COVID-19 lockdowns cause global air pollution declines

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.

Viral respiratory infections and air pollution: A review focused on research in Poland

The COVID-19 pandemic has reinforced an interest in the relationship between air pollution and respiratory viral infections, indicating that their burden can be increased under poor air quality. This paper reviews the pathways through which air pollutants can enhance susceptibility to such infections and aggravate their clinical course and outcome. It also summarizes the research exploring the links between various viral infections and exposure to solid and gaseous pollution in Poland, a region characterized by poor air quality, especially during a heating season. The majority of studies focused on concentrations of particulate matter (PM; 86.7%); the other pollutants, i.e., BaP, benzene, CO, NOx, O3, and SO2, were studied less often and sometimes only in the context of a particular infection type. Most research concerned COVID-19, showing that elevated levels of PM and NO2 correlated with higher morbidity and mortality, while increased PM2.5 and benzo[a]pyrene levels were related to worse clinical course and outcome in hospitalized, regardless of age and dominant SARS-CoV-2 variant. PM10 and PM2.5 levels were also associated with the incidence of influenza-like illness and, along with NO2 concentrations, with a higher rate of children's hospitalizations due to lower respiratory tract RSV infections. Higher levels of air pollutants also increased hospitalization due to bronchitis (PM, NOx, and O3) and emergency department admission due to viral croup (PM10, PM2.5, NOx, CO, and benzene). Although the conducted studies imply only correlations and have other limitations, as discussed in the present paper, it appears that improving air quality through reducing combustion processes in energy production in Poland should be perceived as a part of multilayered protection measures against respiratory viral infections, decreasing the healthcare costs of COVID-19, lower tract RSV infections, influenza, and other respiratory viral diseases prevalent between autumn and early spring, in addition to other health and climate benefits.

Increased global cropland greening as a response to the unusual reduction in atmospheric PM₂.₅ concentrations during the COVID-19 lockdown period

The devastating effects of COVID-19 pandemic have widely affected human lives and economy across the globe. There were significant changes in the global environmental conditions in response to the lockdown (LD) restrictions made due to COVID-19. The direct impact of LD on environment is analysed widely across the latitudes, but its secondary effect remains largely unexplored. Therefore, we examine the changes in particulate matter (PM₂.₅) during LD, and its impact on the global croplands. Our analysis finds that there is a substantial decline in the global PM₂.₅ concentrations during LD (2020) compared to pre-lockdown (PreLD: 2017-2019) in India (10-20%), East China (EC, 10%), Western Europe (WE, 10%) and Nigeria (10%), which are also the cropland dominated regions. Partial correlation analysis reveals that the decline in PM₂.₅ positively affects the cropland greening when the influence of temperature, precipitation and soil moisture are limited. Croplands in India, EC, Nigeria and WE became more greener as a result of the improvement in air quality by the reduction in particulates such as PM₂.₅ during LD, with an increase in the Enhanced Vegetation Index (EVI) of about 0.05-0.1, 0.05, 0.05 and 0.05-0.1, respectively. As a result of cropland greening, increase in the total above ground biomass production (TAGP) and crop yield (TWSO) is also found in EC, India and Europe. In addition, the improvement in PM₂.₅ pollution and associated changes in meteorology also influenced the cropland phenology, where the crop development stage has prolonged in India for wet-rice (1-20%) and maize (1-10%). Therefore, this study sheds light on the response of global croplands to LD-induced improvements in PM₂.₅ pollution. These finding have implications for addressing issues of air pollution, global warming, climate change, environmental conservation and food security to achieve the Sustainable Development Goals (SDGs).

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

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

Atmosphere particulate matter and respiratory diseases during COVID-19 in Korea

We aimed to examine the impact of COVID-19 non-pharmaceutical interventions (NPIs) on the relationship between air pollutants and hospital admissions for respiratory and non-respiratory diseases in six metropolitan cities in South Korea. This study compared the associations between particulate matter (PM10 and PM2.5) and hospital admission for respiratory and non-respiratory diseases before (2016-2019) and during (2020) the implementation of COVID-19 NPIs by using distributed lag non-linear models. In the Pre-COVID-19 period, the association between PM10 and admission risk for asthma and COPD showed an inverted U-shaped pattern. For PM2.5, S-shaped and inverted U-shaped changes were observed in asthma and COPD, respectively. Extremely high and low levels of PM10 and extremely low levels of PM2.5 significantly decreased the risk of admission for asthma and COPD. In the Post-COVID-19 outbreak period, the overall cumulative relationship between PM10 and PM2.5 and respiratory diseases and the effects of extreme levels of PM10 and PM2.5 on respiratory diseases were completely changed. For non-respiratory diseases, PM10 and PM2.5 were statistically insignificant for admission risk during both periods. Our study may provide evidence that implementing NPIs and reducing PM10 and PM2.5 exposure during the COVID-19 pandemic has contributed to reducing hospital admissions for environment-based respiratory diseases.

Air pollution changes due to COVID-19 lockdowns and attributable mortality changes in four countries

COVID-19 lockdowns reduced nitrogen dioxide (NO2) and fine particulate matter (PM2.5) emissions in many countries. We aim to quantify the changes in these pollutants and to assess the attributable changes in mortality in Jiangsu, China; California, U.S.; Central-southern Italy; and Germany during COVID-19 lockdowns in early 2020. Accounting for meteorological impacts and air pollution time trends, we use a machine learning-based meteorological normalization technique and the difference-in-differences approach to quantify the changes in NO2 and PM2.5 concentrations due to lockdowns. Using region-specific estimates of the association between air pollution and mortality derived from a causal modeling approach using data from 2015 to 2019, we assess the changes in mortality attributable to the air pollution changes caused by the lockdowns in early 2020. During the lockdowns, NO2 reductions avoided 1.41 (95% empirical confidence interval [eCI]: 0.94, 1.88), 0.44 (95% eCI: 0.17, 0.71), and 4.66 (95% eCI: 2.03, 7.44) deaths per 100,000 people in Jiangsu, China; California, U.S.; and Central-southern Italy, respectively. Mortality benefits attributable to PM2.5 reductions were also significant, albeit of a smaller magnitude. For Germany, the mortality benefits attributable to NO2 changes were not significant (0.11; 95% eCI: -0.03, 0.25), and an increase in PM2.5 concentrations was associated with an increase in mortality of 0.35 (95% eCI: 0.22, 0.48) deaths per 100,000 people during the lockdown. COVID-19 lockdowns overall improved air quality and brought attributable health benefits, especially associated with NO2 improvements, with notable heterogeneity across regions. This study underscores the importance of accounting for local characteristics when policymakers adapt successful emission control strategies from other regions.

A traffic-induced shift of ultrafine particle sources under COVID-19 soft lockdown in a subtropical urban area

During the unprecedented COVID-19 city lockdown, a unique opportunity arose to dissect the intricate dynamics of urban air quality, focusing on ultrafine particles (UFPs) and volatile organic compounds (VOCs). This study delves into the nuanced interplay between traffic patterns and UFP emissions in a subtropical urban setting during the spring-summer transition of 2021. Leveraging meticulous roadside measurements near a traffic nexus, our investigation unravels the intricate relationship between particle number size distribution (PNSD), VOCs mixing ratios, and detailed vehicle activity metrics. The soft lockdown era, marked by a 20-27% dip in overall traffic yet a surprising surge in early morning motorcycle activity, presented a natural experiment. We observed a consequential shift in the urban aerosol regime: the decrease in primary emissions from traffic substantially amplified the role of aged particles and secondary aerosols. This shift was particularly pronounced under stagnant atmospheric conditions, where reduced dilution exacerbated the influence of alternative emission sources, notably solvent evaporation, and was further accentuated with the resumption of normal traffic flows. A distinct seasonal trend emerged as warmer months approached, with aromatic VOCs such as toluene, ethylbenzene, and xylene not only increasing but also significantly contributing to more frequent particle growth events. These findings spotlight the criticality of targeted strategies at traffic hotspots, especially during periods susceptible to weak atmospheric dilution, to curb UFP and precursor emissions effectively. As we stand at the cusp of widespread vehicle electrification, this study underscores the imperative of a holistic approach to urban air quality management, embracing the complexities of primary emission reductions and the resultant shifts in atmospheric chemistry.

A comprehensive examination of temporal-seasonal variations of PM1.0 and PM2.5 in taiwan before and during the COVID-19 lockdown

COVID-19 has been a significant global concern due to its contagious nature. In May 2021, Taiwan experienced a severe outbreak, leading the government to enforce strict Pandemic Alert Level 3 restrictions in order to curtail its spread. Although previous studies in Taiwan have examined the effects of these measures on air quality, further research is required to compare different time periods and assess the health implications of reducing particulate matter during the Level 3 lockdown. Herein, we analyzed the mass concentrations, chemical compositions, seasonal variations, sources, and potential health risks of PM1.0 and PM2.5 in Central Taiwan before and during the Level 3 lockdown. As a result, coal-fired boilers (47%) and traffic emissions (53%) were identified as the predominant sources of polycyclic aromatic hydrocarbons (PAHs) in PM1.0, while in PM2.5, the dominant sources of PAHs were coal-fired boilers (28%), traffic emissions (50%), and iron and steel sinter plants (22.1%). Before the pandemic, a greater value of 20.9 ± 6.92 μg/m3 was observed for PM2.5, which decreased to 15.3 ± 2.51 μg/m3 during the pandemic due to a reduction in industrial and anthropogenic emissions. Additionally, prior to the pandemic, PM1.0 had a contribution rate of 79% to PM2.5, which changed to 89% during the pandemic. Similarly, BaPeq values in PM2.5 exhibited a comparable trend, with PM1.0 contributing 86% and 65% respectively. In both periods, the OC/EC ratios for PM1.0 and PM2.5 were above 2, due to secondary organic compounds. The incremental lifetime cancer risk (ILCR) of PAHs in PM2.5 decreased by 4.03 × 10-5 during the pandemic, with PM1.0 contributing 73% due to reduced anthropogenic activities.

Elucidating contributions of volatile organic compounds to ozone formation using random forest during COVID-19 pandemic: A case study in China

Ozone has been reported to increase despite nitrogen oxides reductions during the COVID-19 pandemic, and ozone formation needs to be revisited using volatile organic compounds (VOCs), which are rarely measured during the pandemic. Here, a total of 98 VOCs species were monitored in an economy-active city in China from January 2021 to August 2022 to assess contributions to ozone formation during the pandemic. Total VOCs concentrations were 35.55 ± 21.47 ppb during the entire period, among which alkanes account for the largest fraction (13.78 ppb, 38.0%), followed by aromatics (6.16 ppb, 16.8%) and oxygenated VOCs (OVOCs, 5.69 ppb, 15.7%). Most VOCs groups (e.g., alkenes, OVOCs) and individual species (e.g., isoprene, methyl vinyl ketone) display obvious seasonal and diurnal variations, which are related to their sources and reactivities. No weekend effects of VOCs suggest limited influences from traffic emissions during pandemic. Aromatics and alkenes are the major contributors (39% and 33%) to ozone formation potential, largely driven by o/m/p-xylene (21%), ethylene (15%), toluene (9%). Secondary organic aerosol formation potential is dominated by toluene (>50%) despite its low proportion (5%). Further inclusion of VOCs and meteorology in the Random Forest model shows good ozone prediction performance (R2 = 0.77-0.86, RMSE = 11.95-19.91 μg/m3, MAE = 8.89-14.58 μg/m3). VOCs and NO2 contribute >50% of total importance with the largest difference in importance ratio of VOCs/NO2 in the summer and winter, implying ozone formation regime may vary. No seasonal variations in importance of meteorology are observed, while importance of other variables (e.g., PM2.5) is highest in the summer. This work identifies critical VOCs groups and species for ozone formation during the pandemic, and demonstrates the feasibility of machine learning algorithms in elucidation of ozone formation mechanisms.

Alleviation of PM2.5-associated Risk of Daily Influenza Hospitalization by COVID-19 Lockdown Measures: A Time-series Study in Northeastern Thailand

OBJECTIVES

Abrupt changes in air pollution levels associated with the coronavirus disease 2019 (COVID-19) outbreak present a unique opportunity to evaluate the effects of air pollution on influenza risk, at a time when emission sources were less active and personal hygiene practices were more rigorous.

METHODS

This time-series study examined the relationship between influenza cases (n=22 874) and air pollutant concentrations from 2018 to 2021, comparing the timeframes before and during the COVID-19 pandemic in and around Thailand's Khon Kaen province. Poisson generalized additive modeling was employed to estimate the relative risk of hospitalization for influenza associated with air pollutant levels.

RESULTS

Before the COVID-19 outbreak, both the average daily number of influenza hospitalizations and particulate matter with an aerodynamic diameter of 2.5 μm or less (PM2.5) concentration exceeded those later observed during the pandemic (p<0.001). In single-pollutant models, a 10 μg/m3 increase in PM2.5 before COVID-19 was significantly associated with increased influenza risk upon exposure to cumulative-day lags, specifically lags 0-5 and 0-6 (p<0.01). After adjustment for co-pollutants, PM2.5 demonstrated the strongest effects at lags 0 and 4, with elevated risk found across all cumulative-day lags (0-1, 0-2, 0-3, 0-4, 0-5, and 0-6) and significantly greater risk in the winter and summer at lag 0-5 (p<0.01). However, the PM2.5 level was not significantly associated with influenza risk during the COVID-19 outbreak.

CONCLUSIONS

Lockdown measures implemented during the COVID-19 pandemic could mitigate the risk of PM2.5-induced influenza. Effective regulatory actions in the context of COVID-19 may decrease PM2.5 emissions and improve hygiene practices, thereby reducing influenza hospitalizations.

Global poverty estimation using private and public sector big data sources

Household surveys give a precise estimate of poverty; however, surveys are costly and are fielded infrequently. We demonstrate the importance of jointly using multiple public and private sector data sources to estimate levels and changes in wealth for a large set of countries. We train models using 63,854 survey cluster locations across 59 countries, relying on data from satellites, Facebook Marketing information, and OpenStreetMaps. The model generalizes previous approaches to a wide set of countries. On average, across countries, the model explains 55% (min = 14%; max = 85%) of the variation in levels of wealth at the survey cluster level and 59% (min = 0%; max = 93%) of the variation at the district level, and the model explains 4% (min = 0%; max = 17%) and 6% (min = 0%; max = 26%) of the variation of changes in wealth at the cluster and district levels. Models perform best in lower-income countries and in countries with higher variance in wealth. Features from nighttime lights, OpenStreetMaps, and land cover data are most important in explaining levels of wealth, and features from nighttime lights are most important in explaining changes in wealth.

Online public concern about allergic rhinitis and its association with COVID-19 and air quality in China: an informative epidemiological study using Baidu index

BACKGROUND

Allergic rhinitis is a common health concern that affects quality of life. This study aims to examine the online search trends of allergic rhinitis in China before and after the COVID-19 epidemic and to explore the association between the daily air quality and online search volumes of allergic rhinitis in Beijing.

METHODS

We extracted the online search data of allergic rhinitis-related keywords from the Baidu index database from January 23, 2017 to June 23, 2022. We analyzed and compared the temporal distribution of online search behaviors across different themes of allergic rhinitis before and after the COVID-19 pandemic in mainland China, using the Baidu search index (BSI). We also obtained the air quality index (AQI) data in Beijing and assessed its correlation with daily BSIs of allergic rhinitis.

RESULTS

The online search for allergic rhinitis in China showed significant seasonal variations, with two peaks each year in spring from March to May and autumn from August and October. The BSI of total allergic rhinitis-related searches increased gradually from 2017 to 2019, reaching a peak in April 2019, and declined after the COVID-19 pandemic, especially in the first half of 2020. The BSI for all allergic rhinitis themes was significantly lower after the COVID-19 pandemic than before (all p values < 0.05). The results also revealed that, in Beijing, there was a significant negative association between daily BSI and AQI for each allergic rhinitis theme during the original variant strain epidemic period and a significant positive correlation during the Omicron variant period.

CONCLUSION

Both air quality and the interventions used for COVID-19 pandemic, including national and local quarantines and mask wearing behaviors, may have affected the incidence and public concern about allergic rhinitis in China. The online search trends can serve as a valuable tool for tracking real-time public concerns about allergic rhinitis. By complementing traditional disease monitoring systems of health departments, these search trends can also offer insights into the patterns of disease outbreaks. Additionally, they can provide references and suggestions regarding the public's knowledge demands related to allergic rhinitis, which can further be instrumental in developing targeted strategies to enhance population-based disease education on allergic diseases.

A decreased impact of air pollution on hospital pneumonia visits during COVID-19 outbreak in northeastern Thailand

Background

The coronavirus disease 2019 (COVID-19) pandemic had effects on changes in people, society, and pollutant sources. This was a unique research opportunity to assess the effects on the risk of pneumonia resulted from the changes in air pollution and personal hygiene regarding city lockdown.

Methods

This study, we estimated time-series relative risks (RRs) of pneumonia (n=94,288) associated with PM10, PM2.5, NO2, and O3 in Khon Kaen province and its vicinity, using Poison regression with generalized additive model and compared air pollutant-associated risk of pneumonia before vs. during the COVID-19 outbreak [2018-2021].

Results

During the COVID-19 period, pneumonia cases, PM2.5, PM10, and NO2 levels were lower than those before the COVID-19 but the O3 level was significantly higher. The single-pollutant analyses showed that the increase in PM10, PM2.5, and NO2 were significantly associated with pneumonia risks at single-day lag 0 in the earlier two years (2018-2019). For multi-pollutant analyses, there were higher RRs in PM2.5 at lag 0 [RR =1.078, 95% confidence interval (CI): 1.004 to 1.157], lag 4 (RR =1.054, 95% CI: 1.011 to 1.098) and lag 5 (RR =1.090, 95% CI: 1.021 to 1.165) and for all cumulative-day lags, greatest was at lag 0-5 (RR =1.314, 95% CI: 1.200 to 1.439) before the COVID-19 period while there were lower pneumonia RRs of a 10-µg/m3 increase in PM2.5 at single-day lag 1 (RR =1.064, 95% CI: 1.002 to 1.130) and for all cumulative-day lags, greatest was at lag 0-5 (RR =1.201, 95% CI: 1.073 to 1.344) during the COVID-19 outbreak. Multi-pollutant of NO2 significantly increased pneumonia risk in cumulative day exposure before the COVID-19 outbreak at lag 0-3 (RR =1.050, 95% CI: 1.001 to 1.100). It was significantly greater than that risk during the outbreak.

Conclusions

This study revealed that the lockdown measures to control COVID-19 were effective in improving air quality and lowering associated pneumonia risk. These findings would help raise awareness about measures and policies to preserve the air quality to increase respiratory health benefits.

Neural network for evaluation of the impact of the UK COVID-19 national lockdown on atmospheric concentrations of PAHs and PBDEs

The impact of measures to restrict population mobility during the COVID-19 pandemic on atmospheric concentrations of polycyclic aromatic hydrocarbons (PAH) and brominated flame retardants (BFRs) is poorly understood. This study analyses the effects of meteorological parameters and mobility restrictions during the COVID-19 pandemic on concentrations of PAH and BFRs at the University of Birmingham in the UK utilising a neural network (self-organising maps, SOM). Air sampling was performed using Polyurethane Foam (PUF) disk passive samplers between October 2019 and January 2021. Data on concentrations of PAH and BFRs were analysed using SOM and Spearman's rank correlation. Data on meteorological parameters (air temperature, wind, and relative humidity) and mobility restrictions during the pandemic were included in the analysis. Decabromodiphenyl ether (BDE-209) was the most abundant polybrominated diphenyl ether (PBDE) (23-91% Σ7PBDEs) but was detected at lower absolute concentrations (4.2-35.0 pg m-3) than in previous investigations in Birmingham. Air samples were clustered in five groups based on SOM analysis and the effects of meteorology and pandemic-related restrictions on population mobility could be visualised. Concentrations of most PAH decreased during the early stages of the pandemic when mobility was most restricted. SOM analysis also helped to identify the important influence of wind speed on contaminant concentrations, contributing to reduce the concentration of all analysed pollutants. In contrast, concentrations of most PBDEs remained similar or increased during the first COVID-19 lockdown which was attributed to their primarily indoor sources that were either unaffected or increased during lockdown.

Investigating the potential of aggregated mobility indices for inferring public transport ridership changes

Aggregated mobility indices (AMIs) derived from information and communications technologies have recently emerged as a new data source for transport planners, with particular value during periods of major disturbances or when other sources of mobility data are scarce. Particularly, indices estimated on the aggregate user concentration in public transport (PT) hubs based on GPS of smartphones, or the number of PT navigation queries in smartphone applications have been used as proxies for the temporal changes in PT aggregate demand levels. Despite the popularity of these indices, it remains largely untested whether they can provide a reasonable characterisation of actual PT ridership changes. This study aims to address this research gap by investigating the reliability of using AMIs for inferring PT ridership changes by offering the first rigorous benchmarking between them and ridership data derived from smart card validations and tickets. For the comparison, we use monthly and daily ridership data from 12 cities worldwide and two AMIs shared globally by Google and Apple during periods of major change in 2020-22. We also explore the complementary role of AMIs on traditional ridership data. The comparative analysis revealed that the index based on human mobility (Google) exhibited a notable alignment with the trends reported by ridership data and performed better than the one based on PT queries (Apple). Our results differ from previous studies by showing that AMIs performed considerably better for similar periods. This finding highlights the huge relevance of dealing with methodological differences in datasets before comparing. Moreover, we demonstrated that AMIs can also complement data from smart card records when ticketing is missing or of doubtful quality. The outcomes of this study are particularly relevant for cities of developing countries, which usually have limited data to analyse their PT ridership, and AMIs may offer an attractive alternative.

Air pollution benefits from reduced on-road activity due to COVID-19 in the United States

On-road transportation is one of the largest contributors to air pollution in the United States. The COVID-19 pandemic provided the unintended experiment of reduced on-road emissions' impacts on air pollution due to lockdowns across the United States. Studies have quantified on-road transportation's impact on fine particulate matter (PM2.5)-attributable and ozone (O3)-attributable adverse health outcomes in the United States, and other studies have quantified air pollution-attributable health outcome reductions due to COVID-19-related lockdowns. We aim to quantify the PM2.5-attributable, O3-attributable, and nitrogen dioxide (NO2)-attributable adverse health outcomes from traffic emissions as well as the air pollution benefits due to reduced on-road activity during the pandemic in 2020. We estimate 79,400 (95% CI 46,100-121,000) premature mortalities each year due to on-road-attributable PM2.5, O3, and NO2. We further break down the impacts by pollutant and vehicle types (passenger [PAS] vs. freight [FRT] vehicles). We estimate PAS vehicles to be responsible for 63% of total impacts and FRT vehicles 37%. Nitrogen oxide (NOX) emissions from these vehicles are responsible for 78% of total impacts as it is a precursor for PM2.5 and O3. Utilizing annual vehicle miles traveled reductions in 2020, we estimate that 9,300 (5,500-14,000) deaths from air pollution were avoided in 2020 due to the state-specific reductions in on-road activity across the continental United States. By quantifying the air pollution public health benefits from lockdown-related reductions in on-road emissions, the results from this study stress the need for continued emission mitigation policies, like the U.S. Environmental Protection Agency's (EPA) recently proposed NOX standards for heavy-duty vehicles, to mitigate on-road transportation's public health impact.

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.

Air pollution mortality benefits of sustained COVID-19 mobility restrictions in Australian cities

OBJECTIVES

Emissions from road traffic, power generation and industry were substantially reduced during pandemic lockdown periods globally. Thus, we analysed reductions in traffic-related air pollution in Australian capital cities during March-April 2020 and then modelled the mortality benefits that could be realised if similar reductions were sustained by structural policy interventions.

STUDY DESIGN

Satellite, air pollution monitor and land use observations were used to estimate ground-level nitrogen dioxide (NO2) concentrations in all Australian capital cities during: (a) a typical year with no prolonged air pollution events; (b) a hypothetical sustained reduction in NO2 equivalent to the COVID-19 lockdowns.

METHODS

We use the WHO recommended NO2 exposure-response coefficient for mortality (1.023, 95 % CI: 1.008-1.037, per 10 μg/m3 annual average) to assess gains in life expectancy and population-wide years of life from reduced exposure to traffic-related air pollution.

RESULTS

We attribute 1.1 % of deaths to anthropogenic NO2 exposures in Australian cities, corresponding to a total of 13,340 years of life lost annually. Although COVID-19-related reductions in NO2 varied widely between Australian cities during April 2020, equivalent and sustained reductions in NO2 emissions could reduce NO2-attributable deaths by 27 %, resulting in 3348 years of life gained annually.

CONCLUSIONS

COVID-19 mobility restrictions reduced NO2 emissions and population-wide exposures in Australian cities. When sustained to the same extent by policy interventions that reduce fossil fuel consumption by favouring the uptake of electric vehicles, active travel and public transport, the health, mortality and economic benefits will be measurable in Australian cities.

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

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

First close insight into global daily gapless 1 km PM2.5 pollution, variability, and health impact

Here we retrieve global daily 1 km gapless PM2.5 concentrations via machine learning and big data, revealing its spatiotemporal variability at an exceptionally detailed level everywhere every day from 2017 to 2022, valuable for air quality monitoring, climate change, and public health studies. We find that 96%, 82%, and 53% of Earth's populated areas are exposed to unhealthy air for at least one day, one week, and one month in 2022, respectively. Strong disparities in exposure risks and duration are exhibited between developed and developing countries, urban and rural areas, and different parts of cities. Wave-like dramatic changes in air quality are clearly seen around the world before, during, and after the COVID-19 lockdowns, as is the mortality burden linked to fluctuating air pollution events. Encouragingly, only approximately one-third of all countries return to pre-pandemic pollution levels. Many nature-induced air pollution episodes are also revealed, such as biomass burning.

Detection and attribution of an anomaly in terrestrial photosynthesis in Europe during the COVID-19 lockdown

Carbon dioxide (CO2) uptake by plant photosynthesis, referred to as gross primary production (GPP) at the ecosystem level, is sensitive to environmental factors, including pollutant exposure, pollutant uptake, and changes in the scattering of solar shortwave irradiance (SWin) - the energy source for photosynthesis. The 2020 spring lockdown due to COVID-19 resulted in improved air quality and atmospheric transparency, providing a unique opportunity to assess the impact of air pollutants on terrestrial ecosystem functioning. However, detecting these effects can be challenging as GPP is influenced by other meteorological drivers and management practices. Based on data collected from 44 European ecosystem-scale CO2 flux monitoring stations, we observed significant changes in spring GPP at 34 sites during 2020 compared to 2015-2019. Among these, 14 sites showed an increase in GPP associated with higher SWin, 10 sites had lower GPP linked to atmospheric and soil dryness, and seven sites were subjected to management practices. The remaining three sites exhibited varying dynamics, with one experiencing colder and rainier weather resulting in lower GPP, and two showing higher GPP associated with earlier spring melts. Analysis using the regional atmospheric chemical transport model (LOTOS-EUROS) indicated that the ozone (O3) concentration remained relatively unchanged at the research sites, making it unlikely that O3 exposure was the dominant factor driving the primary production anomaly. In contrast, SWin increased by 9.4 % at 36 sites, suggesting enhanced GPP possibly due to reduced aerosol optical depth and cloudiness. Our findings indicate that air pollution and cloudiness may weaken the terrestrial carbon sink by up to 16 %. Accurate and continuous ground-based observations are crucial for detecting and attributing subtle changes in terrestrial ecosystem functioning in response to environmental and anthropogenic drivers.

The weekly cycle of photosynthesis in Europe reveals the negative impact of particulate pollution on ecosystem productivity

Aerosols can affect photosynthesis through radiative perturbations such as scattering and absorbing solar radiation. This biophysical impact has been widely studied using field measurements, but the sign and magnitude at continental scales remain uncertain. Solar-induced fluorescence (SIF), emitted by chlorophyll, strongly correlates with photosynthesis. With recent advancements in Earth observation satellites, we leverage SIF observations from the Tropospheric Monitoring Instrument (TROPOMI) with unprecedented spatial resolution and near-daily global coverage, to investigate the impact of aerosols on photosynthesis. Our analysis reveals that on weekends when there is more plant-available sunlight due to less particulate pollution, 64% of regions across Europe show increased SIF, indicating more photosynthesis. Moreover, we find a widespread negative relationship between SIF and aerosol loading across Europe. This suggests the possible reduction in photosynthesis as aerosol levels increase, particularly in ecosystems limited by light availability. By considering two plausible scenarios of improved air quality-reducing aerosol levels to the weekly minimum 3-d values and levels observed during the COVID-19 period-we estimate a potential of 41 to 50 Mt net additional annual CO2 uptake by terrestrial ecosystems in Europe. This work assesses human impacts on photosynthesis via aerosol pollution at continental scales using satellite observations. Our results highlight i) the use of spatiotemporal variations in satellite SIF to estimate the human impacts on photosynthesis and ii) the potential of reducing particulate pollution to enhance ecosystem productivity.

Using Explainable Machine Learning to Interpret the Effects of Policies on Air Pollution: COVID-19 Lockdown in London

Activity changes during the COVID-19 lockdown present an opportunity to understand the effects that prospective emission control and air quality management policies might have on reducing air pollution. Using a regression discontinuity design for causal analysis, we show that the first UK national lockdown led to unprecedented decreases in road traffic, by up to 65%, yet incommensurate and heterogeneous responses in air pollution in London. At different locations, changes in air pollution attributable to the lockdown ranged from -50% to 0% for nitrogen dioxide (NO2), 0% to +4% for ozone (O3), and -5% to +0% for particulate matter with an aerodynamic diameter less than 10 μm (PM10), and there was no response for PM2.5. Using explainable machine learning to interpret the outputs of a predictive model, we show that the degree to which NO2 pollution was reduced in an area was correlated with spatial features (including road freight traffic and proximity to a major airport and the city center), and that existing inequalities in air pollution exposure were exacerbated: pollution reductions were greater in places with more affluent residents and better access to public transport services.

Redox and inflammatory mechanisms linking air pollution particulate matter with cardiometabolic derangements

Air pollution is the largest environmental risk factor for disease and premature death. Among the different components that are present in polluted air, fine particulate matter below 2.5 μm in diameter (PM2.5) has been identified as the main hazardous constituent. PM2.5 mainly arises from fossil fuel combustion during power generation, industrial processes, and transportation. Exposure to PM2.5 correlates with enhanced mortality risk from cardiovascular diseases (CVD), such as myocardial infarction and stroke. Over the last decade, it has been increasingly suggested that PM2.5 affects CVD already at the stage of risk factor development. Among the multiple biological mechanisms that have been described, the interplay between oxidative stress and inflammation has been consistently highlighted as one of the main drivers of pulmonary, systemic, and cardiovascular effects of PM2.5 exposure. In this context, PM2.5 uptake by tissue-resident immune cells in the lung promotes oxidative and inflammatory mediators release that alter tissue homeostasis at remote locations. This pathway is central for PM2.5 pathogenesis and might account for the accelerated development of risk factors for CVD, including obesity and diabetes. However, transmission and end-organ mechanisms that explain PM2.5-induced impaired function in metabolic active organs are not completely understood. In this review, the main features of PM2.5 physicochemical characteristics related to PM2.5 ability to induce oxidative stress and inflammation will be presented. Hallmark and recent epidemiological and interventional studies will be summarized and discussed in the context of current air quality guidelines and legislation, knowledge gaps, and inequities. Lastly, mechanistic studies at the intersection between redox metabolism, inflammation, and function will be discussed, with focus on heart and adipose tissue alterations. By offering an integrated analysis of PM2.5-induced effects on cardiometabolic derangements, this review aims to contribute to a better understanding of the pathogenesis and potential interventions of air pollution-related CVD.

Impact of the COVID-19 outbreak on urban air, Land surface temperature and air pollution in cold climate zones

The objective of this study was to analyze air pollution and thermal environment in Turkey's cold region before, during, and after COVID-19 in 2019, 2020 and 2021. The CO, NO2, O3, PM10 and SO2 data from the state air quality stations, as well as ground air temperature data from six weather stations, and land satellite images from the USGS website using ArcGIS 10.4.1 software were collected in January, March, April and August of 2019, 2020 an 2021. In order to evaluate the impact of COVID-19 measures and restrictions on cold region cities, air pollution indicators, land surface temperature and air temperature as well as statistical data were analyzed. The results indicated that the CO, NO2, PM10 and SO2 emissions decreased by 14.9%, 14.3%, 47.1% and 28.5%, but O3 increased by 16.9%, respectively, during the COVID-19 lockdown in 2020 as compared to these of the pre-COVID-19 levels in 2019. A positive correlation between air temperature and O3 in 2019 (r2 = 0.80), and in 2020 and 2021 (r2 = 0.64) was obtained. Air temperature in 2020 and 2021 decreased due to lockdowns and quarantine measures that led to lower O3 emissions as compared to 2019. Negative correlations were also found between air temperature and NO2 (r2 = 0.60) and SO2 (r2 = 0.5). There was no correlation between air temperature and PM10. During the COVID-19 lockdown and intense restrictions in April 2020, the average LST and air temperature values dropped by 14.7 °C and 1.6 °C respectively, compared to April 2019. These findings may be beneficial for future urban planning, particularly in cold regions.

Advancing application of satellite remote sensing technologies for linking atmospheric and built environment to health

Background

The intricate interplay between human well-being and the surrounding environment underscores contemporary discourse. Within this paradigm, comprehensive environmental monitoring holds the key to unraveling the intricate connections linking population health to environmental exposures. The advent of satellite remote sensing monitoring (SRSM) has revolutionized traditional monitoring constraints, particularly limited spatial coverage and resolution. This innovation finds profound utility in quantifying land covers and air pollution data, casting new light on epidemiological and geographical investigations. This dynamic application reveals the intricate web connecting public health, environmental pollution, and the built environment.

Objective

This comprehensive review navigates the evolving trajectory of SRSM technology, casting light on its role in addressing environmental and geographic health issues. The discussion hones in on how SRSM has recently magnified our understanding of the relationship between air pollutant exposure and population health. Additionally, this discourse delves into public health challenges stemming from shifts in urban morphology.

Methods

Utilizing the strategic keywords "SRSM," "air pollutant health risk," and "built environment," an exhaustive search unfolded across prestigious databases including the China National Knowledge Network (CNKI), PubMed and Web of Science. The Citespace tool further unveiled interconnections among resultant articles and research trends.

Results

Synthesizing insights from a myriad of articles spanning 1988 to 2023, our findings unveil how SRMS bridges gaps in ground-based monitoring through continuous spatial observations, empowering global air quality surveillance. High-resolution SRSM advances data precision, capturing multiple built environment impact factors. Its application to epidemiological health exposure holds promise as a pioneering tool for contemporary health research.

Conclusion

This review underscores SRSM's pivotal role in enriching geographic health studies, particularly in atmospheric pollution domains. The study illuminates how SRSM overcomes spatial resolution and data loss hurdles, enriching environmental monitoring tools and datasets. The path forward envisions the integration of cutting-edge remote sensing technologies, novel explorations of urban-public health associations, and an enriched assessment of built environment characteristics on public well-being.

Effects of the lockdown measure amid COVID-19 pandemic on outpatient department visits associated with air pollution reduction in Thailand

We investigated the effects of COVID-19 lockdown on air quality and its consequences health and economic benefits in Thailand. The conditional Poisson regression model was applied to examine the association between air pollution and outpatient department (OPD) visits in each province and pooled the province-specific estimates using the random-effects meta-analysis to derive the national estimates. We then applied a random forest model with meteorological normalization approach to predict the concentration of air pollutants by means of business as usual during the lockdown period (April 3-May 3) in 2020 and further calculated the changes in the number of OPD visits and their consequent expenditure attributable to air pollution reduction using the obtained risk function performed earlier. The number of cardiovascular OPD visits attributed to PM10, PM2.5 and NO2 decreased by 4,414 (95% CI 982, 8,401), 4,040 (95% CI 326, 7,770), and 13,917 (95% CI 1,675, 27,278) cases, respectively, leading to reduced medical expenditure by 14,7180.21, 13,4708.31, and 46,4025.04 USD, respectively. The number of respiratory OPD visits attributed to PM10, PM2.5, NO2, and O3 reduction decreased by 2,298 (95% CI 1,223, 3,375), 2,056 (95% CI 740, 3,252), 3,326 (95% CI 542, 6,295), and 1,160 (95% CI 5,26, 1,804) cases, respectively, where the consequent medical expenditure was reduced by 76,618.48, 68,566.36, 11,0908.31, and 38,685.50 USD, respectively. Finding from this study showed that air quality during the lockdown period in Thailand was improved, contributing to the reduction of cardiovascular and respiratory OPD visits, and consequent medical service costs attributable to air pollution.

Cardiovascular mortality in Brazil during the COVID-19 pandemic: a comparison between underlying and multiple causes of death

OBJECTIVES

The COVID-19 pandemic has differentially impacted cardiovascular disease (CVD) mortality worldwide. Causes of death misclassification may be one of the reasons. We evaluated the impact of the pandemic on CVD mortality in Brazil, comparing underlying causes (UCs) and multiple causes (MCs) of death.

STUDY DESIGN

Ecological time-series study.

METHODS

An ecological, time-series study was conducted analysing age-standardised death rates for CVD, from epidemiological week (EW) 10/2020 to 39/2021, using data from the Mortality Information System, Brazil. CVD was defined using the International Classification of Diseases (ICD-10) coding, if reported as UC or MC of death. Observed and expected data (mean for the same EW, 2017-2019) were compared. Risk ratios (RiRs) were analysed, and 95% confidence intervals (CIs) were calculated.

RESULTS

Age-standardised mortality rate for CVD as UC of death was 165.8 (95%CI: 165.4-166.3) per 100,000 inhabitants, similar to what was expected (165.6/100,000, 95%CI: 165.2-166.1, RiR = 1.00). There was increased out-of-hospital mortality (RiR = 1.18; 95%CI: 1.17-1.19) and deaths of ill-defined causes (RiR = 1.43; 95%CI: 1.42-1.44). The increase in out-of-hospital deaths was more pronounced in the North (RiR = 1.33; 95%CI 1.30-1.36) region, with a less resilient health system. Conversely, as MCs of death, there was a 10% increase in CVD mortality (observed: 243.2 [95%CI: 242.7-243.7], expected: 221.6 [95%CI: 221.1-222.1] per 100,000). An increase also occurred in the North and Central West regions (RiR = 1.16; 95%CI: 1.15-1.18), among men (RiR = 1.11; 95%CI: 1.11-1.12) and individuals aged ≥60 years (RiR = 1.11; 95%CI: 1.10-1.11).

CONCLUSIONS

During the pandemic, mortality rates for CVD as MCs of death increased in Brazil, whereas as UC mortality rates did not change. Higher out-of-hospital mortality, misclassification, and competing causes of death may explain this pattern.

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

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

Health and Economic Benefits of Air Pollution Reductions in Vietnam During 2020-2021

Objectives: This paper explores the potential reduction in the number of deaths and the corresponding economic benefits in Vietnam that could have arisen from the decreased in concentrations of particulate matter with a diameter of 2.5 μm or less (PM2.5) and nitrogen dioxide (NO2). Methods: Using Global Exposure Mortality Models, we estimated the potential health and economic benefits on people aged 25 and above across Vietnam's 63 provinces. The counterfactual scenario assumed reducing PM2.5 and NO2 concentrations to levels observed during the two COVID-19 epidemic waves in 2021 with national lockdowns and activity restrictions. Results: In 2019, PM2.5 concentrations ranged from 12.8 to 40.8 μg/m3 while NO2 concentrations ranged between 2.9 and 36.98 μg/m3. The reduced levels of PM2.5 and NO2 resulted in 3,807 (95% CI: 2,845-4,730) and 2,451 (95% CI: 2,845-4,730) avoided deaths of adults aged 25 and above due to non-injury-related causes, respectively. Considering that every prevented death represents potential tangible and intangible cost savings, reduced levels of PM2.5 and NO2 concentrations during COVID-19 restrictions would have resulted in economic benefits of $793.0 million (95% CI: 592.7-985.4) and $510.6 million (95% CI: 381.3-634.9), respectively. Conclusion: The COVID-19 lockdown led to decreased PM2.5 and NO2 concentrations, benefiting health and economy in Vietnam. Our findings highlight the potential advantages of implementing air quality control policies in the country.

The Effect of the COVID-19 Pandemic on Environmental Health (Two Sides of the Same Coin): A Systematic Review

BACKGROUND

The outbreak of the COVID-19 pandemic in late 2019 has led to many changes such as reduced human activities and effects on the environment. There is no big picture of the effects of pandemics on the environment using related evidence.

OBJECTIVES

This study was conducted to investigate the effect of the COVID-19 pandemic on environmental health.

METHODS

A systematic search of English language studies was performed in major electronic databases; Web of Science, PubMed, Scopus, and Google scholar web search engine from December 2019 to February 2022. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) standard guidelines were used to follow up the review process. finally 58 articles entered the review procedure.

RESULTS

The results of indicate a significant reduction of air pollutants and improved air quality. It improved the water quality of some rivers, canals, and seas during the lockdown of the COVID-19 pandemic. The effects of this disease on the environment cannot be fully described yet.

CONCLUSION

In the short term, the amount of air, water, and coastal pollution has been reduced. few studies have examined the effects of pandemics on the environment in the long run, which paves the way for more researches.

A vision for incorporating human mobility in the study of human-wildlife interactions

As human activities increasingly shape land- and seascapes, understanding human-wildlife interactions is imperative for preserving biodiversity. Habitats are impacted not only by static modifications, such as roads, buildings and other infrastructure, but also by the dynamic movement of people and their vehicles occurring over shorter time scales. Although there is increasing realization that both components of human activity substantially affect wildlife, capturing more dynamic processes in ecological studies has proved challenging. Here we propose a conceptual framework for developing a 'dynamic human footprint' that explicitly incorporates human mobility, providing a key link between anthropogenic stressors and ecological impacts across spatiotemporal scales. Specifically, the dynamic human footprint integrates a range of metrics to fully acknowledge the time-varying nature of human activities and to enable scale-appropriate assessments of their impacts on wildlife behaviour, demography and distributions. We review existing terrestrial and marine human-mobility data products and provide a roadmap for how these could be integrated and extended to enable more comprehensive analyses of human impacts on biodiversity in the Anthropocene.

Impact of meteorological parameters and air pollutants on airborne concentration of Betula pollen and Bet v 1 allergen

The intensity of birch pollen season is expressed by seasonal pollen integral (SPIn, the sum of the mean daily pollen concentration during the birch pollination period) and the amount of Bet v 1 allergen released per birch pollen grain expressed by pollen allergen potency (PAP). Betula pollen and Bet v 1 allergen were simultaneously measured in the air of Bratislava from 2019 to 2022 by using two Burkard traps (Hirst-type and cyclone) in order to evaluate the causes of the seasonal variation in the SPIn and PAP levels. The highest SPIn (19,975 pollen/m3) was observed in 2022 and the lowest one (1484 pollen/m3) in 2021. The average daily PAP level (4.0 pg Bet v 1/pollen) was highest in 2019 and lowest (2.5 pg Bet v 1/pollen) in 2020. We found that seasonal variation in SPIn was associated mainly with the changes in environmental conditions during the pre-season period, whereas the year-to-year variation in PAP levels was attributed to environmental conditions during both pre- and in-season periods. Our results indicate that rainy weather in June 2020 and cold overcast weather in January‒February 2021 resulted in low SPIn in 2021. On the other hand, dry weather in June 2021 and warm weather in January‒February 2022 resulted in high SPIn in 2022. The low average daily PAP level in 2020 was associated with (1) low levels of gaseous air pollutants in March, when the ripening of pollen takes place; (2) an earlier start of the birch main pollen season (MPS); and (3) dry weather during the MPS. On the other hand, high PAP level in 2019 was associated with higher levels of air pollutants in March and during the MPS.

Impacts of the COVID-19 economic slowdown on soybean crop yields in the United States

It is without question that the COVID-19 pandemic has taken its toll on the U.S. economy. Stay-at-home orders led to reduced vehicular traffic and widespread declines in anthropogenic emissions (e.g., nitrogen oxides (NOx)). This study is the first to explore the potential consequences of O3 changes resulting from the economic shutdown in the United States on soybean crop yields for 2020. The pandemic's impact on surface O3 is quantified using the NOAA's National Air Quality Forecasting Capability (NAQFC), which is based on the Community Multi-Scale Air Quality (CMAQ) model for May-July 2020. The "would-be", 2020 level business-as-usual (BAU) emissions are compared to a simulation that uses representative COVID-19 (C19) emissions. For each emissions scenario, crop exposures are calculated using the AOT40 cumulative exposure index and then combined with county-level soybean production totals to determine regional yield losses. Exposure changes ranged between - 2 and 2 ppmVhr-1. It was further shown that increased exposures (0.5 to 1.10 ppmVhr-1) in the Southeast U.S. counteracted decreased exposures (0.8 to 0.5 ppmVhr-1) in the other soybean-producing regions. As a result, corresponding yield improvements counteracted yield losses around the Mississippi River Valley and allowed for minimal improvements in soybean production loss totaling $6.5 million over CONUS.

Before the first breath: why ambient air pollution and climate change should matter to neonatal-perinatal providers

Common outdoor air pollutants present threats to fetal and neonatal health, placing neonatal-perinatal clinical specialists in an important role for harm reduction through patient counseling and advocacy. Climate change is intertwined with air pollution and influences air quality. There is increasing evidence demonstrating the unique vulnerability in the development of adverse health consequences from exposures during the preconception, prenatal, and early postnatal periods, as well as promising indications that policies aimed at addressing these toxicants have improved birth outcomes. Advocacy by neonatal-perinatal providers articulating the potential impact of pollutants on newborns and mothers is essential to promoting improvements in air quality and reducing exposures. The goal of this review is to update neonatal-perinatal clinical specialists on the key ambient air pollutants of concern, their sources and health effects, and to outline strategies for protecting patients and communities from documented adverse health consequences.

Abrupt exacerbation in air quality over Europe after the outbreak of Russia-Ukraine war

Much attention has been paid to the world economy and social situations in response to the outbreak of war between Russia and Ukraine in the context of COVID-19. However, much less attention has been paid to the detrimental effect of war on the atmospheric environment. Here, we used an extended deweathered-detrended technique to quantitatively evaluate changes in ambient NO2, O3, and PM2.5 AQI levels arising from emission changes (due to pandemic-driven lockdowns and war-related activities) in European cities. Results show pandemic-induced lockdowns mitigated regional air pollution in Europe, but the war activities led to an average increase of approximately 9.78% in PM2.5 and 10.07% in NO2, along with an average decrease of about 7.93% in O3 levels in cities near the war zones. Moreover, the regional air pollution exacerbated by the war activities has offset the improvements in air quality observed during the COVID-19 pandemic. The potential mechanism analysis show that the increase in atmospheric pollutant emissions driven by the war activities led to the complexity of chemical reactions in the mixed atmospheric system, which posed a huge challenge to the alleviation of air pollution in the region. This study highlights the urgent need for a ceasefire from an environmental perspective.

The worldwide COVID-19 lockdown impacts on global secondary inorganic aerosols and radiative budget

Global lockdown measures to prevent the spread of the coronavirus disease 2019 (COVID-19) led to air pollutant emission reductions. While the COVID-19 lockdown impacts on both trace gas and total particulate pollutants have been widely investigated, secondary aerosol formation from trace gases remains unclear. To that end, we quantify the COVID-19 lockdown impacts on NOx and SO2 emissions and sulfate-nitrate-ammonium aerosols using multiconstituent satellite data assimilation and model simulations. We find that anthropogenic emissions over major polluted regions were reduced by 19 to 25% for NOx and 14 to 20% for SO2 during April 2020. These emission reductions led to 8 to 21% decreases in sulfate and nitrate aerosols over highly polluted areas, corresponding to >34% of the observed aerosol optical depth declines and a global aerosol radiative forcing of +0.14 watts per square meter relative to business-as-usual scenario. These results point to the critical importance of secondary aerosol pollutants in quantifying climate impacts of future mitigation measures.

Impact of COVID-19 restrictions on the concentration and source apportionment of atmospheric ammonia (NH3) across India

The wide spread of the coronavirus disease (COVID-19) has significantly influenced human activities around the world, providing a unique opportunity to investigate the response of air pollution to anthropogenic emission reduction. Compared with numerous studies on conventional air pollutants, atmospheric ammonia (NH₃) that has matched sources from both anthropogenic and natural emissions has been rarely investigated. Here we assess impacts of the COVID-19 lockdown on ambient NH₃ variation across India, one of the most severe NH₃ pollution region in the world. The role of meteorology in shaping emission contribution to NH₃ pollution and respective contribution of each emission source to ambient NH₃ before and after the COVID-19 outbreak were investigated using the XGBoost algorithm coupled with WRF-Chem model. Results showed that ambient NH₃ concentrations in the seven major cities (Hyderabad, Bengaluru, Chennai, Delhi, Lucknow, Kolkata and Mumbai) decreased by 2.1-53.8 % whereas in Ahmedabad increased by 20.3 % during the COVID-19 lockdown period. Obvious decrease in NH₃ in Indo-Gangetic Plain (-17.1 %) was mainly driven by favorable meteorology, whereas the slight decline in NH₃ in South India was mainly driven by epidemic-related emission control (-8.56 %). Source appointment results showed that the contribution of industrial emission (Ind) to ambient NH₃ in most megacities showed a decreasing trend (between 11 % and 26 %) during the lockdown period. However, the reduction effect was mostly compensated by increasing contributions (15-25 %) of residential emission (Res) or agricultural soil emission (Ags). Particularly, in Ahmedabad and Lucknow ambient NH₃ increased by 20.3 % and 12 % during the lockdown period, the reduction effect of Ind on ambient NH₃ (-23 % and -11 %, respectively) was absolutely compensated by enhanced contribution of Res (24 %) and Ags (12 %), respectively. Our results highlight the importance of eliminating residential and agricultural NH₃ emissions especially in North India.

Investing in nature can improve equity and economic returns

Sustainable development requires jointly achieving economic development to raise standards of living and environmental sustainability to secure these gains for the long run. Here, we develop a local-to-global, and global-to-local, earth-economy model that integrates the Global Trade Analysis Project (GTAP)-computable general equilibrium model of the economy with the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model of fine-scale, spatially explicit ecosystem services. The integrated model, GTAP-InVEST, jointly determines land use, environmental conditions, ecosystem services, market prices, supply and demand across economic sectors, trade across regions, and aggregate performance metrics like GDP. We use the integrated model to analyze the contribution of investing in nature for economic prosperity, accounting for the impact of four important ecosystem services (pollination, timber provision, marine fisheries, and carbon sequestration). We show that investments in nature result in large improvements relative to a business-as-usual path, accruing annual gains of $100 to $350 billion (2014 USD) with the largest percentage gains in the lowest-income countries. Our estimates include only a small subset of ecosystem services and could be far higher with inclusion of more ecosystem services, incorporation of ecological tipping points, and reduction in substitutability that limits economic adjustments to declines in natural capital. Our analysis highlights the need for improved environmental-economic modeling and the vital importance of integrating environmental information firmly into economic analysis and policy. The benefits of doing so are potentially very large, with the greatest percentage benefits accruing to inhabitants of the poorest countries.

The impact of the COVID-19 pandemic on global trade-embodied carbon emissions

We evaluate the response of global supply chains to carbon emissions through compiling multi-regional input-output (MRIO) models for import and export shocks in 14 countries/territories dominated by the COVID-19 crisis. Instead of traditional production-based inventories, we achieve CO₂ emissions inventories based on intermediate inputs and final consumption to analyze the connected environmental impacts. In addition, we adopt the available data up to date to construct inventories of carbon emissions involved in imports and exports from different sectors. The results show that global carbon emissions could be decreased by 6.01% during the COVID-19, while export carbon emissions remained basically unchanged. As a result, imported carbon emissions fell by 5.2%, with the energy products sector most affected by the pandemic. Transport sector witnessed 18.42% carbon emission reduction. The impact of developing countries with a large proportion of resource-based industries is comparatively higher than that of developed countries with the technological advantage. International trade plays a crucial role in the choice of supply chain partners to control carbon emissions. Building a sustainable supply chain and reducing the "trade carbon deficit" between countries/regions requires the coordination of all departments of each country/region to promote the trade of energy-saving products, environmental protection services and environmental services.

Inter-regional environmental inequality under lasting pandemic exacerbated by residential response

Pandemics greatly affect transportation, economic and household activities and their associated air pollutant emissions. In less affluent regions, household energy use is often the dominant pollution source and is sensitive to the affluence change caused by a persisting pandemic. Air quality studies on COVID-19 have shown declines in pollution levels over industrialized regions as an immediate response to pandemic-caused lockdown and weakened economy. Yet few have considered the response of residential emissions to altered household affluence and energy choice supplemented by social distancing. Here we quantify the potential effects of long-term pandemics on ambient fine particulate matter pollution (PM_2.5) and resulting premature mortality worldwide, by comprehensively considering the changes in transportation, economic production and household energy use. We find that a persisting COVID-like pandemic would reduce the global gross domestic product by 10.9 % and premature mortality related to black carbon, primary organic aerosols and secondary inorganic aerosols by 9.5 %. The global mortality decline would reach 13.0 % had the response of residential emissions been excluded. Among the 13 aggregated regions worldwide, the least affluent regions exhibit the greatest fractional economic losses with no comparable magnitudes of mortality reduction. This is because their weakened affluence would cause switch to more polluting household energy types on top of longer stay-at-home time, largely offsetting the effect of reduced transportation and economic production. International financial, technological and vaccine aids could reduce such environmental inequality.

Monthly excess mortality across counties in the United States during the COVID-19 pandemic, March 2020 to February 2022

Excess mortality is the difference between expected and observed mortality in a given period and has emerged as a leading measure of the COVID-19 pandemic's mortality impact. Spatially and temporally granular estimates of excess mortality are needed to understand which areas have been most impacted by the pandemic, evaluate exacerbating factors, and inform response efforts. We estimated all-cause excess mortality for the United States from March 2020 through February 2022 by county and month using a Bayesian hierarchical model trained on data from 2015 to 2019. An estimated 1,179,024 excess deaths occurred during the first 2 years of the pandemic (first: 634,830; second: 544,194). Overall, excess mortality decreased in large metropolitan counties but increased in nonmetropolitan counties. Despite the initial concentration of mortality in large metropolitan Northeastern counties, nonmetropolitan Southern counties had the highest cumulative relative excess mortality by July 2021. These results highlight the need for investments in rural health as the pandemic's rural impact grows.

eDNA testing reveals surprising findings on fish population dynamics in Thailand

COVID-19, a global health concern, has an effect on all aspects of the economy. The aquaculture and fishing industries were severely harmed as a result of the closures in multiple nations. Regular systems for inventory monitoring, production, and supply were disrupted. Cancellation of programmes for research, fieldwork, sampling, and tagging influences management-required data. For effective species management, fish dispersion assessments are indispensable. However, due to the difficulty of accessing sampling sites and the associated costs, there is frequently a lack of comprehensive information regarding the distribution and abundance of organisms. The COVID-19 prohibition made fish monitoring more problematic. Due to constant pressure, populations of the stone lapping minnow (Garra cambodgiensis), one of Thailand's overfished fish, are rapidly declining. Therefore, eDNA-based monitoring was devised and implemented to reveal the likely dispersal of the species in Thailand prior to and following the lockdown. At 28 locations within the Chao Phraya River Basin, water samples were collected. qPCR was used to determine the presence or absence of G. cambodgiensis in water samples. In 78 of 252 water samples, a wide range of computed copy numbers for G. cambodgiensis eDNA was observed. It was discovered that samples collected in 2021 (after the lockdown) contain a higher concentration of G. cambodgiensis eDNA than samples collected in 2018 or 2019 (prior to the lockdown). The closure appears to be a boon and may result in a substantial restocking of the fish we have studied. Overall, eDNA-based analysis is an extremely promising new survey instrument.

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.

Impact of coronavirus-driven reduction in aerosols on precipitation in the western United States

Among the many impacts of COVID-19, the pandemic led to improved air quality conditions in the countries under quarantine due to the shutdown of industries, drastically reduced traffic, and lockdowns. Meanwhile, the western United States, particularly the coastal areas from Washington to California, received much less precipitation than normal during early 2020. Is it possible that this reduction in precipitation was driven by the reduced aerosols due to the coronavirus? Here we show that the reduction in aerosols resulted in higher temperatures (up to ∼0.5 °C) and generally lower snow amounts but cannot explain the observed low precipitation amounts over this region. In addition to an assessment of the effects of the coronavirus-related reduction in aerosols on precipitation across the western United States, our findings also provide basic information on the potential impacts different mitigation efforts aimed at reducing anthropogenic aerosols would have on the regional climate.

Twitterati on COVID-19 pandemic-environment linkage: Insights from mining one year of tweets

The COVID-19 pandemic seems to have had positive (although short-lived, e.g., reduction in pollution due to lockdown) as well as negative (e.g., increasing plastic pollution due to use of disposable masks, etc.) impacts on the environment. The pandemic-environment linkage also includes circumstances when regions experienced extreme weather events, such as floods and cyclones, and disaster management became challenging. This study aims to examine the trends in public discourses on Twitter on these interactions between the pandemic and environment. The present study follows the most recent literature on understanding public perceptions - which acknowledges Twitter to be an abundant source of information on public discussions on any global issue, including the pandemic. A Python-based code is developed to extract Twitter data spanning over a year, and analyze the presence of covid-environment related keywords and other attributes. It is found that the Twitterati aggressively viewed the impacts (such as economic slowdown and high mortality) of the pandemic as miniatures of the results of future climate change. The community was also highly concerned about the varying air and plastic pollution levels with the change in lockdown and covid prevention policies. Extreme weather events were a high-frequency topic when they impacted countries such as India, the USA, Australia, the Philippines and Vietnam. This study makes a novel attempt to provide an overview of public discourses on the pandemic-environment linkage and; can be a crucial addition to the literature on assessing public perception of environmental threats through Twitter data mining.

COVID-19 knowledge deconstruction and retrieval: an intelligent bibliometric solution

COVID-19 has been an unprecedented challenge that disruptively reshaped societies and brought a massive amount of novel knowledge to the scientific community. However, as this knowledge flood continues surging, researchers have been disadvantaged by not having access to a platform that can quickly synthesize emerging information and link the new knowledge to the latent knowledge foundation. Aiming to fill this gap, we propose a research framework and develop a dashboard that can assist scientists in identifying, retrieving, and understanding COVID-19 knowledge from the ocean of scholarly articles. Incorporating principal component decomposition (PCD), a knowledge mode-based search approach, and hierarchical topic tree (HTT) analysis, the proposed framework profiles the COVID-19 research landscape, retrieves topic-specific latent knowledge foundation, and visualizes knowledge structures. The regularly updated dashboard presents our research results. Addressing 127,971 COVID-19 research papers from PubMed, the PCD topic analysis identifies 35 research hotspots, along with their inner correlations and fluctuating trends. The HTT result segments the global knowledge landscape of COVID-19 into clinical and public health branches and reveals the deeper exploration of those studies. To supplement this analysis, we additionally built a knowledge model from research papers on the topic of vaccination and fetched 92,286 pre-Covid publications as the latent knowledge foundation for reference. The HTT analysis results on the retrieved papers show multiple relevant biomedical disciplines and four future research topics: monoclonal antibody treatments, vaccinations in diabetic patients, vaccine immunity effectiveness and durability, and vaccination-related allergic sensitization.

Air pollution in marmara region before and during the COVID-19 outbreak

The lockdowns and curfews during the COVID-19 pandemic have halted economic and transportation activities across the world. This study aims to investigate air pollution levels in the Marmara region, particularly in Istanbul, before and during the COVID-19 pandemic. The study used real data provided by the General Directorate of Meteorology and applied three machine learning algorithms (ANN, RBFreg, and SMOreg) to analyze air pollution data. In addition, a one-sample t-test was performed to compare air pollution levels before and during the COVID-19 pandemic in the Marmara region and Istanbul. The results of the study showed a significant reduction in the particulate matter (PM) value, which indicates the degree of air pollution, in both the Marmara region and Istanbul during the COVID-19 pandemic. The one-sample t-test results showed that the reduction in air pollution levels was statistically significant in both areas (t = 11.45, p < .001 for the Marmara region, and t = 3.188, p < .001 for Istanbul). These findings have important practical implications for decision-makers planning for a more sustainable environment. Overall, the study provides valuable insights into the impact of the COVID-19 pandemic on air pollution levels in the Marmara region, particularly in Istanbul. The application of machine learning algorithms and statistical analysis provides a rigorous approach to the investigation of this important issue by comparing before and during the COVID-19 outbreak.

Spatiotemporal variations, photochemical characteristics, health risk assessment and mid pandemic changes of ambient BTEX in a west Asian metropolis

This study examined the concentration of BTEX in Tehran from 2018 to 2020 in five monitoring stations with different backgrounds, which has been accomplished using the combination of passive sampling and GC-FID method. The total concentration of BTEX was estimated to be 65.39 (µg/m3), with a higher average concentration in 2019-2020 (77.79 µg/m3) compared to 2018-2019 (53.48 µg/m3) due to the leaping concentration of Toluene in the pandemic era. Despite a Benzene concentration decline in recent years, the average annual concentration of Benzene (5.66 µg/m3) at five stations remained higher than the EU commission and India standards (5 µg/m3) as well as Japan and Iraq thresholds (3 µg/m3). Toluene dominated other species in terms of concentrations, mass distribution (~0.6%), followed by m,p-Xylene (~0.2%), Benzene (~0.05-0.1) and Ethylbenzene (< 0.05). The evidence regarding seasonal changes of BTEX in 2019 shows the maximum concentration of these compounds in autumn, which is probably due to heavier traffic compared to other seasons. In contrast, in the first half of 2020 (which encompasses the start of the pandemic period and urban lockdown), point sources seem to play a prominent role in concentration fluctuations, as confirmed by changes in interspecies relationships and lower traffic congestion. The highest mean concentrations were observed in high-traffic, residential and suburban sites, respectively. The study reveals that m,p-Xylene possess the highest Ozone formation potential (~109.46), followed by Toluene (~85.34), o-Xylene (~46.87), Ethylbenzene (~13.52) and Benzene (~2.61). Health risk assessment results indicated the high carcinogenic risk of Benzene (mean = 3.6 × 10-6) and the acceptable non-carcinogenic risk of BTEX (hazard index~0.03 < specified limit of 1). Finally, the estimated weighted exposures of BTEX emphasized that residents near the high-traffic districts are more exposed to BTEX.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00477-023-02476-3.