Effects of short-term ambient particulate matter exposure on the risk of severe COVID-19

Outdoor particulate matter and risk of drug resistance for workers and farmers with pulmonary tuberculosis: a population-based time-series study in Suzhou, China

OBJECTIVES

The detrimental effects of particulate matter (PM) on human health have been widely corroborated. We aimed to examine the association between outdoor PM and the drug resistance risk among workers and farmers with pulmonary tuberculosis (PTB).

DESIGN

We performed a population-based time-series study using routinely collected meteorological and TB surveillance data.

SETTING

We selected Suzhou City, China, as the study area. Data on patients with PTB and meteorological factors were extracted from the National Tuberculosis Online Registration System and the China Meteorological Data Sharing Center.

PARTICIPANTS

This study included 7868 patients with PTB diagnosed from January 2017 to December 2021 in Suzhou.

METHODS

The generalised additive model was used to estimate the effects of outdoor PM on the drug resistance risk of TB among workers and farmers who typically work outdoors. Moreover, subgroup analyses were carried out to evaluate the associations in different populations and seasons.

RESULTS

Although there was no significant association between PM with an aerodynamic diameter≤10 µm (PM10) and drug-resistant risk in the overall analysis, subgroup analysis revealed a significant positive association in the winter season. Similarly, PM with an aerodynamic diameter≤2.5 µm (PM2.5) was significantly associated with drug resistance risk among males with a lag of 0-3 days, people ≤60 years with a lag of 0-7 days and in the winter season with a lag of 0-7 days, 0-15 days, 0-90 days or 0-180 days.

CONCLUSIONS

Outdoor PM10 and PM2.5 were positively related to the drug resistance risk of workers and farmers with PTB. Reducing ambient PM pollution might reduce the burden of TB. Further research is required to verify the association through in vitro experiments and extensive cohort studies.

Effects of long-term exposure to outdoor air pollution on COVID-19 incidence: A population-based cohort study accounting for SARS-CoV-2 exposure levels in the Netherlands

Several studies have linked air pollution to COVID-19 morbidity and severity. However, these studies do not account for exposure levels to SARS-CoV-2, nor for different sources of air pollution. We analyzed individual-level data for 8.3 million adults in the Netherlands to assess associations between long-term exposure to ambient air pollution and SARS-CoV-2 infection (i.e., positive test) and COVID-19 hospitalisation risks, accounting for spatiotemporal variation in SARS-CoV-2 exposure levels during the first two major epidemic waves (February 2020-February 2021). We estimated average annual concentrations of PM10, PM2.5 and NO2 at residential addresses, overall and by PM source (road traffic, industry, livestock, other agricultural sources, foreign sources, other Dutch sources), at 1 × 1 km resolution, and weekly SARS-CoV-2 exposure at municipal level. Using generalized additive models, we performed interval-censored survival analyses to assess associations between individuals' average exposure to PM10, PM2.5 and NO2 in the three years before the pandemic (2017-2019) and COVID-19-outcomes, adjusting for SARS-CoV-2 exposure, individual and area-specific confounders. In single-pollutant models, per interquartile (IQR) increase in exposure, PM10 was associated with 7% increased infection risk and 16% increased hospitalisation risk, PM2.5 with 8% increased infection risk and 18% increased hospitalisation risk, and NO2 with 3% increased infection risk and 11% increased hospitalisation risk. Bi-pollutant models suggested that effects were mainly driven by PM. Associations for PM were confirmed when stratifying by urbanization degree, epidemic wave and testing policy. All emission sources of PM, except industry, showed adverse effects on both outcomes. Livestock showed the most detrimental effects per unit exposure, whereas road traffic affected severity (hospitalisation) more than infection risk. This study shows that long-term exposure to air pollution increases both SARS-CoV-2 infection and COVID-19 hospitalisation risks, even after controlling for SARS-CoV-2 exposure levels, and that PM may have differential effects on these COVID-19 outcomes depending on the emission source.

Indirect Effects of PM2.5 Exposure on COVID-19 Mortality in Greater Jakarta, Indonesia: An Ecological Study

Background

Air pollution, including PM2.5, was suggested as one of the primary contributors to COVID-19 fatalities worldwide. Jakarta, the capital city of Indonesia, was recognized as one of the ten most polluted cities globally. Additionally, the incidence of COVID-19 in Jakarta surpasses that of all other provinces in Indonesia. However, no study has investigated the correlation between PM2.5 concentration and COVID-19 fatality in Jakarta.

Objective

To investigate the correlation between short-term and long-term exposure to PM2.5 and COVID-19 mortality in Greater Jakarta area.

Methods

An ecological time-trend study was implemented. The data of PM2.5 ambient concentration obtained from Nafas Indonesia and the National Institute for Aeronautics and Space (LAPAN)/National Research and Innovation Agency (BRIN). The daily COVID-19 death data obtained from the City's Health Office.

Findings

Our study unveiled an intriguing pattern: while short-term exposure to PM2.5 showed a negative correlation with COVID-19 mortality, suggesting it might not be the sole factor in causing fatalities, long-term exposure demonstrated a positive correlation. This suggests that COVID-19 mortality is more strongly influenced by prolonged PM2.5 exposure rather than short-term exposure alone. Specifically, our regression analysis estimate that a 50 µg/m3 increase in long-term average PM2.5 could lead to an 11.9% rise in the COVID-19 mortality rate.

Conclusion

Our research, conducted in one of the most polluted areas worldwide, offers compelling evidence regarding the influence of PM2.5 exposure on COVID-19 mortality rates. It emphasizes the importance of recognizing air pollution as a critical risk factor for the severity of viral respiratory infections.

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

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

Impact of short-term ambient air pollution exposure on the risk of severe COVID-19

Ecological studies suggested a link between air pollution and severe COVID-19 outcomes, while studies accounting for individual-level characteristics are limited. In the present study, we aimed to investigate the impact of short-term ambient air pollution exposure on disease severity among a cohort of 569 laboratory confirmed COVID-19 patients admitted to designated hospitals in Zhejiang province, China, from January 17 to March 3, 2020, and elucidate the possible biological processes involved using transcriptomics. Compared with mild cases, severe cases had higher proportion of medical conditions as well as unfavorable results in most of the laboratory tests, and manifested higher air pollution exposure levels. Higher exposure to air pollutants was associated with increased risk of severe COVID-19 with odds ratio (OR) of 1.89 (95% confidence interval (CI): 1.01, 3.53), 2.35 (95% CI: 1.20, 4.61), 2.87 (95% CI: 1.68, 4.91), and 2.01 (95% CI: 1.10, 3.69) for PM2.5, PM10, NO2 and CO, respectively. OR for NO2 remained significant in two-pollutant models after adjusting for other pollutants. Transcriptional analysis showed 884 differentially expressed genes which mainly were enriched in virus clearance related biological processes between patients with high and low NO2 exposure levels, indicating that compromised immune response might be a potential underlying mechanistic pathway. These findings highlight the impact of short-term air pollution exposure, particularly for NO2, on COVID-19 severity, and emphasize the significance in mitigating the COVID-19 burden of commitments to improve air quality.

Environmental, meteorological and pandemic restriction-related variables affecting SARS-CoV-2 cases

Three years have passed since the outbreak of Coronavirus Disease 2019 (COVID-19) brought the world to standstill. In most countries, the restrictions have ended, and the immunity of the population has increased; however, the possibility of new dangerous variants emerging remains. Therefore, it is crucial to develop tools to study and forecast the dynamics of future pandemics. In this study, a generalized additive model (GAM) was developed to evaluate the impact of meteorological and environmental variables, along with pandemic-related restrictions, on the incidence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Córdoba, Argentina. The results revealed that mean temperature and vegetation cover were the most significant predictors affecting SARS-CoV-2 cases, followed by government restriction phases, days of the week, and hours of sunlight. Although fine particulate matter (PM2.5) and NO2 were less related, they improved the model's predictive power, and a 1-day lag enhanced accuracy metrics. The models exhibited strong adjusted coefficients of determination (R2adj) but did not perform as well in terms of root-mean-square error (RMSE). This suggests that the number of cases may not be the primary variable for controlling the spread of the disease. Furthermore, the increase in positive cases related to policy interventions may indicate the presence of lockdown fatigue. This study highlights the potential of data science as a management tool for identifying crucial variables that influence epidemiological patterns and can be monitored to prevent an overload in the healthcare system.

Nanostructured Bioaerogels as a Potential Solution for Particulate Matter Pollution

Particulate matter (PM) pollution is a significant environmental and public health issue globally. Exposure to high levels of PM, especially fine particles, can have severe health consequences. These particles can come from a variety of sources, including natural events like dust storms and wildfires, as well as human activities such as industrial processes and transportation. Although an extensive development in air filtration techniques has been made in the past few years, fine particulate matter still poses a serios and dangerous threat to human health and to our environment. Conventional air filters are fabricated from non-biodegradable and non-ecofriendly materials which can cause further environmental pollution as a result of their excessive use. Nanostructured biopolymer aerogels have shown great promise in the field of particulate matter removal. Their unique properties, renewable nature, and potential for customization make them attractive materials for air pollution control. In the present review, we discuss the meaning, properties, and advantages of nanostructured aerogels and their potential in particulate matter removal. Particulate matter pollution, types and sources of particulate matter, health effect, environmental effect, and the challenges facing scientists in particulate matter removal are also discussed in the present review. Finally, we present the most recent advances in using nanostructured bioaerogels in the removal of different types of particulate matter and discuss the challenges that we face in these applications.

Particulate matter air pollution and COVID-19 infection, severity, and mortality: A systematic review and meta-analysis

Ecological evidence links ambient particulate matter ≤2.5 mm (PM2.5) and the rate of COVID-19 infections, severity, and deaths. However, such studies are unable to account for individual-level differences in major confounders like socioeconomic status and often rely on imprecise measures of PM2.5. We conducted a systematic review of case-control and cohort studies, which rely on individual-level data, searching Medline, Embase, and the WHO COVID-19 database up to 30 June 2022. Study quality was evaluated using the Newcastle-Ottawa Scale. Results were pooled with a random effects meta-analysis, with Egger's regression, funnel plots, and leave-one-out/trim-and-fill sensitivity analyses to account for publication bias. N = 18 studies met inclusion criteria. A 10 μg/m3 increase in PM2.5 was associated with 66 % (95 % CI: 1.31-2.11) greater odds of COVID-19 infection (N = 7) and 127 % (95 % CI: 1.41-3.66) odds of severe illness (hospitalisation, ICU admission, or requiring respiratory support) (N = 6). Pooled mortality results (N = 5) indicated increased deaths due to PM2.5 but were non-significant (OR 1.40; 0.94 to 2.10). Most studies were rated "good" quality (14/18 studies), though there were numerous methodological issues; few used individual-level data to adjust for socioeconomic status (4/18 studies), instead using area-based indicators (11/18 studies) or no such adjustments (3/18 studies). Most severity (9/10 studies) and mortality studies (5/6 studies) were based on people already diagnosed COVID-19, potentially introducing collider bias. There was evidence of publication bias in studies of infection (p = 0.012) but not severity (p = 0.132) or mortality (p = 0.100). While methodological limits and evidence of bias require cautious interpretation of the findings, we found compelling evidence that PM2.5 increases the risk of COVID-19 infection and severe disease, and weaker evidence of an increase in mortality risk.

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.

Event-Specific Transmission Forecasting of SARS-CoV-2 in a Mixed-Mode Ventilated Office Room Using an ANN

The emerging novel variants and re-merging old variants of SARS-CoV-2 make it critical to study the transmission probability in mixed-mode ventilated office environments. Artificial neural network (ANN) and curve fitting (CF) models were created to forecast the R-Event. The R-Event is defined as the anticipated number of new infections that develop in particular events occurring over the course of time in any defined space. In the spring and summer of 2022, real-time data for an office environment were collected in India in a mixed-mode ventilated office space in a composite climate. The performances of the proposed CF and ANN models were compared with respect to traditional statistical indicators, such as the correlation coefficient, RMSE, MAE, MAPE, NS index, and a20-index, in order to determine the merit of the two approaches. Thirteen input features, namely the indoor temperature (TIn), indoor relative humidity (RHIn), area of opening (AO), number of occupants (O), area per person (AP), volume per person (VP), CO2 concentration (CO2), air quality index (AQI), outer wind speed (WS), outdoor temperature (TOut), outdoor humidity (RHOut), fan air speed (FS), and air conditioning (AC), were selected to forecast the R-Event as the target. The main objective was to determine the relationship between the CO2 level and R-Event, ultimately producing a model for forecasting infections in office building environments. The correlation coefficients for the CF and ANN models in this case study were 0.7439 and 0.9999, respectively. This demonstrates that the ANN model is more accurate in R-Event prediction than the curve fitting model. The results show that the proposed ANN model is reliable and significantly accurate in forecasting the R-Event values for mixed-mode ventilated offices.

Good and bad get together: Inactivation of SARS-CoV-2 in particulate matter pollution from different fuels

Air pollution and associated particulate matter (PM) affect environmental and human health worldwide. The intense vehicle usage and the high population density in urban areas are the main causes of this public health impact. Epidemiological studies have provided evidence on the effect of air pollution on airborne SARS-CoV-2 transmission and COVID-19 disease prevalence and symptomatology. However, the causal relationship between air pollution and COVID-19 is still under investigation. Based on these results, the question addressed in this study was how long SARS-CoV-2 survives on the surface of PM from different origin to evaluate the relationship between fuel and atmospheric pollution and virus transmission risk. The persistence and viability of SARS-CoV-2 virus was characterized in 5 engine exhaust PM and 4 samples of atmospheric PM₁₀. The results showed that SARS-CoV-2 remains on the surface of PM₁₀ from air pollutants but interaction with engine exhaust PM inactivates the virus. Consequently, atmospheric PM₁₀ levels may increase SARS-CoV-2 transmission risk thus supporting a causal relationship between these factors. Furthermore, the relationship of pollution PM and particularly engine exhaust PM with virus transmission risk and COVID-19 is also affected by the impact of these pollutants on host oxidative stress and immunity. Therefore, although fuel PM inactivates SARS-CoV-2, the conclusion of the study is that both atmospheric and engine exhaust PM negatively impact human health with implications for COVID-19 and other diseases.