High PM2 Research Articles

Insight of effects of air quality and sustainable aviation fuel blend on energy saving and emission reduction in airport

Air quality in airport attracts a widespread attention due to the emission of GHGs and pollutants related with aircraft flight. Sustainable aviation fuel (SAF) has confirmed PM2.5 reduction due to free of aromatics and sulphur, and thus air quality improvement in airport is prospected by SAF blend. Two types of SAF were assessed the potential of energy saving and emission reduction by ZF850 jet engine. FT fuel is characterized with only paraffins without aromatics and cycloparaffins while HCHJ fuels is characterized with no aromatics. The descend of air quality and SAF blend were both investigated the effect on the engine performance and emission characteristic. The critical parameters were extracted from fuel compositions and air pollutants. Ambient air with a higher PM2.5 could lead to the rise of engine emission especially in UHC and PM2.5 despite at the low thrust setting and high thrust setting, and even couple with 3.2% rise in energy consumption and 1% reduction in combustion efficiency. CO, NO and NO2 in ambient air show less influence on engine performance and emission characteristic than PM2.5. Both types of SAF blend were observed significant reductions in PM2.5 and UHC. PM2.5 reduction obtained 37.9%—99.8% by FT blend and 0.64%-93.9% by HCHJ blend through the whole trust settings. There are almost 6.67% positive benefit in TSFC through the whole thrust setting by 7% FT blend. The effects of air quality and SAF blend on engine emission present significant changes on PM and UHC but the slight change on CO and NOx. By SAF blend, the energy saving and pollutant reduction obtained could be both benefit for air quality improvement in airport and further reduce engine emission as the feedback of less pollutants in ambient air.Graphical

Air Pollution and Parkinson Disease in a Population-Based Study

The role of air pollution in risk and progression of Parkinson disease (PD) is unclear. To assess whether air pollution is associated with increased risk of PD and clinical characteristics of PD. This population-based case-control study included patients with PD and matched controls from the Rochester Epidemiology Project from 1998 to 2015. Data were analyzed from January to June 2024. Mean annual exposure to particulate matter with a diameter of 2.5 µm or less (PM2.5) from 1998 to 2015 and mean annual exposure to nitrogen dioxide (NO2) from 2000 to 2014. Outcomes of interest were PD risk, all-cause mortality, presence of tremor-predominant vs akinetic rigid PD, and development of dyskinesia. Models were adjusted for age, sex, race and ethnicity, year of index, and urban vs rural residence. A total of 346 patients with PD (median [IQR] age 72 [65-80] years; 216 [62.4%] male) were identified and matched on age and sex with 4813 controls (median [IQR] age, 72 [65-79] years, 2946 [61.2%] male). Greater PM2.5 exposure was associated with increased PD risk, and this risk was greatest after restricting to populations within metropolitan cores (odds ratio [OR], 1.23; 95% CI, 1.11-1.35) for the top quintile of PM2.5 exposure compared with the bottom quintile. Greater NO2 exposure was also associated with increased PD risk when comparing the top quintile with the bottom quintile (OR, 1.13; 95% CI, 1.07-1.19). Air pollution was associated with a 36% increased risk of akinetic rigid presentation (OR per each 1-μg/m3 increase in PM2.5, 1.36; 95% CI, 1.02-1.80). In analyses among patients with PD only, higher PM2.5 exposure was associated with greater risk for developing dyskinesia (HR per 1-μg/m3 increase in PM2.5, 1.42; 95% CI, 1.17-1.73), as was increased NO2 exposure (HR per 1 μg/m3 increase in NO2, 1.13; 95% CI, 1.06-1.19). There was no association between PM2.5 and all-cause mortality among patients with PD. In this case-control study of air pollution and PD, higher levels of PM2.5 and NO2 exposure were associated with increased risk of PD; also, higher levels of PM2.5 exposure were associated with increased risk of developing akinetic rigid PD and dyskinesia compared with patients with PD exposed to lower levels. These findings suggest that reducing air pollution may reduce risk of PD, modify the PD phenotype, and reduce risk of dyskinesia.

Exposure to ultrafine particles, black carbon and particulate matter during commute in a suburb from the Yangtze River Delta, China

This study evaluated the exposure of commuters to particulate matter (PM), black carbon (BC), and ultrafine particulate matter (UFP) concentrations in five different traffic microenvironments (bike, bus, taxi, electric motorbike, and subway) under various conditions, such as regular and dust days, weekdays and weekends, and peak and off-peak commute hours in Nanjing, China. In the morning peak hours, bicycle commuters were exposed to the highest PM concentration, those on buses to the highest BC concentration (3.4 ± 3.0 μg/m3), and those on buses and taxis to the highest UFP number concentration. Throughout the rides, the commuter on the subway had the lowest mean exposure concentration of PM, BC, and UFP number. Bicycle commuters (16.25 μg/km) had the highest total inhaled dose of PM2.5, followed by e-bikers (1.98 μg/km), bus riders (1.28 μg/km), taxi riders (0.69 μg/km), and subway users (0.45 μg/km). Weekday PM2.5 exposure levels (115 μg/m3) and BC exposure levels (1.8 μg/m3) were higher than weekend levels (170% and 158%, respectively). The mean exposure concentration of PM10 (1299 ± 1286 μg/m3) in the evening peak of dust days was 803% and 994% higher than the mean concentrations on weekdays and holidays, respectively. PM2.5, PM10, and BC concentrations on dust days were significantly higher than those on weekdays and holidays. The overall mean ratios of PM1/PM2.5 and PM2.5/PM10 were 0.47 and 0.36, respectively, and the five commuting microenvironments had higher proportions of 2.5–10 μm and 1–2.5 μm particles in PM10 and PM2.5, respectively. Reducing the sources of particulate matter in the corresponding particle size ranges could lower exposure levels.

Interpretable machine learning tools to analyze PM2.5 sensor network data so as to quantify local source impacts and long-range transport

Sensor networks provide spatially resolved information about the time variation of PM2.5 concentrations in urban areas around the world. With relatively simple improvements to the control of the temperature and humidity of incoming air, and with proper quality assurance and calibration protocols, a low cost monitor was developed that provides measurements that were highly correlated with a reference PM2.5 monitor. These have sufficient resolution and reliability to quantify small differences within an urban area. Nevertheless, many sites report similar concentrations and it can be difficult to interpret the results or distinguish local from regional effects. Generalized Additive Models are an effective Machine Learning method to distinguish the impact of factors across very different scales. As a type of Interpretable Machine Learning / eXplainable Artificial Intelligence, they provide direct information on the link between specific factors and PM2.5 concentrations. GAM simulations were developed for sensors located around Dhaka, Bangladesh, for both the dry and the wet seasons. The simulations show that the largest contributor to high PM2.5 concentration variations across both urban and peri-urban sites is the boundary layer height which represent the vertical mixing of the urban plume. Using Trajectory Cluster Concentration Impacts, the simulations showed that robust estimates of long-range transport could be obtained from measurements located within a polluted environment, and the model further showed that enhancements of more than 40μg/m3 were associated with air transport from the Indo-Gangetic Plain in the dry season. Finally, interaction maps of the effect of horizontal wind speed and direction showed that these could be associated with up to +/−20 % variation in PM2.5 from site to site. Most of the enhancements are related to very calm winds and appear to be more strongly associated with road emissions than with point sources. Overall, the sensor network shows that air is polluted throughout the Dhaka area and into the peripheries, and that a multipronged approach will be needed to improve air quality for its inhabitants.

Mapping air quality trends across 336 cities in India: Insights from three decades of monitoring (1987–2019)

Over a span of 34 years (1987–2019), an in-depth analysis of PM10, SO2, and NO2 trends across India was conducted using data from the National Ambient Air Quality Monitoring Programme’s manual monitoring stations in 336 cities. The study encompassed six geographical regions over three time blocks, revealing a correlation between the expansion of monitoring networks and the nation’s economic growth. Regions like the densely populated Indo-Gangetic Plains (IGP) and Central India consistently hosted more monitoring stations, while the Himalayan and Northeast regions saw substantial increases from initial scarcity. SO2 concentrations showed a declining trend, while NO2 levels remained relatively stable with intermittent fluctuations. Conversely, national average PM10 concentrations exhibited an upward trajectory, notably spiking by 128 % between 2006 and 2009 due to economic activities, construction, network expansion, the 2009 drought, and heightened coal consumption. Spatially, pollutant concentrations across three blocks demonstrated improved SO2 levels, several cities exceeding NO2 standards, and persistently high PM10 levels in the IGP. PM10 levels in block 3 were lower than in block 2, reflecting effective policy interventions. State rankings, however, did not consistently reflect pollutant trends across blocks. Regionally, the IGP consistently had the highest PM10 concentrations, while the Northeast recorded the lowest. Population-weighted exposure levels indicated an overall increase in public exposure to PM10. Analysis of major city per region aligned with national trends, as evidenced by Delhi (IGP), Guwahati (Northeast), Vadodara (Northwest), and Bhopal (Central) showing increased PM10 concentrations since 2006, followed by intermittent declines. In contrast, Shimla (Himalayan) and Chennai (Southern) exhibited distinct patterns. Major industrial cities such as Parwanoo, Bongaigaon, Angul and Talcher, and Visakhapatnam mirrored national trends, with PM10 levels rising since 2009, highlighting the significant impact of industrial activities on air quality. This research underscores the need for targeted, effective mitigation strategies based on spatial and temporal pollutant trends.

Influence of meteorological conditions on the variability of indoor and outdoor particulate matter concentrations in a selected Polish health resort

The article evaluates air pollution by particulate matter (PM) in indoor and outdoor air in one of the Polish health resorts, where children and adults with respiratory diseases are treated. The highest indoor PM concentrations were recorded during the winter season. Therefore, the maximum average daily concentration values in indoor air for the PM10, PM2.5, and PM1 fractions were 50, 42 and 23 µg/m3, respectively. In the case of outdoor air, the highest average daily concentrations of PM2.5 reached a value of 40 µg/m3. The analyses and backward trajectories of episodes of high PM concentrations showed the impact of supra-regional sources and the influx of pollutants from North Africa on the variability of PM concentrations. The correlation between selected meteorological parameters and PM concentrations shows the relationship between PM concentrations and wind speed. For example, the correlation coefficients between PM1(I) and PM1(O) concentrations and wind speed were − 0.8 and − 0.7 respectively. These factors determined episodes of high PM concentrations during winter periods in the outdoor air, which were then transferred to the indoor air. Elevated concentrations in indoor air during summer were also influenced by chimney/gravity ventilation and the appearance of reverse chimney effect.

Association Between Long-Term Exposure to Environmental Fine Particulate Matter and the Prevalence of Thyroid Disorders: A National Cross-Sectional Study in China.

Background: Exposure to particles with an aerodynamic diameter of ≤2.5 μm (PM2.5) is associated with the occurrence of thyroid dysfunction among pregnant women and neonates, but it is not known if this association occurs in the general population. We aimed to determine the association of prolonged exposure to PM2.5 with the prevalence of thyroid disorders among adults in China. Methods: A nationally representative cross-sectional study of thyroid disorders, iodine status, and diabetes status was carried out in all 31 provinces across China from 2015 to 2017. In total, 73,900 adults aged 18 years and older were included. Serum concentrations of thyroid hormones, thyrotropin, and thyroid antibodies and the urine iodine concentration were measured. The environmental concentration of PM2.5 for each participant's residential address at a spatial resolution of 1 × 1 km was estimated. Results: The average long-term exposure to PM2.5 at residential addresses was 66.41 μg/m3, ranging from 17.58 μg/m3 to 120.40 μg/m3. Compared with that of individuals with lower exposure levels, the prevalence of thyroid diseases such as autoimmune thyroiditis and subclinical hypothyroidism was greater in those with PM2.5 concentrations within the third quartile range (60.18 to 73.78 μg/m3). Compared with those in the first quartile (17.58 to 46.38 μg/m3), participants in the highest PM2.5 quartile (73.78 to 120.40 μg/m3) presented an increased risk of overt hypothyroidism (OR 1.23 [CI 0.94-1.61]), subclinical hypothyroidism (1.10 [1.01-1.21]), autoimmune thyroiditis (1.09 [1.00-1.18]), and thyroglobulin antibody positivity (1.17 [1.07-1.29]). However, there was no association between PM2.5 exposure and overt hyperthyroidism, subclinical hyperthyroidism, Graves' disease, or thyroid peroxidase antibody positivity (p > 0.05). Each 10 μg/m³ increase in the PM2.5 concentration was associated with an increased risk of overt hypothyroidism (OR 1.05 [1.00-1.11]), subclinical hypothyroidism (1.02 [1.00-1.03]), and thyroglobulin antibody positivity (1.02 [1.00-1.04]). Furthermore, a nearly linear exposure-response relationship was observed between long-term PM2.5 exposure and thyroglobulin antibody positivity. Conclusions: PM2.5 exposure was associated with thyroid disorders among Chinese adults. A dose-response relationship between PM2.5 exposure and autoimmune thyroiditis, as well as thyroglobulin antibody positivity, was also observed.

Evaluating the Effects of Long-Term Exposure to Fine Particulate Matter (PM2.5) on Depressive Mood among Korean Older Adults Using Multilevel Analysis.

The harmful effects of particulate matter (PM) are amplified in older adults, who experience a decline in physiological function, reducing their ability to expel and detoxify inhaled PM. Moreover, older adults may be more vulnerable to fine particulate toxicity due to underlying medical conditions. We assessed the effects of long-term exposure to fine particulate matter (PM2.5) on depressive mood (DM) in adults aged > 65 yr using community-based data. In the 2017, Korean Community Health Survey (KCHS) data, data of PM2.5 and community factors were constructed based on participants who responded to DM in a sample of 67,802 individuals. To evaluate the effect of PM2.5 on DM among older adults, a multilevel regression model was constructed using individual-(KCHS) and community-level data (PM2.5, green area per capita, urban area, social welfare budget, health, and social business). For PM2.5, an independent variable, quartiles were used to classify regions according to concentration. A positive correlation was found between PM2.5 and DM of older adults. Compared to Q1, the odds ratio increased to 1.15 (95% CI 0.76-1.74) in Q2, 1.55 (95% CI 1.02-2.35) in Q3, and 1.60 (95% CI 1.16-2.20) in Q4. DM may increase in older individuals living in areas with high PM2.5. Systematic mental health management is required for older individuals residing in such areas.

Contextual Deprivation, Race and Ethnicity, and Income in Air Pollution and Cardiovascular Disease

Socioeconomically disadvantaged subpopulations are more vulnerable to fine particulate matter (PM2.5) exposure. However, as prior studies focused on individual-level socioeconomic characteristics, how contextual deprivation modifies the association of PM2.5 exposure with cardiovascular health remains unclear. To assess disparities in PM2.5 exposure association with cardiovascular disease among subpopulations defined by different socioeconomic characteristics. This cohort study used longitudinal data on participants with electronic health records (EHRs) from the All of Us Research Program between calendar years 2016 and 2022. Statistical analysis was performed from September 25, 2023, through February 23, 2024. Satellite-derived 5-year mean PM2.5 exposure at the 3-digit zip code level according to participants' residential address. Incident myocardial infarction (MI) and stroke were obtained from the EHRs. Stratified Cox proportional hazards regression models were used to estimate the hazard ratio (HR) between PM2.5 exposure and incident MI or stroke. We evaluated subpopulations defined by 3 socioeconomic characteristics: contextual deprivation (less deprived, more deprived), annual household income (≥$50 000, <$50 000), and race and ethnicity (non-Hispanic Black, non-Hispanic White). We calculated the ratio of HRs (RHR) to quantify disparities between these subpopulations. A total of 210 554 participants were analyzed (40% age >60 years; 59.4% female; 16.7% Hispanic, 19.4% Non-Hispanic Black, 56.1% Non-Hispanic White, 7.9% other [American Indian, Asian, more than 1 race and ethnicity]), among whom 954 MI and 1407 stroke cases were identified. Higher PM2.5 levels were associated with higher MI and stroke risks. However, disadvantaged groups (more deprived, income <$50 000 per year, Black race) were more vulnerable to high PM2.5 levels. The disparities were most pronounced between groups defined by contextual deprivation. For instance, increasing PM2.5 from 6 to 10 μg/m3, the HR for stroke was 1.13 (95% CI, 0.85-1.51) in the less-deprived vs 2.57 (95% CI, 2.06-3.21) in the more-deprived cohort; 1.46 (95% CI, 1.07-2.01) in the $50 000 or more per year vs 2.27 (95% CI, 1.73-2.97) in the under $50 000 per year cohort; and 1.70 (95% CI, 1.35-2.16) in White individuals vs 2.76 (95% CI, 1.89-4.02) in Black individuals. The RHR was highest for contextual deprivation (2.27; 95% CI, 1.59-3.24), compared with income (1.55; 95% CI, 1.05-2.29) and race and ethnicity (1.62; 95% CI, 1.02-2.58). In this cohort study, while individual race and ethnicity and income remained crucial in the adverse association of PM2.5 with cardiovascular risks, contextual deprivation was a more robust socioeconomic characteristic modifying the association of PM2.5 exposure.

Skin and Scalp Health Benefits of a Specific Botanical Extract Blend: Results from a Double-Blind Placebo-Controlled Study in Urban Outdoor Workers

Environmental pollution is increasingly recognized as a significant contributor to skin and scalp damage. Oral supplementation with a specific blend of four standardized botanical extracts (Rosmarinus officinalis, Lippia citriodora, Olea europaea leaf, and Sophora japonica) has been previously demonstrated to enhance skin health in individuals exposed to high environmental stress. Thus, it might represent a convenient strategy to also improve their scalp health aspect, particularly in subjects with sensitive scalps. To support these effects, a 12-week double-blind, randomized, placebo-controlled trial was performed in 66 women working outdoors in urban areas with high particulate matter (PM) levels and taking 250 mg of the botanical blend daily. Product efficacy was measured as follows: skin antioxidant status (FRAP); skin and scalp moisturization (corneometer), transepidermal water loss (tewameter), and oiliness (sebumeter); skin radiance and colour (spectrophotometer), elasticity and firmness (cutometer) and wrinkle depth (image analysis); and scalp clinical evaluation. Despite constant exposure to increased levels of PM, the tested product positively influenced all monitored parameters compared to both baseline and the placebo-treated group, in as early as 4 weeks. At the end of the study, key improvements included increased skin FRAP (21.9%), moisturization (9.5%), radiance (24.9%) and reduced wrinkle depth (−16.5%), dark spot pigmentation (−26.2%), and skin oiliness (−19.3%). For the scalp, moisturization increased (14.1%), TEWL decreased (−13.8%), and sebum content reduced by 16.2%. Additionally, 71% of subjects with sensitive scalps experienced reduced redness. These findings highlight the extensive benefits of the ingredient, expanding its application beyond conventional skin treatments to also alleviate scalp issues.

Spatial source, simulating improvement, and short-term health effect of high PM2.5 exposure during mutation event in the key urban agglomeration regions in China

Air quality in China has significantly improved owing to the effective implementation of pollution control measures. However, mutation events caused by short-term spikes in PM2.5 in urban agglomeration regions continue to occur frequently. Identifying the spatial sources and influencing factors, as well as improving the prediction accuracy of high PM2.5 during mutation events, are crucial for public health. In this study, we firstly introduced discrete wavelet transform (DWT) to identify the mutation events with high PM2.5 concentration in the four key urban agglomerations, and evaluated the spatial sources for the polluted scenario using Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model. Additionally, DWT was combined with a widely used artificial neural network (ANN) to improve the prediction accuracy of PM2.5 concentration seven days in advance (seven-day forecast). Results indicated that mutation events commonly occurred in the northern regions during winter time, which were under the control of both short-range transportation of dirty airmass as well as negative meteorology conditions. Compared with the ANN model alone, the average band errors decreased by 9% when using DWT-ANN model. The average correlation coefficient (R) and root mean square error (RMSE) obtained using the DWT-ANN improved by 10% and 12% compared to those obtained using the ANN, indicating the efficiency and accuracy of simulating PM2.5, by combining the DWT and ANN. The short-term mortality during mutation events was then calculated, with the total averted all-cause, cardiovascular, and respiratory deaths in the four regions, being 4751, 2554, and 582 persons, respectively. A declining trend in prevented deaths from 2018 to 2020 demonstrated that the pollution intensity during mutation events gradually decreased owing to the implementation of the Three-Year Action Plan to Win the Blue Sky Defense War. The method proposed in this study can be used by policymakers to take preventive measures in response to a sudden increase in PM2.5, thereby ensuring public health.

Impact of Exposure to Environmental Particulate Matter on the Onset of Giant Cell Arteritis.

The aim of this study was to investigate the association between exposure to particulate matter with an aerodynamic diameter ≤10 μm (PM10) and the development of giant cell arteritis (GCA) and its ischemic complications. This was case-crossover study on consecutive patients who received a diagnosis of GCA in three hospitals in northern Italy between 2013 and 2021. The PM10 hourly and daily average concentrations, collected in the Italian monitoring network and archived by Istituto Superiore per la Protezione e la Ricerca Ambientale, were determined using European reference. We used a Bayesian hierarchical model to determine patients' daily exposures to them. We employed conditional logistic regression to estimate the effect of exposure on GCA symptoms onset or ischemic complications. We included 232 patients. A positive association was observed between exposure to PM10 and GCA risk, with an incremental odd of 27.1% (95% confidence interval 5.8-52.6) for every 10-μg/m3 increase in PM10 concentration within a 60-day period. We did not find any significant association for shorter periods or with ischemic complications. Subgroup analysis found a significantly higher incremental risk at a 60-day lag for patients ≥70 years old. Comparing patients who were chronically exposed to high PM10 levels (26.9 ± 13.8 μg/m3) to those who were not (11.9 ± 7.9 μg/m3) revealed that only in the former group was there an association between GCA onset and increased PM10 levels in the preceding 60 days. Exposure to environmental PM10 in the preceding 60 days seems to be associated with an increased risk of developing GCA, especially in older individuals with prolonged exposure to high levels of air pollution.

Effects of Meteorological Conditions in Summer and Early Autumn on PM2.5 and O3 Pollution in Beijing-Tianjin-Hebei and Surrounding Areas

PM2.5 pollution remains prominent in autumn, whereas O3 pollution gradually manifests in summer. To understand the dual high characteristics and meteorological effects of PM2.5 and O3 in the summer and early autumn of 2021 in the Beijing-Tianjin-Hebei and surrounding areas, the spatiotemporal distribution characteristics of PM2.5 and O3 concentrations, as well as meteorological conditions, subtropical high index, and weather situation in the Beijing-Tianjin-Hebei and surrounding areas were analyzed. The results showed that PM2.5 concentration and DPO3 （O3 daily maximum 8h mean minus O3 concentration at 06：00） from June to September 2021 decreased compared with those in the same period in 2020 and 2022, and high concentrations were mainly occurring in June. The overall PM2.5 concentration and DPO3 showed a gradual decrease from the middle to the north and south, with synchronous spatiotemporal changes. The hourly value of PM2.5 concentration presented a multimodal distribution, reaching the peak at 07：00-10：00 and 22：00-24：00. O3 concentration showed an opposite trend of change with PM2.5 concentration, reaching their peak from 14：00-16：00. When DPO3 and the concentration of PM2.5 were high, the characteristics of near-surface meteorological elements were characterized by temperatures ranging from 24.0-28.0℃, relative humidity concentrated at 58.4%-76.3%, and wind speeds ranging from 1.5-3 m·s-1. There was a high lag correlation between the subtropical high index and DPO3. When the subtropical high was farther and stronger from the Beijing-Tianjin-Hebei and surrounding areas, DPO3 was higher. The major weather patterns with both high PM2.5 and O3 concentrations in the study area were near surface low-pressure front, high-pressure type, and frontal type. The high altitude was controlled by high-pressure ridges, and the subtropical high ridge line was stable between 21°-28°N.

Ambient fine particulate matter and Life's essential 8 and mortality in adults in China: A Nationwide retrospective cohort study

BackgroundEnhanced cardiovascular health (CVH) is linked to reduced mortality risks, whereas long-term exposure to fine particulate matter (PM2.5), elevates these risks. Whether long-term exposure to PM2.5 counteracts the health benefits of high CVH is unknown. The study aims to evaluate whether the association of CVH assessed by Life's Essential 8 (LE8) with death was consistent between participants with different PM2.5 exposures. MethodsWe included 134,727 participants in the field survey of China Chronic Disease and Risk Factor Surveillance which was conducted from August 2013 to June 2014. The deaths of participants were obtained by linking to the National Mortality Surveillance System (2013–2018). The environmental data is obtained by satellite inversion. The participants' CVH scores were calculated using the LE8 method. Hazard ratio (HR) and 95% confidence intervals (95%CI) for mortality were calculated using Cox regression models. ResultsA total of 2,936 all-cause deaths and 1,158 cardiovascular disease (CVD) deaths were recorded. Compared to those with low CVH, adults with high CVH demonstrated a reduced risk of all-cause mortality, irrespective of their PM2.5 exposure levels (P < 0.05, all P for interaction >0.05). Furthermore, in comparison to those with low CVH and highest PM2.5 exposure, adults with high CVH and lowest PM2.5 exposure exhibited HR of 0.18 (95%CI, 0.12–0.25) for all-cause mortality and 0.13 (95%CI, 0.08–0.22) for CVD mortality. ConclusionsHigh CVH is associated with reduced all-cause mortality risk, regardless of PM2.5 exposure levels. For Chinese adults, sustaining high CVH is advisable, irrespective of their residential location.

Ambient air pollution and survival in childhood cancer: A nationwide survival analysis.

Particulate matter consisting of fine particles measuring 2.5 microns or less in diameter (PM2.5), a component of air pollution, has been linked to adverse health outcomes. The objective of this study was to assess the association between ambient PM2.5 exposure and survival in children with cancer in the United States. Individuals aged birth to 19 years who were diagnosed with cancer between January 1, 2004, and December 31, 2019, were selected from the National Cancer Database. The association between the annual PM2.5 level at the patient's zip code of residence at the time of diagnosis and overall survival was evaluated using time-varying Cox proportional hazards models (crude and adjusted for diagnosis year and age). To address concerns that exposure to air pollution is correlated with other social determinants of health, the authors tested the association between PM2.5 levels and survival among sociodemographic subgroups. Of the 172,550 patients included, 27,456 (15.9%) resided in areas with annual PM2.5 concentrations above the US Environmental Protection Agency (EPA) annual PM2.5 standard of 12 μg/m3. Residing in these high-pollution areas was associated with worse overall survival (adjusted hazard ratio [aHR], 1.06; 95% confidence interval [CI], 1.012-1.10). Similarly, when PM2.5 was evaluated as a linear measure, each unit increase in PM2.5 exposure was associated with worse survival (aHR, 1.011; CI, 1.005-1.017). Exposure to PM2.5 at levels above the EPA standards was also significantly associated with worse overall survival among sociodemographic subgroups. Exposure to PM2.5 was significantly associated with worse overall survival among children with cancer, even at levels below EPA air quality standards. These results underscore the importance of setting appropriate air quality standards to protect the health of this sensitive population. The authors investigated how living in areas with high air pollution (defined as particulate matter consisting of fine particles measuring 2.5 microns or less in diameter; PM2.5) affects the overall survival of children with cancer in the United States. The results indicated that children living in areas with higher PM2.5 levels, and even at levels below prior and current US Environmental Protection Agency standards, had lower survival rates than children living in areas with lower levels of PM2.5. This finding emphasizes the need for stricter air quality standards to better protect children, particularly those with serious health conditions like childhood cancer.

Effects on the Indoor Environment in a Stable for Horses in Winter: A Case Study

The aim of this article is to show the most significant factors influencing the indoor environment in winter considering the operating conditions of an older stable modified for housing 12 horses and an indoor riding arena for teaching and sports purposes. This research focused on assessing the influences affecting the internal environment from the point of view of the construction of the building and ventilation control in the operating conditions of working and non-working days. The analysis of the results showed that the massive masonry structure has sufficient thermal insulation and accumulation, which was manifested by good temperature stability inside the stable of 7.2 ± 1.7 °C when the outside air temperature was −4.80 ± 1.5 °C. At low outdoor air temperatures of −6.44 ± 0.4 °C, the following conditions were found: a higher relative air humidity (76.0 ± 5.3%), a high CO2 concentration 2317.1 ± 931.7 ppm, and a high airborne dust concentration PM10 = 231.94 ± 19.13 μg·m−3 and PM2.5 = 160.13 ± 6.28 μg·m−3. Therefore, it is necessary to improve the solution and function of the stable ventilation. The small size of the windows and their uneven distribution (average daylight factor ei from 0.313 ± 0.154 to 0.835 ± 0.309) caused insufficient daylight in some individual boxes.

A long-term Doppler wind LiDAR study of heavy pollution episodes in western Yangtze River Delta region, China

Previous studies are primarily based on surface air pollution for a single event, leaving research on the temporal characteristics of aerosols and wind fields in the atmospheric boundary layer (ABL) and their associations unclear due to a lack of long-term vertical profile data. This study was the first to conduct long-term Doppler wind LiDAR measurements from Sep. 2019 to Aug. 2022 in Hefei in western Yangtze River Delta (YRD) region, China. The spatiotemporal characteristics of retrieved aerosol backscatter coefficient (β) and wind profiles were analyzed from the perspective of long-term statistics and typical heavy pollution episodes (HPEs). The seasonal profile of β showed a peak in the near-surface layer meanwhile the overall β profile (<0.5 km) was the highest in winter but lowest in summer. Combined with ground-based meteorological and air quality observations, 12 HPEs were identified and classified into dust-related events and fog-haze episodes. The results showed a consistency between hourly variations in PM10 (particulate matter smaller than 10 μm) concentration and β retrieved at 300 m during a high PM10 concentration (>150 μg/m3) period. Different roles of horizontal wind at different altitudes and its associated time delay effect on surface PM10 pollution were evaluated based on correlation coefficients (Ф) and air pollution diffusion conditions. Significant positive Ф values were noticed below 0.5 km during the entire dust-related EP4 and EP6, indicating that the higher wind speeds could exacerbate PM10 pollution. In contrast, large negative Ф values meant the removal of PM10 pollutants by strong winds below 0.8 km during 24 h after peak in EP3. Combining with backward trajectory analysis and meteorological condition, notable transboundary pollution with positive contributions from upper winds (>1.5 km) was discovered in dust-related EP1, EP4, EP7, EP9, and EP10. Time delay effect (1-h/2-h lag) of upper winds (>0.8 km) on surface PM10 pollution was explored in EP5, EP6, EP7, EP8, and EP9. In spring dust-related HPEs, surface PM10 pollution was mostly contributed by long-range transport of aerosol particles at higher altitudes (>1.5 km) from the northwest direction, driven by the Mongolian cyclone and the cold front system. Transboundary aerosols originated from the northern part of Anhui province and the YRD region in the middle altitudes (∼0.5 km) was the main contributor to fog-haze HPEs. The findings demonstrated the ability of the real-time Doppler wind LiDAR system to monitor transboundary air pollution and provided a scientific reference for policy makers.

Application of Acinetobacter indicus to promote cigarette smoke particulate matter phytoremediation: removal efficiency and plant-microbe interactions.

Particulate matter (PM) is one of the most hazardous atmospheric pollutants. Several plant species show high potential to reduce air pollutants and are widely used as green belts to provide clean outdoor spaces for human well-being. However, high PM concentrations cause physiological changes and stress in plants. In this study, 11 species of Thai native perennial plants were exposed to PM generated from tobacco smoke. Wrightia religiosa (Teijsm. & Binn.) Benth. ex Kurz, Bauhinia purpurea DC. ex Walp. and Tectona grandis L.f. reduced PM effectively (which is in the typical range of 43.95 to 52.97%) compared to other plant species. In addition, the responses of perennial plants under PM stress at the proteomic level were also evaluated. Proteomic analysis of these three plant species showed that plants respond negatively to high PM concentrations, such as reducing several photosynthetic-related proteins and increasing plant stress response proteins. To improve PM phytoremediation efficiency and reduce plant stress from PM, perennial plant-microbe interactions were investigated. W. religiosa was inoculated with Acinetobacter indicus PS1, and high biosurfactant-producing strains clearly showed a higher PM removal efficiency than non-inoculated plants (9.48, 9.5 and 12.6% for PM1.0, PM2.5 and PM10, respectively). Inoculating W. religiosa with A. indicus PS1 maintained chlorophyll a and b concentrations. Moreover, the malondialdehyde (MDA) concentration of W. religiosa inoculated with A. indicus PS1 was lower than that of non-inoculated W. religiosa. The leaf wax content (µg/cm2) and biosurfactant (µg/cm2) of W. religiosa inoculated with A. indicus PS1 were also higher than those of non-inoculated W. religiosa. This study clearly showed that inoculating plants with A. indicus PS1 can help plants remediate PM and improve their PM stress response.

Associations between indoor fine particulate matter (PM2.5) and sleep-disordered breathing in an urban sample of school-aged children

ObjectivesEnvironmental risk factors may contribute to sleep-disordered breathing. We investigated the association between indoor particulate matter ≤2.5µm in aerodynamic diameter (PM2.5) and sleep-disordered breathing in children in an urban US community. MethodsThe sample consisted of children aged 6-12years living in predominantly low-income neighborhoods in Boston, Massachusetts. Indoor PM2.5 was measured in participants’ main living areas for 7days using the Environmental Multipollutant Monitoring Assembly device. High indoor PM2.5 exposure was defined as greater than the sample weekly average 80th percentile level (≥15.6 μg/m3). Sleep-disordered breathing was defined as an Apnea-Hypopnea-Index (AHI) or Oxygen-Desaturation-Index (ODI) (≥3% desaturation) of ≥5 events/hour. Habitual loud snoring was defined as caregiver-report of loud snoring (most or all the time each week) over the past 4weeks. We examined the associations of PM2.5 with sleep-disordered breathing or snoring using logistic regression adjusting for potential confounders. ResultsThe sample included 260 children (mean age 9.6years; 41% female), with 32% (n = 76) classified as having sleep-disordered breathing. In a logistic regression model adjusted for socioeconomics and seasonality, children exposed to high indoor PM2.5 levels (n = 53) had a 3.53-fold increased odds for sleep-disordered breathing (95%CI: 1.57, 8.11, p = .002) compared to those with lower indoor PM2.5. This association persisted after additional adjustments for physical activity, outdoor PM2.5, environmental tobacco smoke, and health characteristics. Similar associations were observed for snoring and indoor PM2.5. ConclusionsChildren with higher indoor PM2.5 exposure had greater odds of sleep-disordered breathing and habitual loud snoring, suggesting that indoor air quality contributes to sleep disparities.

Spatial and Temporal Distribution Characteristics and Cytotoxicity of Atmospheric PM2.5 in the Main Urban Area of Lanzhou City.

PM2.5, as one of the most harmful pollutant in the atmospheric environment and population health, has received much attention. We monitored PM2.5 levels at five sampling sites in the Lanzhou City and collected PM2.5 particles from two representative sites for cytotoxicity experiment. The cytotoxicity of PM2.5 samples on A549 cells and migration ability of the cells were respectively detected by Cell Counting kit-8 (CCK-8) assay and scratch assay. We detected the levels of cellular inflammatory factors and oxidative damage-related biochemical indexes. RT-qPCR was used to detect the mRNA levels of NF-κB and epithelial-mesenchymal transition (EMT)-related genes. We found that the Lanlian Hotel station had the highest PM2.5 annual average concentration. The annual average concentration change curve of PM2.5 showed a roughly "U"-shaped distribution during the whole sampling period. The cytotoxicity experiment showed the viability of A549 cells decreased and the scratch healing rate increased in the 200 and 400μg/mL PM2.5-treated groups. We also found 400μg/mL PM2.5 induced changes in the mRNA levels of NF-κB and EMT-related genes, the mRNA levels of IKK-α, NIK, and NF-κB in the 400μg/mL PM2.5 group were higher than those in the control group. The mRNA levels of E-cadherin decreased and α-SMA increased in the 400μg/mL PM2.5 groups, and the mRNA levels of Fibronectin increased in the 400μg/mL PM2.5 groups. Moreover, we found hydroxyl radical scavenging ability and T-AOC levels were lower, and LPO levels were higher in the 200 and 400μg/mL PM2.5 groups, and the SOD activity of cells in the 400µg/mL PM2.5 group decreased. And compared with the control group, the levels of TNF-α were higher in the 200 and 400μg/mL PM2.5 groups and the levels of IL-1 were higher in the 400μg/mL PM2.5 group. The results indicated that the cytotoxicity of atmospheric PM2.5 was related to oxidative damage, inflammatory response, NF-κB activity and EMT.