Assessing the relationship between surface levels of PM2.5 and PM10 particulate matter impact on COVID-19 in Milan, Italy

The pathogenic burden potential of airborne particles in emanating from the respiratory area of COVID-19 patients (a case study)

The pathogenic potential of airborne particles carrying the SARS-CoV-2 viral genome was examined by considering the size distribution of airborne particles at given distances from the respiratory zone of an infected patient after coughing or sneezing with a focus on time, temperature, and relative humidity. The results show an association between the size distribution of airborne particles, particularly PM1 and PM2.5, and the presence of viral genome in different stations affected by the distance from the respiratory zone and the passage of time. The correlation with time was strong with all the dependent factors except PM1. Also, the effect of time intervals on the median concentration of airborne PM in the range of PM7 and PM10 was significant. Accordingly, in the first 20 min after coughing, the COVID-19 patient was more likely to be exposed to PM-carrying RNA genomes of SARS-CoV-2. The other finding was that the two distances of 0.25 m to the patient's left of the respiratory zone and 1.0 m above the breathing zone showed positive results for the presence of SARS-CoV-2 in all studied time intervals. The patterns of results suggested that there was a high potential for distribution of the virus in an infected patient based on position and airflow and that the severity of infection and viral load may influence the presence of viral load in droplets when coughing. Based on the results, one can conclude that ventilation plays a key role in mitigating the risk of airborne virus transmission in indoor environments, and it has been shown that reductions in particulate concentrations occur when portable air purifiers are placed near the breathing zone. The use of personal protective equipment for the patient and healthcare personnel to minimize the distribution of virus particles in the air is recommended.

Association between short-term exposure to PM2.5 and its components and mumps incidence in Lanzhou, China

To date, a limited number of studies have assessed the impact of individual and combined PM2.5 components on mumps incidence. Between 2013 and 2019, we collected data on 6270 mumps cases in Lanzhou, along with corresponding PM2.5 and its components, to analyze their temporal and spatial distributions. A generalized additive mixed model was constructed to examine the association between PM2.5 components and mumps incidence. Additionally, Bayesian kernel machine regression was used to evaluate the combined and interactive effects of co-exposure to PM2.5 components on mumps incidence and to identify key contributing components. A significant linear correlation was found between PM2.5 and mumps incidence at lag 1 month, with a relative risk (RR) of 1.85 (95 % CI: 1.14, 3.02) for each unit increase in PM2.5 (log-transformed PM2.5 concentration). Organic matter (OM) at lag 0 and 1 month, as well as black carbon (BC) at lag 1 month, were significantly positively correlated with mumps incidence. Furthermore, the joint exposure-effect curve for PM2.5 components and mumps incidence displayed an approximate V-shape. The effects of PM2.5 and its components on mumps incidence were more pronounced during the warm season. These findings suggest a significant association between short-term exposure to PM2.5 and its components and mumps incidence in Lanzhou, with potential variations in effect depending on the specific components of PM2.5.

Environmental Issues in Global Pediatric Health: Technical Report

Pediatricians and pediatric trainees in North America are increasingly involved in caring for children and adolescents in or from low- and middle-income countries (LMICs). In many LMICs, toxic environmental exposures-notably outdoor and household air pollution, water pollution, lead, hazardous waste disposal, pesticides, and other manufactured chemicals-are highly prevalent and account for twice as great a proportion of disease and deaths among young children as in North America. Climate change will likely worsen these exposures. It is important that pediatricians and other pediatric health professionals from high-income countries who plan to work in LMICs be aware of the disproportionately severe impacts of environmental hazards, become knowledgeable about the major toxic threats to children's health in the countries and communities where they will be working, and consider environmental factors in their differential diagnoses. Likewise, pediatricians in high-income countries who care for children and adolescents who have emigrated from LMICs need to be aware that these children may be at elevated risk of diseases caused by past exposures to toxic environmental hazards in their countries of origin as well as ongoing exposures in products such as traditional foods, medications, and cosmetics imported from their original home countries. Because diseases of toxic environmental origin seldom have unique physical signatures, the environmental screening history, supplemented by laboratory testing, is the principal diagnostic tool. The goal of this technical report is to enhance pediatricians' ability to recognize, diagnose, and manage disease caused by hazardous environmental exposures, especially toxic chemical exposures, in all countries and especially in LMICs.

Environmental Issues in Global Pediatric Health: Policy Statement

Pediatricians and pediatric trainees in North America are increasingly involved in caring for children and adolescents in or from low- and middle-income countries (LMICs). In many LMICs, hazardous environmental exposures-notably outdoor and household air pollution, water pollution, lead, pesticides, and other manufactured chemicals-are highly prevalent and account for twice the proportion of disease and deaths among young children as in North America. Climate change will likely worsen these exposures. It is important that pediatricians and other pediatric health professionals from high-income countries who work in LMICs be aware of the disproportionately severe impacts of toxic environmental hazards, become knowledgeable about the major local/regional environmental threats, and consider environmental factors in their differential diagnoses. Likewise, pediatricians in high-income countries who care for patients who have emigrated from LMICs need to be aware that these children may be at elevated risk of toxic environmental diseases from past exposures to toxic environmental hazards in their countries of origin as well as ongoing exposures in products imported from their home countries, including traditional foods, medications, and cosmetics. Because diseases of toxic environmental origin seldom have unique physical signatures, pediatricians can utilize the environmental screening history, supplemented by laboratory testing, as a diagnostic tool. To prepare pediatricians to care for children in and from LMICs, pediatric organizations could increase the amount of environmental health and climate change content offered in continuing medical education (CME) credits, maintenance of certification (MOC) credits, and certification and recertification examinations. Broadly, it is important that governments and international agencies increase resources directed to pollution prevention, strengthen the environmental health workforce, and expand public health infrastructure in all countries.

Unraveling Various Sources of Particulates Matter in Air-Dust Samples Collected from North India Through Elemental Mapping and Concentration Correlation Using Micro-Particle-Induced X-Ray Emission Spectroscopy

Particulate matter (PM) found in the air is one of the major sources of pollution and air-borne diseases. Therefore, it is imperative to examine the elemental concentration distribution of the PM to identify the pollutant sources. In this study, it has demonstrated the capabilities of micro-particle-induced X-ray emission (micro-PIXE) spectroscopy in quantitative analysis of air samples collected from the Old Delhi outdoor market and indoor locations in the Panjab University hostel in the winter months. A 2-million electronvolts energetic scanning proton micro-beam (diameter ≈1 µm2) is used in micro-PIXE experiments generating high-resolution elemental maps of different regions of interest (ROI). Micro-PIXE along with the GeoPIXE analysis provides a non-destructive, standard-less, and ng/mg level-sensitive tool for the investigation of elemental distributions and highlighting pixels, which correlates to specific concentration ratios between elements at ROIs, thereby enabling a comprehensive understanding of the source of each elemental particulate. Si, Ca, and K detected in indoor PM suggest the source to soil erosion and crop burning, while high S levels in outdoor PM are primarily associated with coal power plants. Additionally, Sc, Ti, Cr, Mn, and Zn are found in outdoor samples, while indoor locations also contained trace amounts of V, Co, and Cu.

Understanding the drivers of PM2.5 concentrations in Chinese cities: A comprehensive study of anthropogenic and environmental factors

Understanding the factors that drive PM2.5 concentrations in cities with varying population and land areas is crucial for promoting sustainable urban population health. This knowledge is particularly important for countries where air pollution is a significant challenge. Most existing studies have investigated either anthropogenic or environmental factors in isolation, often in limited geographic contexts; however, this study fills this knowledge gap. We employed a multimethodological approach, using both multiple linear regression models and geographically weighted regression (GWR), to assess the combined and individual effects of these factors across different cities in China. The variables considered were urban built-up area, land consumption rate (LCR), population size, population growth rate (PGR), longitude, and latitude. Compared with other studies, this study provides a more comprehensive understanding of PM2.5 drivers. The findings of this study showed that PGR and population size are key factors affecting PM2.5 concentrations in smaller cities. In addition, the extent of urban built-up areas exerts significant influence in medium and large cities. Latitude was found to be a positive predictor for PM2.5 concentrations across all city sizes. Interestingly, the northeast, south, and southwest regions demonstrated lower PM2.5 levels than the central, east, north, and northwest regions. The GWR model underscored the importance of considering spatial heterogeneity in policy interventions. However, this research is not without limitations. For instance, international pollution transfers were not considered. Despite the limitation, this study advances the existing literature by providing an understanding of how both anthropogenic and environmental factors, in conjunction with city scale, shape PM2.5 concentrations. This integrated approach offers invaluable insights for tailoring more effective air pollution management strategies across cities of different sizes and characteristics.

The Effects of Environmental Factors on General Human Health: A Scoping Review

Background/Objectives: The external environment constantly influences human health through many factors, including air quality, access to green spaces, exposure to pollutants, and climate change. Contamination poses a substantial threat to human well-being; conversely, environmental factors also positively impact health. The purpose of this study is to provide a comprehensive review of the complex relationship between various environmental factors and human health. While individual studies have explored specific aspects, a broader integrative understanding is lacking. Methods: Through databases (PubMed, Cochrane, Copernicus), 4888 papers were identified, with 166 selected for detailed analysis. Results: We summarized recent research, identifying multiple associations between environmental factors such as air pollution, climate change, solar radiation, and meteorological conditions and their impact on various health outcomes, including respiratory, cardiovascular, metabolic and gastrointestinal, renal and urogenital, neurological and psychological health, infectious and skin diseases, and major cancers. We use chord diagrams to illustrate these links. We also show the interaction between different environmental factors. Findings begin with exploring the direct impact of environmental factors on human health; then, the interplay and combined effects of environmental factors, elucidating their (often indirect) interaction and collective contribution to human health; and finally, the implications of climate change on human health. Conclusions: Researchers and policymakers need to consider that individuals are exposed to multiple pollutants simultaneously, the "multipollutant exposure phenomenon". It is important to study and regulate environmental factors by considering the combined impact of various pollutants rather than looking at each pollutant separately. We emphasize actionable recommendations and solutions.

Quantifying the magnitude of the general contextual effect in a multilevel study of SARS-CoV-2 infection in Ontario, Canada: application of the median rate ratio in population health research

BACKGROUND

Regional variations in SARS-CoV-2 infection were observed in Canada and other countries. Studies have used multilevel analyses to examine how a context, such as a neighbourhood, can affect the SARS-CoV-2 infection rates of the people within it. However, few multilevel studies have quantified the magnitude of the general contextual effect (GCE) in SARS-CoV-2 infection rates and assessed how it may be associated with individual- and area-level characteristics. To address this gap, we will illustrate the application of the median rate ratio (MRR) in a multilevel Poisson analysis for quantifying the GCE in SARS-CoV-2 infection rates in Ontario, Canada.

METHODS

We conducted a population-based, two-level multilevel observational study where individuals were nested into regions (i.e., forward sortation areas [FSAs]). The study population included community-dwelling adults in Ontario, Canada, between March 1, 2020, and May 1, 2021. The model included seven individual-level variables (age, sex, asthma, diabetes, hypertension, congestive heart failure, and chronic obstructive pulmonary disease) and four FSA census-based variables (household size, household income, employment, and driving to work). The MRR is a median value of the rate ratios comparing two patients with identical characteristics randomly selected from two different regions ordered by rate. We examined the attenuation of the MRR after including individual-level and FSA census-based variables to assess their role in explaining the variation in rates between regions.

RESULTS

Of the 11 789 128 Ontario adult community-dwelling residents, 343 787 had at least one SARS-CoV-2 infection during the study period. After adjusting for individual-level and FSA census-based variables, the MRR was attenuated to 1.67 (39% reduction from unadjusted MRR). The strongest FSA census-based associations were household size (RR = 1.88, 95% CI: 1.71-1.97) and driving to work (RR = 0.68, 95% CI: 0.65-0.71).

CONCLUSIONS

The individual- and area-level characteristics in our study accounted for approximately 40% of the between-region variation in SARS-CoV-2 infection rates measured by MRR in Ontario, Canada. These findings suggest that population-based policies to address social determinants of health that attenuate the MRR may reduce the observed between-region heterogeneity in SARS-CoV-2 infection rates.

Individual and contextual factors associated with measles infection in Malaysia: a multilevel analysis

BACKGROUND

Despite effective vaccination strategies, measles remains a global public health challenge. The study explored individual and contextual factors associated with measles infection in Malaysia from 2018 to 2022, informing the development of targeted public health interventions.

METHODS

This cross-sectional study utilised data from the Ministry of Health, the Department of Statistics, and the Department of Environment Malaysia. Multilevel logistic regression analysis was employed to examine individual-level factors, including age, sex, ethnicity, nationality, contact history, travel history, and vaccination status. Concurrently, contextual factors were assessed, encompassing district-level determinants such as population density, median household income, urbanisation, the number of health and rural clinics, vaccination rates, fine particulate matter less than 2.5 μm (PM2.5) levels, relative humidity, and temperature, to determine their impact on measles infection risk.

RESULTS

Measles infection was significantly associated with various individual factors. These included age (adjusted odds ratio [aOR], 1.02; 95% confidence interval [CI], 1.02-1.03), ethnicity, non-Malaysian nationality (aOR, 34.53; 95% CI, 8.42- 141.51), prior contact with a measles case (aOR, 2.36; 95% CI, 2.07-2.69), travel history (aOR, 2.30; 95% CI, 1.13-4.70), and vaccination status (aOR, 0.76; 95% CI, 0.72-0.79). Among contextual factors, urbanisation (aOR, 1.56; 95% CI, 1.16- 2.10) and the number of clinics (aOR, 0.98; 95% CI, 0.97-0.99) were significant determinants.

CONCLUSION

This multilevel logistic regression analysis illuminates the complexities of measles transmission, advocating public health interventions tailored to individual and contextual vulnerabilities. The findings highlight the need for a synergistic approach that combines vaccination campaigns, healthcare accessibility improvements, and socioeconomic interventions to effectively combat measles.

Multi-source observations on the effect of atmospheric blocking on air quality in İstanbul: a study case

Air pollution is affected by the atmospheric dynamics. This study aims to determine that air pollution concentration values in İstanbul increased significantly and reached peak values due to atmospheric blocking between the 30th of December 2022 and the 5th of January 2023. In this study, hourly pollutant data was obtained from 16 air quality monitoring stations (AQMS), the exact reanalysis data was extracted from ERA5 database, and inversion levels and meteorological and synoptic analyses were used to determine the effects of atmospheric blocking on air pollution. Also, cloud base heights and vertical visibility measurements were taken with a ceilometer. Statistical calculations and data visualizations were performed using the R and Grads program. Omega-type blocking, which started in İstanbul on December 30, 2022, had a significant impact on the 1st and 2nd of January 2023, and PM10 and PM2.5 concentration values reached their peak values at 572.8 and 254.20 µg/m3, respectively. In addition, it was found that the average concentration values in the examined period in almost all stations were higher than the averages for January and February. As a result, air quality in İstanbul was determined as "poor" between these calendar dates. It was found that the blocking did not affect the ozone (µg/m3) concentration. It was also found that the concentrations of particulate matter (PM) 10 µm or less in diameter (PM10) and PM 2.5 µm or less in diameter (PM2.5) were increased by the blocking effect in the İstanbul area. Finally, according to the data obtained using the ceilometer, cloud base heights decreased to 30 m and vertical visibility to 10 m.

Exploring the association between ambient air pollution and COVID-19 risk: A comprehensive meta-analysis with meta-regression modelling

Introduction

Air pollution is speculated to increase the risk of Coronavirus disease-2019 (COVID-19). Nevertheless, the results remain inconsistent and inconclusive. This study aimed to explore the association between ambient air pollution (AAP) and COVID-19 risks using a meta-analysis with meta-regression modelling.

Methods

The inclusion criteria were: original studies quantifying the association using effect sizes and 95 % confidence intervals (CIs); time-series, cohort, ecological or case-crossover peer-reviewed studies in English. Exclusion criteria encompassed non-original studies, animal studies, and data with common errors. PubMed, Web of Science, Embase and Google Scholar electronic databases were systemically searched for eligible literature, up to 31, March 2023. The risk of bias (ROB) was assessed following the Agency for Healthcare Research and Quality parameters. A random-effects model was used to calculate pooled risk ratios (RRs) and their 95 % CIs.

Results

A total of 58 studies, between 2020 and 2023, met the inclusion criteria. The global representation was skewed, with major contributions from the USA (24.1 %) and China (22.4 %). The distribution included studies on short-term (43.1 %) and long-term (56.9 %) air pollution exposure. Ecological studies constituted 51.7 %, time-series-27.6 %, cohorts-17.2 %, and case crossover-3.4 %. ROB assessment showed low (86.2 %) and moderate (13.8 %) risk. The COVID-19 incidences increased with a 10 μg/m3 increase in PM2.5 [RR = 4.9045; 95 % CI (4.1548-5.7895)], PM10 [RR = 2.9427: (2.2290-3.8850)], NO2 [RR = 3.2750: (3.1420-3.4136)], SO2 [RR = 3.3400: (2.7931-3.9940)], CO [RR = 2.6244: (2.5208-2.7322)] and O3 [RR = 2.4008: (2.1859-2.6368)] concentrations. A 10 μg/m3 increase in concentrations of PM2.5 [RR = 3.0418: (2.7344-3.3838)], PM10 [RR = 2.6202: (2.1602-3.1781)], NO2 [RR = 3.2226: (2.1411-4.8504)], CO [RR = 1.8021 (0.8045-4.0370)] and O3 [RR = 2.3270 (1.5906-3.4045)] was significantly associated with COVID-19 mortality. Stratified analysis showed that study design, exposure period, and country influenced exposure-response associations. Meta-regression model indicated significant predictors for air pollution-COVID-19 incidence associations.

Conclusion

The study, while robust, lacks causality demonstration and focuses only on the USA and China, limiting its generalizability. Regardless, the study provides a strong evidence base for air pollution-COVID-19-risks associations, offering valuable insights for intervention measures for COVID-19.

Application of GIS and spatiotemporal analyses in viral infection modelling using multiple datasets - A case study on the SARS-CoV-2 epidemic

INTRODUCTION/OBJECTIVE

Viral and infectious diseases such as COVID-19 continue to pose a significant public health threat. In order to create an early warning system for new pandemics or emerging versions of the virus, it is imperative to study its epidemiology. In this study, we created a geospatial model to predict the weekly contagion and lethality rates of COVID-19 in Ireland.

METHODS

More than forty parameters including atmospheric pollutants, metrological variables, sociodemographic factors, and lockdown phases were introduced as input variables to the model. The significant parameters in predicting the number of new cases and the death toll were identified. QGIS software was employed to process input data, and a principal component regression (PCR) model was developed using the statistical add-on XLSTAT.

RESULTS AND CONCLUSIONS

The developed models were able to predict more than half of the variations in contagion and lethality rates. This indicates that the proposed model can serve to help prediction systems for the identification of future high-risk conditions. Nevertheless, there are additional parameters that could be included in future models, such as the number of deaths in care homes, the percentage of contagion and mortality among health workers, and the degree of compliance with social distancing.

Contribution of Particulates to Airborne Disease Transmission and Severity: A Review

The emergence of coronavirus disease 2019 (COVID-19) has catalyzed great interest in the spread of airborne pathogens. Airborne infectious diseases are classified into viral, bacterial, and fungal infections. Environmental factors can elevate their transmission and lethality. Air pollution has been reported as the leading environmental cause of disease and premature death worldwide. Notably, ambient particulates of various components and sizes are harmful pollutants. There are two prominent health effects of particles in the atmosphere: (1) particulate matter (PM) penetrates the respiratory tract and adversely affects health, such as heart and respiratory diseases; and (2) bioaerosols of particles act as a medium for the spread of pathogens in the air. Particulates contribute to the occurrence of infectious diseases by increasing vulnerability to infection through inhalation and spreading disease through interactions with airborne pathogens. Here, we focus on the synergistic effects of airborne particulates on infectious disease. We outline the concepts and characteristics of bioaerosols, from their generation to transformation and circulation on Earth. Considering that microorganisms coexist with other particulates as bioaerosols, we investigate studies examining respiratory infections associated with airborne PM. Furthermore, we discuss four factors (meteorological, biological, physical, and chemical) that may impact the influence of PM on the survival of contagious pathogens in the atmosphere. Our review highlights the significant role of particulates in supporting the transmission of infectious aerosols and emphasizes the need for further research in this area.

From Local Issues to Global Impacts: Evidence of Air Pollution for Romania and Turkey

Air pollution significantly threatens human health and natural ecosystems and requires urgent attention from decision makers. The fight against air pollution begins with the rigorous monitoring of its levels, followed by intelligent statistical analysis and the application of advanced machine learning algorithms. To effectively reduce air pollution, decision makers must focus on reducing primary sources such as industrial plants and obsolete vehicles, as well as policies that encourage the adoption of clean energy sources. In this study, data analysis was performed for the first time to evaluate air pollution based on the SPSS program. Correlation coefficients between meteorological parameters and particulate matter concentrations (PM1, PM2.5, PM10) were calculated in two urban regions of Romania (Craiova and Drobeta-Turnu Severin) and Turkey (Adana). This study establishes strong relationships between PM concentrations and meteorological parameters with correlation coefficients ranging from -0.617 (between temperature and relative humidity) to 0.998 (between PMs). It shows negative correlations between temperature and particulate matter (-0.241 in Romania and -0.173 in Turkey) and the effects of humidity ranging from moderately positive correlations with PMs (up to 0.360 in Turkey), highlighting the valuable insights offered by independent PM sensor networks in assessing and improving air quality.

Causal effect of air pollution and meteorology on the COVID-19 pandemic: A convergent cross mapping approach

Environmental factors have been suspected to influence the propagation and lethality of COVID-19 in the global population. However, most of the studies have been limited to correlation analyses and did not use specific methods to address the dynamic of the causal relationship between the virus and its external drivers. This work focuses on inferring and understanding the causal effect of critical air pollutants and meteorological parameters on COVID-19 by using an Empirical Dynamic Modeling approach called Convergent Cross Mapping. This technique allowed us to identify the time-delayed causation and the sign of interactions. Considering its remarkable urban environment and mortality rate during the pandemic, Quito, Ecuador, was chosen as a case study. Our results show that both urban air pollution and meteorology have a causal impact on COVID-19. Even if the strength and the sign of the causality vary over time, a general trend can be drawn. NO2, SO2, CO and PM2.5 have a positive causation for COVID-19 infections (ρ > 0.35 and ∂ > 9.1). Contrary to current knowledge, this study shows a rapid effect of pollution on COVID-19 cases (1 < lag days <24) and a negative impact of O3 on COVID-19-related deaths (ρ = 0.53 and ∂ = -0.3). Regarding the meteorology, temperature (ρ = 0.24 and ∂ = -0.4) and wind speed (ρ = 0.34 and ∂ = -3.9) tend to mitigate the epidemiological consequences of SARS-CoV-2, whereas relative humidity seems to increase the excess deaths (ρ = 0.4 and ∂ = 0.05). A causal network is proposed to synthesize the interactions between the studied variables and to provide a simple model to support the management of coronavirus outbreaks.

Simultaneous Detection of Infectious Diseases Using Aptamer-Conjugated Gold Nanoparticles in the Lateral Flow Immunoassay-Based Signal Amplification Platform

Various platforms for the accurate diagnosis of infectious diseases have been studied because of the emergence of coronavirus disease (COVID-19) in 2019. Recently, it has become difficult to distinguish viruses with similar symptoms due to the continuous mutation of viruses, and there is an increasing need for a diagnostic method to detect them simultaneously. Therefore, we developed a paper-based rapid antigen diagnostic test using DNA aptamers for the simultaneous detection of influenza A, influenza B, and COVID-19. Aptamers specific for each target viral antigen were selected and attached to AuNPs for application in a rapid antigen diagnosis kit using our company's heterogeneous sandwich-type aptamer screening method (H-SELEX). We confirmed that the three viruses could be detected on the same membrane without cross-reactivity based on the high stability, specificity, and binding affinity of the selected aptamers. Further, the limit of detection was 2.89 pg·mL-1 when applied to develop signal amplification technology; each virus antigen was detected successfully in diluted nasopharyngeal samples. We believe that the developed simultaneous diagnostic kit, based on such high accuracy, can distinguish various infectious diseases, thereby increasing the therapeutic effect and contributing to the clinical field.

Particulate Matter and Its Impact on Macrophages: Unraveling the Cellular Response for Environmental Health

Particulate matter (PM) imposes a significant impact to environmental health with deleterious effects on the human pulmonary and cardiovascular systems. Macrophages (Mφ), key immune cells in lung tissues, have a prominent role in responding to inhaled cells, accommodating inflammation, and influencing tissue repair processes. Elucidating the critical cellular responses of Mφ to PM exposure is essential to understand the mechanisms underlying PM-induced health effects. The present review aims to give a glimpse on literature about the PM interaction with Mφ, triggering the cellular events causing the inflammation, oxidative stress (OS) and tissue damage. The present paper reviews the different pathways involved in Mφ activation upon PM exposure, including phagocytosis, intracellular signaling cascades, and the release of pro-inflammatory mediators. Potential therapeutic strategies targeting Mφ-mediated responses to reduce PM-induced health effects are also discussed. Overall, unraveling the complex interplay between PM and Mφ sheds light on new avenues for environmental health research and promises to develop targeted interventions to reduce the burden of PM-related diseases on global health.

Climate Changes and COVID-19

Climatic change, which influences population growth and land usage, has been theorized to be linked to the emergence and spread of new viruses like the currently unfolding COVID-19 pandemic. In this chapter, we explain how climate change may have altered the beginning, transmission, and maybe even the sickness consequences of the COVID-19 pandemic. Where possible, we also provide mechanistic explanations for how this may have occurred. We have presented evidence that suggests climate change may have had a role in the establishment and transmission of SARS-CoV-2 infection, and most possibly even in some of its clinical effects. Human activities bringing people into closer contact with bats and animals like pangolins that potentially represent the intermediate hosts, and evidence that climate-induced changes in vegetation are the main reservoir source of coronaviruses for human infection, are among the explanations. Although there are still unsubstantiated indications that the first viral pathogen may have escaped from a laboratory, it is possible that this encounter took place in the field or in marketplaces in the instance of COVID-19. We also present the argument that climate change is working to enhance transmission between diseased and uninfected humans, and this is true regardless of the source of the original development of the disease. Changes in temperature and humidity make it easier for viruses to survive, and the impacts of industrial pollution induce people to cough and sneeze, which releases highly infectious aerosols into the air. These three factors combine to make this a more likely scenario than it would otherwise be. We suggest that changes in climate are contributing to create conditions that are favorable for the development of more severe symptoms of illness. It is more difficult to build the argument for this circumstance, and much of it is indirect. However, climate change has caused some communities to adjust their nutritional habits, both in terms of the quantity of food they eat and the quality of the foods they consume. The effects frequently become apparent as a result of alterations that are imposed on the microbiome of the gut, which, in turn, influence the types of immune responses that are produced. The incidence of comorbidities like diabetes and animal vectors like bats that transmit other illnesses that modify vulnerability to SARS-CoV-2 are also two examples of the factors that have been affected by climate change. In order to curb the development of infectious illnesses caused by new viruses, it is necessary to understand the connection between environmental dynamics and the emergence of new coronaviruses. This knowledge should lead to initiatives aimed at reducing global greenhouse gas emissions.

Exposure to outdoor particulate matter and risk of respiratory diseases: a systematic review and meta-analysis

According to epidemiological studies, particulate matter (PM) is an important air pollutant that poses a significant threat to human health. The relationship between particulate matter and respiratory diseases has been the subject of numerous studies, but these studies have produced inconsistent findings. The purpose of this systematic review was to examine the connection between outdoor particulate matter (PM2.5 and PM10) exposure and respiratory disorders (COPD, lung cancer, LRIs, and COVID-19). For this purpose, we conducted a literature search between 2012 and 2022 in PubMed, Web of Science, and Scopus. Out of the 58 studies that were part of the systematic review, meta-analyses were conducted on 53 of them. A random effect model was applied separately for each category of study design to assess the pooled association between exposure to PM2.5 and PM10 and respiratory diseases. Based on time-series and cohort studies, which are the priorities of the strength of evidence, a significant relationship between the risk of respiratory diseases (COPD, lung cancer, and COVID-19) was observed (COPD: pooled HR = 1.032, 95% CI: 1.004-1.061; lung cancer: pooled HR = 1.017, 95% CI: 1.015-1.020; and COVID-19: pooled RR = 1.004, 95% CI: 1.002-1.006 per 1 μg/m3 increase in PM2.5). Also, a significant relationship was observed between PM10 and respiratory diseases (COPD, LRIs, and COVID-19) based on time-series and cohort studies. Although the number of studies in this field is limited, which requires more investigations, it can be concluded that outdoor particulate matter can increase the risk of respiratory diseases.

Distribution characteristics and potential risks of bioaerosols during scattered farming

In most economically underdeveloped areas, scattered farming and human‒livestock cohabitation are common. However, production of bioaerosols and their potential harm in these areas have not been previously researched. In this study, bioaerosol characteristics were analyzed in scattered farming areas in rural Northwest China. The highest bacteria, fungi, and Enterobacteria concentrations were 125609 ± 467 CFU/m³, 25175 ± 10305 CFU/m³, and 4167 ± 592 CFU/m³, respectively. Most bioaerosols had particle sizes >3.3 μm. A total of 71 bacterial genera and 16 fungal genera of potential pathogens were identified, including zoonotic potential pathogenic genera. Moreover, our findings showed that the scattered farming pattern of human‒animal cohabitation can affect the indoor air environment in the surrounding area, leading to chronic respiratory diseases in the occupants. Therefore, relevant government departments and farmers should enhance their awareness of bioaerosol risks and consider measures that may be taken to reduce them.

Spatial Distribution of PM2.5 Mass and Number Concentrations in Paris (France) from the Pollutrack Network of Mobile Sensors during 2018-2022

The presence of particulate matter smaller than 2.5 µm in diameter (PM2.5) in ambient air has a direct pejorative effect on human health. It is thus necessary to monitor the urban PM2.5 values with high spatial resolution to better evaluate the different exposure levels that the population encounters daily. The Pollutrack network of optical mobile particle counters on the roofs of hundreds of vehicles in Paris was used to produce maps with a 1 km2 resolution (108 squares to cover the Paris surface). The study was conducted during the 2018-2022 period, showing temporal variability due to different weather conditions. When averaging all the data, the highest air pollution was found along the Paris motorway ring. Also, the mean mass concentrations of PM2.5 pollution increased from southwest to northeast, due to the typology of the city, with the presence of canyon streets, and perhaps due to the production of secondary aerosols during the transport of airborne pollutants by the dominant winds. The number of days above the new daily threshold of 15 µg.m-3 recommended by the WHO in September 2021 varies from 3.5 to 7 months per year depending on the location in Paris. Pollutrack sensors also provide the number concentrations for particles greater than 0.5 µm. Using number concentrations of very fine particles instead of mass concentrations corresponding to the dry residue of PM2.5 is more representative of the pollutants citizens actually inhale. Some recommendations for the calibration of the sensors used to provide such number concentrations are given. Finally, the consequences of such pollution on human health are discussed.

Experimental and in silico evaluations of the possible molecular interaction between airborne particulate matter and SARS-CoV-2

During the early stage of the COVID-19 pandemic (winter 2020), the northern part of Italy has been significantly affected by viral infection compared to the rest of the country leading the scientific community to hypothesize that airborne particulate matter (PM) could act as a carrier for the SARS-CoV-2. To address this controversial issue, we first verified and demonstrated the presence of SARS-CoV-2 RNA genome on PM2.5 samples, collected in the city of Bologna (Northern Italy) in winter 2021. Then, we employed classical molecular dynamics (MD) simulations to investigate the possible recognition mechanism(s) between a newly modelled PM2.5 fragment and the SARS-CoV-2 Spike protein. The potential molecular interaction highlighted by MD simulations suggests that the glycans covering the upper Spike protein regions would mediate the direct contact with the PM2.5 carbon core surface, while a cloud of organic and inorganic PM2.5 components surround the glycoprotein with a network of non-bonded interactions resulting in up to 4769 total contacts. Moreover, a binding free energy of -207.2 ± 3.9 kcal/mol was calculated for the PM-Spike interface through the MM/GBSA method, and structural analyses also suggested that PM attachment does not alter the protein conformational dynamics. Although the association between the PM and SARS-CoV-2 appears plausible, this simulation does not assess whether these established interactions are sufficiently stable to carry the virus in the atmosphere, or whether the virion retains its infectiousness after the transport. While these key aspects should be verified by further experimental analyses, for the first time, this pioneering study gains insights into the molecular interactions between PM and SARS-CoV-2 Spike protein and will support further research aiming at clarifying the possible relationship between PM abundance and the airborne diffusion of viruses.

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

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.

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

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

Environmental Stability and Transmissibility of Enveloped Viruses at Varied Animate and Inanimate Interfaces

Enveloped viruses have been the leading causative agents of viral epidemics in the past decade, including the ongoing coronavirus disease 2019 outbreak. In epidemics caused by enveloped viruses, direct contact is a common route of infection, while indirect transmissions through the environment also contribute to the spread of the disease, although their significance remains controversial. Bridging the knowledge gap regarding the influence of interfacial interactions on the persistence of enveloped viruses in the environment reveals the transmission mechanisms when the virus undergoes mutations and prevents excessive disinfection during viral epidemics. Herein, from the perspective of the driving force, partition efficiency, and viral survivability at interfaces, we summarize the viral and environmental characteristics that affect the environmental transmission of viruses. We expect to provide insights for virus detection, environmental surveillance, and disinfection to limit the spread of severe acute respiratory syndrome coronavirus 2.

A comprehensive SERS, SEM and EDX study of individual atmospheric PM2.5 particles in Chengdu, China

Characterization of atmospheric fine particulate matter (PM_2.5) in large cities has important implications for the study of their sources and formation mechanisms, as well as in developing effective measures to control air pollution. Herein, we report a holistic physical and chemical characterization of PM_2.5 by combining surface-enhanced Raman scattering (SERS) with scanning electron microscopy (SEM) and electron-induced X-ray spectroscopy (EDX). PM_2.5 particles were collected in a suburban area of Chengdu, a large city in China with a population over 21 million. A special SERS chip composed of inverted hollow Au cone (IHAC) arrays was designed and fabricated to allow direct loading of PM_2.5 particles. SERS and EDX were used to reveal the chemical composition, and particle morphologies were analyzed from SEM images. SERS data of atmospheric PM_2.5 indicated qualitatively the presence of carbonaceous particulate matter, sulfate, nitrate, metal oxides and bioparticles. The EDX showed the presence of the elements C, N, O, Fe, Na, Mg, Al, Si, S, K, and Ca in the collected PM_2.5. Morphology analysis showed that the particulates were mainly in the form of flocculent clusters, spherical, regular crystal shaped or irregularly shaped particles. Our chemical and physical analyses also revealed that the main sources of PM_2.5 are automobile exhaust, secondary pollution caused by photochemical reactions in the air, dust, emission from nearby industrial exhaust, biological particles, other aggregated particles, and hygroscopic particles. SERS and SEM data collected during three different seasons showed that carbon-containing particles are the principal sources of PM_2.5. Our study demonstrates that the SERS based technique, when combined with standard physicochemical characterization methods, is a powerful analytical tool to determine the sources of ambient PM_2.5 pollution. Results obtained in this work may be valuable to the prevention and control of PM_2.5 pollution in air.

Peculiar weather patterns effects on air pollution and COVID-19 spread in Tokyo metropolis

As a pandemic hotspot in Japan, between March 1, 2020-October 1, 2022, Tokyo metropolis experienced seven COVID-19 waves. Motivated by the high rate of COVID-19 incidence and mortality during the seventh wave, and environmental/health challenges we conducted a time-series analysis to investigate the long-term interaction of air quality and climate variability with viral pandemic in Tokyo. Through daily time series geospatial and observational air pollution/climate data, and COVID-19 incidence and death cases, this study compared the environmental conditions during COVID-19 multiwaves. In spite of five State of Emergency (SOEs) restrictions associated with COVID-19 pandemic, during (2020-2022) period air quality recorded low improvements relative to (2015-2019) average annual values, namely: Aerosol Optical Depth increased by 9.13% in 2020 year, and declined by 6.64% in 2021, and 12.03% in 2022; particulate matter PM2.5 and PM10 decreased during 2020, 2021, and 2022 years by 10.22%, 62.26%, 0.39%, and respectively by 4.42%, 3.95%, 5.76%. For (2021-2022) period the average ratio of PM2.5/PM10 was (0.319 ± 0.1640), showing a higher contribution to aerosol loading of traffic-related coarse particles in comparison with fine particles. The highest rates of the daily recorded COVID-19 incidence and death cases in Tokyo during the seventh COVID-19 wave (1 July 2022-1 October 2022) may be attributed to accumulation near the ground of high levels of air pollutants and viral pathogens due to: 1) peculiar persistent atmospheric anticyclonic circulation with strong positive anomalies of geopotential height at 500 hPa; 2) lower levels of Planetary Boundary Layer (PBL) heights; 3) high daily maximum air temperature and land surface temperature due to the prolonged heat waves (HWs) in summer 2022; 4) no imposed restrictions. Such findings can guide public decision-makers to design proper strategies to curb pandemics under persistent stable anticyclonic weather conditions and summer HWs in large metropolitan areas.

Effects of fine particulate matter (PM2.5) and meteorological factors on the daily confirmed cases of COVID-19 in Bangkok during 2020-2021, Thailand

The ongoing global pandemic caused by the SARS-CoV-2 virus, known as COVID-19, has disrupted public health, businesses, and economies worldwide due to its widespread transmission. While previous research has suggested a possible link between environmental factors and increased COVID-19 cases, the evidence regarding this connection remains inconclusive. The purpose of this research is to determine whether or not there is a connection between the presence of fine particulate matter (PM2.5) and meteorological conditions and COVID-19 infection rates in Bangkok, Thailand. The study employs a statistical method called Generalized Additive Model (GAM) to find a positive and non-linear association between RH, AH, and R and the number of verified COVID-19 cases. The impacts of the seasons (especially summer) and rainfall on the trajectory of COVID-19 cases were also highlighted, with an adjusted R-square of 0.852 and a deviance explained of 85.60%, both of which were statistically significant (p < 0.05). The study results assist in preventing the future seasonal spread of COVID-19, and public health authorities may use these findings to make informed decisions and assess their policies.

A bibliometric analysis of the impact of COVID-19 social lockdowns on air quality: research trends and future directions

Social lockdowns improved air quality during the COVID-19 pandemic. Governments had previously spent a lot of money addressing air pollution without success. This bibliometric study measured the influence of COVID-19 social lockdowns on air pollution, identified emerging issues, and discussed future perspectives. The researchers examined the contributions of countries, authors, and most productive journals to COVID-19 and air pollution research from January 1, 2020, to September 12, 2022, from the Web of Sciences Core Collection (WoS). The results showed that (a) publications on the COVID-19 pandemic and air pollution were 504 (research articles) with 7495 citations, (b) China ranked first in the number of publications (n = 151; 29.96% of the global output) and was the main country in international cooperation network, followed by India (n = 101; 20.04% of the total articles) and the USA (n = 41; 8.13% of the global output). Air pollution plagues China, India, and the USA, calling for many studies. After a high spike in 2020, research published in 2021 declined in 2022. The author's keywords have focused on "COVID-19," "air pollution," "lockdown," and "PM25." These keywords suggest that research in this area is focused on understanding the health impacts of air pollution, developing policies to address air pollution, and improving air quality monitoring. The COVID-19 social lockdown served as a specified procedure to reduce air pollution in these countries. However, this paper provides practical recommendations for future research and a model for environmental and health scientists to examine the likely impact of COVID-19 social lockdowns on urban air pollution.

Air pollution-associated shifts in the human airway microbiome and exposure-associated molecular events

Publications addressing air pollution-induced human respiratory microbiome shifts are reviewed in this article. The healthy respiratory microbiota is characterized by a low density of bacteria, fungi and viruses with high diversity, and usually consists of Bacteroidetes, Firmicutes, Proteobacteria, Actinobacteria, Fusobacteria, viruses and fungi. The air's microbiome is highly dependent on air pollution levels and is directly reflected within the human respiratory microbiome. In addition, pollutants indirectly modify the local environment in human respiratory organs by reducing antioxidant capacity, misbalancing proteolysis and modulating inflammation, all of which regulate local microbiomes. Improving air quality leads to more diverse and healthy microbiomes of the local air and, subsequently, residents' airways.

Representativeness of the particulate matter pollution assessed by an official monitoring station of air quality in Santiago, Chile: projection to human health

Santiago, capital city of Chile, presents air pollution problems for decades mainly by particulate matter, which significantly affects population health, despite national authority efforts to improve air quality. Different properties of the particulate matter (PM₁₀, PM_2.5 and PM₁ fractions, particle surface and number) were measured with an optical spectrometer. The sampling was done during spring 2019 at different sites within the official representative area of Independencia monitoring station (ORMS-IS). The results of this study evidence large variations in PM mass concentration at small-scale areas within the ORMS-IS representative zone, which reports the same value for the total area. Results from PM properties such as PM₁, particle number and particle surface distribution show that these properties should be incorporated in regular monitoring in order to improve the understanding of the effects of these factors on human health. The use of urban-climate canopy-layer models in a portion of the sampled area around the monitoring station demonstrates the influence of street geometry, building densities and vegetation covers on wind velocity and direction. These factors, consequently, have an effect on the potential for air pollutants concentrations. The results of this study evidence the existence of hot spots of PM pollution within the area of representativeness of the ORMS-IS. This result is relevant from the point of view of human health and contributes to improve the effectiveness of emission reduction policies.

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

Objectives

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

Methods

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

Results

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

Conclusion

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

Machine learning-based country-level annual air pollutants exploration using Sentinel-5P and Google Earth Engine

Climatic condition is triggering human health emergencies and earth's surface changes. Anthropogenic activities, such as built-up expansion, transportation development, industrial works, and some extreme phases, are the main reason for climate change and global warming. Air pollutants are increased gradually due to anthropogenic activities and triggering the earth's health. Nitrogen Dioxide (NO₂), Carbon Monoxide (CO), and Aerosol Optical Depth (AOD) are truthfully important for air quality measurement because those air pollutants are more harmful to the environment and human's health. Earth observational Sentinel-5P is applied for monitoring the air pollutant and chemical conditions in the atmosphere from 2018 to 2021. The cloud computing-based Google Earth Engine (GEE) platform is applied for monitoring those air pollutants and chemical components in the atmosphere. The NO₂ variation indicates high during the time because of the anthropogenic activities. Carbon Monoxide (CO) is also located high between two 1-month different maps. The 2020 and 2021 results indicate AQI change is high where 2018 and 2019 indicates low AQI throughout the year. The Kolkata have seven AQI monitoring station where high nitrogen dioxide recorded 102 (2018), 48 (2019), 26 (2020) and 98 (2021), where Delhi AQI stations recorded 99 (2018), 49 (2019), 37 (2020), and 107 (2021). Delhi, Kolkata, Mumbai, Pune, and Chennai recorded huge fluctuations of air pollutants during the study periods, where ~ 50-60% NO₂ was recorded as high in the recent time. The AOD was noticed high in Uttar Pradesh in 2020. These results indicate that air pollutant investigation is much necessary for future planning and management otherwise; our planet earth is mostly affected by the anthropogenic and climatic conditions where maybe life does not exist.

72-hour real-time forecasting of ambient PM2.5 by hybrid graph deep neural network with aggregated neighborhood spatiotemporal information

The observation-based air pollution forecasting method has high computational efficiency over traditional numerical models, but a poor ability in long-term (after 6 h) forecasting due to a lack of detailed representation of atmospheric processes associated with the pollution transport. To address such limitation, here we propose a novel real-time air pollution forecasting model that applies a hybrid graph deep neural network (GNN_LSTM) to dynamically capture the spatiotemporal correlations among neighborhood monitoring sites to better represent the physical mechanism of pollutant transport across the space with the graph structure which is established with features (angle, wind speed, and wind direction) of neighborhood sites to quantify their interactions. Such design substantially improves the model performance in 72-hour PM_2.5 forecasting over the whole Beijing-Tianjin-Hebei region (overall R² increases from 0.6 to 0.79), particularly for polluted episodes (PM_2.5 concentration > 55 µg/m³) with pronounced regional transport to be captured by GNN_LSTM model. The inclusion of the AOD feature further enhances the model performance in predicting PM_2.5 over the sites where the AOD can inform additional aloft PM_2.5 pollution features related to regional transport. The importance of neighborhood site (particularly for those in the upwind flow pathway of the target area) features for long-term PM_2.5 forecast is demonstrated by the increased performance in predicting PM_2.5 in the target city (Beijing) with the inclusion of additional 128 neighborhood sites. Moreover, the newly developed GNN_LSTM model also implies the "source"-receptor relationship, as impacts from distanced sites associated with regional transport grow along with the forecasting time (from 0% to 38% in 72 h) following the wind flow. Such results suggest the great potential of GNN_LSTM in long-term air quality forecasting and air pollution prevention.

Developing PM2.5 and PM10 prediction models on a national and regional scale using open-source remote sensing data

Clean air is the precursor to a healthy life. Air quality is an issue that has been getting under its well-deserved spotlight in the last few years. From a remote sensing point of view, the first Copernicus mission with the main purpose of monitoring the atmosphere and tracking air pollutants, the Sentinel-5P TROPOMI mission, has been widely used worldwide. Particulate matter of a diameter smaller than 2.5 and 10 μm (PM_2.5 and PM₁₀) significantly determines air quality. Still, there are no available satellite sensors that allow us to track them remotely with high accuracy, but only using ground stations. This research aims to estimate PM_2.5 and PM₁₀ using Sentinel-5P and other open-source remote sensing data available on the Google Earth Engine (GEE) platform for heating (December 2021, January, and February 2022) and non-heating seasons (June, July, and August 2021) on the territory of the Republic of Croatia. Ground stations of the National Network for Continuous Air Quality Monitoring were used as a starting point and as ground truth data. Raw hourly data were matched to remote sensing data, and seasonal models were trained at the national and regional scale using machine learning. The proposed approach uses a random forest algorithm with a percentage split of 70% and gives moderate to high accuracy regarding the temporal frame of the data. The mapping gives us visual insight between the ground and remote sensing data and shows the seasonal variations of PM_2.5 and PM₁₀. The results showed that the proposed approach and models could efficiently estimate air quality.

An investigation of PM2.5 concentration changes in Mid-Eastern China before and after COVID-19 outbreak

With the Chinese government revising ambient air quality standards and strengthening the monitoring and management of pollutants such as PM_2.5, the concentrations of air pollutants in China have gradually decreased in recent years. Meanwhile, the strong control measures taken by the Chinese government in the face of COVID-19 in 2020 have an extremely profound impact on the reduction of pollutants in China. Therefore, investigations of pollutant concentration changes in China before and after COVID-19 outbreak are very necessary and concerning, but the number of monitoring stations is very limited, making it difficult to conduct a high spatial density investigation. In this study, we construct a modern deep learning model based on multi-source data, which includes remotely sensed AOD data products, other reanalysis element data, and ground monitoring station data. Combining satellite remote sensing techniques, we finally realize a high spital density PM_2.5 concentration change investigation method, and analyze the seasonal and annual, the spatial and temporal characteristics of PM_2.5 concentrations in Mid-Eastern China from 2016 to 2021 and the impact of epidemic closure and control measures on regional and provincial PM_2.5 concentrations. We find that PM_2.5 concentrations in Mid-Eastern China during these years is mainly characterized by "north-south superiority and central inferiority", seasonal differences are evident, with the highest in winter, the second highest in autumn and the lowest in summer, and a gradual decrease in overall concentration during the year. According to our experimental results, the annual average PM_2.5 concentration decreases by 3.07 % in 2020, and decreases by 24.53 % during the shutdown period, which is probably caused by China's epidemic control measures. At the same time, some provinces with a large share of secondary industry see PM_2.5 concentrations drop by more than 30 %. By 2021, PM_2.5 concentrations rebound slightly, rising by 10 % in most provinces.

An Improvement Strategy for Indoor Air Quality Monitoring Systems

Air quality has a huge impact on the comfort and healthiness of various environments. According to the World Health Organization, people who are exposed to chemical, biological and/or physical agents in buildings with low air quality and poor ventilation are more prone to be affected by psycho-physical discomfort, respiratory tract and central nervous system diseases. Moreover, in recent years, the time spent indoors has increased by around 90%. If we consider that respiratory diseases are mainly transmitted from human to human through close contact, airborne respiratory droplets and contaminated surfaces, and that there is a strict relationship between air pollution and the spread of the diseases, it becomes even more necessary to monitor and control these environmental conditions. This situation has inevitably led us to consider renovating buildings with the aim of improving both the well-being of the occupants (safety, ventilation, heating) and the energy efficiency, including monitoring the internal comfort using sensors and the IoT. These two objectives often require opposite approaches and strategies. This paper aims to investigate indoor monitoring systems to increase the quality of life of occupants, proposing an innovative approach consisting of the definition of new indices that consider both the concentration of the pollutants and the exposure time. Furthermore, the reliability of the proposed method was enforced using proper decision-making algorithms, which allows one to consider measurement uncertainty during decisions. Such an approach allows for greater control over the potentially harmful conditions and to find a good trade-off between well-being and the energy efficiency objectives.

Air pollution and respiratory infections: the past, present, and future

Air pollution levels across the globe continue to rise despite government regulations. The increase in global air pollution levels drives detrimental human health effects, including 7 million premature deaths every year. Many of these deaths are attributable to increased incidence of respiratory infections. Considering the COVID-19 pandemic, an unprecedented public health crisis that has claimed the lives of over 6.5 million people globally, respiratory infections as a driver of human mortality is a pressing concern. Therefore, it is more important than ever to understand the relationship between air pollution and respiratory infections so that public health measures can be implemented to ameliorate further morbidity and mortality. This article aims to review the current epidemiologic and basic science research on interactions between air pollution exposure and respiratory infections. The first section will present epidemiologic studies organized by pathogen, followed by a review of basic science research investigating the mechanisms of infection, and then conclude with a discussion of areas that require future investigation.

The effect of air quality parameters on new COVID-19 cases between two different climatic and geographical regions in Turkey

Different health management strategies may need to be implemented in different regions to cope with diseases. The current work aims to evaluate the relationship between air quality parameters and the number of new COVID-19 cases in two different geographical locations, namely Western Anatolia and Western Black Sea in Turkey. Principal component analysis (PCA) and regression model were utilized to describe the effect of environmental parameters (air quality and meteorological parameters) on the number of new COVID-19 cases. A big difference in the mean values for all air quality parameters has appeared between the two areas. Two regression models were developed and showed a significant relationship between the number of new cases and the selected environmental parameters. The results showed that wind speed, SO₂, CO, NO_X, and O₃ are not influential variable and does not affect the number of new cases of COVID-19 in the Western Black Sea area, while only wind speed, SO₂, CO, NO_X, and O₃ are influential parameters on the number of new cases in Western Anatolia. Although the environmental parameters behave differently in each region, these results revealed that the relationship between the air quality parameters and the number of new cases is significant.

Spatio-Temporal Distribution Characteristics and Drivers of PM2.5 Pollution in Henan Province, Central China, before and during the COVID-19 Epidemic

PM_2.5 is the main cause of haze pollution, and studying its spatio-temporal distribution and driving factors can provide a scientific basis for prevention and control policies. Therefore, this study uses air quality monitoring information and socioeconomic data before and during the COVID-19 outbreak in 18 prefecture-level cities in Henan Province from 2017 to 2020, using spatial autocorrelation analysis, ArcGIS mapping, and the spatial autocorrelation analysis. ArcGIS mapping and the Durbin model were used to reveal the characteristics of PM_2.5 pollution in Henan Province in terms of spatial and temporal distribution characteristics and analyze its causes. The results show that: (1) The annual average PM_2.5 concentration in Henan Province fluctuates, but decreases from 2017 to 2020, and is higher in the north and lower in the south. (2) The PM_2.5 concentrations in Henan Province in 2017-2020 are positively autocorrelated spatially, with an obvious spatial spillover effect. Areas characterized by a high concentration saw an increase between 2017 and 2019, and a decrease in 2020; values in low-concentration areas remained stable, and the spatial range showed a decreasing trend. (3) The coefficients of socio-economic factors that increased the PM_2.5 concentration were construction output value > industrial electricity consumption > energy intensity; those with negative effects were: environmental regulation > green space coverage ratio > population density. Lastly, PM_2.5 concentrations were negatively correlated with precipitation and temperature, and positively correlated with humidity. Traffic and production restrictions during the COVID-19 epidemic also improved air quality.

The clinical outcomes of COVID-19 critically ill patients co-infected with other respiratory viruses: a multicenter, cohort study

BACKGROUND

Previous studies have shown that non-critically ill COVID-19 patients co-infected with other respiratory viruses have poor clinical outcomes. However, limited studies focused on this co-infections in critically ill patients. This study aims to evaluate the clinical outcomes of critically ill patients infected with COVID-19 and co-infected by other respiratory viruses.

METHODS

A multicenter retrospective cohort study was conducted for all adult patients with COVID-19 who were hospitalized in the ICUs between March, 2020 and July, 2021. Eligible patients were sub-categorized into two groups based on simultaneous co-infection with other respiratory viruses throughout their ICU stay. Influenza A or B, Human Adenovirus (AdV), Human Coronavirus (i.e., 229E, HKU1, NL63, or OC43), Human Metapneumovirus, Human Rhinovirus/Enterovirus, Middle East Respiratory Syndrome Coronavirus (MERS-CoV), Parainfluenza virus, and Respiratory Syncytial Virus (RSV) were among the respiratory viral infections screened. Patients were followed until discharge from the hospital or in-hospital death.

RESULTS

A total of 836 patients were included in the final analysis. Eleven patients (1.3%) were infected concomitantly with other respiratory viruses. Rhinovirus/Enterovirus (38.5%) was the most commonly reported co-infection. No difference was observed between the two groups regarding the 30-day mortality (HR 0.39, 95% CI 0.13, 1.20; p = 0.10). The in-hospital mortality was significantly lower among co-infected patients with other respiratory viruses compared with patients who were infected with COVID-19 alone (HR 0.32 95% CI 0.10, 0.97; p = 0.04). Patients concomitantly infected with other respiratory viruses had longer median mechanical ventilation (MV) duration and hospital length of stay (LOS).

CONCLUSION

Critically ill patients with COVID-19 who were concomitantly infected with other respiratory viruses had comparable 30-day mortality to those not concomitantly infected. Further proactive testing and care may be required in the case of co-infection with respiratory viruses and COVID-19. The results of our study need to be confirmed by larger studies.

The COVID-19 Mortality Rate Is Associated with Illiteracy, Age, and Air Pollution in Urban Neighborhoods: A Spatiotemporal Cross-Sectional Analysis

There are different area-based factors affecting the COVID-19 mortality rate in urban areas. This research aims to examine COVID-19 mortality rates and their geographical association with various socioeconomic and ecological determinants in 350 of Tehran's neighborhoods as a big city. All deaths related to COVID-19 are included from December 2019 to July 2021. Spatial techniques, such as Kulldorff's SatScan, geographically weighted regression (GWR), and multi-scale GWR (MGWR), were used to investigate the spatially varying correlations between COVID-19 mortality rates and predictors, including air pollutant factors, socioeconomic status, built environment factors, and public transportation infrastructure. The city's downtown and northern areas were found to be significantly clustered in terms of spatial and temporal high-risk areas for COVID-19 mortality. The MGWR regression model outperformed the OLS and GWR regression models with an adjusted R2 of 0.67. Furthermore, the mortality rate was found to be associated with air quality (e.g., NO2, PM10, and O3); as air pollution increased, so did mortality. Additionally, the aging and illiteracy rates of urban neighborhoods were positively associated with COVID-19 mortality rates. Our approach in this study could be implemented to study potential associations of area-based factors with other emerging infectious diseases worldwide.

Seasonal and Spatial Variations of the Oxidative Properties of Ambient PM2.5 in the Po Valley, Italy, before and during COVID-19 Lockdown Restrictions

This study describes the chemical and toxicological characteristics of fine particulate matter (PM2.5) in the Po Valley, one of the largest and most polluted areas in Europe. The investigated samples were collected in the metropolitan area of Milan during the epidemic lockdown and their toxicity was evaluated by the oxidative potential (OP), measured using ascorbic acid (OPAA) and dithiothreitol (OPDTT) acellular assays. The study was also extended to PM2.5 samples collected at different sites in the Po Valley in 2019, to represent the baseline conditions in the area. Univariate correlations were applied to the whole dataset to link the OP responses with the concentrations of the major chemical markers of vehicular and biomass burning emissions. Of the two assays, OPAA was found mainly sensitive towards transition metals released from vehicular traffic, while OPDTT towards the PM carbonaceous components. The impact of the controlling lockdown restrictions on PM2.5 oxidative properties was estimated by comparing the OP values in corresponding time spans in 2020 and 2019. We found that during the full lockdown the OPAA values decreased to 80-86% with respect to the OP data in other urban sites in the area, while the OPDTT values remained nearly constant.

Environmental Pollutants PM2.5, PM10, Nitrogen Dioxide (NO2), and Ozone (O3) Association with the Incidence of Monkeypox Cases in European Countries

Background

Monkeypox, also known as monkeypox disease, is caused by the monkeypox virus (MPXV), which is a zoonotic infection. The swift spread of human monkeypox cases has caused an alarming situation worldwide. This novel study aimed to investigate the association of particulate matter air pollutants PM2.5, PM10, Nitrogen dioxide (NO₂), and Ozone (O₃) on the incidence of monkeypox cases from May 1, 2022, to July 15, 2022.

Methods

The data on air pollutants PM2.5, PM10, NO₂, and O₃ and monkeypox cases were recorded from the date of occurrence of the first case of monkeypox in the United Kingdom, Spain, France, Germany, Italy, the Netherlands, Switzerland, and Portugal from May 1, 2022, to July 15, 2022. The daily concentrations of PM2.5, PM10, NO₂, and O₃ were recorded from the metrological website "Air Quality Index-AQI," and daily human monkeypox cases were recorded from the official website of "Our World in Data." The mean values along with simple, multiple, and Spearman Rho correlations were performed to investigate the relationship and strength of association between the concentrations of air pollutants and cases of monkeypox.

Results

The environmental pollutants PM2.5, PM10, NO₂, and O₃ were positively associated with monkeypox cases in the United Kingdom, Spain, France, Germany, Italy, the Netherlands, Switzerland, and Portugal. The analysis further revealed that for each 10-unit increase in PM2.5, PM10, and NO₂, levels, the number of monkeypox cases was significantly augmented by 29.6%, 9.7%, 13%, and 80.6%, respectively.

Conclusions

Environmental pollutants PM2.5, PM10, NO₂, and O₃ have been positively linked to the number of daily monkeypox cases in European countries. Environmental pollution is a risk factor for the increasing incidence of monkeypox daily cases. The regional and international authorities must implement policies to curtail air pollution to combat the cases of monkeypox in European countries and worldwide.

Unanswered questions on the airborne transmission of COVID-19

Policies and measures to control pandemics are often failing. While biological factors controlling transmission are usually well explored, little is known about the environmental drivers of transmission and infection. For instance, respiratory droplets and aerosol particles are crucial vectors for the airborne transmission of the severe acute respiratory syndrome coronavirus 2, the causation agent of the coronavirus 2019 pandemic (COVID-19). Once expectorated, respiratory droplets interact with atmospheric particulates that influence the viability and transmission of the novel coronavirus, yet there is little knowledge on this process or its consequences on virus transmission and infection. Here we review the effects of atmospheric particulate properties, vortex zones, and air pollution on virus survivability and transmission. We found that particle size, chemical constituents, electrostatic charges, and the moisture content of airborne particles can have notable effects on virus transmission, with higher survival generally associated with larger particles, yet some viruses are better preserved on small particles. Some chemical constituents and surface-adsorbed chemical species may damage peptide bonds in viral proteins and impair virus stability. Electrostatic charges and water content of atmospheric particulates may affect the adherence of virion particles and possibly their viability. In addition, vortex zones and human thermal plumes are major environmental factors altering the aerodynamics of buoyant particles in air, which can strongly influence the transport of airborne particles and the transmission of associated viruses. Insights into these factors may provide explanations for the widely observed positive correlations between COVID-19 infection and mortality with air pollution, of which particulate matter is a common constituent that may have a central role in the airborne transmission of the novel coronavirus.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10311-022-01557-z.

Concentration Characteristics and Correlations with Other Pollutants of Atmospheric Particulate Matter as Affected by Relevant Policies

With the increase in global environmental pollution, it is important to understand the concentration characteristics and correlations with other pollutants of atmospheric particulate matter as affected by relevant policies. The data presented in this paper were obtained at monitoring stations in Xi'an, China, in the years from 2016 to 2020, and the spatial distribution characteristics of the mass and quantity concentrations of particulate matter in the atmosphere, as well as its correlation with other pollutants, were analyzed in depth. The results showed that the annual average concentrations of PM10 and PM2.5 decreased year by year from 2016 to 2020. The annual concentrations of PM2.5 decreased by 20.3 μg/m3, and the annual concentrations of PM10 decreased by 47.3 μg/m3. The days with concentrations of PM10 exceeding the standards decreased by 82 days, with a decrease of 66.7%. The days with concentrations of PM2.5 exceeding the standards decreased by 40 days, with a decrease of 35.4%. The concentration values of PM10 and PM2.5 were roughly consistent with the monthly and daily trends. The change in monthly concentrations was U-shaped, and the change in daily concentrations showed a double-peak behavior. The highest concentrations of particulate matter appeared at about 8:00~9:00 am and 11:00 pm, and they were greatly affected by human activity. The proportion of particles of 0~1.0 μm decreased by 1.94%, and the proportion of particles of 0~2.5 μm decreased by 2.00% from 2016 to 2020. A multivariate linear regression model to calculate the concentrations of the pollutants was established. This study provides a reference for the comprehensive analysis and control of air pollutants in Xi'an and even worldwide.

Bayesian profile regression to study the ecologic associations of correlated environmental exposures with excess mortality risk during the first year of the Covid-19 epidemic in lombardy, Italy

Many countries, including Italy, have experienced significant social and spatial inequalities in mortality during the Covid-19 pandemic. This study applies a multiple exposures framework to investigate how joint place-based factors influence spatial inequalities of excess mortality during the first year of the Covid -19 pandemic in the Lombardy region of Italy. For the Lombardy region, we integrated municipality-level data on all-cause mortality between 2015 and 2020 with 13 spatial covariates, including 5-year average concentrations of six air pollutants, the average temperature in 2020, and multiple socio-demographic factors, and health facilities per capita. Using the clustering algorithm Bayesian profile regression, we fit spatial covariates jointly to identify clusters of municipalities with similar exposure profiles and estimated associations between clusters and excess mortality in 2020. Cluster analysis resulted in 13 clusters. Controlling for spatial autocorrelation of excess mortality and health-protective agency, two clusters had significantly elevated excess mortality than the rest of Lombardy. Municipalities in these highest-risk clusters are in Bergamo, Brescia, and Cremona provinces. The highest risk cluster (C11) had the highest long-term particulate matter air pollution levels (PM2.5 and PM10) and significantly elevated NO2 and CO air pollutants, temperature, proportion ≤18 years, and male-to-female ratio. This cluster is significantly lower for income and ≥65 years. The other high-risk cluster, Cluster 10 (C10), is elevated significantly for ozone but significantly lower for other air pollutants. Covariates with elevated levels for C10 include proportion 65 years or older and a male-to-female ratio. Cluster 10 is significantly lower for income, temperature, per capita health facilities, ≤18 years, and population density. Our results suggest that joint built, natural, and socio-demographic factors influenced spatial inequalities of excess mortality in Lombardy in 2020. Studies must apply a multiple exposures framework to guide policy decisions addressing the complex and multi-dimensional nature of spatial inequalities of Covid-19-related mortality.

Long-term air pollution exposure and COVID-19 case-severity: An analysis of individual-level data from Switzerland

Several studies are pointing out that exposure to elevated air pollutants could contribute to increased COVID-19 mortality. However, literature on the associations between air pollution exposure and COVID-19 severe morbidity is rather sparse. In addition, the majority of the studies used an ecological study design and were applied in regions with rather high air pollution levels. Here, we study the differential effects of long-term exposure to air pollution on severe morbidity and mortality risks from COVID-19 in various population subgroups in Switzerland, a country known for clean air. We perform individual-level analyses using data covering the first two major waves of COVID-19 between February 2020 and May 2021. High-resolution maps of particulate matter (PM_2.5) and nitrogen dioxide (NO₂) concentrations were produced for the 6 years preceding the pandemic using Bayesian geostatistical models. Air pollution exposure for each patient was measured by the long-term average concentration across the municipality of residence. The models were adjusted for the effects of individual characteristics, socio-economic, health-system, and climatic factors. The variables with an important association to COVID-19 case-severity were identified using Bayesian spatial variable selection. The results have shown that the individual-level characteristics are important factors related to COVID-19 morbidity and mortality in all the models. Long-term exposure to air pollution appears to influence the severity of the disease only when analyzing data during the first wave; this effect is attenuated upon adjustment for health-system related factors during the entire study period. Our findings suggest that the burden of air pollution increased the risks of COVID-19 in Switzerland during the first wave of the pandemic, but not during the second wave, when the national health system was better prepared.

COVID-19: Reducing the risk via diet and lifestyle

This review shows that relatively simple changes to diet and lifestyle can significantly, and rapidly, reduce the risks associated with coronavirus disease 2019 (COVID-19) in terms of infection risk, severity of disease, and even disease-related mortality. A wide range of interventions including regular exercise, adequate sleep, plant-based diets, maintenance of healthy weight, dietary supplementation, and time in nature have each been shown to have beneficial effects for supporting more positive health outcomes with COVID-19, in addition to promoting better overall health. This paper brings together literature from these areas and presents the argument that non-pharmaceutical approaches should not be overlooked in our response to COVID-19. It is noted that, in several cases, interventions discussed result in risk reductions equivalent to, or even greater than, those associated with currently available vaccines. Where the balance of evidence suggests benefits, and the risk is minimal to none, it is suggested that communicating the power of individual actions to the public becomes morally imperative. Further, many lives could be saved, and many harms from the vaccine mandates avoided, if we were willing to embrace this lifestyle-centred approach in our efforts to deal with COVID-19.

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

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

SUPPLEMENTARY INFORMATION

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