Possible environmental effects on the spread of COVID-19 in China

Rain, rain, go away, come again another day: do climate variations enhance the spread of COVID-19?

The spread of infectious diseases was further promoted due to busy cities, increased travel, and climate change, which led to outbreaks, epidemics, and even pandemics. The world experienced the severity of the 125 nm virus called the coronavirus disease 2019 (COVID-19), a pandemic declared by the World Health Organization (WHO) in 2019. Many investigations revealed a strong correlation between humidity and temperature relative to the kinetics of the virus's spread into the hosts. This study aimed to solve the riddle of the correlation between environmental factors and COVID-19 by applying RepOrting standards for Systematic Evidence Syntheses (ROSES) with the designed research question. Five temperature and humidity-related themes were deduced via the review processes, namely 1) The link between solar activity and pandemic outbreaks, 2) Regional area, 3) Climate and weather, 4) Relationship between temperature and humidity, and 5) the Governmental disinfection actions and guidelines. A significant relationship between solar activities and pandemic outbreaks was reported throughout the review of past studies. The grand solar minima (1450-1830) and solar minima (1975-2020) coincided with the global pandemic. Meanwhile, the cooler, lower humidity, and low wind movement environment reported higher severity of cases. Moreover, COVID-19 confirmed cases and death cases were higher in countries located within the Northern Hemisphere. The Blackbox of COVID-19 was revealed through the work conducted in this paper that the virus thrives in cooler and low-humidity environments, with emphasis on potential treatments and government measures relative to temperature and humidity. HIGHLIGHTS: • The coronavirus disease 2019 (COIVD-19) is spreading faster in low temperatures and humid area. • Weather and climate serve as environmental drivers in propagating COVID-19. • Solar radiation influences the spreading of COVID-19. • The correlation between weather and population as the factor in spreading of COVID-19.

A New Auto-Regressive Multi-Variable Modified Auto-Encoder for Multivariate Time-Series Prediction: A Case Study with Application to COVID-19 Pandemics

The SARS-CoV-2 global pandemic prompted governments, institutions, and researchers to investigate its impact, developing strategies based on general indicators to make the most precise predictions possible. Approaches based on epidemiological models were used but the outcomes demonstrated forecasting with uncertainty due to insufficient or missing data. Besides the lack of data, machine-learning models including random forest, support vector regression, LSTM, Auto-encoders, and traditional time-series models such as Prophet and ARIMA were employed in the task, achieving remarkable results with limited effectiveness. Some of these methodologies have precision constraints in dealing with multi-variable inputs, which are important for problems like pandemics that require short and long-term forecasting. Given the under-supply in this scenario, we propose a novel approach for time-series prediction based on stacking auto-encoder structures using three variations of the same model for the training step and weight adjustment to evaluate its forecasting performance. We conducted comparison experiments with previously published data on COVID-19 cases, deaths, temperature, humidity, and air quality index (AQI) in São Paulo City, Brazil. Additionally, we used the percentage of COVID-19 cases from the top ten affected countries worldwide until May 4th, 2020. The results show 80.7% and 10.3% decrease in RMSE to entire and test data over the distribution of 50 trial-trained models, respectively, compared to the first experiment comparison. Also, model type#3 achieved 4th better overall ranking performance, overcoming the NBEATS, Prophet, and Glounts time-series models in the second experiment comparison. This model shows promising forecast capacity and versatility across different input dataset lengths, making it a prominent forecasting model for time-series tasks.

Investigating the cumulative lag effects of environmental exposure under urban differences on COVID-19

Although all walks of life are paying less attention to COVID-19, the spread of COVID-19 has never stopped. As an infectious disease, its transmission speed is closely related to the atmosphere environment, particularly the temperature (T) and PM2.5 concentrations. However, How T and PM2.5 concentrations are related to the spread of SARS-CoV-2 and how much their cumulative lag effect differ across cities is unclear. To identify the characteristics of cumulative lag effects of environmental exposure under city differences, this study used a generalized additive model to investigate the associations between T/PM2.5 concentrations and the daily number of new confirmed COVID-19 cases (NNCC) during the outbreak period in the second half of 2021 in Shaoxing, Shijiazhuang, and Dalian. The results showed that except for PM2.5 concentrations in Shaoxing, the NNCC in the three cities generally increased with the unit increase of T and PM2.5 concentrations. In addition, the cumulative lag effects of T/PM2.5 concentrations on NNCC in the three cities reached a peak at lag 26/25, lag 10/26, and lag 18/13 days, respectively, indicating that the response of NNCC to T and PM2.5 concentrations varies among different regions. Therefore, combining local meteorological and air quality conditions to adopt responsive measures is an important way to prevent and control the spread of SARS-CoV-2.

Evaluation of intervention measures in reducing the driver's exposure to respiratory particles in a taxi with infected passengers

In the fifth wave of the COVID-19 epidemic in Hong Kong in early 2022, the large number of infected persons caused a shortage of ambulances and transportation vehicles operated by the government. To solve the problem, taxi drivers were recruited to transport infected persons to hospitals in their taxis. However, many of the drivers were infected after they began to participate in the plan. To tackle this issue, the present study numerically evaluated the effectiveness of several intervention measures in reducing the infection risk for taxi drivers. First, experiments were conducted inside a car to validate the large-eddy simulation (LES)-Lagrangian model for simulation of particle transport in a car. The validated model was then applied to calculate the particle dispersion and deposition in a Hong Kong taxi with intervention measures that included opening windows, installing partitions, and using a far-UVC lamp. The results show that opening the windows can significantly reduce the driver's total exposure by 97.4 %. Installing partitions and using a far-UVC lamp can further reduce the infection risk of driver by 55.9 % and 32.1 %, respectively. The results of this study can be used to support the implementation of effective intervention measures to protect taxi drivers from infection.

Effect of a Total Extract and Saponins from Astragalus glycyphyllos L. on Human Coronavirus Replication In Vitro

Members of the family Coronaviridae cause diseases in mammals, birds, and wildlife (bats), some of which may be transmissible to humans or specific to humans. In the human population, they can cause a wide range of diseases, mainly affecting the respiratory and digestive systems. In the scientific databases, there are huge numbers of research articles about the antiviral, antifungal, antibacterial, antiviral, and anthelmintic activities of medicinal herbs and crops with different ethnobotanical backgrounds. The subject of our research is the antiviral effect of isolated saponins, a purified saponin mixture, and a methanol extract of Astragalus glycyphyllos L. In the studies conducted for the cytotoxic effect of the substances, CC50 (cytotoxic concentration 50) and MTC (maximum tolerable concentration) were determined by the colorimetric method (MTT assay). The virus was cultured in the MDBK cell line. As a result of the experiments carried out on the influence of substances on viral replication (using MTT-based colorimetric assay for detection of human Coronavirus replication inhibition), it was found that the extract and the purified saponin mixture inhibited 100% viral replication. The calculated selective indices are about 13 and 18, respectively. The obtained results make them promising for a preparation with anti-Coronavirus action.

Impact of environmental factors on COVID-19 transmission: spatial variations in the world

The COVID-19 pandemic caused enormous destruction to global health and the economy and has surged worldwide with colossal morbidity and mortality. The pattern of the COVID infection varies in diverse regions of the world based on the variations in the geographic environment. The multivariate generalized linear regression models: zero-inflated negative binomial regression, and the zero-inflated Poisson regression model, have been employed to determine the significant meteorological factors responsible for the spread of the pandemic in different continents. Asia experienced a high COVID-19 infection, and death was extreme in Europe. Relative humidity, air pressure, and wind speed are the salient factors significantly impacting the spread of COVID-19 in Africa. Death due to COVID-19 in Asia is influenced by air pressure, temperature, precipitation, and relative humidity. Air pressure and temperature substantially affect the spread of the pandemic in Europe.

Role of meteorological parameters with the spread of Covid-19 in Pakistan: application of autoregressive distributed lag approach

This research focuses on the impacts of different meteorological parameters (temperature, humidity, rainfall, and evapotranspiration) on the transmission of Covid-19 in the administrative regions and provinces of Pakistan, i.e., Azad Jammu and Kashmir, Gilgit Baltistan, Khyber Pakhtunkhwa, Islamabad, Punjab, Sindh, and Balochistan from June 10, 2020, to August 31, 2021. This study analyzes the relation between Covid-19-confirmed cases and the meteorological parameters with the help of the autoregressive distributed lag model. In this research, additional tools (t-statistics, f-statistics, and time series analysis) are used for the motive of examining the linear relationship, the productivity of the model, and for the significant association between dependent and independent variables, lnccc and lnevp, lnhum, lnrain, lntemp, respectively. Values of t-statistics and f-statistics reveal that variables have a connection and individual significance for the model exist. Time series display that the Covid-19 spread increased from June 10, 2020, to August 31, 2021, in Pakistan. Temperature positively influenced the Covid-19-confirmed cases in all provinces of Pakistan in the long run. Evapotranspiration and rainfall influenced positively, while specific humidity influenced negatively on the confirmed Covid-19 cases in Azad Jammu Kashmir, Khyber Pakhtunkhwa, and Punjab. Specific humidity had a positive impact, while evapotranspiration and rainfall had the negative impact on the Covid-19-confirmed cases in Sindh and Balochistan. Evapotranspiration and specific humidity influenced positively, while rainfall influenced the Covid-19-confirmed cases negatively in Gilgit Baltistan. Evapotranspiration influenced positively, while specific humidity and rainfall influenced negatively on the Covid-19-confirmed cases in Islamabad.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13762-023-04997-4.

Impact of Covid-19 pandemic lockdown on the urban litter and clean environment index

Changing the level of pollution in the urban environment is one of the consequences of Covid-19. Litter are one of the most important urban pollutants affected by the Covid-19 pandemic. In this research, the pollution level of urban areas during the Covid-19 pandemic was investigated by studying the urban environment. To this end, the protocol of observation and counting was used and litter were studied in two groups including common litter and Covid-19 related litter in Yasuj, Iran. The results were interpreted based on the clean environment index (CEI). The time of observation was selected based on the peak of the disease and the decline in the incidence rate. The results showed that on average, at the peak of the disease, the density of the litter was reduced by 19% compared to the low lockdown related to Covid-19. The CEI on average was 4.76 at the peak of the disease that was interpreted in the clean status, while the CEI on average was 5.94 at the low lockdown related to Covid-19 so interpreted in the moderate status. Among urban land uses, recreational areas with a difference of more than 60% showed the greatest impact caused by Covid-19, while in commercial areas this difference was less than 3%. The effect of Covid-19 related litter on the calculated index was 73% in the worst case and 0.8% in the lowest case. Although Covid-19 decreased the number of litter in urban areas, the emergence of Covid-19 lockdown related litter was a cause for concern and led to increasing the CEI.

Critical air pollutant assessments and health effects attributed to PM2.5 during and after COVID-19 lockdowns in Iran: application of AirQ+ models

Objectives

The aim of this study was to evaluate changes in air quality index (AQI) values before, during, and after lockdown, as well as to evaluate the number of hospitalizations due to respiratory and cardiovascular diseases attributed to atmospheric PM_2.5 pollution in Semnan, Iran in the period from 2019 to 2021 during the COVID-19 pandemic.

Methods

Daily air quality records were obtained from the global air quality index project and the US Environmental Protection Administration (EPA). In this research, the AirQ+ model was used to quantify health consequences attributed to particulate matter with an aerodynamic diameter of <2.5 μm (PM_2.5).

Results

The results of this study showed positive correlations between air pollution levels and reductions in pollutant levels during and after the lockdown. PM_2.5 was the critical pollutant for most days of the year, as its AQI was the highest among the four investigated pollutants on most days. Mortality rates from chronic obstructive pulmonary disease (COPD) attributed to PM_2.5 in 2019-2021 were 25.18% in 2019, 22.55% in 2020, and 22.12% in 2021. Mortality rates and hospital admissions due to cardiovascular and respiratory diseases decreased during the lockdown. The results showed a significant decrease in the percentage of days with unhealthy air quality in short-term lockdowns in Semnan, Iran with moderate air pollution. Natural mortality (due to all-natural causes) and other mortalities related to COPD, ischemic heart disease (IHD), lung cancer (LC), and stroke attributed to PM_2.5 in 2019-2021 decreased.

Conclusion

Our results support the general finding that anthropogenic activities cause significant health threats, which were paradoxically revealed during a global health crisis/challenge.

Evaluation of the ventilation and pollutant exposure risk level inside 3D street canyon with void deck under different wind directions

With continuous global warming, growing urban population density, and increasing compactness of urban buildings, VD (void deck) street design has become increasingly popular in city planning, especially in tropical countries. However, understanding on traffic pollutant dispersion inside the street canyons with VDs is still at early stage. This paper evaluates quantitatively the effects of VD location and wind direction on the ventilation and traffic pollutant exposure inside the street canyon with VDs. The results show that under seven wind directions (0°, 15°, 30°, 45°, 60°, 75°, and 90°), the VD provides higher ACH than that of the regular canyon, especially at high α (angle between the approaching wind and the canyon axis). Also, mean K (dimensionless pollutant concentration) values of the canyon wall and pedestrian respiration plane on one side where VD is located are significantly reduced compared to the regular canyon. Therefore, when VDs are at both buildings, both pedestrian respiration planes and walls have the lowest K values, thus providing the best living environment for pedestrians and near-road residents. In addition, as α increases, the K values on both respiration planes significantly decrease except for the leeward respiration plane of the canyon with the windward VD. These findings can help to design urban street canyons for mitigating traffic pollution risk and improving ventilation in tropical cities with frequently changing wind directions.

A Literature Review of the Effects of Air Pollution on COVID-19 Health Outcomes Worldwide: Statistical Challenges and Data Visualization

Several peer-reviewed papers and reviews have examined the relationship between exposure to air pollution and COVID-19 spread and severity. However, many of the existing reviews on this topic do not extensively present the statistical challenges associated with this field, do not provide comprehensive guidelines for future researchers, and review only the results of a relatively small number of papers. We reviewed 139 papers, 127 of which reported a statistically significant positive association between air pollution and adverse COVID-19 health outcomes. Here, we summarize the evidence, describe the statistical challenges, and make recommendations for future research. To summarize the 139 papers with data from geographical locations around the world, we also present anopen-source data visualization tool that summarizes these studies and allows the research community to contribute evidence as new research papers are published.

The impact of meteorological factors on the spread of COVID-19

Background

Clinical studies suggest that warmer climates slow the spread of viral infections. In addition, exposure to cold weakens human immunity.

Aim

This study describes the relationship between meteorological indicators, the number of cases, and mortality in patients with confirmed coronavirus disease 2019 (COVID-19).

Patients and Methods

This was a retrospective observational study. Adult patients who presented to the emergency department with confirmed COVID-19 were included in the study. Meteorological data [mean temperature, minimum (min) temperature, maximum (max) temperature, relative humidity, and wind speed] for the city of Istanbul were collected from the Istanbul Meteorology 1^st Regional Directorate.

Results

The study population consisted of 169,058 patients. The highest number of patients were admitted in December (n = 21,610) and the highest number of deaths (n = 46) occurred in November. In a correlation analysis, a statistically significant, negative correlation was found between the number of COVID-19 patients and mean temperature (rho = -0.734, P < 0.001), max temperature (rho = -0.696, P < 0.001) or min temperature (rho = -0.748, P < 0.001). Besides, the total number of patients correlated significantly and positively with the mean relative humidity (rho = 0.399 and P = 0.012). The correlation analysis also showed a significant negative relationship between the mean, maximum, and min temperatures and the number of deaths and mortality.

Conclusion

Our results indicate an increased number of COVID-19 cases during the 39-week study period when the mean, max, and min temperatures were consistently low and the mean relative humidity was consistently high.

Evaluation on ventilation and traffic pollutant dispersion in asymmetric street canyons with void decks

With continuous global warming, growing urban population density, and increasing compactness of urban buildings, the "void deck" street canyon design has become increasingly popular in city planning, especially for urban streets located in tropical areas. Nevertheless, research on traffic pollutant dispersion in street canyons with void decks (VDs) is still at its early stage. This study quantitatively evaluates the effects of void deck height and location on the canyon ventilation and pollutant dispersion in asymmetric street canyons with void decks, and the pollutant exposure risk level for pedestrians and street dwellers. Void decks introduce more fresh air, thereby greatly improving the ventilation properties of the asymmetric canyon. The air exchange rate (ACH: 147.9%, 270.9%) and net escape velocity (NEV*: 416.7%, 915.8%) of the step-up and step-down canyons with VDs (3 m high at full scale) at both buildings are higher than those of regular asymmetric canyons. Moreover, the mean dimensionless pollutant concentration (K) on the building wall and pedestrian respiration plane in which VDs are located stands at a low level, because pollutants are removed by the airflow entering or exiting through the void decks. Increased VD height (4.5 m at full scale) enhances the strength of airflow flowing into and out of the canyon, significantly increasing ACH (177.3%, 380.9%) and NEV* (595.2%, 1268.4%) and decreasing the mean K on both pedestrian respiration planes and canyon walls. In particular, the K values on both pedestrian respiration planes and both walls are almost zero for the canyons with VDs at both buildings. Therefore, among the three VD locations, both VDs provide the best living environment for pedestrians and near-road residents. These findings can help to design urban street canyons for mitigating traffic pollution risk and improving ventilation in tropical cities.

Environment and COVID-19 incidence: A critical review

The coronavirus disease 2019 (COVID-19) pandemic is an unprecedented worldwide health crisis. Many previous research studies have found and investigated its links with one or some natural or human environmental factors. However, a review on the relationship between COVID-19 incidence and both the natural and human environment is still lacking. This review summarizes the inter-correlation between COVID-19 incidence and environmental factors. Based on keyword searching, we reviewed 100 relevant peer-reviewed articles and other research literature published since January 2020. This review is focused on three main findings. One, we found that individual environmental factors have impacts on COVID-19 incidence, but with spatial heterogeneity and uncertainty. Two, environmental factors exert interactive effects on COVID-19 incidence. In particular, the interactions of natural factors can affect COVID-19 transmission in micro- and macro- ways by impacting SARS-CoV-2 survival, as well as human mobility and behaviors. Three, the impact of COVID-19 incidence on the environment lies in the fact that COVID-19-induced lockdowns caused air quality improvement, wildlife shifts and socio-economic depression. The additional value of this review is that we recommend future research perspectives and adaptation strategies regarding the interactions of the environment and COVID-19. Future research should be extended to cover both the effects of the environment on the COVID-19 pandemic and COVID-19-induced impacts on the environment. Future adaptation strategies should focus on sustainable environmental and public policy responses.

Epidemiological geography at work: An exploratory review about the overall findings of spatial analysis applied to the study of CoViD-19 propagation along the first pandemic year

The present work aims to give an overview on the international scientific papers related to the territorial spreading of SARS-CoV-2, with a specific focus upon applied quantitative geography and territorial analysis, to define a general structure for epidemiological geography research. The target publications were based on GIS spatial analysis, both in the sense of topological analysis and descriptive statistics or lato sensu geographical approaches. The first basic purpose was to organize and enhance the vast knowledge developments generated hitherto by the first pandemic that was studied "on-the-fly" all over the world. The consequent target was to investigate to what extent researchers in geography were able to draw scientifically consistent conclusions about the pandemic evolution, as well as whether wider generalizations could be reasonably claimed. This implied an analysis and a comparison of their findings. Finally, we tested what geographic approaches can say about the pandemic and whether a reliable spatial analysis routine for mapping infectious diseases could be extrapolated. We selected papers proposed for publication during 2020 and 209 articles complied with our parameters of query. The articles were divided in seven categories to enhance existing commonalities. In some cases, converging conclusions were extracted, and generalizations were derived. In other cases, contrasting or inconsistent findings were found, and possible explanations were provided. From the results of our survey, we extrapolated a routine for the production of epidemiological geography analyses, we highlighted the different steps of investigation that were attained, and we underlined the most critical nodes of the methodology. Our findings may help to point out what are the most critical conceptual challenges of epidemiological mapping, and where it might improve to engender informed conclusions and aware outcomes.

How do temperature, humidity, and air saturation state affect the COVID-19 transmission risk?

Environmental parameters have a significant impact on the spread of respiratory viral diseases (temperature (T), relative humidity (RH), and air saturation state). T and RH are strongly correlated with viral inactivation in the air, whereas supersaturated air can promote droplet deposition in the respiratory tract. This study introduces a new concept, the dynamic virus deposition ratio (α), that reflects the dynamic changes in viral inactivation and droplet deposition under varying ambient environments. A non-steady-state-modified Wells-Riley model is established to predict the infection risk of shared air space and highlight the high-risk environmental conditions. Findings reveal that a rise in T would significantly reduce the transmission of COVID-19 in the cold season, while the effect is not significant in the hot season. The infection risk under low-T and high-RH conditions, such as the frozen seafood market, is substantially underestimated, which should be taken seriously. The study encourages selected containment measures against high-risk environmental conditions and cross-discipline management in the public health crisis based on meteorology, government, and medical research.

Effects of void deck on the airflow and pollutant dispersion in 3D street canyons

In general, urban canyons are the areas most clearly affected by traffic pollutants since the ability of the canyon to self-ventilate is inhibited due to blockage of buildings or other urban structures. However, previous studies have aimed to improve the pedestrian-level wind speed with void deck in single buildings or short canyons. This study investigated the effects of void deck height and location, and the building height on the airflow field and the traffic pollutant diffusion in a long canyon with L/H = 10, validated by wind-tunnel experiment data. The results show that the void decks have a significant effect on the airflow and pollutant distribution inside the canyon. Air exchange rates (ACH) of the canyons with the void deck are much larger than that of regular canyons, and the perturbation changes of turbulence (ACH') decrease. For the windward void deck, purging flow rate (PFR) and normalized net escape velocity (NEV*) increase by 6.4 times compared to the regular canyon, and for the leeward void deck, increase by 13 times. In particular, when the void decks are at both buildings, they are increased by 38.3 times. Also, for the canyons with the void deck, traffic pollutants are removed out of the canyon by the strong airflow through the void deck. Therefore, unlike the regular canyons, as the void deck and the building height increases, the strength of the airflow through the void deck becomes stronger, and as a result, the mean pollutant concentration is significantly reduced at both walls and the pedestrian respiration level. The mean pollutant concentration on the wall of the building with the void deck and on the pedestrian respiration plane close to it is near zero. These findings can help ease traffic pollution inside the street canyons composed of high-rise buildings, especially in tropical cities.

Role of Potential COVID-19 Immune System Associated Genes and the Potential Pathways Linkage with Type-2 Diabetes

BACKGROUND

Coronavirus is an enclosed positive-sense RNA virus with club-like spikes extending from its surface. It is most typically associated with acute respiratory infections in humans, but its capacity to infect many host species and cause multiple illnesses makes it a complicated pathogen. The frequent encounters between wild animals and humans are a typical cause of infection. The zoonotic infections SARS-CoV and MERS-CoV are among the most common causes of serious respiratory illnesses in humans.

AIM

The main goal of this research was to look at gene expression profiles in human samples that were either infected with coronavirus or were not, and compare the varied expression patterns and their functional implications.

METHODS

The previously researched samples were acquired from a public database for this purpose, and the study was conducted, which included gene expression analysis, pathway analysis, and network-level comprehension. The results for differentially expressed genes, enriched pathways, and networks for prospective genes and gene sets are presented in the analysis. In terms of COVID-19 gene expression and its relationship to type 2 diabetes.

RESULTS

We see a lot of genes that have different gene expression patterns than normal for coronavirus infection, but in terms of pathways, it appears that there are only a few sets of functions that are affected by altered gene expression, and they are related to infection, inflammation, and the immune system.

CONCLUSION

Based on our study, we conclude that the potential genes which are affected due to infection are NFKBIA, MYC, FOXO3, BIRC3, ICAM1, IL8, CXCL1/2/5, GADD45A, RELB, SGK1, AREG, BBC3, DDIT3/4, EGR1, MTHFD2, and SESN2 and the functional changes are mainly associated with these pathways: TNF, cytokine, NF-kB, TLR, TCR, BCR, Foxo, and TGF signaling pathways are among them and there are additional pathways such as hippo signaling, apoptosis, estrogen signaling, regulating pluropotency of stem cells, ErbB, Wnt, p53, cAMP, MAPK, PI3K-AKT, oxidative phosphorylation, protein processing in endoplasmic reticulum, prolactin signaling, adipocytokine, neurotrophine signaling, and longevity regulating pathways. SMARCD3, PARL, GLIPR1, STAT2, PMAIP1, GP1BA, and TOX genes and PI3K-Akt, focal adhesion, Foxo, phagosome, adrenergic, osteoclast differentiation, platelet activation, insulin, cytokine- cytokine interaction, apoptosis, ECM, JAK-STAT, and oxytocin signaling appear as the linkage between COVID-19 and Type-2 diabetes.

Machine learning analysis on the impacts of COVID-19 on India's renewable energy transitions and air quality

India is severely affected by the COVID-19 pandemic and is facing an unprecedented public health emergency. While the country's immediate measures focus on combating the coronavirus spread, it is important to investigate the impacts of the current crisis on India's renewable energy transition and air quality. India's economic slowdown is mainly compounded by the collapse of global oil prices and the erosion of global energy demand. A clean energy transition is a key step in enabling the integration of energy and climate. Millions in India are affected owing to fossil fuel pollution and the increasing climate heating that has led to inconceivable health impacts. This paper attempts to study the impact of COVID-19 on India's climate and renewable energy transitions through machine learning algorithms. India is observing a massive collapse in energy demand during the lockdown as its coal generation is suffering the worst part of the ongoing pandemic. During this current COVID-19 crisis, the renewable energy sector benefits from its competitive cost and the Indian government's must-run status to run generators based on renewable energy sources. In contrast to fossil fuel-based power plants, renewable energy sources are not exposed to the same supply chain disruptions in this current pandemic situation. India has the definite potential to surprise the global community and contribute to cost-effective decarbonization. Moreover, the country has a good chance of building more flexibility into the renewable energy sector to avoid an unstable future.

Association Between Air Pollution, Climate Change, and COVID-19 Pandemic: A Review of the Recent Scientific Evidence

Background: Recent studies indicated the possible relationship between climate change, environmental pollution, and Coronavirus Disease 2019 (COVID-19) pandemic. This study reviewed the effects of air pollution, climate parameters, and lockdown on the number of cases and deaths related to COVID-19. Methods: The present review was performed to determine the effects of weather and air pollution on the number of cases and deaths related to COVID-19 during the lockdown. Articles were collected by searching the existing online databases, such as PubMed, Science Direct, and Google Scholar, with no limitations on publication dates. Afterwards, this review focused on outdoor air pollution, including PM2.5, PM10, NO2, SO2, and O3, and weather conditions affecting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)/COVID-19. Results: Most reviewed investigations in the present study showed that exposure to air pollutants, particularly PM2.5 and NO2, is positively related to COVID-19 patients and mortality. Moreover, these studies showed that air pollution could be essential in transmitting COVID-19. Local meteorology plays a vital role in coronavirus spread and mortality. Temperature and humidity variables are negatively correlated with virus transmission. The evidence demonstrated that air pollution could lead to COVID-19 transmission. These results support decision-makers in curbing potential new outbreaks. Conclusions: Overall, in environmental perspective-based COVID-19 studies, efforts should be accelerated regarding effective policies for reducing human emissions, bringing about air pollution and weather change. Therefore, using clean and renewable energy sources will increase public health and environmental quality by improving global air quality.

Modeling the impact of the COVID‐19 outbreak on environment, health sector and energy market

The global outbreak of COVID‐19 disease had a significant impact on the entire globe. Such a notable public health event can be seen as a “black swan” that brings unpredictable and unusual forces into the economic context and that it could typically lead to a chain of adverse reactions and market disruptions. Hence, the purpose of this study is to examine how COVID‐19 affects the environment, health, and the oil and energy markets. To achieve this objective, we used daily data for several measures that refer to the environment, health, and oil and energy, for the first wave of the COVID‐19 pandemic (December 31, 2019–May 22, 2020). The variable integration mix led to the approach of the ARDL model, and the Granger causality test was also employed. These empirical techniques allowed us to examine the cointegration between variables and causal relationships. The econometric results of the ARDL models exhibited that the global new cases and new deaths of COVID‐19 have short and long‐term effects on the environment, the health sector, the oil, and energy measures. However, no significant causal connection was found between the pandemic and the environment, the health sector, or the oil and energy industry, according to the Granger causality test. The uniqueness of current approach consists in the investigation of pandemic impact on the health, environment, oil, and energy sector by applying the ARDL model that permits the analysis of cointegration both in the long run and in the short term. This study provides important insights for investors and policy makers.

The COVID-19 Pandemic: Quantification of Temporal Variations in Air Pollutants Before, During and Post the Lockdown in Jeddah City, Saudi Arabia

The government of Saudi Arabia imposed a strict lockdown between March and July 2020 to stop the spread of the coronavirus disease (COVID-19), which has led to a sharp decline in economic activities. The daily temporal variations of PM₁₀, PM_2.5, carbon monoxide (CO), nitrogen dioxide (NO₂), and ozone (O₃) were used to investigate the changes in air quality in response to COVID-19 lockdown control measures from January to December 2020 in Jeddah, Saudi Arabia. Meteorological parameters (wind speed, direction, temperature, relative humidity) were also analyzed to understand the changes during the pandemic. As a result, significant reductions in the concentrations of NO₂ (- 44.5%), CO (- 41.5%), and PM_2.5, PM₁₀ (- 29.5%, each) were measured in the capital city of Jeddah during the quarantine compared to the pre-lockdown average. In contrast, the lockdown caused a significant increase in O₃ by 41%. The changes in air quality during the COVID-19 outbreak by comparing the average pollutant concentration before lockdown (January 1-March 21, 2020) and the following 12 weeks during the partial lockdown (March 22-July 28, 2020), reveal a very significant decrease in pollutants, and consequently a significant improvement in air quality. Observed differences are attributable to changes in point source emissions associated with changes in localized activities, possibly related to decreased economic and industrial activity in response to the lockdown. The results of the present study show during the study period indicated a positive response to lockdown during the COVID-19 pandemic. Furthermore, the results can be used to establish future control measures and strategies to improve air quality.

Detection rate of SARS-CoV-2 RNA in relation to isolation time and environmental surface type

Severe acute respiratory syndrome coronavirus (SARS-CoV-2) causes environmental contamination via respiratory droplets and persists on contaminants and environmental surfaces for anywhere from a few hours to 6 days. Therefore, it is particularly important to understand the transmission and containment of SARS-CoV-2 on the surface of objects within isolated environments. In this study, 356 environmental surface samples were collected and 79 tested positive, with the highest contamination rate (56.96%) in the wood category (bedside tables, wood floors, and walls). This study revealed differences in the detection rates of environmental surfaces in hospitalized and discharged rooms of patients with confirmed COVID-19 in 2 isolated settings (A: p = 0.001; B: p = 0.505) and suggested that environmental contamination may be an important route of virus transmission, providing a reference to guide the enhancement of ventilation, the use of hotel isolation model, the advocacy of cotton masks, and the effective suppression of virus transmission.

Boundary conditions for exhaled airflow from a cough with a surgical or N95 mask

Wearing surgical or N95 masks is effective in reducing the infection risks of airborne infectious diseases. However, in the literature there are no detailed boundary conditions for airflow from a cough when a surgical or N95 mask is worn. These boundary conditions are essential for accurate prediction of exhaled particle dispersion by computational fluid dynamics (CFD). This study first constructed a coughing manikin with an exhalation system to simulate a cough from a person. The smoke visualization method was used to measure the airflow profile from a cough. To validate the setup of the coughing manikin, the results were compared with measured data from subject tests reported in the literature. The validated coughing manikin was then used to measure the airflow boundary conditions for a cough when a surgical mask was worn and when an N95 mask was worn, respectively. Finally, this study applied the developed airflow boundary conditions to calculate person-to-person particle transport from a cough when masks are worn. The calculated exhaled particle patterns agreed well with the smoke pattern in the visualization experiments. Furthermore, the calculated results indicated that, when the index person wore a surgical and a N95 mask, the total exposure of the receptor was reduced by 93.0% and 98.8%, respectively.

Analysis of Spatial and Temporal Changes of AQI in Wuhan City under the Urban Blockade of COVID-19 Pandemic

Wuhan was the primary city in the world to adopt lockdown measures during the Coronavirus Disease 2019 pandemic. The influence of the abrupt halt of human activities on the air quality of Wuhan is a subject of considerable attention. This study is based on air quality data from 21 monitoring stations in Wuhan from 2016 to 2020. The lag effect and seasonal factors of AQI were taken into account to analyze the changes in air quality in Wuhan under the influence of the pandemic blockade. The results showed the following during the city closure: (1) A lagging effect is observed in air quality changes, with the change point occurring on the 14th day after the city closure; (2) the air quality index is substantially decreased, demonstrating a reduction in spatial differences; (3) NO2, PM10, and PM2.5 significantly decreased whilst O3 increased, and SO2 and CO did not change significantly; (4) except for the insignificant changes in spatial differences of PM10, all pollutants demonstrated a changing pattern of decreasing geographical differences. This paper provides a reference for studying the influence of human activities on the natural environment.

Exploring the risk factors of COVID-19 Delta variant in the USA based on Bayesian spatio-temporal analysis

The transmission of coronavirus disease‐2019 (COVID‐19) epidemic is a global emergency, which is worsened by the genetic mutations of SARS‐CoV‐2. However, till date, few statistical studies have researched the COVID‐19 spread patterns in terms of the variant cases. Hence, this paper aims to explore the associated risk factors of Delta variant, the most contagious strain of COVID‐19. The study collected the state‐level COVID‐19 Delta variant cases in the United States during a 12‐week period and included potential environmental, socioeconomic, and public prevention factors as independent variables. Instead of regarding the covariate effects as constant, this paper proposes a flexible Bayesian hierarchical model with spatio‐temporally varying coefficients to account for data heterogeneity. The method enables us to cluster the states into distinctive groups based on the temporal trends of the coefficients and simultaneously identify significant risk factors for each cluster. The findings contribute novel insight into the dynamics of covariate effects on the COVID‐19 Delta variant over space and time, which could help the government develop targeted prevention measures for vulnerable regions based on the selected risk factors.

Assessing work resumption in hospitals during the COVID-19 epidemic in China using multiscale geographically weighted regression

The resumption of work and production is one of the key issues during the novel coronavirus (COVID-19) post-epidemic phase. We used location-based service data of mobile devices to assess the work resumption of 22,098 hospitals in mainland China. The multiscale influences of the determinants on work resumption in hospitals, including medical-service capacity, human movement, and epidemic severity, were examined using the multiscale geographically weighted regression technique. This study provides a novel insight into the assessment of work resumption in hospitals and its determinants, and is flexible to be extended to evaluate the work resumption of other industries. The findings can introduce helpful information for other countries to implement the strategies of work recovery during the post-epidemic phase.

Wealth, health, and beyond: Is COVID-19 less likely to spread in rich neighborhoods?

Since December 2019, the COVID-19 pandemic has quickly spread across the world. The traditional understanding of the relationship between wealth and the spread of contagious diseases is that similar to many precedent epidemics, the pandemic spread easily in poor neighborhoods in many countries. The environmental and socioeconomic implications of the COVID-19 pandemic are still poorly understood, thus this paper examines the relationship between neighborhood characteristics and the spread of the pandemic through a case study of Shenzhen, a Chinese megacity with many low-income rural migrants. The major finding is that wealthier and larger neighborhoods in Shenzhen were more likely to be infected in the first wave of the pandemic in 2020. This spread pattern is likely to result from China’s strict control to prevent the pandemic, human mobility, and demographic characteristics such as income. This finding reveals a new phenomenon that contrasts with the traditional understanding of the influence of wealth on the spread of epidemics. This paper enriches the understanding of the role of neighborhoods in the spread of the pandemic, and it has important public policy implications.

Measuring the mobility impact on the COVID-19 pandemic

This assessment aims at measuring the impact of different location mobility on the COVID-19 pandemic. Data over time and over the 27 Brazilian federations in 5 regions provided by Google's COVID-19 community mobility reports and classified by place categories (retail and recreation, grocery and pharmacy, parks, transit stations, workplaces, and residences) are autoregressed on the COVID-19 incidence in Brazil using generalized linear regressions to measure the aggregate dynamic impact of mobility on each socioeconomic category. The work provides a novel multicriteria approach for selecting the most appropriate estimation model in the context of this application. Estimations for the time gap between contagion and data disclosure for public authorities' decision-making, estimations regarding the propagation rate, and the marginal mobility contribution for each place category are also provided. We report the pandemic evolution on the dimensions of cases and a geostatistical analysis evaluating the most critical cities in Brazil based on optimized hotspots with a brief discussion on the effects of population density and the carnival.

Global Gene Expression and Docking Profiling of COVID-19 Infection

Coronavirus is an enclosed positive-sense RNA virus with club-like spikes protruding from its surface that causes acute respiratory infections in humans. Because it is considered a member of the complex pathogen group, it has been found to infect different host species and cause a variety of diseases. So far, it has been discovered that it may affect the immune, infection, and inflammatory systems, leading to the hypothesis that the immune and inflammatory systems (signaling pathways and components) fail to control infection, opening the door to look for potential targets primarily in these systems. The study’s main purpose is to identify highly overexpressed genes and their functional implications as a result of COVID-19 infection, as well as to investigate probable infections, inflammation, and immune systems to better understand the impact of coronavirus infection. We explored the genes and pathways mostly linked with infection, inflammation, and the immune systems using the datasets available for COVID-19 infection gene expression compendium. NFKBIA, FN1, FAP, KANK4, COMP, FAM101B, COL1A2, ANKRD1, TAGLN, SPARC, ADAM19, OLFM4, CXCL10/11, OASL, FOS, APOBEC3A, IFI44L, IFI27, IFIT1, RSAD2, NDUFS1, SRSF6, HECTD1, CBX3, and DDX17 are among the genes that may be impacted by infection, according to our findings. The functional changes are mainly associated with these pathways TNF, cytokine, NF—kB, TLR, TCR, BCR, Foxo, and TGF signaling pathways are among them and there are additional pathways such as hippo signaling, apoptosis, estrogen signaling, regulating pluropotency of stem cells, ErbB, Wnt, p53, cAMP, MAPK, PI3K—AKT, oxidative phosphorylation, protein processing in endoplasmic reticulum, prolactin signaling, adipocytokine, neurotrophine signaling, and longevity regulating pathways. Moreover, we have also explored the potential herbal drug (apigenin, quercetin, and resveratrol) targets for the top-rated genes based on the overall analysis where we observe that quercetin and resveratrol as most effective.

Decomposing PM2.5 air pollution rebounds in Northern China before COVID-19

China's efforts to curb air pollution have drastically reduced its concentrations of fine particulate matter (PM_2.5) from 2013 to 2018 nationwide. However, few studies examined the most recent changes in PM_2.5 concentrations and questioned if the previous PM_2.5 declining trend was sustained or not. This study took a deep dive into the PM_2.5 trend for 136 northern cities of China from 2015 to early 2020 before the coronavirus disease 2019 (the COVID-19 hereafter) crisis, using ground-based PM_2.5 data notably adjusted for a key measurement method change. We find that mean PM_2.5 concentrations in northern China increased by 5.16 µg/m³ in 2019, offsetting 80% of the large reduction achieved in 2018. The rebound was more significant during the heating seasons (HS; Nov to next Mar) over the 2 years: 10.49 µg/m³ from the 2017 HS to the 2019 HS. A multiple linear regression analysis further revealed that anthropogenic factors contributed to around 50% of the PM_2.5 rebound in northern cities of China. Such a significant role of anthropogenic factors in driving the rebound was tightly linked to deep cuts in PM_2.5 concentrations in the previous year, systemic adjustment of policy targets and mitigation measures by the government, and the rising marginal cost of these measures. These findings suggest the need to chart a more sustainable path for future PM_2.5 emission reductions, with an emphasis on key regions during key pollution periods.

"SARS-CoV-2 is transmitted by particulate air pollution": Misinterpretations of statistical data, skewed citation practices, and misuse of specific terminology spreading the misconception

In epidemiology, there are still outdated myths associated with the spread of respiratory infections. Recently, we have witnessed the origination of a new misconception, to the effect that SARS-CoV-2 is transmitted in the open air by way of particulate air pollution (atmospheric particulate matter (PM)). There is no evidence to support the idea behind this misconception. Nevertheless, more and more people are involved in animated debate and the number of studies concerning atmospheric PM as a carrier of SARS-CoV-2 is growing rapidly. In this work, the origin of the misconception was investigated, and the published papers which have contributed to the spread of this myth were analyzed. The results show that the following factors lie behind the origin and spread of the misconception: a) The specific terminology is not always clearly defined or consistently used by scientists. In particular, the terms 'particulate matter', 'atmospheric aerosol particles', 'air pollutants', and 'atmospheric aerosols' need to be clarified, and besides they are often equated to 'infectious aerosols', 'virus-bearing aerosols', 'bio-aerosols', 'virus-laden particles', 'respiratory aerosol/droplets', and 'droplet nuclei'. b) Authors misinterpret statistical data and information from other sources. Interpretation of the correlation between PM levels and the increasing incidence and severity of COVID-19 infection, is often changed from "PM may reflect the indirect action of certain atmospheric conditions that maintain infectious nuclei suspended for prolonged periods, parameters that also act on atmospheric pollutants" to "PM could cause an increase in infectious droplets/aerosols containing SARS-CoV-2." This is a dramatic change to the meaning. Moreover, it is often not taken into account that PM may reflect activities in areas with high population density and this population density at the same time contributes to the spread COVID-19. c) Skewed citation practices. Many authors cite a hypothetical conclusion from an original study, then other authors cite the papers of these authors as primary sources. This practice leads to the effect that there are many witnesses to a 'phenomenon' that did not ever occur. Thus, the terminology used in interdisciplinary communications should be more nuanced and defined precisely. Authors should be more careful when citing unconfirmed data (and hypotheses) as well as in interpreting statistical data so as to avoid confusion and spreading false information. This is especially important now in the era of the COVID-19 pandemic.

Ambient air pollution and COVID-19 risk: Evidence from 35 observational studies

BACKGROUND AND AIMS

The coronavirus disease 2019 (COVID-19) pandemic is severely threatening and challenging public health worldwide. Epidemiological studies focused on the influence of outdoor air pollution (AP) on COVID-19 risk have produced inconsistent conclusions. We aimed to quantitatively explore this association using a meta-analysis.

METHODS

We searched for studies related to outdoor AP and COVID-19 risk in the Embase, PubMed, and Web of Science databases. No language restriction was utilized. The search date entries were up to August 13, 2021. Pooled estimates and 95% confidence intervals (CIs) were obtained with random-/fixed-effects models. PROSPERO registration number: CRD42021244656.

RESULTS

A total of 35 articles were eligible for the meta-analysis. For long-term exposure to AP, COVID-19 incidence was positively associated with 1 μg/m³ increase in nitrogen dioxide (NO₂; effect size = 1.042, 95% CI 1.017-1.068), particulate matter with diameter <2.5 μm (PM_2.5; effect size = 1.056, 95% CI 1.039-1.072), and sulfur dioxide (SO₂; effect size = 1.071, 95% CI 1.002-1.145). The COVID-19 mortality was positively associated with 1 μg/m³ increase in nitrogen dioxide (NO₂; effect size = 1.034, 95% CI 1.006-1.063), PM_2.5 (effect size = 1.047, 95% CI 1.025-1.1071). For short-term exposure to air pollutants, COVID-19 incidence was positively associated with 1 unit increase in air quality index (effect size = 1.001, 95% CI 1.001-1.002), 1 μg/m³ increase NO₂ (effect size = 1.014, 95% CI 1.011-1.016), particulate matter with diameter <10 μm (PM₁₀; effect size = 1.005, 95% CI 1.003-1.008), PM_2.5 (effect size = 1.003, 95% CI 1.002-1.004), and SO₂ (effect size = 1.015, 95% CI 1.007-1.023).

CONCLUSIONS

Outdoor air pollutants are detrimental factors to COVID-19 outcomes. Measurements beneficial to reducing pollutant levels might also reduce the burden of the pandemic.

Novel Evidence Showing the Possible Effect of Environmental Variables on COVID-19 Spread

Coronavirus disease (COVID-19) remains a serious issue, and the role played by meteorological indicators in the process of virus spread has been a topic of academic discussion. Previous studies reached different conclusions due to inconsistent methods, disparate meteorological indicators, and specific time periods or regions. This manuscript is based on seven daily meteorological indicators in the NCEP reanalysis data set and COVID-19 data repository of Johns Hopkins University from 22 January 2020 to 1 June 2021. Results showed that worldwide average temperature and precipitable water (PW) had the strongest correlation (ρ > 0.9, p < 0.001) with the confirmed COVID-19 cases per day from 22 January to 31 August 2020. From 22 January to 31 August 2020, positive correlations were observed between the temperature/PW and confirmed COVID-19 cases/deaths in the northern hemisphere, whereas negative correlations were recorded in the southern hemisphere. From 1 September to 31 December 2020, the opposite results were observed. Correlations were weak throughout the near full year, and weak negative correlations were detected worldwide (|ρ| < 0.4, p ≤ 0.05); the lag time had no obvious effect. As the latitude increased, the temperature and PW of the maximum confirmed COVID-19 cases/deaths per day generally showed a decreasing trend; the 2020-year fitting functions of the response latitude pattern were verified by the 2021 data. Meteorological indicators, although not a decisive factor, may influence the virus spread by affecting the virus survival rates and enthusiasm of human activities. The temperature or PW threshold suitable for the spread of COVID-19 may increase as the latitude decreases.

Regression Analysis for COVID-19 Infections and Deaths Based on Food Access and Health Issues

COVID-19, or SARS-CoV-2, is considered as one of the greatest pandemics in our modern time. It affected people’s health, education, employment, the economy, tourism, and transportation systems. It will take a long time to recover from these effects and return people’s lives back to normal. The main objective of this study is to investigate the various factors in health and food access, and their spatial correlation and statistical association with COVID-19 spread. The minor aim is to explore regression models on examining COVID-19 spread with these variables. To address these objectives, we are studying the interrelation of various socio-economic factors that would help all humans to better prepare for the next pandemic. One of these critical factors is food access and food distribution as it could be high-risk population density places that are spreading the virus infections. More variables, such as income and people density, would influence the pandemic spread. In this study, we produced the spatial extent of COVID-19 cases with food outlets by using the spatial analysis method of geographic information systems. The methodology consisted of clustering techniques and overlaying the spatial extent mapping of the clusters of food outlets and the infected cases. Post-mapping, we analyzed these clusters’ proximity for any spatial variability, correlations between them, and their causal relationships. The quantitative analyses of the health issues and food access areas against COVID-19 infections and deaths were performed using machine learning regression techniques to understand the multi-variate factors. The results indicate a correlation between the dependent variables and independent variables with a Pearson correlation R²-score = 0.44% for COVID-19 cases and R² = 60% for COVID-19 deaths. The regression model with an R²-score of 0.60 would be useful to show the goodness of fit for COVID-19 deaths and the health issues and food access factors.

COVID-19 Lockdown and the Impact on Mobility, Air Quality, and Utility Consumption: A Case Study from Sharjah, United Arab Emirates

This study presents an analysis of the impact of COVID-19 lockdown on people’s mobility trends, air quality, and utility consumption in Sharjah, United Arab Emirates (UAE). Records of lockdown and subsequent easing measures, infection and vaccination rates, community mobility reports, remotely sensed and ground-based air quality data, and utility (electricity, water, and gas) consumption data were collected and analyzed in the study. The mobility trends reflected the stringency of the lockdown measures, increasing in the residential sector but decreasing in all other sectors. The data showed significant improvement in air quality corresponding to the lockdown measures in 2020 followed by gradual deterioration as the lockdown measures were eased. Electricity and water consumption increased in the residential sector during the lockdown; however, overall utility consumption did not show significant changes. The changes in mobility were correlated with the relevant air quality parameters, such as NO2, which in turn was highly correlated to O3. The study provides data and analysis to support future planning and response efforts in Sharjah. Furthermore, the methodology used in the study can be applied to assess the impacts of COVID-19 or similar events on people’s mobility, air quality and utility consumption at other geographical locations.

Release of tens of thousands of microfibers from discarded face masks under simulated environmental conditions

While mechanical abrasion by water and sediment is a primary and critical step in weathering process, the upsurge of discarded face masks will undoubtedly become a potential source of micro-/nanofibers owing to the spread of novel coronavirus (COVID-19) pneumonia. However, effects of mechanical abrasion on discarded face masks have neither been seriously addressed nor understood. Therefore, we conducted a simulated experiment to explore abundance, size distribution and morphology of microfibers released from common, surgical and face filtering piece (FFP) masks after mechanical abrasion. Technologies such as Fourier transform infrared spectrometry, fluorescence microscopy, scanning electron microscopy, and confocal laser scanning microscopy were used. Results showed that the abundance of released microfibers followed order of surgical > common > FFP in both water and sediment environments, and the maximum abundance reached 272 ± 12.49 items per square centimeter of mask (items·cm^-2) after sediment abrasion. Taking surgical mask for further investigation, the length of released fiber was observed to vary from 47.78 μm to 3.93 mm, and 72.41-89.58% of the total number of released microfibers fell in the range of 0.1-1 mm. However, microfibers with a very small length (1-100 μm) can occupy 0.09-13.59% of the total number of released fibers in sediment environment. The roughness of fiber surface after sediment abrasion was successively increased. Furthermore, the morphology analysis showed significant changes with countless cracks and many prominent protrusions on fiber surface after sediment abrasion. The cracks and protrusions may further accelerate mask decomposition, thereby potentially resulting in the adsorption of other contaminants and the release of self-containing chemicals. This study provides a valuable database of microfibers released from discarded face masks at the primary but critical stage, and further contributes knowledge on environmental impact of discarded personal protective equipment due to COVID-19.

Applications of GIS and geospatial analyses in COVID-19 research: A systematic review

Background: Geographic information science (GIS) has established itself as a distinct domain and incredibly useful whenever the research is related to geography, space, and other spatio-temporal dimensions. However, the scientific landscape on the integration of GIS in COVID-related studies is largely unknown. In this systematic review, we assessed the current evidence on the implementation of GIS and other geospatial tools in the COVID-19 pandemic.

Methods: We systematically retrieved and reviewed 79 research articles that either directly used GIS or other geospatial tools as part of their analysis. We grouped the identified papers under six broader thematic groups based on the objectives and research questions of the study- environmental, socio-economic, and cultural, public health, spatial transmission, computer-aided modeling, and data mining.

Results: The interdisciplinary nature of how geographic and spatial analysis was used in COVID-19 research was notable among the reviewed papers. Geospatial techniques, especially WebGIS, have even been widely used to visualize the data on a map and were critical to informing the public regarding the spread of the virus, especially during the early days of the pandemic. This review not only provided an overarching view on how GIS has been used in COVID-19 research so far but also concluded that geospatial analysis and technologies could be used in future public health emergencies along with statistical and other socio-economic modeling techniques. Our review also highlighted how scientific communities and policymakers could leverage GIS to extract useful information to make an informed decision in the future.

Conclusions:  Despite the limited applications of GIS in identifying the nature and spatio-temporal pattern of this raging pandemic, there are opportunities to utilize these techniques in handling the pandemic. The use of spatial analysis and GIS could significantly improve how we understand the pandemic as well as address the underserviced demographic groups and communities.

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

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

Less COVID-19 deaths in southern and insular Italy explained by forest bathing, Mediterranean environment, and antiviral plant volatile organic compounds

The coronavirus disease 2019 (COVID-19) is causing major sanitary and socioeconomic issues, yet some locations are less impacted than others. While densely populated areas are likely to favor viral transmission, we hypothesize that other environmental factors could explain lower cases in some areas. We studied COVID-19 impact and population statistics in highly forested Mediterranean Italian regions versus some northern regions where the amount of trees per capita is much lower. We also evaluated the affinity of Mediterranean plant-emitted volatile organic compounds (VOCs) isoprene, α-pinene, linalool and limonene for COVID-19 protein targets by molecular docking modeling. Results show that while mean death number increased about 4 times from 2020 to 2021, the percentage of deaths per population (0.06-0.10%) was lower in the greener Mediterranean regions such as Sardinia, Calabria and Basilica versus northern regions with low forest coverage, such as Lombardy (0.33%) and Emilia Romagna (0.29%). Data also show that the pandemic severity cannot be explained solely by population density. Modeling reveals that plant organic compounds could bind and interfere with the complex formed by the receptor binding domain of the coronavirus spike protein with the human cell receptor. Overall, our findings are likely explained by sea proximity and mild climate, Mediterranean diet and the abundance of non-deciduous Mediterranean plants which emit immunomodulatory and antiviral compounds. Potential implications include 'forest bathing' as a therapeutic practice, designing nasal sprays containing plant volatile organic compounds, and preserving and increasing forest coverage.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s10311-021-01309-5.

COVID-19 PCR test performance on samples stored at ambient temperature

The WHO-named Coronavirus Disease 2019 (COVID-19) infection had become a pandemic within a short time period since it was detected in Wuhan. The outbreak required the screening of millions of samples daily and overwhelmed diagnostic laboratories worldwide. During this pandemic, the handling of patient specimens according to the universal guidelines was extremely difficult as the WHO, CDC and ECDC required cold chain compliance during transport and storage of the swab samples. The aim of this study was to compare the effects of two different storage conditions on the COVID-19 real-time PCR assay on 30 positive nasopharyngeal and/or oropharyngeal samples stored at both ambient temperature (22 ± 2 °C) and +4 °C. The results revealed that all the samples stored at ambient temperature remain PCR positive for at least six days without any false-negative result. In conclusion, transporting and storing these types of swab samples at ambient temperature for six days under resource-limited conditions during the COVID-19 pandemics are acceptable.

The casual effects of COVID-19 lockdown on air quality and short-term health impacts in China

The outbreak of coronavirus (COVID-19) has forced China to lockdown many cities and restrict transportation, industrial, and social activities. This provides a great opportunity to look at the impacts of pandemic quarantine on air quality and premature death due to exposure to air pollution. In this study, we applied the difference-in-differences (DID) model to quantify the casual impacts of COVID-19 lockdown on air quality at 278 cities across China. A widely used exposure-response function was further utilized to estimate the short-term health impacts associated with changes in PM_2.5 due to lockdown. Results show that lockdown has caused drastic reduction in air pollution level in terms of all criteria pollutants except ozone. On average, concentrations of PM_2.5, PM₁₀, NO₂, SO₂ and CO are estimated to drop by 14.3 μg/m³, 22.2 μg/m³, 17.7 μg/m³, 2.9 μg/m³, and 0.18 mg/m³ as the result of lockdown. Cities with more confirmed cases of COVID-19 are related to stronger responses in air quality, despite that similar lockdown measures were implemented by the local governments. The improvement of air quality caused by COVID-19 lockdown in northern cities is found to be smaller than that of southern cities. Avoided premature death associated with PM_2.5 exposures over the 278 cities was estimated to be 50.8 thousand. Our results re-emphasize the effectiveness of emission controls on air quality and associated health impacts. The high cost of lockdown, still high level of air pollution during lockdown and smaller effects in northern cities implies that source-specific mitigation policies are needed for continuous and sustainable reduction of air pollution.

What determines urban resilience against COVID-19: City size or governance capacity?

This study analyzed the effects of urban governance and city size on COVID-19 prevention and control measures. Based on real-time data in 276 prefecture-level Chinese cities, we used the ordinary least squares plus robust standard error strategy. It was found that: (1) despite the non-significant effect of city size, urban governance capacity was an important factor affecting the prevention and control of the COVID-19 pandemic; urban governance capacity was particularly significant in the late control of the pandemic, but not significant in the early prevention; for every unit increase of urban governance capacity, the number of recovered COVID-19 cases per capita increased by 2.4%. Moreover, (2) the influence mechanism of anti-pandemic measures in cities could be divided into the workforce, financial, and material effects, and their contribution rates were 26.15%, 32.55%, and 37.20%, respectively; namely, the effective/timely assistance from Chinese central government regarding the workforce, financial, and material resources in key pandemic areas and nationwide played a major role in pandemic control. Additionally, (3) cities with a high level of smart city construction were more capable of enhancing the pandemic prevention and control effect, indicating that smart city construction is conducive to enhanced coping with public crises.

An analysis on the effect of coronavirus (COVID-19) pandemic movement control order (MCOS) on the solid waste generation in Peninsular Malaysia

This study intends to deal with the environmental consequences of the COVID-19 pandemic in Malaysia, by providing a summary of the effects of COVID-19 on municipal solid waste (MSW). In this analysis, the data on domestic waste collection were collected from the Solid Waste Management and Public Cleaning Corporation (SWCorp) from 1 January 2020 to 31 December 2020 to evaluate the relative changes in MSW percentage via a waste weighing method. The data consisted of the cumulative tonnage of MSW for every local authority in Peninsular Malaysia and was classified according to MCO phases; before the MCO, during the MCO, during the conditional MCO (CMCO) and during the recovery MCO (RMCO) phases. The results indicated that the enforcement of the early MCO showed a positive effect by decreasing the volume of MSW. This decrease was noted across 41 local authorities, which accounts for 87.23% of Peninsular Malaysia. However, the amount of MSW began to increase again when the MCO reached the conditional and recovery stages. From this, it can be concluded that the implementation of the MCO, in its various incarnations, has shown us that our lifestyles can have a harmful impact on our environment. While the pandemic was still spreading and limitations were still in place in Malaysia, local governments and waste management companies had to quickly alter their waste management systems and procedures. The current circumstance allows us to rethink our social and economic structures while improving environmental and social inclusion.

What county-level factors influence COVID-19 incidence in the United States? Findings from the first wave of the pandemic

Effective control of the COVID-19 pandemic via appropriate management of the built environment is an urgent issue. This study develops a research framework to explore the relationship between COVID-19 incidence and influential factors related to protection of vulnerable populations, intervention in transmission pathways, and provision of healthcare resources. Relevant data for regression analysis and structural equation modeling is collected during the first wave of the pandemic in the United States, from counties with over 100 confirmed cases. In addition to confirming certain factors found in the existing literature, we uncover six new factors significantly associated with COVID-19 incidence. Furthermore, incidence during the lockdown is found to significantly affect incidence after the reopening, highlighting that timely quarantining and treating of patients is essential to avoid the snowballing transmission over time. These findings suggest ways to mitigate the negative effects of subsequent waves of the pandemic, such as special attention of infection prevention in neighborhoods with unsanitary and overcrowded housing, minimization of social activities organized by neighborhood associations, and contactless home delivery service of healthy food. Also worth noting is the need to provide support to people less capable of complying with the stay-at-home order because of their occupations or socio-economic disadvantage.

Characterizing the interruption-recovery patterns of urban air pollution under the COVID-19 lockdown in China

The COVID-19 pandemic provides an opportunity to study the effects of urban lockdown policies on the variation in pollutant concentrations and to characterize the recovery patterns of urban air pollution under the interruption of COVID-19 lockdown policies. In this paper, interruption-recovery models and regression discontinuity design were developed to characterize air pollution interruption-recovery patterns and analyze environmental impacts of the COVID-19 lockdown, using air pollution data from four Chinese metropolises (i.e., Shanghai, Wuhan, Tianjin, and Guangzhou). The results revealed the air pollutant interruption-recovery curve represented by the three lockdown response periods (Level I, Level II and Level III) during COVID-19. The curve decreased during Level I (A 25.3%-48.8% drop in the concentration of NO₂ has been observed in the four metropolises compared with the same period in 2018-2019.), then recovered around reopening, but decreased again during Level III. Moreover, the interruption-recovery curve of the year-on-year air pollution difference suggests a process of first decreasing during Level I and gradually recovering to a new equilibrium during Level III (e.g., the unit cumulative difference of NO₂ mass concentrations in Shanghai was 21.7, 22.5, 11.3 (μg/m³) during Level I, II, and III and other metropolises shared similar results). Our findings reveal general trends in the air quality externality of different lockdown policies, hence could provide valuable insights into air pollutant interruption-recovery patterns and clear scientific guides for policymakers to estimate the effect of different lockdown policies on urban air quality.

COVID-19 and urban spaces: A new integrated CFD approach for public health opportunities

Safe urban public spaces are vital owing to their impacts on public health, especially during pandemics such as the ongoing COVID-19 pandemic. Urban public spaces and urbanscape elements must be designed with the risk of viral transmission in mind. This work therefore examines how the design of urbanscape elements can be revisited to control COVID-19 transmission dynamics. Nine proposed models of urban public seating were thus presented and assessed using a transient three-dimensional computational fluid dynamics (CFD) model, with the Eulerian-Lagrangian method and discrete phase model (DPM). The proposed seating models were evaluated by their impact on the normalized air velocity, the diameter of coughing droplets, and deposition fraction. Each of the proposed models demonstrated an increase in the normalized velocity, and a decrease in the deposition fraction by >29%. Diagonal cross linear and curved triangle configurations demonstrated an improved airflow momentum and turbulent flow, which decreased the droplets deposition fraction by 68%, thus providing an improved, healthier urban public seating option.

Environmental spatial heterogeneity of the impacts of COVID-19 on the top-20 metropolitan cities of Asia-Pacific

This study investigated the environmental spatial heterogeneity of novel coronavirus (COVID-19) and spatial and temporal changes among the top-20 metropolitan cities of the Asia-Pacific. Remote sensing-based assessment is performed to analyze before and during the lockdown amid COVID-19 lockdown in the cities. Air pollution and mobility data of each city (Bangkok, Beijing, Busan, Dhaka, Delhi, Ho Chi Minh, Hong Kong, Karachi, Mumbai, Seoul, Shanghai, Singapore, Tokyo, Wuhan, and few others) have been collected and analyzed for 2019 and 2020. Results indicated that almost every city was impacted positively regarding environmental emissions and visible reduction were found in Aerosol Optical Depth (AOD), sulfur dioxide (SO2), carbon monoxide (CO), and nitrogen dioxide (NO2) concentrations before and during lockdown periods of 2020 as compared to those of 2019. The highest NO2 emission reduction (~ 50%) was recorded in Wuhan city during the lockdown of 2020. AOD was highest in Beijing and lowest in Colombo (< 10%). Overall, 90% movement was reduced till mid-April, 2020. A 98% reduction in mobility was recorded in Delhi, Seoul, and Wuhan. This analysis suggests that smart mobility and partial shutdown policies could be developed to reduce environmental pollutions in the region. Wuhan city is one of the benchmarks and can be replicated for the rest of the Asian cities wherever applicable.

Association between environmental factors and COVID-19 in Shanghai, China

The outbreak of coronavirus disease 2019 (COVID-19) continues to spread worldwide and has led to recession, rising unemployment, and the collapse of the health-care system. The aim of this study was to explore the exposure-response relationship between daily confirmed COVID-19 cases and environmental factors. We used a time-series generalized additive model (GAM) to investigate the short-term association between COVID-19 and environmental factors by using daily meteorological elements, air pollutant concentration, and daily confirmed COVID-19 cases from January 21, 2020, to February 29, 2020, in Shanghai, China. We observed significant negative associations between daily confirmed COVID-19 cases and mean temperature (Tave), temperature humidity index (THI), and index of wind effect (K), whereas air quality index (AQI), PM2.5, PM10 NO2, and SO2 were significantly associated with the increase in daily confirmed COVID-19 cases. A 1 °C increase in Tave, one-unit increase in THI, and 10-unit increase in K (lag 0-7 days) were associated with 4.7, 1.8, and 1.6% decrease in daily confirmed cases, respectively. Daily Tave, THI, K, PM10, and SO2 had significant lag and persistence (lag 0-7 days), whereas the lag and persistence of AQI, PM2.5, and NO2 were significant at both lag 0-7 and 0-14 days. A 10-μg/m3 increase in PM10 and 1-μg/m3 increase in SO2 was associated with 13.9 and 5.7% increase in daily confirmed cases at lag 0-7 days, respectively, whereas a 10-unit increase in AQI and a 10-μg/m3 increase in PM2.5 and NO2 were associated with 7.9, 7.8, and 10.1% increase in daily confirmed cases at lag 0-14 days, respectively. Our findings have important implications for public health in the city of Shanghai.