Next Article in Journal
Unlocking the Viral Universe: Metagenomic Analysis of Bat Samples Using Next-Generation Sequencing
Next Article in Special Issue
Boutonneuse Fever in Southeastern Romania
Previous Article in Journal
The Combination of β-Glucan and Astragalus Polysaccharide Effectively Resists Nocardia seriolae Infection in Largemouth Bass (Micropterus salmoides)
Previous Article in Special Issue
Antimicrobial Resistance of Actinobacillus pleuropneumoniae, Streptococcus suis, and Pasteurella multocida Isolated from Romanian Swine Farms
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Microorganisms
Volume 11
Issue 10
10.3390/microorganisms11102531
microorganisms-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Linkage between Airborne Particulate Matter and Viral Pandemic COVID-19 in Bucharest

by
Maria Zoran
*,
Roxana Savastru
,
Dan Savastru
,
Marina Tautan
and
Daniel Tenciu
C Department, National Institute of R&D for Optoelectronics, 409 Atomistilor Street, MG5, 077125 Magurele, Romania
*
Author to whom correspondence should be addressed.
Microorganisms 2023, 11(10), 2531; https://doi.org/10.3390/microorganisms11102531
Submission received: 7 September 2023 / Revised: 21 September 2023 / Accepted: 25 September 2023 / Published: 10 October 2023
(This article belongs to the Special Issue State-of-the-Art Parasitic and Bacterial Infections in Romania)
Browse Figures
Review Reports Versions Notes

Abstract

:
The long-distance spreading and transport of airborne particulate matter (PM) of biogenic or chemical compounds, which are thought to be possible carriers of SARS-CoV-2 virions, can have a negative impact on the incidence and severity of COVID-19 viral disease. Considering the total Aerosol Optical Depth at 550 nm (AOD) as an atmospheric aerosol loading variable, inhalable fine PM with a diameter ≤2.5 µm (PM2.5) or coarse PM with a diameter ≤10 µm (PM10) during 26 February 2020–31 March 2022, and COVID-19’s five waves in Romania, the current study investigates the impact of outdoor PM on the COVID-19 pandemic in Bucharest city. Through descriptive statistics analysis applied to average daily time series in situ and satellite data of PM2.5, PM10, and climate parameters, this study found decreased trends of PM2.5 and PM10 concentrations of 24.58% and 18.9%, respectively compared to the pre-pandemic period (2015–2019). Exposure to high levels of PM2.5 and PM10 particles was positively correlated with COVID-19 incidence and mortality. The derived average PM2.5/PM10 ratios during the entire pandemic period are relatively low (<0.44), indicating a dominance of coarse traffic-related particles’ fraction. Significant reductions of the averaged AOD levels over Bucharest were recorded during the first and third waves of COVID-19 pandemic and their associated lockdowns (~28.2% and ~16.4%, respectively) compared to pre-pandemic period (2015–2019) average AOD levels. The findings of this research are important for decision-makers implementing COVID-19 safety controls and health measures during viral infections.

1. Introduction

Surveillance of viral disease outbreaks and the influx of data on the evolution of viruses and other pathogenic microorganisms highlight the need for in-depth investigation and severe measures to mitigate the potential transmission of airborne viruses and pathogens, particularly through outdoor particulate matter contaminants. The recent coronavirus disease 2019 (COVID-19) pandemic, caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and its mutations, and known as the third human disease outbreak of the 21st century is responsible for more than 68,262 deaths and more than 3,410,957 infected people from 26 February 2020 to 20 August 2023 in Romania. Bucharest, the capital of Romania, recorded about 16.97% of the total confirmed COVID-19 cases and 8.83% of deaths in Romania [1].
Due to the hydrophobic properties of the SARS-CoV-2 spike protein, COVID-19 viral respiratory infection is believed to be transmitted mainly through the inhalation of virus-laden respiratory droplets [2,3], airborne diffusion [4,5,6], direct contact with infected persons, fomites, feco-oral routes [7], or the incineration of COVID-19 sewage sludge and recovery of residue ash as building material [8,9]. The relative importance of different viral transmission routes is variable in different spatio-temporal climates and topographic and socioeconomic conditions. Several scientific studies considered international trade indicators and complex human-to-human interactions to be more important pathways than demographic, pollution, and economic aspects that must be used to describe the transmission of COVID-19 dynamics in different countries [10,11,12]. Examining the relative significance of different transmission routes is crucial for developing and adopting targeted infection-control strategies.
As new coronavirus species may emerge in the future with different intensities of the waves, it is essential to understand the COVID-19 pandemic spreading related to air pollution and associated bioaerosols in the large metropolitan area of Bucharest. During the COVID-19 pandemic period, due to intensive air pollution control in European community countries, there was a decrease in fine PM2.5 and coarse PM10 particulate matter concentrations at the ground level. However, the issue of high air pollution complexity in metropolitan cities remains a serious threat to the environment. According to the air quality standards of the European Environmental Agency, Bucharest has an average concentration of fine particulate matter PM2.5 of 16.4 µg/m3, classifying it among the poor air quality metropolises in Europe [13]. High levels of aerosols and bioaerosols recorded in European cities harm air quality, local and regional climate systems, and radiative forcing, all of which pose a significant risk to human public health.
Prolonged exposure and the inhalation of high concentrations of airborne fine particles lead to direct deposition in the lower respiratory system bronchi and alveoli sacs, while coarse particles are deposited in the upper respiratory system, airways region and lower respiratory tract’s trachea and bronchi [14]. Coronavirus-laden fine and coarse particulate matter, known as “pathogenic“, may decrease the respiratory system immunity through intra-host induced mutagenesis of the SARS-CoV-2 genome. Under daily average peaks of PM2.5 and PM10 and bioaerosols, these airborne pollutants may be active viral vectors mode of various diseases, including influenza A (H1N1) and COVID-19 spreading both indoor and outdoor environments [15,16].
Several epidemiological studies found a positive association between short-term and long-term exposure to solid air pollutants (especially PM2.5 and PM10) in transmission and the severity of respiratory viral diseases such as COVID-19, rhinovirus, respiratory syncytial virus (RSV), influenza and influenza-like illness [17,18]. Through damaging airway epithelial cell cilia and affecting antiviral immunity cell types, including neutrophils, macrophages, dendritic cells and lymphocytes, PM can increase susceptibility to viral infections inducing oxidative stress and stimulating proinflammatory cytokine release and other inflammasome responses [19,20]. Some studies indicate that PM2.5 may act as a SARS-CoV-2 carrier for both outdoor and indoor transmission [21,22]. There is worldwide epidemiological evidence that COVID-19 incidence and severity is associated with high levels of ambient air pollution PM (ultrafine, fine and coarse) that worsen COVID-19 outcomes [23,24,25].
Also, high concentrations of inhalable air pollutant gases outdoors and indoors (ozone (O3), nitrogen dioxide (NO2), carbon monoxide (CO), carbon dioxide (CO2), sulfur dioxide (SO2), volatile organic compounds (VOCs), etc.) may decrease the human immunity system and the severity of COVID-19 disease outcomes.
Linking weather conditions to the urban micro- and macro-climate context demonstrated that climate variables seasonality has a high impact on airborne microbial SARS-CoV-2 temporal patterns, being affected by seasonal changes of air temperature, pressure, humidity, solar surface irradiance, wind speed intensity and direction, Planetary Boundary Layer height, and synoptic meteorological patterns [26,27,28].
Several studies show that environmental (green and blue spaces), demographic, social, and clinical factors play an important role in exposure to SARS-CoV-2 virions and COVID-19 viral infection severity transmission, but human host-specific genetic factors may contribute to revealing biological mechanisms involved in therapeutic relevance results [29]. However, the understanding of COVID-19 spreading requires a complex interdisciplinary, multidimensional, and transdisciplinary approach [30,31,32,33].
This paper investigates the synergy between exposure to the main ambient air pollutants particulate matter PM2.5 and PM10 and weather-related factors, which may increase the viral pathogens’ impact on human health and the COVID-19 viral infection diffusion and mortality in Bucharest. Using regression models and descriptive statistics applied to the daily in situ and satellite time-series data registered during several seasons and over a long period (1 January 2020–31 March 2022), and five COVID-19 pandemic waves, this study provides an accurate assessment of the linkage between urban air quality related to climate factor variability and the epidemiologic evolution of the COVID-19 viral disease in the metropolitan city of Bucharest. As a measure of the aerosol loading in the lower atmosphere over Bucharest city, this study used a fundamental variable, total Aerosol Optical Depth (AOD) at 550 nm, which is a marker of air pollution that expresses the sunlight attenuation by aerosols. Also, this study investigated temporal patterns of the daily observational and satellite time-series data of PM2.5, PM10, and total AOD at 550 nm data in the different time windows, before the outbreak of the epidemic (2015–2019) years, during the lockdown and beyond. The diffusion pattern of SARS-CoV-2 virions in the Bucharest metropolitan city is a multifactorial process involving among other factors outdoor and indoor air pollution, meteorological parameters variability, and viral inactivation.

2. Materials and Methods

2.1. Ambient Particulate Matter, Bioaerosols and COVID-19 Disease

Outdoor ambient air pollution, which includes diverse man-made (traffic-related, construction-related, energy-generating, etc.) and natural sources (mine dust, biomass burning, etc.), is a significant environmental risk factor for human health, and it is influenced by a high range of local and regional atmospheric processes. Bioaerosols are a subset of atmospheric particles that are released from the biosphere into the atmosphere and contain both living and dead microorganisms (viruses, bacteria, and fungi as well as their excretions such as endotoxins, glucans, mycotoxins, fungal spores, and plant pollen) [34]. These particles pose a serious threat to human health as pathogens and allergens. According to Figure 1, aerosol particle sizes can range from nanometers to nearly a tenth of a millimeter. The upper limit of this range is affected by a number of atmospheric processes, including rapid sedimentation, etc.
Inhalable ambient particles may contain a wide range of organic chemicals, metals, salts, and potentially pathogenic biological species (bacteria, viruses, fungi, proteins, lipids from plants, etc.) [35,36]. Different size fractions of PM (ultrafine particles PM0.1 with an aerodynamic diameter less than 0.1 µm, fine particles PM2.5 with an aerodynamic diameter less than 2.5 µm, and coarse particles PM10 with an aerodynamic diameter greater than 2.5 µm and less than 10 µm) predominate in agglomerated metropolitan regions [37,38]. Due to its composition, which includes an inert carbonaceous core, nitrate, sulfate, organic chemicals, metals, and crustal elements, as well as potential adsorbed organic pollutants, viruses, bacteria, fungi, and toxic heavy metals on its surface, PM2.5 is thought to have a higher level of toxicity [39,40,41].
Epidemiological and toxicological studies revealed that the elevated level of cytotoxicity of ambient ultrafine nanoparticles and fine particles (PM2.5, including PM0.1) in comparison with coarse particles may be attributed to increased bio-reactivity. Its small size and high surface-to-mass ratio allow for deep penetration into the lung airways and through the circulatory system into the organs while also carrying large amounts of potential toxins with allergenic and inflammatory potential [42,43,44] and associated increased morbidity and lethality. In terms of the compartmental deposition of inhaled particulate matter in different size fractions on the respiratory tract [45], according to Figure 2, PM10 deposits primarily in the upper and large conducting lungs airways, while PM0.1 and PM2.5 deposit in the lower respiratory tract, primarily in small airways bronchi/bronchioles and alveoli, being potentially more harmful to health [46,47] through increased risk of lung infections by affecting the function of alveolar macrophages and epithelial cells [48,49]. The exposure of the nasopharyngeal, tracheobronchial, and pulmonary regions of the human respiratory tract to potentially toxic inhaled particulate matter of different size fractions, bioaerosols, and gases is a critical issue in interpreting the response to injury [50,51].
The function of age, the strength of the immune system, seasonal or local and regional atmospheric circulation and weather conditions, geographic location, and epidemiologic studies found that both short-term and long-term exposure to high levels at the ground or street-level PM and bioaerosols concentrations can be linked with a variety of airway diseases seasonality and increased respiratory system symptoms, including rhinitis, airway inflammation, asthma, bronchitis, organic dust toxic syndrome, seasonal influenza, severe acute respiratory syndrome, and coronavirus disease (COVID-19), through lung function decrease and the development of different respiratory symptoms (cough, shortness of breath, pain on deep inspiration, etc.) [52,53,54,55].

2.2. Study Test Site

Bucharest city, Romania’s capital, with a 240 km2 surface, located in the southeastern part of Romania and southeastern part of Europe, is centered at (44.43° N, 26.10° E), and it is considered to be the greatest carbon emitter in Romania. Due to its extensive traffic-related and industrial pollution, it is one of the most polluted metropolitan cities in Europe. Its climate is temperate continental, with western European climate circulation influences, east-European anticyclone, and the synoptic meteorological Mediterranean cyclones, which are characterized by very hot summers, especially during heat waves, and cold humid winters, with frequent extreme climate events. Bucharest metropolitan city has about 1.7945 million residents [56]. The main air pollutant sources in this region are associated with fossil fuels (coal and natural gas) used for home heating and the intensive use of old cars.

2.3. Data Collection

For analysis of the COVID-19 viral infection patterns related to air quality and climate variability in Bucharest (Daily New Cases (DNCs), Daily New Deaths (DNDs), this study used available data provided by websites [57,58]. Additional COVID-19 data for the 26 February 2020 to 31 March 2022 period have been delivered by other websites [59,60].
Time series of the average daily concentration of air pollutants of PM2.5 and PM10 for Bucharest were provided by [61,62,63]. This study used also MERRA-2 time-series data collected from Modern-Era Retrospective analysis for Research and Applications Version 2 and derived total Aerosol Optical Depth (AOD) at 550 nm products provided by National Aeronautics and Space Administration (NASA) and Copernicus Atmosphere Monitoring Service (CAMS) data [64].
Also, Modern-Era Retrospective Analysis for Research and Applications Version 2 MERRA-2 [65] provided the available daily time series of meteorological data, including average temperature (T), maximum (Tmax) and minimum air temperatures (Tmin), air pressure (p), relative humidity (RH), and average wind speed intensity (w) for the Bucharest metropolitan region. Other climate data have been collected from the Climate Change Service of Copernicus (C3S) [66] and meteorological Romanian networks.

2.4. Statistical Analysis Used

To evaluate the similarity between two time-series data of the averaged outdoor daily PM in two size fractions (PM2.5 and PM10) and the average daily AOD levels, climate observables (air temperature and relative humidity, wind speed, surface solar irradiance Planetary Boundary Layer heights), and COVID-19 incidence and mortality in Bucharest, we used cross-correlation analysis. The dependence between pairs of the daily time-series data was assessed in this study by statistical standard tools, Spearman rank-correlation, and rank-correlation non-parametric test coefficients as well as linear regression analysis. The normality of the average daily time-series data sets was assessed through Kolmogorov–Smirnov tests of normality. Because the daily new COVID-19 cases (DNCs) and daily new COVID-19 deaths (DNDs) have a non-normal distribution, Spearman rank correlation was selected to identify the linear correlation between the important variables: (1) air pollutants PM2.5, PM10 concentrations, total Aerosol Optical Depth at 550 nm, climate variables and (2) COVID-19 incidence and mortality rates. We used the p-value (p < 0.05) to determine the statistical significance of the correlation. ORIGIN 10.0 software version 2021 for Microsoft Windows was used for data processing.

3. Results and Discussion

3.1. Particulate Matter PM2.5 and PM10 and COVID-19

To assess the impact of air pollutants on COVID-19 disease transmission and lethality during the 26 February 2020–31 March 2022 period, with five recorded waves of COVID-19 in Bucharest, this study analyzed time series of the daily average PM2.5 and PM10. In good accordance with the numerous studies which have explicitly examined the harmful effects of particulate matter on COVID-19 transmission [67,68,69], the results of this research show direct positive correlations of PM2.5 concentrations, PM10 concentrations, and the derived PM2.5/PM10 ratio with daily new COVID-19 cases (DNCs) and deaths (DNDs) (Table 1). The outdoor PM2.5 and PM10 temporal patterns, for the entire analyzed period, show seasonal variation with lower values during the spring–summer periods and higher values for the fall–winter seasons (Figure 3). For the entire analyzed pandemic period, this study found a decreased average daily PM2.5 concentration (23.83 ± 14.05 µg/m3) in comparison with the daily average PM2.5 concentration for the pre-pandemic period (2015–2019) of 32.67 ± 13.24 µg/m3. A similar decreased value of the average daily PM10 concentrations for the same reported period (62.52 ± 23.50 µg/m3) was found in comparison with the same pre-pandemic period of (76.39 ± 26.19 µg/m3). The reported decreased concentrations of PM2.5 and PM10 found during implementation of the total or partial lockdowns may be explained through adopted draconian measures to mitigate the potential transmission of airborne SARS-CoV2 virions. However, like in other European metropolitan areas, especially during pandemic events, there is an urgent need to improve urban air quality in Bucharest’s densely populated area [70,71].
If the particulate matter (PM) concentration variability constitutes an important indicator of the degree of air pollution in megacities, PM2.5/PM10 ratios quantify the ability to affect human health and atmospheric processes [72,73,74]. When PM2.5/PM10 ratios are less than 0.5, it is considered that fine particles (PM2.5) have more adverse effects on human health than coarse particles (PM2.5–PM10). However, while PM2.5 is a proxy of exhaust emissions, PM2.5–10 is associated with non-exhaust contributions. In our study, using the observation data across the Bucharest metropolis from 1 January 2020 to 31 March 2022 and the daily time-series distribution of PM2.5 and PM10 with the daily COVID-19 incidence and mortality for the entire investigated period, the average ratio PM2.5/PM10 was (0.44 ± 0.221), which means a lower contribution of fine particles (PM2.5) as compared to coarse particles (PM10).
Our results show that outdoor PM2.5 is high in winter and low in summer, while PM10 is high in winter and spring and low during summer and autumn. Temporal analysis of PM2.5/PM10 ratios from 1 January 2020 to 31 March 2022 in the Bucharest area presents the highest values in winter and the lowest values in spring seasons. The derived PM2.5/PM10 ratios, which show a strong independence on PM2.5 and PM10, can provide extra useful information about the type of aerosol pollution. Similar findings have been reported in the previous studies focused on this specific topic [75,76].
This study confirms the results of the scientific literature: exceeding the recommended threshold levels and prolonged exposure to harmful traffic-related pollutants, particularly PM, CO, and CO2, has detrimental health effects and potential risks for the severity of viral diseases such as COVID-19 especially for some of the more vulnerable socioeconomic groups [77,78]. Also, by considering the contribution of air pollutants’ seasonal variability at the ground levels [79,80], this study highlights the association of the average daily PM2.5 and PM10 increased concentrations during the second, the fourth, and the fifth COVID-19 waves with high numbers of total daily new COVID-19 cases in Bucharest (Table 2). Considering the mutual interaction of increasing ecotoxicological levels of air pollutants and city inhabitants, this study proved the harmful effects of PM2.5 and PM10 on COVID-19 incidence and lethality in Bucharest, the result being consistent with previous studies [81,82,83]. Also, this finding supports the hypothesis that particulate matter in different size fractions can be considered a viral vector of SARS-CoV-2 pathogens in large cities through the reduction in pulmonary function and emergence of new viral variants. Presently, particulate matter PM2.5 including ultrafine particles is considered the fourth leading risk factor for death and disability in the world [84,85].

3.2. AOD Temporal Pattern during COVID-19

Compared to the long-term average AOD level (2015–2019) for the same periods of the year, our findings highlight the reduction in the total Aerosol Optical Depth (AOD) at 550 nm levels over Bucharest metropolitan city (~28.2%) during the first COVID-19 wave associated with the total lockdown period (15 March–15 May 2020) and a decrease of ~16.4% recorded during the third COVID-19 wave when a few restrictions had been adopted (Figure 4). Like other studies found in different metropolises, this article reported the reduction in PM2.5 and PM10 ambient particles and the increase/decrease in trace gases O3/NO2 during the implemented lockdown periods [86,87,88].
Figure 4 shows the seasonal variation of total AOD at 550 nm over Bucharest metropolitan city during the investigated COVID-19 pre- and pandemic period with minimum in autumn and winter and maxima in summer and spring. Recorded high AOD values may be associated with different atmospheric processes in the peri-urban areas (secondary aerosols and pollutants formation due to biomass combustion after crop harvesting, hygroscopic growth of aerosols, etc.), which favor pollutants accumulation in this region.
In the spring season, the increased AOD levels due to high dust concentrations are sometimes attributed to transboundary pollution sources like Saharan intrusions. Like several other studies [89,90,91], this research underlines the negative role of both short-term and long-term outdoor exposure to high levels of air pollutants concentrations in Bucharest city on COVID-19 pandemic transmission and severity and suggests the urgent need for a reduction in air pollutants sources during pandemic outbreaks. However, to improve air quality in large cities, lockdown implementation measures are welcome during strong pandemic periods [92,93,94,95].

3.3. Meteorological Variables and COVID-19

Based on statistical analysis of the daily time series of meteorological variables, we found that air temperature and surface solar irradiance are inversely correlated (Figure 5) with the confirmed COVID-19 daily new cases (DNCs, r = −0.51, p < 0.01; and r = −0.60; p < 0.01) and deaths (DNDs, r = −0.67, p < 0.01; r = −0.65, p < 0.01), and respectively; the results are comparable with the scientific literature in the field [96,97,98].
Another important finding shows that Planetary Boundary Layer height is inversely correlated with DNCs (r = −0.70; p < 0.01) and DNDs, respectively (r = −0.72; p < 0.01). Like other studies [99,100,101,102,103], this research found positive linear Spearman rank correlations between average daily air relative humidity with DNCs (r = 0.42, p < 0.01) and DNDs (r = 0.47; p < 0.01) and air pressure with DNCs (r = 0.27, p < 0.01) and DNDs (r = 0.35; p < 0.01). Low negative correlations have been recorded between the average daily wind speed intensity and daily COVID-19 new cases and deaths (r = −0.32, p < 0.01; and r = −0.38; p < 0.01). Also, similar findings have been reported by previous studies [104,105,106,107,108].
Similar results have been reported by some previous studies, which demonstrated an association of weather factors (mostly air temperature, humidity, solar radiation) and COVID-19 transmission in specific regions of the world during different time windows [109,110]. Despite being very important factors in COVID-19 transmission, the airborne pathway is considered to be crucial [111].
Due to its topographic location in a large plain area surrounded by Carpathians Mountain barriers, particularly during the late fall and winter seasons, the Bucharest metropolis has strong tropospheric anomalous synoptic anticyclonic circulation with downwards airflows, which create proper conditions during atmospheric inversions for the accumulation of air pollutants and SARS-CoV-2 viral pathogens near the ground. This anomalous atmospheric circulation may be associated with the high rates of infections reported during the third and the fifth COVID-19 waves. Frequent spring Saharan dust storms over the southeastern part of Romania and Bucharest are responsible for the particulate matter and bioaerosols concentrations increasing several times over, which may explain the high rate of confirmed positive cases recorded during the 5th COVID-19 wave. Researchers demonstrated that sandstorms inject newly emerging pathogens into the atmosphere with adverse effects on urban air quality and built environments. Previous studies found that during the sandstorm events, the particulate matter (PM) and pathogenic bacterial community concentrations in the atmosphere were extremely high, posing a significant hazard to human health, as small bioaerosols (0.65–1.1 μm) remained suspended for a long time in the atmosphere [112,113,114,115].
However, experimental studies [116,117] found that the bacterial and fungal abundances in ultrafine particulate matter PM1.0 were higher than those in PM2.5 and PM10 across different seasons in large cities, showing a strong positive correlation with air quality index. Also, the bacterial gene abundances were higher than fungi, presenting stronger seasonality variation and shifts in the available microbial sources in the urban atmosphere [118,119]). Also, human toxicological and epidemiological studies established a high correlation between health risks and the degrees of exposure (long term or just short term) to high levels of PM in ambient air. Such studies took into account the reduction in lung function and respiratory symptoms including cough, shortness of breath, and pain on deep inhalation. The benefits of improved air quality depend on the dose–response relationship and individual susceptibility at different thresholds of PM concentrations [120,121,122]. During COVID-19 pandemic periods, besides gaseous air pollutants and PM2.5 particles, public risk perception of urban air pollution is associated with high levels of PM10 concentrations from industrial sources [123] and their biogenic or chemical toxic components as well as with climate and sociodemographic factors [124,125]. Among the various risks/hazards induced by air pollutants on human health, microorganisms in PM2.5 and PM10 are considered to be responsible for various allergies and for the spread of respiratory diseases [126,127]. To provide information on the allergenic and pathogenic potentials of different factors, future studies must consider metagenomic methods to analyze the microbial composition of PM in urban metropolitan areas. The COVID-19 viral pandemic infection caused by the SARS-CoV-2 has produced several outbreaks worldwide, which have had a high rate of viral variants and subvariants, which in synergy with other viral or bacterial diseases, and under the pressure of environmental, socioeconomic, and demographic stressors, are significantly related to lethality and transmissibility [128,129,130].

3.4. Strengths and Limitations

Our study has several strengths in having a longer observation period of air pollution and climate variables related to COVID-19 epidemiology in the Bucharest metropolitan region, which spanned several seasons from 26 February 2020 to 31 March 2022, allowing us to explore a large database. Also, a few studies considered the analysis of the aerosol optical depth satellite MERRA-2 product as a measure for aerosol loading over Bucharest during COVID-19 multiwaves comparative analysis. A strength of this investigation consists of its useful information on air pollution and climate variability impacts on COVID-19 pandemic transmission and the severity provided for policymakers in Romania and the public worldwide. Some limitations of this study may be acknowledged. COVID-19 incidence and death data can have some uncertainties due to under-testing and underreporting cases. Also, due to COVID-19-related sanitary restrictions, it was not possible to measure lower air pollution levels, which limit the statistical analysis results.

4. Conclusions

The complex statistical analysis carried out in this study suggests that exposure to high levels of air pollution, particularly particulate matter (PM) as potential carriers of SARS-CoV-2 virions, could increase the transmission and severity of COVID-19 viral infection through clusters of aerosols, which can harm the integrity of the upper and lower human respiratory tract and possibly form condensation nuclei for viral attachment. The inactivation processes of this viral aerosol transmission, which mostly involves fine particulate matter, are influenced by time periods and meteorological conditions.
The results of this study highlight the importance of implementing the total COVID-19 lockdown during the 1st COVID-19 wave and some restrictions adopted during the second and the third waves that improved air quality in the short term through a significant reduction in PM2.5, PM10, and AOD levels over the metropolitan city of Bucharest compared to the long-term average AOD level (2015–2019) for the same periods of the year.
Also, the results of this study show a negative correlation between COVID-19 incidence and severity with air temperature, PBL heights, and surface solar irradiance, supporting the idea that COVID-19 will spread more readily during the colder months. COVID-19 can spread via the airborne route and over long distances. A significant negative impact on COVID-19 transmission and human health will also result from the occurrence of severe haze or fog episodes during particularly synoptic anticyclonic conditions linked to autumn/winter atmospheric inversions. These episodes reflect the synergetic effects caused by interactions between local and regional air masses, transport, anthropogenic emissions, and atmospheric physicochemical processes. Time-series analysis of investigated climate variables in this study demonstrated that a sudden change in outdoor temperature might activate the COVID-19 epidemic in the temperate climate of Bucharest, and relative humidity will facilitate aerosol spread. The effects of global changes on urbanization and climate patterns, including an increased frequency of extreme climate events, which have been recorded during the last few years in Romania and Europe, will lead to specific changes in the intensity of future viral epidemics. In particular, increasing the amplitude of seasonal fluctuations in aerosols and bioaerosols concentrations and the meteorological variables regime lead to more intense epidemics and a high potential transmission of viral infections in metropolitan areas.
However, the ongoing increase in confirmed new cases worldwide and the novel Omicron subvariants imply the adoption of safety risk strategies for imported viruses. Urban intensely polluted areas may implement targeted decisions to reduce the main sources of air pollution and improve air quality through adopting cleaner energy sources and electric vehicle use.

Author Contributions

M.Z.: Conceptualization; Methodology, Supervision, Writing—Review & Editing. R.S.: Validation, Review. D.S.: Validation, Review. M.T.: Data processing, Validation. D.T.: Software. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Romanian Ministry of Research, Innovation and Digitalization, through Program 1—Development of the national research-development system, Subprogram 1.2—Institutional performance—Projects to finance Contract no. 18PFE/30 December 2021; This work was carried out through the Core Program within the National Research Development and Innovation Plan 2022–2027, carried out with the support of MCID, project no. PN 23 05; This work was supported by a grant of the Ministry of Research, Innovation and Digitization, CNCS-UEFISCDI, project number PN-III-P4-PCE-2021-0585, within PNCDI III. This research was funded by the Romanian Ministry of European Investment and Projects & Romanian Ministry of Research, Innovation and Digitalization, contract no.8/1.2.1 PTI ap.2/17 February 2023. We are thankful to NASA MERRA-2 derived products provided by Copernicus Atmosphere Monitoring Service (CAMS) and NASA Giovanni Platform.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Worldometer Info. Available online: https://www.worldometers.info/ (accessed on 25 March 2023).
  2. Horne, J.; Dunne, N.; Singh, N.; Safiuddin, M.; Esmaeili, N.; Erenler, M.; Ho, I.; Luk, E. Building parameters linked with indoor transmission of SARS-CoV-2. Environ. Res. 2023, 238, 117156. [Google Scholar] [CrossRef] [PubMed]
  3. Karimzadeh, S.; Bhopal, R.; Nguyen Tien, H. Review of infective dose, routes of transmission and outcome of COVID-19 caused by the SARS-CoV-2: Comparison with other respiratory viruses. Epidemiol. Infect. 2021, 149, e96. [Google Scholar] [CrossRef] [PubMed]
  4. Bontempi, E. First data analysis about possible COVID-19 virus airborne diffusion due to air particulate matter (PM): The case of Lombardy (Italy). Environ. Res. 2020, 186, 109639. [Google Scholar] [CrossRef] [PubMed]
  5. Nor, N.S.M.; Wai, Y.C.; Ibrahim, N.; Rashid, Z.Z.; Mustafa, N.; Hamid, H.H.A.; Chandru, K.; Latif, M.T.; Saw, P.E.; Lin, C.Y. Particulate matter (PM2.5) as a potential SARS-CoV-2 carrier. Sci. Rep. 2021, 11, 2508. [Google Scholar] [CrossRef]
  6. Pivato, A.; Amoruso, I.; Formenton, G.; Di Maria, F.; Bonato, T.; Vanin, S.; Marion, A.; Baldovin, T. Evaluating the presence of SARS-CoV-2 RNA in the particulate matters during the peak of COVID-19 in Padua, northern Italy. Sci. Total Environ. 2021, 784, 147129. [Google Scholar] [CrossRef]
  7. Targoński, R.; Gąsecka, A.; Prowancki, A.; Targoński, R. An alternative to airborne droplet transmission route of SARS-CoV-2, the feco-oral route, as a factor shaping COVID-19 pandemic. Med. Hypotheses 2022, 166, 110903. [Google Scholar] [CrossRef]
  8. Ducoli, S.; Zacco, A.; Bontempi, E. Incineration of sewage sludge and recovery of residue ash as building material: A valuable option as a consequence of the COVID-19 pandemic. J. Environ. Manag. 2021, 282, 111966. [Google Scholar] [CrossRef]
  9. Belosi, F.; Conte, M.; Gianelle, V.; Santachiara, G.; Contini, D. On the concentration of SARS-CoV-2 in outdoor air and the interaction with pre-existing atmospheric particles. Environ. Res. 2021, 193, 110603. [Google Scholar] [CrossRef]
  10. Bontempi, E. Commercial exchanges instead of air pollution as possible origin of COVID-19 initial diffusion phase in Italy: More efforts are necessary to address interdisciplinary research. Environ. Res. 2020, 188, 109775. [Google Scholar] [CrossRef]
  11. Bontempi, E.; Coccia, M. International trade as critical parameter of COVID-19 spread that outclasses demographic, economic, environmental, and pollution factors. Environ. Res. 2021, 201, 111514. [Google Scholar] [CrossRef]
  12. Bontempi, E.; Coccia, M.; Vergalli, S.; Zanoletti, A. Can commercial trade represent the main indicator of the COVID-19 diffusion due to human-to-human interactions? A comparative analysis between Italy, France, and Spain. Environ. Res. 2021, 201, 111529. [Google Scholar] [CrossRef]
  13. EEA. Available online: https://www.eea.europa.eu/themes/air/urban-air-quality/european-city-air-quality-viewer (accessed on 19 June 2023).
  14. Passi, A.; Shiva Nagendra, S.M.; Maiya, M.P. Assessment of exposure to airborne aerosol and bio-aerosol particles and their deposition in the respiratory tract of subway metro passengers and workers. Atmos. Pollut. Res. 2022, 12, 101218. [Google Scholar] [CrossRef]
  15. Ma, Y.; Cheng, B.; Li, H.; Feng, F.; Zhang, Y.; Wang, W.; Qin, P. Air pollution and its associated health risks before and after COVID-19 in Shaanxi Province, China. Environ. Pollut. 2023, 320, 121090. [Google Scholar] [CrossRef] [PubMed]
  16. Dai, H.; Zhao, B. Association between the infection probability of COVID-19 and ventilation rates: An update for SARS-CoV-2 variants. Build. Simul. 2023, 16, 3–12. [Google Scholar] [CrossRef] [PubMed]
  17. Bergmann, M.L.; Andersen, Z.J.; Amini, H.; Khan, J.; Lim, Y.H.; Loft, S.; Mehta, A.; Westendorp, R.G.; Cole-Hunter, T. Ultrafine particle exposure for bicycle commutes in rush and non-rush hour traffic: A repeated measures study in Copenhagen, Denmark. Environ. Pollut. 2022, 294, 118631. [Google Scholar] [CrossRef]
  18. Borro, M.; Di Girolamo, P.; Gentile, G.; De Luca, O.; Preissner, R.; Marcolongo, A.; Ferracuti, S.; Simmaco, M. Evidence-based considerations exploring relations between SARS-CoV-2 pandemic and air pollution: Involvement of PM2.5-mediated up-regulation of the viral receptor ACE-2. Int. J. Environ. Res. Public Health 2020, 17, 5573. [Google Scholar] [CrossRef]
  19. Bowe, B.; Xie, Y.; Gibson, A.K.; Cai, M.; van Donkelaar, A.; Martin, R.V.; Burnett, R.; AlAly, Z. Ambient fine particulate matter air pollution and the risk of hospitalization among COVID-19 positive individuals: Cohort study. Environ. Int. 2021, 154, 106564. [Google Scholar] [CrossRef]
  20. Garcia, E.; Marian, B.; Chen, Z.; Li, K.; Lurmann, F.; Gilliland, F.; Eckel, S.P. Long-term air pollution and COVID-19 mortality rates in California: Findings from the Spring/Summer and Winter surges of COVID-19. Environ. Pollut. 2022, 292, 118396. [Google Scholar] [CrossRef]
  21. Dragic, N.; Bijelovic, S.; Jevtic, M.; Velicki, R.; Radic, I. Short-term health effects of air quality changes during the COVID-19 pandemic in the City of Novi Sad, the Republic of Serbia. Int. J. Occup. Med. Environ. Health 2021, 34, 223–237. [Google Scholar] [CrossRef]
  22. Borisova, T.; Komisarenko, S. Air pollution particulate matter as a potential carrier of SARS-CoV-2 to the nervous system and/or neurological symptom enhancer: Arguments in favor. Environ. Sci. Pollut. Res. Int. 2021, 28, 40371–40377. [Google Scholar] [CrossRef]
  23. Jerrett, M.; Nau, C.L.; Young, D.R.; Butler, R.K.; Batteate, C.M.; Su, J.; Burnett, R.T.; Kleeman, M.J. Air pollution and meteorology as risk factors for COVID-19 death in a cohort from Southern California. Environ. Int. 2023, 171, 107675. [Google Scholar] [CrossRef] [PubMed]
  24. Yates, E.F.; Zhang, K.; Naus, A.; Forbes, C.; Wu, X.; Dey, T.A. Review on the biological, epidemiological, and statistical relevance of COVID-19 paired with air pollution. Environ. Adv. 2022, 8, 100250. [Google Scholar] [CrossRef] [PubMed]
  25. Travaglio, M.; Yu, Y.; Popovic, R.; Selley, L.; Leal, N.S.; Martins, L.M. Links between air pollution and COVID-19 in England. Environ. Pollut. 2021, 268, 115859. [Google Scholar] [CrossRef]
  26. Azuma, K.; Kagi, N.; Kim, H.; Hayash, M. Impact of climate and ambient air pollution on the epidemic growth during COVID-19 outbreak in Japan. Environ. Res. 2020, 190, 110042. [Google Scholar] [CrossRef] [PubMed]
  27. Al-Khateeb, M.S.; Abdulla, F.A.; Al-Delaimy, W.K. Long-term spatiotemporal analysis of the climate related impact on the transmission rate of COVID-19. Environ. Res. 2023, 236, 116741. [Google Scholar] [CrossRef] [PubMed]
  28. Islam, N.; Bukhari, Q.; Jameel, Y.; Shabnam, S.; Erzurumluoglu, A.M.; Siddique, M.A.; Massaro, J.M.; D’Agostino, R.B., Sr. COVID-19 and climatic factors: A global analysis. Environ. Res. 2021, 193, 110355. [Google Scholar] [CrossRef]
  29. Casado-Aranda, L.A.; Sánchez-Fernández, J.; Viedma-del-Jesús, M.I. Analysis of the scientific production of the effect of COVID-19 on the environment: A bibliometric study. Environ. Res. 2021, 193, 110416. [Google Scholar] [CrossRef]
  30. Bontempi, E.; Vergalli, S.; Squazzoni, F. Understanding COVID-19 diffusion requires an interdisciplinary, multi-dimensional approach. Environ. Res. 2020, 188, 109814. [Google Scholar] [CrossRef]
  31. Maleki, M.; Anvari, E.; Hopke, P.K.; Noorimotlagh, Z.; Mirzaee, S.A. An updated systematic review on the association between atmospheric particulate matter pollution and prevalence of SARS-CoV-2. Environ. Res. 2021, 195, 110898. [Google Scholar] [CrossRef]
  32. Anand, U.; Cabreros, C.; Mal, J.; Ballesteros, F., Jr.; Sillanpää, M.; Tripathi, V.; Bontempi, E. Novel coronavirus disease 2019 (COVID-19) pandemic: From transmission to control with an interdisciplinary vision. Environ. Res. 2021, 197, 111126. [Google Scholar] [CrossRef]
  33. Depero, L.E.; Bontempi, E. Comparing the spreading characteristics of monkeypox (MPX) and COVID-19: Insights from a quantitative model. Environ. Res. 2023, 235, 116521. [Google Scholar] [CrossRef] [PubMed]
  34. Fröhlich-Nowoisky, J.; Kampf, C.J.; Weber, B.; Huffman, J.A.; Pöhlker, C.; Andreae, M.O.; Lang-Yona, N.; Burrows, S.M.; Gunthe, S.S.; Elbert, W.; et al. Bioaerosols in the Earth system: Climate, health, and ecosystem interactions. Atmos. Res. 2016, 182, 346–376. [Google Scholar] [CrossRef]
  35. Tung, N.T.; Cheng, P.C.; Chi, K.H.; Hsiao, T.C.; Jones, T.; BéruBé, K.; Ho, K.F.; Chuang, H.C. Particulate matter and SARS-CoV-2: A possible model of COVID-19 transmission. Sci. Total Environ. 2021, 750, 141532. [Google Scholar] [CrossRef] [PubMed]
  36. Wang, T.; Rovira, J.; Sierra, J.; Blanco, J.; Chen, S.-J.; Mai, B.-X.; Schuhmacher, M.; Domingo, J.L. Characterization of airborne particles and cytotoxicity to a human lung cancer cell line in Guangzhou, China. Environ. Res. 2021, 196, 110953. [Google Scholar] [CrossRef]
  37. Sarmadi, M.; Moghanddam, V.K.; Dickerson, A.S.; Martelletti, L. Association of COVID-19 distribution with air quality, sociodemographic factors, and comorbidities: An ecological study of US states. Air Qual. Atmos. Health 2021, 14, 455–465. [Google Scholar] [CrossRef]
  38. Sulaymon, I.D.; Zhang, Y.; Hopke, P.K.; Guo, S.; Ye, F.; Sun, J.; Zhu, Y.; Hu, J. Using the COVID-19 lockdown to identify atmospheric processes and meteorology influences on regional PM2.5 pollution episodes in the Beijing-Tianjin-Hebei, China. Atmos. Res. 2023, 294, 106940. [Google Scholar] [CrossRef]
  39. Juarez, P.D.; Ramesh, A.; Hood, D.B.; Alcendor, D.J.; Valdez, R.B.; Aramandla, M.P.; Tabatabai, M.; Matthews-Juarez, P.; Langston, M.A.; Al-Hamdan, M.Z.; et al. The effects of air pollution, meteorological parameters, and climate change on COVID-19 comorbidity and health disparities: A systematic review. Environ. Chem. Ecotoxicol. 2022, 4, 194–210. [Google Scholar] [CrossRef]
  40. Zoran, M.A.; Savastru, R.S.; Savastru, D.M.; Tautan, M.N. Assessing the relationship between surface levels of PM2.5 and PM10 particulate matter impact on COVID-19 in Milan, Italy. Sci. Total Environ. 2020, 738, 139825. [Google Scholar] [CrossRef]
  41. Prinz, A.L.; Richter, D.J. Long-term exposure to fine particulate matter air pollution: An ecological study of its effect on COVID-19 cases and fatality in Germany. Environ. Res. 2022, 204 Pt A, 111948. [Google Scholar] [CrossRef]
  42. Mu, G.; Zhou, M.; Wang, B.; Cao, L.; Yang, S.; Qiu, W.; Nie, X.; Ye, Z.; Zhou, Y.; Chen, W. Personal PM2.5 exposure and lung function: Potential mediating role of systematic inflammation and oxidative damage in urban adults from the general population. Sci. Total Environ. 2021, 755, 142522. [Google Scholar] [CrossRef]
  43. Marquès, M.; Correig, E.; Domingo, J.L. Long-term exposure to PM10 above WHO guidelines exacerbates COVID-19 severity and mortality. Environ. Int. 2022, 158, 106930. [Google Scholar] [CrossRef] [PubMed]
  44. Marquès, M.; Domingo, J.L. Positive association between outdoor air pollution and the incidence and severity of COVID-19. A review of the recent scientific evidences. Environ. Res. 2022, 203, 111930. [Google Scholar] [CrossRef] [PubMed]
  45. Sugiyama, J.T.; Ueda, K.; Seposo, X.T.; Nakashima, A.; Kinoshita, M.; Matsumoto, H.; Ikemori, F.; Honda, A.; Takano, H.; Michikawa, T.; et al. Health effects of PM2.5 sources on children’s allergic and respiratory symptoms in Fukuoka. Sci. Total Environ. 2020, 709, 136023. [Google Scholar] [CrossRef]
  46. Marquès, M.; Rovira, J.; Nadal, M.; Domingo, J.L. Effects of air pollution on the potential transmission and mortality of COVID-19: A preliminary case-study in Tarragona Province (Catalonia, Spain). Environ. Res. 2021, 192, 110315. [Google Scholar] [CrossRef] [PubMed]
  47. Baron, Y.M.; Camilleri, L. The Emergence of Ten SARS-CoV-2 Variants and Airborne PM2.5. Virol. Curr. Res. 2021, 5, 141. [Google Scholar]
  48. Baron, Y.M. Are there medium to outdoor multifaceted effects of the airborne pollutant PM2.5 determining the emergence of SARS-CoV-2 variants? Med. Hypotheses 2022, 158, 110718. [Google Scholar] [CrossRef] [PubMed]
  49. Asadi, S.; Bouvier, N.; Wexler, A.S.; Ristenpart, W.D. The coronavirus pandemic and aerosols: Does COVID-19 transmit via expiratory particles? Aerosol Sci. Technol. 2020, 54, 635–638. [Google Scholar] [CrossRef]
  50. Neupane, B.; Jerrett, M.; Burnett, R.T.; Marrie, T.; Arain, A.; Loeb, M. Long-term exposure to ambient air pollution and risk of hospitalization with community-acquired pneumonia in older adults. Am. J. Respir. Crit. Care Med. 2010, 181, 47–53. [Google Scholar] [CrossRef]
  51. Gao, C.; Li, S.; Liu, M.; Zhang, F.; Achal, V.; Tu, Y.; Zhang, S.; Cai, C. Impact of the COVID-19 pandemic on air pollution in Chinese megacities from the perspective of traffic volume and meteorological factors. Sci. Total Environ. 2021, 773, 145545. [Google Scholar] [CrossRef]
  52. Dai, S.; Chen, X.; Liang, J.; Li, X.; Li, S.; Chen, G.; Chen, Z.; Bin, J.; Tang, Y.; Li, X. Response of PM2.5 pollution to meteorological and anthropogenic emissions changes during COVID-19 lockdown in Hunan Province based on WRF-Chem model. Environ. Pollut. 2023, 331, 121886. [Google Scholar] [CrossRef]
  53. Seposo, X.; Ueda, K.; Sugata, S.; Yoshino, A.; Takami, A. Outdoor effects of air pollution on daily single- and co-morbidity cardiorespiratory outpatient visits. Sci. Total Environ. 2020, 729, 138934. [Google Scholar] [CrossRef] [PubMed]
  54. Setti, L.; Passarini, F.; de Gennaro, G.; Barbieri, P.; Pallavicini, A.; Ruscio, M.; Piscitelli, P.; Colao, A.; Miani, A. Searching for SARS-COV-2 on particulate matter: A possible early indicator of COVID-19 epidemic recurrence. Int. J. Environ. Res. Public Health 2020, 17, 2986. [Google Scholar] [CrossRef] [PubMed]
  55. Setti, L.; Passarini, F.; de Gennaro, G.; Barbieri, P.; Perrone, M.G.; Borelli, M.; Palmisani, J.; Di Gilio, A.; Torboli, V.; Fontana, F.; et al. SARS-Cov-2RNA found on particulate matter of Bergamo in Northern Italy: First evidence. Environ. Res. 2020, 188, 109754. [Google Scholar] [CrossRef] [PubMed]
  56. Worldpopulation. Available online: https://worldpopulationreview.com/world-cities/bucharest-population (accessed on 25 June 2021).
  57. World Health Organization. Available online: https://www.who.int/emergencies/diseases/novel-coronavirus-2019 (accessed on 25 June 2023).
  58. Statista. Available online: https://www.statista.com/statistics/1220938/most-polluted-capital-cities-in-europe/ (accessed on 30 June 2023).
  59. Johns Hopkins Coronavirus Resource Center. COVID-19 Dashboard by the Center for Systems Science And Engineering (CSSE). Available online: https://coronavirus.jhu.edu/map (accessed on 27 June 2023).
  60. MAI. Available online: https://www.mai.gov.ro (accessed on 3 June 2022).
  61. COPERNICUS Atmosphere Data. Available online: https://www.copernicus.eu/en/copernicus-services/atmosphere (accessed on 23 June 2023).
  62. ANM. Available online: https://www.anm.ro (accessed on 28 June 2023).
  63. AQICN. Available online: https://aqicn.org/city/romania/municipiul-bucuresti/ (accessed on 30 June 2023).
  64. SODA-PRO Radiation Data. Available online: http://www.soda-pro.com/web-services/radiation/cams-mcclear (accessed on 25 March 2023).
  65. SODA-PRO Meteo Data. Available online: http://www.soda-pro.com/web-services/meteo-data/merra (accessed on 25 March 2023).
  66. COPERNICUS Climate Data. Available online: https://www.copernicus.eu/en/copernicus-services/climate (accessed on 30 June 2023).
  67. Shao, L.; Cao, Y.; Jones, T.; Santosh, M.; Silva, L.F.; Ge, S.; da Boit, K.; Feng, X.; Zhang, M.; BéruBé, K. COVID-19 mortality and exposure to airborne PM2.5: A lag time correlation. Sci. Total Environ. 2022, 806, 151286. [Google Scholar] [CrossRef]
  68. Domingo, J.L.; Marquès, M.; Rovira, J. Influence of airborne transmission of SARS-CoV-2 on COVID-19 pandemic. A review. Environ. Res. 2020, 188, 109861. [Google Scholar] [CrossRef]
  69. Marquès, M.; Domingo, J.L. Contamination of inert surfaces by SARS-CoV-2: Persistence, stability and infectivity. A review. Environ. Res. 2021, 193, 110559. [Google Scholar] [CrossRef]
  70. Beloconi, A.; Vounatsou, P. Long-term air pollution exposure and COVID-19 case-severity: An analysis of individual-level data from Switzerland. Environ. Res. 2023, 216, 114481. [Google Scholar] [CrossRef]
  71. Aboura, S. The influence of climate factors and government interventions on the Covid-19 pandemic: Evidence from 134 countries. Environ. Res. 2022, 208, 112484. [Google Scholar] [CrossRef]
  72. Berg, K.; Present, R.; Richardson, K. Long-term air pollution and other risk factors associated with COVID-19 at the census tract level in Colorado. Environ. Pollut. 2021, 287, 117584. [Google Scholar] [CrossRef]
  73. Xu, L.; Taylor, J.E.; Kaiser, J. Outdoor air pollution exposure and COVID-19 infection in the United States. Environ. Pollut. 2022, 292, 118369. [Google Scholar] [CrossRef]
  74. Chakrabarty, R.K.; Beeler, P.; Liu, P.; Goswami, S.; Harvey, R.D.; Pervez, S.; van Do, A.; Martin, R.V. Ambient PM2.5 exposure and rapid spread of COVID-19 in the United States. Sci. Total Environ. 2021, 760, 143391. [Google Scholar] [CrossRef] [PubMed]
  75. Lipsitt, J.; Chan-Golston, A.M.; Liu, J.; Su, J.; Zhu, Y.; Jerrett, M. Spatial analysis of COVID-19 and traffic-related air pollution in Los Angeles. Environ. Int. 2021, 153, 106531. [Google Scholar] [CrossRef] [PubMed]
  76. Liu, X.; Huang, J.; Li, C.; Zhao, Y.; Wang, D.; Huang, Z.; Yang, K. The role of seasonality in the spread of COVID-19 pandemic. Environ. Res. 2021, 195, 110874. [Google Scholar] [CrossRef] [PubMed]
  77. Naqvi, A.; Peer, S.; Müller, J.; Straub, M. The spatial–temporal exposure to traffic-related Particulate Matter emissions. Transp. Res. Part D Transp. Environ. 2023, 123, 103899. [Google Scholar] [CrossRef]
  78. Altuwayjiri, A.; Soleimanian, E.; Moroni, S.; Palomba, P.; Borgini, A.; De Marco, C.; Ruprecht, A.A.; Sioutas, C. The impact of stay-home policies during Coronavirus-19 pandemic on the chemical and toxicological characteristics of ambient PM2.5 in the metropolitan area of Milan, Italy. Sci. Total Environ. 2021, 758, 143582. [Google Scholar] [CrossRef]
  79. Fan, H.; Zhao, C.; Yang, Y.; Yang, X. Spatio-Temporal Variations of the PM2.5/PM10 Ratios and Its Application to Air Pollution Type Classification in China. Front. Environ. Sci. 2021, 9, 692440. [Google Scholar] [CrossRef]
  80. Abbass, R.A.; Kumar, P.; El-Gendy, A. Car users exposure to particulate matter and gaseous air pollutants in megacity Cairo. Sustain. Cities Soc. 2020, 56, 102090. [Google Scholar] [CrossRef]
  81. Zoran, M.; Savastru, R.; Savastru, D.; Tautan, M.; Baschir, L.; Tenciu, D. Assessing the impact of air pollution and climate seasonality on COVID-19 multiwaves in Madrid, Spain. Environ. Res. 2022, 203, 111849. [Google Scholar] [CrossRef]
  82. Collivignarelli, M.C.; Bellazzi, S.; Caccamo, F.M.; Carnevale Miino, M. Discussion about the Latest Findings on the Possible Relation between Air Particulate Matter and COVID-19. Int. J. Environ. Res. Public Health 2023, 20, 5132. [Google Scholar] [CrossRef]
  83. Bilal; Bashir, M.F.; Benghoul, M.; Numan, U.; Shakoor, A.; Komal, B.; Bashir, M.A.; Bashir, M.; Tan, D. Environmental pollution and COVID-19 outbreak: Insights from Germany. Air Qual. Atmos. Health 2020, 3, 1385–1394. [Google Scholar] [CrossRef]
  84. Carballo, I.H.; Bakola, M.; Stuckler, D. The impact of air pollution on COVID-19 incidence, severity, and mortality: A systematic review of studies in Europe and North America. Environ. Res. 2022, 215, 114155. [Google Scholar] [CrossRef]
  85. Xu, G.; Ren, X.; Xiong, K.; Li, L.; Bi, X.; Wu, Q. Analysis of the driving factors of PM2.5 concentration in the air: A case study of the Yangtze River Delta, China. Ecol. Indic. 2020, 110, 105889. [Google Scholar] [CrossRef]
  86. Byun, W.S.; Heo, S.W.; Jo, G.; Kim, J.W.; Kim, S.; Lee, S.; Park, H.E.; Baek, J.-H. Is coronavirus disease (COVID-19) seasonal? A critical analysis of empirical and epidemiologic studies at global and local scales. Environ. Res. 2021, 196, 110972. [Google Scholar] [CrossRef] [PubMed]
  87. WHO. Ambient Air Pollution: A Global Assessment of Exposure and Burden of Disease; World Health Organization: Geneva, Switzerland, 2016; Available online: https://apps.who.int/iris/handle/10665/250141 (accessed on 14 April 2023).
  88. Collivignarelli, M.C.; De Rose, C.; Abbà, A.; Baldi, M.; Bertanza, G.; Pedrazzani, R.; Sorlini, S.; Miino, M.C. Analysis of lockdown for CoViD-19 impact on NO2 in London, Milan and Paris: What lesson can be learnt? Process Saf. Environ. Prot. 2021, 146, 952–960. [Google Scholar] [CrossRef] [PubMed]
  89. He, C.; Hong, S.; Zhang, L.; Mu, H.; Xin, A.; Zhou, Y.; Liu, J.; Liu, N.; Su, Y.; Tian, Y.; et al. Global, continental, and national variation in PM2.5, O3, and NO2 concentrations during the early 2020 COVID-19 lockdown. Atmos. Pollut. Res. 2021, 12, 136–145. [Google Scholar] [CrossRef]
  90. Linillos-Pradillo, B.; Rancan, L.; Ramiro, E.D.; Vara, E.; Artíñano, B.; Arias, J. Determination of SARS-CoV-2 RNA in different particulate matter size fractions of outdoor air samples in Madrid during the lockdown. Environ. Res. 2021, 195, 110863. [Google Scholar] [CrossRef]
  91. Ho, C.-C.; Hung, S.-C.; Ho, W.-C. Effects of short- and long-term exposure to atmospheric pollution on COVID-19 risk and fatality: Analysis of the first epidemic wave in northern Italy. Environ. Res. 2021, 199, 111293. [Google Scholar] [CrossRef]
  92. Bu, X.; Xie, Z.; Liu, J.; Wei, L.; Wang, X.; Chen, M.; Ren, H. Global PM2.5-attributable health burden from 1990 to 2017: Estimates from the Global Burden of disease study 2017. Environ. Res. 2021, 197, 111123. [Google Scholar] [CrossRef]
  93. Fang, B.; Zeng, H.; Zhang, L.; Wang, H.; Liu, J.; Hao, K.; Zheng, G.; Wang, M.; Wang, Q.; Yang, W. Toxic metals in outdoor/indoor airborne PM2.5 in port city of Northern, China: Characteristics, sources, and personal exposure risk assessment. Environ. Pollut. 2021, 279, 116937. [Google Scholar] [CrossRef]
  94. Han, J.; Yin, J.; Wu, X.; Wang, D.; Li, C. Environment and COVID-19 incidence: A critical review. J. Environ. Sci. 2023, 124, 933–951. [Google Scholar] [CrossRef]
  95. Orak, N.H.; Ozdemir, O. The impacts of COVID-19 lockdown on PM10 and SO2 concentrations and association with human mobility across Turkey. Environ. Res. 2021, 197, 111018. [Google Scholar] [CrossRef] [PubMed]
  96. Jin, B.; Ji, J.; Yang, W.; Yao, Z.; Huang, D.; Xu, C. Analysis on the spatio-temporal characteristics of COVID-19 in mainland China. Process Saf. Environ. Prot. 2021, 152, 291–303. [Google Scholar] [CrossRef]
  97. Rayasam, S.D.G.; Aung, M.T.; Cooper, C.; Kwiatkowski, C.; Germolec, D.R.; Rooney, A.A.; Walker, V.R.; Forte, C.; Woodruff, T.J.; Chartres, N. Identifying Environmental Factors that Influence Immune Response to SARS-CoV-2: Systematic Evidence Map Protocol. Environ. Int. 2022, 164, 107230. [Google Scholar] [CrossRef] [PubMed]
  98. Srivastava, A. COVID-19 and air pollution and meteorology-an intricate relationship: A review. Chemosphere 2021, 263, 128297. [Google Scholar] [CrossRef]
  99. Tian, F.; Liu, X.; Chao, Q.; Qian, Z.M.; Zhang, S.; Qi, L.; Niu, Y.; Arnold, L.D.; Zhang, S.; Li, H.; et al. Ambient air pollution and low temperature associated with case fatality of COVID-19: A nationwide retrospective cohort study in China. Innovation 2021, 2, 100139. [Google Scholar] [CrossRef]
  100. Tignat-Perrier, R.; Dommergue, A.; Thollot, A.; Magand, O.; Amato, P.; Joly, M.; Sellegri, K.; Vogel, T.M.; Larose, C. Seasonal shift in airborne microbial communities. Sci. Total Environ. 2020, 716, 137129. [Google Scholar] [CrossRef] [PubMed]
  101. Linares, C.; Culqui, D.; Belda, F.; López-Bueno, J.A.; Luna, Y.; Sánchez-Martínez, G.; Hervella, B.; Díaz, J. Impact of environmental factors and Sahara dust intrusions on incidence and severity of COVID-19 disease in Spain. Effect in the first and second pandemic waves. Environ. Sci. Pollut. Res. 2021, 28, 51948–51960. [Google Scholar] [CrossRef]
  102. To, T.; Zhang, K.; Maguire, B.; Terebessy, E.; Fong, I.; Parikh, S.; Zhu, J.; Su, Y. UV, ozone, and COVID-19 transmission in Ontario, Canada using generalised linear models. Environ. Res. 2021, 194, 110645. [Google Scholar] [CrossRef]
  103. Jana, A.; Kundu, S.; Shaw, S.; Chakraborty, S.; Chattopadhyay, A. Spatial shifting of COVID-19 clusters and disease association with environmental parameters in India: A time series analysis. Environ. Res. 2023, 222, 115288. [Google Scholar] [CrossRef]
  104. aldo-Aubanell, Q.; Campillo i López, F.; Bach, A.; Serra, I.; Olivet-Vila, J.; Saez, M.; Pino, D.; Maneja, R. Community risk factors in the COVID-19 incidence and mortality in Catalonia (Spain). A population-based study. Int. J. Environ. Res. Public Health 2021, 18, 3768. [Google Scholar] [CrossRef]
  105. Cai, H.; Yu, Z.; Amanze, C.; Wang, S.; Yu, R.; Zeng, W.; Wu, X.; Shen, L.; Li, J. Variations of Airborne Bacterial Community with Seasons and Environmental Factors in Changsha, China. Air Qual. Atmos. Health 2022, 15, 773–783. [Google Scholar] [CrossRef]
  106. Pegoraro, V.; Heiman, F.; Levante, A.; Urbinati, D.; Peduto, I. Italian individual-level data study investigating on the association between air pollution exposure and Covid-19 severity in primary-care setting. BMC Public Health 2021, 21, 902. [Google Scholar] [CrossRef] [PubMed]
  107. Isphording, I.E.; Pestel, N. Pandemic meets pollution: Poor air quality increases deaths by COVID-19. J. Environ. Econ. Manag. 2021, 108, 102448. [Google Scholar] [CrossRef] [PubMed]
  108. Suligowski, R.; Ciupa, T. Five waves of the COVID-19 pandemic and green–blue spaces in urban and rural areas in Poland. Environ. Res. 2023, 216, 114662. [Google Scholar] [CrossRef]
  109. Manik, S.; Mandal, M.; Pal, S.; Patra, S.; Acharya, S. Impact of climate on COVID-19 transmission: A study over Indian states. Environ. Res. 2022, 211, 113110. [Google Scholar] [CrossRef]
  110. Scapini, V.; Torres, S.; Rubilar-Torrealba, R. Meteorological, PM2.5 and PM10 factors on SARS-CoV-2 transmission: The case of southern regions in Chile. Environ. Pollut. 2023, 322, 120961. [Google Scholar] [CrossRef]
  111. Núñez-Delgado, A.; Bontempi, E.; Coccia, M.; Kumar, M.; Farkas, K.; Domingo, J.L. SARS-CoV-2 and other pathogenic microorganisms in the environment. Environ. Res. 2021, 201, 111606. [Google Scholar] [CrossRef]
  112. Feng, B.; Lian, J.; Yu, F.; Zhang, D.; Chen, W.; Wang, Q.; Shen, Y.; Xie, G.; Wang, R.; Teng, Y.; et al. Impact of short-term ambient air pollution exposure on the risk of severe COVID-19. J. Environ. Sci. 2024, 135, 610–618. [Google Scholar] [CrossRef]
  113. An, T.; Liang, Z.; Chen, Z.; Li, G. Recent progress in online detection methods of bioaerosols. Fundam. Res. 2023, in press. [Google Scholar] [CrossRef]
  114. Rodríguez-Arias, R.M.; Rojo, J.; Fernández-González, F.; Pérez-Badia, R. Desert dust intrusions and their incidence on airborne biological content. Review and case study in the Iberian Peninsula. Environ. Pollut. 2023, 316, 120464. [Google Scholar] [CrossRef]
  115. Ranjan, A.K.; Patrab, A.K.; Gorai, A.K. Effect of lockdown due to SARS COVID-19 on aerosol optical depth (AOD) over urban and mining regions in India. Sci. Total Environ. 2020, 745, 141024. [Google Scholar] [CrossRef] [PubMed]
  116. Hammer, M.S.; van Donkelaar, A.; Martin, R.V.; McDuffie, E.E.; Lyapustin, A.; Sayer, A.M.; Hsu, N.C.; Levy, R.C.; Garay, M.J.; Kalashnikova, O.V.; et al. Effects of COVID-19 lockdowns on fine particulate matter concentrations. Sci. Adv. 2021, 7, eabg7670. [Google Scholar] [CrossRef] [PubMed]
  117. Jiang, S.; Sun, B.; Zhu, R.; Che, C.; Ma, D.; Wang, R.; Dai, H. Airborne microbial community structure and potential pathogen identification across the PM size fractions and seasons in the urban atmosphere. Sci. Total Environ. 2022, 831, 154665. [Google Scholar] [CrossRef] [PubMed]
  118. Tao, Y.; Zhang, X.; Qiu, G.; Spillmann, M.; Ji, Z.; Wang, J. SARS-CoV-2 and other airborne respiratory viruses in outdoor aerosols in three Swiss cities before and during the first wave of the COVID-19 pandemic. Environ. Int. 2022, 164, 107266. [Google Scholar] [CrossRef] [PubMed]
  119. Chen, H.; Du, R.; Zhang, Y. Evolution of PM2.5 bacterial community structure in Beijing’s suburban atmosphere. Sci. Total Environ. 2021, 799, 149387. [Google Scholar] [CrossRef]
  120. Liu, H.; Hu, Z.; Zhou, M.; Hu, J.; Yao, X.; Zhang, H. The distribution variance of airborne microorganisms in urban and rural environments. Environ. Pollut. 2019, 247, 898–906. [Google Scholar] [CrossRef]
  121. Liu, H.; Hu, Z.; Zhou, M.; Zhang, H.; Li, Z.; Zhang, H. Airborne microorganisms exacerbate the formation of atmospheric ammonium and sulfate. Environ. Pollut. 2020, 263, 114293. [Google Scholar] [CrossRef]
  122. Mebrahtu, T.F.; Santorelli, G.; Yang, T.C.; Wright, J.; Tate, J.; McEachan, R.R.C. The effects of exposure to NO2, PM2.5 and PM10 on health service attendances with respiratory illnesses: A time-series analysis. Environ. Pollut. 2023, 333, 122123. [Google Scholar] [CrossRef]
  123. Zanoletti, A.; Cornelio, A.; Bontempi, E. A post-pandemic sustainable scenario: What actions can be pursued to increase the raw materials availability? Environ. Res. 2021, 202, 111681. [Google Scholar] [CrossRef]
  124. Alex, F.J.; Tan, G.; Kyei, S.K.; Ansah, P.O.; Agyeman, P.K.; Fayzullayevich, J.V.; Olayode, I.O. Transmission of viruses and other pathogenic microorganisms via road dust: Emissions, characterization, health risks, and mitigation measures. Atmos. Pollut. Res. 2023, 14, 101642. [Google Scholar] [CrossRef]
  125. Pignocchino, G.; Di Baldassarre, G.; Mondino, E.; Raffetti, E. Public risk perception of air pollution in the general population of Italy and Sweden during the COVID-19 pandemic: Environmental and socio-demographic drivers. Prev. Med. 2023, 173, 107601. [Google Scholar] [CrossRef] [PubMed]
  126. Sturm, R. Total deposition of ultrafine particles in the lungs of healthy men and women: Experimental and theoretical results. Ann. Transl. Med. 2016, 4, 234. [Google Scholar] [CrossRef]
  127. Sturm, R. Modelling the deposition of fine particulate matter (PM2.5) in the human respiratory tract. AME Med. J. 2020, 5, 14. [Google Scholar] [CrossRef]
  128. Shahhosseini, N.; Babuadze, G.; Wong, G.; Kobinger, G.P. Mutation Signatures and In Silico Docking of Novel SARS-CoV-2 Variants of Concern. Microorganisms 2021, 9, 926. [Google Scholar] [CrossRef] [PubMed]
  129. Bennett, J.C.; Hetrich, M.K.; Garcia Quesada, M.; Sinkevitch, J.N.; Deloria Knoll, M.; Feikin, D.R.; Zeger, S.L.; Kagucia, E.W.; Cohen, A.L.; Ampofo, K.; et al. Changes in Invasive Pneumococcal Disease Caused by Streptococcus pneumoniae Serotype 1 following Introduction of PCV10 and PCV13: Findings from the PSERENADE Project. Microorganisms 2021, 9, 696. [Google Scholar] [CrossRef]
  130. Lewis, C.R.; Bonham, K.S.; McCann, S.H.; Volpe, A.R.; D’Sa, V.; Naymik, M.; De Both, M.D.; Huentelman, M.J.; Lemery-Chalfant, K.; Highlander, S.K.; et al. Family SES Is Associated with the Gut Microbiome in Infants and Children. Microorganisms 2021, 9, 1608. [Google Scholar] [CrossRef]
Microorganisms 11 02531 g001
Figure 1. Characteristic size ranges of atmospheric particles and bioaerosols.
Figure 1. Characteristic size ranges of atmospheric particles and bioaerosols.
Microorganisms 11 02531 g001
Microorganisms 11 02531 g002
Figure 2. Compartmental deposition of particulate matter in different size fraction on the respiratory tract.
Figure 2. Compartmental deposition of particulate matter in different size fraction on the respiratory tract.
Microorganisms 11 02531 g002
Microorganisms 11 02531 g003
Figure 3. Temporal patterns of the average daily ground levels of PM2.5 and PM10 concentrations and daily new confirmed COVID-19 cases (DNCs) and deaths (DNDs) for the investigated period during the pandemic in Bucharest city.
Figure 3. Temporal patterns of the average daily ground levels of PM2.5 and PM10 concentrations and daily new confirmed COVID-19 cases (DNCs) and deaths (DNDs) for the investigated period during the pandemic in Bucharest city.
Microorganisms 11 02531 g003
Microorganisms 11 02531 g004
Figure 4. The monthly distribution of AOD in Bucharest metropolis for the years 2019–2021.
Figure 4. The monthly distribution of AOD in Bucharest metropolis for the years 2019–2021.
Microorganisms 11 02531 g004
Microorganisms 11 02531 g005
Figure 5. Temporal distribution of the average daily meteorological parameters (air relative humidity, temperature at 2 m height, Planetary Boundary Layer height, surface solar irradiance, and COVID-19 incidence and mortality in Bucharest during the five waves of the COVID-19 pandemic period).
Figure 5. Temporal distribution of the average daily meteorological parameters (air relative humidity, temperature at 2 m height, Planetary Boundary Layer height, surface solar irradiance, and COVID-19 incidence and mortality in Bucharest during the five waves of the COVID-19 pandemic period).
Microorganisms 11 02531 g005
Table 1. Spearman rank correlation coefficients and p-values between COVID-19 cases and average daily PM concentrations and PM2.5/PM10 ratios for Bucharest city for the analyzed pandemic period, 26 February 2020–31 March 2022.
Table 1. Spearman rank correlation coefficients and p-values between COVID-19 cases and average daily PM concentrations and PM2.5/PM10 ratios for Bucharest city for the analyzed pandemic period, 26 February 2020–31 March 2022.
BucharestAverage Daily Air Pollutant Concentration
COVID-19 incidencePM2.5 (µg/m3)PM10 (µg/m3)PM2.5/PM10
Daily New Cases (DNCs)0.39 *0.37 *0.51 *
Daily New Deaths (DNDs)0.44 *0.42 *0.56 *
Note: PM2.5 (particulate matter of 2.5 µm size), PM10 (particulate matter of 10 µm size); * p < 0.01.
Table 2. Cumulative statistical analysis of COVID-19 cases and deaths per waves, periods and the average daily PM2.5 and PM10 concentrations for the 26 February 2020–31 March 2022 period in Bucharest.
Table 2. Cumulative statistical analysis of COVID-19 cases and deaths per waves, periods and the average daily PM2.5 and PM10 concentrations for the 26 February 2020–31 March 2022 period in Bucharest.
Time PeriodDaily New COVID-19 Cases (DNCs)Daily New COVID-19 Deaths (DNDs)Daily Average PM2.5 (µg/m3)Daily Average
PM10 (µg/m3)
1st COVID-19 wave and lockdown
26 February 2020–15 June 2020		239812723.865 ± 18.09465.034 ± 13.265
Pre-2nd COVID-19 wave
15 July 2020–30 September 2020		13,64926620.773 ± 7.80160.092 ± 12.783
2nd COVID-19 wave 01 October 2020–31 January 2021101,018142124.772 ± 11.15472.584 ± 27.405
3rd COVID-19 wave 01 February 2021–01 June 202164,848116622.013 ± 10.79361.053 ± 26.272
4th COVID-19 wave 01 September 2021–21 December 2021120,986209828.212 ± 10.53460.592 ± 24.165
5th COVID-19 wave 22 December 2021–31 March 2022235,18558425.135 ± 11.65267.721 ± 22.823
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Zoran, M.; Savastru, R.; Savastru, D.; Tautan, M.; Tenciu, D. Linkage between Airborne Particulate Matter and Viral Pandemic COVID-19 in Bucharest. Microorganisms 2023, 11, 2531. https://doi.org/10.3390/microorganisms11102531

AMA Style

Zoran M, Savastru R, Savastru D, Tautan M, Tenciu D. Linkage between Airborne Particulate Matter and Viral Pandemic COVID-19 in Bucharest. Microorganisms. 2023; 11(10):2531. https://doi.org/10.3390/microorganisms11102531

Chicago/Turabian Style

Zoran, Maria, Roxana Savastru, Dan Savastru, Marina Tautan, and Daniel Tenciu. 2023. "Linkage between Airborne Particulate Matter and Viral Pandemic COVID-19 in Bucharest" Microorganisms 11, no. 10: 2531. https://doi.org/10.3390/microorganisms11102531

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop