High PM2 Research Articles

Assessment of Deep Neural Network Models for Direct and Recursive Multi-Step Prediction of PM10 in Southern Spain

Western Europe has been strongly affected in the last decades by Saharan dust incursions, causing a high PM10 concentration and red rain. In this study, dust events and the performance of seven neural network prediction models, including convolutional neural networks (CNN) and recurrent neural networks (RNN), have been analyzed in a PM10 concentration series from a monitoring station in Córdoba, southern Spain. The models were also assessed here for recursive multi-step prediction over different forecast periods in three different situations: background concentration, a strong dust event, and an extreme dust event. A very important increase in the number of dust events has been identified in the last few years. Results show that CNN models outperform the other models in terms of accuracy for direct 24 h prediction (RMSE values between 10.00 and 10.20 μg/m3), whereas the recursive prediction is only suitable for background concentration in the short term (for 2–5-day forecasts). The assessment and improvement of prediction models might help the development of early-warning systems for these events. From the authors’ perspective, the evaluation of trained models beyond the direct multi-step predictions allowed to fill a gap in this research field, which few articles have explored in depth.

Data Augmentation Strategies for Improved PM2.5 Forecasting Using Transformer Architectures

Breathing in fine particulate matter of diameter less than 2.5 µm (PM2.5) greatly increases an individual’s risk of cardiovascular and respiratory diseases. As climate change progresses, extreme weather events, including wildfires, are expected to increase, exacerbating air pollution. However, models often struggle to capture extreme pollution events due to the rarity of high PM2.5 levels in training datasets. To address this, we implemented cluster-based undersampling and trained Transformer models to improve extreme event prediction using various cutoff thresholds (12.1 µg/m3 and 35.5 µg/m3) and partial sampling ratios (10/90, 20/80, 30/70, 40/60, 50/50). Our results demonstrate that the 35.5 µg/m3 threshold, paired with a 20/80 partial sampling ratio, achieved the best performance, with an RMSE of 2.080, MAE of 1.386, and R2 of 0.914, particularly excelling in forecasting high PM2.5 events. Overall, models trained on augmented data significantly outperformed those trained on original data, highlighting the importance of resampling techniques in improving air quality forecasting accuracy, especially for high-pollution scenarios. These findings provide critical insights into optimizing air quality forecasting models, enabling more reliable predictions of extreme pollution events. By advancing the ability to forecast high PM2.5 levels, this study contributes to the development of more informed public health and environmental policies to mitigate the impacts of air pollution, and advanced the technology for building better air quality digital twins.

Evaluating the Laboratory Performance of Pellet-Fueled Semigasifier Cookstoves.

This study examines three representative semigasifier cookstove models each burning four types of pelletized-biomass fuel (hardwood, peanut hull, rice husk, and wheat straw) using the International Organization for Standardization (ISO) 19867-1:2018 protocol. ISO tier ratings for fine particulate matter (PM2.5) and carbon monoxide (CO) emissions ranged 1-4 and 2-5 (where 5 = cleanest), respectively, suggesting that pellet-fueled cookstoves may provide substantial emissions reductions, dependent upon stove/fuel matching and operation, over other biomass-fueled cooking alternatives. PM2.5 emission factors based on useful energy delivered (EFd) varied by up to 25-fold, and organic and elemental carbon (OC and EC) EFd values respectively varied by >200- and ∼100-fold, reflecting complex variability in PM2.5 composition. These semigasifier cookstoves showed higher ultrafine particle (UFP) emissions but lower bulk PM2.5 emissions. Operation of pellet-fueled cookstoves at higher firepower resulted in higher PM2.5 and UFP emission factors and higher EC-to-total-carbon ratios; operation at lower firepower resulted in higher gaseous pollutant emission factors. Results of this work provide technical guidance for stove developers, users, and policy-makers. These ISO-protocol-based emission factors are also pertinent to health and climate modeling efforts.

Preparation of a Highly Effective PMIA/BaTiO3 Nanofiber Membrane for Particulate Matter Removal Under High Temperature

ABSTRACTParticulate matter (PM) pollution is widely recognized as a major threat to public health, making the development of thermally stable filters for particulate removal in high‐temperature environments critically important. However, the common polymer‐based nanofiber air filters are inadequate stability when exposed to high temperature. In this study, the poly (m‐phenylene isophthalamide) (PMIA) and nano barium titanate (BaTiO3) which showed remarkable thermal endurance was utilized to prepare the PMIA/BaTiO3 composite nanofiber air filter. The structure and performance of nanofibers were regulated by changing the addition of BaTiO3. The PMIA/BaTiO3 nanofiber air filter with nano‐protrusion structure exhibits high PM2.5 filtration efficiency (98.7%) and low pressure drop (60.4 Pa). Even after being exposed to treatment at 250°C, the PMIA/BaTiO3 composite nanofiber air filter can still maintain stable filtration performance. The results showed that PMIA/BaTiO3 nanofiber materials hold promise for eliminating particulate matter in high‐temperature environments.

P0804 Morning administration of biologic therapy is associated with decreased risk of treatment failure in patients with Inflammatory Bowel Disease

Abstract Background Biological therapy has greatly improved treatment outcomes in inflammatory bowel disease (IBD) management, but primary non-response rates of 10-30% and even higher secondary non-response rates have been reported1. Chronotherapy, which involves administering treatments in alignment with the body’s biological clock has shown promise in improving treatment efficacy across multiple diseases2,3. However, its impact on IBD management remains largely unexplored4. Here, we investigated the effect of infusion timing in IBD patients receiving infliximab or vedolizumab. Methods We conducted a retrospective observational study using electronic health records from the Mount Sinai Health System. The cohort included IBD patients with hospital encounters at least 3 months pre- and 12 months post-index date (i.e., treatment initiation) (Fig.1). Treatment failure was defined as a composite outcome of biologic discontinuation, IBD-related surgery, or hospitalisation within one year after the index date. Infusion times were categorised as AM (between 07:16 and 11:59) or PM (between 12:00 and 23:45), and the PM ratio was calculated as the percentage of PM treatments. To assess associations between timing and outcomes, we employed mixed effects logistic regression (LR) and Extreme Gradient Boosting (XGBoost) models. Models were adjusted for demographics (age, sex, race), body mass index, and clinical factors (prior IBD-related medications, hospitalization, surgeries, and concomitant immunomodulator use). Results The study included 1,211 IBD patients with 1,316 treatment courses (54.8% Crohn's disease, 45.2% ulcerative colitis; 50.1% infliximab, 49.9% vedolizumab). During the one-year follow-up period, 44.5% of cases resulted in treatment failure. 42.6% of treatments were administered AM, and 57.3% PM. The LR model showed that PM administration was associated with 10% higher odds of treatment failure (odds ratio: 1.101, 95% confidence interval: 1.009-1.201, p=0.03). The XGBoost model improved with PM ratio variable added, compared to baseline model (area under the receiver operating characteristics curve: 0.819 vs 0.623). SHapley Additive exPlanations (SHAP) analysis identified PM ratio as the most influential feature (Fig. 2A), with higher PM ratio more strongly associated with treatment failure (Fig. 2B). Conclusion Our findings suggest that infusion timing may influence biological therapy outcomes in IBD, with morning administrations being associated with better treatment outcome. These data support the potential relevance of chronotherapy in the management of IBD.

Foliar image-based characterization of airborne particulate matter in an urban area and its implications for remediation

This study addresses the pervasive issue of particulate matter (PM) emission in urban areas, proposing a better approach using scanning electron microscope (SEM) techniques to identify plant species effective in airborne PM removal. Conducted in Bilaspur city, the research strategically selected six plant species across four distinct sites and applied the SEM-Image J method for analysis, yielding significant insights, especially in the respirable PM range. Among the tested plant species, Senna Siamea and Dalbergia Sissoo emerged as consistent and standout performers, displaying the highest PM removal efficiency across all sites. Notably, the smaller leaves of Senna siamea and Dalbergia sissoo prevent PM from being resuspended in the air by strong winds, enhancing their overall performance in combating PM pollution. The SEM-EDS analysis was then employed for morphological and chemical characterizations of the PM, revealing anthropogenic sources as the primary contributors to pollution. Hazardous elements, including arsenic (As), antimony (Sb), iron (Fe), indium (In), terbium (Tb), chlorine (Cl), and iodine (I), were identified, underscoring potential health risks associated with the PM composition. The study underscores the significance of SEM-EDS based plant selection for mitigating airborne PM pollution and improving air quality. Senna Siamea and Dalbergia Sissoo are identified as top choices for effective PM removal, marking a significant step towards sustainable urban environments. The findings contribute valuable insights into the chemical makeup of PM, facilitating a deeper understanding of its sources and potential health implications. Overall, this research serves as a crucial step in developing strategies to combat air pollution and fosters the creation of healthier and more sustainable urban environments.

Changes in the Dominant Contributions of Nitrate Formation and Sources During Haze Episodes: Insights From Dual Isotopic Evidence

AbstractInorganic nitrate (NO3−), a crucial component of fine particulate matter (PM2.5), has not shown a consistent decrease, despite an obvious decrease of nitrogen oxide (NOx) and PM2.5. The atmospheric oxidation process for nitrate formation has been deemed a key factor in pollution; however, the changes of sources and oxidation pathways during a particular haze episode require further investigation. Here, daily dual isotopes (δ15N and δ18O) were used to quantify the sources and oxidation pathways of nitrate formation in Qingdao, a port city in Northern China, from September 2017 to February 2018. This study also includes a detailed introduction to two haze episodes. δ15N and δ18O results show that both fractions of nocturnal oxidation for nitrate formation and NOx from coal combustion were lower in warmer season and higher in colder season. The fractions increased with increasing PM2.5 under low PM2.5 concentration while the fractions were not significantly changed under higher PM2.5 concentration, dominated by nocturnal oxidation (70.6% ± 9.7%) and coal combustion (66.1% ± 18.2%), respectively. The haze episode 1 was attributed to smoke transported over long distances, which provided a large amount of aerosol particles to absorb more locally formed gaseous HNO3 or N2O5. In this episode, meteorological and air quality factors, nitrate sources, and formation mechanism did not obviously change. Haze episode 2 was caused by unfavorable meteorological factors that enhanced local nitrate accumulation. As pollution worsened, the oxidation pathway shifted from OH oxidation to N2O5 hydrolysis, and the primary source changed from coal combustion to more vehicle exhaust.

Precision and Accuracy Analysis of PM2.5 Light-Scattering Sensor: Field and Laboratory Experiments

The widely used low-cost particulate matter (PM) sensors in Thailand, such as the DustBoy, require performance improvements to ensure their data align with the established standards set by the US Environmental Protection Agency (US EPA). This study evaluates the accuracy and reliability of the DustBoy, a commonly used PM2.5 monitoring device in Thailand. A comparative analysis was conducted between the DustBoy and the US EPA’s Federal Reference Method (FRM) and Federal Equivalent Method (FEM). The research involved both laboratory and field testing, where the DustBoy’s performance was analyzed at various particulate matter concentration levels and environmental conditions. The study demonstrated that the DustBoy readings diverged from those of standard monitors at higher PM2.5 concentrations; however, a positive correlation between the devices remained evident. Below 100 µg/m3, the DustBoy overestimated PM concentrations compared to the FRM devices but underestimated them compared to the FEM devices. At higher concentrations, the DustBoy showed a significant overestimation, although the data trends aligned with those of standard devices. The sensor performance was also affected by factors such as the sensor age and device model. Corrections were developed to adjust the DustBoy readings to match the reference devices more closely, enhancing the accuracy post-adjustment. These corrections will refine the DustBoy’s public data reporting and serve as guidelines for other low-cost sensors in Thailand.

Demographic inequities and cumulative environmental burdens within communities near superfund sites on Long Island, New York.

Nassau and Suffolk Counties of Long Island, New York are densely populated and contain 34 federally-designated and 449 state-designated Superfund sites, potentially exposing communities to toxic releases. We conducted a distributive justice analysis assessing proximity to Superfund sites, community socio-demographics, and other environmental burdens. Socio-demographic and environmental variables for 665 census tracts were obtained from the United States Census and Environmental Protection Agency's Environmental Justice Screening and Mapping Tool. Hierarchical Bayesian spatial Poisson regression models evaluated the relationship between socio-demographic and environmental variables and counts of Superfund sites per census tract. Analyses were further stratified by county and site type (Federal versus State). A 10% increase in low-income residents was associated with a 47% increase in Superfund sites (Risk Ratio [RR]: 1.47; 95% credible interval (CI): 1.20-1.81). A 10% increase in Hispanic/Latino residents was associated with a 20% increase (RR: 1.20; 95%CI: 1.02-1.42). Higher PM2.5 concentrations (RR:1.64, 95% CI: 1.09-2.48), higher toxic air releases (RR: 1.27, 95%CI: 1.03-1.61), and greater proximity to underground gas storage tanks (RR: 1.27, 95%CI: 1.09-1.48) were associated with increases in Superfund counts. Stratified analyses revealed that low-income residents are concentrated near state not federal Superfund sites. County stratification found that only Suffolk County residents near Superfund sites have increased lead exposure potential, and Black residents in Suffolk (not Nassau) were more likely to live near Superfund sites. We observed localized distributive inequities in community demographics near Superfund sites on Long Island, and communities near Superfund sites are more likely to experience other environmental burdens.

Mitigating Air Pollution and Protecting Public Health: Analyzing the Impact of National Clean Air Programme in Kota, Rajasthan

The clean air plan in India involves a set of rules, policies, and initiatives targeted at improving the air quality and public health by way of decreasing emissions from various sources. The study aims to evaluate the impact of National Clean Air Program (NCAP) on lowering air pollution levels and improving public health outcomes in Kota city, Rajasthan, highlighting progress, challenges, and the need for sustained emission control efforts. Kota's selection for this study highlights its significance as an educational hub, attracting students from all over India. The rapid population growth and increased vehicle emissions in the city cause adverse impacts on air quality. Improving air quality will not only enhance the health of residents and students but also contribute to a more conducive learning environment. The action plan of NCAP involves enforcing the construction and demolition waste management rules 2016, implementing emission control measures like water sprinkling and covered transport for construction activities, and extensive campaigns against open burning of biomass and waste. It also includes regular checks on industrial emissions, proper waste collection and disposal, and mandatory green belt development in residential areas. It employs a mixed-method approach, combining air quality monitoring data collected from the Central and State Pollution Control Boards from 2014 to 2023. It also examines trends in key pollutants, including NO2, SO2, and PM10, and analyzes the effect of regulatory measures such as emission controls and waste management rules. The study reveals a decreasing trend in NO2 (nitrogen dioxide) levels in Kota city, Rajasthan from 35.35 µg/m³ to 29.90 µg/m³ during 2014 to 2023, showing a significant drop during the COVID-19 lockdown. Similarly, PM10 (particulate matter) levels peaked at 153.28 µg/m³ in 2018 but saw a significant reduction to104.80 µg/m³ by 2020, indicating an improvement in air quality. However, SO2 (sulfur dioxide) concentrations slightly increased in 2019-2023 compared to 2014-2018. The air quality index (AQI) improved modestly but frequently surpassed 100, indicating hazardous air quality for vulnerable populations. The study concludes that while the NCAP in India has significantly improved air quality, challenges remain, with NO2 levels rebounding post-COVID-19 lockdown and persistent high particulate matter levels. It is recommended to ensure stricter enforcement of emission control measures, enhanced monitoring systems, and public awareness campaigns. Future work should focus on the long-term health impacts of particulate matter and strategies to achieve sustained air quality improvements in high-risk regions.

Spatiotemporal Distribution and Potential Source Areas of PM2.5 Pollution in Xianyang City, Guanzhong Basin

This study employed PM2.5 concentration data for Xianyang City spanning the years 2014 to 2021, in conjunction with the global data assimilation system （GDAS）. Various analytical techniques, including backward trajectory clustering analysis, potential source contribution function （PSCF）, concentration weighted trajectory analysis （CWT）, and relevant statistical methods, were employed to investigate the temporal and spatial variations in PM2.5 pollution. Furthermore, this research aimed to elucidate the source characteristics and potential areas contributing to PM2.5 pollution within Xianyang City. The results revealed a fluctuation in PM2.5 pollution concentration in Xianyang City from 2014 to 2021, with an initial increase followed by a subsequent decrease. The peak average annual concentration, reaching 81.25 μg·m-3, was recorded in 2016. Seasonal variations indicated higher PM2.5 concentrations in autumn and winter, contrasting with lower levels in spring and summer. Winter exhibited the highest PM2.5 concentration at 116 μg·m-3, while the lowest was recorded in summer at 31.58 μg·m-3. Spatially, the annual mean distribution of PM2.5 in Xianyang City demonstrated heightened pollution in the southern and central regions, juxtaposed with lower pollution in the northern areas. Cluster analysis highlighted that Xianyang City experienced substantial influence from northwest airflow during spring, autumn, and winter, while short-distance transport dominated during the summer months. PSCF and CWT analyses indicated that the high-value potential source contribution areas were most extensive during winter, followed by spring and autumn. Conversely, the high-value area in summer was the smallest. The potential source areas were concentrated within Xianyang City and extended northwest to southeast, encompassing regions in western Inner Mongolia, central and eastern Gansu Province, central and southern Ningxia, and central Henan. In analyzing periods of heavy pollution, the study demonstrated that PM2.5 pollution in Xianyang City was primarily induced by westerly airflow. High PM2.5 concentrations were influenced by the surrounding areas of Xianyang City and the source zone, akin to regions with high potential source values during the winter. As a consequence, mitigating PM2.5 pollution in Xianyang City necessitates stringent control measures for both local and regional pollution sources. Additionally, regional collaborative efforts should be emphasized to prevent external pollution sources from exacerbating persistent pollution episodes in the region.

Bidirectional effect modifications of temperature and PM2.5 on myocardial infarction morbidity and mortality in Beijing, China from 2007 to 2021.

Ambient temperatures and PM2.5 can trigger myocardial infarction (MI), while little is known about the complex interplay between these two factors on MI, especially morbidity. To investigate bidirectional effect modifications of temperature and PM2.5 on MI morbidity and mortality. A time-stratified case-crossover study was conducted utilizing high-resolution data of temperature and PM2.5, along with 498,077 MI cases from the citywide registry in Beijing, China from 2007 to 2021. A conditional logistic regression model combined with a distributed lag non-linear model was used to examine linear and categorical effect modifications of temperature and PM2.5 on MI morbidity and mortality. The PM2.5 effect on MI morbidity, modified by temperature, showed a progressive increase of odds ratio from 1.013 (95% CI: 1.001, 1.025) to 1.027 (95% CI: 1.012, 1.042) with rising temperatures. Stratified analysis revealed a greater PM2.5 effect in high temperature strata (1.049, 95 % CI: 1.029, 1.069) compared with low strata (1.007, 95 % CI: 0.993, 1.021) on MI morbidity (PZ test<0.001). The temperature effect on MI morbidity was also modified by PM2.5, with a gradual upward risk trend observed with increasing PM2.5 concentration. Specifically, the heat wave effect was greater at high PM2.5 concentration strata (1.097, 95 % CI: 1.042, 1.155) than at low strata (0.954, 95 % CI: 0.890, 1.023) (PZ test=0.002). The cold spell effect was greater at high PM2.5 concentration strata (1.181, 95 % CI: 1.117, 1.249) than at low strata (0.883, 95 % CI: 0.740, 1.053) (PZ test=0.002). A similar bidirectional effect modification of temperature and PM2.5 was also found in MI mortality. Temperature and PM2.5 bidirectionally modify their effect on MI morbidity and mortality. Elevated temperatures exacerbate PM2.5 effect, while increased concentrations of PM2.5 amplify temperature effect. The combined effect of temperatures and PM2.5 should be stressed, encompassing not only extreme conditions but also the entire range of exposures.

Real-world outdoor air exposure effects in a model of the human airway epithelium - A comparison of healthy and asthmatic individuals using a mobile laboratory setting.

We developed a mobile laboratory allowing field exposure of lung tissue models to ambient air at localities with various pollution sources (Background, Industrial, Traffic, Urban) in different seasons (summer/fall/winter). In samples originating from healthy and asthmatic individuals, we assessed the parameters of toxicity, lipid peroxidation and immune response; we further performed comprehensive monitoring of air pollutants at sampling sites. We measured lactate dehydrogenase (LDH) and adenylate kinase (AK) production and transepithelial electrical resistance (TEER), analyzed 15-F2t-isopostane (IsoP) and a panel of 20 cytokines/chemokines/growth factors. In the ambient air, we detected particulate matter (PM), and other relevant chemicals (benzene, benzo[a]pyrene (BaP), NOx). In the Traffic locality, we found very high concentrations of ultrafine particles and NOx and observed low TEER values in the exposed samples, indicating significant traffic-related toxicity of the ambient air. In the Urban locality, sampled in winter, we observed high PM and BaP levels. We found lower AK levels in samples from healthy individuals exposed in this locality than in the asthmatic samples. In the samples from the Industrial locality, sampled in summer, we detected higher concentrations of TNFα, MIP-1α, Eotaxin, GROα, GM-CSF, IL-6 and IL-7 than in the Urban locality samples. We hypothesize that pollen or other plant-related components of the ambient air were responsible for this response. In conclusion, our data proved the feasibility of our mobile laboratory for field measurements of the biological response of lung tissue models exposed to ambient air, reflecting not only the levels of toxic compounds, but also season-specific parameters.

Spatio-temporal analysis of extreme air pollution and risk assessment.

Extreme air pollution poses global health and environmental threats, necessitating robust policy interventions. This study first analyses the surface mass concentration of major aerosols (such as black carbon, organic carbon, dust, sea salts, and sulphates) to estimate global PM2.5 concentrations from 1980 to 2023. The developed model-estimated PM2.5 database was validated against data from 526 cities worldwide, showing strong accuracy, with RMSE, r, and R2 values of 7.47μg/m³, 0.87, and 0.75, respectively. The motivation arises from the need to understand whether recent pollution increases are driven by rising emissions or natural variability, given the significant impacts on life and property. To assess both short-and long-term pollution trends, magnitudes, and risks, we proposed twelve novel extreme pollution indices, which comprehensively characterize the spatial and temporal variations in pollution. The highest PM2.5 concentrations were observed in regions near the Saharan Desert, reaching up to 90,000μg/m³. However, significant PM2.5TOT (total pollution) concentrations were also found in the Indo-Gangetic Plain (IGP) and eastern China, ranging from 20,000 to 40,000μg/m³. Persistent pollution burdens North Africa for approximately 350 days annually, while the IGP and eastern China experience extreme pollution for over 200 days yearly. Other pollution indices highlight the intensity and frequency of pollution in regions such as North Africa, IGP, Eastern Russia, Western USA, and Eastern China, revealing critical regional air quality challenges. Our analysis identifies cities in low-income and middle-income countries, such as New Delhi, Lahore, Dhaka, and Dammam, as being at extreme risk scores above 90 out of 100. Meanwhile, cities like Ghaziabad, Chongqing, Kolkata, Mumbai, and East London fall into the high-risk category, scoring between 60 and 80. Conversely, most cities in the EU, USA, and Canada are at very low risk, a result of the effective implementation of strategic air pollution norms and policies. The study promotes a phased approach for low- and middle-income regions, emphasizing achievable air quality standards, low-cost monitoring, targeted interventions, urban greening, public awareness, and innovative financing for improvements.

Demonstrating multi-benefits of green infrastructure to schools through collaborative approach.

Green infrastructure (GI) is known to reduce road air pollution exposure, but their implementation in schools and associated benefits remain under-researched. In this study, two GI solutions, green screen and green gate, were co-designed and installed at a primary school in Guildford using collaborative and participatory methods. By assessing changes in air pollution levels, noise, and public perception before and after GI installation, we aimed to understand their impact on reducing children's exposure and evaluate other co-benefits. Without considering wind direction's effect, a maximum reduction of up to 32%, 10% and 12% in the average daily concentration of PM10 (green gate), PM2.5 (green screen) and PM1 (green gate), respectively, when compared with in-front concentration. The decay in concentration decreases with distance from the GI, and different wind directions result in varying percentage reductions in PM concentration. For the green screen, 'parallel to the screen' and for the green gate, 'away from the gate' wind directions provided the highest PM reduction. The horizontal abatement efficiency of GI varied with PM size, with the highest being PM10. Continuous monitoring behind the green screen revealed a decrease in PM concentration after installation, and this relative concentration varied from 0.29 to 0.90 compared to before installation. The green gate effectively lowered noise by 5dB(A), and the green screen did not report a noticeable impact on noise levels. Most parents perceived the installation of GI in school as significantly decreasing air pollution exposure and slightly reducing noise levels, resembling the changes in their levels observed in monitoring. The successful co-creation and co-implementation of GI interventions and resulting co-benefits underscore the importance of community engagement and participatory approaches in urban planning and environmental management. This study paves the way for the wider-scale application of innovative strategies involving local communities, stakeholders, and policymakers in implementing GI projects to ensure their sustainability and effectiveness.

Short-term effects of combined environmental factors on respiratory disease mortality in Qingdao city: A time-series investigation.

It is crucial to comprehend the interplay between air pollution and meteorological conditions in relation to population health within the framework of "dual-carbon" targets. The purpose of this study was to investigate the impact of intricate environmental factors, encompassing both meteorological conditions and atmospheric pollutants, on respiratory disease (RD) mortality in Qingdao, a representative coastal city in China. The RD mortality cases were collected from the Chronic Disease Surveillance Monitoring System in Qingdao during Jan 1st, 2014 and Dec 31st, 2020. The distributed-lag nonlinear model and generalized additivity model were used to assess the association between daily mean temperature (DMT), air pollutant exposure and RD mortality. To ascertain the robustness of the model and further investigate this relationship, a stratified analysis and sensitivity analysis were conducted to mitigate potential confounding factors. A total of 19,905 mortalities from RD were recorded. The minimum mortality temperature (MMT) was determined to be 23.5°C, and DMT and RD mortality showed an N-shaped relationship. At the MMT of 23.5°C, the cumulative relative risk (cumRR) for mortality within a lag period of 0-14 days from the highest temperature (31°C) was estimated at 2.114 (95% confidence interval [CI]: 1.475 ~ 3.028). The effect value of particulate matter (PM) also increased with a longer cumulative lag time. In the single pollutant model, the highest risk of RD mortality was observed on the lag1-day of per 10 μg/m3 increase in PM2.5 exposure, with an excess risk ratio (ER) of 0.847% (95% CI: 0.335% ~ 1.362%). The largest cumulative effect was found at a lag of 8 days, with an ER of 1.546% (95% CI: 0.483% ~ 2.621%). A similar trend was found for PM10. For O3 exposure, the highest risk was observed on the lag1-day of per 10 μg/m3 increase, with an ER of 1.073% (95% CI: 0.502% ~ 1.647%), and the largest cumulative effect occurred at a lag of 2 days with an ER of 1.113% (95%CI: 0.386% ~ 1.844%). Results from the dual-pollutants model demonstrated that the effect of PM on the risk of RD mortality remained significant and slightly increased in magnitude. Moreover, composite pollutants exhibited a higher risk effect, reaching its peak after one week; however, there was a decrease in single-day cumulative effects as more pollutant types were included. Subgroup analysis showed that females, elderly individuals, and those exposed during warm seasons demonstrated greater susceptibility to PM exposure. The present study revealed a significant association between short-term exposure to high temperature, PM2.5, PM10 and O3 and the risk of RD mortality in Qingdao, even in dual- and composite-pollutants models. Furthermore, our findings indicate that females, the elderly population, and warm seasons exhibit heightened sensitivity to PM exposure.

Evaluation of a gravimetrical method with a cyclonic pre-separator against the reference method for measuring high PM10 mass concentrations in livestock houses

Evaluation of a gravimetrical method with a cyclonic pre-separator against the reference method for measuring high PM10 mass concentrations in livestock houses

CARDIOMETABOLIC DISEASE RISK PARTIALLY MEDIATES RELATIONSHIP BETWEEN FINE PARTICULATE MATTER AND COGNITION

Abstract PM2.5 pollution is expected to worsen in many places due to climate change. This is because of hotter temperatures, less precipitation, and increases in windspeed. PM2.5 exposure is associated with many adverse health effects. Less is known about how physical and mental health challenges associated with aging, like the onset of cardiometabolic disease (CMB) and changes in psychological wellbeing, might mediate linkages between PM2.5 exposure and aging-related cognitive and physical limitations. Longitudinal data from the Midlife in the United States study (MIDUS 1: 2004-05; MIDUS 2: 2013-14) were adopted. A sample of 4,272 individuals aged 32 to 84 was included. Based on individuals’ residential addresses at each wave, we linked census tract identifiers to PM2.5 exposure, which was defined by five-year annual averages. Using structural equation models, we examined cross-sectional and longitudinal associations between PM2.5 exposure and executive functioning and instrumental activities of daily living (IADL) limitations, and tested CMB risk and psychological well-being as mediators using the product of coefficients method. Results suggested that higher PM2.5 exposure was associated with lower executive functioning cross-sectionally and with larger decreases over time. No significant associations were found between PM2.5 and IADL limitations. The cross-sectional association between PM2.5and executive functioning was partially mediated by CMB risk, accounting for 14% of the estimate, and the longitudinal association showed a similar, albeit nonsignificant mediation pattern. Psychological wellbeing was not a significant mediator. Findings suggest the importance of considering the complex and indirect ways in which climate change may impact health of older adults.

Estimation of global ground-level PM10 concentrations using a stacking model

ABSTRACT Atmospheric environmental pollution has gained significant attention recently. Inhalable particulate matter (PM10) impacts societal development and poses serious health threats. Consequently, PM10 has become a primary focus of air pollutant research. In this article, we propose a stacking model incorporating a back propagation neural network and extremely randomized trees (Stacking-BP-ET model). This model combined aerosol optical depth products with auxiliary factors, including meteorology, elevation and population distribution, to construct a global PM10 dataset with a 1 km spatial resolution from 2015 to 2021. The product had an out-of-station and out-of-year spatiotemporal cross-validation coefficient of determination (R2) of 0.833, and the mean absolute error (MAE) and root mean square error (RMSE) were 6.411 and 14.071 μg/m3, respectively. High PM10 concentrations were found in areas with frequent dust storm events such as Mongolia, the Arabian Peninsula and Northwest China and wildfire disasters such as the west coast of the United States and Australia. The PM10 concentrations in these areas had shown an increasing trend in recent years as the disaster frequency has increased. Overall, the global PM10 dataset may be useful for future large-scale studies of air pollution.

Analyzing numerical simulation of ventilation system and filter membrane: The exposure of airborne nanoparticles in health interior Space

Improving air quality in indoor areas or interior spaces is a vital need. High PM 2.5 concentration or excessive air pollution causes severe damage to human health and the respiratory system. Air ventilation systems in health spaces are more necessary as there is a higher human crowd flow in viral-susceptible areas. The recent COVID situation has made us rethink the re-arrangement of hospitals' interior space and air ventilation systems. For that reason, improving high-efficiency particulate air filters is the need of the hour. The main objective of this research is the optimization and redesign of the air filtration system in health centers by integrating a hybrid combination system of HEPA filters based on computer modeling and numerical simulation. To analyze the effectiveness of the hybrid system, first, we simulated the existing air filtration system by using a successful and proven air filtering computational model in COMSOL Multiphysics. After that, we generated comparative numerical results and graphical analysis obtained by COMSOL to demonstrate the competency of the hybrid system over the conventional air filtration system. Finally, we have shown the most optimum and effective way to implement the hybrid system in health centers by analyzing the Computational Fluid Dynamics of different types of opening and ventilation systems. This research will play an essential role in future interior air quality improvement and effective anti-viral design of health spaces.