The association of wildfire air pollution with COVID-19 incidence in New South Wales, Australia

Revealing the impact of wildfires on groundwater quality: Insights from Sierra de la Culebra (Spain)

Wildfires induce changes in soil and vegetation composition, significantly impacting the hydrological cycle and altering future runoff and infiltration patterns. Ash residue on the ground can infiltrate the subsoil along with water, leading to modifications in groundwater hydrochemistry. Climate change and summer heatwaves can create favourable conditions for severe wildfires, such as the one that occurred in Zamora, Spain, in 2022. Fourteen simultaneous points of origin across various locations in Zamora triggered the worst environmental disaster in this province, as well as the largest fire recorded in the history of Spain. Following the severe wildfires in Sierra de la Culebra, Zamora, groundwater samples were obtained to compare the hydrochemistry with pre-fire background data spanning several years. A general decline in pH across all sampling points was observed, most notably at Z1, likely due to its very high permeability and leaching of organic acids from burned vegetation. Increases in major ions such as SO42- and NO3- were detected at Z1-2, while HCO3- levels decreased, indicating possible oxidation of soil organic matter and the introduction of wildfire-derived organic acids into the groundwater system. Elevated concentrations of Na+, K+, Mg2+, and Ca2+ were observed at Z3, suggesting possible ash residue infiltration. Despite the severity of the wildfires, the results indicate that there were no significant long-lasting impacts on groundwater quality overall. This finding suggests that the groundwater systems in the study area are resilient to such environmental catastrophes.

Spatiotemporal hierarchical Bayesian analysis to identify factors associated with COVID-19 in suburban areas in Colombia

Introduction

The pandemic had a profound impact on the provision of health services in Cúcuta, Colombia where the neighbourhood-level risk of Covid-19 has not been investigated. Identifying the sociodemographic and environmental risk factors of Covid-19 in large cities is key to better estimate its morbidity risk and support health strategies targeting specific suburban areas. This study aims to identify the risk factors associated with the risk of Covid-19 in Cúcuta considering inter -spatial and temporal variations of the disease in the city's neighbourhoods between 2020 and 2022.

Methods

Age-adjusted rate of Covid-19 were calculated in each Cúcuta neighbourhood and each quarter between 2020 and 2022. A hierarchical spatial Bayesian model was used to estimate the risk of Covid-19 adjusting for socioenvironmental factors per neighbourhood across the study period. Two spatiotemporal specifications were compared (a nonparametric temporal trend; with and without space-time interaction). The posterior mean of the spatial and spatiotemporal effects was used to map the Covid-19 risk.

Results

There were 65,949 Covid-19 cases in the study period with a varying standardized Covid-19 rate that peaked in October-December 2020 and April-June 2021. Both models identified an association of the poverty and stringency indexes, education level and PM10 with Covid-19 although the best fit model with a space-time interaction estimated a strong association with the number of high-traffic roads only. The highest risk of Covid-19 was found in neighbourhoods in west, central, and east Cúcuta.

Conclusions

The number of high-traffic roads is the most important risk factor of Covid-19 infection in Cucuta. This indicator of mobility and connectivity overrules other socioenvironmental factors when Bayesian models include a space-time interaction. Bayesian spatial models are important tools to identify significant determinants of Covid-19 and identifying at-risk neighbourhoods in large cities. Further research is needed to establish causal links between these factors and Covid-19.

Quantifying the impact of lockdown measures on air pollution levels: A comparative study of Bhopal and Adelaide

This research study presents an in-depth comparison of air quality in Bhopal, India, and Adelaide, Australia, focusing on the impact of COVID-19 restrictions. Utilizing air quality data from 2019 to 2022, the research analyzed the concentrations of pollutants like PM2.5, PM10, NO2, and O3, during pre-lockdown, lockdown, and post-lockdown periods. The findings demonstrate a significant reduction in PM2.5and PM10 levels during lockdown in cities such as Delhi and Haryana in India, and various Chinese cities, while also highlighting complex sources of air pollution like bushfires in regions like Sydney, Australia. In contrast, the study revealed nuanced trends in Bhopal and Adelaide, influenced by local geographical, climatic, and anthropogenic factors. Bhopal exhibited a notable decrease in PM10 and PM2.5levels, but inconsistent patterns in NO2 and CO, while Adelaide experienced marginal changes. The study emphasizes the temporary effectiveness of lockdowns and underscores the need for region-specific, sustainable air quality management strategies. Future implications include considerations for regional specificities, broader atmospheric chemistry, and international collaboration. The research provides valuable insights for urban air quality policy formulation, stressing a data-driven, long-term approach.

Long-term effects of extreme smoke exposure on COVID-19: A cohort study

BACKGROUND

In 2014, the Hazelwood coalmine fire shrouded the regional Australian town of Morwell in smoke and ash for 6 weeks. One of the fire's by-products, PM2.5 , is associated with an increased risk of COVID-19 and severe disease. However, it is unclear whether the effect persisted for years after exposure. In this study, we surveyed a cohort established prior to the pandemic to determine whether PM2.5 from the coalmine fire increased long-term vulnerability to COVID-19 and severe disease.

METHODS

From August to December 2022, 612 members of the Hazelwood Health Study's adult cohort, established in 2016/17, participated in a follow-up survey that included standardized items to capture COVID-19 cases, as well as questions about hospitalization and vaccinations. Associations were evaluated in crude and adjusted logistic regression models.

RESULTS

A total of 268 (44%) participants self-reported or met symptom criteria for having had COVID-19 at least once. All models found a positive association, with odds of COVID-19 increasing by between 4% and 30% for a 10 μg/m3 increase in coalmine fire-related PM2.5 exposure. However, the association was significant in only 2 of the 18 models. There were insufficient hospitalizations to examine severity (n = 7; 1%).

CONCLUSION

The findings are inconclusive on the effect of coalmine fire-related PM2.5 exposure on long-term vulnerability to COVID-19. Given the positive association that was robust to modelling variations as well as evidence for a causal mechanism, it would be prudent to treat PM2.5 from fire events as a long-term risk factor until more evidence accumulates.

Air pollution exposure, SARS-CoV-2 infection, and immune response in a cohort of healthcare workers of a large university hospital in Milan, Italy

Several studies have examined the possible relationship between air pollutants and the risk of COVID-19 but most returned controversial findings. We tried to assess the association between (short- and long-term) exposure to particulate and gaseous pollutants, SARS-CoV-2 infections, and immune response in a population of healthcare workers (HCWs) with well-characterized individual data. We collected occupational and clinical characteristics of all HCWs who performed a nasopharyngeal swab (NPS) for detecting SARS-CoV-2 at the Policlinico Hospital in Milan (Lombardy, Italy) between February 24, 2020 (day after first documented case of COVID-19 in our hospital) and December 26, 2020 (day before start of the vaccination campaign). Each subject was assigned daily average levels of particulate matter ≤10 μm (PM10), nitrogen dioxide (NO2), and ozone (O3) retrieved from the air quality monitoring station closest to his/her residential address. Air pollution data were treated as time-dependent variables, generating person-days at risk. Multivariate Poisson regression models were fit to evaluate the rate of positive NPS and to assess the association between air pollution and antibody titer among NPS-positive HCWs. Among 3712 included HCWs, 635 (17.1%) had at least one positive NPS. A 10 μg/m3 increase in NO2 average concentration in the four days preceding NPS was associated with a higher risk of testing positive [Incidence Rate Ratio (IRR) = 1.08, 95% confidence interval (CI): 1.01; 1.16)]. When considering a 1 μg/m3 increase in 2019 annual NO2 average, we observed a higher risk of infection (IRR: 1.02, 95%CI: 1.00; 1.03) and an increased antibody titer (+2.4%, 95%CI: 1.1; 3.6%). Findings on PM10 and O3 were less consistent and, differently from NO2, were not confirmed in multipollutant models. Our study increases the body of evidence suggesting an active role of air pollution exposure on SARS-CoV-2 infection and confirms the importance of implementing pollution reduction policies to improve public health.

Mapping the risk of respiratory infections using suburban district areas in a large city in Colombia

BACKGROUND

Acute respiratory infections (ARI) in Cúcuta -Colombia, have a comparatively high burden of disease associated with high public health costs. However, little is known about the epidemiology of these diseases in the city and its distribution within suburban areas. This study addresses this gap by estimating and mapping the risk of ARI in Cúcuta and identifying the most relevant risk factors.

METHODS

A spatial epidemiological analysis was designed to investigate the association of sociodemographic and environmental risk factors with the rate of ambulatory consultations of ARI in urban sections of Cúcuta, 2018. The ARI rate was calculated using a method for spatial estimation of disease rates. A Bayesian spatial model was implemented using the Integrated Nested Laplace Approximation approach and the Besag-York-Mollié specification. The risk of ARI per urban section and the hotspots of higher risk were also estimated and mapped.

RESULTS

A higher risk of IRA was found in central, south, north and west areas of Cúcuta after adjusting for sociodemographic and environmental factors, and taking into consideration the spatial distribution of the city's urban sections. An increase of one unit in the percentage of population younger than 15 years; the Index of Multidimensional Poverty and the rate of ARI in the migrant population was associated with a 1.08 (1.06-1.1); 1.04 (1.01-1.08) and 1.25 (1.22-1.27) increase of the ARI rate, respectively. Twenty-four urban sections were identified as hotspots of risk in central, south, north and west areas in Cucuta.

CONCLUSION

Sociodemographic factors and their spatial patterns are determinants of acute respiratory infections in Cúcuta. Bayesian spatial hierarchical models can be used to estimate and map the risk of these infections in suburban areas of large cities in Colombia. The methods of this study can be used globally to identify suburban areas and or specific communities at risk to support the implementation of prevention strategies and decision-making in the public and private health sectors.

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.

The COVID-19-wildfire smoke paradox: Reduced risk of all-cause mortality due to wildfire smoke in Colorado during the first year of the COVID-19 pandemic

BACKGROUND

In 2020, the American West faced two competing challenges: the COVID-19 pandemic and the worst wildfire season on record. Several studies have investigated the impact of wildfire smoke (WFS) on COVID-19 morbidity and mortality, but little is known about how these two public health challenges impact mortality risk for other causes.

OBJECTIVES

Using a time-series design, we evaluated how daily risk of mortality due to WFS exposure differed for periods before and during the COVID-19 pandemic.

METHODS

Our study included daily data for 11 counties in the Front Range region of Colorado (2010-2020). We assessed WFS exposure using data from the National Oceanic and Atmospheric Administration and used mortality counts from the Colorado Department of Public Health and Environment. We estimated the interaction between WFS and the pandemic (an indicator variable) on mortality risk using generalized additive models adjusted for year, day of week, fine particulate matter, ozone, temperature, and a smoothed term for day of year.

RESULTS

WFS impacted the study area on 10% of county-days. We observed a positive association between the presence of WFS and all-cause mortality risk (incidence rate ratio (IRR) = 1.03, 95%CI: 1.01-1.04 for same-day exposures) during the period before the pandemic; however, WFS exposure during the pandemic resulted in decreased risk of all-cause mortality (IRR = 0.90, 95%CI: 0.87-0.93 for same-day exposures).

DISCUSSION

We hypothesize that mitigation efforts during the first year of the pandemic, e.g., mask mandates, along with high ambient WFS levels encouraged health behaviors that reduced exposure to WFS and reduced risk of all-cause mortality. Our results suggest a need to examine how associations between WFS and mortality are impacted by pandemic-related factors and that there may be lessons from the pandemic that could be translated into health-protective policies during future wildfire events.

Do wildfires exacerbate COVID-19 infections and deaths in vulnerable communities? Evidence from California

Understanding whether and how wildfires exacerbate COVID-19 outcomes is important for assessing the efficacy and design of public sector responses in an age of more frequent and simultaneous natural disasters and extreme events. Drawing on environmental and emergency management literatures, we investigate how wildfire smoke (PM_2.5) impacted COVID-19 infections and deaths during California's 2020 wildfire season and how public housing resources and hospital capacity moderated wildfires' effects on COVID-19 outcomes. We also hypothesize and empirically assess the differential impact of wildfire smoke on COVID-19 infections and deaths in counties exhibiting high and low social vulnerability. To test our hypotheses concerning wildfire severity and its disproportionate impact on COVID-19 outcomes in socially vulnerable communities, we construct a county-by-day panel dataset for the period April 1 to November 30, 2020, in California, drawing on publicly available state and federal data sources. This study's empirical results, based on panel fixed effects models, show that wildfire smoke is significantly associated with increases in COVID-19 infections and deaths. Moreover, wildfires exacerbated COVID-19 outcomes by depleting the already scarce hospital and public housing resources in local communities. Conversely, when wildfire smoke doubled, a one percent increase in the availability of hospital and public housing resources was associated with a 2 to 7 percent decline in COVID-19 infections and deaths. For California communities exhibiting high social vulnerability, the occurrence of wildfires worsened COVID-19 outcomes. Sensitivity analyses based on an alternative sample size and different measures of social vulnerability validate this study's main findings. An implication of this study for policymakers is that communities exhibiting high social vulnerability will greatly benefit from local government policies that promote social equity in housing and healthcare before, during, and after disasters.

Severe Acute Respiratory Syndrome and Particulate Matter Exposure: A Systematic Review

BACKGROUND

Particulate matter (PM) exposure is responsible for seven million deaths annually and has been implicated in the pathogenesis of respiratory infections such as severe acute respiratory syndrome (SARS). Understanding modifiable risk factors of high mortality, resource burdensome C19 and exposure risks such as PM is key to mitigating their devastating effects. This systematic review focuses on the literature available, identifying the spatial and temporal variation in the role of quantified PM exposure in SARS disease outcome and planning our future experimental studies.

METHODS

The systematic review utilized keywords adhered to the PRISMA guidelines. We included original human research studies in English.

RESULTS

Initial search yielded N = 906, application of eligibility criteria yielded N = 46. Upon analysis of risk of bias N = 41 demonstrated high risk. Studies found a positive association between elevated PM2.5, PM10 and SARS-related outcomes. A geographic and temporal variation in both PM and C19's role was observed.

CONCLUSION

C19 is a high mortality and resource intensive disease which devastated the globe. PM exposure is also a global health crisis. Our systematic review focuses on the intersection of this impactful disease-exposure dyad and understanding the role of PM is important in the development of interventions to prevent future spread of viral infections.

The Temporal-Spatial Distribution and Information-Diffusion-Based Risk Assessment of Forest Fires in China

As forest fires are becoming a recurrent and severe issue in China, their temporal-spatial information and risk assessment are crucial for forest fire prevention and reduction. Based on provincial-level forest fire data during 1998–2017, this study adopts principal component analysis, clustering analysis, and the information diffusion theory to estimate the temporal-spatial distribution and risk of forest fires in China. Viewed from temporality, China’s forest fires reveal a trend of increasing first and then decreasing. Viewed from spatiality, provinces characterized by high population density and high coverage density are seriously affected, while eastern coastal provinces with strong fire management capabilities or western provinces with a low forest coverage rate are slightly affected. Through the principal component analysis, Hunan (1.33), Guizhou (0.74), Guangxi (0.51), Heilongjiang (0.48), and Zhejiang (0.46) are found to rank in the top five for the severity of forest fires. Further, Hunan (1089), Guizhou (659), and Guanxi (416) are the top three in the expected number of general forest fires, Fujian (4.70), Inner Mongolia (4.60), and Heilongjiang (3.73) are the top three in the expected number of large forest fires, and Heilongjiang (59,290), Inner Mongolia (20,665), and Hunan (5816) are the top three in the expected area of the burnt forest.