Changes in U.S. air pollution during the COVID-19 pandemic

Capabilities of satellite Geostationary Environment Monitoring Spectrometer (GEMS) NO2 data for hourly ambient NO2 exposure modeling

The Geostationary Environment Monitoring Spectrometer (GEMS) is the world's first geostationary instrument that monitors hourly gaseous air pollutant levels, including nitrogen dioxide (NO2). Using the first-of-its-kind capabilities of GEMS NO2 data, we examined how well GEMS NO2 levels can explain the spatiotemporal variabilities in hourly NO2 concentrations in the Republic of Korea for the year 2022. A correlation analysis between hourly GEMS NO2 levels and ground NO2 concentrations showed a higher spatial correlation [Pearson r= 0.56 (SD= 0.20)] than a temporal one [Pearson r= 0.42 (SD= 0.14)], on average. To take advantage of the enhanced spatial predictability of GEMS NO2 data, we employed a mixed effects model to allow hour-specific relationships between GEMS NO2 and NO2 concentrations on a given day in each region and subsequently estimated hourly NO2 concentrations in all urban and rural areas. The 10-fold cross validation demonstrated R2= 0.72, mean absolute error (MAE)= 3.7 ppb, and root mean squared error (RMSE)= 5.5 ppb. The hourly variations of the relationships were attributed particularly to those of wind speed among meteorological parameters considered in this study. The spatial distributions of hourly estimated NO2 concentrations were highly correlated between hours [average r= 0.91 (SD= 0.06)]. Nonetheless, they represented the diurnal patterns of urban versus rural NO2 contrasts during the day [urban/rural NO2 ratios from 1.22 (5 pm) to 1.37 (12 pm)]. The newly retrieved GEMS NO2 data enable temporally as well as spatially resolved NO2 exposure assessment. In combination with the time-activity patterns of individual subjects, the GEMS NO2 data can generate 'sub-population' exposure estimates and therefore enhance health effect studies.

Implications of circular economy, digitalization and technological innovation to achieve sustainable environmental goal: Pre and post-vision 2030

The current study contributes to the existing literature by constructing a digitalization index to investigate the significance of digitalization in controlling the environmental footprint. Moreover, the dataset is divided into pre-Vision 2030 and post-Vision 2030 implementation to scrutinize the progress of Saudi Vision 2030 to counter the environmental challenges. Vision 2030 is a strategic framework to reduce Saudi Arabia's dependence on oil, diversify its economy, and develop public service sectors such as health, education, infrastructure, recreation, and tourism. The findings have documented the negative coefficients for post-Vision 2030 and post-COVID-19 estimations, reflecting that a significant digitalization increase is useful for controlling the environmental externalities in Saudi Arabia. In the case of post-Vision 2030, the role of environmental technology turns out to be significant and negative, but with a lower magnitude. The study results are useful for drawing significant environmental policies through enhancing the digitalization parameters and advancement of technology.

Effects of COVID-19 on coastal and marine environments: Aggravated microplastic pollution, improved air quality, and future perspective

The COVID-19 pandemic during 2020-2023 has wrought adverse impacts on coastal and marine environments. This study conducts a comprehensive review of the collateral effects of COVID-19 on these ecosystems through literature review and bibliometric analysis. According to the output and citation analysis of these publications, researchers from the coastal countries in Asia, Europe, and America payed more attentions to this environmental issue than other continents. Specifically, India, China, and USA were the top three countries in the publications, with the proportion of 19.55%, 18.99%, and 12.01%, respectively. The COVID-19 pandemic significantly aggravated the plastic and microplastic pollution in coastal and marine environments by explosive production and unproper management of personal protective equipment (PPE). During the pandemic, the estimated mismanaged PPE waste ranged from 16.50 t/yr in Sweden to 250,371.39 t/yr in Indonesia. In addition, the PPE density ranged from 1.13 × 10-5 item/m2 to 2.79 item/m2 in the coastal regions worldwide, showing significant geographical variations. Besides, the emerging contaminants released from PPE into the coastal and marine environments cannot be neglected. The positive influence was that the COVID-19 lockdown worldwide reduced the release of air pollutants (e.g., fine particulate matter, NO2, CO, and SO2) and improved the air quality. The study also analyzed the relationships between sustainable development goals (SDGs) and the publications and revealed the dynamic changes of SDGs in different periods the COVID-19 pandemic. In conclusion, the air was cleaner due to the lockdown, but the coastal and marine contamination of plastic, microplastic, and emerging contaminants got worse during the COVID-19 pandemic. Last but not least, the study proposed four strategies to deal with the coastal and marine pollution caused by COVID-19, which were regular marine monitoring, performance of risk assessment, effective regulation of plastic wastes, and close international cooperation.

A review of the CAMx, CMAQ, WRF-Chem and NAQPMS models: Application, evaluation and uncertainty factors

With the gradual deepening of the research and governance of air pollution, chemical transport models (CTMs), especially the third-generation CTMs based on the "1 atm" theory, have been recognized as important tools for atmospheric environment research and air quality management. In this review article, we screened 2396 peer-reviewed manuscripts on the application of four pre-selected regional CTMs in the past five years. CAMx, CMAQ, WRF-Chem and NAQPMS models are well used in the simulation of atmospheric pollutants. In the simulation study of secondary pollutants such as O3, secondary organic aerosol (SOA), sulfates, nitrates, and ammonium (SNA), the CMAQ model has been widely applied. Secondly, model evaluation indicators are diverse, and the establishment of evaluation criteria has gone through the long-term efforts of predecessors. However, the model performance evaluation system still needs further specification. Furthermore, temporal-spatial resolution, emission inventory, meteorological field and atmospheric chemical mechanism are the main sources of uncertainty, and have certain interference with the simulation results. Among them, the inventory and mechanism are particularly important, and are also the top priorities in future simulation research.

Air pollution trend in Chandigarh during 2019-2022: status and influence of meteorological factors

The degradation of ambient air quality is a pressing global concern, and India, as a developing nation, has witnessed a rapid surge in industrial activities in recent decades. This surge has resulted in numerous Indian cities ranking among the world's most polluted urban areas. Chandigarh, strategically positioned within the Indo-Gangetic plains (IGP), has not escaped this distressing trend, experiencing a significant spike in air pollution levels. This study focuses on comprehending and addressing the air quality issues in Chandigarh, shedding light on the evolution of air pollution trends and their dependence on meteorological factors. Notably, the study reveals that, with the exception of O3, pollutant concentrations surge during the rice stubble burning season. These pollutants persist in the atmosphere for prolonged periods, exacerbating the situation during winter due to lower temperatures and heightened use of fossil fuels for heating by low-income households. In contrast, the wheat stubble burning period does not significantly impact pollutant concentrations. The study also identifies a spring peak in surface O3 concentrations, attributed to favorable high temperatures that promote the photochemical reactions responsible for this phenomenon, a distinctive feature in South Asia and the Himalayas. An examination of the connection between pollutant concentrations and meteorological parameters underscores that elevated pollutant levels, except for CO, are linked to lower relative humidity and temperatures. This suggests that current development patterns have contributed to the escalation of air pollution in Chandigarh, necessitating urgent interventions to preserve the city's aesthetics and the health of its residents. Furthermore, to model and monitor pollutant behavior in Chandigarh, more extensive and extended studies are imperative. Both short-term and long-term investigations into the environmental and health impacts of air pollutants, including primary and secondary pollutants, are of paramount importance. These endeavors are essential for the well-being of both the environment and the population of Chandigarh.

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.

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.

Hypertension effects of the COVID-19 lockdowns: Evidence from a repeated cross-sectional survey in Peru

Using data from Peru and a quasi-experimental approach, we document significant increases in arterial blood pressure and in the incidence of arterial hypertension caused by the restrictive measures employed by the Peruvian authorities during the COVID-19 pandemic. The effects are more pronounced for women, older respondents, and urban residents. The effects are statistically significant and high in magnitude relative to the pre-pandemic incidence of disease in the Peruvian population. A main channel of disease propagation seems to be the changes in dietary habits and physical activity imposed by the COVID-19 lockdowns, which affected several anthropometric measurements that are common risk factors for hypertension.

Wildfire and smoke association with COVID-19 cases in the Pantanal wetland, Brazil

OBJECTIVES

In 2020, Brazil experienced two concurrent public health challenges related to respiratory disease: wildfires and increased mortality due to the coronavirus (COVID-19) pandemic. Smoke from these wildfires contributed to a variety of air pollutants, including fine particulate matter (PM2.5). The present study aims to investigate the effects of environmental and socio-economic factors on COVID-19 hospitalisation in the Pantanal.

STUDY DESIGN

Ecological retrospective study.

METHODS

We applied a multilevel negative binomial model to relate monthly hospitalisation data with environmental variables.

RESULTS

We showed that monthly PM2.5 concentration levels had the greatest influence on the increase in hospitalisations by COVID-19 in the elderly (23 % increase). The Gini index, a coefficient that reflects income inequalities, also had a positive association with COVID-19 hospitalisations (18 % increase). Higher temperatures and humidity were protective factors, showing a 15 % and 14 % decrease in hospitalisations, respectively. The results of the present study suggest that high PM2.5 exposure contributed to the increase in COVID-19 hospitalisations, as did the social inequalities of each municipality.

CONCLUSIONS

The present study highlights the importance of gathering evidence supported by multiple information sources to guide decision-making and identify populations needing better public health systems.

Using neural networks and remote sensing for spatio-temporal prediction of air pollution during the COVID-19 pandemic

The study aims to monitor air pollution in Iranian metropolises using remote sensing, specifically focusing on pollutants such as O3, CH4, NO2, CO2, SO2, CO, and suspended particles (aerosols) in 2001 and 2019. Sentinel 5 satellite images are utilized to prepare maps of each pollutant. The relationship between these pollutants and land surface temperature (LST) is determined using linear regression analysis. Additionally, the study estimates air pollution levels in 2040 using Markov and Cellular Automata (CA)-Markov chains. Furthermore, three neural network models, namely multilayer perceptron (MLP), radial basis function (RBF), and long short-term memory (LSTM), are employed for predicting contamination levels. The results of the research indicate an increase in pollution levels from 2010 to 2019. It is observed that temperature has a strong correlation with contamination levels (R2 = 0.87). The neural network models, particularly RBF and LSTM, demonstrate higher accuracy in predicting pollution with an R2 value of 0.90. The findings highlight the significance of managing industrial towns to minimize pollution, as these areas exhibit both high pollution levels and temperatures. So, the study emphasizes the importance of monitoring air pollution and its correlation with temperature. Remote sensing techniques and advanced prediction models can provide valuable insights for effective pollution management and decision-making processes.

Impact of the lockdown period during the COVID-19 pandemic on the metal content of the anemone Anemonia sulcata in the Canary Islands (CE Atlantic, Spain)

Anemones, specifically the species Anemonia sulcata, are cnidarians that serve as bioindicators in marine ecosystems, indicating the health of the environment and changes in environmental conditions. Monitoring anemone populations and studying their well-being and distribution provide valuable insights into marine ecosystem conditions. This study aimed to investigate the impact of the SARS-CoV-2 pandemic on the metal content of Anemonia sulcata. Over a six-year period (2017-2022), twenty specimens of Anemonia sulcata were collected in Tenerife, Spain. The results showed that in 2020, during the two-month lockdown in Spain from March to May when tourism was halted, A. sulcata exhibited the lowest concentrations of various metals studied (Al, Cd, Cu, Fe, Pb, and Zn). This finding suggests that the reduced anthropogenic pressure on the coast due to the absence of tourism significantly decreased pollution levels. Therefore, the study emphasizes the importance of promoting sustainable tourism worldwide. The research highlights that minimizing human impact on coastal areas through responsible tourism practices can effectively reduce pollution in marine ecosystems.

How do density, employment and transit affect the prevalence of COVID-19 pandemic? A study of 3,141 counties across the United States

Previous research has explored the effect of the built environment on the spread of the coronavirus disease (COVID-19) pandemic. This study extends the existing literature by examining the relationship between pandemic prevalence and density, employment, and transit factors at the county level. Using multilinear spatial-lag regressions and time series clustering analyses on the Smart Location Database encompassing 3141 counties in the United States, our findings reveal the following: (1) Density, employment, and transit variables yield heterogeneous effects to infection rate, death rate, and mortality rate. (2) Pedestrian-oriented road density is positively correlated to the prevalence of COVID-19, every 0.011 miles/acre increase is associated with 1% increase in the infection rate. (3) A consistent negative correlation is observed between jobs per household and infection rate, while a decrease in unemployment rate leads to an increase in the death rate. (4) The results from time series analysis suggest that areas characterized by low auto-oriented intersection density but high pedestrian-oriented road density are more susceptible to the impacts of pandemics. This highlights the need to prioritize pandemic prevention efforts in the suburban and rural areas with low population density, as emphasized in existing literature emphasized.

Pathogen inhibition and indication by gelatin nonwoven mats with incorporation of polyphenol derivatives

There is a need for non-pharmaceutical intervention methods that can prevent and indicate the risk of airborne disease spread. In this study, we developed a nonwoven mat based on the polyphenol gallic acid, which can inhibit pathogens growth and also indicate pathogen levels in the surrounding environment. Using nuclear magnetic resonance, Fourier-transform infrared spectroscopy, and high-performance liquid chromatography, we characterized this novel gelatin-based nonwoven mat and investigated the mechanism governing its ability to indicate pathogen levels. We demonstrated that the incorporation of gallic acid serves a vital role in indicating the presence of bacteria, causing the nonwoven mat to change in color from white to brown. We have proposed a plausible mechanism for this color change behavior based on a reaction of gallic acid with components excreted by bacteria, including glutamate, valine, and leucine. The concentrations of these components reflect the bacterial counts, enabling a real-time indication of pathogen levels in the surrounding air. In summary, the nonwoven mat presented herein can serve as an excellent antibacterial agent and as an indicator of nearby bacteria for fabricating personal protection equipment like filtration mask.

Investigating changes in atmospheric aerosols properties over the Indo-Gangetic Plain during different phases of COVID-19-induced lockdowns

Impact of COrona VIrus Diseases 2019 (COVID-19) restrictive measures on aerosol optical depth (AOD) and black carbon (BC) concentration is investigated for the western, central, and eastern Indo-Gangetic Plain (IGP) using satellite-based observations. Due to COVID-19-induced lockdown measures, a noticeable decline in AOD and BC concentrations was observed across the IGP when compared to pre-lockdown period of 2020 and the lockdown concurrent period of 2015-2019. During the total lockdown period, a maximum drop in AOD and BC was observed in the central IGP (26.5 % and 10.1 %), followed by western IGP (24.9% and 5.2%) and eastern IGP (23.2 % and 4.9 %) with respect to the same period of 2015-2019. We have removed seasonal influences on aerosol properties during the COVID-19 lockdown, by taking average seasonal variations during the period of 2015-2019 as reference and projecting the hypothetical AOD and BC for the lockdown period under normal scenario. The difference between the hypothetical AOD and BC (under normal scenario) and the retrieved AOD and BC for the lockdown period is the absolute percentage change in AOD and BC concentration due to the lockdown alone. This elimination of seasonal influence is a novel approach. Central IGP showed an absolute decrease in AOD and BC of 38.5% and 18.2% during the lockdown period followed by western IGP (34.6% and 7.7%) and eastern IGP (25.9% and 11.5%). The observed absolute reduction in AOD, 26-39 %, is significantly higher than the global average reduction in AOD of 2-5%. CALIPSO-derived aerosol sub-types over major location of the western, central, and eastern IGP suggests prevalence of anthropogenic activities during pre- and post-lockdown periods. During the lockdown, IGP was influenced by aerosols from natural sources, with mineral dust and polluted dust in the western and central IGP, and aerosols from marine regions in the eastern IGP. Replenishment of aerosols within the boundary layer were far quicker when compared to total column during post-lockdown. Overall, the study reveals a reduction in anthropogenic emissions during the COVID-19-induced lockdowns, leading to temporary improvements in air quality over the IGP. Our study presents a comprehensive analysis of COVID-19 lockdown impact on aerosols properties over the IGP and highlights unprecedented reductions in AOD (~ 40 %) and BC (~ 20 %), due to imposition of lockdown and subsequent cessation of aerosol sources, by removing seasonal influences.

Testing the theory of Kuznet curve on environmental pollution during pre- and post-Covid-19 era

Covid-19 has brought about significant changes in people's daily lives, leading to a slowdown in economic activities and the implementation of restrictions and lockdowns. As a result, there have been noticeable effects on the environment. In this study, we examine the impact of Covid-19 total cases on the monthly average of carbon monoxide emissions in developed economies known for heavy pollution, covering the period from 2014 to 2023. We apply the Ambiental Kuznets curve approach to analyze the data. By employing different panel estimation techniques such as fixed effects and Driscoll-Kraay regressions, we observe a marked shift in environmental dynamics during the post-Covid era. This shift alters the statistical significance of the N-shaped Kuznets curve, rendering the relationship between economic activity and environmental impact non-significant. Interestingly, the Covid-related variables utilized in the various estimations are not statistically significant in explaining the long-term environmental effects.

Air quality changes in NE Romania during the first Covid 19 pandemic wave

This study analyzes for the first time uniformly and causally the level of pollution and air quality for the NE-Romania Region, one of the poorest region in the European Union. Knowing the level of pollution and air quality in this region, which can be taken as a benchmark due to its positional and economic-geographical attributes, responds to current scientific and practical needs. The study uses an hourly database (for five pollutants and five climate elements), from 2009 to 2020, from 19 air quality monitoring stations in northeastern Romania. Pollutant levels were statistically and graphically/cartographically modeled for the entire 2009-2020 interval on the distributive-spatial and regime, temporal component. Inter-station differences and similarities were analyzed causally. Taking advantage of the emergency measures between March 16 and May 14, 2020, we observed the impact of the event on the regional air quality in northeastern Romania. During the emergency period, the metropolitan area of Suceava (with over 100,000 inhabitants) was quarantined, which allowed us to analyze the impact of the quarantine period on the local air quality. We found that, in this region, air quality falls into class I (for NO2, SO2 and CO), II for O3 and III for PM10. During the lockdown periods NO2 and SO2 decreased for the entire region by 8.6 and 14.3%, respectively, and in Suceava by 13.9 and 40.1%, respectively. The causes of the reduction were anthropogenic in nature.

Impact of the emergency response to COVID-19 on air quality and its policy implications: Evidence from 290 cities in China

The emergency response to the COVID-19 pandemic had an extreme exogenous impact on society and the economy. This paper aims to explore the impacts of the national emergency response and the subsequent emergency response termination on air quality and its policy implications through regression discontinuity design (RDD) estimation by employing panel data on daily air quality from January 1, 2019, to July 31, 2020, for 290 cities in China. The empirical results showed that the emergency response resulted in a significant decrease in most of the major pollutant concentrations within a short time frame, and the average air quality index (AQI) decreased by approximately 11.0%. The concentrations of PM_2.5, PM₁₀, SO₂, NO₂, and CO decreased by approximately 18.8%, 13.1%, 13.5%, 11.1% and 6.7%, respectively, while the O₃ concentration did not change significantly. Further causal analysis found that mandatory traffic restrictions and the shutdown of industries were two important factors that contributed greatly to air quality improvement. Moreover, since the process of returning to normal daily activities and promoting the economy were gradual, the results showed that air pollution did not rebound immediately after the government called for the "resumption of production and work" and announced the "termination of the emergency response". Our findings suggest that to achieve a substantial and sustainable improvement in air quality, it is necessary to continuously implement strict emission control routines and take co-control measures for various VOCs precursors of ozone.

Impact of COVID-19 lockdown on water quality in China during 2020 and 2022: two case surges

The COVID-19 severely affected the world in 2020. Taking the two outbreaks in China in 2020 and 2022 as examples, the spatiotemporal changes in surface water quality levels and COD_Mn and NH₃-N concentrations were analyzed, and the relationships between the variations in the two pollutants and environmental and social factors were evaluated. The results showed that during the two lockdowns, due to the total water consumption (including industrial, agricultural, and domestic water) decreased, the proportion of good water quality increased by 6.22% and 4.58%, and the proportion of polluted water decreased by 6.00% and 3.98%, the quality of water environment has been improved significantly. However, the proportion of excellent water quality decreased by 6.19% after entering the unlocking period. Before the second lockdown period, the average COD_Mn concentration exhibited a "falling, rising, and falling" trend, while the average NH₃-N concentration changed in the opposite direction. The correlation analysis revealed that the increasing trend of pollutant concentrations was positively correlated with longitude and latitude, and weakly correlated with DEM and precipitation. A slight decrease trend in NH₃-N concentration was negatively correlated with the population density variation and positively correlated with the temperature variation. The relationship between the change in the number of confirmed cases in provincial regions and the change in pollutant concentrations was uncertain, with positive and negative correlations. This study demonstrates the impact of lockdowns on water quality and the possibility of improving water quality through artificial regulation, which can provide a reference basis for water environmental management.

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.

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.

Toxicological Effects of Fine Particulate Matter (PM2.5): Health Risks and Associated Systemic Injuries-Systematic Review

Previous studies focused on investigating particulate matter with aerodynamic diameter ≤ 2.5 µm (PM2.5) have shown the risk of disease development, and association with increased morbidity and mortality rates. The current review investigate epidemiological and experimental findings from 2016 to 2021, which enabled the systemic overview of PM2.5's toxic impacts on human health. The Web of Science database search used descriptive terms to investigate the interaction among PM2.5 exposure, systemic effects, and COVID-19 disease. Analyzed studies have indicated that cardiovascular and respiratory systems have been extensively investigated and indicated as the main air pollution targets. Nevertheless, PM2.5 reaches other organic systems and harms the renal, neurological, gastrointestinal, and reproductive systems. Pathologies onset and/or get worse due to toxicological effects associated with the exposure to this particle type, since it can trigger several reactions, such as inflammatory responses, oxidative stress generation and genotoxicity. These cellular dysfunctions lead to organ malfunctions, as shown in the current review. In addition, the correlation between COVID-19/Sars-CoV-2 and PM2.5 exposure was also assessed to help better understand the role of atmospheric pollution in the pathophysiology of this disease. Despite the significant number of studies about PM2.5's effects on organic functions, available in the literature, there are still gaps in knowledge about how this particulate matter can hinder human health. The current review aimed to approach the main findings about the effect of PM2.5 exposure on different systems, and demonstrate the likely interaction of COVID-19/Sars-CoV-2 and PM2.5.

The effects of contemporaneous air pollution on COVID-19 morbidity and mortality

We examine the relationship between contemporaneous fine particulate matter exposure and COVID-19 morbidity and mortality using an instrumental variable approach. Harnessing daily changes in county-level wind direction, we show that arguably exogenous fluctuations in local air quality impact the incidence of confirmed COVID-19 cases and deaths. We find that a one μ g/m 3 increase in PM 2.5, or 15% of the average PM 2.5 concentration in a county, increases the number of same-day confirmed cases by 1.8% from the mean case incidence in a county. A one μ g/m 3 increase in PM 2.5 increases the same-day death rate by just over 4% from the mean. These effects tend to increase in magnitude over longer time horizons and are robust to a host of sensitivity tests. When analyzing potential mechanisms, we also demonstrate that an additional unit of PM 2.5 increases COVID-19-related hospitalizations by 0.8% and use of intensive care units by 0.5% on the same day. Using individual case records, we also show that higher PM 2.5 exposure at the time of case confirmation increases risk of later mechanical ventilation and mortality. These results suggest that air pollution plays an important role in mediating the severity of respiratory syndromes such as COVID-19.

Evaluating TROPOMI and MODIS performance to capture the dynamic of air pollution in São Paulo state: A case study during the COVID-19 outbreak

Atmospheric pollutant data retrieved through satellite sensors are continually used to assess changes in air quality in the lower atmosphere. During the COVID-19 pandemic, several studies started to use satellite measurements to evaluate changes in air quality in many different regions worldwide. However, although satellite data is continuously validated, it is known that its accuracy may vary between monitored areas, requiring regionalized quality assessments. Thus, this study aimed to evaluate whether satellites could measure changes in the air quality of the state of São Paulo, Brazil, during the COVID-19 outbreak; and to verify the relationship between satellite-based data [Tropospheric NO₂ column density and Aerosol Optical Depth (AOD)] and ground-based concentrations [NO₂ and particulate material (PM; coarse: PM₁₀ and fine: PM_2.5)]. For this purpose, tropospheric NO₂ obtained from the TROPOMI sensor and AOD retrieved from MODIS sensor data by using the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm were compared with concentrations obtained from 50 automatic ground monitoring stations. The results showed low correlations between PM and AOD. For PM₁₀, most stations showed correlations lower than 0.2, which were not significant. The results for PM_2.5 were similar, but some stations showed good correlations for specific periods (before or during the COVID-19 outbreak). Satellite-based Tropospheric NO₂ proved to be a good predictor for NO₂ concentrations at ground level. Considering all stations with NO₂ measurements, correlations >0.6 were observed, reaching 0.8 for specific stations and periods. In general, it was observed that regions with a more industrialized profile had the best correlations, in contrast with rural areas. In addition, it was observed about 57% reductions in tropospheric NO₂ throughout the state of São Paulo during the COVID-19 outbreak. Variations in air pollutants were linked to the region economic vocation, since there were reductions in industrialized areas (at least 50% of the industrialized areas showed >20% decrease in NO₂) and increases in areas with farming and livestock characteristics (about 70% of those areas showed increase in NO₂). Our results demonstrate that Tropospheric NO₂ column densities can serve as good predictors of NO₂ concentrations at ground level. For MAIAC-AOD, a weak relationship was observed, requiring the evaluation of other possible predictors to describe the relationship with PM. Thus, it is concluded that regionalized assessment of satellite data accuracy is essential for assertive estimates on a regional/local level. Good quality information retrieved at specific polluted areas does not assure a worldwide use of remote sensor data.

Assessment of NO2 concentrations over industrial state Jharkhand, at the time frame of pre, concurrent, and post-COVID-19 lockdown along with the meteorological behaviour: an overview from satellite and ground approaches

Burning of fossil fuels in the form of coal or gasoline in thermal power plants, industries, and automobiles is a prime source of nitrogen dioxide (NO₂), a major air pollutant causing health problems. In this paper, spatio-temporal unevenness of NO₂ concentrations via both spaceborne Sentinel-5P and ground-based in situ data have been studied for the period of 2017-2021. Annual and seasonal distribution of TROPOMI-NO₂ depict consistency over the Jharkhand region, highlighting six hotspot regions. As compared to 2019, a notable dip of 11% in the spatial annual average TROPOMI-NO₂ was achieved in 2020, which were elevated again by 22% in 2021 as the lockdown gradually goes out of the picture. Among eight ground-monitoring stations, Tata and Golmuri stations always displayed a higher level of TROPOMI-NO₂ ranges up to 15.2 ×10¹⁵molecules.cm^-2 and 16.9 ×10¹⁵molecules.cm^-2 respectively, as being located in the highly industrialised district of Jamshedpur. A big percentage reduction of up to 30% in TROPOMI-NO₂ has been reported in Jharia and Bastacola stations in Dhanbad in the lockdown phase of 2020 compared to 2019. Good agreement between TROPOMI-NO₂ and surface-NO₂ has been achieved with R = 0.8 and R = 0.71 during winter and post-monsoon respectively. Among four meteorological parameters, TROPOMI-NO₂ was majorly found to be influenced by precipitation, having R = 0.6-0.8 for almost all stations. More advanced satellite algorithms and ground-based data may be used to estimate NO₂ in places where monitoring facilities are limited and thus can help in air pollution control policy.

Assessment of light-duty versus heavy-duty diesel on-road mobile source emissions using general additive models applied to traffic volume and air quality data and COVID-19 responses

Following the outbreak of the COVID-19 pandemic, several papers have examined the effect of the pandemic response on urban air pollution worldwide. This study uses observed traffic volume and near-road air pollution data for black carbon (BC), oxides of nitrogen (NO_x), and carbon monoxide (CO) to estimate the emissions contributions of light-duty and heavy-duty diesel vehicles in five cities in the continental United States. Analysis of mobile source impacts in the near-road environment has several health and environmental justice implications. Data from the initial COVID-19 response period, defined as March to May in 2020, were used with data from the same period over the previous two years to develop general additive models (GAMs) to quantify the emissions impact of each vehicle class. The model estimated that light-duty traffic contributes 4-69%, 14-65%, and 21-97% of BC, NO_x, and CO near-road levels, respectively. Heavy-duty diesel traffic contributes an estimated 26-46%, 17-63%, and -7-18% of near-road levels of the three pollutants. The estimated mobile source impacts were used to calculate NO_x to CO and BC to NO_x emission ratios, which were between 0.21-0.32 μg m^-3 NO_x (μg m^-3 CO)^-1 and 0.013-0.018 μg m^-3 BC (μg m^-3 NO_x)^-1. These ratios can be used to assess existing emission inventories for use in determining air pollution standards. These results agree moderately well with recent National Emissions Inventory estimates and other empirically-derived estimates, showing similar trends among the pollutants. However, a limitation of this study was the recurring presence of an implausible air pollution impact estimate in 41% of the site-pollutant combinations, where a vehicle class was estimated to account for either a negative impact or an impact higher than the total estimated pollutant concentration. The variations seen in the GAM estimates are likely a result of location-specific factors, including fleet composition, external pollution sources, and traffic volumes.Implications: Drastic reductions in traffic and air pollution during the lockdowns of the COVID-19 pandemic present a unique opportunity to assess vehicle emissions. A General Additive Modeling approach is developed to relate traffic levels, observed air pollution, and meteorology to identify the amount vehicle types contribute to near-road levels of traffic-related air pollutants (TRAPs), which is important for future emission regulation and policy, given the significant health and environmental justice implications of vehicle-related pollution along major roadways. The model is used to evaluate emission inventories in the near-road environment, which can be used to refine existing estimates. By developing a locally data-driven method to readily characterize impacts and distinguish between heavy and light duty vehicle effects, local regulations can be used to target policies in major cities around the country, thus addressing local health disbenefits and disparities occurring as a result of exposure to near-road air pollution.

Predicting Grocery Store Visits During the Early Outbreak of COVID-19 with Machine Learning

While non-essential travel was canceled during the coronavirus infectious disease (COVID-19) pandemic, grocery shopping was essential. The objectives of this study were to: 1) examine how grocery store visits changed during the early outbreak of COVID-19, and 2) estimate a model to predict the change of grocery store visits in the future, within the same phase of the pandemic. The study period (February 15-May 31, 2020) covered the outbreak and phase-one re-opening. Six counties/states in the United States were examined. Grocery store visits (in-store or curbside pickup) increased over 20% when the national emergency was declared on March 13 and then decreased below the baseline within a week. Grocery store visits on weekends were affected more significantly than those on workdays before late April. Grocery store visits in some states (including California, Louisiana, New York, and Texas) started returning to normal by the end of May, but that was not the case for some of the counties (including those with the cities of Los Angeles and New Orleans). With data from Google Mobility Reports, this study used a long short-term memory network to predict the change of grocery store visits from the baseline in the future. The networks trained with the national data or the county data performed well in predicting the general trend of each county. The results from this study could help understand mobility patterns of grocery store visits during the pandemic and predict the process of returning to normal.

Effects of shared governance and cost redistribution on air pollution control: a study of game theory-based cooperation

This study seeks cost-effective strategies for PM2.5 reduction to generate insights into minimizing pollution abatement costs subject to different scenarios. This study theorizes that the cooperation of PM2.5 abatement has potential gains for participants and develop an empirical way to compare the costs and efficiency of PM2.5 abatement involving the variation of environmental conditions. This study revises the cooperative game model in the context of threshold effects using data obtained from the Beijing-Tianjin-Hebei metropolitan cluster in China. In general, the results support the key assertion that cooperation in the metropolitan cluster plays a vital role in optimizing the efficiency and costs of PM2.5 abatement. In addition to extending the application of the revised model, this study provides a way to estimate the costs and the mitigation benefits of meeting the pollution targets for each coparticipant and take the scenario of multiparty cooperation into account as well as the scenarios involving other types of pollutants. The empirical findings have important policy implications for regional shared governance, decentralization, and resource reallocation. Economic incentive-based shared governance and cost reallocation work better than traditional regulations.

Spatial shifting of COVID-19 clusters and disease association with environmental parameters in India: A time series analysis

BACKGROUND

The viability and virulence of COVID-19 are complex in nature. Although the relationship between environmental parameters and COVID-19 is well studied across the globe, in India, such studies are limited. This research aims to explore long-term exposure to weather conditions and the role of air pollution on the infection spread and mortality due to COVID-19 in India.

METHOD

District-level COVID-19 data from April 26, 2020 to July 10, 2021 was used for the study. Environmental determinants such as land surface temperature, relative humidity (RH), Sulphur dioxide (SO₂), Nitrogen dioxide (NO₂), Ozone (O₃), and Aerosol Optical Depth (AOD) were considered for analysis. The bivariate spatial association was used to explore the spatial relationship between Case Fatality Rate (CFR) and these environmental factors. Further, the Bayesian multivariate linear regression model was applied to observe the association between environmental factors and the CFR of COVID-19.

RESULTS

Spatial shifting of COVID-19 cases from Western to Southern and then Eastern parts of India were well observed. The infection rate was highly concentrated in most of the Western and Southern regions of India, while the CFR shows more concentration in Northern India along with Maharashtra. Four main spatial clusters of infection were recognized during the study period. The time-series analysis indicates significantly more CFR with higher AOD, O₃, and NO₂ in India.

CONCLUSIONS

COVID-19 is highly associated with environmental parameters and air pollution in India. The study provides evidence to warrant consideration of environmental parameters in health models to mediate potential solutions. Cleaner air is a must to mitigate COVID-19.

COVID-19, travel restrictions and environmental consequences

The component of human life that has been most significantly altered by the COVID-19 epidemic is travel. Due to the upheaval produced by the pandemic breakout, countries are becoming increasingly avaricious and are scrambling to stockpile vaccines. The world has been locked down to reduce/control the pandemic outbreak, driving countries to shut their doors to other people from countries. The recent pandemic has had a short-term, positive effect on the environment, but travel restrictions have caused problems for the common person and are expected to deteriorate more soon, necessitating longer quarantines, vaccination requirements, vaccine passports, and immunization certificates required by countries for safe travel. Thus, this study has three objectives. First, we investigate the impact of COVID-19 on travel and the environment, as well as the role that tourists play in the transmission of the virus. Second, we examine how countries are handling COVID-19 vaccines. Finally, we pinpoint differences in vaccination coverage.

COVID-19 pandemic, port congestion, and air quality: Evidence from China

The emergency of COVID-19 leads to almost all unnecessary activities being banned because of city lockdowns, which results in the economy and human mobility being strictly restricted. While affecting economic development, it has brought some environmental benefits. As a critical link to collection and distribution, ports have been deeply impacted by COVID-19, including quarantine time and operational efficiency, and even cause unexpected port congestion. This study empirically examines the relationship between the COVID-19 pandemic, port congestion and air quality in Chinese port cities using classical and system panel models. We find that the COVID-19 pandemic and port congestion significantly influence air quality in port cities. Managerial implications include the ensuring of port workers' shifts, the unblocking of port logistics, and the cooperation between transportation, customs, and quarantine departments, which can reduce the time of ships at berths and improve the air quality in port cities.

Childhood asthma diagnoses declined during the COVID-19 pandemic in the United States

BACKGROUND

Prior studies have documented declines in pediatric asthma exacerbations and asthma-related health care utilization during the COVID-19 pandemic, but less is known about the incidence of asthma during the pandemic.

METHODS

We conducted a retrospective cohort study of children under age 18 without a prior diagnosis of asthma within a large US commercial claims database. Incident asthma was defined using a combination of diagnosis codes, location of services, and medication dispensing. Crude quarterly rates of asthma diagnosis per 1000 children were calculated, and the incidence rate ratio and 95% confidence interval were estimated for newly diagnosed asthma during versus before the pandemic using negative binomial regression, adjusted for age, sex, region, and season.

RESULTS

Compared with 3 years prior to the pandemic, crude incident diagnosis rates of asthma decreased by 52% across the first four quarters of the US pandemic. The covariate-adjusted pandemic-associated incidence rate ratio was 0.47 (95% confidence interval 0.43, 0.51).

CONCLUSIONS

New diagnoses of childhood asthma in the US declined by half during the first year of the pandemic. These findings raise important questions whether pandemic-related changes in infectious or other triggers truly altered the incidence of childhood asthma beyond the well-described disruptions in healthcare access.

Changes in physicochemical, heavy metals and air quality linked to spot Aplocheilus panchax along Mahanadi industrial belt of India under COVID-19-induced lockdowns

Positive effects of COVID-19-induced lockdowns on environment are well documented although pre-planned experiments for such analyses and appearance of fish species are lacking. We hypothesize that spotting the fish Aplocheilus panchax along the industrial belt of Mahanadi River near Cuttack in a never seen manner could be due to the regenerated environment. Heavy metals, water and air qualities along with spotting A. panchax in up, mid and downstream of Mahanadi River near Jagatpur industrial basins were analysed during pre-(end of March 2020) and after 60 days of lockdowns (last week of May 2020). An overall 45, 61, 79, 100, 97 and 90% reduction of Fe, Cu, Ni, Cd, Pb and Zn was recorded in the studied area after lockdowns, respectively. Similarly, dissolved oxygen and pH were elevated by 26 and 7%, respectively. Water temperature, conductivity and total dissolved solute levels were reduced by 7, 46 and 15%, respectively, which were again elevated during post-lockdowns during 2021 as observed from the Landsat-8 OLI satellite data. Air NO₂, SO₂, NH₃, PM_2.5, PM₁₀ and CO levels were alleviated by 58.75, 80.33, 72.22, 76.28, 77.33 and 80.15%, respectively. Finally, for the first time, about 12 A. panchax fish per 100 m shore line in the area were spotted. The observed lockdown-induced environmental healing at the studied area could contribute to the appearance of A. panchax in the study site and therefore a stringent environmental audit is suggested during post-COVID-19 periods to make the regenerated environmental status long lasting in such habitats.

Noise complaints, the COVID-19 pandemic, and compact developments: evidence from five American cities

The COVID-19 continues to take its toll on human life. Even though to a less threatening extent, and insignificant to some, noise turns out to be one of its consequences without consensus. While individuals experience multiple restrictions and restrain from exuberant activities by spending most of their time at home, reducing public transportation and personal vehicles, overall, they end up reduce anthropogenic noise pressure. On another level, people continue reporting noise concerns at various degrees during the COVID-19 pandemic. To draw a bigger picture as to whether or not these complaints have increased during the COVID-19 compared to the same period last year, this research examines them in five major American cities: New York, Chicago, San Francisco, Phoenix, and Dallas. Furthermore, the study also assessed the complaint patterns, whether reported in compact or sprawled areas. The findings highlight that either the noise complaints increased or decreased during the COVID-19 crisis. Accordingly, four of the five selected cities, except San Francisco, showed a decrease in reported noise. As it turns out, compact developments correlate significantly and positively with noise complaints in all study areas, except in Phoenix. These findings call for regulating and prioritizing noise-related policies. Planners and urban designers can thus advise to sustain environmental planning and public health issues, especially in planning compact developments.

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

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

The positive impact of the Omicron pandemic lockdown on air quality and human health in cities around Shanghai

The Omicron pandemic broke out in Shanghai in March 2022, and some infected people spread to some cities in the Yangtze River Delta (YRD) region. To achieve the dynamic zero-COVID target as soon as possible, Shanghai and nine cities that were heavily affected by Shanghai implemented the lockdown measures. This paper aims to quantify the impact of the lockdown on air quality and human health. A difference-in-difference (DID) model was first used to measure the impact of the lockdown on air quality in these ten cities. Based on the results of the DID model, we estimated the PM2.5-related health and economic benefits using the concentration-response function and the value of statistical life method. Results showed that the lockdown has reduced the concentrations of PM2.5, PM10, SO2, NO2, and CO by 9.87 μg/m3, 17.31 μg/m3, 0.75 μg/m3, 9.03 μg/m3, and 0.07 mg/m3, respectively. The number of avoided premature deaths due to PM2.5 reduction was estimated to be 35,342. The resulting economic benefits totaled 18.86 billion US dollars. We investigated the reasons for the air quality improvement in these ten cities and found the "3 + 11" policy has had a great impact on air quality. Compared with the first COVID-19 lockdown in early 2020, the effect of the lockdown in 2022 was smaller. These findings demonstrated that reductions in anthropogenic emissions would achieve substantial air quality improvement and health benefits. This paper re-emphasized continuous efforts to improve air quality are essential to protect public health.

Impact of mobility restrictions on NO2 concentrations in key Latin American cities during the first wave of the COVID-19 pandemic

Between March and June 2020, activity in the major cities of Latin America declined due to containment efforts implemented by local governments to avoid the rapid spread of COVID-19. Our study compared 2020 with the previous year and demonstrated a considerable drop in tropospheric NO₂ levels obtained by the SENTINEL 5P satellite in major Latin American cities. Lima (47.5%), Santiago (36.1%), São Paulo (27%), Rio de Janeiro (23%), Quito (18.6%), Bogota (17.5%), Buenos Aires (16.6%), Guayaquil (15.3%), Medellin (14.2%), La Paz (9.5%), Belo Horizonte (7.8%), Mexico (7.6%) and Brasilia (5.9%) registered statistically significant decreases in NO₂ concentrations during the study period. In addition, we analyzed mobility data from Google and Apple reports as well as meteorological information from atmospheric reanalysis data along with satellite fields between 2011 and 2020, and performed a refined multivariate analysis (non-negative matrix approximation) to show that this decrease was associated with a reduction in population mobility rather than meteorological factors. Our findings corroborate the argument that confinement scenarios may indicate how air pollutant concentrations can be effectively reduced and managed.

Impact of COVID-19 restrictions on air quality and surface urban heat island effect within the main urban area of Urumqi, China

The outbreak of coronavirus in 2019 (COVID-19) posed a serious global threat. However, the reduction in man-made pollutants during COVID-19 restrictions did improve the ecological environment of cities. Using multi-source remote sensing data, this study explored the spatiotemporal variations in air pollutant concentrations during the epidemic prevention and control period in Urumqi and quantitatively analyzed the impact of different air pollutants on the surface urban heat island intensity (SUHII) within the study area. Urumqi, located in the hinterland of the Eurasian continent, northwest of China, in the central and northern part of Xinjiang was selected as the study area. The results showed that during COVID-19 restrictions, concentrations of air pollutants decreased in the main urban area of Urumqi, and air quality improved. The most evident decrease in NO2 concentration, by 77 ± 1.05% and 15 ± 0.98%, occurred in the middle of the first (January 25 to March 20, 2020) and second (July 21 to September 1, 2020) COVID-19 restriction periods, respectively, compared with the corresponding period in 2019. Air pollutant concentrations and the SUHIIs were significantly and positively correlated, and NO2 exhibited the strongest correlation with the SUHIIs. We revealed that variations in the air quality characteristics and thermal environment were observed in the study area during the COVID-19 restrictions, and their quantitative relationship provides a theoretical basis and reference value for improving the air and ecological environment quality within the study area.

Assessing the ambient air quality patterns associated to the COVID-19 outbreak in the Yangtze River Delta: A random forest approach

The novel coronavirus (COVID-19), first identified at the end of December 2019, has significant impacts on all aspects of human society. In this study, we aimed to assess the ambient air quality patterns associated to the COVID-19 outbreak in the Yangtze River Delta (YRD) region using a random forest (RF) model. To estimate the accuracy of the model, the cross-validation (CV), determination coefficient R2, root mean squared error (RMSE) and mean absolute error (MAE) were used. The results demonstrate that the RF model achieved the best performance in the prediction of PM10 (R2 = 0.78, RMSE = 8.81 μg/m3), PM2.5 (R2 = 0.76, RMSE = 6.16 μg/m3), SO2 (R2 = 0.76, RMSE = 0.70 μg/m3), NO2 (R2 = 0.75, RMSE = 4.25 μg/m3), CO (R2 = 0.81, RMSE = 0.4 μg/m3) and O3 (R2 = 0.79, RMSE = 6.24 μg/m3) concentrations in the YRD region. Compared with the prior two years (2018-19), significant reductions were recorded in air pollutants, such as SO2 (-36.37%), followed by PM10 (-33.95%), PM2.5 (-32.86%), NO2 (-32.65%) and CO (-20.48%), while an increase in O3 was observed (6.70%) during the COVID-19 period (first phase). Moreover, the YRD experienced rising trends in the concentrations of PM10, PM2.5, NO2 and CO, while SO2 and O3 levels decreased in 2021-22 (second phase). These findings provide credible outcomes and encourage the efforts to mitigate air pollution problems in the future.

Exposure to the early COVID-19 pandemic and early, moderate and overall preterm births in the United States: A conception cohort approach

BACKGROUND

The United States (US) data suggest fewer-than-expected preterm births in 2020, but no study has examined the impact of exposure to the early COVID-19 pandemic at different points in gestation on preterm birth.

OBJECTIVE

Our objective was to determine-among cohorts exposed to the early COVID-19 pandemic-whether observed counts of overall, early and moderately preterm birth fell outside the expected range.

METHODS

We used de-identified, cross-sectional, national birth certificate data from 2014 to 2020. We used month and year of birth and gestational age to estimate month of conception for birth. We calculated the count of overall (<37 weeks gestation), early (<33 weeks gestation) and moderately (33 to <37 weeks gestation) preterm birth by month of conception. We employed time series methods to estimate expected counts of preterm birth for exposed conception cohorts and identified cohorts for whom the observed counts of preterm birth fell outside the 95% detection interval of the expected value.

RESULTS

Among the 23,731,146 births in our study, the mean prevalence of preterm birth among monthly conception cohorts was 9.7 per 100 live births. Gestations conceived in July, August or December of 2019-that is exposed to the early COVID-19 pandemic in the first or third trimester-yielded approximately 3245 fewer moderately preterm and 3627 fewer overall preterm births than the expected values for moderate and overall preterm. Gestations conceived in August and October of 2019-that is exposed to the early COVID-19 pandemic in the late second to third trimester-produced approximately 498 fewer early preterm births than the expected count for early preterm.

CONCLUSIONS

Exposure to the early COVID-19 pandemic may have promoted longer gestation among close-to-term pregnancies, reduced risk of later preterm delivery among gestations exposed in the first trimester or induced selective loss of gestations.

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

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

Weekend effect of air pollutants in small and medium-sized cities: The role of policies stringency to COVID-19 containment

Although the pattern of air pollutants has been extensively studied during the COVID-19 pandemic, the weekend effect has been rarely investigated. In order to understand the impact of policies stringency as well as the interruption-recovery pattern, the aim of the study was to investigate the levels of air pollutants (O3, NO2, SO2, PM2.5, PM10) and PM2.5/PM10 ratio before and after the COVID-19 pandemic in four cities in the state of Rio Grande do Sul, Brazil, evaluating the weekend effect at these two scenarios and also identifying how the restriction measures applied locally had an impact on this effect. For this, daily data from two years of monitoring of air pollutants were collected and the weekend effect was calculated based on the levels from Monday to Friday (weekday) and Saturday and Sunday (weekend). There was a positive weekend effect for almost all criteria air pollutants in the four cities, and an intrinsic relation between the weekend effect and the restriction measures adopted. A negative weekend effect was observed in the scenario characterized by less restrictive and more permissive policies for daily and occupational activities. Conversely, when more stringent measures were implemented, this trend was reversed and higher intensities of positive weekend effect were observed as restrictions increased. In conclusion, the COVID-19 perturbation to air quality changed as regions tighten and loosen restrictions on human mobility. These insights that can guide responsible authorities about future strategies and policies for air quality control.

New-type urbanization ecologically reshaping China

Addressing critical ecological issues is one of the core objectives of the Chinese National New-type Urbanization Plan (NTU). However, there is not enough research to clearly demonstrate NTU's impact on environmental pollution. There is a need to provide solid empirical evidence from evaluation of the effects of NTU on environmental pollution. This study implements a series of propensity score matching and difference-in-difference analyses based on a comprehensive panel dataset spanning the period 2006-2017. The quantitative results show that NTU is largely conducive to curbing environmental pollution, with PM_2.5 concentrations as a proxy, and its robustness is demonstrated by the parallel trend test and placebo test. Further, the ecological effect of NTU is heterogeneous in terms of city level, location, and former pollution level. Small and medium cities benefit more than larger or central cities from NTU, and eastern cities exhibit better air quality improvement than cities in western regions. In addition, through quantile regression, we find that the positive outcomes of NTU are restricted by level of pollution, i.e., heavier pollution adds greatly to the difficulty of abatement.

Public policy for healthy living: How COVID-19 has changed the landscape

The coronavirus disease 2019 (COVID-19) pandemic had a transformational impact on public policy as governments played a leading role, working alongside and coordinating with business/industry, healthcare, public health, education, transportation, researchers, non-governmental organizations, philanthropy, and media/communications. This paper summarizes the impact of the pandemic on different areas of public policy affecting healthy living and cardiovascular health including prevention (i.e., nutrition, physical activity, air quality, tobacco use), risk factors for chronic disease (hypertension, diabetes, obesity, substance abuse), access to health care, care delivery and payment reform, telehealth and digital health, research, and employment policy. The paper underscores where public policy is evolving and where there are needs for future evidence base to inform policy development, and the intersections between the public and private sectors across the policy continuum. There is a continued need for global multi-sector coordination to optimize population health.

How do the stay-at-home (SAH) orders affect air quality? Evidence from the northeastern USA

This paper examines whether the SAH orders, implemented in the USA from mid-March to late May 2020, improved air quality in the northeastern states. The estimates are based on panel data from the Environmental Protection Agency and an identification strategy that exploits the exogenous variation in the timing of the SAH orders. We find that the SAH orders reduced the concentrations of the air pollutants nitrogen dioxide ( NO 2 ) and carbon monoxide (CO), whose dominant source is motor vehicle emissions, by approximately 24% and 13%, respectively. The effects were larger for areas of high population density and areas near major roads. We also find that the reductions got smaller, and air pollution gradually approached normal levels, after the orders were lifted. This suggests that the air quality improvements were temporary.

The lock-down effects of COVID-19 on the air pollution indices in Iran and its neighbors

Introduction

The COVID-19 restrictions have a lot of various peripheral negative and positive effects, like economic shocks and decreasing air pollution, respectively. Many studies showed NO2 reduction in most parts of the world.

Methods

Iran and its land and maritime neighbors have about 7.4% of the world population and 6.3% and 5.8% of World COVID-19 cases and deaths, respectively. The air pollution indices of them such as CH₄ (Methane), CO_1 (CO), H₂O (Water), HCHO (Tropospheric Atmospheric Formaldehyde), NO₂ (Nitrogen oxides), O₃ (ozone), SO₂ (Sulfur Dioxide), UVAI_AAI [UV Aerosol Index (UVAI)/Absorbing Aerosol Index (AAI)] are studied from the First quarter of 2019 to the fourth quarter of 2021 with Copernicus Sentinel 5 Precursor (S5P) satellite data set from Google Earth Engine. The outliers are detected based on the depth functions. We use a two-sample t test, Wilcoxon test, and interval-wise testing for functional data to control the familywise error rate.

Result

The adjusted p value comparison between Q2 of 2019 and Q2 of 2020 in NO₂ for almost all countries is statistically significant except Iraq, UAE, Bahrain, Qatar, and Kuwait. But, the CO and HCHO are not statistically significant in any country. Although CH₄, O₃, and UVAI_AAI are statistically significant for some countries. In the Q2 comparison for NO₂ between 2020 and 2021, only Iran, Armenia, Turkey, UAE, and Saudi Arabia are statistically significant. However, Ch₄ is statistically significant for all countries except Azerbaijan.

Conclusions

The comparison with and without adjusted p values declares the decreases in some air pollution in these countries.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40808-022-01528-x.

Comparison of bacterial community structure in PM2.5 during hazy and non-hazy periods in Guilin, South China

In recent years, significant efforts have been made to study changes in the levels of air pollutants at regional and urban scales, and changes in bioaerosols during air pollution events have attracted increasing attention. In this study, the bacterial structure of PM_2.5 was analysed under different environmental conditions during hazy and non-hazy periods in Guilin. A total of 32 PM_2.5 samples were collected in December 2020 and July 2021, and the microbial community structures were analysed using high-throughput sequencing methods. The results show that air pollution and climate change alter the species distribution and community diversity of bacteria in PM_2.5, particularly Sphingomonas and Pseudomonas. The structure of the bacterial community composition is related to diurnal variation, vertical height, and urban area and their interactions with various environmental factors. This is a comprehensive study that characterises the variability of bacteria associated with PM_2.5 in a variety of environments, highlighting the impacts of environmental effects on the atmospheric microbial community. The results will contribute to our understanding of haze trends in China, particularly the relationship between bioaerosol communities and the urban environment.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s10453-022-09777-0.

Air quality changes in Taiwan over the past decades and during the COVID-19 crisis

Over the past decades, Taiwan has achieved remarkable goals in air pollution reduction with the concentrations of several common air pollutants such as CO, NOx, PM10, PM2.5, and SO2 going down. In contrast to these achievements, the mitigation of O3 remains extremely tough due to the complexity of its formation process involving synergistic effects of precursor reductions and meteorological influences. During the local COVID-19 crises in Taiwan and the Level 3 alert in 2021, air pollutants directly emitted from the traffic such as CO and NOx present clear relationships with the drop of the recorded freeway traffic volume due to the alert, while PM10 and PM2.5 which are also relevant to the traffic do not show indications of being greatly influenced by the decrease of the traffic flow. Although road traffic is not regarded as a main source of SO2 by current understanding, the unusual SO2 variation patterns found in this study suggest a prolonged impact for months from the changes of travel behavior during the epidemic. In contrast, the epidemic did not exert influences on industrial SO2 concentration which accounts for a large portion of total SO2 in Taiwan, and a similar scenario is also seen in each type of O3 monitoring. Although some results discussed in this study are not in line with current consensuses and understandings in terms of the nation of certain air pollutants, these findings may disclose new perspectives which could be a potential benefit to air quality improvement projects in the future.

Ambient air pollutants concentration prediction during the COVID-19: A method based on transfer learning

Research on the correlation analysis between COVID-19 and air pollution has attracted increasing attention since the COVID-19 pandemic. While many relevant issues have been widely studied, research into ambient air pollutant concentration prediction (APCP) during COVID-19 is still in its infancy. Most of the existing study on APCP is based on machine learning methods, which are not suitable for APCP during COVID-19 due to the different distribution of historical observations before and after the pandemic. Therefore, to fulfill the predictive task based on the historical observations with a different distribution, this paper proposes an improved transfer learning model combined with machine learning for APCP during COVID-19. Specifically, this paper employs the Gaussian mixture method and an optimization algorithm to obtain a new source domain similar to the target domain for further transfer learning. Then, several commonly used machine learning models are trained in the new source domain, and these well-trained models are transferred to the target domain to obtain APCP results. Based on the real-world dataset, the experimental results suggest that, by using the improved machine learning methods based on transfer learning, our method can achieve the prediction with significantly high accuracy. In terms of managerial insights, the effects of influential factors are analyzed according to the relationship between these influential factors and prediction results, while their importance is ranked through their average marginal contribution and partial dependence plots.

Prediction and assessment of the impact of COVID-19 lockdown on air quality over Kolkata: a deep transfer learning approach

The present study focuses on the prediction and assessment of the impact of lockdown because of coronavirus pandemic on the air quality during three different phases, viz., normal periods (1 January 2018-23 March 2020), complete lockdown (24 March 2020-31 May 2020), and partial lockdown (1 June 2020-30 September 2020). We identify the most important air pollutants influencing the air quality of Kolkata during three different periods using Random Forest, a tree-based machine learning (ML) algorithm. It is found that the ambient air quality of Kolkata is mainly affected with the aid of particulate matter or PM (PM₁₀ and PM_2.5). However, the effect of the lockdown is most prominent on PM_2.5 which spreads in the air of Kolkata due to diesel-driven vehicles, domestic and commercial combustion activities, road dust, and open burning. To predict urban PM_2.5 and PM₁₀ concentrations 24 h in advance, we use a deep learning (DL) model, namely, stacked-bidirectional long short-term memory (stacked-BDLSTM). The model is trained during the normal periods, and it shows the superiority over some supervised ML models, like support vector machine, K-nearest neighbor classifier, multilayer perceptron, long short-term memory, and statistical time series forecasting model autoregressive integrated moving average. This pre-trained stacked-BDLSTM is applied to predict the concentrations of PM_2.5 and PM₁₀ during the pandemic situation of two cases, viz., complete lockdown and partial lockdown using a deep model-based transfer learning (TL) approach (TLS-BDLSTM). Transfer learning aims to utilize the information gained from one problem to improve the predictive performance of a learning model for a different but related problem. Our work helps to demonstrate how TL is useful when there is a scarcity of data during the COVID-19 pandemic regarding the drastic change in concentration of pollutants. The results reveal the best prediction performance of TLS-BDLSTM with a lead time of 24 h as compared to some well-known traditional ML and statistical models and the pre-trained stacked-BDLSTM. The prediction is then validated using the real-time data obtained during the complete lockdown due to COVID second wave (16 May-15 June 2021) with different time steps, e.g., 24 h, 48 h, 72 h, and 96-120 h. TLS-BDLSTM involving transfer learning is seen to outperform the said comparing methods in modeling the long-term temporal dependency of multivariate time series data and boost the forecast efficiency not only in single step, but also in multiple steps. The proposed methodologies are effective, consistent, and can be used by operational organizations to utilize in monitoring and management of air quality.

The Effects of COVID-19 Lockdown on Air Pollutant Concentrations across China: A Google Earth Engine-Based Analysis

To overcome the spread of the severe COVID-19 outbreak, various lockdown measures have been taken worldwide. China imposed the strictest home-quarantine measures during the COVID-19 outbreak in the year 2020. This provides a valuable opportunity to study the impact of anthropogenic emission reductions on air quality. Based on the GEE platform and satellite imagery, this study analyzed the changes in the concentrations of NO₂, O₃, CO, and SO₂ in the same season (1 February-1 May) before and after the epidemic control (2019-2021) for 16 typical representative cities of China. The results showed that NO₂ concentrations significantly decreased by around 20-24% for different types of metropolises, whereas O₃ increased for most of the studied metropolises, including approximately 7% in megacities and other major cities. Additionally, the concentrations of CO and SO₂ showed no statistically significant changes during the study intervals. The study also indicated strong variations in air pollutants among different geographic regions. In addition to the methods in this study, it is essential to include the differences in meteorological impact factors in the study to identify future references for air pollution reduction measures.