A global observational analysis to understand changes in air quality during exceptionally low anthropogenic emission conditions

In-depth analysis of ambient air pollution changes due to the COVID-19 pandemic in the Asian Monsoon region

The COVID-19 pandemic has given a chance for researchers and policymakers all over the world to study the impact of lockdowns on air quality in each country. This review aims to investigate the impact of the restriction of activities during the lockdowns in the Asian Monsoon region on the main criteria air pollutants. The various types of lockdowns implemented in each country were based on the severity of the COVID-19 pandemic. The concentrations of major air pollutants, especially particulate matter (PM) and nitrogen dioxide (NO2), reduced significantly in all countries, especially in South Asia (India and Bangladesh), during periods of full lockdown. There were also indications of a significant reduction of sulfur dioxide (SO2) and carbon monoxide (CO). At the same time, there were indications of increasing trends in surface ozone (O3), presumably due to nonlinear chemistry associated with the reduction of oxides of nitrogens (NOX). The reduction in the concentration of air pollutants can also be seen in satellite images. The results of aerosol optical depth (AOD) values followed the PM concentrations in many cities. A significant reduction of NO2 was recorded by satellite images in almost all cities in the Asian Monsoon region. The major reductions in air pollutants were associated with reductions in mobility. Pakistan, Bangladesh, Myanmar, Vietnam, and Taiwan had comparatively positive gross domestic product growth indices in comparison to other Asian Monsoon nations during the COVID-19 pandemic. A positive outcome suggests that the economy of these nations, particularly in terms of industrial activity, persisted during the COVID-19 pandemic. Overall, the lockdowns implemented during COVID-19 suggest that air quality in the Asian Monsoon region can be improved by the reduction of emissions, especially those due to mobility as an indicator of traffic in major cities.

Health risks and sources of trace elements and black carbon in PM2.5 from 2019 to 2021 in Beijing

A comprehensive health risk assessment of PM2.5 is meaningful to understand the current status and directions regarding further improving air quality from the perspective of human health. In this study, we evaluated the health risks of PM2.5 as well as highly toxic inorganic components, including heavy metals (HMs) and black carbon (BC) based on long-term observations in Beijing from 2019 to 2021. Our results showed that the relative risks of chronic obstructive pulmonary disease, lung cancer, acute lower respiratory tract infection, ischemic heart disease, and stroke decreased by 4.07%-9.30% in 2020 and 2.12%-6.70% in 2021 compared with 2019. However, they were still at high levels ranging from 1.26 to 1.77, in particular, stroke showed the highest value in 2021. Mn had the highest hazard quotient (HQ, from 2.18 to 2.56) for adults from 2019 to 2021, while Ni, Cr, Pb, As, and BC showed high carcinogenic risks (CR > 1.0×10-6) for adults. The HQ values of Mn and As and the CR values of Pb and As showed constant or slight upwards trends during our observations, which is in contrast to the downward trends of other HMs and PM2.5. Mn, Cr, and BC are crucial toxicants in PM2.5. A significant shrink of southern region sourcesof HMs and BCshrank suggests the increased importance of local sources. Industry, dust, and biomass burning are the major contributors to the non-carcinogenic risks, while traffic emissions and industry are the dominant contributors to the carcinogenic risks in Beijing.

Air quality modeling in the metropolitan area of São Paulo, Brazil: A review

Numerous studies have used air quality models to estimate pollutant concentrations in the Metropolitan Area of São Paulo (MASP) by using different inputs and assumptions. Our objectives are to summarize these studies, compare their performance, configurations, and inputs, and recommend areas of further research. We examined 29 air quality modeling studies that focused on ozone (O3) and fine particulate matter (PM2.5) performed over the MASP, published from 2001 to 2023. The California Institute of Technology airshed model (CIT) was the most used offline model, while the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) was the most used online model. Because the main source of air pollution in the MASP is the vehicular fleet, it is commonly used as the only anthropogenic input emissions. Simulation periods were typically the end of winter and during spring, seasons with higher O3 and PM2.5 concentrations. Model performance for hourly ozone is good with half of the studies with Pearson correlation above 0.6 and root mean square error (RMSE) ranging from 7.7 to 27.1 ppb. Fewer studies modeled PM2.5 and their performance is not as good as ozone estimates. Lack of information on emission sources, pollutant measurements, and urban meteorology parameters is the main limitation to perform air quality modeling. Nevertheless, researchers have used measurement campaign data to update emission factors, estimate temporal emission profiles, and estimate volatile organic compounds (VOCs) and aerosol speciation. They also tested different emission spatial disaggregation approaches and transitioned to global meteorological reanalysis with a higher spatial resolution. Areas of research to explore are further evaluation of models' physics and chemical configurations, the impact of climate change on air quality, the use of satellite data, data assimilation techniques, and using model results in health impact studies. This work provides an overview of advancements in air quality modeling within the MASP and offers practical approaches for modeling air quality in other South American cities with limited data, particularly those heavily impacted by vehicle emissions.

Associations between cold spells of different time types and coronary heart disease severity

Limited evidence showed the association between cold spells and the severity of coronary heart disease (CHD). This study was to investigate the association between cold spells with their different time types and CHD severity. We collected data on CHD patients admitted to the Zhongnan Hospital, Wuhan, China from 2016 to 2021. CHD severity was quantified using the SYNTAX score and transformed into a binomial variable. Daily mean, maximum and minimum temperature were collected during the study period. We first used daily mean temperature to find the optimum definition among multiple thresholds and durations. The daily maximum and minimum temperatures were used to define different types of cold spells (daytime, nighttime and compound) based on the optimum definition. Annual cold spell days were included to assess individual exposure to cold spells. Logistic regression models were performed to fit the association between cold spell days and CHD severity stratified by different tertiles of PM2.5 and NDVI. In this study, 1937 CHD patients were included. The cold spell defined as at least four consecutive days with daily mean temperature below the 5th percentile exhibited the optimum model. We found that a 4-day increase in cold spell days was associated with more severe CHD (OR = 1.170, 95% CI: 1.074, 1.282). Such an association was more pronounced under higher levels of PM2.5 by OR = 1.270 (1.086, 1.494) and lower levels of greenness by OR = 1.240 (1.044, 1.476). Compared with daytime and compound cold spells, nighttime cold spells showed the strongest association with CHD severity by OR = 1.141 (1.026, 1.269). This study showed that exposure to cold spells was positively associated with CHD severity, especially the nighttime cold spells. The association between cold spells and CHD severity was more significant in high levels of PM2.5 and low levels of greenness.

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

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

A short climatology of black and brown carbon and their sources at a suburban site impacted by smoke in Brazil

Emissions from biomass burning challenge efforts to curb air pollution in cities downwind of fire-prone regions, as they contribute large amounts of brown carbon (BrC) and black carbon (BC) particles. We investigated the patterns of BrC and BC concentrations using Aethalometer data (at λ = 370 and 880 nm, respectively) spanning four years at a site impacted by the outflow of smoke. The data required to be post processed for the shadowing effect since, without correction, concentrations would be between 29% and 35% underestimated. The BrC concentrations were consistently higher than the BC concentrations, indicating the prevalence of aerosols from biomass burning. The results were supported by the Ångström coefficient (Å370/880), with values predominantly larger than 1 (mean ± standard deviation: 1.25 ± 0.31). Å370/880 values below 1 were more prevalent during the wet season, which suggests a contribution from fossil fuel combustion. We observed sharp BrC and BC seasonal signals, with mean minimum concentrations of 0.40 µg/m3 and 0.36 µg/m3, respectively, in the wet season, and mean maximum concentrations of 2.05 µg/m3 and 1.53 µg/m3 in the dry season. The largest concentrations were observed when northerly air masses moved over regions with a high density of fire spots. Local burning of residential solid waste and industrial combustion caused extreme BrC and BC concentrations under favourable wind directions. Although neither pollutant is included in any ambient air quality standards, our results suggest that transboundary smoke may hamper efforts to meet the World Health Organization guidelines for fine particles.

Air pollution analysis in Northwestern South America: A new Lagrangian framework

This study examines the spatiotemporal variations of PM2.5, PM10, SO2, O3, NO, and NO2 concentrations in Northwestern South America (NWSA). We assess the efficacy of existing policies, identify underlying phenomena, and highlight areas for further research. Significant findings have emerged by analyzing reanalysis and in-situ data, employing the WRF-Chem model, and utilizing a new Lagrangian framework designed to overcome some drawbacks common to analysis of pollution Long-Range Transport. Wildfires in the first half of the year and volcanic activity (for SO2) in July-August contribute to over 90 % of the pollutant's advection, leading to high pollution levels in urban areas. SO2 volcanic emissions contribute to secondary PM, explaining the peak in PM concentrations in Cali in July. In the second half of the year, pollutant behavior varies based on factors such as city characteristics, vehicular-volume, air temperature, wind speed, and boundary layer height, and O3 is influenced by solar radiation and the NO/NO2 ratio. Diurnal variations of PM and NOx correlate with vehicular density, SO2 with industrial activity, and O3 depends on solar radiation. Trend analysis reveals decreasing PM10 levels except in three Cundinamarca cities and Cali suggesting the need to implement/evaluate control plans in those locations. Although data is limited, NO and NO2 levels show an increasing trend due to the rising number of vehicles. SO2 levels are decreasing, except in Cali, potentially influenced by the nearby industrial and polluted city of Yumbo. O3 displays a downward trend in most cities, except Bogotá, due to the NO/NO2 ratio favoring O3 increase. These findings provide a starting point for further research to deepen our understanding of NWSA air pollution. Such investigations are essential before modifying existing policies or enacting new ones. Collaborative efforts at the international, regional, and inter-city levels are crucial for effective air quality management.

COVID-19 perturbation on US air quality and human health impact assessment

The COVID-19 stay-at-home orders issued in the United States caused significant reductions in traffic and economic activities. To understand the pandemic's perturbations on US emissions and impacts on urban air quality, we developed near-real-time bottom-up emission inventories based on publicly available energy and economic datasets, simulated the emission changes in a chemical transport model, and evaluated air quality impacts against various observations. The COVID-19 pandemic affected US emissions across broad-based energy and economic sectors and the impacts persisted to 2021. Compared with 2019 business-as-usual emission scenario, COVID-19 perturbations resulted in annual decreases of 10-15% in emissions of ozone (O3) and fine particle (PM2.5) gas-phase precursors, which are about two to four times larger than long-term annual trends during 2010-2019. While significant COVID-induced reductions in transportation and industrial activities, particularly in April-June 2020, resulted in overall national decreases in air pollutants, meteorological variability across the nation led to local increases or decreases of air pollutants, and mixed air quality changes across the United States between 2019 and 2020. Over a full year (April 2020 to March 2021), COVID-induced emission reductions led to 3-4% decreases in national population-weighted annual fourth maximum of daily maximum 8-h average O3 and annual PM2.5. Assuming these emission reductions could be maintained in the future, the result would be a 4-5% decrease in premature mortality attributable to ambient air pollution, suggesting that continued efforts to mitigate gaseous pollutants from anthropogenic sources can further protect human health from air pollution in the future.

Achievements and challenges in improving air quality in China: Analysis of the long-term trends from 2014 to 2022

Due to the implementation of air pollution control measures in China, air quality has significantly improved, although there are still additional issues to be addressed. This study used the long-term trends of air pollutants to discuss the achievements and challenges in further improving air quality in China. The Kolmogorov-Zurbenko (KZ) filter and multiple-linear regression (MLR) were used to quantify the meteorology-related and emission-related trends of air pollutants from 2014 to 2022 in China. The KZ filter analysis showed that PM2.5 decreased by 7.36 ± 2.92% yr-1, while daily maximum 8-h ozone (MDA8 O3) showed an increasing trend with 3.71 ± 2.89% yr-1 in China. The decrease in PM2.5 and increase in MDA8 O3 were primarily attributed to changes in emission, with the relative contribution of 85.8% and 86.0%, respectively. Meteorology variations, including increased ambient temperature, boundary layer height, and reduced relative humidity, also contributed to the reduction of PM2.5 and the enhancement of MDA8 O3. The emission-related trends of PM2.5 and MDA8 O3 exhibited continuous decrease and increase, respectively, from 2014 to 2022, while the variation rates slowed during 2018-2020 compared to that during 2014-2017, highlighting the challenges in further improving air quality, particularly in simultaneously reducing PM2.5 and O3. This study recommends reducing NH3 emissions from the agriculture sector in rural areas and transport emissions in urban areas to further decrease PM2.5 levels. Addressing O3 pollution requires the reduction of O3 precursor gases based on site-specific atmospheric chemistry considerations.

Contrasting effects of clean air actions on surface ozone concentrations in different regions over Beijing from May to September 2013-2020

Due to the nonlinear impacts of meteorology and precursors, the response of ozone (O3) trends to emission changes is very complex over different regions in megacity Beijing. Based on long-term in-situ observations at 35 air quality sites (four categories, i.e., urban, traffic, northern suburban and southern suburban sites) and satellite data, spatiotemporal variability of O3, gaseous precursors, and O3-VOCs-NOx sensitivity were explored through multiple metrics during the warm season from 2013 to 2020. Additionally, the contribution of meteorology and emissions to O3 was separated by a machine-learning-based de-weathered method. The annual averaged MDA8 O3 and O3 increased by 3.7 and 2.9 μg/m3/yr, respectively, with the highest at traffic sites and the lowest in northern suburb, and the rate of Ox (O3 + NO2) was 0.2 μg/m3/yr with the highest in southern suburb, although NO2 declined strongly and HCHO decreased slightly. However, the increment of O3 and Ox in the daytime exhibited decreasing trends to some extent. Additionally, NOx abatements weakened O3 loss through less NO titration, which drove narrowing differences in urban-suburban O3 and Ox. Due to larger decrease of NO2 in urban region and HCHO in northern suburb, the extent of VOCs-limited regime fluctuated over Beijing and northern suburb gradually shifted to transition or NOx-limited regime. Compared with the directly observed trends, the increasing rate of de-weathered O3 was lower, which was attributed to favorable meteorological conditions for O3 generation after 2017, especially in June (the most polluted month); whereas the de-weathered Ox declined except in southern suburb. Overall, clean air actions were effective in reducing the atmospheric oxidation capacity in urban and northern suburban regions, weakening local photochemical production over Beijing and suppressing O3 deterioration in northern suburb. Strengthening VOCs control and keeping NOx abatement, especially in June, will be vital to reverse O3 increase trend in Beijing.

Urban surface classification using semi-supervised domain adaptive deep learning models and its application in urban environment studies

High-resolution urban surface information, e.g., the fraction of impervious/pervious surface, is pivotal in studies of local thermal/wind environments and air pollution. In this study, we introduced and validated a domain adaptive land cover classification model, to automatically classify Google Earth images into pixel-based land cover maps. By combining domain adaptation (DA) and semi-supervised learning (SSL) techniques, our model demonstrates its effectiveness even when trained with a limited dataset derived from Gaofen2 (GF2) satellite images. The model's overall accuracy on the translated GF2 dataset improved significantly from 19.5% to 75.2%, and on the Google Earth image dataset from 23.1% to 61.5%. The overall accuracy is 2.9% and 3.4% higher than when using only DA. Furthermore, with this model, we derived land cover maps and investigated the impact of land surface composition on the local meteorological parameters and air pollutant concentrations in the three most developed urban agglomerations in China, i.e., Beijing, Shanghai and the Great Bay Area (GBA). Our correlation analysis reveals that air temperature exhibits a strong positive correlation with neighboring artificial impervious surfaces, with Pearson correlation coefficients higher than 0.6 in all areas except during the spring in the GBA. However, the correlation between air pollutants and land surface composition is notably weaker and more variable. The primary contribution of this paper is to provide an efficient method for urban land cover extraction which will be of great value for assessing the urban surface composition, quantifying the impact of land use/land cover, and facilitating the development of informed policies.

Elemental composition of atmospheric PM10 during COVID-19 lockdown and recovery periods in Moscow (April-July 2020)

Changes in the concentrations of PM10-bound potentially toxic elements (PTEs) during the COVID-19 lockdown period and after the revocation of restrictions were analyzed using the data received at the Aerosol Complex of Moscow State University in April-July 2020. During the lockdown, the input of biomass combustion products enriched in PTEs from the Moscow region hindered the decrease in pollutant concentrations. After the introduction of the self-isolation regime, lower concentrations of most PTEs occurred due to the decrease in anthropogenic activity and the rainy meteorological conditions. After the revocation of restrictive measures, the PTE concentrations began to increase. Multivariate statistical analysis (APCA-MLR) identified the main sources of atmospheric pollutants as urban dust, non-exhaust traffic emissions, and combustion and exhaust traffic emissions. PM10 particles were significantly enriched with Sb, Cd, Sn, Bi, S, Pb, Cu, Mo, and Zn. The total non-carcinogenic and carcinogenic risks, calculated according to the U.S. EPA model, decreased by 24% and 23% during the lockdown; after the removal of restrictions, they increased by 61% and 72%, respectively. The study provides insight into the PTE concentrations and their main sources at different levels of anthropogenic impact.

Long-term exposure to ambient ozone at workplace is positively and non-linearly associated with incident hypertension and blood pressure: longitudinal evidence from the Beijing-Tianjin-Hebei medical examination cohort

BACKGROUND

There is limited longitudinal evidence on the hypertensive effects of long-term exposure to ambient O3. We investigated the association between long-term O3 exposure at workplace and incident hypertension, diastolic blood pressure (DBP), systolic blood pressure (SBP), pulse pressure (PP), and mean arterial pressure (MAP) in general working adults.

METHODS

We conducted a cohort study by recruiting over 30,000 medical examination attendees through multistage stratified cluster sampling. Participants completed a standard questionnaire and comprehensive medical examination. Three-year ambient O3 concentrations at each employed participant's workplace were estimated using a two-stage machine learning model. Mixed-effects Cox proportional hazards models and linear mixed-effects models were used to examine the effect of O3 concentrations on incident hypertension and blood pressure parameters, respectively. Generalized additive mixed models were used to explore non-linear concentration-response relationships.

RESULTS

A total of 16,630 hypertension-free working participants at baseline finished the follow-up. The mean (SD) O3 exposure was 45.26 (2.70) ppb. The cumulative incidence of hypertension was 7.11 (95% CI: 6.76, 7.47) per 100 person-years. Long-term O3 exposure was independently, positively and non-linearly associated with incident hypertension (Hazard ratios (95% CI) for Q2, Q3, and Q4 were 1.77 (1.34, 2.36), 2.06 (1.42, 3.00) and 3.43 (2.46, 4.79), respectively, as compared with the first quartile (Q1)), DBP (β (95% CI) was 0.65 (0.01, 1.30) for Q2, as compared to Q1), SBP (β (95% CI) was 2.88 (2.00, 3.77), 2.49 (1.36, 3.61) and 2.61 (1.64, 3.58) for Q2, Q3, and Q4, respectively), PP (β (95% CI) was 2.12 (1.36, 2.87), 2.03 (1.18, 2.87) and 2.14 (1.38, 2.90) for Q2, Q3, and Q4, respectively), and MAP (β (95% CI) was 1.39 (0.76, 2.02), 1.04 (0.24, 1.84) and 1.12 (0.43, 1.82) for Q2, Q3, and Q4, respectively). The associations were robust across sex, age, BMI, and when considering PM2.5 and NO2.

CONCLUSIONS

To our knowledge, this is the first cohort study in the general population that demonstrates the non-linear hypertensive effects of long-term O3 exposure. The findings are particularly relevant for policymakers and researchers involved in ambient pollution and public health, supporting the integration of reduction of ambient O3 into public health interventions.

Spatial heterogeneity in global atmospheric CO during the COVID-19 lockdown: Implications for global and regional air quality policies

The COVID-19 lockdown (LD) provided a unique opportunity to examine the changes in regional and global air quality. Changes in the atmospheric carbon monoxide (CO) during LD warrant a thorough analysis as CO is a major air pollutant that affects human health, ecosystem and climate. Our analysis reveals a decrease of 5-10% in the CO column during LD (April-May 2020) compared to the pre-lockdown (PreLD, March 2020) periods in regions with high anthropogenic activity, such as East China (EC), Indo-Gangetic Plain (IGP), North America, parts of Europe and Russia. However, this reduction did not occur in the regions of frequent and intense wildfires and agricultural waste burning (AWB). We find high heterogeneity in the CO column distributions, from regional to city scales during the LD period. To determine the sources of CO emissions during LD, we examined the ratios of nitrogen dioxide (NO2), sulfur dioxide (SO2) to CO for major cities in the world. This facilitated the identification of contributions from different sources; including vehicles, industries and biomass burning during LD. The comparison between CO levels during the LD and PreLD periods indicates a notable reduction in the global tropospheric CO, but no significant change in the stratosphere. It is found that CO emissions decreased during LD in the hotspot regions, but rebounded after the LD restrictions were lifted. This study, therefore, highlights the importance of policy decisions and their implementations in the global and regional scales to improve the air quality, and thus to protect public health and environment.

Climate change, air quality, and respiratory health: a focus on particle deposition in the lungs

This review article delves into the multifaceted relationship between climate change, air quality, and respiratory health, placing a special focus on the process of particle deposition in the lungs. We discuss the capability of climate change to intensify air pollution and alter particulate matter physicochemical properties such as size, dispersion, and chemical composition. These alterations play a significant role in influencing the deposition of particles in the lungs, leading to consequential respiratory health effects. The review paper provides a broad exploration of climate change's direct and indirect role in modifying particulate air pollution features and its interaction with other air pollutants, which may change the ability of particle deposition in the lungs. In conclusion, climate change may play an important role in regulating particle deposition in the lungs by changing physicochemistry of particulate air pollution, therefore, increasing the risk of respiratory disease development.

Impact of COVID-19 restrictions on hourly levels of PM10, PM2.5 and black carbon at an industrial suburban site in northern Spain

Due to the COVID-19 pandemic, lockdown restrictions were established around the world. Many studies have assessed whether these restrictions affected atmospheric pollution. Comparison between them is difficult as the periods of time considered are generally not the same and thus, different conclusions may be reached. Besides, most of them consider mean daily pollutant concentration, despite differences being observed according to the time of day. In this study, the hourly levels of PM10, PM2.5 and black carbon (BC) in an industrial suburban area in the north of Spain were analysed from May 2019 to June 2020 and compared with those from the literature, using the same period in each case. In general, the highest concentrations were reached when the wind direction came from the southwest (where a steelworks, a coal-fired power plant and other industries are located) and during the night-time, both before and during the lockdown. The highest concentrations of PM10, PM2.5 and BC were observed from December to February (on average: 45, 17 and 1.3 μg m^-3, respectively). The decrease/increase in those pollutants levels during the lockdown were found to be highly dependent on the period considered. Indeed, PM10 can be found to decrease by up to 39% or increase by 12%; PM2.5 can decrease by 21% or increase by up to 36%; and BC, although it generally decreases (by up to 42%), can increase by 7.4%.

Biological effects of particulate matter samples during the COVID-19 pandemic: a comparison with the pre-lockdown period in Northwest Italy

In 2020, during the COVID-19 pandemic, containment measures were applied inducing potential changes in air pollutant concentrations and thus in air toxicity. This study evaluates the role of restrictions on biological effects of particulate matter (PM) in different Northwest Italy sites: urban background, urban traffic, rural, and incinerator. Daily PM samples collected in 2020 were pooled according to restrictions: January/February (no restrictions), March and April (first lockdown), May/June and July/August/September (low restrictions), October/November/December (second lockdown). The 2019 samples (pre-pandemic period) were pooled as 2020 for comparison. Pools were extracted with organic solvents and extracts were tested to assess cytotoxicity (WST-1 assay) and genotoxicity (comet assay) on BEAS-2B cells, mutagenicity (Ames test) on TA98 and TA100 Salmonella typhimurium strains, and estrogenic activity (gene reporter assay) on MELN cells. Pollutant concentrations were also analyzed (PM10, PM2.5, polycyclic aromatic hydrocarbons). No difference was observed for PM and polycyclic aromatic hydrocarbon concentrations between 2020 and 2019. During lockdown months (2020), PM cytotoxicity/genotoxicity was significantly lower in some sites than during 2019, while considering PM mutagenicity/estrogenic activity some differences were detected but without statistical significance. PM extract effects decreased in some sites during 2020; this may be due to lockdowns that reduced/modified pollutant emissions and may be related also to complex PM origin/formation and to meteorological conditions. In conclusion, the study confirms that PM biological effects cannot be assessed considering only the PM concentration and suggests to include a battery of bioassay for air quality monitoring in order to protect human health from air pollution effects.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s11869-023-01381-6.

The impact of COVID-19 public health restrictions on particulate matter pollution measured by a validated low-cost sensor network in Oxford, UK

Emergency responses to the COVID-19 pandemic led to major changes in travel behaviours and economic activities with arising impacts upon urban air quality. To date, these air quality changes associated with lockdown measures have typically been assessed using limited city-level regulatory monitoring data, however, low-cost air quality sensors provide capabilities to assess changes across multiple locations at higher spatial-temporal resolution, thereby generating insights relevant for future air quality interventions. The aim of this study was to utilise high-spatial resolution air quality information utilising data arising from a validated (using a random forest field calibration) network of 15 low-cost air quality sensors within Oxford, UK to monitor the impacts of multiple COVID-19 public heath restrictions upon particulate matter concentrations (PM₁₀, PM_2.5) from January 2020 to September 2021. Measurements of PM₁₀ and PM_2.5 particle size fractions both within and between site locations are compared to a pre-pandemic related public health restrictions baseline. While average peak concentrations of PM₁₀ and PM_2.5 were reduced by 9-10 μg/m³ below typical peak levels experienced in recent years, mean daily PM₁₀ and PM_2.5 concentrations were only ∼1 μg/m³ lower and there was marked temporal (as restrictions were added and removed) and spatial variability (across the 15-sensor network) in these observations. Across the 15-sensor network we observed a small local impact from traffic related emission sources upon particle concentrations near traffic-oriented sensors with higher average and peak concentrations as well as greater dynamic range, compared to more intermediate and background orientated sensor locations. The greater dynamic range in concentrations is indicative of exposure to more variable emission sources, such as road transport emissions. Our findings highlight the great potential for low-cost sensor technology to identify highly localised changes in pollutant concentrations as a consequence of changes in behaviour (in this case influenced by COVID-19 restrictions), generating insights into non-traffic contributions to PM emissions in this setting. It is evident that additional non-traffic related measures would be required in Oxford to reduce the PM₁₀ and PM_2.5 levels to within WHO health-based guidelines and to achieve compliance with PM_2.5 targets developed under the Environment Act 2021.

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

In December 2019, the New Crown Pneumonia (the COVID-19) outbroke around the globe, and China imposed a nationwide lockdown starting as early as January 23, 2020. This decision has significantly impacted China's air quality, especially the sharp decrease in PM_2.5 (aerodynamic equivalent diameter of particulate matter less than or equal to 2.5 μm) pollution. Hunan Province is located in the central and eastern part of China, with a "horseshoe basin" topography. The reduction rate of PM_2.5 concentrations in Hunan province during the COVID-19 (24.8%) was significantly higher than the national average (20.3%). Through the analysis of the changing character and pollution sources of haze pollution events in Hunan Province, more scientific countermeasures can be provided for the government. We use the Weather Research and Forecasting with Chemistry (WRF-Chem, V4.0) model to predict and simulate the PM_2.5 concentrations under seven scenarios before the lockdown (2020.1.1-2020.1.22) and during the lockdown (2020.1.23-2020.2.14). Then, the PM_2.5 concentrations under different conditions is compared to differentiate the contribution of meteorological conditions and local human activities to PM_2.5 pollution. The results indicate the most important cause of PM_2.5 pollution reduction is anthropogenic emissions from the residential sector, followed by the industrial sector, while the influence of meteorological factors contribute only 0.5% to PM_2.5. The explanation is that emission reductions from the residential sector contribute the most to the reduction of seven primary contaminants. Finally, we trace the source and transport path of the air mass in Hunan Province through the Concentration Weight Trajectory Analysis (CWT). We found that the external input of PM_2.5 in Hunan Province is mainly from the air mass transported from the northeast, accounting for 28.6%-30.0%. To improve future air quality, there is an urgent need to burn clean energy, improve the industrial structure, rationalize energy use, and strengthen cross-regional air pollution synergy control.

Spatiotemporal variations of tropospheric ozone in Spain (2008-2019)

This study aims to support the development of Spain's Ozone Mitigation Plan by evaluating the present-day spatial variation (2015-2019) and trends (2008-2019) for seven ground-level ozone (O₃) metrics relevant for human/ecosystems exposure and regulatory purposes. Results indicate that the spatial variation of O₃ depends on the part of the O₃ distribution being analyzed. Metrics associated with moderate O₃ concentrations depict an increasing O₃ gradient between the northern and Mediterranean coasts due to climatic factors, while for metrics considering the upper end of the O₃ distribution, this climatic gradient tends to attenuate in favor of hotspot regions pointing to relevant local/regional O₃ formation. A classification of atmospheric regions in Spain is proposed based on their O₃ pollution patterns, to identify priority areas (or O₃ hotspots) where local/regional precursor abatement might significantly reduce O₃ during pollution episodes. The trends assessment reveals a narrowing of the O₃ distribution at the national level, with metrics influenced by lower concentrations tending to increase over time, and those reflecting the higher end of the O₃ distribution tending to decrease. While most stations show no statistically significant variations, contrasting O₃ trends are evident among the O₃ hotspots. The Madrid area exhibits the majority of upward trends across all metrics, frequently with the highest increasing rates, implying increasing O₃ associated with both chronic and episodic exposure. The Valencian Community area exhibits a mixed variation pattern, with moderate to high O₃ metrics increasing and peak metrics decreasing, while O₃ in areas downwind of Barcelona, the Guadalquivir Valley and Puertollano shows no variations. Sevilla is the only large Spanish city with generalized O₃ decreasing trends. The different O₃ trends among hotspots highlight the need for mitigation measures to be designed at a local/regional scale to be effective. This approach may offer valuable insights for other countries developing O₃ mitigation plans.

A yearlong monitoring campaign of polycyclic aromatic compounds and other air pollutants at three sites in Sweden: Source identification, in vitro toxicity and human health risk assessment

Air pollution is a complex mixture of gases and particulate matter (PM) with local and non-local emission sources, resulting in spatiotemporal variability in concentrations and composition, and thus associated health risks. To study this in the greater Stockholm area, a yearlong monitoring campaign with in situ measurements of PM₁₀, PM₁, black carbon, NO_x, O₃, and PM₁₀-sampling was performed. The locations included an Urban and a Rural background site and a Highway site. Chemical analysis of PM₁₀ was performed to quantify monthly levels of polycyclic aromatic compounds (PACs), which together with other air pollution data were used for source apportionment and health risk assessment. Organic extracts from PM₁₀ were tested for oxidative potential in human bronchial epithelial cells. Strong seasonal patterns were found for most air pollutants including PACs, with higher levels during the winter months than summer e.g., highest levels of PM₁₀ were detected in March at the Highway site (33.2 μg/m³) and lowest in May at the Rural site (3.6 μg/m³). In general, air pollutant levels at the sites were in the order Highway > Urban > Rural. Multivariate analysis identified several polar PACs, including 6H-Benzo[cd]pyren-6-one, as possible discriminatory markers for these sites. The main sources of particulate pollution for all sites were vehicle exhaust and biomass burning emissions, although diesel exhaust was an important source at the Highway site. In vitro results agreed with air pollutant levels, with higher oxidative potential from the winter samples. Estimated lung cancer cases were in the order PM₁₀ > NO₂ > PACs for all sites, and with less evident seasonal differences than in vitro results. In conclusion, our study presents novel seasonal data for many PACs together with air pollutants more traditionally included in air quality monitoring. Moreover, seasonal differences in air pollutant levels correlated with differences in toxicity in vitro.

Spatiotemporal characteristics and socioeconomic factors of PM2.5 heterogeneity in mainland China during the COVID-19 epidemic

Spatiotemporal variation of PM_2.5 in 2018 and 2020 were compared to analyze the impacts of COVID-19, the spatial heterogeneity of PM_2.5, and meteorological and socioeconomic impacts of PM_2.5 concentrations heterogeneity in China in 2020 were investigated. The results showed that the annual average PM_2.5 concentration in 2020 was 32.73 μg/m³ existing a U-shaped variation pattern, which has decreased by 6.38 μg/m³ compared to 2018. A consistent temporal pattern was found in 2018 and 2020 with significant high values in winter and low in summer. PM_2.5 declined dramatically in eastern and central China, where are densely populated and economically developed areas during the COVID-19 epidemic compared with previous years, indicating that the significantly decline of social activities had an important effect on the reduction of PM_2.5 concentrations. The lowest PM_2.5 was found in August because that precipitation had a certain dilution effect on pollutants. January was the most polluted due to centralized coal burning for heating in North China. Overall, the PM_2.5 concentrations in China were spatially agglomerated. The highly polluted contiguous zones were mainly located in northwest China and the central plains city group, while the coastal area and Inner Mongolia were areas with good air quality. Negative correlations were found between natural factors (temperature, precipitation, wind speed and relative humidity) and PM_2.5 concentrations, with precipitation has the greatest impact on PM_2.5, which are beneficial for reducing PM_2.5 concentrations. Among the socio-economic factors, proportion of the secondary industry, number of taxis, per capita GDP, population, and industrial nitrogen oxide emissions have positive correlation effects on PM_2.5, while the overall social electricity consumption, industrial sulfur dioxide emissions, green coverage in built-up areas, and total gas and liquefied gas supply have negative correlation effects on the PM_2.5.

Elemental Composition and Sources of Fine Particulate Matter (PM2.5) in Delhi, India

In this study we have analysed the elemental composition of fine particulate matter (PM_2.5) to examine the seasonal changes and sources of the elements in Delhi, India from January, 2017 to December, 2021. During the entire sampling period, 19 elements (Al, Fe, Ti, Cu, Zn, Cr, Ni, As, Mo, Cl, P, S, K, Pb, Na, Mg, Ca, Mn, and Br) of PM_2.5 were identified by Wavelength Dispersive X-ray Fluorescence Spectrometer. The higher annual mean concentrations of S (2.29 µg m^-3), Cl (2.26 µg m^-3), K (2.05 µg m^-3), Ca (0.96 µg m^-3) and Fe (0.93 µg m^-3) were recorded during post-monsoon season followed by Zn > Pb > Al > Na > Cu > Ti > As > Cr > Mo > Br > Mg > Ni > Mn > and P. The annual mean concentrations of elemental composition of PM_2.5 accounted for 10% of PM_2.5 (pooled estimate of 5 year). Principal Component Analysis (PCA) identified the five main sources [crustal/soil/road dust, combustion (BB + FFC), vehicular emissions (VE), industrial emissions (IE) and mixed source (Ti, Cr and Mo rich-source)] of PM_2.5 in Delhi, India.

Air pollution and mobility patterns in two Ugandan cities during COVID-19 mobility restrictions suggest the validity of air quality data as a measure for human mobility

We explored the viability of using air quality as an alternative to aggregated location data from mobile phones in the two most populated cities in Uganda. We accessed air quality and Google mobility data collected from 15th February 2020 to 10th June 2021 and augmented them with mobility restrictions implemented during the COVID-19 lockdown. We determined whether air quality data depicted similar patterns to mobility data before, during, and after the lockdown and determined associations between air quality and mobility by computing Pearson correlation coefficients ([Formula: see text]), conducting multivariable regression with associated confidence intervals (CIs), and visualized the relationships using scatter plots. Residential mobility increased with the stringency of restrictions while both non-residential mobility and air pollution decreased with the stringency of restrictions. In Kampala, PM2.5 was positively correlated with non-residential mobility and negatively correlated with residential mobility. Only correlations between PM2.5 and movement in work and residential places were statistically significant in Wakiso. After controlling for stringency in restrictions, air quality in Kampala was independently correlated with movement in retail and recreation (- 0.55; 95% CI =  - 1.01- - 0.10), parks (0.29; 95% CI = 0.03-0.54), transit stations (0.29; 95% CI = 0.16-0.42), work (- 0.25; 95% CI =  - 0.43- - 0.08), and residential places (- 1.02; 95% CI =  - 1.4- - 0.64). For Wakiso, only the correlation between air quality and residential mobility was statistically significant (- 0.99; 95% CI =  - 1.34- - 0.65). These findings suggest that air quality is linked to mobility and thus could be used by public health programs in monitoring movement patterns and the spread of infectious diseases without compromising on individuals' privacy.

Concentration and size distribution of atmospheric particles in southern Italy during COVID-19 lockdown period

Many countries imposed lockdown (LD) to limit the spread of COVID-19, which led to a reduction in the emission of anthropogenic atmospheric pollutants. Several studies have investigated the effects of LD on air quality, mostly in urban settings and criteria pollutants. However, less information is available on background sites, and virtually no information is available on particle number size distribution (PNSD). This study investigated the effect of LD on air quality at an urban background site representing a near coast area in the central Mediterranean. The analysis focused on equivalent black carbon (eBC), particle mass concentrations in different size fractions: PM_2.5 (aerodynamic diameter D_a < 2.5 μm), PM₁₀ (D_a < 10 μm), PM_10-2.5 (2.5 < D_a < 10 μm); and PNSD in a wide range of diameters (0.01-10 μm). Measurements in 2020 during the national LD in Italy and period immediately after LD (POST-LD period) were compared with those in the corresponding periods from 2015 to 2019. The results showed that LD reduced the frequency and intensity of high-pollution events. Reductions were more relevant during POST-LD than during LD period for all variables, except quasi-ultrafine particles and PM_10-2.5. Two events of long-range transport of dust were observed, which need to be identified and removed to determine the effect of LD. The decreases in the quasi-ultrafine particles and eBC concentrations were 20%, and 15-22%, respectively. PM_2.5 concentration was reduced by 13-44% whereas PM_10-2.5 concentration was unaffected. The concentration of accumulation mode particles followed the behaviour of PM_2.5, with reductions of 19-57%. The results obtained could be relevant for future strategies aimed at improving air quality and understanding the processes that influence the number and mass particle size distributions.

Lockdown effects of the COVID-19 on the spatio-temporal distribution of air pollution in Beijing, China

To prevent the spread of COVID-19, China enacted a series of strict policies, which reduced anthropogenic activities to a near standstill. This provided a precious window to explore its effects on the spatio-temporal distribution of air pollution in Beijing, China. In this study, continuous wavelet transforms and spatial interpolation methods were used to explore the spatiotemporal variations in air pollutants and their lockdown effects. The results indicate that except O₃, the annual average concentration of NO₂, PM_2.5 and SO₂ showed a decreasing trend during 2016 and 2019; NO₂, PM_2.5 and SO₂ show a trend of "low in summer and high in winter"; the diurnal variation of NO₂ concentration was mainly related to the rush hours of traffic volume, with the first peak at the morning peak (7:00), and then accumulating gradually to second peak (22:00). The continuous wavelet analysis shows that PM2.5, SO₂ and NO₂ had four primary periods, while O₃ only had two primary periods. The high NO₂ concentration areas were mainly in Dongcheng, Xicheng, Chaoyang and Fengtai, while the low concentration areas were located in the northern areas, such as Miyun and Huairou; the PM_2.5 concentration decreased from south to north; this characteristic presented more obviously in winter. Compared to the pre-lockdown, NO₂ and SO₂ decreased considerably during lockdown, whereas PM_2.5 and O₃ increased dramatically. The contribution rates of transportation activities to the NO₂, O₃, PM_2.5 and SO₂ were estimated be 9.4 % ∼ 17.2 %, -76.4 % ∼ -42.9 %, -39.5 % ∼ -22.8 % and 5.7 % ∼ 43.7 %, respectively; the contribution rates of industrial activities were 19.9 % ∼ 26.7 %, 7.8 % ∼ 30.9 %, 1.6 % ∼ 36.2 % and -10.5 % ∼ 15.9 %, respectively. Considering meteorological factors, we inferred that pauses in anthropogenic activities indeed help improving air pollution, but it is difficult to offset the impact of extreme weather. These findings can enhance our understanding on the sources of air pollution, and can therefore provide insights on urban air pollution mitigation.

COVID-19 response in Africa: impacts and lessons for environmental management and climate change adaptation

The COVID-19 pandemic adds pressure on Africa; the most vulnerable continent to climate change impacts, threatening the realization of most Sustainable Development Goals (SDGs). The continent is witnessing an increase in intensity and frequency of extreme weather events, and environmental change. The COVID-19 was managed relatively well across in the continent, providing lessons and impetus for environmental management and addressing climate change. This work examines the possible impact of the COVID-19 pandemic on the environment and climate change, analyses its management and draws lessons from it for climate change response in Africa. The data, findings and lessons are drawn from peer reviewed articles and credible grey literature on COVID-19 in Africa. The COVID-19 pandemic spread quickly, causing loss of lives and stagnation of the global economy, overshadowing the current climate crisis. The pandemic was managed through swift response by the top political leadership, research and innovations across Africa providing possible solutions to COVID-19 challenges, and redirection of funds to manage the pandemic. The well-coordinated COVID-19 containment strategy under the African Centers for Disease Control and Prevention increased sharing of resources including data was a success in limiting the spread of the virus. These strategies, among others, proved effective in limiting the spread and impact of COVID-19. The findings provide lessons that stakeholders and policy-makers can leverage in the management of the environment and address climate change. These approaches require solid commitment and practical-oriented leadership.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10668-023-02956-0.

A retrospect of ozone formation mechanisms during the COVID-19 lockdown: The potential role of isoprene

Wuhan took strict measures to prevent the spread of COVID-19 from January 26 to April 7 in 2020. The lockdown reduced the concentrations of atmospheric pollutants, except ozone (O₃). To investigate the increase in O₃ during the lockdown, trace gas pollutants were collected. The initial concentrations of volatile organic compounds (VOCs) were calculated based on a photochemical ratio method, and the ozone formation potential (OFP) was obtained using the initial and measured VOC concentrations. The O₃ formation regime was NO_X-limited based on the VOCs/NO_X diurnal ratios during the lockdown period. The reduced nitric oxide (NO) concentrations and lower wind speed (WS) could explain the night-time O₃ accumulation. The initial total VOCs (TVOCs) during the lockdown were 47.6 ± 2.9 ppbv, and alkenes contributed 48.1%. The photochemical loss amounts of alkenes were an order of magnitude higher than those of alkenes in the same period in 2019 and increased from 16.6 to 28.0 ppbv in the daytime. The higher initial alkene concentrations sustained higher OFP during the lockdown, reaching between 252.4 and 504.4 ppbv. The initial isoprene contributed approximately 35.0-55.0% to the total OFP and had a positive correlation with the increasing O₃ concentrations. Approximately 75.5% of the temperatures were concentrated in the range of 5 and 20 °C, which were higher than those in 2019. In addition to stronger solar radiation, the higher temperatures induced higher isoprene emission rates, partially accounting for the higher isoprene concentrations. Lower isoprene-emitting trees should be considered for future urban vegetation to control O₃ episodes.

Surface, satellite ozone variations in Northern South America during low anthropogenic emission conditions: a machine learning approach

2020 presented the ideal conditions for studying the air quality response to several emission reductions due to the COVID-19 lockdowns. Numerous studies found that the tropospheric ozone increased even in lockdown conditions, but its reasons are not entirely understood. This research aims to better understand the ozone variations in Northern South America. Satellite and reanalysis data were used to analyze regional ozone variations. An analysis of two of the most polluted Colombian cities was performed by quantifying the changes of ozone and its precursors and by doing a machine learning decomposition to disentangle the contributions that precursors and meteorology made to form O₃. The results indicated that regional ozone increased in most areas, especially where wildfires are present. Meteorology is associated with favorable conditions to promote wildfires in Colombia and Venezuela. Regarding the local analysis, the machine learning ensemble shows that the decreased titration process associated with the NO plummeting owing to mobility reduction is the main contributor to the O₃ increase (≈50%). These tools lead to conclude that (i) the increase in O₃ produced by the reduction of the titration process that would be associated with an improvement in mobile sources technology has to be considered in the new air quality policies, (ii) a boost in international cooperation is essential to control wildfires since an event that occurs in one country can affect others and (iii) a machine learning decomposition approach coupled with sensitivity experiments can help us explain and understand the physicochemical mechanism that drives ozone formation.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11869-023-01303-6.

Impact of Confinement on the Reduction of Pollution and Particulate Matter Concentrations. Reflections for Public Transport Policies

Different initiatives have been implemented to improve air quality in large cities, such as encouraging travel by sustainable modes of transport, promoting electro-mobility, or the car-free day. However, to date, we have not found statistics that indicate to what extent the concentration levels of particulate matter PM 2.5 , PM 10 and nitrogen oxides (NO x ) pollutants decrease as a result of public policy. We used official data from the Chilean Government’s national air quality information system (SINCA) for the Santiago metropolitan region and estimated the impact of the confinement by COVID-19 on the ambient concentration average values of NO x gases and particulate matter PM 2.5 and PM 10 , which are the main air pollutants produced by the transport sector after CO 2 . We found that in general there are significant differences between the average levels of gas emissions for 2020 compared to 2019. In particular, we found that, for the months of total confinement May-July, the monthly average levels decreased between 7% and 19% for particulate matter PM 2.5 , between 18% and 50% for PM 10 and between 34% and 48% for NO x . With the return to the new normality, these improvements in ambient concentration levels may be affected by the increase in private transport trips, due to the reluctance of citizens to return to mass public transport. Our results, therefore, represent the maximum impact that can be expected in reducing ambient concentration levels in the city of Santiago of Chile when a mobility reduction of gasoline vehicles is implemented.

The reduction of PM 2.5 , PM 10 and NO x was no more than 7%, 18% and 34%, respectively.

The average concentration of PM 2.5 decreased by 7–19% compared to previous years.

The average concentration of PM 10 decreased by 18% and 50% compared to previous years.

Concentrating commuting on public transport would help reduce levels of PM 10 and PM 2.5 .

Spatio-temporal analysis of PM2.5 and policies in Northwestern South America

This paper analyzes the spatio-temporal variations, and exceedances of the PM_2.5 concentrations in Northwestern South America at different scales to assess the implemented policies and identify the involved phenomena. Through reanalysis and ground-based data, we found that high PM_2.5 levels in most cities of the region are caused by wildfires and local emissions, including the capital cities of Venezuela, Ecuador, Colombia, and Panamá. In-situ measurements suggest that the majority of the cities comply with the local but not with the WHO guidelines, indicating that local annual limits should be more restrictive. Two peaks in the daily variations of PM_2.5 (related to vehicle emissions) and also a steeper decrease around noon (associated with an increase in wind speed and in the boundary layer height) were identified. The trend-analysis shows that Bogotá and Medellín have a decreasing PM_2.5 annual-trend (between -0.8μgm^-3 and -1.7μgm^-3) that corresponds to effective policies. In contrast, Cali has a positive annual-trend (0.8μgm^-3) most likely because of Short-Range Transport produced by a northerly-flow from a highly polluted neighboring city, which also affects Cali's PM_2.5 diurnal cycle, or by local-dynamics. The exceedances show that the policies are working on an annual but not at a daily time-scale. These results serve as a first input for additional studies, with the aim of gaining a better understanding of the contaminant before adapting current policies or implementing new policies and measures that need to include a joint international, regional, and inter-city efforts regarding pollution transport.

Lessons learnt for air pollution mitigation policies from the COVID-19 pandemic: The Italian perspective

Policies to improve air quality need to be based on effective plans for reducing anthropogenic emissions. In 2020, the outbreak of COVID-19 pandemic resulted in significant reductions of anthropogenic pollutant emissions, offering an unexpected opportunity to observe their consequences on ambient concentrations. Taking the national lockdown occurred in Italy between March and May 2020 as a case study, this work tries to infer if and what lessons may be learnt concerning the impact of emission reduction policies on air quality. Variations of NO₂, O₃, PM₁₀ and PM_2.5 concentrations were calculated from numerical model simulations obtained with business as usual and lockdown specific emissions. Both simulations were performed at national level with a horizontal resolution of 4 km, and at local level on the capital city Rome at 1 km resolution. Simulated concentrations showed a good agreement with in-situ observations, confirming the modelling systems capability to reproduce the effects of emission reductions on ambient concentration variations, which differ according to the individual air pollutant. We found a general reduction of pollutant concentrations except for ozone, that experienced an increase in Rome and in the other urban areas, and a decrease elsewhere. The obtained results suggest that acting on precursor emissions, even with sharp reductions like those experienced during the lockdown, may lead to significant, albeit complex, reduction patterns for secondary pollutant concentrations. Therefore, to be more effective, reduction measures should be carefully selected, involving more sectors than those related to mobility, such as residential and agriculture, and integrated on different scales.

Changes in air pollution due to COVID-19 lockdowns in 2020: Limited effect on NO2, PM2.5, and PM10 annual means compared to the new WHO Air Quality Guidelines

Background

Lockdowns have been fundamental to decreasing disease transmission during the COVID-19 pandemic even after vaccines were available. We aimed to evaluate and compare changes in air quality during the first year of the pandemic in different cities around the world, investigate how these changes correlate with changes in mobility, and analyse how lockdowns affected air pollutants' annual means.

Methods

We compared the concentrations of NO₂, PM_2.5, and PM₁₀ in 42 cities around the world in the first months of the pandemic in 2020 to data from 2016-2019 and correlated them with changes in mobility using Human Development Indexes (HDIs). Cities with the highest decreases in air pollutants during this period were evaluated for the whole year 2020. We calculated the annual means for these cities and compared them to the new World Health Organization (WHO) Air Quality Guidelines. A Student's t-test (95% confidence interval) was used to evaluate significant changes.

Results

Highest decreases in NO₂, PM_2.5, and PM₁₀ were between -50 and -70%. Cities evaluated for the whole year 2020 generally showed a recovery in air pollution levels after the initial months of the pandemic, except for London. These changes positively correlated with year-long mobility indexes for NO₂ and PM_2.5 for some cities. The highest reductions in air pollutants' annual means were from -20 to -35%. In general, decreases were higher for NO₂, compared to PM_2.5 and PM₁₀. All analysed cities showed annual means incompliant with the new WHO Air Quality Guidelines for NO₂ of 10 μg/m³, with values 1.7 and 4.3 times higher. For PM_2.5, all cities showed values 1.3 to 7.6 times higher than the WHO Guidelines of 5 μg/m³, except for New Delhi, with a value 18 times higher. For PM₁₀, only New York complied with the new guidelines of 15 μg/m³ and all the other cities were 1.1 to 4.2 times higher, except for New Delhi, which was 11 times higher.

Conclusions

These data show that even during a pandemic that highly affected mobility and economic activities and decreased air pollution around the world, complying with the new WHO Guidelines will demand a global strategical effort in the way we generate energy, move in and around the cities, and manufacture products.

Links between the concentrations of gaseous pollutants measured in different regions of Estonia

The factors that determine the concentrations of air pollutants (NO, NO₂, SO₂, O₃), measured in 8 monitoring stations (4 rural background, 3 urban, and 1 industrial) in Estonia, are studied applying the factor analysis. The factor analysis reveals remarkable impact of COVID-19 lockdown, effects caused by dramatic decrease in oil-shale based energy production in Estonia provoked by new socio-economic conditions such as elevated price for CO₂ emission quota, differences between rural and urban stations, maritime-continental difference for NO₂ and ozone, and specific industrial impact in case of SO₂. The multiple regression analysis to predict the ozone concentration in one rural background station at Tahkuse was performed, based on the ozone concentrations measured in other stations and the concentrations of NO, NO₂, and CO₂, recorded in the same station. It was found that the ozone concentration at Tahkuse is rather well predictable (determination coefficient, i.e., correlation coefficient squared, R ² = 0.714), using only the concentrations from another rural station at Saarejärve that is about 110 km away from Tahkuse. Adding all the available data into the list of regression analysis arguments, the model predictability is improved moderately (determination coefficient R ² = 0.795). Large model residuals above all tend to occur with the values measured and predicted at summer nights. Surprisingly, neither NO nor NO₂ concentration measured in the Tahkuse station did appear a good predictor for ozone (R ² = 0.02 and 0.05, respectively), possibly long-range transport of ozone (that has also experienced NO and/or NO₂ influence during transport) overrides the local effects of NO and/or NO₂.

Changing weekend effects of air pollutants in Beijing under 2020 COVID-19 lockdown controls

In 2020, lockdown control measures were implemented to prevent a novel coronavirus disease 19 (COVID-19) pandemic in many places of the world, which largely reduced human activities. Here, we detect changes in weekly cycles of PM2.5, NO2, SO2, CO and O3 concentrations in 2020 compared to 2018 and 2019 using the observed data at 32 stations in Beijing. Distinct weekly cycles of annual average PM2.5, NO2, SO2 and CO concentrations existed in 2018, while the weekend effects changed in 2020. In addition, the weekly cycle magnitudes of PM2.5, NO2, SO2, and O3 concentrations in 2020 decreased by 29.60-69.26% compared to 2018, and 4.49-47.21% compared to 2019. We propose that the changing weekend effects and diminishing weekly cycle magnitudes may be tied to the COVID-19 lockdown controls, which changed human working and lifestyle cycles and reduced anthropogenic emissions of air pollutants on weekends more than weekdays.

Spatiotemporal impact of COVID-19 on Taiwan air quality in the absence of a lockdown: Influence of urban public transportation use and meteorological conditions

The unprecedented outbreak of COVID-19 significantly improved the atmospheric environment for lockdown-imposed regions; however, scant evidence exists on its impacts on regions without lockdown. A novel research framework is proposed to evaluate the long-term monthly spatiotemporal impact of COVID-19 on Taiwan air quality through different statistical analyses, including geostatistical analysis, change detection analysis and identification of nonattainment pollutant occurrence between the average mean air pollutant concentrations from 2018-2019 and 2020, considering both meteorological and public transportation impacts. Contrary to lockdown-imposed regions, insignificant or worsened air quality conditions were observed at the beginning of COVID-19, but a delayed improvement occurred after April in Taiwan. The annual mean concentrations of PM₁₀, PM_2.5, SO₂, NO₂, CO and O₃ in 2020 were reduced by 24%, 18%, 15%, 9.6%, 7.4% and 1.3%, respectively (relative to 2018-2019), and the overall occurrence frequency of nonattainment air pollutants declined by over 30%. Backward stepwise regression models for each air pollutant were successfully constructed utilizing 12 meteorological parameters (R² > 0.8 except for SO₂) to simulate the meteorological normalized business-as-usual concentration. The hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) model simulated the fate of air pollutants (e.g., local emissions or transboundary pollution) for anomalous months. The changes in different public transportation usage volumes (e.g., roadway, railway, air, and waterway) moderately reduced air pollution, particularly CO and NO₂. Reduced public transportation use had a more significant impact than meteorology on air quality improvement in Taiwan, highlighting the importance of proper public transportation management for air pollution control and paving a new path for sustainable air quality management even in the absence of a lockdown.

Long-Term Variation in Carbonaceous Components of PM2.5 from 2012 to 2021 in Delhi

Carbonaceous species [organic carbon (OC), elemental carbon (EC), elemental matter (EM), primary organic carbon (POC), secondary organic carbon (SOC), total carbon (TC), and total carbonaceous matter (TCM)] of PM_2.5 were analyzed to study the seasonal variability and long-term trend of carbonaceous aerosols (CAs) in megacity Delhi, India from January, 2012 to April, 2021. The average concentrations (± standard deviation) of PM_2.5, OC, EC, TC, EM, TCM, POC and SOC were 127 ± 77, 15.7 ± 11.6, 7.4 ± 5.1, 23.1 ± 16.5, 8.2 ± 5.6, 33.3 ± 23.9, 9.3 ± 6.3 and 6.5 ± 5.3 µg m^-3, respectively during the sampling period (10-year average). The average CAs accounted for 26% of PM_2.5 concentration during the entire sampling period. In addition, the seasonal variations in PM_2.5, OC, EC, POC, SOC, and TCM levels were recorded with maxima in post-monsoon and minima in monsoon seasons. The linear relationship of OC and EC, OC/EC and EC/TC ratios suggested that the vehicular emissions (VE), fossil fuel combustion (FFC) and biomass burning (BB) are the major sources of CAs at megacity Delhi, India.

Ambient concentrations and dosimetry of inhaled size-segregated particulate matter during periods of low urban mobility in Bragança, Portugal

The restrictive measures in place during the COVID-19 pandemic provided a timely scenario to investigate the effects of human activities on air quality, and the extent to which mobility reduction strategies can impact atmospheric pollutant levels. Real-time concentrations of PM₁, PM_2.5 and PM₁₀ were measured using a mobile platform in a small city of Portugal, during morning and afternoon rush hours, in two distinct phases of the pandemic: emergency phase (cold period, lockdown) and calamity phase (warm period, less restricted). The Multiple-Path Particle Dosimetry Model (MPPD) was used to calculate the PM deposition for adults. Large spatio-temporal variabilities and pronounced changes in mean PM concentrations were observed, with lower concentrations in the calamity phase: PM₁ = 2.33 ± 1.61 μg m^-3; PM_2.5 = 5.15 ± 2.77 μg m^-3; PM₁₀ = 23.30 ± 21.53 μg m^-3 than in the emergency phase: PM₁ = 16.85 ± 31.80 μg m^-3; PM_2.5 = 30.92 ± 31.93 μg m^-3; PM₁₀ = 111.27 ± 104.53 μg m^-3. These changes are explained by a combination of meteorological factors and local emissions, mainly residential firewood burning. Regarding regional deposition, PM₁ was the main contributor to deposition in the tracheobronchial (5%) and pulmonary (12%) regions, and PM₁₀ in the head region (92%). In general, total deposition doses were higher for males than for females. This work quantitatively demonstrated that even with a 38% reduction in urban mobility during the lockdown, the use of firewood for residential heating is the main contributor to the high concentrations of PM and the respective inhaled dose.

Analysis of the lockdown effects due to the COVID-19 on air pollution in Brescia (Lombardy)

SARS-CoV-2 virus (COVID-19) pandemic has impacted several countries, with also some differences at local levels. When lockdown restrictions were imposed, the concentrations of some air pollutants were reduced, as reported in some other cities in the world. This was often considered a positive by-product of the pandemic. However, often literature reporting the connection of air quality (AQ) and lockdown, suffers of limited and incomplete data analysis, not considering, for example, some confounding factors. This work presents a methodology, and the results of its application, to assess the impact of pandemic restrictions on AQ (in particular nitrogen oxides, NO₂ and particulate matter, PM₁₀) in spring 2020 in Brescia, located in one of the most affected areas in terms of virus diffusion and in one of the most polluted areas in Europe (Po Valley, Italy). In particular, the proposed methodology integrates data and AQ modelling simulations to distinguish between the changes in the PM₁₀ and NO₂ pollutants concentration that occurred due to the restriction measures and due to other factors, like spatial-temporal characteristics (for example the seasonality), meteorological factors, and governmental actions that were introduced in the past to improve the air quality. Results show that NO₂ is strongly dependent to traffic emission. On the contrary, although the expected decrease in PM₁₀ concentrations, the results highlight that the reduction of transport emission would not help to avoid severe air pollution, due to the other pollution sources that contribute to its origin. The results presented for the first time in this work are of particular interest because they may be used as a basis to investigate in more details the sources that can impact on the air quality in Brescia, with the aim to propose effective measures able to reduce it.

High Resolution On-Road Air Pollution Using a Large Taxi-Based Mobile Sensor Network

Traffic-related air pollution (TRAP) was monitored using a mobile sensor network on 125 urban taxis in Shanghai (November 2019/December 2020), which provide real-time patterns of air pollution at high spatial resolution. Each device determined concentrations of carbon monoxide (CO), nitrogen dioxide (NO₂), and PM_2.5, which characterised spatial and temporal patterns of on-road pollutants. A total of 80% road coverage (motorways, trunk, primary, and secondary roads) required 80-100 taxis, but only 25 on trunk roads. Higher CO concentrations were observed in the urban centre, NO₂ higher in motorway concentrations, and PM_2.5 lower in the west away from the city centre. During the COVID-19 lockdown, concentrations of CO, NO₂, and PM_2.5 in Shanghai decreased by 32, 31 and 41%, compared with the previous period. Local contribution related to traffic emissions changed slightly before and after COVID-19 restrictions, while changing background contributions relate to seasonal variation. Mobile networks are a real-time tool for air quality monitoring, with high spatial resolution (~200 m) and robust against the loss of individual devices.

Sustainable development goals under threat? Multidimensional impact of COVID-19 on our planet and society outweigh short term global pollution reduction

The Sustainable Development Goals (SDGs) call on all nations to accomplish 17 broad global development goals by 2030. However, the COVID-19 pandemic presents a challenging period in human history, causing large-scale impacts on society and the environment as governments shift priorities and divert funding in response to this pandemic. Through a literature survey and data acquirement from various international organizations (e.g. United Nations and European Space Agency), this manuscript is intended to provide critical insights into the impacts of the COVID-19 pandemic on the SDGs. We briefly describe this pandemic's positive and short-term effects on the environment, followed by a critical evaluation of its potential long-term impacts on the environment, society, and the SDGs. On the basis of COVID-19 effects, the SDGs are classified into three categories: directly-affected SDGs, indirectly-affected SDGs, and a stand-alone category. The COVID-19-induced lockdowns and restrictions resulted in a short-term decline in environmental pollution and greenhouse gases (GHG) emissions, providing valuable data for climate advocates and researchers. These positive impacts were essentially temporary due to the synchronized global response to the pandemic. The halted focus on the progress of the SDGs greatly impacts the global green transition to a healthy and sustainable world. COVID-19 threatens to impede the progress toward a prosperous, environment-friendly, and sustainable global development in multiple ways. These multi-dimensional threats have been critically evaluated, along with a description of potential solutions to curtail the adverse effects of COVID-19 on the SDGs. Considering the limited data regarding the impacts of the pandemic on the SDGs, diverse collaborative studies at the regional and global levels are recommended.

Interannual variations, sources, and health impacts of the springtime ozone in Shanghai

In spring, ozone (O₃) pollution frequently occurrs in eastern China, but key drivers remain uncertain. In this study, interannual variations in springtime ozone in Shanghai, China, from 2013 to 2021, were investigated to assess the health impacts and the effectiveness of recent air pollution control measures. A combination of ground-level measurements of regulated air pollutants, lidar observations, and backward trajectories of air masses was used to identify the key drivers for enhancing springtime O₃. The results show that external imports of O₃ driven by atmospheric circulation are notable sources of springtime surface O₃. For example, the downward transport from the free troposphere could contribute to over 50% of surface O₃ in the morning. The surface O₃ mixing ratios in spring exhibited an upward trend of 0.93 ppb yr^-1 (p < 0.05) from 2013 to 2021. The change in meteorological variables, particularly the increase in air temperature, could explain nearly 87% of the springtime O₃ upward trend. The change in anthropogenic emissions of precursors only contributed to a small fraction (<13%) of the increase in springtime O₃. The cumulative exposure of urban residents to O₃ in spring also exhibited a significant upward trend (111 ppb yr^-1, p < 0.05). With the rapid increase in surface O₃, premature respiratory mortality attributable to O₃ exposure has fluctuated at approximately 2933 deaths per year since 2016, even though the total deaths from respiratory diseases have significantly declined. Long-term exposure to high O₃ concentrations is a significant contributor to premature respiratory mortality.

Determination of the human impact on the drop in NO2 air pollution due to total COVID-19 lockdown using Human-Influenced Air Pollution Decrease Index (HIAPDI)

This study investigates the relationship between territorial human influence and decreases in NO₂ air pollution during a total COVID-19 lockdown in Metropolitan France. NO₂ data from the confinement period and the Human Influence Index (HII) were implemented to address the problem. The relative change in tropospheric NO₂ was calculated using Sentinel-5P (TROPOMI) satellite data. Hotspot-Coldspot analysis was performed to examine the change in NO₂. Moreover, the novel Human-Influenced Air Pollution Decrease Index (HIAPDI) was developed. Weather bias was investigated by implementing homogeneity analysis with χ² test. The correlations between variables were tested with the statistical T-test. Likewise, remote observations were validated with data from in-situ monitoring stations. The study showed a strong correlation between the NO₂ decrease during April 2020 under confinement measures and HII. The greater the anthropogenic influence, the greater the reduction of NO₂ in the regions (R² = 0.62). The new HIAPDI evidenced the degree of anthropogenic impact on NO₂ change. HIAPDI was found to be a reliable measure to determine the correlation between human influence and change in air pollution (R² = 0.93). It is concluded that the anthropogenic influence is a determining factor in the phenomenon of near-surface NO₂ reduction. The implementation of HIAPDI is recommended in the analysis of other polluting gases.

Long-Term COVID-19 Restrictions in Italy to Assess the Role of Seasonal Meteorological Conditions and Pollutant Emissions on Urban Air Quality

A year-round air quality analysis was addressed over four Italian cities (Milan, Turin, Bologna, and Florence) following the outbreak of the Coronavirus 2019 (COVID-19) pandemic. NO2, O3, PM2.5, and PM10 daily observations were compared with estimations of meteorological variables and observations of anthropogenic emission drivers as road traffic and heating systems. Three periods in 2020 were analysed: (i) the first (winter/spring) lockdown, (ii) the (spring/summer) partial relaxation period, and (iii) the second (autumn/winter) lockdown. During the first lockdown, only NO2 concentrations decreased systematically (and significantly, between −41.9 and −53.9%), mainly due to the drastic traffic reduction (−70 to −74%); PM2.5 varied between −21 and +18%, PM10 varied between −23 and +9%, and O3 increased (up to +17%). During the partly relaxation period, no air quality issues were observed. The second lockdown was particularly critical as, although road traffic significantly reduced (−30 to −44%), PM2.5 and PM10 concentrations dramatically increased (up to +87 and +123%, respectively), mostly due to remarkably unfavourable weather conditions. The latter was confirmed as the main driver of PM’s most critical concentrations, while strong limitations to anthropogenic activity—including traffic bans—have little effect when taken alone, even when applied for more than two months and involving a whole country.

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 NO_x-limited regime, while a few NAAs remain NO_x-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.

COVID-19 Lockdown in Israel: The Environmental Effect on Ultrafine Particle Content in the Airway

Inhaled ultrafine particle (UFP) content in exhaled breath condensate (EBC) was observed as an airway inflammatory marker and an indicator of exposure to particulate matter (PM). The exceptional decline in air pollution during the COVID-19 lockdown was an opportunity to evaluate the effect of environmental changes on UFP airway content. We collected EBC samples from 30 healthy subjects during the first lockdown due to COVID-19 in Israel (March–April 2020) and compared them to EBC samples retrieved during April–June 2016 from 25 other healthy subjects (controls) living in the same northern Israeli district. All participants underwent EBC collection and blood sampling. Ambient air pollutant levels were collected from the Israeli Ministry of Environmental Protection’s online database. Data were acquired from the monitoring station closest to each subject’s home address, and means were calculated for a duration of 1 month preceding EBC collection. UFP contents were measured in the EBC and blood samples by means of the NanoSight LM20 system. There was a dramatic reduction in NO, NO₂, SO₂, and O₃ levels during lockdown compared to a similar period in 2016 (by 61%, 26%, 50%, and 45%, respectively). The specific NO₂ levels were 8.3 ppb for the lockdown group and 11.2 ppb for the controls (p = 0.01). The lockdown group had higher UFP concentrations in EBC and lower UFP concentrations in serum compared to controls (0.58 × 10⁸/mL and 4.3 × 10⁸/mL vs. 0.43 × 10⁸/mL and 6.7 × 10⁸/mL, p = 0.05 and p = 0.03, respectively). In this observational study, reduced levels of air pollution during the COVID-19 lockdown were reflected in increased levels of UFP airway contents. The suggested mechanism is that low airway inflammation levels during lockdown resulted in a decreased UFP translocation to serum. Further studies are needed to confirm this hypothesis.

Variability of Near-Surface Aerosol Composition in Moscow in 2020–2021: Episodes of Extreme Air Pollution of Different Genesis

During 2020–2021, a comprehensive experiment was conducted to study the composition of near-surface atmospheric aerosol in Moscow. The paper considers the experimental data together with synoptic and meteorological conditions. Attention is focused on six episodes of extremely high aerosol mass concentration values: in March and October 2020, as well in March, April, May and July 2021. In all these cases (and only in them), the average daily mass concentration of PM10 aerosol exceeded the Maximum Permissible Concentration (MPC) value (according to Russian standards, 60 μg/m3). The origin of the aerosol during these periods of extreme pollution is revealed, which is the main result of the work. It was shown that the July episode of 2021 was associated with a local intensive anthropogenic source that arose as a result of the active dismantling and demolition of multistory industrial buildings. The remaining spring and autumn episodes were caused by atmospheric transport of both smoke aerosol from various regions with strong biomass fires and dust aerosol from arid zones of the south of European territory of Russia (ETR) with dust wind storms. The cases of atmospheric pollution transport to Moscow region from the other regions are confirmed with the help of air mass transport trajectories (HYSPLIT 4 model) and MERRA-2 reanalysis data on black carbon and/or dust distribution in the atmosphere over ETR. Differences in the elemental composition of the near-surface aerosol of Moscow air during periods with extremely high aerosol concentrations are analyzed in comparison with each other and with unperturbed conditions for the season.