Search Results (48)

The atmospheric air quality is one of the crucial factors determining people’s health, duration and quality of life. The importance of ammonia (NH₃) and ethylene (C₂H₄) is due to the fact that they are precursors of secondary organic aerosols (SOA) and phytotoxicants, which significantly affect air quality, cause human diseases and damage plants. The Fourier Transform Infrared (FTIR) spectrometry is a powerful tool for long-term monitoring of the atmospheric gas composition, including toxic gases. The paper presents the results of atmospheric FTIR measurements of NH₃ and C₂H₄ at the St. Petersburg State University observational site (59.88° N, 29.83° E, 20 m above sea level) located in a suburb of greater Saint Petersburg. This work demonstrates the applicability of the ground-based atmospheric FTIR spectroscopy to long-term monitoring of air pollution in urbanized areas and in particular to provide information on the NH₃ and C₂H₄ abundance in the atmosphere, including the analysis of their annual cycle, long-term trends, and positive anomalies. It was shown that for NH₃ and C₂H₄, a statistically significant decrease in column-averaged dry-air mole fraction values (X_NH3 and X_C2H4) was observed, amounting to (−2.3 ± 0.2)%/year for the 2009–2025 period and with the rate (−2.2 ± 0.4)%/year for the 2016–2025 period, respectively. Periodically recorded X_NH3 anomalies indicate the presence of intensive emission sources in the region, subjecting ecosystems in adjacent areas to constant exposure to NH₃ concentrations exceeding the critical level. Anomalously high values of X_NH3 and X_C2H4 were recorded simultaneously only once—on 17 October 2017. Using data on HCN total column (as a forest fire indicator) and the results of atmospheric dispersion modeling, it was shown that this pollution event was caused by the influence of biomass burning products emitted from wildfires located approximately 250 km to the north-west from the observational site in the Helsinki area (Finland). Full article

Cross-sectional surveys of psychological distress using the Kessler-6 tool (K6+) were conducted among training cohorts per year of New Jersey (NJ) secondary school teachers between January 2022 and December 2024. Data downloaded for 12–18 annual virtual synchronous live session training date ranges related to specified teacher cohorts, consisting of 30 calendar days prior to its date to relate to K6+ questions (575 unique participants across 42 total live sessions). Utilizing data from federal/state air quality monitoring stations (AQMS), we constructed a database of estimated exposures to ambient/outdoor air quality. Cohorts were broken down by school district (SD) and paired with AQMS based on approximate geographic proximity for each SD’s school’s physical address utilizing NJ-GeoWeb. Once addresses were reported and associated with two AQMS, associated reviewed daily criteria pollutant data (2021–2024) were retrieved for particulate matter (PM, PM₁₀ and PM_2.5) and ozone. Data were averaged for relevant stations. Analyses suggested prior 30-day PM_2.5 showed a significant negative correlation with K6+ scores, −0.32 with PM_2.5 concentration (p = 0.04) and −0.48 with PM_2.5 AQI (p = 0.002); however, wind speed had a positive association, 0.33, with K6+ scores (p = 0.03). These results suggested how specific events and meteorological conditions affected ambient air quality for only some of the prior 30 days yet still potentially influenced K6+ scores for some cohorts, e.g., large wildfires then prevailing winds. More research with improved exposure assessment is warranted. This initial environmental epidemiology study with ecological design can inform future collaborative research and practice work on mental health and the effects of environmental factors. Full article

Background: Prenatal exposure to air pollutants may increase the risk of adverse respiratory outcomes, particularly in offspring of asthmatic mothers. Evidence on wildfire-related PM_2.5 exposure during pregnancy remains limited. This study investigated associations between early gestational wildfire-related PM_2.5 exposure, infant lung function, and respiratory outcomes at 6 years. Methods: Gestational wildfire-related PM_2.5 exposure patterns were characterised using group-based trajectory modelling and linked to infant lung function outcomes. Infant respiratory measurements were obtained at six weeks of age during behaviourally defined quiet sleep using tidal-breathing flow–volume loops (TBFVL). Airway mechanics at six years were assessed by impulse oscillometry (IOS) following international guideline standards. Trajectory modelling of PM_2.5 during gestation was conducted in SAS (PROC TRAJ); all additional statistical analyses were performed in Stata IC 16.1. Results: Increased mean tidal inspiratory flow (MTIF, beta coefficient [β]: 10.51 mL/s, 95% CI: 3.66 to 17.36, p = 0.003) and peak tidal inspiratory flow (PTIF, β: 12.49 mL/s, 95% CI: 2.48 to 22.51, p = 0.014) were observed in infants born to mothers with higher wildfire-related PM_2.5 exposure during early gestation (n = 420; n = 411 not exposed, n = 9 exposed). β-coefficients from infant mixed models were then used as proxy indicators and applied in linear regression models and associated with higher reactance at 5 Hz frequency (n = 73) at 6 years of age (PTIF: β: 9.88 mL/s, 95% CI: 0.10 to 19.67, p = 0.048 and MTIF: β: 13.43 mL/s, 95% CI: 1.43 to 25.44, p = 0.029). PTIF was further associated with asthma diagnoses at 6 years (aOR: 1.36, 95% CI: 1.07 to 1.73, p = 0.012; n = 259; n = 116 asthma). Conclusion: Early gestational exposure to wildfire-related PM_2.5 may be linked with altered respiratory patterns in infancy and differences in airway reactance during childhood. Findings also suggest a relationship with asthma risk, although mechanisms remain uncertain. Full article

Wildfires have emerged as a critical environmental and public health challenge globally, with their rising frequency and severity largely attributed to climate change. Although wildfire smoke is well recognized for its detrimental effects on respiratory and cardiovascular health, a growing body of evidence indicates that its immunological impacts are equally consequential. Composed of a complex mixture of particulate matter, volatile gases, and organic chemicals, wildfire smoke can disrupt immune homeostasis through multiple, interconnected pathways. Recent findings underscore the susceptibility of natural killer (NK) cells—key effectors of the innate immune system—to wildfire smoke-induced dysregulation. This review synthesizes current knowledge on the immunotoxicological effects of wildfire smoke with a specific focus on NK cell biology. It examines how both acute and chronic smoke exposures alter NK cell frequency, phenotype, and cytotoxic function, and explores the mechanistic contributions of inflammation, oxidative stress, and pollutant-mediated receptor modulation. Furthermore, the review considers potential long-term consequences of NK cell impairment, including heightened vulnerability to viral infections, diminished tumor surveillance, and broader disruptions in innate–adaptive immune crosstalk. Collectively, the evidence highlights the need for targeted research to delineate the pathways by which wildfire smoke compromises NK cell-mediated immunity and to inform strategies for mitigating these risks in exposed populations. Full article

Wildfires are a major source of greenhouse gases (GHGs), particulate matter (PM), and atmospheric pollutants, exerting widespread impacts on air quality, human health, and global climate. To address knowledge gaps, this study conducts a literature review of GHG emissions from wildfires across diverse ecosystems and fire regimes. The analysis quantifies emission magnitudes and compositions, evaluates their influence on regional and global climate processes, and synthesizes trends and methodological advances. Results show that the burned area is the main determinant of total emissions, with CO₂ as a robust predictor for estimated CO and CH₄, reflecting coupled emission behavior under varying combustion conditions. The Modified Combustion Efficiency (MCE) demonstrates a stronger predictive capacity for the CO/CO₂ ratio than for CH₄/CO₂, suggesting that CO/CO₂ can be predicted from MCE. Complete combustion dominates most fire events, while incomplete combustion increases the release of CO, CH₄, N₂O, and PM, contributing to tropospheric ozone formation and enhanced radiative forcing. Exposure to PM_2.5 and ozone remains a major health concern in fire-affected regions. This review provides a quantitative synthesis linking combustion efficiency and GHG co-variability, offering insights to refine emission modeling and guide climate mitigation strategies. Full article

Urban populations in Latin America are highly exposed to traffic-related pollutants, yet monitoring networks remain limited. This study proposes a low-cost methodology to identify urban pollution hotspots in the city of Córdoba, Argentina, by categorizing 20 sites based on traffic categories using Google Traffic data. Measurements of PM_2.5, polycyclic aromatic hydrocarbons (PAHs), and equivalent sound pressure level (LA_eq) were conducted over a 21-day cold-season period. Mean PM_2.5 concentrations ranged from 7.5 to 27.3 µg/m³, and total PAHs ranged from 1.4 to 7.9 ng/m³. Sites with high and medium traffic density exhibited significantly higher PAH concentrations and noise levels, with LAeq₅ values exceeding 65 dB at all urban core locations. Conversely, PM_2.5 concentrations were higher at peripheral sites due to topography, dust resuspension, and wildfire events. Strong correlations were found between vehicular flow and noise (r = 0.94), and between heavy-vehicle proportion and noise (r = 0.60). The lifetime lung cancer risk associated with PAH exposure was classified as “low” according to USEPA criteria. This traffic-based categorization approach provides a rapid and cost-effective tool for identifying high-risk areas in resource-limited settings, supporting urban planning and public health interventions. Full article

Wildfires are a source of air pollution, which impacts air quality in proximity to and at great distances from fires. Wildfire smoke exposure is seasonal and episodic, with exposure levels and durations that can vary considerably. Exposure to wildfire smoke is associated with numerous health effects, including an increased risk of mortality and exacerbation of respiratory diseases. In Canada, the health risks of wildfire smoke are communicated to the public via air quality (AQ) alerts, when levels of wildfire smoke are currently or are forecasted to be relatively high, posing a risk to the general population. To better understand the population at risk due to wildfire smoke, a population-based exposure metric was developed based on geolocated AQ alerts and population data. This metric, measured in person-days, quantifies the number of people at risk of experiencing adverse health effects of wildfire smoke during a given time period. Data from the 2023 wildfire season were used to evaluate the metric. The greatest numbers of person-days were associated with population centres and regions that experienced periods of prolonged, intense smoke exposure. For example, Toronto, a large population centre, had 12 days with AQ alerts issued, corresponding to 33.5 M person-days. This approach could be expanded to other environmental or extreme weather conditions. Full article

Poor air quality can harm human health and the environment. Air quality data are needed to understand and reduce exposure to air pollution. Air sensor data can supplement national air monitoring data, allowing for a better understanding of localized air quality and trends. However, these sensors can have limitations, biases, and inaccuracies that must first be controlled to generate data of adequate quality, and analyzing sensor data often requires extensive data analysis. To address these issues, an R-Shiny application has been developed to assist air quality professionals in (1) understanding air sensor data quality through comparison with nearby ambient air reference monitors, (2) applying basic quality assurance and quality control, and (3) understanding local air quality conditions. This tool provides agencies with the ability to more quickly analyze and utilize air sensor data for a variety of purposes while increasing the reproducibility of analyses. While more in-depth custom analysis may still be needed for some sensor types (e.g., advanced correction methods), this tool provides an easy starting place for analysis. This paper highlights two case studies using the tool to explore PM_2.5 sensor performance under the conditions of wildfire smoke impacts in the Midwestern United States and the performance of O₃ sensors for a year. Full article

Wildfires in North America, particularly in western states, have caused widespread environmental, economic, social, and health impacts. Smoke from these fires travels long distances, spreading pollutants and worsening the air quality across continents. Vulnerable groups, such as children, the elderly, and those with preexisting conditions, face heightened health risks, as do firefighters working in extreme conditions. Wildfire firefighters are of particular concern as they are fighting fires in extreme conditions with minimal protective equipment. This study examined wildfire smoke during July–August 2021, when intense fires in Canada and the western U.S. led to cross-continental smoke transport and caused significant impacts on the air quality across North America. Using the GEOS-Chem model, we simulated the transport and distribution of PM_2.5 (particulate matter with a diameter of 2.5 μm or smaller), identifying significant carcinogenic risks for adults, children, and firefighters using dosimetry risk methodologies established by the U.S. EPA. Significant carcinogenic risks for adult, child, and firefighter populations due to exposure to PM_2.5 were identified over the two-month period of evaluation. The findings emphasize the need for future studies to assess the toxic chemical mixtures in wildfire smoke and consider the risks to underrepresented communities. Full article

Numerous epidemiologic studies have used remote sensing to quantify the contribution of greenness, air pollution, and wildfire smoke to asthma and other respiration outcomes. This is the first review paper to evaluate the influence of remote sensing exposures on specific outcome severity and risk factors in different ecological settings. Literature searches utilizing PubMed and Google Scholar identified 61 unique studies published between 2009 and 2023, with 198 specific outcomes. Respiration-specific outcomes were lower in greenness and higher in air pollution and wildfire ecological settings. Aerosol optical depth (AOD)-PM_2.5 readings and specific outcomes were higher in economically developing than in economically developed countries. Prospective studies found prenatal and infant exposure to higher ambient AOD-PM_2.5 concentration level readings contributed to higher childhood asthma incidence. Lung function was higher in greenness and lower in the other two ecological settings. Age, environment, gender, other, and total risk factors showed significant differences between health outcomes and ecological settings. Published studies utilized physiologic mechanisms of immune, inflammation, and oxidative stress to describe obtained results. Individual and total physiologic mechanisms differed between ecological settings. Study results were used to develop a descriptive physiologic asthma model and propose updated population-based asthma intervention program guidelines. Full article

Environmental characteristics, such as proximity to green spaces and exposure to roads, can significantly influence atmospheric factors like air quality. For instance, areas with abundant green spaces typically exhibit better air quality, while high road density often correlates with increased air pollution, both of which can affect students’ cognitive functioning and academic performance. This study aimed to evaluate the association between the environmental and atmospheric conditions—specifically green spaces (measured by the NDVI and green space area), roads (total road length), and wildfires—around students’ schools and homes in the Federal District (FD), Brazil, and their impact on academic performance. We analyzed data from 344,175 public school students across 256 schools in the FD, covering the years 2017 to 2020. Using a mixed-effects regression model, we investigated how neighborhood characteristics such as green spaces, road density, and wildfire frequency influence individual-level academic performance while controlling for temporal, socioeconomic, and school-specific factors. Our findings indicate that the environmental factors around schools, particularly green spaces and road density, have significant associations with academic outcomes. Specifically, a higher road density around schools was linked to lower academic performance, whereas green space presence had a generally positive impact, especially around schools. Wildfires, while negatively associated with performance around homes, had mixed effects around schools. These results underscore the importance of considering environmental and atmospheric factors in urban planning and education policy to enhance student performance. Full article

At the end of the dry season, from early March to early April each year, extensive agricultural biomass waste burnings occur throughout insular mainland Southeast Asia. During this biomass-burning period, smoke aerosols blanketed the whole region and were transported and dispersed by predominant westerly and southwesterly winds to southern China, Taiwan, and as far southern Japan and the Philippines. The extensive and intense burnings coincided with some wildfires in the forests due to high temperatures, making the region one of the global hot spots of biomass fires. In this study, we focus on the effect of pollutants emitted from biomass burnings in March 2019 at the height of the burning period on the exposed population and their health impact. The Weather Research Forecast-Chemistry (WRF-Chem) model was used to predict the PM_2.5 concentration over the simulating domain, and health impacts were then assessed on the exposed population in the four countries of Southeast Asia, namely Thailand, Laos, Cambodia, and Vietnam. Using the health impact based on log-linear concentration-response function and Integrated Exposure Response (IER), the results show that at the peak period of the burnings from 13 to 20 March 2019, Thailand experienced the highest impact, with an estimated 2170 premature deaths. Laos, Vietnam, and Cambodia followed, with estimated mortalities of 277, 565, and 315 deaths, respectively. However, when considering the impact per head of population, Laos exhibited the highest impact, followed by Thailand, Cambodia, and Vietnam. The results highlight the significant health impact of agricultural waste burnings in Southeast Asia at the end of the dry season. Hence, policymakers should take these into account to design measures to reduce the negative impact of widespread burnings on the exposed population in the region. Full article

The frequency of extreme wildfire events (EWEs) is expected to increase due to climate change, leading to higher levels of atmospheric pollutants being released into the air, which could cause significant short-term impacts on human health (both for the population and firefighters) and on visibility. This study aims to gain a better understanding of the effects of EWEs’ smoke on air quality, its short-term impacts on human health, and how it reduces visibility by applying a modelling system to the Portuguese EWEs of October 2017. The Weather Research and Forecasting Model was combined with a semi-empirical fire spread algorithm (WRF-SFIRE) to simulate particulate matter smoke dispersion and assess its impacts based on up-to-date numerical approaches. Hourly simulated particulate matter values were compared to hourly monitored values, and the WRF-SFIRE system demonstrated accuracy consistent with previous studies, with a correlation coefficient ranging from 0.30 to 0.76 and an RMSE varying between 215 µg/m³ and 418 µg/m³. The estimated daily particle concentration levels exceeded the European air quality limit value, indicating a potential strong impact on human health. Health indicators related to exposure to particles were estimated, and their spatial distribution showed that the highest number of hospital admissions (>300) during the EWE, which occurred downwind of the fire perimeters, were due to the combined effect of high smoke pollution levels and population density. Visibility reached its worst level at night, when dispersion conditions were poorest, with the entire central and northern regions registering poor visibility levels (with a visual range of less than 2 km). This study emphasises the use of numerical models to predict, with high spatial and temporal resolutions, the population that may be exposed to dangerous levels of air pollution caused by ongoing wildfires. It offers valuable information to the public, civil protection agencies, and health organisations to assist in lessening the impact of wildfires on society. Full article

Wildfires generate substantial smoke containing fine particulate matter (PM_2.5) that adversely impacts health. This study develops machine learning models integrating pre-wildfire factors like weather and fuel conditions with post-wildfire health impacts to provide a holistic understanding of smoke exposure risks. Various data-driven models including Support Vector Regression, Multi-layer Perceptron, and three tree-based ensemble algorithms (Random Forest, Extreme Gradient Boosting (XGBoost), and Natural Gradient Boosting (NGBoost)) are evaluated in this study. Ensemble models effectively predict PM_2.5 levels based on temperature, humidity, wind, and fuel moisture, revealing the significant roles of radiation, temperature, and moisture. Further modelling links smoke exposure to deaths from chronic obstructive pulmonary disease (COPD) and lung cancer using age, sex, and pollution type as inputs. Ambient pollution is the primary driver of COPD mortality, while age has a greater influence on lung cancer deaths. This research advances atmospheric and health impact understanding, aiding forest fire prevention and management. Full article

Worsening wildfire seasons in recent years are reversing decadal progress on the reduction of harmful air pollutants in the US, particularly in Western states. Measurements of the contributions of wildfire smoke to ambient air pollutants, such as fine particulate matter (PM_2.5), at fine resolution scales would be valuable to public health research on climate vulnerable populations and compound climate risks. We estimate the influence of wildfire smoke emissions on daily PM_2.5 at fine-resolution, 3 km, for California 2011–2020, using a geostatistical modeled ambient PM_2.5 estimate and wildfire smoke plume data from NOAA Hazard Mapping System. Additionally, we compare this product with the US Environmental Protection Agency (EPA) daily and annual standards for PM_2.5 exposure. Our results show wildfires significantly influence PM_2.5 in California and nearly all exceedances of the daily US EPA PM_2.5 standard were influenced by wildfire smoke, while annual exceedances were increasingly attributed to wildfire smoke influence in recent years. This wildfire-influenced PM_2.5 product can be applied to public health research to better understand source-specific air pollution impacts and assess the combination of multiple climate hazard risks. Full article