Levels Of Particulate Matter Research Articles

Ambient Air Particulate Matter and Hospital Admissions for Chronic Kidney Disease in China: A Nationwide Case-Crossover Study.

Incident chronic kidney disease (CKD) may be accelerated (could be indicated by repeated admissions to the hospital) by environmental triggers such as ambient particulate matter (PM). Additionally, hospital admission is a sensitive proxy reflecting the disease burden. However, the association of PM exposure with hospital admissions for CKD is still unknown, let alone the excess risks (ERs) in hospital admissions for CKD due to high PM level exposure. In this study, a two-stage time-stratified case-crossover study was conducted to investigate the association of ambient air PM exposure with hospital admission for CKD in 282 Chinese cities of prefecture-level or above during 2013-2017. City-specific associations of single and cumulative 0-3 days lagged exposure to fine particulate matter (PM2.5) and inhalable particles (PM10) with hospital admissions for total CKD and its subtypes were evaluated by the conditional logistic regression model, then were pooled using the random-effect model. A total of 3,490,416 hospital admissions for CKD were identified. We found that per interquartile range (IQR) increment in PM2.5 at lag02 and per IQR increment in PM10 at lag03 were associated with increases of 2.36% (95%CI: 1.58%, 3.14%) and 2.87% (95% CI:1.91%, 3.85%) in hospital admissions for total CKD, respectively. Compared to control concentrations (PM2.5: 35 μg/m3; PM10: 50 μg/m3), the largest ERs in hospital admissions for total CKD were 2.63% (95% CI: 2.15%-3.11%) and 4.45% (95% CI: 3.85%-5.06%) in association with exposure to heavily excessive PM2.5 (≥75 μg/m3) and PM10 (≥150 μg/m3), respectively. Moreover, the attributable fractions (AFs) for CKD admissions were 2.83% for PM2.5 and 3.46% for PM10 during the study period. These findings suggested that exposure to PM2.5 and PM10 is associated with substantially increased risk and burden of CKD admissions.

Microbial dynamics and climatic interactions in pig sheds: Insights into airborne microbes and particulate matter concentrations.

Emissions of airborne pollutants from livestock buildings affect indoor air quality, the health and well-being of farmers, animals and the environment. This study aimed to evaluate the microbial count within pig sheds and its relationship with meteorological variables (temperature, relative humidity and air velocity) and particulate matter (PM10 and PM2.5) and microbial diversity. Sampling was conducted both inside and outside of two pig sheds over three seasons (summer, rainy and winter), with regular monitoring at fortnightly intervals. Results showed that the bacterial and fungal counts ranged from 0.07 to 3.98 x 103 cfu/m3 inside the sheds and 0.01 to 1.82 x 103 cfu/m3 outside. Seasonal variations were observed, with higher concentrations of particulate matter detected during the winter season, followed by summer. Climatic variables such as temperature, air velocity and relative humidity demonstrated significant impacts on the abundance of Enterobacteriaceae and fungi, while air velocity specifically influenced the presence of mesophilic bacteria and staphylococci. Importantly, no significant disparities were found between microbial counts and particulate matter levels. Staphylococcaceae emerged as the predominant bacterial family, while Aspergillus and Cladosporium spp. were the dominant fungal species within the pig sheds. The average levels of airborne bacteria and fungi in pig sheds were found to be within the recommended range, which can be attributed to the loose housing design and lower animal population on the farms.

Fine particulate matter (PM2.5) promotes chemoresistance and aggressive phenotype of A549 lung cancer cells

Lung cancer is one of the most aggressive malignancies with a high mortality rate. In large cities, particulate matter (PM) is a common air pollutant. High PM levels with aerodynamic size ≤2.5 μm (PM2.5) associates with lung cancer incidence and mortality. In this work, we explored PM2.5 effects on the behavior of lung cancer cells. To this, we chronically exposed A549 cells to increasing PM2.5 concentrations collected in México City, then evaluating cell proliferation, chemoresponse, migration, invasion, spheroid formation, and P-glycoprotein and N-cadherin expression. Chronic PM2.5 exposure from 1 μg/cm2 stimulated A549 cell proliferation, migration, and chemoresistance and upregulated P-glycoprotein and N-cadherin expression. PM2.5 also induced larger multicellular tumor spheroids (MCTS) and less disintegration compared with control cells. Therefore, these results indicate lung cancer patients exposed to airborne PM2.5 as urban pollutant could develop more aggressive tumor phenotypes, with increased cell proliferation, migration, and chemoresistance.

Poor air quality at school and educational inequality by family socioeconomic status in Italy

This paper investigates social stratification in the context of poor air quality's impact on educational achievement in Italy, a country characterized by high levels of air pollution and significant geographical diversity. We address two primary questions: firstly, whether students from different socio-economic backgrounds vary in their exposure to high levels of particulate matter (PM2.5) at school, and secondly, if the effect of exposure to poor air quality on academic performance differs between children from high and low socio-economic status families. Utilizing a novel dataset comprising test scores in math and reading for 456,508 8th-grade students, collected nationally in Italy in 2019, we geocode the locations of 6882 schools based on their addresses and link the level of air pollution in the surrounding areas using PM2.5 data from the Atmospheric Composition Analysis Group. To address potential confounding factors, we estimate municipality and province fixed effects and control for indicators of school neighborhood characteristics and school quality. Our analysis yields three key findings. Firstly, students from higher socio-economic backgrounds tend to attend schools with higher PM2.5 levels. However, the positive association between SES and exposure to PM2.5 disappears when adding province and municipality fixed effects, suggesting that the positive association can be explained by selection into provinces and municipalities by SES. Secondly, we identify a small yet consistent negative effect of PM2.5 on math and reading test scores. Thirdly, this adverse impact is primarily observed among students from low socio-economic backgrounds. We conclude that the relationship between environmental risks and disparities in educational achievement based on social background in Italy is nuanced and critically influenced by the country's specific context.

Montana Statewide Google Earth Engine-Based Wildfire Hazardous Particulate (PM2.5) Concentration Estimation

Wildfires pose a direct threat to the property, life, and well-being of the population of Montana, USA, and indirectly to their health through hazardous smoke and gases emitted into the atmosphere. Studies have shown that elevated levels of particulate matter cause impacts to human health ranging from early death, to neurological and immune diseases, to cancer. Although there is currently a network of ground-based air quality sensors (n = 20) in Montana, the geographically sparse network has large gaps and lacks the ability to make accurate predictions for air quality in many areas of the state. Using the random forest method, a predictive model was developed in the Google Earth Engine (GEE) environment to estimate PM2.5 concentrations using satellite-based aerosol optical depth (AOD), dewpoint temperature (DPT), relative humidity (RH), wind speed (WIND), wind direction (WDIR), pressure (PRES), and planetary-boundary-layer height (PBLH). The validity of the prediction model was evaluated using 10-fold cross validation with a R2 value of 0.572 and RMSE of 9.98 µg/m3. The corresponding R2 and RMSE values for ‘held-out data’ were 0.487 and 10.53 µg/m3. Using the validated prediction model, daily PM2.5 concentration maps (1 km-resolution) were estimated from 2012 to 2023 for the state of Montana. These concentration maps are accessible via an application developed using GEE. The product provides valuable insights into spatiotemporal trends of PM2.5 concentrations, which will be useful for communities to take appropriate mitigation strategies and minimize hazardous PM2.5 exposure.

Prenatal and postnatal early life exposure to greenness and particulate matter of different size fractions in relation to childhood rhinitis - A multi-center study in China

The impact of early life exposure to residential greenness on childhood rhinitis and its interaction with particulate matter (PM) of different size fractions remain inconsistent. Herein, we recruited 40,486 preschool children from randomly selected daycare centers in 7 cities in China from 2019 to 2020, and estimated exposure to residential greenness by the normalized difference vegetation index (NDVI) with a 500 m buffer. Exposure to ambient PM (PM1, PM2.5, and PM10) was evaluated using a satellite-based prediction model (daily, at a resolution of 1 km × 1 km). By mixed-effect logistic regression, NDVI values during pregnancy, in the first (0–1 year old) and the second (1–2 years old) year of life were negatively associated with lifetime rhinitis (LR) and current rhinitis (CR) (P < 0.001). PM in the same time windows was associated with increased risks of LR and CR in children, with smaller size fraction of PM showing greater associations. The negative associations between prenatal and postnatal NDVI and LR and CR in preschool children remained robust after adjusting for concomitant exposure to PM, whereas the associations of postnatal NDVI and rhinitis showed significant interactions with PM. At lower levels of PM, postnatal NDVI remained negatively associated with rhinitis and was partly mediated by PM (10.0–40.9 %), while at higher levels of PM, the negative associations disappeared or even turned positive. The cut-off levels of PM were identified for each size fraction of PM. In conclusion, prenatal exposure to greenness had robust impacts in lowering the risk of childhood rhinitis, while postnatal exposure to greenness depended on the co-exposure levels to PM. This study revealed the complex interplay of greenness and PM on rhinitis in children. The exposure time window in prenatal or postnatal period and postnatal concomitant PM levels played important roles in influencing the associations between greenness, PM and rhinitis.

Differential serum inflammatory cytokine elaboration in Nepali brick factory workers

Previous studies involving workers at brick kilns in the Kathmandu Valley of Nepal have revealed chronic exposure to hazardous levels of fine particulate matter (PM2.5) common in ambient and occupational environments. Such exposures are known to cause and/or exacerbate chronic respiratory diseases including chronic obstructive pulmonary disease (COPD). However, there is a paucity of data regarding the systemic inflammatory status of exposed workers at brick manufacturing facilities. In the current study, we sought to elucidate systemic inflammatory responses by quantifying the molecular cytokine/chemokine profiles in serum from study participants. A sample of participants (N = 48; 54% female; 31.77±9.76 years-old) were distributed among four different job categories. Blood was procured from participants on-site, allowed to clot at room temperature, and centrifuged to obtain total serum. A human cytokine antibody array was used to screen cytokines/chemokines in serum samples. Comparisons were generated between a control group of administration workers vs. fire master workers, administration vs. green brick hand molders, and administration vs. top loaders. We discovered significantly increased concentrations of eotaxin-1, eotaxin-2, GCSF, GM-CSF, IFN-γ, IL-1γα, IL-1β, IL-6, IL-8, TGF-β1, TNF-α, and TIMP-2 in serum samples from fire master workers vs. administration (p &lt; 0.03). Each of these molecules, with the exception of TIMP-2, were also significantly elevated in the serum from green brick hand molders (p &lt; 0.02). In addition, each molecule was significantly elevated in serum from top loaders compared to administration workers (p &lt; 0.03). With few exceptions, the fire master workers expressed significantly more systemic inflammatory molecular abundance when compared to all other job descriptions. These results reveal an association between pulmonary exposure to PM2.5 and systemic inflammatory responses likely mediated by cytokine/chemokine elaboration. Additional characterization of a broader array of inflammatory molecules may provide valuable insight into susceptibility to lung diseases including COPD. This work was supported in part by funding from Undergraduate Mentoring and a Widtsoe Research Grant from Brigham Young University. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Indoor and outdoor air quality in street corner kiosks in a large metropolitan area

Poor air quality in workplaces constitutes a great concern on human health as a good fraction of our time is spent at work. In Greece, very unique workplaces are the street corner kiosks, which are freestanding boxes placed on sidewalks next to city streets and vehicular traffic, where one can find many consumer goods. As such, its employees are exposed to both outdoor and indoor air pollutants. Very few studies have examined the occupational exposure of kiosk workers to air pollutants, and thus the magnitude of this unique indoor and outdoor exposure remains unknown. The objective of this study is to investigate and compare the levels of indoor and outdoor particulate matter (PM10 and PM2.5), ultrafine particles (UFPs) and black carbon (BC) in different kiosks located in Athens, Greece, in urban-traffic and urban-background environments. Continuous measurements of the above-mentioned pollutants were carried out on a 24-h basis over 7 consecutive days at three kiosks from September to October 2019. Indoor PM10 concentrations in the urban kiosk ranged from 19.0 to 44.0 μg/m3, PM2.5 values ranged from 14.0 to 33.0 μg/m3, whereas BC concentrations ranged from 1.2 to 7.0 μg/m3 and UFPs from almost 9.5 to 47.0 × 103 pt/cm3. Outdoor PM10 and PM2.5 measurements ranged from 29.0 to 59.0 μg/m3 and from 22.0 to 39.0 μg/m3, respectively. BC outdoor concentrations ranged from 1.1 to 2.2 μg/m3. The mean hazard quotient (HQ) for PM10 (4.9) and PM2.5 (4.7) among all participants was >1. The health risk of exposure to PM10 and PM2.5 was found to be at moderate hazard levels, although in some cases we observed HQ values higher than 10 due to high PM10 and PM2.5 concentrations in the kiosks. Overall our study indicates that people working at kiosks can be exposed to very high concentrations on particulate pollution depending on a number of factors including the traffic that strongly depends on location and the time of the day.

Evaluating the Particulate Matter and Carbon Dioxide Reduction of Four Broad-leaved Evergreen Plants

Background and objective: Since people spend 70-80% of their time indoors, the quality of indoor air has become a crucial factor in overall health. Therefore, poor indoor air quality can have significant adverse effects on our well-being. Common indoor air pollutants are particulate matter (PM) and carbon dioxide (CO2). Plants can remove PM and CO2 through the photosynthesis process and leaf surfaces, and regulate the temperature and humidity of the air. By analyzing the PM and CO2 reduction of four broad-leaved evergreen plants, this study aims to provide data for air purification in indoor spaces.Methods: The four different plant species (Neolitsea sericea (Blume) Koidz., Coffea arabica L., Photinia glabra(Thunb.) Franch. and Sav., and Farfugium japonicum (L.) Kitam.) were selected for this study. Mosquito coils and a CO2 cylinder were the primary sources of PM and CO2. These pollutants were injected into a closed acrylic chamber with plants, and the air quality within the chamber was monitored for a duration of five hours. The plants' effectiveness in reducing carbon dioxide was evaluated through the clean air delivery rate (CADR), while their ability to reduce PM was assessed by analyzing the PM reduction rate. Photosynthetic rates and leaf area were also measured to determine the correlation between air pollution removal and these factors.Results: The ability to remove PM and CO2 varied among plants. Plants with higher rates of photosynthesis were more effective in reducing PM and CO2 than those with lower rates. Among the four plant species, C. arabica and P. glabra were more effective in removing PM and CO2 than the other species. The chamber containing plants exhibited higher humidity and lower temperatures compared to the chamber without plants.Conclusion: These findings suggest that plants can play a significant role in improving indoor air quality. Not only do they effectively reduce levels of PM and CO2, but they also contribute to the regulation of indoor temperature and humidity. The implications of these results highlight the potential of integrating plants into indoor spaces as a natural and multifaceted solution for creating healthier and more comfortable environments.

Efficient calibration of cost-efficient particulate matter sensors using machine learning and time-series alignment

Atmospheric particulate matter (PM) poses a significant threat to human health, infiltrating the lungs and brain and leading to severe issues such as heart and lung diseases, cancer, and premature death. The main sources of PM pollution are vehicular and industrial emissions, construction and agricultural activities, and natural phenomena such as wildfires. Research underscores the absence of a safe threshold for particulate exposure, highlighting the crucial need for monitoring PM levels to develop and implement effective risk mitigation measures. Notwithstanding, accurate measurement of PM concentration relies on expensive and cumbersome equipment. Despite the rising popularity of low-cost alternatives, their reliability remains questionable, given their sensitivity to environmental conditions, inherent instability, and manufacturing imperfections. This article proposes a novel approach to efficient correction of low-cost PM sensors. The primary calibration model is a feedforward artificial neural network (ANN), which directly renders predicted output of the corrected sensor based on environmental variables such as temperature, humidity, and atmospheric pressure. The ANN hyper-parameters are identified by aligning time series of prior reference and low-cost sensor readings, which enables the network to learn typical temporal changes of the sensor outcome as a function of the aforementioned parameters as well as operational relationships between the sensor and the reference device. The architecture of the ANN is optimized in terms of the number of neurons in each layer to enhance its generalization capability. Our methodology has been demonstrated using a custom-designed portable monitoring platform and reference data acquired from public stations in Gdansk. The results are indicative of excellent calibration reliability. The achieved correlation coefficients w.r.t. the reference readings are 0.86, 0.88, and 0.72 for PM1, PM10, and PM2.5, respectively, whereas RMSE values are only 3.0, 3.9, and 5.4 µg/m³.

Does diet quality moderate the long-term effects of discrete but extreme PM2.5 exposure on respiratory symptoms? A study of the Hazelwood coalmine fire

In 2014, a fire at an open cut coalmine in regional Victoria, Australia burned for 6 weeks. Residents of the nearby town of Morwell were exposed to smoke, which included high levels of fine particulate matter (PM2.5). We investigated whether the long-term effects of PM2.5 on respiratory health were moderated by diet quality. A cross-sectional analysis was conducted of data collected 8.5 years after the mine fire from 282 residents of Morwell and 166 residents from the nearby unexposed town of Sale. Primary outcomes were respiratory symptoms. Exposure was coalmine fire-related PM2.5 and diet quality was assessed as Australian Recommended Food Score (ARFS) derived using the Australian Eating Survey (AES). The moderating effect of diet quality on respiratory outcomes associated with PM2.5 was assessed using logistic regression models, adjusting for potential confounders. Diet quality was poor in this sample, with 60% in the lowest category of overall diet quality. Overall diet quality and fruit and vegetable quality significantly attenuated the association between PM2.5 and prevalence of chronic cough and phlegm. Sauce/condiment intake was associated with a greater effect of PM2.5 on COPD prevalence. No other moderating effects were significant. The moderating effects of overall diet quality and vegetable and fruit intake aligned with a priori hypotheses, suggesting potential protective benefits. While more evidence is needed to confirm these findings, improving diets, especially fruit and vegetable intake, may provide some protection against the effects of smoke exposure from fire events.

The Impact of Utilizing Waste Sunflower Oil as a Biodiesel Blend on Four-Stroke Engine Performance and Emissions

The blending of biodiesel with petroleum diesel attracts much attention due to its high potential in reducing emissions. In this work, waste sunflower oil was converted to biodiesel by the trans-esterification method, and it was blended with petroleum diesel in three ratios (10, 30, and 50%). The impact of using these blended fuels in a four-stroke engine on engine performance and exhaust emissions at three engine loads (2, 4, and 6 N.m) was investigated and compared with the use of petroleum diesel and biodiesel. The engine performance was evaluated by determining the brake-specific fuel consumption (BSFC), engine effective power (Ne), brake-specific energy consumption (BSEC), brake thermal efficiency (BTE), and noise intensity. The evaluation of emissions from the engine exhaust was carried out by measuring the levels of carbon oxides (CO and CO2), hydrocarbons (HC), nitrogen oxides (NO and NO2), and particulate matter (PM). The results show that blending diesel with up to 30% biodiesel can reduce CO, HC, and PM emissions by 29.6 ± 1%, 26.0 ± 4%, and 31.0 ± 3%, respectively. However, this decrease is associated with increasing CO2 and NOx emissions by 18.5 ± 2.5% and 29.0 ± 6%, respectively. In addition, the engine showed acceptable performance when using up to 30% biodiesel, where the increase in fuel consumption was limited to 5.8 ± 0.3%. In addition, the engine’s effective power increased with the blending ratio of 10% by 2.0 ± 0.6%, but then decreased with the blending ratio of 30% by only 2.0 ± 0.6%. The noise intensity was also decreased by 2.4%, while BSEC and BTE were reduced by only 2.9 ± 0.9% and 3.5 ± 1%, respectively. The results of this work provide deep insights regarding the utilization of waste sunflower oil as biodiesel to be blended with petroleum diesel, which is a considerable novel approach in the energy and environmental sectors.

Comprehending particulate matter dynamics in transit-oriented developments: Traffic as a generator and design as a captivator

In Transit-Oriented Development (TOD), the close integration of residential structures with community activities and traffic heightens residents' exposure to traffic-related pollutants. Despite traffic being a primary source of particulate matter (PM), the compact design of TODs, together with the impact of urban heat island (UHI), increases the likelihood of trapping emitted PM from traffic, leading to heightened exposure of TOD residents to PM. Although PM originates from two distinct sources in road traffic, exhaust and non-exhaust emissions (NEE), current legislation addressing traffic-related PM from non-exhaust emissions sources remains limited. This paper focuses on two TOD typologies in Manchester City—Manchester Piccadilly and East Didsbury—to understand the roles of TOD traffic as a PM generator and TOD place design as a PM container and trapper. The investigation aims to establish correlations between street design canyon ratios, vehicular Speed, and PM10/PM2.5, providing design guidance and effective traffic management strategies to control PM emissions within TODs. Through mapping the canyon ratio and utilising the Breezometer API for PM monitoring, the paper revealed elevated PM levels in both TOD areas, exceeding World Health Organization (WHO) recommendations, particularly for PM2.5. Correlation analysis between canyon configuration and PM2.5/PM10 highlighted the importance of considering building heights and avoiding the creation of deep canyons in TOD design to minimise the limited dispersion of PM. Leveraging UK road statistics and the PTV Group API for vehicle speed calculations, the paper studied the average speeds on the TOD roads concerning PM. Contrary to conventional assumption, the correlation analyses have revealed a noteworthy association shift between vehicular speed and PM concentrations. A positive correlation existed between speed increase and PM increases on arterial roads. However, a negative correlation emerged on main, collector, and local streets, indicating that PM levels rise for both PM10 and PM2.5 as Speed decreases. These findings challenge the traditional assumption that higher Speed leads to increased emissions, highlighting the potential impact of NEE on PM concentrations. This paper calls for thorough design considerations and traffic management strategies in TOD, especially in dense areas, considering building height, optimising traffic flow, and enhancing compromised air quality associated with vehicular emissions.

Deciphering air quality index through sample entropy: A nonlinear time series analysis

Pollution and its impacts on human health have become a crisis in regions with poor air quality index (AQI), which is an indicator of concentrations of pollutants, prompting the United Nations (UN) to set sustainable development goals (SDG). The present study proposes a surrogate sample entropy-based method in tune with UN’s SDG, to assess AQI from the time series of any of the pollutants. New Delhi is one of the world's most polluted state capital, with a higher level of particulate matter (PM). The temporal data of the pollutants in New Delhi, recorded in the one-hour interval during the years 2016 and 2017, are subjected to time series analysis. The data collected from the Central Pollution Control Board of India are analyzed with special reference to PM and compared with the World Air Quality Report 2021 and University of Washington data. The dependence of PM2.5 concentration on humidity and rain is also studied. The study reveals the increase in complexity with the concentration of pollutants through the phase portrait. The sample entropy analysis of the nonlinear time series of the pollutants exhibits a linear relation with AQI suggesting the possibility of using sample entropy as a surrogate measure of AQI.

Human health impacts attributable to inhalation exposure of particulate matter (PM10 and PM2.5) during the Holi festival

Objective The present study focuses on residential areas of Delhi to identify the elevated levels of ambient PM10 and PM2.5 due to biomass burning followed by the coloring activity in the Holi festival celebrated at the end of the winter season. This study also focuses on the health risk assessment and mortality among different age groups due to the change in particulate matter levels during the Holi festival in Delhi, India. Materials and Methods Secondary data of particulate matters have been procured from the Central Pollution Control Board (CPCB), Delhi Pollution Control Committee (DPCC), and Indian Institute of Tropical Meteorology (IITM), Pune for the period of the pre-, during, and post-Holi period for the year 2018-2020 at four selected residential locations in Delhi, India. The health impacts of particle inhalation were quantified using the AirQ + models. Results The results indicated the levels of PM10 and PM2.5 rise about 3-4 times higher during the Holi festival than on normal days, resulting in health risks and causing an excess number of mortality and Asthma cases in Delhi. Such cases were also found to be higher in 2018, followed by 2019 and 2020 at all the selected locations in Delhi. Conclusions The study linked the increasing particulate levels in the Holi festival with the increased health risk through short-term exposure of the population. The excess number of cases (ENCs) of mortality, all causes of mortality among adults (age > 30 years) associated with short-term exposure to particulate were also identified.

The Impact of Acute Peri-operative Particulate Matter Exposure on Endoscopic Sinus Surgery Outcomes: A Preliminary Multi-site Investigation.

Environmental exposures have been postulated to play an important role in the pathophysiology of chronic rhinosinusitis (CRS). Particulate matter (PM) is one of the most widely studied ambient air pollutants, but its peri-operative impact on CRS is unknown. To determine the effect of acute, peri-operative PM exposure on outcomes after endoscopic sinus surgery (ESS). Participants with CRS who self-selected ESS were prospectively enrolled. The 22-item SinoNasal Outcome Test (SNOT-22) and Medical Outcomes Study Questionnaire Short-Form 6-D (SF-6D) health utility values scores were recorded. Using residence zip codes, a secondary analysis of patient exposure to PM <2.5 μm and <10 μm (PM2.5 and PM10, respectively) was performed for the month of surgery utilizing data from Environmental Protection Agency air quality monitors. Spearman's correlation coefficients (ρ), 95% confidence intervals (CIs), and effect estimates (β) were used to determine the magnitudes of association. Simple, multivariate regression analysis was also completed. One hundred and seven patients from four geographically unique institutions across the US were enrolled with a follow-up of 6 months. Patients with higher peri-operative PM2.5 exposure had less improvement in their SNOT-22 scores after ESS compared to those with less exposure using both univariate analysis (ρ = 0.26, 95% CI: 0.08, 0.43; P = .01) and after covariate adjustment with multivariate analysis (B = 1.06, 95% CI: 0.001, 2.14, P = .05). Similar associations were not found with SF-6D outcomes or with PM10 as an exposure of interest. No significant correlations were found between peri-operative PM levels and Lund-Kennedy endoscopy scores post-operatively. Preliminary data from this pilot study reveal that PM exposure at the time of ESS may negatively associate with post-operative improvement in sinonasal quality-of-life. Larger, population-based studies with more standardized PM exposure windows are needed to confirm the clinical significance of the present findings.

Short-term and long-term effects of exposure to PM10

IntroductionA dust storm is a harmful event in the environment and human health resulting from certain chemicals in the air that transcend predefined levels. Developing countries, such as Iran, suffer from this basic threat (dust particles) to health. The purpose of the research was to evaluate the effects of PM10 particulate matter on mortality related to cardiovascular diseases (CVD) in the year 2022–2023 in Andimeshk City, with the AirQ + programmed. Material and methodThe recommended approach for evaluating effects on health, as recognized by the world health organization (WHO), uses the Air Quality Health Impact Assessment Software (Air Q 2.2.3). The polluted air information required for model runs was obtained from the Environmental Department of Khuzestan. The Khuzestan Meteorological Organization gave information on the levels of particulate matter for both dusty and non-dusty days. The mortality numbers came from the Iranian Statistics Centre and the Health Ministry. The data was collected in a volumetric manner for the year 2022–2023. ResultThe lowest PM10 concentration reported in a year in Andimeshk city belonged to January 9 (14.19 μg/m3), while the most polluted day of the year was November 6 (5634.61 μg/m3). The total monthly concentration of PM10 for April, May, June, July, August, September, October, November, December, January, and February respectively, 2917.9, 3227.82, 3175.89, 2966.24, 3520.46, 3393.1, 4671.07, 13121.26, 3042.63, 1851.67, 2627.52, and 6758.12 μg/m3. ConclusionThe results of this study showed that the studied area is a polluted city in terms of the concentration of dust particles. So, during a year, only 8 days, PM10 concentration did not exceed the standard. One of the main reasons for air pollution in Andimshek city is the neighborhood of Khuzestan province with Iraq and Saudi Arabia, which have dry areas and dust storms.

Machine learning insights into PM2.5 changes during COVID-19 lockdown: LSTM and RF analysis in Mashhad.

This study seeks to investigate the impact of COVID-19 lockdown measures on air quality in the city of Mashhad employing two strategies. We initiated our research using basic statistical methods such as paired sample t-tests to compare hourly PM2.5 data in two scenarios: before and during quarantine, and pre- and post-lockdown. This initial analysis provided a broad understanding of potential changes in air quality. Notably, a low reduction of 2.40% in PM2.5 was recorded when compared to air quality prior to the lockdown period. This finding highlights the wide range of factors that impact the levels of particulate matter in urban settings, with the transportation sector often being widely recognized as one of the principal causes of this issue. Nevertheless, throughout the period after the quarantine, a remarkable decrease in air quality was observed characterized by distinct seasonal patterns, in contrast to previous years. This finding demonstrates a significant correlation between changes in human mobility patterns and their influence on the air quality of urban areas. It also emphasizes the need to use air pollution modeling as a fundamental tool to evaluate and understand these linkages to support long-term plans for reducing air pollution. To obtain a more quantitative understanding, we then employed cutting-edge machine learning methods, such as random forest and long short-term memory algorithms, to accurately determine the effect of the lockdown on PM2.5 levels. Our models' results demonstrated remarkable efficacy in assessing the pollutant concentration in Mashhad during lockdown measures. The test set yielded an R-squared value of 0.82 for the long short-term memory network model, whereas the random forest model showed a calculated cross-validation R-squared of 0.78. The required computational cost for training the LSTM and the RF models across all data was 25min and 3s, respectively. In summary, through the integration of statistical methods and machine learning, this research attempts to provide a comprehensive understanding of the impact of human interventions on air quality dynamics.

Temporal variation of the PM2.5/PM10 ratio and its association with meteorological factors in a South American megacity: Metropolitan Area of Lima-Callao, Peru.

The Metropolitan Area of Lima-Callao (MALC) is a South American megacity that has suffered a serious deterioration in air quality due to high levels of particulate matter (PM2.5 and PM10). Studies on the behavior of the PM2.5/PM10 ratio and its temporal variability in relation to meteorological parameters are still very limited. The objective of this study was to analyze the temporal trends of the PM2.5/PM10 ratio, its temporal variability, and its association with meteorological variables over a period of 5years (2015-2019). For this, the Theil-Sen estimator, bivariate polar plots, and correlation analysis were used. The regions of highest mean concentrations of PM2.5 and PM10 were identified at eastern Lima (ATE station-41.2µg/m3) and southern Lima (VMT station-126.7µg/m3), respectively. The lowest concentrations were recorded in downtown Lima (CDM station-16.8µg/m3 and 34.0µg/m3, respectively). The highest average PM2.5/PM10 ratio was found at the CDM station (0.55) and the lowest at the VMT station (0.27), indicating a predominance of emissions from the vehicular fleet within central Lima and a greater emission of coarse particles by resuspension in southern Lima. The temporal progression of the ratio of PM2.5/PM10 showed positive and highly significant trends in northern and central Lima with values of 0.03 and 0.1 units of PM2.5/PM10 per year, respectively. In the southern region of Lima, the trend was also significant, showcasing a value of 0.02 units of PM2.5/PM10 per year. At the hourly and monthly level, the PM2.5/PM10 ratio presented a negative and significant correlation with wind speed and air temperature, and a positive and significant correlation with relative humidity. These findings offer insights into identifying the sources of PM pollution and are useful for implementing regulations to reduce air emissions considering both anthropogenic sources and meteorological dispersion patterns.

Particle analysis of surgical lung biopsies from deployed and non-deployed US service members during the Global War on Terrorism.

The role that inhaled particulate matter plays in the development of post-deployment lung disease among US service members deployed to Southwest Asia during the Global War on Terrorism has been difficult to define. There is a persistent gap in data addressing the relationship between relatively short-term (months to a few years) exposures to high levels of particulate matter during deployment and the subsequent development of adverse pulmonary outcomes. Surgical lung biopsies from deployed service members and veterans (DSMs) and non-deployed service members and veterans (NDSMs) who develop lung diseases can be analyzed to potentially identify residual deployment-specific particles and develop associations with pulmonary pathological diagnoses. We examined 52 surgical lung biopsies from 25 DSMs and 27 NDSMs using field emission scanning electron microscopy (FE-SEM) with energy dispersive x-ray spectroscopy (EDS) to identify any between-group differences in the number and composition of retained inorganic particles, then compared the particle analysis results with the original histopathologic diagnoses. We recorded a higher number of total particles in biopsies from DSMs than from NDSMs, and this difference was mainly accounted for by geologic clays (illite, kaolinite), feldspars, quartz/silica, and titanium-rich silicate mixtures. Biopsies from DSMs deployed to other Southwest Asia regions (SWA-Other) had higher particle counts than those from DSMs primarily deployed to Iraq or Afghanistan, due mainly to illite. Distinct deployment-specific particles were not identified. Particles did not qualitatively associate with country of deployment. The individual diagnoses of the DSMs and NDSMs were not associated with elevated levels of total particles, metals, cerium oxide, or titanium dioxide particles. These results support the examination of particle-related lung disease in DSMs in the context of comparison groups, such as NDSMs, to assist in determining the strength of associations between specific pulmonary pathology diagnoses and deployment-specific inorganic particulate matter exposure.