Plain Pre- to Post-COVID-19

Under the clean air action plans and the lockdown to constrain the coronavirus disease 2019 (COVID-19), the air quality improved significantly. However, fine particulate matter (PM2.5) pollution still occurred on the North China Plain (NCP). This study analyzed the variations of PM2.5, nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), and ozone (O3) during 2017-2021 on the northern (Beijing) and southern (Henan) edges of the NCP. Furthermore, the drivers for the PM2.5 pollution episodes pre- to post-COVID-19 in Beijing and Henan were explored by combining air pollutant and meteorological datasets and the weighted potential source contribution function. Results showed air quality generally improved during 2017-2021, except for a slight rebound (3.6%) in NO2 concentration in 2021 in Beijing. Notably, the O3 concentration began to decrease significantly in 2020. The COVID-19 lockdown resulted in a sharp drop in the concentrations of PM2.5, NO2, SO2, and CO in February of 2020, but PM2.5 and CO in Beijing exhibited a delayed decrease in March. For Beijing, the PM2.5 pollution was driven by the initial regional transport and later secondary formation under adverse meteorology. For Henan, the PM2.5 pollution was driven by the primary emissions under the persistent high humidity and stable atmospheric conditions, superimposing small-scale regional transport. Low wind speed, shallow boundary layer, and high humidity are major drivers of heavy PM2.5 pollution. These results provide an important reference for setting mitigation measures not only for the NCP but for the entire world.

Regional PM2.5 pollution confined by atmospheric internal boundaries in the North China Plain: Analysis based on surface observations

There are plenty of mesoscale meteorological discontinuities in the atmosphere, acting as atmospheric internal boundaries (AIBs). In conjunction with the atmospheric boundary layer in the vertical direction, they form confined three-dimensional structures that significantly affect air pollution. However, the role of AIBs in regional pollution has not been systematically elucidated. Based on surface observations, this study investigates PM_2.5 pollution distributions under the forcing of various AIBs in the North China Plain. A total of 98 regional pollution episodes are identified during the autumn and winter of 2014-2020, and are further classified according to the impact of AIBs. In the pollution formation-maintenance stage, there are three categories. The frontal category (with a frequency of 41%), including the frontal trough type and frontal inverted trough type, displays the most polluted air masses along the mountains. The frontal AIB defines the lateral border of the pollution zone and forms a frontal inversion above, creating a closed and stable structure wherein the highest concentration of PM_2.5 accumulates. The wind shear category (29%) is decided by the dynamic convergence AIB, which causes lighter PM_2.5 pollution with diverse spatial patterns corresponding to west-southwest shear, southeast-east shear, and south-north shear. The topographic obstruction category (14%) presents as a narrow arc-shaped pollution belt at the foot of the windward mountains, resulting from the cold air damming AIB with dynamical obstruction and thermal stratification. Pollution diffuses in three ways: northwest, west, and northeast, respectively. The first one is the strongest and most frequent (42%), with both strong horizontal wind and vertical mixing. The second category is relatively rare (17%), characterized by foehn-induced active vertical ventilation. The last one is frequent (41%), but relatively weak, mainly relying on horizontal diffusion. Some evolution details of the AIB affecting PM_2.5 pollution are also illustrated by a typical case.

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.

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.

Diverse spillover effects of COVID-19 control measures on air quality improvement: evidence from typical Chinese cities

The COVID-19 prevention and control measures are taken by China's government, especially traffic restrictions and production suspension, had spillover effects on air quality improvement. These effects differed among cities, but these differences have not been adequately studied. To provide more knowledge, we studied the air quality index (AQI) and five air pollutants (PM2.5, PM10, SO2, NO2, and O3) before and after the COVID-19 outbreak in Shanghai, Wuhan, and Tangshan. The pollution data from two types of monitoring stations (traffic and non-traffic stations) were separately compared and evaluated. We used monitoring data from the traffic stations to study the emission reduction caused by traffic restrictions. Based on monitoring data from the non-traffic stations, we established a difference-in-difference model to study the emission reduction caused by production suspension. The COVID-19 control measures reduced AQI and the concentrations of all pollutants except O3 (which increased greatly), but the magnitude of the changes differed among the three cities. The control measures improved air quality most in Wuhan, followed by Shanghai and then Tangshan. We investigated the reasons for these differences and found that differences in the characteristics of these three types of cities could explain these differences in spillover effects. Understanding these differences could provide some guidance and support for formulating differentiated air pollution control measures in different cities. For example, whole-process emission reduction technology should be adopted in cities with the concentrated distribution of continuous process enterprises, whereas vehicles that use cleaner energy and public transport should be vigorously promoted in cities with high traffic development level.

COVID-19 pandemic: impacts on air quality and economy before, during and after lockdown in China in 2020

ABSTRACTThis paper comprehensively evaluates the dynamic effects on China's environment and economy during the COVID-19 pandemic. Results show that the COVID-19 lockdown resulted in a temporary improvement in air quality. Furthermore, nitrogen dioxide (NO₂) levels in the atmosphere in China were 36% lower than in the week after last year's Lunar New Year holiday, but this also led to an economic downturn. Moreover, the aerosol optical depth (AOD) decreased significantly. During the back-to-work period, the economy recovered and there was an increase in energy consumption, and CO₂, NO₂ emissions sharply increased to pre-lockdown levels. In the post-lockdown period, the AOD was lower than that of the same period last year. This study can provide reference for environmental policy making, as it demonstrates to what extent the control of pollution sources can improve air quality. Precise emission reduction and regional joint prevention and control are important and effective means for the prevention and control of O₃ pollution. The health and economic benefits of COVID-19 pandemic control measures are incalculable. And this can provide an effective scientific basis and theoretical support for the prevention and control of air pollution.

A Clustering Spatial Estimation of Marginal Economic Losses for Vegetation Due to the Emission of VOCs as a Precursor of Ozone

The economic losses of vegetation caused by ozone were usually evaluated with existing ozone concentrations. However, in the case a new project is assessed, the marginal losses induced by the additional emissions of ozone’s precursors are required. As ozone is VOC-sensitive in China, this study used novel approaches to assess the marginal economic losses (MELs) for vegetation due to the emission of VOCs as a precursor of ozone, which integrated the geographically constrained AHC algorithm with the spatial regression and applied the cluster-specific coefficients of VOC emissions to the MEL estimation. The new approaches reduce the regression sigma2 from 94.5 to 64.6. The marginal contributions of VOC emissions to ozone concentrations range from 0.123 to 1.180 μg/m3 per kilotonne of emissions per year per 0.25 × 0.25 degree. Negative marginal contributions of NOx emissions were found in Southeast China and the Yunan Guizhou Plateau. County-level marginal increases in AOT40s and MELs due to VOC emissions for crops, semi-natural products, and coniferous and deciduous forests were presented as maps. These values are exceedingly large in Northeast China and the Yunan Guizhou Plateau. Due to the high timber prices, sensitivities to ozone, and long growing seasons, MELs of forests are higher than those of other vegetation types, and thus factories with VOC emissions should be away from the surrounding areas of forests.

Quantification of SO2 Emission Variations and the Corresponding Prediction Improvements Made by Assimilating Ground-Based Observations

In this research, a new time-resolved emission inversion system was developed to investigate variations in SO2 emission in China during the COVID-19 (Corona Virus Disease 2019) lockdown period based on a four-dimensional variational (4DVar) inversion method to dynamically optimize the SO2 inventory by assimilating the ground-based hourly observation data. The inversion results obtained were validated in the North China Plain (NCP). Two sets of experiments were carried out based on the original and optimized inventories during the pre-lockdown and lockdown period to quantify the SO2 emission variations and the corresponding prediction improvement. The SO2 emission changes due to the lockdown in the NCP were quantified by the differences in the averaged optimized inventories between the pre-lockdown and lockdown period. As a response to the lockdown control, the SO2 emissions were reduced by 20.1% on average in the NCP, with ratios of 20.7% in Beijing, 20.2% in Tianjin, 26.1% in Hebei, 18.3% in Shanxi, 19.1% in Shandong, and 25.9% in Henan, respectively. These were mainly attributed to the changes caused by the heavy industry lockdown in these areas. Compared to the model performance based on the original inventory, the optimized daily SO2 emission inventory significantly improved the model SO2 predictions during the lockdown period, with the correlation coefficient (R) value increasing from 0.28 to 0.79 and the root-mean-square error (RMSE) being reduced by more than 30%. Correspondingly, the performance of PM2.5 was slightly improved, with R-value increasing from 0.67 to 0.74 and the RMSE being reduced by 8% in the meantime. These statistics indicate the good optimization ability of the time-resolved emission inversion system.

Assessing the change of ambient air quality patterns in Jiangsu Province of China pre-to post-COVID-19

Following the outbreak of the novel coronavirus in early 2020, to effectively prevent the spread of the disease, major cities across China suspended work and production. While the rest of the world struggles to control COVID-19, China has managed to control the pandemic rapidly and effectively with strong lockdown policies. This study investigates the change in air pollution (focusing on the air quality index (AQI), six ambient air pollutants nitrogen dioxide (NO2), ozone (O3), sulphur dioxide (SO2), carbon monoxide (CO), particulate matter with aerodynamic diameters ≤10 μm (PM10) and ≤2.5 μm (PM2.5)) patterns for three periods: pre-COVID (from 1 January to May 30, 2019), active COVID (from 1 January to May 30, 2020) and post-COVID (from 1 January to May 30, 2021) in the Jiangsu province of China. Our findings reveal that the change in air pollution from pre-COVID to active COVID was greater than in previous years due to the government's lockdown policies. Post-COVID, air pollutant concentration is increasing. Mean change PM2.5 from pre-COVID to active COVID decreased by 18%; post-COVID it has only decreased by 2%. PM10 decreased by 19% from pre-COVID to active COVID, but post-COVID pollutant concentration has seen a 23% increase. Air pollutants show a positive correlation with COVID-19 cases among which PM2.5, PM10 and NO2 show a strong correlation during active COVID-19 cases. Metrological factors such as minimum temperature, average temperature and humidity show a positive correlation with COVID-19 cases while maximum temperature, wind speed and air pressure show no strong positive correlation. Although the COVID-19 pandemic had numerous negative effects on human health and the global economy, the reduction in air pollution and significant improvement in ambient air quality likely had substantial short-term health benefits; the government must implement policies to control post-COVID environmental issues.

Insights on In-Situ Photochemistry Associated with Ozone Reduction in Guangzhou during the COVID-19 Lockdown

Increases in ground-level ozone (O3) have been observed during the COVID-19 lockdown in many places around the world, primarily due to the uncoordinated emission reductions of O3 precursors. In Guangzhou, the capital of Guangdong province in South China, O3 distinctively decreased during the lockdown. Such a phenomenon was attributed to meteorological variations and weakening of local O3 formation, as indicated by chemical transport models. However, the emission-based modellings were not fully validated by observations, especially for volatile organic compounds (VOCs). In this study, we analyzed the changes of O3 and its precursors, including VOCs, from the pre-lockdown (Pre-LD) to lockdown period (LD) spanning 1 week in Guangzhou. An observation-based box model was applied to understand the evolution of in-situ photochemistry. Indeed, the ambient concentrations of O3 precursors decreased significantly in the LD. A reduction of 20.7% was identified for the total mixing ratios of VOCs, and the transportation-related species experienced the biggest declines. However, the reduction of O3 precursors would not lead to a decrease of in-situ O3 production if the meteorology did not change between the Pre-LD and LD periods. Sensitivity tests indicated that O3 formation was limited by VOCs in both periods. The lower temperature and photolysis frequencies in the LD reversed the increase of O3 that would be caused by the emission reductions otherwise. This study reiterates the fact that O3 abatement requires coordinated control strategies, even if the emissions of O3 precursors can be significantly reduced in the short term.

The Different Impacts of Emissions and Meteorology on PM2.5 Changes in Various Regions in China: A Case Study

Emissions and meteorology are significant factors affecting aerosol pollution, but it is not sufficient to understand their relative contributions to aerosol pollution changes. In this study, the observational data and the chemical model (GRAPES_CUACE) are combined to estimate the drivers of PM2.5 changes in various regions (the Beijing–Tianjin–Hebei (BTH), the Central China (CC), the Yangtze River Delta (YRD), and the Pearl River Delta (PRD)) between the first month after COVID-19 (FMC_2020) (i.e., from 23 January to 23 February 2020) and the corresponding period in 2019 (FMC_2019). The results show that PM2.5 mass concentration increased by 26% (from 61 to 77 µg m−3) in the BTH, while it decreased by 26% (from 94 to 70 µg m−3) in the CC, 29% (from 52 to 37 µg m−3) in the YRD, and 32% (from 34 to 23 µg m−3) in the PRD in FMC_2020 comparing with FMC_2019, respectively. In the BTH, although emissions reductions partly improved PM2.5 pollution (−5%, i.e., PM2.5 mass concentration decreased by 5% due to emissions) in FMC_2020 compared with that of FMC_2019, the total increase in PM2.5 mass concentration was dominated by more unfavorable meteorological conditions (+31%, i.e., PM2.5 mass concentration increased by 31% due to meteorology). In the CC and the YRD, emissions reductions (−33 and −36%) played a dominating role in the total decrease in PM2.5 in FMC_2020, while the changed meteorological conditions partly worsened PM2.5 pollution (+7 and +7%). In the PRD, emissions reductions (−23%) and more favorable meteorological conditions (−9%) led to a total decrease in PM2.5 mass concentration. This study reminds us that the uncertainties of relative contributions of meteorological conditions and emissions on PM2.5 changes in various regions are large, which is conducive to policymaking scientifically in China.

Potency of the pandemic on air quality: An urban resilience perspective

Since the outbreak of COVID-19 pandemic, the lockdown policy across the globe has brought improved air quality while fighting against the coronavirus. After the closure, urban air quality was subject to emission reduction of air pollutants and rebounded to the previous level after the potency period of recession. Different response patterns exhibit divergent sensitivities of urban resilience in regard to air pollution. In this paper, we investigate the post-lockdown AQI values of 314 major cities in China to analyse their differential effects on the influence factors of urban resilience. The major findings of this paper include: 1) Cities exhibit considerable range of resilience with their AQI values which are dropped by 21.1% per day, took 3.97 days on average to reach the significantly decreased trough point, and reduced by 49.3% after the lockdown initiatives. 2) Mega cities and cities that locate as the focal points of transportation for nearby provinces, together with those with high AQI values, were more struggling to maintain a good air quality with high rebounds. 3) Urban resilience shows divergent spatial sensitivities to air pollution controls. Failing to consider multi-dimensional factors besides from geomorphological and economical activities could lead to uneven results of environmental policies. The results unveil key drivers of urban air pollution mitigation, and provide valuable insights for prediction of air quality in response to anthropogenic interference events under different macro-economic contexts. Research findings in this paper can be adopted for prevention and management of public health risks from the perspective of urban resilience and environmental management in face of disruptive outbreak events in future.

Pollutant Concentration Changes During the COVID-19 Lockdown in Barcelona and Surrounding Regions: Modification of Diurnal Cycles and Limited Role of Meteorological Conditions

One of the consequences of the COVID-19 lockdowns has been the modification of the air quality in many cities around the world. This study focuses on the variations in pollutant concentrations and how important meteorological conditions were for those variations in Barcelona and the surrounding area during the 2020 lockdown. Boundary-layer height, wind speed, and precipitation were compared between mid-March and April 2016-2019 (pre-lockdown) and the same period in 2020 (during lockdown). The results show the limited influence of meteorological factors on horizontal and vertical dispersion conditions. Compared with the pre-lockdown period, during lockdown the boundary-layer height slightly increased by between 5% and 9%, mean wind speed was very similar, and the fraction of days with rainfall increased only marginally, from 0.33 to 0.34, even though April 2020 was extremely wet in the study area. Variations in nitrogen dioxide ( NO 2 ), particulate matter with a diameter less than 10 μ m (PM10), and ozone ( O 3 ) concentrations over a 10-year period showed a 66% reduction in NO 2 , 37% reduction in PM10, and 27% increase in O 3 at a traffic station in Barcelona. The differences in the daily concentration cycle between weekends and weekdays were heavily smoothed for all pollutants considered. The afternoon NO 2 peak at the traffic station was suppressed compared with the average daily cycle. The analysis of ozone was extended to the regional scale, revealing lower concentrations at rural sites and higher ones in urban zones, especially in Barcelona and the surrounding area. The results presented not only complement previous air quality COVID-19 lockdown studies but also provide insights into the effects of road-traffic reduction.

Does the joint prevention and control regulation improve the air quality? A quasi-experiment in the Beijing economic belt during the COVID-19 pandemic

This study aims to clarify the correlation between air pollution of cities in Beijing Economic Belt from a time-varying perspective and estimate effects of joint prevention and control regulation of air pollution. The COVID-19 pandemic provides a unique opportunity. Based on daily data of air quality, we used TVP-VAR model and utilize the pandemic as a quasi-experiment to assess the policies. The results show air pollution in surrounding cities does influence Beijing's air quality, but the relationship has been weakening year by year, mainly due to industrial adjustment which have achieved progress on alleviating the path of air pollution. Therefore, it is necessary to implement joint regulation in areas with serious pollution. Specifically, the relationship between the air quality of Beijing and Zhangjiakou, Chengde, Tianjin decreased as the pandemic became worse. In contrast, there was no significant decline in Langfang and Baoding. So unlike Baoding and Langfang, industrial production increased relationships between air quality of Beijing and the other three cities, which highlights the validity of restrictions. However, restrictions implemented on Baoding and Langfang affect economic development but have little effect on Beijing's air governance. Therefore, joint regulation contributes to realizing sustainable cities, but more targeted policies should be formulated.

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

The coronavirus disease (COVID-19) spread rapidly worldwide in the first half of 2020. Stringent national lockdown policies imposed by China to prevent the spread of the virus reduced anthropogenic emissions and improved air quality. A weather research and forecasting model coupled with chemistry was applied to evaluate the impact of meteorology and emissions on air quality during the COVID-19 outbreak (from January 23 to February 29, 2020) in mid-eastern China. The results show that air pollution episodes still occurred on polluted days and accounted for 31.6%-60.5% of the total number of outbreak days in mid-eastern China from January 23 to February 29, 2020. However, anthropogenic emissions decreased significantly, indicating that anthropogenic emission reduction cannot completely offset the impact of unfavorable meteorological conditions on air quality. Favorable meteorological conditions in 2019 improved the overall air quality for a COVID-19 outbreak in 2019 instead of 2020. PM_2.5 concentrations decreased by 4.2%-29.2% in Beijing, Tianjin, Shijiazhuang, and Taiyuan, and increased by 6.1%-11.5% in Jinan and Zhengzhou. PM_2.5 concentrations increased by 10.9%-20.5% without the COVID-19 outbreak of 2020 in mid-eastern China, and the frequency of polluted days increased by 5.3%-18.4%. Source apportionment of PM_2.5 during the COVID-19 outbreak showed that industry and residential emissions were the dominant PM_2.5 contributors (32.7%-49.6% and 26.0%-44.5%, respectively) followed by agriculture (18.7%-24.0%), transportation (7.7%-15.5%), and power (4.1%-5.9%). In Beijing, industrial and residential contributions to PM_2.5 concentrations were lower (32.7%) and higher (44.5%), respectively, than in other cities (38.7%-49.6% for industry and 26.0%-36.2% for residential). Therefore, enhancing regional cooperation and implementing a united air pollution control are effective emission mitigation measures for future air quality improvement, especially the development of new technologies for industrial and cooking fumes.

The impact of coal combustion, nitrous oxide emissions, and traffic emissions on COVID-19 cases: a Markov-switching approach

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spread to more than 200 countries with a current case fatality ratio (CFR) of more than 2% globally. The concentration of air pollutants is considered a critical factor responsible for transmitting coronavirus disease among the masses. The photochemical process and coal combustions create respiratory disorders that lead to coronavirus disease. Based on the crucial fact, the study evaluated the impact of nitrous oxide (N2O) emissions, coal combustion, and traffic emissions on COVID-19 cases in a panel of 39 most affected countries of the world. These three air pollution factors are considered to form a lethal smog that negatively affects the patient's respiratory system, leading to increased susceptibility to coronavirus worldwide. The study used the Markov two-step switching regime regression model for obtaining parameter estimates. In contrast, an innovation accounting matrix is used to assess smog factors' intensity on possibly increasing coronavirus cases over time. The results show that N2O emissions, coal combustion, and traffic emissions increase COVID-19 cases in regime-1. On the other hand, N2O emissions significantly increase coronavirus cases in regime-2. The innovation accounting matrix shows that N2O emissions would likely have a more significant share of increasing coronavirus cases with a variance of 33.902%, followed by coal combustion (i.e., 6.643%) and traffic emissions (i.e., 2.008%) over the time horizon. The study concludes that air quality levels should be maintained through stringent environmental policies, such as carbon pricing, sustainable urban planning, green technology advancement, renewable fuels, and pollution less accessible vehicles. All these measures would likely decrease coronavirus cases worldwide.

Nonlinear response of nitrate to NOx reduction in China during the COVID-19 pandemic

In recent years, nitrate plays an increasingly important role in haze pollution and strict emission control seems ineffective in reducing nitrate pollution in China. In this study, observations of gaseous and particulate pollutants during the COVID-19 lockdown, as well as numerical modelling were integrated to explore the underlying causes of the nonlinear response of nitrate mitigation to nitric oxides (NO_x) reduction. We found that, due to less NO_x titration effect and the transition of ozone (O₃) formation regime caused by NO_x emissions reduction, a significant increase of O₃ (by ∼ 69%) was observed during the lockdown period, leading to higher atmospheric oxidizing capacity and facilitating the conversion from NO_x to oxidation products like nitric acid (HNO₃). It is proven by the fact that 26-61% reduction of NO_x emissions only lowered surface HNO₃ by 2-3% in Hebi and Nanjing, eastern China. In addition, ammonia concentration in Hebi and Nanjing increased by 10% and 40% during the lockdown, respectively. Model results suggested that the increasing ammonia can promote the gas-particle partition and thus enhance the nitrate formation by up to 20%. The enhanced atmospheric oxidizing capacity together with increasing ammonia availability jointly promotes the nitrate formation, thereby partly offsetting the drop of NO_x. This work sheds more lights on the side effects of a sharp NO_x reduction and highlights the importance of a coordinated control strategy.

Impacts of the COVID-19 economic slowdown on ozone pollution in the U.S

In this work, we use observations and experimental emissions in a version of NOAA's National Air Quality Forecasting Capability to show that the COVID-19 economic slowdown led to disproportionate impacts on near-surface ozone concentrations across the contiguous U.S. (CONUS). The data-fusion methodology used here includes both U.S. EPA Air Quality System ground and the NASA Aura satellite Ozone Monitoring Instrument (OMI) NO2 observations to infer the representative emissions changes due to the COVID-19 economic slowdown in the U.S. Results show that there were widespread decreases in anthropogenic (e.g., NOx) emissions in the U.S. during March-June 2020, which led to widespread decreases in ozone concentrations in the rural regions that are NOx-limited, but also some localized increases near urban centers that are VOC-limited. Later in June-September, there were smaller decreases, and potentially some relative increases in NOx emissions for many areas of the U.S. (e.g., south-southeast) that led to more extensive increases in ozone concentrations that are partly in agreement with observations. The widespread NOx emissions changes also alters the O3 photochemical formation regimes, most notably the NOx emissions decreases in March-April, which can enhance (mitigate) the NOx-limited (VOC-limited) regimes in different regions of CONUS. The average of all AirNow hourly O3 changes for 2020-2019 range from about +1 to -4 ppb during March-September, and are associated with predominantly urban monitoring sites that demonstrate considerable spatiotemporal variability for the 2020 ozone changes compared to the previous five years individually (2015-2019). The simulated maximum values of the average O3 changes for March-September range from about +8 to -4 ppb (or +40 to -10%). Results of this work have implications for the use of widespread controls of anthropogenic emissions, particularly those from mobile sources, used to curb ozone pollution under the current meteorological and climate conditions in the U.S.

COVID-19 lockdown and particle exposure of road users

INTRODUCTION

In 2020, due to the outbreak of COVID-19, there has been an unprecedented decrease in road traffic in almost all urbanized areas around the globe. This has undoubtedly affected the ambient air quality.

METHODS

In this study mobile and fixed-site measurements of aerosol particle concentrations in the ambient air in one of the busiest streets in Lublin, a mid-sized city in Central Europe (Poland) during the COVID-19 lockdown in the spring of 2020 were performed. Based on the measurements particle doses received by road users during different times of the day were assessed. The obtained results were compared with corresponding pre-COVID-19 measurements also performed in the spring which were available only from 2017.

RESULTS

During lockdown the mass concentration of traffic-related submicrometer PM1 particles and number concentration of ultrafine PN0.1 particles was significantly reduced. This resulted in a decrease of doses inhaled by road users as well as of particle doses deposited in their respiratory tracks. The greatest reductions of respectively over 2 times and over 5 times were observed during the day for total particles and traffic-related particles. Smaller reductions indicating the existence of relatively intensive non-traffic emissions were reported at night.

CONCLUSIONS

Substantial decrease in traffic intensity in the city caused by lockdown restrictions resulted in a significant reduction in the concentration of vehicle-generated particles in the ambient air. This in turn could have resulted in smaller doses inhaled by the inhabitants, specifically road users, which should have a positive impact on their health.

COVID-19 outbreak and air quality of Lahore, Pakistan: evidence from asymmetric causality analysis

This paper aims to examine the impact of COVID-19 restrictions on the air quality of Lahore city of Pakistan for the period 26th February, 2020 to 31st August, 2020. The study employs asymmetrical Granger causality tests for analyzing the effects of COVID-19 cases and deaths on particulate matter (PM_2.5) emissions in the city. The results show positive shocks in COVID-19 cases and deaths improve the air quality of the city. This implies that the pandemic has lowered down environmental pressure in one of the top most polluted cities of the world. Further, the problem of hazardous air pollution in Lahore city is manmade mainly caused by everyday human activities. When these human activities were restricted owing to a rise in COVID-19 cases and deaths, the air pollution in the city resultantly reduces. Therefore, this study recommends controlling unnecessary production and consumption activities that degrades the environment so that air pollution in the city can be manageable after the COVID-19.

The Dynamic Impact of the COVID-19 Pandemic on Air Quality: The Beijing Lessons

Air pollution is one of the major environmental problems that endanger human health. The COVID-19 pandemic provided an excellent opportunity to investigate the possible methods to improve Beijing's air quality meanwhile considering Beijing's economic impact. We used the TVP-VAR model to analyze the dynamic relationship among the pandemic, economy and air quality based on the daily data from 1 January to 30 August 2020. The result shows that the COVID-19 pandemic indeed had a positive effect on air governance which was good for human health, while doing business as usual would gradually weaken this effect. It shows that the Chinese authority's production restriction effectively deals with air pollution in a short period of time since the pandemic is just like a quasi-experiment that suddenly suspended all the companies. However, as the limitation stops, the improvement decreases. It is not sustainable. In addition, a partial quarantine also has a positive impact on air quality, which means a partial limitation was also helpful in improving air quality and also played an important role in protecting people's health. Second, the control measures really hurt Beijing's economy. However, the partial quarantine had fewer adverse effects on the economy than the lockdown. It is supposed to be a reference for air governance and pandemic control. Third, the more the lag periods were, the smaller their impact. Thus, restrictions on production can only be used in emergencies, such as some international meetings, while it is hard to improve the air quality and create a healthy and comfortable living environment only by limitation in the long-term.

Effects of COVID-19 lockdowns on fine particulate matter concentrations

Lockdowns during the COVID-19 pandemic provide an unprecedented opportunity to examine the effects of human activity on air quality. The effects on fine particulate matter (PM2.5) are of particular interest, as PM2.5 is the leading environmental risk factor for mortality globally. We map global PM2.5 concentrations for January to April 2020 with a focus on China, Europe, and North America using a combination of satellite data, simulation, and ground-based observations. We examine PM2.5 concentrations during lockdown periods in 2020 compared to the same periods in 2018 to 2019. We find changes in population-weighted mean PM2.5 concentrations during the lockdowns of -11 to -15 μg/m3 across China, +1 to -2 μg/m3 across Europe, and 0 to -2 μg/m3 across North America. We explain these changes through a combination of meteorology and emission reductions, mostly due to transportation. This work demonstrates regional differences in the sensitivity of PM2.5 to emission sources.

COVID-19 and air pollution in Vienna-a time series approach

We performed a time series analysis in Vienna, Austria, investigating the temporal association between daily air pollution (nitrogen dioxide, NO₂ and particulate matter smaller than 10 µm, PM10) concentration and risk of coronavirus disease 2019 (COVID-19) infection and death. Data covering about 2 months (March–April 2020) were retrieved from public databases. Infection risk was defined as the ratio between infected and infectious. In a separate sensitivity analysis different models were applied to estimate the number of infectious people per day. The impact of air pollution was assessed through a linear regression on the natural logarithm of infection risk. Risk of COVID-19 mortality was estimated by Poisson regression. Both pollutants were positively correlated with the risk of infection with the coefficient for NO₂ being 0.032 and for PM10 0.014. That association was significant for the irritant gas (p = 0.012) but not for particles (p = 0.22). Pollutants did not affect COVID-19-related mortality. The study findings might have wider implications on an interaction between air pollution and infectious agents.

Ambient PM2.5 Estimates and Variations during COVID-19 Pandemic in the Yangtze River Delta Using Machine Learning and Big Data

The lockdown of cities in the Yangtze River Delta (YRD) during COVID-19 has provided many natural and typical test sites for estimating the potential of air pollution control and reduction. To evaluate the reduction of PM2.5 concentration in the YRD region by the epidemic lockdown policy, this study employs big data, including PM2.5 observations and 29 independent variables regarding Aerosol Optical Depth (AOD), climate, terrain, population, road density, and Gaode map Point of interesting (POI) data, to build regression models and retrieve spatially continuous distributions of PM2.5 during COVID-19. Simulation accuracy of multiple machine learning regression models, i.e., random forest (RF), support vector regression (SVR), and artificial neural network (ANN) were compared. The results showed that the RF model outperformed the SVR and ANN models in the inversion of PM2.5 in the YRD region, with the model-fitting and cross-validation coefficients of determination R2 reached 0.917 and 0.691, mean absolute error (MAE) values were 1.026 μg m−3 and 2.353 μg m−3, and root mean square error (RMSE) values were 1.413 μg m−3, and 3.144 μg m−3, respectively. PM2.5 concentrations during COVID-19 in 2020 have decreased by 3.61 μg m−3 compared to that during the same period of 2019 in the YRD region. The results of this study provide a cost-effective method of air pollution exposure assessment and help provide insight into the atmospheric changes under strong government controlling strategies.

COVID-19 and Air Pollution: Measuring Pandemic Impact to Air Quality in Five European Countries

The rapid spread of the coronavirus (COVID-19) pandemic affected the economy, trade, transport, health care, social services, and other sectors. To control the rapid dispersion of the virus, most countries imposed national lockdowns and social distancing policies. This led to reduced industrial, commercial, and human activities, followed by lower air pollution emissions, which caused air quality improvement. Air pollution monitoring data from the European Environment Agency (EEA) datasets were used to investigate how lockdown policies affected air quality changes in the period before and during the COVID-19 lockdown, comparing to the same periods in 2018 and 2019, along with an assessment of the Index of Production variation impact to air pollution changes during the pandemic in 2020. Analysis results show that industrial and mobility activities were lower in the period of the lockdown along with the reduced selected pollutant NO2, PM2.5, PM10 emissions by approximately 20–40% in 2020.