The impact of ambient fine particles on influenza transmission and the modification effects of temperature in China: A multi-city study

Exploring the Relationship between Acute Coronary Syndrome, Lower Respiratory Tract Infections, and Atmospheric Pollution

Background: Respiratory infections were found to be connected with the incidence of acute coronary syndrome (ACS). The proposed pathway of this connection includes inflammation, oxidative stress, pro-coagulation, and atherosclerotic plaque destabilization. This can cause rapture and thrombus formation, leading to ACS. Our study aimed to assess the risk factors for coronary artery thrombosis as a manifestation of ACS and for lower respiratory tract infections (LRTIs) in patients with ACS. Methods: The study included 876 patients with ACS from January 2014 to December 2018. Both the clinical data and air pollution data were analyzed. Statistical tests used for analysis included Student's t-test, the Mann-Whitney U-test, the Chi-squared test, and the odds ratio Altman calculation. Results: LRTIs were found in 9.13% patients with ACS. The patients with LRTI had a higher risk of coronary artery thrombosis (OR: 2.4903; CI: 1.3483 to 4.5996). Moreover, they had increased values of inflammatory markers, were older, had a lower BMI, and a higher rate of atrial fibrillation. The average atmospheric aerosols with a maximum diameter of 2.5 μm (PM2.5 concentration) from three consecutive days before hospitalization for ACS were higher in patients with LRTI. Conclusions: The occurrence of coronary artery thrombosis was higher among the patients with LRTI during ACS. PM2.5 exposition was higher in the three consecutive days before hospitalization in patients with LRTI during ACS.

Differential associations of fine and coarse particulate air pollution with cause-specific pneumonia mortality: A nationwide, individual-level, case-crossover study

BACKGROUND

The connections between fine particulate matter (PM2.5) and coarse particulate matter (PM2.5-10) and daily mortality of viral pneumonia and bacterial pneumonia were unclear.

OBJECTIVES

To distinguish the connections between PM2.5 and PM2.5-10 and daily mortality due to viral pneumonia and bacterial pneumonia.

METHODS

Using a comprehensive national death registry encompassing all areas of mainland China, we conducted a case-crossover investigation from 2013 to 2019 at an individual level. Residential daily particle concentrations were evaluated using satellite-based models with a spatial resolution of 1 km. To analyze the data, we employed the conditional logistic regression model in conjunction with polynomial distributed lag models.

RESULTS

We included 221,507 pneumonia deaths in China. Every interquartile range (IQR) elevation in concentrations of PM2.5 (lag 0-2 d, 37.6 μg/m3) was associated with higher magnitude of mortality for viral pneumonia (3.03%) than bacterial pneumonia (2.14%), whereas the difference was not significant (p-value for difference = 0.38). An IQR increase in concentrations of PM2.5-10 (lag 0-2 d, 28.4 μg/m3) was also linked to higher magnitude of mortality from viral pneumonia (3.06%) compared to bacterial pneumonia (2.31%), whereas the difference was not significant (p-value for difference = 0.52). After controlling for gaseous pollutants, their effects were all stable; however, with mutual adjustment, the associations of PM2.5 remained, and those of PM2.5-10 were no longer statistically significant. Greater magnitude of associations was noted in individuals aged 75 years and above, as well as during the cold season.

CONCLUSION

This nationwide study presents compelling evidence that both PM2.5 and PM2.5-10 exposures could increase pneumonia mortality of viral and bacterial causes, highlighting the more robust effects of PM2.5 and somewhat higher sensitivity of viral pneumonia.

Differentiated impacts of short-term exposure to fine particulate constituents on infectious diseases in 507 cities of Chinese children and adolescents: A nationwide time-stratified case-crossover study from 2008 to 2021

This study assesses the association of short-term exposure to PM2.5 (particles ≤2.5 μm) on infectious diseases among Chinese children and adolescents. Analyzing data from 507 cities (2008-2021) on 42 diseases, it focuses on PM2.5 components (black carbon (BC), ammonium (NH4+), inorganic nitrate (NO3-), organic matter (OM), and sulfate (SO42-)). PM2.5 constituents significantly associated with incidence. Sulfate showed the most substantial effect, increasing all-cause infectious disease risk by 2.72 % per interquartile range (IQR) increase. It was followed by BC (2.04 % increase), OM (1.70 %), NO3- (1.67 %), and NH4+ (0.79 %). Specifically, sulfate and BC had pronounced impacts on respiratory diseases, with sulfate linked to a 10.73 % increase in seasonal influenza risk and NO3- to a 16.39 % rise in tuberculosis. Exposure to PM2.5 also marginally increased risks for gastrointestinal, enterovirus, and vectorborne diseases like dengue (7.46 % increase with SO42-). Sexually transmitted and bloodborne diseases saw an approximate 6.26 % increase in incidence, with specific constituents linked to diseases like hepatitis C and syphilis. The study concludes that managing PM2.5 levels could substantially reduce infectious disease incidence, particularly in China's middle-northern regions. It highlights the necessity of stringent air quality standards and targeted disease prevention, aligning PM2.5 management with international guidelines for public health protection.

A Review of the Interactive Effects of Climate and Air Pollution on Human Health in China

PURPOSE OF REVIEW

Through a systematic search of peer-reviewed epidemiologic studies, we reviewed the literature on the human health impacts of climate and ambient air pollution, focusing on recently published studies in China. Selected previous literature is discussed where relevant in tracing the origins.

RECENT FINDINGS

Climate variables and air pollution have a complex interplay in affecting human health. The bulk of the literature we reviewed focuses on the air pollutants ozone and fine particulate matter and temperatures (including hot and cold extremes). The interaction between temperature and ozone presented substantial interaction, but evidence about the interactive effects of temperature with other air pollutants is inconsistent. Most included studies used a time-series design, usually with daily mean temperature and air pollutant concentration as independent variables. Still, more needs to be studied about the co-occurrence of climate and air pollution. The co-occurrence of extreme climate and air pollution events is likely to become an increasing health risk in China and many parts of the world as climate changes. Climate change can interact with air pollution exposure to amplify risks to human health. Challenges and opportunities to assess the combined effect of climate variables and air pollution on human health are discussed in this review. Implications from epidemiological studies for implementing coordinated measures and policies for addressing climate change and air pollution will be critical areas of future work.

The association between humidex and tuberculosis: a two-stage modelling nationwide study in China

BACKGROUND

Under a changing climate, the joint effects of temperature and relative humidity on tuberculosis (TB) are poorly understood. To address this research gap, we conducted a time-series study to explore the joint effects of temperature and relative humidity on TB incidence in China, considering potential modifiers.

METHODS

Weekly data on TB cases and meteorological factors in 22 cities across mainland China between 2011 and 2020 were collected. The proxy indicator for the combined exposure levels of temperature and relative humidity, Humidex, was calculated. First, a quasi-Poisson regression with the distributed lag non-linear model (DLNM) was constructed to examine the city-specific associations between humidex and TB incidence. Second, a multivariate meta-regression model was used to pool the city-specific effect estimates, and to explore the potential effect modifiers.

RESULTS

A total of 849,676 TB cases occurred in the 22 cities between 2011 and 2020. Overall, a conspicuous J-shaped relationship between humidex and TB incidence was discerned. Specifically, a decrease in humidex was positively correlated with an increased risk of TB incidence, with a maximum relative risk (RR) of 1.40 (95% CI: 1.11-1.76). The elevated RR of TB incidence associated with low humidex (5th humidex) appeared on week 3 and could persist until week 13, with a peak at approximately week 5 (RR: 1.03, 95% CI: 1.01-1.05). The effects of low humidex on TB incidence vary by Natural Growth Rate (NGR) levels.

CONCLUSION

A J-shaped exposure-response association existed between humidex and TB incidence in China. Humidex may act as a better predictor to forecast TB incidence compared to temperature and relative humidity alone, especially in regions with higher NGRs.

Air Pollution and Influenza: A Systematic Review and Meta-Analysis

Background

Influenza is the first infectious disease that implements global monitoring and is one of the major public health issues in the world. Air pollutants have become an important global public health issue, in recent years, and much epidemiological and clinical evidence has shown that air pollutants are associated with respiratory diseases.

Methods

We comprehensively searched articles published up to 15 November 2022 in PubMed, Web of Science, China National Knowledge Infrastructure (CNKI), Database of Chinese sci-tech periodicals, and Wanfang Database. The search strategies were based on keyword combinations related to influenza and air pollutants. The air pollutants included particulate matter (PM2.5, PM10), nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), and ozone (O3). Meta-analysis was performed using the R programming language (R4.2.1).

Results

A total of 2926 records were identified and 1220 duplicates were excluded. Finally, 19 studies were included in the meta-analysis according to inclusion and exclusion criteria. We observed a significant association between partial air pollutants (PM2.5, NO2, PM10 and SO2) and the incidence risk of influenza. The RRs were 1.0221 (95% CI: 1.0093~1.0352), 1.0395 (95% CI: 1.0131~1.0666), 1.007 (95% CI: 1.0009~1.0132), and 1.0352 (95% CI. 1.0076~1.0635), respectively. However, there was no significant relationship between CO and O3 exposure and influenza, and the RRs were 1.2272 (95% CI: 0.9253~1.6275) and 1.0045 (95% CI: 0.9930~1.0160), respectively.

Conclusion

Exposure to PM2.5, NO2, PM10, and SO2 was significantly associated with influenza, which may be risk factors for influenza. The association of CO and O3 with influenza needs further investigation.

Short-Term Exposure to Ambient Air Pollution and Influenza: A Multicity Study in China

BACKGROUND

Air pollution is a major risk factor for planetary health and has long been suspected of predisposing humans to respiratory diseases induced by pathogens like influenza viruses. However, epidemiological evidence remains elusive due to lack of longitudinal data from large cohorts.

OBJECTIVE

Our aim is to quantify the short-term association of influenza incidence with exposure to ambient air pollutants in Chinese cities.

METHODS

Based on air pollutant data and influenza surveillance data from 82 cities in China over a period of 5 years, we applied a two-stage time series analysis to assess the association of daily incidence of reported influenza cases with six common air pollutants [particulate matter with aerodynamic diameter ≤ 2.5 μ m (PM 2.5 ), particulate matter with aerodynamic diameter ≤ 10 μ m (PM 10 ), NO 2 , SO 2 , CO, and O 3 ], while adjusting for potential confounders including temperature, relative humidity, seasonality, and holiday effects. We built a distributed lag Poisson model for one or multiple pollutants in each individual city in the first stage and conducted a meta-analysis to pool city-specific estimates in the second stage.

RESULTS

A total of 3,735,934 influenza cases were reported in 82 cities from 2015 to 2019, accounting for 72.71% of the overall case number reported in the mainland of China. The time series models for each pollutant alone showed that the daily incidence of reported influenza cases was positively associated with almost all air pollutants except for ozone. The most prominent short-term associations were found for SO 2 and NO 2 with cumulative risk ratios of 1.094 [95% confidence interval (CI): 1.054, 1.136] and 1.093 (95% CI: 1.067, 1.119), respectively, for each 10   μ g / m 3 increase in the concentration at each of the lags of 1-7 d. Only NO 2 showed a significant association with the daily incidence of influenza cases in the multipollutant model that adjusts all six air pollutants together. The impact of air pollutants on influenza was generally found to be greater in children, in subtropical cities, and during cold months.

DISCUSSION

Increased exposure to ambient air pollutants, particularly NO 2 , is associated with a higher risk of influenza-associated illness. Policies on reducing air pollution levels may help alleviate the disease burden due to influenza infection. https://doi.org/10.1289/EHP12146.

Associations between Short-Term Exposure to Ambient Air Pollution and Influenza: An Individual-Level Case-Crossover Study in Guangzhou, China

BACKGROUND

Influenza imposes a heavy burden on public health. Little is known, however, of the associations between detailed measures of exposure to ambient air pollution and influenza at an individual level.

OBJECTIVE

We examined individual-level associations between six criteria air pollutants and influenza using case-crossover design.

METHODS

In this individual-level time-stratified case-crossover study, we linked influenza cases collected by the Guangzhou Center for Disease Control and Prevention from 1 January 2013 to 31 December 2019 with individual residence-level exposure to particulate matter (PM 2.5 and PM 10 ), sulfur dioxide (SO 2 ), nitrogen dioxide (NO 2 ), ozone (O 3 ) and carbon monoxide (CO). The exposures were estimated for the day of onset of influenza symptoms (lag 0), 1-7 d before the onset (lags 1-7), as well as an 8-d moving average (lag07), using a random forest model and linked to study participants' home addresses. Conditional logistic regression was developed to investigate the associations between short-term exposure to air pollution and influenza, adjusting for mean temperature, relative humidity, public holidays, population mobility, and community influenza susceptibility.

RESULTS

N = 108,479 eligible cases were identified in our study. Every 10 - μ g / m 3 increase in exposure to PM 2.5 , PM 10 , NO 2 , and CO and every 5 - μ g / m 3 increase in SO 2 over 8-d moving average (lag07) was associated with higher risk of influenza with a relative risk (RR) of 1.028 (95% CI: 1.018, 1.038), 1.041 (95% CI: 1.032, 1.049), 1.169 (95% CI: 1.151, 1.188), 1.004 (95% CI: 1.003, 1.006), and 1.134 (95% CI: 1.107, 1.163), respectively. There was a negative association between O 3 and influenza with a RR of 0.878 (95% CI: 0.866, 0.890).

CONCLUSIONS

Our findings suggest that short-term exposure to air pollution, except for O 3 , is associated with greater risk for influenza. Further studies are necessary to decipher underlying mechanisms and design preventive interventions and policies. https://doi.org/10.1289/EHP12145.

The associations of ambient fine particles with tuberculosis incidence and the modification effects of ambient temperature: A nationwide time-series study in China

Ambient fine particulate matter (PM2.5) is a major air pollutant that poses significant risks to human health. However, little is known about the association of PM2.5 with tuberculosis (TB) incidence, and whether temperature modifies the association.This study aimed to explore the association between ambient PM2.5 exposure and TB incidence in China and the modification effects of temperature. Weekly meteorological data, PM2.5 concentrations, and TB incidence numbers were collected for 22 cities across Mainland China, from 2011 to 2020. A quasi-Poisson regression with the distributed lag non-linear model was used to assess city-specific PM2.5-TB associations. A multivariate meta-regression model was then used to pool the city-specific effect estimates, at the national and regional levels. A J-shaped PM2.5-TB relationship was observed at the national level for China. Compared to those with minimum PM2.5-TB risk, people who were exposed to the highest PM2.5 concentrations had a 26 % (RR:1.26, 95 % confidence interval [CI]: 1.05, 1.52) higher risk for TB incidence. J-shaped PM2.5-TB associations were also observed for most sub-groups, however, no significant modifying effects were found. While a trend was observed between low temperatures and increased exposure-response associations, these results were not significant. Overall, approximately 20 % of TB cases in the 22 study cities, over the period 2011-2020, could be attributed to PM2.5 exposure. Strengthening the monitoring and emission control of PM2.5 could aid the prevention and control of TB incidence.

LSTM-based recurrent neural network provides effective short term flu forecasting

BACKGROUND

Influenza virus is responsible for a yearly epidemic in much of the world. To better predict short-term, seasonal variations in flu infection rates and possible mechanisms of yearly infection variation, we trained a Long Short-Term Memory (LSTM)-based deep neural network on historical Influenza-Like-Illness (ILI), climate, and population data.

METHODS

Data were collected from the Centers for Disease Control and Prevention (CDC), the National Center for Environmental Information (NCEI), and the United States Census Bureau. The model was initially built in Python using the Keras API and tuned manually. We explored the roles of temperature, precipitation, local wind speed, population size, vaccination rate, and vaccination efficacy. The model was validated using K-fold cross validation as well as forward chaining cross validation and compared to several standard algorithms. Finally, simulation data was generated in R and used for further exploration of the model.

RESULTS

We found that temperature is the strongest predictor of ILI rates, but also found that precipitation increased the predictive power of the network. Additionally, the proposed model achieved a +1 week prediction mean absolute error (MAE) of 0.1973. This is less than half of the MAE achieved by the next best performing algorithm. Additionally, the model accurately predicted simulation data. To test the role of temperature in the network, we phase-shifted temperature in time and found a predictable reduction in prediction accuracy.

CONCLUSIONS

The results of this study suggest that short term flu forecasting may be effectively accomplished using architectures traditionally reserved for time series analysis. The proposed LSTM-based model was able to outperform comparison models at the +1 week time point. Additionally, this model provided insight into the week-to-week effects of climatic and biotic factors and revealed potential patterns in data series. Specifically, we found that temperature is the strongest predictor of seasonal flu infection rates. This information may prove to be especially important for flu forecasting given the uncertain long-term impact of the SARS-CoV-2 pandemic on seasonal influenza.

Spatio-temporal evolution patterns of influenza incidence and its nonlinear spatial correlation with environmental pollutants in China

BACKGROUND

Currently, the influenza epidemic in China is at a high level and mixed with other respiratory diseases. Current studies focus on regional influenza and the impact of environmental pollutants on time series, and lack of overall studies on the national influenza epidemic and the nonlinear correlation between environmental pollutants and influenza. The unclear spatial and temporal evolution patterns of influenza as well as the unclear correlation effect between environmental pollutants and influenza epidemic have greatly hindered the prevention and treatment of influenza epidemic by relevant departments, resulting in unnecessary economic and human losses.

METHOD

This study used Chinese influenza incidence data for 2007-2017 released by the China CDC and air pollutant site monitoring data. Seasonal as well as inter monthly differences in influenza incidence across 31 provinces of China have been clarified through time series. Space-Time Cube model (STC) was used to investigate the spatio-temporal evolution of influenza incidence in 315 Chinese cities during 2007-2017. Then, based on the spatial heterogeneity of influenza incidence in China, Generalized additive model (GAM) was used to identify the correlation effect of environmental pollutants (PM2.5, PM10, CO, SO2, NO2, O3) and influenza incidence.

RESULT

The influenza incidence in China had obvious seasonal changes, with frequent outbreaks in winter and spring. The influenza incidence decreased significantly after March, with only sporadic outbreaks occurring in some areas. In the past 11 years, the influenza epidemic had gradually worsened, and the clustering of influenza had gradually expanded, which had become a serious public health problem. The correlation between environmental pollutants and influenza incidence was nonlinear. Generally, PM2.5, CO and NO2 were positively correlated at high concentrations, while PM10 and SO2 were negatively correlated. O3 was not strongly correlated with the influenza incidence.

CONCLUSION

The study found that the influenza epidemic in China was in a rapidly rising stage, and several regions had a multi-year outbreak trend and the hot spots continue to expand outward. The association between environmental pollutants and influenza incidence was nonlinear and spatially heterogeneous. Relevant departments should improve the monitoring of influenza epidemic, optimize the allocation of resources, reduce environmental pollution, and strengthen vaccination to effectively prevent the aggravation and spread of influenza epidemic in the high incidence season and areas.

Airborne fine particles drive H1N1 viruses deep into the lower respiratory tract and distant organs

Mounting data suggest that environmental pollution due to airborne fine particles (AFPs) increases the occurrence and severity of respiratory virus infection in humans. However, it is unclear whether and how interactions with AFPs alter viral infection and distribution. We report synergetic effects between various AFPs and the H1N1 virus, regulated by physicochemical properties of the AFPs. Unlike infection caused by virus alone, AFPs facilitated the internalization of virus through a receptor-independent pathway. Moreover, AFPs promoted the budding and dispersal of progeny virions, likely mediated by lipid rafts in the host plasma membrane. Infected animal models demonstrated that AFPs favored penetration of the H1N1 virus into the distal lung, and its translocation into extrapulmonary organs including the liver, spleen, and kidney, thus causing severe local and systemic disorders. Our findings revealed a key role of AFPs in driving viral infection throughout the respiratory tract and beyond. These insights entail stronger air quality management and air pollution reduction policies.

The indirect impacts of nonpharmacological COVID-19 control measures on other infectious diseases in Yinchuan, Northwest China: a time series study

BACKGROUND

Various nonpharmaceutical interventions (NPIs) against COVID-19 continue to have an impact on socioeconomic and population behaviour patterns. However, the effect of NPIs on notifiable infectious diseases remains inconclusive due to the variability of the disease spectrum, high-incidence endemic diseases and environmental factors across different geographical regions. Thus, it is of public health interest to explore the influence of NPIs on notifiable infectious diseases in Yinchuan, Northwest China.

METHODS

Based on data on notifiable infectious diseases (NIDs), air pollutants, meteorological data, and the number of health institutional personnel in Yinchuan, we first fitted dynamic regression time series models to the incidence of NIDs from 2013 to 2019 and then estimated the incidence for 2020. Then, we compared the projected time series data with the observed incidence of NIDs in 2020. We calculated the relative reduction in NIDs at different emergency response levels in 2020 to identify the impacts of NIPs on NIDs in Yinchuan.

RESULTS

A total of 15,711 cases of NIDs were reported in Yinchuan in 2020, which was 42.59% lower than the average annual number of cases from 2013 to 2019. Natural focal diseases and vector-borne infectious diseases showed an increasing trend, as the observed incidence in 2020 was 46.86% higher than the estimated cases. The observed number of cases changed in respiratory infectious diseases, intestinal infectious diseases and sexually transmitted or bloodborne diseases were 65.27%, 58.45% and 35.01% higher than the expected number, respectively. The NIDs with the highest reductions in each subgroup were hand, foot, and mouth disease (5854 cases), infectious diarrhoea (2157 cases) and scarlet fever (832 cases), respectively. In addition, it was also found that the expected relative reduction in NIDs in 2020 showed a decline across different emergency response levels, as the relative reduction dropped from 65.65% (95% CI: -65.86%, 80.84%) during the level 1 response to 52.72% (95% CI: 20.84%, 66.30%) during the level 3 response.

CONCLUSIONS

The widespread implementation of NPIs in 2020 may have had significant inhibitory effects on the incidence of respiratory infectious diseases, intestinal infectious diseases and sexually transmitted or bloodborne diseases. The relative reduction in NIDs during different emergency response levels in 2020 showed a declining trend as the response level changed from level 1 to level 3. These results can serve as essential guidance for policy-makers and stakeholders to take specific actions to control infectious diseases and protect vulnerable populations in the future.

Dynamics and calculation of the basic reproduction number for a nonlocal dispersal epidemic model with air pollution

In order to reflect the dispersal of pollutants in non-adjacent areas and the large-scale movement of individuals, this paper proposes an epidemic model of nonlocal dispersal with air pollution, where the transmission rate is related to the concentration of pollutants. This paper checks the uniqueness and existence of the global positive solution and defines the basic reproduction number, R 0 . We simultaneously explore the global dynamics: when R 0 < 1 , the disease-free stable point is global asymptotic stability; when R 0 > 1 , the disease is uniformly persistent. Additionally, in order to approximate R 0 , a numerical method has been introduced. Illustrative examples are used to verify the theoretical outcomes and show the effect of the dispersal rate on the basic reproduction number R 0 .

Inferring a Causal Relationship between Environmental Factors and Respiratory Infections Using Convergent Cross-Mapping

The incidence of respiratory infections in the population is related to many factors, among which environmental factors such as air quality, temperature, and humidity have attracted much attention. In particular, air pollution has caused widespread discomfort and concern in developing countries. Although the correlation between respiratory infections and air pollution is well known, establishing causality between them remains elusive. In this study, by conducting theoretical analysis, we updated the procedure of performing the extended convergent cross-mapping (CCM, a method of causal inference) to infer the causality between periodic variables. Consistently, we validated this new procedure on the synthetic data generated by a mathematical model. For real data in Shaanxi province of China in the period of 1 January 2010 to 15 November 2016, we first confirmed that the refined method is applicable by investigating the periodicity of influenza-like illness cases, an air quality index, temperature, and humidity through wavelet analysis. We next illustrated that air quality (quantified by AQI), temperature, and humidity affect the daily influenza-like illness cases, and, in particular, the respiratory infection cases increased progressively with increased AQI with a time delay of 11 days.

PM10 promotes an inflammatory cytokine response that may impact SARS-CoV-2 replication in vitro

Introduction

In the last decades, a decrease in air quality has been observed, mainly associated with anthropogenic activities. Air pollutants, including particulate matter (PM), have been associated with adverse effects on human health, such as exacerbation of respiratory diseases and infections. High levels of PM in the air have recently been associated with increased morbidity and mortality of COVID-19 in some regions of the world.

Objective

To evaluate the effect of coarse particulate matter (PM10) on the inflammatory response and viral replication triggered by SARS-CoV-2 using in vitro models.

Methods

Peripheral blood mononuclear cells (PBMC) from healthy donors were treated with PM10 and subsequently exposed to SARS-CoV-2 (D614G strain, MOI 0.1). The production of pro-inflammatory cytokines and antiviral factors was quantified by qPCR and ELISA. In addition, using the A549 cell line, previously exposed to PM, the viral replication was evaluated by qPCR and plaque assay.

Results

SARS-CoV-2 stimulation increased the production of pro-inflammatory cytokines in PBMC, such as IL-1β, IL-6 and IL-8, but not antiviral factors. Likewise, PM10 induced significant production of IL-6 in PBMCs stimulated with SARS-CoV-2 and decreased the expression of OAS and PKR. Additionally, PM10 induces the release of IL-1β in PBMC exposed to SARS-CoV-2 as well as in a co-culture of epithelial cells and PBMCs. Finally, increased viral replication of SARS-CoV-2 was shown in response to PM10.

Conclusion

Exposure to coarse particulate matter increases the production of pro-inflammatory cytokines, such as IL-1β and IL-6, and may alter the expression of antiviral factors, which are relevant for the immune response to SARS-CoV-2. These results suggest that pre-exposure to air particulate matter could have a modest role in the higher production of cytokines and viral replication during COVID-19, which eventually could contribute to severe clinical outcomes.

Stronger susceptibilities to air pollutants of influenza A than B were identified in subtropical Shenzhen, China

Air pollution was indicated to be a key factor contributing to the aggressive spread of influenza viruses, whereas uncertainty still exists regarding to whether distinctions exist between influenza subtypes. Our study quantified the impact of five air pollutants on influenza subtype outbreaks in Shenzhen, China, a densely populated and highly urbanized megacity. Daily influenza outbreak data of laboratory-confirmed positive cases were obtained from the Shenzhen CDC, from May 1, 2013 to Dec 31, 2015. Concentrations of nitrogen dioxide (NO₂), sulfur dioxide (SO₂), particulate matters ≤2.5 μm (PM_2.5), particulate matters ≤10 μm (PM₁₀), and ozone (O₃), were retrieved from the 18 national monitoring stations. The generalized additive model (GAM) and distributed lag non-linear model (DLNM) were used to calculate the concentration-response relationships between environmental inducers and outbreak epidemics, respectively for influenza A (Flu-A) and B (Flu-B). There were 1687 positive specimens were confirmed during the study period. The cold season was restricted from Nov. 4th to Apr. 20th, covering all seasons other than the long-lasting summer. Relatively heavy fine particle matter (PM_2.5) and NO₂ pollution was observed in cold months, with mean concentrations of 46.06 μg/m³ and 40.03 μg/m³, respectively. Time-series analysis indicated that high concentrations of NO₂, PM_2.5, PM₁₀, and O₃ were associated with more influenza outbreaks at short lag periods (0-5 d). Although more Flu-B (679 cases) epidemics occurred than Flu-A (382 cases) in the cold season, Flu-A generally showed higher susceptibility to air pollutants. A 10 μg/m³ increment in concentrations of PM_2.5, PM₁₀, and O₃ at lag 04, was associated with a 2.103 (95%CI: 1.528-2.893), 1.618 (95%CI: 1.311-1.996), and 1.569 (95%CI: 1.214-2.028) of the relative risk (RR) of Flu-A, respectively. A 5 μg/m³ increase in NO₂ was associated with higher risk of Flu-A at lag 03 (RR = 1.646, 95%CI: 1.295-2.092) and of Flu-B at lag 04 (RR = 1.319, 95%CI: 1.095-1.588). Nevertheless, barely significant effect of particulate matters (PM_2.5, PM₁₀) on Flu-B and SO₂ on both subtypes was detected. Further, the effect estimates of NO₂ increased for both subtypes when coexisting with other pollutants. This study provides evidence that declining concentrations of main pollutants including NO₂, O₃, and particulate matters, could substantially decrease influenza risk in subtropical Shenzhen, especially for influenza A.

Risk Factors for Respiratory Viral Infections: A Spotlight on Climate Change and Air Pollution

Climate change has both direct and indirect effects on human health, and some populations are more vulnerable to these effects than others. Viral respiratory infections are most common illnesses in humans, with estimated 17 billion incident infections globally in 2019. Anthropogenic drivers of climate change, chiefly the emission of greenhouse gases and toxic pollutants from burning of fossil fuels, and the consequential changes in temperature, precipitation, and frequency of extreme weather events have been linked with increased susceptibility to viral respiratory infections. Air pollutants like nitrogen dioxide, particulate matter, diesel exhaust particles, and ozone have been shown to impact susceptibility and immune responses to viral infections through various mechanisms, including exaggerated or impaired innate and adaptive immune responses, disruption of the airway epithelial barrier, altered cell surface receptor expression, and impaired cytotoxic function. An estimated 90% of the world's population is exposed to air pollution, making this a topic with high relevance to human health. This review summarizes the available epidemiologic and experimental evidence for an association between climate change, air pollution, and viral respiratory infection.

Impact of PM2.5 and ozone on incidence of influenza in Shijiazhuang, China: a time-series study

Most of the studies are focused on influenza and meteorological factors for influenza. There are still few studies focused on the relationship between pollution factors and influenza, and the results are not consistent. This study conducted distributed lag nonlinear model and attributable risk on the relationship between influenza and pollution factors, aiming to quantify the association and provide a basis for the prevention of influenza and the formulation of relevant policies. Environmental data in Shijiazhuang from 2014 to 2019, as well as the data on hospital-confirmed influenza, were collected. When the concentration of PM_2.5 was the highest (621 μg/m³), the relative risk was the highest (RR: 2.39, 95% CI: 1.10-5.17). For extremely high concentration PM_2.5 (348 μg/m³), analysis of cumulative lag effect showed statistical significance from cumulative lag0-1 to lag0-6 day, and the minimum cumulative lag effect appeared in lag0-2 (RR: 0.760, 95% CI: 0.655-0.882). In terms of ozone, the RR value was 2.28(1.19,4.38), when O₃ concentration was 310 μg/m³, and the RR was 1.65(1.26,2.15), when O₃ concentration was 0 μg/m³. The RR of this lag effect increased with the increase of lag days, and reached the maximum at lag0-7 days, RR and 95% CI of slightly low concentration and extremely high concentration were 1.217(1.108,1.337) and 1.440(1.012,2.047), respectively. Stratified analysis showed that there was little difference in gender, but in different age groups, the cumulative lag effect of these two pollutants on influenza was significantly different. Our study found a non-linear relationship between two pollutants and influenza; slightly low concentrations were more associated with contaminant-related influenza. Health workers should encourage patients to get the influenza vaccine and wear masks when going out during flu seasons.

The influence of air quality and meteorological variations on influenza A and B virus infections in a paediatric population in Singapore

INTRODUCTION

Influenza is an important cause of paediatric illness across the globe. However, information about the relationships between air pollution, meteorological variability and paediatric influenza A and B infections in tropical settings is limited.

METHODS

We analysed all daily reports of influenza A and B infections in children <5 years old obtained from the largest specialist women and children's hospital in Singapore. In separate negative binomial regression models, we assessed the dependence of paediatric influenza A and B infections on air quality and meteorological variability, using multivariable fractional polynomial modelling and adjusting for time-varying confounders.

RESULTS

Approximately 80% of 7329 laboratory-confirmed reports were caused by influenza A. We observed positive associations between sulphur dioxide (SO₂) exposure and the subsequent risk of infection with both influenza types. We observed evidence of a harvesting effect of SO₂ on Influenza A but not Influenza B. Ambient temperature was associated with a decline in influenza A reports (Relative Risk at lag 5 [RR_lag5]: 0.949, 95% CI: 0.916-0.983). Rainfall was positively associated with a subsequent increase in influenza A reports (RR_lag3: 1.044, 95% CI: 1.017-1.071). Nitrogen dioxide (NO₂) concentration was positively associated with influenza B reports (RR_lag5: 1.015, 95% CI: 1.005-1.025). There was a non-linear association between CO and influenza B reports. Absolute humidity increased the ensuing risk of influenza B (RR_lag5: 4.799, 95% CI: 2.277-10.118). Influenza A and B infections displayed dissimilar but predictable within-year seasonal patterns.

CONCLUSIONS

We observed different independent associations between air quality and meteorological variability with paediatric influenza A and B infections. Anticipated seasonal infection peaks and variations in air quality and meteorological parameters can inform the timing of community measures aimed at reducing influenza infection risk.

Influence of air pollution on influenza-like illness in China: a nationwide time-series analysis

BACKGROUND

Evidence concerning effects of air pollution on influenza-like illness (ILI) from multi-center is limited and little is known about how regional factors might modify this relationship.

METHODS

In this ecological study, ILI cases defined as outpatients with temperature ≥38 °C, accompanied by cough or sore throat, were collected from National Influenza Surveillance Network in China. We adopted generalized additive model with quasi-Poisson to estimate province-specific association between air pollution and ILI in 30 Chinese provinces during 2015-2019, after adjusting for time trend and meteorological factors. We then pooled province-specific association by using random-effect meta-analysis. Potential effect modifications of season and regional characteristics were explored.

FINDINGS

A total of 26, 004, 853 ILI cases and 777, 223, 877 hospital outpatients were collected. In general, effects of air pollutants were acute. An inter-quartile range increase of PM_2.5, SO₂, PM₁₀, NO₂ and CO at lag0, and O₃ at lag0-2 was associated with 3.08% (95% CI: 1.91%, 4.27%), 3.00% (1.86%, 4.16%), 6.46% (4.71%, 8.25%), 7.21% (5.73%, 8.71%), 4.37% (3.05%, 5.70%), and -9.26% (-11.32%, -7.14%) change of ILI at national level, respectively. Associations between air pollutants and ILI varied by season and regions, with higher effect estimates in cold season, eastern and central regions and provinces with more humid condition and larger population.

INTERPRETATION

This study indicated that most air pollutants increased the risk of ILI in China. Our findings might provide implications for the development of policies to protect public health from air pollution and influenza.

FUNDING

National Natural Science Foundation of China and Chongqing Health Commission Program.

Exposure to ultrafine particles and childhood obesity: A cross-sectional analysis of the Seven Northeast Cities (SNEC) Study in China

BACKGROUND

Studies on the obesogenic effect of air pollution on children have been mixed and sparse. Moreover, due to insufficient air monitoring, few studies have investigated the role of more tiny but unregulated particles (ambient particles with a diameter of 0.1 μm or less, ultrafine particles).

OBJECTIVE

We sought to explore the associations between long-term exposure to ambient ultrafine particles (UFPs) and childhood obesity in Chinese children.

METHODS

In this cross-sectional study, we randomly recruited 47,990 children, aged 6-18 years, from seven cities in Northeastern China between 2012 and 2013. Child age- and sex-specific z-scores for body mass index (BMI Z-score) and weight status were generated using the World Health Organization growth reference. Four-year average concentrations of UFPs and airborne particulates of diameter ≤ 1 μm (PM₁), ≤2.5 μm (PM_2.5), and ≤10 μm (PM₁₀) were estimated at home, using neural network simulated WRF-Chem model and spatiotemporal model, respectively. Confounder-adjusted generalized linear mixed models examined the associations between air pollution and BMI Z-score and the prevalence of childhood obesity.

RESULT

We found that UFPs exposure was associated with greater childhood BMI Z-score and a higher likelihood of obesity. Compared with the lowest quartile, higher quartiles of UFPs were associated with greater odds for obesity prevalence in children (i.e., the adjusted OR was 1.25; 95 % CI, 1.12-1.39; 1.43; 95 % CI, 1.27-1.61; and 1.41; 95 % CI, 1.25-1.58 for the second, third, and fourth quartile, respectively). Similar associations were observed for PM₁, PM_2.5, and PM₁₀, and were greater in boys and children living close to roadways.

CONCLUSIONS

Long-term UFPs exposure was associated with a greater likelihood of childhood obesity, and stronger associations on BMI Z-score were observed in boys and children living close to roadways. This study indicates that more attention should be paid to the health effects of UFPs, and routinely monitoring of UFPs should be considered.

Community-level ambient fine particulate matter and seasonal influenza among children in Guangzhou, China: A Bayesian spatiotemporal analysis

BACKGROUND

Influenza is a major preventable infectious respiratory disease. However, there is little detailed long-term evidence of its associations with PM_2.5 among children. We examined the community-level associations between exposure to ambient PM_2.5 and incident influenza in Guangzhou, China.

METHODS

We used data from the city-wide influenza surveillance system collected by Guangzhou Centre for Disease Control and Prevention (GZCDC) over the period 2013 and 2019. Incident influenza was defined as daily new influenza (both clinically diagnosed and laboratory confirmed) cases as per standard diagnostic criteria. A 200-meter city-wide grid of daily ambient PM_2.5 exposure was generated using a random forest model. We developed spatiotemporal Bayesian hierarchical models to examine the community-level associations between PM_2.5 and the influenza adjusting for meteorological and socioeconomic variables and accounting for spatial autocorrelation. We also calculated community-wide influenza cases attributable to PM_2.5 levels exceeding the China Grade 1 and World Health Organization (WHO) regulatory thresholds.

RESULTS

Our study comprised N = 191,846 children from Guangzhou aged ≤19 years and diagnosed with influenza between January 1, 2013 and December 31, 2019. Each 10 μg/m³ increment in community-level PM_2.5 measured on the day of case confirmation (lag 0) and over a 6-day moving average (lag 0-5 days) was associated with higher risks of influenza (RR = 1.05, 95% CI: 1.05-1.06 for lag 0 and RR = 1.15, 95% CI: 1.14-1.16 for lag 05). We estimated that 8.10% (95%CI: 7.23%-8.57%) and 20.11% (95%CI: 17.64%-21.48%) influenza cases respectively were attributable to daily PM_2.5 exposure exceeding the China Grade I (35 μg/m³) and the WHO limits (25 μg/m³). The risks associated with PM_2.5 exposures were more pronounced among children of the age-group 10-14 compared to other age groups.

CONCLUSIONS

More targeted non-pharmaceutical interventions aimed at reducing PM_2.5 exposures at home, school and during commutes among children may constitute additional influenza prevention and control polices.

SARS-CoV-2 and other airborne respiratory viruses in outdoor aerosols in three Swiss cities before and during the first wave of the COVID-19 pandemic

Caused by the SARS-CoV-2 virus, Coronavirus disease 2019 (COVID-19) has been affecting the world since the end of 2019. While virus-laden particles have been commonly detected and studied in the aerosol samples from indoor healthcare settings, studies are scarce on air surveillance of the virus in outdoor non-healthcare environments, including the correlations between SARS-CoV-2 and other respiratory viruses, between viruses and environmental factors, and between viruses and human behavior changes due to the public health measures against COVID-19. Therefore, in this study, we collected airborne particulate matter (PM) samples from November 2019 to April 2020 in Bern, Lugano, and Zurich. Among 14 detected viruses, influenza A, HCoV-NL63, HCoV-HKU1, and HCoV-229E were abundant in air. SARS-CoV-2 and enterovirus were moderately common, while the remaining viruses occurred only in low concentrations. SARS-CoV-2 was detected in PM10 (PM below 10 µm) samples of Bern and Zurich, and PM2.5 (PM below 2.5 µm) samples of Bern which exhibited a concentration positively correlated with the local COVID-19 case number. The concentration was also correlated with the concentration of enterovirus which raised the concern of coinfection. The estimated COVID-19 infection risks of an hour exposure at these two sites were generally low but still cannot be neglected. Our study demonstrated the potential functionality of outdoor air surveillance of airborne respiratory viruses, especially at transportation hubs and traffic arteries.

In the Seeking of Association between Air Pollutant and COVID-19 Confirmed Cases Using Deep Learning

The COVID-19 pandemic raises awareness of how the fatal spreading of infectious disease impacts economic, political, and cultural sectors, which causes social implications. Across the world, strategies aimed at quickly recognizing risk factors have also helped shape public health guidelines and direct resources; however, they are challenging to analyze and predict since those events still happen. This paper intends to invesitgate the association between air pollutants and COVID-19 confirmed cases using Deep Learning. We used Delhi, India, for daily confirmed cases and air pollutant data for the dataset. We used LSTM deep learning for training the combination of COVID-19 Confirmed Case and AQI parameters over the four different lag times of 1, 3, 7, and 14 days. The finding indicates that CO is the most excellent model compared with the others, having on average, 13 RMSE values. This was followed by pressure at 15, PM_2.5 at 20, NO₂ at 20, and O₃ at 22 error rates.

Aerosol exposure of staff during dental treatments: a model study

Background

Due to exposure to potentially infectious aerosols during treatments, the dental personnel is considered being at high risk for aerosol transmitted diseases like COVID-19. The aim of this study was to evaluate aerosol exposure during different dental treatments as well as the efficacy of dental suction to reduce aerosol spreading.

Methods

Dental powder-jet (PJ; Air-Flow^®), a water-cooled dental handpiece with a diamond bur (HP) and water-cooled ultrasonic scaling (US) were used in a simulation head, mounted on a dental unit in various treatment settings. The influence of the use of a small saliva ejector (SE) and high-volume suction (HVS) was evaluated. As a proxy of aerosols, air-born particles (PM10) were detected using a Laser Spectrometer in 30 cm distance from the mouth. As control, background particle counts (BC) were measured before and after experiments.

Results

With only SE, integrated aerosol levels [median (Q25/Q75) µg/m³ s] for PJ [91,246 (58,213/118,386) µg/m³ s, p < 0.001, ANOVA] were significantly increased compared to BC [7243 (6501/8407) µg/m³ s], whilst HP [11,119 (7190/17,234) µg/m³ s, p > 0.05] and US [6558 (6002/7066) µg/m³ s; p > 0.05] did not increase aerosol levels significantly. The use of HVS significantly decreased aerosol exposure for PJ [37,170 (29,634/51,719) µg/m³ s; p < 0.01] and HP [5476 (5066/5638) µg/m³ s; p < 0.001] compared to SE only, even reaching lower particle counts than BC levels for HP usage (p < 0.001).

Conclusions

To reduce the exposure to potentially infectious aerosols, HVS should be used during aerosol-forming dental treatments.

Short-term exposure to ambient air pollution and individual emergency department visits for COVID-19: a case-crossover study in Canada

Background

Ambient air pollution is thought to contribute to increased risk of COVID-19, but the evidence is controversial.

Objective

To evaluate the associations between short-term variations in outdoor concentrations of ambient air pollution and COVID-19 emergency department (ED) visits.

Methods

We conducted a case-crossover study of 78 255 COVID-19 ED visits in Alberta and Ontario, Canada between 1 March 2020 and 31 March 2021. Daily air pollution data (ie, fine particulate matter with diameter less than 2.5 µm (PM_2.5), nitrogen dioxide (NO₂) and ozone were assigned to individual case of COVID-19 in 10 km × 10 km grid resolution. Conditional logistic regression was used to estimate associations between air pollution and ED visits for COVID-19.

Results

Cumulative ambient exposure over 0–3 days to PM2.5 (OR 1.010; 95% CI 1.004 to 1.015, per 6.2 µg/m³) and NO₂ (OR 1.021; 95% CI 1.015 to 1.028, per 7.7 ppb) concentrations were associated with ED visits for COVID-19. We found that the association between PM_2.5 and COVID-19 ED visits was stronger among those hospitalised following an ED visit, as a measure of disease severity, (OR 1.023; 95% CI 1.015 to 1.031) compared with those not hospitalised (OR 0.992; 95% CI 0.980 to 1.004) (p value for effect modification=0.04).

Conclusions

We found associations between short-term exposure to ambient air pollutants and COVID-19 ED visits. Exposure to air pollution may also lead to more severe COVID-19 disease.

Dynamics and approximation of positive solution of the stochastic SIS model affected by air pollutants

In this paper, we develop a stochastic susceptible-infective-susceptible (SIS) model, in which the transmission coefficient is a function of air quality index (AQI). By using Markov semigroup theory, the existence of kernel operator is obtained. Then, the sufficient conditions that guarantee the stationary distribution and extinction are given by Foguel alternative, Khasminsk$\check{\rm l}$ function and Itô formula. Next, a positivity-preserving numerical method is used to approximate the stochastic SIS model, meanwhile for all $ p > 0 $, we show that the algorithm has the $ p $th-moment convergence rate. Finally, numerical simulations are carried out to illustrate the corresponding theoretical results.

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

BACKGROUND AND AIMS

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

METHODS

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

RESULTS

A total of 35 articles were eligible for the meta-analysis. For long-term exposure to AP, COVID-19 incidence was positively associated with 1 μg/m3 increase in nitrogen dioxide (NO2; effect size = 1.042, 95% CI 1.017-1.068), particulate matter with diameter <2.5 μm (PM2.5; effect size = 1.056, 95% CI 1.039-1.072), and sulfur dioxide (SO2; effect size = 1.071, 95% CI 1.002-1.145). The COVID-19 mortality was positively associated with 1 μg/m3 increase in nitrogen dioxide (NO2; effect size = 1.034, 95% CI 1.006-1.063), PM2.5 (effect size = 1.047, 95% CI 1.025-1.1071). For short-term exposure to air pollutants, COVID-19 incidence was positively associated with 1 unit increase in air quality index (effect size = 1.001, 95% CI 1.001-1.002), 1 μg/m3 increase NO2 (effect size = 1.014, 95% CI 1.011-1.016), particulate matter with diameter <10 μm (PM10; effect size = 1.005, 95% CI 1.003-1.008), PM2.5 (effect size = 1.003, 95% CI 1.002-1.004), and SO2 (effect size = 1.015, 95% CI 1.007-1.023).

CONCLUSIONS

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

Short-term impact of ambient temperature on the incidence of influenza in Wuhan, China

Few studies have estimated the nonlinear association of ambient temperature with the risk of influenza. We therefore applied a time-series analysis to explore the short-term effect of ambient temperature on the incidence of influenza in Wuhan, China. Daily influenza cases were collected from Hubei Provincial Center for Disease Control and Prevention (Hubei CDC) from January 1, 2014, to December 31, 2017. The meteorological and daily pollutant data was obtained from the Hubei Meteorological Service Center and National Air Quality Monitoring Stations, respectively. We used a generalized additive model (GAM) coupled with the distributed lag nonlinear model (DLNM) to explore the exposure-lag-response relationship between the short-term risk of influenza and daily average ambient temperature. Analyses were also performed to assess the extreme cold and hot temperature effects. We observed that the ambient temperature was statistically significant, and the exposure-response curve is approximately S-shaped, with a peak observed at 23.57 ℃. The single-day lag curve showed that extreme hot and cold temperatures were both significantly associated with influenza. The extreme hot temperature has an acute effect on influenza, with the most significant effect observed at lag 0-1. The extreme cold temperature has a relatively smaller effect but lasts longer, with the effect exerted continuously during a lag of 2-4 days. Our study found significant nonlinear and delayed associations between ambient temperature and the incidence of influenza. Our finding contributes to the establishment of an early warning system for airborne infectious diseases.

Association between ozone exposure and prevalence of mumps: a time-series study in a Megacity of Southwest China

In the present study, we aim to evaluate the delayed and cumulative effect of ozone (O₃) exposure on mumps in a megacity with high population density and high humidity. We took Chongqing, a megacity in Southwest China, as the research area and 2013-2017 as the research period. A total of 49,258 confirmed mumps cases were collected from 122 hospitals of Chongqing. We employed the distributed lag nonlinear models with quasi-Poisson link to investigate the relationship between prevalence of mumps and O₃ exposure after adjusting for the effects of meteorological conditions. The results show that the effect of O₃ exposure on mumps was mainly manifested in the lag of 0-7 days. The single-day ;lag effect was the most obvious on the 4th day, with the relative risk (RR) of mumps occurs of 1.006 (95% CI: 1.003-1.007) per 10 μg/m³ in the O₃ exposure. The cumulative RR within 7 days was 1.025 (95% CI: 1.013-1.038). Our results suggest that O₃ exposure can increase the risk of mumps infection, which fills the gap of relevant research in mountainous areas with high population density and high humidity.

Outdoor PM2.5 concentration and rate of change in COVID-19 infection in provincial capital cities in China

This study investigates thoroughly whether acute exposure to outdoor PM2.5 concentration, P, modifies the rate of change in the daily number of COVID-19 infections (R) across 18 high infection provincial capitals in China, including Wuhan. A best-fit multiple linear regression model was constructed to model the relationship between P and R, from 1 January to 20 March 2020, after accounting for meteorology, net move-in mobility (NM), time trend (T), co-morbidity (CM), and the time-lag effects. Regression analysis shows that P (β = 0.4309, p < 0.001) is the most significant determinant of R. In addition, T (β = -0.3870, p < 0.001), absolute humidity (AH) (β = 0.2476, p = 0.002), P × AH (β = -0.2237, p < 0.001), and NM (β = 0.1383, p = 0.003) are more significant determinants of R, as compared to GDP per capita (β = 0.1115, p = 0.015) and CM (Asthma) (β = 0.1273, p = 0.005). A matching technique was adopted to demonstrate a possible causal relationship between P and R across 18 provincial capital cities. A 10 µg/m3 increase in P gives a 1.5% increase in R (p < 0.001). Interaction analysis also reveals that P × AH and R are negatively correlated (β = -0.2237, p < 0.001). Given that P exacerbates R, we recommend the installation of air purifiers and improved air ventilation to reduce the effect of P on R. Given the increasing observation that COVID-19 is airborne, measures that reduce P, plus mandatory masking that reduces the risks of COVID-19 associated with viral-particulate transmission, are strongly recommended. Our study is distinguished by the focus on the rate of change instead of the individual cases of COVID-19 when modelling the statistical relationship between R and P in China; causal instead of correlation analysis via the matching analysis, while taking into account the key confounders, and the individual plus the interaction effects of P and AH on R.

Investigating the roles of meteorological factors in COVID-19 transmission in Northern Italy

The novel COVID-19 is a highly invasive, pathogenic, and transmittable disease that has stressed the health care sector and hampered global development. Information of other viral respiratory diseases indicates that COVID-19 transmission could be affected by varying weather conditions; however, the impact of meteorological factors on the COVID-19 death counts remains unexplored. By investigating the impact of meteorological factors (absolute humidity, relative humidity, and temperature), this study will contribute both theoretically and practically to the concerned domain of pandemic management to be better prepared to control the spread of the disease. For this study, data is collected from 23 February to 31 March 2020 for Milan, Northern Italy, one of the badly hit regions by COVID-19. The generalized additive model (GAM) is applied, and a nonlinear relationship is examined with penalized spline methods. A sensitivity analysis is conducted for the verification of model results. The results reveal that temperature, relative humidity, and absolute humidity have a significant but negative relationship with the COVID-19 mortality rate. Therefore, it is possible to postulate that cool and dry environmental conditions promote virus transmission, leading to an increase in COVID-19 death counts. The results may facilitate health care policymakers in developing and implementing effective control measures in a timely and efficient way.

Mortality risk attributable to wildfire-related PM2·5 pollution: a global time series study in 749 locations

BACKGROUND

Many regions of the world are now facing more frequent and unprecedentedly large wildfires. However, the association between wildfire-related PM2·5 and mortality has not been well characterised. We aimed to comprehensively assess the association between short-term exposure to wildfire-related PM2·5 and mortality across various regions of the world.

METHODS

For this time series study, data on daily counts of deaths for all causes, cardiovascular causes, and respiratory causes were collected from 749 cities in 43 countries and regions during 2000-16. Daily concentrations of wildfire-related PM2·5 were estimated using the three-dimensional chemical transport model GEOS-Chem at a 0·25° × 0·25° resolution. The association between wildfire-related PM2·5 exposure and mortality was examined using a quasi-Poisson time series model in each city considering both the current-day and lag effects, and the effect estimates were then pooled using a random-effects meta-analysis. Based on these pooled effect estimates, the population attributable fraction and relative risk (RR) of annual mortality due to acute wildfire-related PM2·5 exposure was calculated.

FINDINGS

65·6 million all-cause deaths, 15·1 million cardiovascular deaths, and 6·8 million respiratory deaths were included in our analyses. The pooled RRs of mortality associated with each 10 μg/m3 increase in the 3-day moving average (lag 0-2 days) of wildfire-related PM2·5 exposure were 1·019 (95% CI 1·016-1·022) for all-cause mortality, 1·017 (1·012-1·021) for cardiovascular mortality, and 1·019 (1·013-1·025) for respiratory mortality. Overall, 0·62% (95% CI 0·48-0·75) of all-cause deaths, 0·55% (0·43-0·67) of cardiovascular deaths, and 0·64% (0·50-0·78) of respiratory deaths were annually attributable to the acute impacts of wildfire-related PM2·5 exposure during the study period.

INTERPRETATION

Short-term exposure to wildfire-related PM2·5 was associated with increased risk of mortality. Urgent action is needed to reduce health risks from the increasing wildfires.

FUNDING

Australian Research Council, Australian National Health & Medical Research Council.

The Correlation Between COVID-19 Activities and Climate Factors in Different Climate Types Areas

OBJECTIVE

To investigate the epidemiological characteristics of human infection with corona virus disease 2019 (COVID-19) in Moscow, Lima, Kuwait, and Singapore to analyze the effects of climate factors on the incidence of COVID-19.

METHODS

Collect the daily incidence of COVID-19 and related climate data in four areas, construct a negative binomial regression model, and analyze the correlation between the incidence of COVID-19 and meteorological factors.

RESULTS

AH was the climate factor affecting the incidence of COVID-19 in Moscow, Lima, and Singapore; Ta and RH were the climate factors affecting the incidence of COVID-19 in Kuwait.

CONCLUSIONS

The incidence of COVID-19 in four areas were all associated with the humidity, and climate factors should be taken into consideration when epidemic prevention measures are taken, and environment humidification may be a feasible approach to decrease COVID-19 virus transmission.

Independent association between meteorological factors, PM2.5, and seasonal influenza activity in Hangzhou, Zhejiang province, China

BACKGROUND

Due to variations in climatic conditions, the effects of meteorological factors and PM2.5 on influenza activity, particularly in subtropical regions, vary in existing literature. In this study, we examined the relationship between influenza activity, meteorological parameters, and PM2.5 .

METHODS

A total of 20 165 laboratory-confirmed influenza cases in Hangzhou, Zhejiang province, were documented in our dataset and aggregated into weekly counts for downstream analysis. We employed a combination of the quasi-Poisson-generalized additive model and the distributed lag non-linear model to examine the relationship of interest, controlling for long-term trends, seasonal trends, and holidays.

RESULTS

A hockey-stick association was found between absolute humidity and the risk of influenza infections. The overall cumulative adjusted relative risk (ARR) was statistically significant when weekly mean absolute humidity was low (<10 µg/m3 ) and high (>17.5 µg/m3 ). A slightly higher ARR was observed when weekly mean temperature reached over 30.5°C. A statistically significantly higher ARR was observed when weekly mean relative humidity dropped below 67%. ARR increased statistically significantly with increasing rainfall. For PM2.5 , the ARR was marginally statistically insignificant. In brief, high temperature, wet and dry conditions, and heavy rainfall were the major risk factors associated with a higher risk of influenza infections.

CONCLUSIONS

The present study contributes additional knowledge to the understanding of the effects of various environmental factors on influenza activities. Our findings shall be useful and important for the development of influenza surveillance and early warning systems.

Limited evidence for the role of environmental factors in the unusual peak of influenza in Brisbane during the 2018-2019 Australian summer

OBJECTIVE

To explore the contribution of environmental factors in the unusual pattern of influenza activity observed in Brisbane, Australia during the summer of 2018-2019.

METHODS

Distributed lag nonlinear models (DLNMs) were used to estimate the effect of environmental factors on weekly influenza incidence in Brisbane. Next generation sequencing was then employed to analyze minor and majority variants in influenza strains isolated from Brisbane children during this period.

RESULTS

There were limited marked differences in the environmental factors observed in Brisbane between the 2018-2019 summer period and the same period of the proceeding years, with the exception of significant reduction in rainfall. DLNM showed that reduced rainfall in Brisbane (at levels consistent with the 2018-2019 period) correlated with a dramatic increase in the relative risk of influenza. Sulfur dioxide (SO₂) levels were also increased in the 2018-2019 period, although these levels did not correlate with an increased risk of influenza. Sequencing of a limited number of pediatric influenza virus strains isolated during the 2018-2019 showed numerous mutations within the viral HA.

CONCLUSIONS

Taken together, these data suggest a limited role for key environmental factors in the influenza activity observed in Brisbane, Australia during the summer of 2018-2019. One alternative explanation may that viral factors, in addition to other factors not studied herein, contributed to the unusual influenza season. Our findings provide fundamental information that may be beneficial to a better understanding of the seasonal trends of influenza virus.

Haloculture: A system to mitigate the negative impacts of pandemics on the environment, society and economy, emphasizing COVID-19

COVID-19 (coronavirus disease) is a global pandemic that started in China in 2019 and has negatively affected all economic sectors of the world, including agriculture. However, according to estimates in different countries, agriculture has suffered less than other sectors such as construction, industry and tourism, so agricultural development can be a good option to compensate for the economic damage caused to other sectors. The quality of available water and soil resources for agricultural development is not only limited, but is also decreasing incrementally, so the use of saline and unconventional soil and water resources is inevitable. Biosaline agriculture or haloculture is a system in which highly saline water and soil resources are used sustainably for the economic production of agricultural crops. It seems that in the current situation of the world (with COVID-19's impact on agriculture on the one hand and the quantitative and qualitative decline of freshwater and soil on the other), haloculture with a re-reading of territorial capabilities has good potential to provide a part of human food supply. In this review article, the potential of haloculture to offset the adverse impacts of the pandemic is analyzed from five perspectives: increasing the area under cultivation, using unconventional water, stabilizing dust centers, increasing the body's immune resistance, and reducing losses in agribusiness due to the coronavirus. Overall, haloculture is an essential system, which COVID-19 has accelerated in the agricultural sector.

Air pollutants and outpatient visits for influenza-like illness in Beijing, China

Background

Air pollution leads to many adverse health conditions, mainly manifested by respiratory or cardiac symptoms. Previous studies are limited as to whether air pollutants were associated to influenza-like illness (ILI). This study aimed to explore the association between air pollutants and outpatient visits for ILI, especially during an outbreak of influenza.

Methods

Daily counts of hospital visits for ILI were obtained from Peking University Third Hospital between January 1, 2015, and March 31, 2018. A generalized additive Poisson model was applied to examine the associations between air pollutants concentrations and daily outpatient visits for ILI when adjusted for the meteorological parameters.

Results

There were 35862 outpatient visits at the fever clinic for ILI cases. Air quality index (AQI), PM_2.5, PM₁₀, CO and O₃ on lag0 days, as well as nitrogen dioxide (NO₂) and sulfur dioxide (SO₂) on lag1 days, were significantly associated with an increased risk of outpatient visits for ILI from January 2015 to November 2017. From December 2017 to March 2018, on lag0 days, air pollutants PM_2.5 [risk ratio (RR) = 0.971, 95% CI: 0.963-0.979], SO₂ (RR = 0.892, 95% CI: 0.840–0.948) and CO (RR = 0.306, 95% CI: 0.153–0.612) were significantly associated with a decreased risk of outpatient visits for ILI. Interestingly, on the lag2 days, all the pollutants were significantly associated with a reduced risk of outpatient visits for ILI except for O₃. We did not observe the linear correlations between the outpatient visits for ILI and any of air pollutants, which were instead associated via a curvilinear relationship.

Conclusions

We found that the air pollutants may be associated with an increased risk of outpatient visits for ILI during the non-outbreak period and with a decreased risk during the outbreak period, which may be linked with the use of disposable face masks and the change of outdoor activities. These findings expand the current knowledge of ILI outpatient visits correlated with air pollutants during an influenza pandemic.

Impact of biometeorological conditions and air pollution on influenza-like illnesses incidence in Warsaw

In order to assess the influence of atmospheric conditions and particulate matter (PM) on the seasonally varying incidence of influenza-like illnesses (ILI) in the capital of Poland-Warsaw, we analysed time series of ILI reported for the about 1.75 million residents in total and for different age groups in 288 approximately weekly periods, covering 6 years 2013-2018. Using Poisson regression, we predicted ILI by the Universal Thermal Climate Index (UTCI) as biometeorological indicator, and by PM2.5 and PM10, respectively, as air quality measures accounting for lagged effects spanning up to 3 weeks. Excess ILI incidence after adjusting for seasonal and annual trends was calculated by fitting generalized additive models. ILI morbidity increased with rising PM concentrations, for both PM2.5 and PM10, and with cooler atmospheric conditions as indicated by decreasing UTCI. While the PM effect focused on the actual reporting period, the atmospheric influence exhibited a more evenly distributed lagged effect pattern over the considered 3-week period. Though ILI incidence adjusted for population size significantly declined with age, age did not significantly modify the effect sizes of both PM and UTCI. These findings contribute to better understanding environmental conditionings of influenza seasonality in a temperate climate. This will be beneficial to forecasting future dynamics of ILI and to planning clinical and public health resources under climate change scenarios.

Determination of SARS-CoV-2 RNA in different particulate matter size fractions of outdoor air samples in Madrid during the lockdown

BACKGROUND

Previous studies described the presence of SARS-CoV-2 in outdoor air particulate matter (PM) in urban areas of northern Italy and USA. The city of Madrid was heavily affected by COVID-19 during March-June 2020. Also, this city usually displays high concentrations of PM under several atmospheric situations. This is mandatory to assess the presence of viral RNA in PM, as an indicator of epidemic recurrence. Our study was aimed at investigating the presence of SARS-CoV-2 RNA in outdoor air samples (on PM10, PM2.5 and PM1).

METHODS

Six samples of PM10, PM2.5 and PM1 were collected between the May 4th and 22nd 2020 in Madrid, on quartz fiber filters by using MCV high volume samplers (30 m³ h^-1 flow) with three inlets (Digitel DHA-80) for sampling PM10, PM2.5 and PM1. RNA extraction and amplification was performed according to the protocol recently set by Setti et al.2020c in Italy. Up to three highly specific molecular marker genes (N1, N2, and RP) were used to test the presence of SARS-CoV-2 RNA.

RESULTS

After RNA extraction and expression measurements of N1, N2 and RP genes from all the collected filters, no presence of SARS-CoV-2 RNA was observed. Control tests to exclude false positive results were successfully accomplished.

CONCLUSIONS

No presence of SARS-CoV-2 in quartz fiber filters samplers for PM10, PM2.5 and PM1 fractions was observed in our study carried out in Madrid during the month of May 2020. Nevertheless, the absence of viral genomes could be due to different factors including: limited social interactions and economic activities resulting in reduced circulation of the coronavirus, lower daily PM concentration in outdoor air, as well as to meteorological stability and higher temperature that characterize spring season. Further research should be carried out during winter, in presence of higher viral circulation and daily PM exceedances.

Did anomalous atmospheric circulation favor the spread of COVID-19 in Europe?

The current pandemic of coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is having negative health, social and economic consequences worldwide. In Europe, the pandemic started to develop strongly at the end of February and beginning of March 2020. Subsequently, it spread over the continent, with special virulence in northern Italy and inland Spain. In this study we show that an unusual persistent anticyclonic situation prevailing in southwestern Europe during February 2020 (i.e. anomalously strong positive phase of the North Atlantic and Arctic Oscillations) could have resulted in favorable conditions, e.g., in terms of air temperature and humidity among other factors, in Italy and Spain for a quicker spread of the virus compared with the rest of the European countries. It seems plausible that the strong atmospheric stability and associated dry conditions that dominated in these regions may have favored the virus propagation, both outdoors and especially indoors, by short-range droplet and aerosol (airborne) transmission, or/and by changing social contact patterns. Later recent atmospheric circulation conditions in Europe (July 2020) and the U.S. (October 2020) seem to support our hypothesis, although further research is needed in order to evaluate other confounding variables. Interestingly, the atmospheric conditions during the Spanish flu pandemic in 1918 seem to have resembled at some stage with the current COVID-19 pandemic.

Did anomalous atmospheric circulation favor the spread of COVID-19 in Europe?

The current pandemic of coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is having negative health, social and economic consequences worldwide. In Europe, the pandemic started to develop strongly at the end of February and beginning of March 2020. Subsequently, it spread over the continent, with special virulence in northern Italy and inland Spain. In this study we show that an unusual persistent anticyclonic situation prevailing in southwestern Europe during February 2020 (i.e. anomalously strong positive phase of the North Atlantic and Arctic Oscillations) could have resulted in favorable conditions, e.g., in terms of air temperature and humidity among other factors, in Italy and Spain for a quicker spread of the virus compared with the rest of the European countries. It seems plausible that the strong atmospheric stability and associated dry conditions that dominated in these regions may have favored the virus propagation, both outdoors and especially indoors, by short-range droplet and aerosol (airborne) transmission, or/and by changing social contact patterns. Later recent atmospheric circulation conditions in Europe (July 2020) and the U.S. (October 2020) seem to support our hypothesis, although further research is needed in order to evaluate other confounding variables. Interestingly, the atmospheric conditions during the Spanish flu pandemic in 1918 seem to have resembled at some stage with the current COVID-19 pandemic.

Our future: Experiencing the coronavirus disease 2019 (COVID-19) outbreak and pandemic

Outbreaks of the novel coronavirus disease (severe acute respiratory syndrome coronavirus 2: SARS-CoV-2) (coronavirus disease 2019; COVID-19) remind us once again of the mechanisms of zoonotic outbreaks. Climate change and the expansion of agricultural lands and infrastructures due to population growth will ultimately reduce or eliminate wildlife and avian habitats and increase opportunities for wildlife and birds to come into contact with livestock and humans. Consequently, infectious pathogens are transmitted from wildlife and birds to livestock and humans, promoting zoonotic diseases. In addition, the spread of diseases has been associated with air pollution and social inequities, such as racial discrimination, gender inequality, and racial, economic, and educational disparities. The COVID-19 pandemic is a fresh reminder of the significance of excessive greenhouse gas excretion and air pollution, highlighting social inequities and distortions. This provides us with an opportunity to reflect on the appropriateness of our trajectory. Therefore, this review glances through the COVID-19 pandemic and discusses our future.

Housing Quality in a Rural and an Urban Settlement in South Africa

During 2016 to 2018, a prospective household cohort study of influenza and respiratory syncytial virus community burden and transmission dynamics (the PHIRST study) was undertaken to examine the factors associated with influenza and other respiratory pathogen transmissions in South Africa. We collected information on housing conditions in the PHIRST study sites: Rural villages near Agincourt, Bushbuckridge Municipality, Mpumalanga Province, and urban Jouberton Township in North West Province. Survey data were collected from 159 and 167 study households in Agincourt and Jouberton, respectively. Multiple housing-related health hazards were identified in both sites, but particularly in Agincourt. In Agincourt, 75% (119/159) of households reported daily or weekly interruptions in water supply and 98% (154/159) stored drinking water in miscellaneous containers, compared to 1% (1/167) and 69% (115/167) of households in Jouberton. Fuels other than electricity (such as wood) were mainly used for cooking by 44% (70/159) and 7% (11/167) of Agincourt and Jouberton households, respectively; and 67% (106/159) of homes in Agincourt versus 47% (79/167) in Jouberton were located on unpaved roads, which is associated with the generation of dust and particulate matter. This study has highlighted housing conditions in Agincourt and Jouberton that are detrimental to health, and which may impact disease severity or transmission in South African communities.

SARS-CoV-2 infection, COVID-19 pathogenesis, and exposure to air pollution: What is the connection?

Exposure to air pollutants has been previously associated with respiratory viral infections, including influenza, measles, mumps, rhinovirus, and respiratory syncytial virus. Epidemiological studies have also suggested that air pollution exposure is associated with increased cases of SARS-CoV-2 infection and COVID-19-associated mortality, although the molecular mechanisms by which pollutant exposure affects viral infection and pathogenesis of COVID-19 remain unknown. In this review, we suggest potential molecular mechanisms that could account for this association. We have focused on the potential effect of exposure to nitrogen dioxide (NO2 ), ozone (O3 ), and particulate matter (PM) since there are studies investigating how exposure to these pollutants affects the life cycle of other viruses. We have concluded that pollutant exposure may affect different stages of the viral life cycle, including inhibition of mucociliary clearance, alteration of viral receptors and proteases required for entry, changes to antiviral interferon production and viral replication, changes in viral assembly mediated by autophagy, prevention of uptake by macrophages, and promotion of viral spread by increasing epithelial permeability. We believe that exposure to pollutants skews adaptive immune responses toward bacterial/allergic immune responses, as opposed to antiviral responses. Exposure to air pollutants could also predispose exposed populations toward developing COIVD-19-associated immunopathology, enhancing virus-induced tissue inflammation and damage.

Short-term effects of ambient air pollution on the incidence of influenza in Wuhan, China: A time-series analysis

BACKGROUND

Evidence suggests that air pollution is associated with many adverse health outcomes such as cardiovascular diseases (CVD), respiratory diseases, cancer, and birth defects. Yet few studies dig into the relationship between air pollution and airborne infectious diseases.

METHODS

Daily data on influenza incidence were obtained from Hubei Provincial Center for Disease Control and Prevention (Hubei CDC). Data on air pollutants including nitrogen dioxide (NO₂), sulfur dioxide (SO₂), ground-level ozone (O₃), particulate matter (PM) with aerodynamic diameter ≤ 2.5 μm (PM_2.5), and PM with aerodynamic diameter ≤ 10 μm (PM₁₀) were retrieved from ten national air sampling stations located at Wuhan. We applied generalized additive model (GAM) to estimate the associations between air pollution and the risk of influenza in Wuhan, China during 2015-2017.

RESULTS

In the single-day lag model, the largest effect estimates were observed at lag 0. An increased relative risk (RR) of influenza was significantly associated with a 10 μg/m³ increase in SO₂ (RR: 1.099; 95% confidence interval [CI]: 1.011-1.195), NO₂ (RR: 1.039; 95% CI: 1.013-1.065), and O₃ (RR: 1.005; 95% CI: 0.994-1.016), respectively. In the multi-day lag model, concentrations of SO₂, NO₂, and O₃ were statistically significantly associated with the risk of influenza at lag 0-1. The seasonal analysis suggests that the influence of air pollution on influenza is greater in the cold season as compared in the warm season in the early lag days. The multi-pollutant model indicates that NO₂ may be a potential confounder for co-pollutants.

CONCLUSIONS

Our study shows that air pollution may be associated with the risk of influenza in a broad sense. Therefore, when formulating policies to deal with influenza outbreaks in the future, factors regarding air pollution should be taken into consideration.

Climate and Human coronaviruses 229E and Human coronaviruses OC43 Infections: Respiratory Viral Infections Prevalence in Hospitalized Children in Cheonan, Korea

The study of climate and respiratory viral infections using big data may enable the recognition and interpretation of relationships between disease occurrence and climatic variables. In this study, realtime reverse transcription quantitative PCR (qPCR) methods were used to identify Human respiratory coronaviruses (HCoV). infections in patients below 10 years of age with respiratory infections who visited Dankook University Hospital in Cheonan, South Korea, from January 1, 2012, to December 31, 2018. Out of the 9010 patients who underwent respiratory virus real-time reverse transcription qPCR test, 364 tested positive for HCoV infections. Among these 364 patients, 72.8% (n = 265) were below 10 years of age. Data regarding the frequency of infections was used to uncover the seasonal pattern of the two viral strains, which was then compared with local meteorological data for the same time period. HCoV-229E and HCoV-OC43 showed high infection rates in patients below 10 years of age. There was a negative relationship between HCoV-229E and HCoV-OC43 infections with air temperature and wind-chill temperatures. Both HCoV-229E and HCoV-OC43 rates of infection were positively related to atmospheric pressure, while HCoV-229E was also positively associated with particulate matter concentrations. Our results suggest that climatic variables affect the rate in which children below 10 years of age are infected with HCoV. These findings may help to predict when prevention strategies may be most effective.

Airborne particulate matter, population mobility and COVID-19: a multi-city study in China

BACKGROUND

Coronavirus disease 2019 (COVID-19) is an emerging infectious disease, which has caused numerous deaths and health problems worldwide. This study aims to examine the effects of airborne particulate matter (PM) pollution and population mobility on COVID-19 across China.

METHODS

We obtained daily confirmed cases of COVID-19, air particulate matter (PM_2.5, PM₁₀), weather parameters such as ambient temperature (AT) and absolute humidity (AH), and population mobility scale index (MSI) in 63 cities of China on a daily basis (excluding Wuhan) from January 01 to March 02, 2020. Then, the Generalized additive models (GAM) with a quasi-Poisson distribution were fitted to estimate the effects of PM₁₀, PM_2.5 and MSI on daily confirmed COVID-19 cases.

RESULTS

We found each 1 unit increase in daily MSI was significantly positively associated with daily confirmed cases of COVID-19 in all lag days and the strongest estimated RR (1.21, 95% CIs:1.14 ~ 1.28) was observed at lag 014. In PM analysis, we found each 10 μg/m³ increase in the concentration of PM₁₀ and PM_2.5 was positively associated with the confirmed cases of COVID-19, and the estimated strongest RRs (both at lag 7) were 1.05 (95% CIs: 1.04, 1.07) and 1.06 (95% CIs: 1.04, 1.07), respectively. A similar trend was also found in all cumulative lag periods (from lag 01 to lag 014). The strongest effects for both PM₁₀ and PM_2.5 were at lag 014, and the RRs of each 10 μg/m³ increase were 1.18 (95% CIs:1.14, 1.22) and 1.23 (95% CIs:1.18, 1.29), respectively.

CONCLUSIONS

Population mobility and airborne particulate matter may be associated with an increased risk of COVID-19 transmission.

Association between NO2 cumulative exposure and influenza prevalence in mountainous regions: A case study from southwest China

While accumulating evidence shows that air pollution exposure is an important risk factor to influenza prevalence, their association has been inadequately investigated in mountainous regions with dense populations and high humidity. We aim to estimate the association and exposure-outcome effects between exposure to nitrogen dioxide (NO₂) and influenza prevalence in a mountainous region with a dense population and high humidity. We investigated 14,993 patients with confirmed influenza cases from January 2013 to December 2017 in Chongqing, a mountainous city in southwest China. We developed distributed lag non-linear models with quasi-Poisson link to take into account the lag and non-linear effects of NO₂ exposure on influenza prevalence. We estimated that the cumulative effect of a 10 μg/m³ increase in NO₂ with seven-day lag (i.e., summing all the contributions up to seven days) corresponded to relative risk of 1.24 (95% CI: 1.17-1.31) in daily influenza prevalence. Comparing to annual mean of the World Health Organization air quality guidelines of 40 μg/m³ for NO₂, we estimated that 14.01% (95% CI: 10.69-17.08%) of the influenza cases were attributable to excessive NO₂ exposure. Our results suggest that NO₂ exposure could worsen the risk of influenza infection in this mountainous city, filling the gap of relevant researches in densely populated and mountainous cities. Our findings provide evidence for developing influenza surveillance and early warning systems.