The link between COVID-19 mortality and PM2.5 emissions in rural and medium-size municipalities considering population density, dust events, and wind speed

An explicit review and proposal of an integrated framework system to mitigate the baffling complexities induced by road dust-associated contaminants

Road dust-associated contaminants (RD-AC) are gradually becoming a much thornier problem, as their monotonous correlations render them carcinogenic, mutagenic, and teratogenic. While many studies have examined the harmful effects of road dust on both humans and the environment, few studies have considered the co-exposure risk and gradient outcomes given the spatial extent of RD-AC. In this spirit, this paper presents in-depth elucidation into the baffling complexities induced by both major and emerging contaminants of road dust through a panorama-to-profile up-to-date review of diverse studies unified by the goal of advancing innovative methods to mitigate these contaminants. The paper thoroughly explores the correlations between RD-AC and provides insights to understand their potential in dispersing saprotrophic microorganisms. It also explores emerging challenges and proposes a novel integrated framework system aimed at thermally inactivating viruses and other pathogenic micro-organisms commingled with RD-AC. The main findings are: (i) the co-exposure risk of both major and emerging contaminants add another layer of complexity, highlighting the need for more holistic framework strategies, given the geospatial morphology of these contaminants; (ii) road dust contaminants show great potential for extended prevalence and severity of viral particles pollution; (iii) increasing trend of environmentally persistent free radicals (EPFRs) in road dust, with studies conducted solely in China thus far; and (iv) substantial hurdle exists in acquiring data concerning acute procedural distress and long-term co-exposure risk to RD-ACs. Given the baffling complexities of RD-ACs, co-exposure risk and the need for innovative mitigation strategies, the study underscore the significance of establishing robust systems for deep road dust contaminants control and future research efforts while recognizing the interconnectivity within the contaminants associated with road dust.

Spatiotemporally continuous PM2.5 dataset in the Mekong River Basin from 2015 to 2022 using a stacking model

With the potential to cause millions of deaths, PM2.5 pollution has become a global concern. In Southeast Asia, the Mekong River Basin (MRB) is experiencing heavy PM2.5 pollution and the existing PM2.5 studies in the MRB are limited in terms of accuracy and spatiotemporal coverage. To achieve high-accuracy and long-term PM2.5 monitoring of the MRB, fused aerosol optical depth (AOD) data and multi-source auxiliary data are fed into a stacking model to estimate PM2.5 concentrations. The proposed stacking model takes advantage of convolutional neural network (CNN) and Light Gradient Boosting Machine (LightGBM) models and can well represent the spatiotemporal heterogeneity of the PM2.5-AOD relationship. In the cross-validation (CV), comparison with CNN and LightGBM models shows that the stacking model can better suppress overfitting, with a higher coefficient of determination (R2) of 0.92, a lower root mean square error (RMSE) of 5.58 μg/m3, and a lower mean absolute error (MAE) of 3.44 μg/m3. For the first time, the high-accuracy PM2.5 dataset reveals spatially and temporally continuous PM2.5 pollution and variations in the MRB from 2015 to 2022. Moreover, the spatiotemporal variations of annual and monthly PM2.5 pollution are also investigated at the regional and national scales. The dataset will contribute to the analysis of the causes of PM2.5 pollution and the development of mitigation policies in the MRB.

A new efficiency calibration methodology for different atmospheric filter geometries by using coaxial Ge detectors

The study of the different pollutants present in atmospheric aerosols such as trace elements and radionuclides is essential to assess the air quality. To analyze the particulate matter (PM), atmospheric filters with different dimensions and geometries (rectangular, circular, slotted, and square filters) are usually employed. Regarding the pollutants existing in atmospheric aerosols, radionuclides are usually analyzed due to their multiple applications such as either in the environmental radiological control or as tracers of atmospheric processes. Therefore, this study aims to develop a new and general methodology to calibrate in efficiency coaxial Ge detectors to properly determine radionuclides present in the PM by gamma-ray spectrometry for several filter types. For this, granular certified reference materials (CRM) containing only natural radionuclides (238U-series, 232Th-series, and 40 K) were selected. Several granular solid CRMs were chosen allowing us to reproduce the same PM deposition geometry and to assure the homogeneity of the added CRMs. These are the main advantages in relation to the typical methods that use liquid CRMs. Furthermore, for filters whose surfaces are relatively large, they were cut in several pieces and placed one on top of the other, achieving the same geometry than the PM deposited onto the filter. Then, the experimental full-energy peak efficiencies (FEPEs) were obtained for each energy of interest (Eγ) and they were fitted versus Eγ, finding a general FEPE function for each filter type. Finally, this methodology was validated for both natural and artificial radionuclides (from 46 to 1332 keV) by using different filter types employed in proficiency test exercises, obtaining |zscore|< 2 for all cases.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11869-023-01336-x.

Changes in temporal pattern and spatial distribution of environmental pollutants in 8 Asian countries owing to COVID-19 pandemic

This study investigated the changes in air pollutant's concentration, spatio-temporal distribution and sensitivity of changes in air pollutant's concentration during pre and post COVID-19 outbreak. We employed Google Earth Engine Platform to access remote sensing datasets of air pollutants across Asian continent. Air pollution and cumulative confirmed-COVID cases data of Asian countries (Afghanistan, Bangladesh, China, India, Iran, Iraq, Pakistan, and Saudi Arabia) have been collected and analyzed for 2019 and 2020. The results indicate that aerosol index (AI) and nitrogen dioxide (NO₂) is significantly reduced during COVID outbreak i.e. in year 2020. In addition, we found significantly positive (P < 0.05, 95% confidence interval, two-tailed) correlation between changes in AI and NO₂ concentration for net active-COVID case increment in almost each country. For other atmospheric gases i.e. carbon monoxide (CO), formaldehyde (HCHO), ozone (O₃), and Sulfur dioxide (SO₂), insignificant and/or significant negative correlation is also observed. These results suggest that the atmospheric concentration of AI and NO₂ are good indicators of human activities. Furthermore, the changes in O₃ shows significantly negative correlation for net active-COVID case increment. In conclusion, we observed significant positive environmental impact of COVID-19 restrictions in Asia. This study would help and assist environmentalist and policy makers in restraining air pollution by implementing efficient restrictions on human activities with minimal economic loss.

A simple and precise methodology to determine particulate matter mass in atmospheric filters; validation and application cases

In the past decades, particulate matter (PM) measurements have been used extensively in atmospheric sciences, as it allows studying the evolution of tracers for different atmospheric processes and the effects of atmospheric pollution on human health. However, measuring PM mass requires a constant control of the laboratory conditions due to its capacity to absorb humidity. For this reason, this study was focused on developing a novel, simple and precise methodology to determine the corrections of the filter mass due to humidity changes. The control and corrections are possible using a "control filter", which is always adapted to the environmental conditions of the laboratory. To check the consistency of this method, it was proved that the mass of any problem filter and that of the control filter behave in a very similar way. This allows quantifying the mass changes of any problem filter by using the control filter, where the problem filters and the control filter must have the same chemical composition and dimensions. To validate this methodology, a comparison was made between the methodology proposed in this study (Method-1) and the one proposed by the EPA (Method-2), which is generally applied. The particulate matter mass (m) was obtained for a problem filter for different weights, achieving similar values using both methods. However, Method-1 still provided reliable mass measurements for relative humidities very different from 50%, even as low as 18%. It was also proved that the adsorption or loss of water by the particulate matter can be neglected, since m is much smaller than the blank filter mass. Method-1 was also employed in several samplings carried out using three PM₁₀ samplers to determine contaminants, such as ⁷Be and ²¹⁰Pb, obtaining a good agreement between all particulate masses and activities measured by the three samplers for all samplings.

Analysis of airborne sputum droplets flow dynamic behaviors under different ambient conditions and aerosol size effects

The coronavirus (COVID-19) is becoming more threatening with the emergence of new mutations. New virus transmission and infection processes remain challenging and re-examinations of proper protection methods are urgently needed. From fluid dynamic viewpoint, the transmission of virus-carrying droplets and aerosols is one key to understanding the virus-transmission mechanisms. This study shows virus transmission by incorporating flow-evaporation model into the Navier-Stokes equation to describe the group of airborne sputum droplets exhaled under Rosin-Rammler distribution. Solid components and humidity field evolution are incorporated in describing droplet and ambient conditions. The numerical model is solved by an inhouse code using advection-diffusion equation for the temperature field and the humidity field, discretized by applying the total-variation diminishing Runge-Kutta method. The results of this study are presented in detail to show the different trends under various ambient conditions and to reveal the major viral-transmission routes as a function of droplet size.

COVID-19 in the U.S. during pre-vaccination period: Shifting impact of sociodemographic factors and air pollution

Pandemic "wave" usually refers to the rise and fall of the infections with time, however, for a large country, the variations due to geographical location could be considerable. In this work, we investigated COVID-19 infection and fatality across the U.S. during the pandemic waves in the pre-vaccination period (January 2020-December 2020). Focusing on counties with a population ≥100,000, the data from the entire period were first segmented into two equal phases roughly corresponding to the first pandemic wave and subsequent surge, and each phase was further divided into two zones based on infection rate. We studied the potential influences of six sociodemographic variables (population density, age, poverty, education, and percentage of Hispanic and African American population) and four air pollutants (PM2.5, NO2, SO2, and O3) on the differences in infection and fatality observed among different phases and zones. We noticed a distinct difference in the overall impact of COVID-19 between the two phases of the pre-vaccination period with a substantial decrease in the fatality in the second phase despite an increase in the infection. Analysis using log-linear regression modeling further revealed a shift in the impact of several risk factors considered in this study. For example, population density and lesser education were found to be significant for infection during the first phase of the pandemic alone. Furthermore, population density and lesser education along with poverty and NO2 level had a significant contribution to fatality during the first phase of the pandemic, while age over 65 years was important in both phases. Interestingly, the effects of many of these factors were found to be significant only in the zones with higher infection rates. Our findings indicate that the impacts of several well-known sociodemographic and environmental risk factors for COVID-19 are not constant throughout the course of the pandemic, and therefore, careful considerations should be made about their role when developing preventative and mitigative measures.

Impacts of exposure to air pollution, radon and climate drivers on the COVID-19 pandemic in Bucharest, Romania: A time series study

During the ongoing global COVID-19 pandemic disease, like several countries, Romania experienced a multiwaves pattern over more than two years. The spreading pattern of SARS-CoV-2 pathogens in the Bucharest, capital of Romania is a multi-factorial process involving among other factors outdoor environmental variables and viral inactivation. Through descriptive statistics and cross-correlation analysis applied to daily time series of observational and geospatial data, this study aims to evaluate the synergy of COVID-19 incidence and lethality with air pollution and radon under different climate conditions, which may exacerbate the coronavirus' effect on human health. During the entire analyzed period 1 January 2020-21 December 2021, for each of the four COVID-19 waves were recorded different anomalous anticyclonic synoptic meteorological patterns in the mid-troposphere, and favorable stability conditions during fall-early winter seasons for COVID-19 disease fast-spreading, mostly during the second, and the fourth waves. As the temporal pattern of airborne SARS-CoV-2 and its mutagen variants is affected by seasonal variability of the main air pollutants and climate parameters, this paper found: 1) the daily outdoor exposures to air pollutants (particulate matter PM2.5 and PM10, nitrogen dioxide-NO₂, sulfur dioxide-SO₂, carbon monoxide-CO) and radon - ²²²Rn, are directly correlated with the daily COVID-19 incidence and mortality, and may contribute to the spread and the severity of the pandemic; 2) the daily ground ozone-O₃ levels, air temperature, Planetary Boundary Layer height, and surface solar irradiance are anticorrelated with the daily new COVID-19 incidence and deaths, averageingful for spring-summer periods. Outdoor exposure to ambient air pollution associated with radon is a non-negligible driver of COVID-19 transmission in large metropolitan areas, and climate variables are risk factors in spreading the viral infection. The findings of this study provide useful information for public health authorities and decision-makers to develop future pandemic diseases strategies in high polluted metropolitan environments.

Factors Modulating COVID-19: A Mechanistic Understanding Based on the Adverse Outcome Pathway Framework

Addressing factors modulating COVID-19 is crucial since abundant clinical evidence shows that outcomes are markedly heterogeneous between patients. This requires identifying the factors and understanding how they mechanistically influence COVID-19. Here, we describe how eleven selected factors (age, sex, genetic factors, lipid disorders, heart failure, gut dysbiosis, diet, vitamin D deficiency, air pollution and exposure to chemicals) influence COVID-19 by applying the Adverse Outcome Pathway (AOP), which is well-established in regulatory toxicology. This framework aims to model the sequence of events leading to an adverse health outcome. Several linear AOPs depicting pathways from the binding of the virus to ACE2 up to clinical outcomes observed in COVID-19 have been developed and integrated into a network offering a unique overview of the mechanisms underlying the disease. As SARS-CoV-2 infectibility and ACE2 activity are the major starting points and inflammatory response is central in the development of COVID-19, we evaluated how those eleven intrinsic and extrinsic factors modulate those processes impacting clinical outcomes. Applying this AOP-aligned approach enables the identification of current knowledge gaps orientating for further research and allows to propose biomarkers to identify of high-risk patients. This approach also facilitates expertise synergy from different disciplines to address public health issues.

Exposure Detection Applications Acceptance: The Case of COVID-19

The pandemic's context is rife with numerous dangerous threats and high fear levels, influencing human decision-making. Such characteristics are identified by investigating the acceptance of exposure detection apps from the technology acceptance model (TAM) perspective. This study purposed a model to investigate protection technology acceptance, specifically exposure detection apps in the context of COVID-19. Quantitative study approach and a cross-section design targeted 586 participants from Saudi Arabia. As the study model is complex, the study hypotheses were analysed using the structural equation modelling-partial least squares (SEM-PLS3) approach. The findings support the entire model hypothesis except the link between social media awareness and exposure detection apps' intention. Mediation of COVID-19 anxiety and influence was confirmed as well. The current paper contributes to the technologies acceptance domain by developing a context-driven model comprising the major pandemic characteristics that lead to various patterns of technology acceptance. This study also fills the literature gap regarding mediating effects of social influence and COVID-19 anxiety in the relationship between trust in government and exposure detection apps implementation, and between COVID-19 anxiety and exposure detection apps implementation, respectively. The results may assist government agencies, health policymakers, and health organisations in the wide world and specifically Saudi Arabia, in their attempts to contain the COVID-19 pandemic spread.

Analysis of Particulate Matter Concentration Changes before, during, and Post COVID-19 Lockdown: A Case Study from Victoria, Mexico

The lockdown measures implemented due to the SARS-CoV-2 pandemic to reduce the epidemic curve, in most cases, have had a positive impact on air quality indices. Our study describes the changes in the concentration levels of PM2.5 and PM10 during the lockdown and post-lockdown in Victoria, Mexico, considering the following periods: before the lockdown (BL) from 16 February to 14 March, during the lockdown (DL) from 15 March to 2 May, and in the partial lockdown (PL) from 3 May to 6 June. When comparing the DL period of 2019 and 2020, we document a reduction in the average concentration of PM2.5 and PM10 of −55.56% and −55.17%, respectively. Moreover, we note a decrease of −53.57% for PM2.5 and −51.61% for PM10 in the PL period. When contrasting the average concentration between the DL periods of 2020 and 2021, an increase of 91.67% for PM2.5 and 100.00% for PM10 was identified. Furthermore, in the PL periods of 2020 and 2021, an increase of 38.46% and 31.33% was observed for PM2.5 and PM10, respectively. On the other hand, when comparing the concentrations of PM2.5 in the three periods of 2020, we found a decrease between BL and DL of −50.00%, between BL and PL a decrease of −45.83%, and an increase of 8.33% between DL and PL. In the case of PM10, a decrease of −48.00% between BL and DL, −40.00% between BL and PL, and an increase of 15.38% between the DL and PL periods were observed. In addition, we performed a non-parametric statistical analysis, where a significant statistical difference was found between the DL-2020 and DL-2019 pairs (x2 = 1.204) and between the DL-2021 and DL-2019 pairs (x2 = 0.372), with a p<0.000 for PM2.5, and the contrast between pairs of PM10 (DL) showed a significant difference between all pairs with p<0.01.

Comparative Analysis of Age, Sex, and Viral Load in Outpatients during the Four Waves of SARS-CoV-2 in A Mexican Medium-Sized City

Governments have implemented measures to minimize SARS-CoV-2 spread. However, these measures were relaxed, and the appearance of new variants has prompted periods of high contagion known as waves. In Mexico, four waves distributed between July and August 2020, January and February 2021, August and September 2021, and January and February 2022 have appeared. Current health policies discourage mass sampling, preferring to focus on the corrective treatment of severe cases. Outpatients are only advised to undergo brief voluntary confinement and symptomatic treatment, with no follow-up. Therefore, the present study aimed to analyze sex, age, and viral load in outpatients during the four waves in a medium-sized city in Mexico. For each wave, the date of peak contagion was identified, and data were collected within ±15 days. In this regard, data from 916 patients (434 men and 482 women) were analyzed. The age range of positive patients (37-45 years) presented a higher frequency during the first and third waves, while 28-36 years was the most frequent age range during the second and fourth waves, while the viral load values were significantly higher, for both sexes, during the fourth wave. Obtained data of COVID-19 prevalence in population segments can be used for decision-making in the design of effective public health policies.

Use of Low-Cost Sensors to Characterize Occupational Exposure to PM2.5 Concentrations Inside an Industrial Facility in Santa Ana, CA: Results from a Worker- and Community-Led Pilot Study

PM2.5 is an air contaminant that has been widely associated with adverse respiratory and cardiovascular health, leading to increased hospital admissions and mortality. Following concerns reported by workers at an industrial facility located in Santa Ana, California, workers and community leaders collaborated with experts in the development of an air monitoring pilot study to measure PM2.5 concentrations to which employees and local residents are exposed during factory operating hours. To detect PM2.5, participants wore government-validated AtmoTube Pro personal air monitoring devices during three separate workdays (5 AM–1:30 PM) in August 2021. Results demonstrated a mean PM2.5 level inside the facility of 112.3 µg/m3, nearly seven-times greater than outdoors (17.3 µg/m3). Of the eight workers who wore personal indoor sampling devices, five showed measurements over 100 μg/m3. Welding-related activity inside the facility resulted in the greatest PM2.5 concentrations. This study demonstrates the utility of using low-cost air quality sensors combined with employee knowledge and participation for the investigation of workplace air pollution exposure as well as facilitation of greater health-related awareness, education, and empowerment among workers and community members. Results also underscore the need for basic measures of indoor air pollution control paired with ongoing air monitoring within the Santa Ana facility, and the importance of future air monitoring studies aimed at industrial facilities.

Is the epithelial barrier hypothesis the key to understanding the higher incidence and excess mortality during COVID-19 pandemic? The case of Northern Italy

The high incidence and increased mortality of COVID-19 make Italy among the most impacted countries by SARS-CoV-2 outbreak. In the beginning of the pandemic, Northern regions accounted for 40% of cases and 45% of deaths from COVID-19 in Italy. Several factors have been suggested to explain the higher incidence and excess mortality from COVID-19 in these regions. It is noticed that Northern Italian regions, and particularly the cities in Po Valley, are the areas with the highest air pollution due to commercial vehicle traffic, industry and a stagnant meteorological condition, with one of the highest levels in Italy and Europe of fine particulate matter 2.5 micron or smaller in size (PM2.5). PM2.5, the major environmental pollutant deriving mainly by factory and automobile exhaust emissions and coal combustion, increases the expression of angiotensin-converting enzyme 2, the epithelial cell entry receptor for SARS-CoV-2, and thus increase the susceptibility to this virus. The epithelial barrier hypothesis proposes that many diverse diseases may rise from the disruption of epithelial barrier of skin, respiratory tract and gastrointestinal system, including allergic diseases, metabolic and autoimmune diseases, and chronic neuropsychiatric conditions. There is evidence of a close correlation between air pollution and airway epithelial barrier dysfunction. Air pollution, causing lung epithelial barrier dysfunction, may contribute to local chronic inflammation, microbiome dysbiosis and impaired antiviral immune response against SARS-CoV-2, all of which contribute to the high incidence and excess mortality from COVID-19. In addition, air pollution and epithelial barrier dysfunction contribute also to the higher prevalence of several comorbidities of COVID-19, such as diabetes, chronic obstructive pulmonary disease and obesity, which have been identified as risk factors for mortality of COVID-19. In this article, on the basis of epidemiological and environmental monitoring data in Northern Italy, it is suggested that epithelial barrier hypothesis may help to understand the excess burden and mortality from COVID-19.

Evidence of the Relationship between Social Vulnerability and the Spread of COVID-19 in Urban Spaces

Modeling the social-spatial structure of urban spaces can facilitate the development of guidelines aimed at curbing the spread of the COVID-19 pandemic while also acting as an instrument that helps decision-making concerning mitigation policies. The modeling process starts with categorization of urban spaces based on the concept of social vulnerability. A model is created based on this concept and the theory of analysis of social areas. Statistical techniques of factor analysis and geostatistics are applied. This generates a map of social differentiation that, when related to data on the evolution of the contagion, generates a multidimensional model of social vulnerability. The application of this model towards people (social structure) and the environment where they live (spatial structure) is specified. Our model assumes the uniqueness of cities, and it is intended to be a broadly applicable model that can be extrapolated to other urban areas if pertinent revisions are made. Our work demonstrates that aspects of the social and urban structures may be validly used to analyze and explain the spatial spread of COVID-19.

COVID-19 mortality in Italy varies by patient age, sex and pandemic wave

SARS-CoV-2 has caused a worldwide epidemic of enormous proportions, which resulted in different mortality rates in different countries for unknown reasons. We analyzed factors associated with mortality using data from the Italian national database of more than 4 million SARS-CoV-2-positive cases diagnosed between January 2020 and July 2021, including > 415 thousand hospitalized for coronavirus disease-19 (COVID-19) and > 127 thousand deceased. For patients for whom age, sex and date of infection detection were available, we determined the impact of these variables on mortality 30 days after the date of diagnosis or hospitalization. Multivariable weighted Cox analysis showed that each of the analyzed variables independently affected COVID-19 mortality. Specifically, in the overall series, age was the main risk factor for mortality, with HR > 100 in the age groups older than 65 years compared with a reference group of 15-44 years. Male sex presented a two-fold higher risk of death than female sex. Patients infected after the first pandemic wave (i.e. after 30 June 2020) had an approximately threefold lower risk of death than those infected during the first wave. Thus, in a series of all confirmed SARS-CoV-2-infected cases in an entire European nation, elderly age was by far the most significant risk factor for COVID-19 mortality, confirming that protecting the elderly should be a priority in pandemic management. Male sex and being infected during the first wave were additional risk factors associated with COVID-19 mortality.

Does air pollution explain COVID-19 fatality and mortality rates? A multi-city study in São Paulo state, Brazil

Since air pollution compromise the respiratory system and COVID-19 disease is caused by a respiratory virus, it is expected that air pollution plays an important role in the current COVID-19 pandemic. Exploratory studies have observed positive associations between air pollution and COVID-19 cases, deaths, fatality, and mortality rate. However, no study focused on Brazil, one of the most affected countries by the pandemic. Thus, this study aimed to understand how long-term exposure to PM₁₀, PM_2.5, and NO₂ contributed to COVID-19 fatality and mortality rates in São Paulo state in 2020. Air quality data between 2015 and 2019 in 64 monitoring stations within 36 municipalities were considered. The COVID-19 fatality was calculated considering cases and deaths from the government's official data and the mortality rate was calculated considering the 2020 population. Linear regression models were well-fitted for PM_2.5 concentration and fatality (R² = 0.416; p = 0.003), NO₂ concentration and fatality (R² = 0.232; p = 0.005), and NO₂ concentration and mortality (R² = 0.273; p = 0.002). This study corroborates other authors' findings and enriches the discussion for having considered a longer time series to represent long-term exposure to the pollutants and for having considered one of the regions with the highest incidence of COVID-19 in the world. Thus, it reinforces measures to reduce the concentration of air pollutants which are essential for public health and will increase the chance to survive in future respiratory disease epidemics.

Community-Engaged Use of Low-Cost Sensors to Assess the Spatial Distribution of PM2.5 Concentrations across Disadvantaged Communities: Results from a Pilot Study in Santa Ana, CA

PM_2.5 is an air pollutant that is widely associated with adverse health effects, and which tends to be disproportionately located near low-income communities and communities of color. We applied a community-engaged research approach to assess the distribution of PM_2.5 concentrations in the context of community concerns and urban features within and around the city of Santa Ana, CA. Approximately 183 h of one-minute average PM_2.5 measurements, along with high-resolution geographic coordinate measurements, were collected by volunteer community participants using roughly two dozen low-cost AtmoTube Pro air pollution sensors paired with real-time GPS tracking devices. PM_2.5 varied by region, time of day, and month. In general, concentrations were higher near the city’s industrial corridor, which is an area of concern to local community members. While the freeway systems were shown to correlate with some degree of elevated air pollution, two of four sampling days demonstrated little to no visible association with freeway traffic. Concentrations tended to be higher within socioeconomically disadvantaged communities compared to other areas. This pilot study demonstrates the utility of using low-cost air pollution sensors for the application of community-engaged study designs that leverage community knowledge, enable high-density air monitoring, and facilitate greater health-related awareness, education, and empowerment among communities. The mobile air-monitoring approach used in this study, and its application to characterize the ambient air quality within a defined geographic region, is in contrast to other community-engaged studies, which employ fixed-site monitoring and/or focus on personal exposure. The findings from this study underscore the existence of environmental health inequities that persist in urban areas today, which can help to inform policy decisions related to health equity, future urban planning, and community access to resources.

Positive association between outdoor air pollution and the incidence and severity of COVID-19. A review of the recent scientific evidences

In June 2020, we published a review focused on assessing the influence of various air pollutants on the transmission of SARS-CoV-2, and the severity of COVID-19 in patients infected by the coronavirus. The results of most of those reviewed studies suggested that chronic exposure to certain air pollutants might lead to more severe and lethal forms of COVID-19, as well as delays/complications in the recovery of the patients. Since then, a notable number of studies on this topic have been published, including also various reviews. Given the importance of this issue, we have updated the information published since our previous review. Taking together the previous results and those of most investigations now reviewed, we have concluded that there is a significant association between chronic exposure to various outdoor air pollutants: PM_2.5, PM₁₀, O₃, NO₂, SO₂ and CO, and the incidence/risk of COVID-19 cases, as well as the severity/mortality of the disease. Unfortunately, studies on the potential influence of other important air pollutants such as VOCs, dioxins and furans, or metals, are not available in the scientific literature. In relation to the influence of outdoor air pollutants on the transmission of SARS-CoV-2, although the scientific evidence is much more limited, some studies point to PM_2.5 and PM₁₀ as potential airborne transmitters of the virus. Anyhow, it is clear that environmental air pollution plays an important negative role in COVID-19, increasing its incidence and mortality.

Air quality index variation before and after the onset of COVID-19 pandemic: a comprehensive study on 87 capital, industrial and polluted cities of the world

BACKGROUND

Coronavirus disease 2019 (COVID-19) pandemic provided an opportunity for the environment to reduce ambient pollution despite the economic, social and health disruption to the world. The purpose of this study was to investigate the changes in the air quality indexes (AQI) in industrial, densely populated and capital cities in different countries of the world before and after 2020. In this ecological study, we used AQI obtained from the free available databases such as the World Air Quality Index (WAQI). Bivariate correlation analysis was used to explore the correlations between meteorological and AQI variables. Mean differences (standard deviation: SD) of AQI parameters of different years were tested using paired-sample t-test or Wilcoxon signed-rank test as appropriate. Multivariable linear regression analysis was conducted to recognize meteorological variables affecting the AQI parameters.

RESULTS

AQI-PM2.5, AQI-PM10 and AQI-NO2 changes were significantly higher before and after 2020, simultaneously with COVID-19 restrictions in different cities of the world. The overall changes of AQI-PM2.5, AQI-PM10 and AQI-NO2 in 2020 were - 7.36%, - 17.52% and - 20.54% compared to 2019. On the other hand, these results became reversed in 2021 (+ 4.25%, + 9.08% and + 7.48%). In general, the temperature and relative humidity were inversely correlated with AQI-PM2.5, AQI-PM10 and AQI-NO2. Also, after adjusting for other meteorological factors, the relative humidity was inversely associated with AQI-PM2.5, AQI-PM10 and AQI-NO2 (β = - 1.55, β = - 0.88 and β = - 0.10, P < 0.01, respectively).

CONCLUSIONS

The results indicated that air quality generally improved for all pollutants except carbon monoxide and ozone in 2020; however, changes in 2021 have been reversed, which may be due to the reduction of some countries' restrictions. Although this quality improvement was temporary, it is an important result for planning to control environmental pollutants.

Assessment of COVID-19 pandemic effects on ship pollutant emissions in major international seaports

This study aims to investigate the coronavirus disease (COVID-19) pandemic effects and associated restrictive rules on ship activities and pollutant emissions (CO2, SOX, NOX, PM, CO, CH4) in four major seaports, namely the Ports of Singapore, Long Beach, Los Angeles, and Hamburg. We used 2019 as the baseline year to show the business-as-usual emission and compared with the estimated quantity during the July 2020-July 2021 pandemic period. We also project future ship emissions from August 2021-August 2022 to illustrate two potential port congestion scenarios due to COVID-19. The results show that the ship emissions in all four ports generally increased by an average of 79% because of the prolonged turnaround time in port. Importantly, majority of ship emissions occurred during the extended hoteling time at berth and anchorage areas as longer operational times were needed due to pandemic-related delays, with increases ranging from 27 to 123% in the total emissions across ports. The most affected shipping segments were the container ships and dry bulk carriers which the total emissions of all pollutants increased by an average of 94-142% compared with 2019. Overall, the results of this study provide a comprehensive review of the ship emission outlook amid the pandemic uncertainty.