Fine Particulate Matter Exposure Research Articles

Risk/benefit tradeoff of habitual physical activity and air pollution on chronic pulmonary obstructive disease: findings from a large prospective cohort study

BackgroundThe combined health impact of physical activity (PA) and air pollution on chronic obstructive pulmonary disease (COPD) remains unclear. We investigated the joint effects of habitual PA and long-term fine particulate matter (PM2.5) exposure on COPD incidence in a prospective population-based cohort.MethodsA prospective cohort study was conducted using data from the UK Biobank. Incidence of COPD was ascertained through linkage to the UK National Health Services register. Annual mean PM2.5 concentration was obtained using land use regression model. PA was measured by questionnaire and wrist-worn accelerometer. Cox proportional hazard models were applied to examine the associations between PM2.5, PA, and COPD. Additive and multiplicative interactions were examined.ResultsA total of 266,280 participants free of COPD at baseline were included in data analysis with an average follow-up of 10.64 years, contributing to around 2.8 million person-years. Compared with participants with low level of PA, those with higher PA levels had lower risks of COPD incidence [hazard ratio (HR): 0.769, 95% CI: 0.720, 0.820 for moderate level; HR: 0.726, 95% CI: 0.679, 0.776 for high level]. By contrast, PM2.5 was associated with increased risk of COPD (HR per interquartile range increment: 1.065, 95% CI: 1.032, 1.099). Limited evidence of interaction between habitual PA and PM2.5 exposure was found. Similar results were found for accelerometer-measured PA.ConclusionsOur study suggests that habitual PA could reduce risk of COPD incidence, and such protective effects were not affected by ambient PM2.5 pollution exposure.

Projecting Lifetime Health Outcomes and Costs Associated with the Ambient Fine Particulate Matter Exposure among Adult Women in Korea.

We sought to estimate the lifetime healthcare costs and outcomes associated with the exposure to the escalated concentration of fine particulate matter (particle size < 2.5 μm, PM2.5) among adult Korean women. We adapted a previously developed Markov model, and a hypothetical cohort composed of Korean women was exposed to either a standard (15 μg/m3) or increased (25 μg/m3) concentration of PM2.5. The time horizon of the analysis was 60 years, and the cycle length was 1 year. The outcomes were presented as direct healthcare costs and quality-adjusted life years (QALYs), and costs were discounted annually at 5%. Deterministic and probabilistic sensitivity analyses were performed. The model estimated that when the exposure concentration was increased by 10 μg/m3, the lifetime healthcare cost increased by USD 9309, which is an 11.3% increase compared to the standard concentration group. Women exposed to a higher concentration of PM2.5 were predicted to live 30.64 QALYs, compared to 32.08 QALYs for women who were exposed to the standard concentration of PM2.5. The tendency of a higher cost and shorter QALYs at increased exposure was consistent across a broad range of sensitivity analyses. The negative impact of PM2.5 was higher on cost than on QALYs and accelerated as the exposure time increased, emphasizing the importance of early intervention.

Sensitive inflammatory biomarkers of acute fine particulate matter exposure among healthy young adults: Findings from a randomized, double-blind crossover trial on air filtration

The short-term alteration of peripheral cytokines may be an early adverse health effect of PM2.5 exposure and may be further associated with cardiovascular disease. We conducted a randomized, double-blind crossover trial using true or sham air filtration among 54 healthy college students in Beijing to investigate the potential benefits of short-term indoor air filtration and the adverse health effects of time-weighted personal PM2.5 exposure through inflammatory cytokines. The participants randomly received true or sham air filtration intervention for a week, and the treatment was changed after a two-week washout period. Peripheral blood samples were collected after each intervention period to measure 38 inflammatory cytokines. A linear mixed-effects model was applied to estimate the impacts of air purification or a 10 μg/m3 PM2.5 exposure increase on cytokines. Lag effects of PM2.5 exposure were analyzed using single-day and moving average lag models. Air filtration reduced indoor and time-weighted average personal PM2.5 concentrations by 69.0% (from 33.6 to 10.4 μg/m3) and 40.3% (from 40.6 to 24.3 μg/m3), respectively. We observed a significant association of PM2.5 exposure with growth-regulated alpha protein (GRO-α) of −11.3% (95%CI: 17.0%, −5.4%). In the lag models, significant associations between personal PM2.5 exposure and interleukin-1 receptor antagonist (IL-1Ra), monocyte chemotactic protein (MCP-1), and eotaxin were obtained at lag0, while associations with cytokines including vascular endothelial growth factor (VEGF), epidermal growth factor (EGF), fibroblast growth factor-2 (FGF-2), granulocyte colony-stimulating factor (G-CSF), macrophage inflammatory protein-1β (MIP-1β), IL-4, tumor necrosis factor-α (TNF-α), and interferon-γ (IFN-γ) were noted at relatively long lagged exposure windows (lag5-lag6). No significant alteration in cytokines was observed under true air filtration intervention. Our study indicates the effectiveness of air filtration on indoor PM2.5 reduction. PM2.5 exposure may decrease GRO-α levels and change different cytokine levels time-varyingly. Further study is still needed to explore the mechanisms of PM2.5 exposure on the inflammatory response.

Surrogate-Assisted Fine Particulate Matter Exposure Assessment in an Underground Subway Station.

With the increase in subway travelers, the air quality of underground enclosed spaces at subway stations has attracted much more attention. The study of pollutants exposure assessment, especially fine particulate matter, is important in both pollutant control and metro station design. In this paper, combining pedestrian flow analysis (PFA) and computational fluid dynamics (CFD) simulations, a novel surrogate-assisted particulate matter exposure assessment method is proposed, in which PFA is used to analyze the spatial-temporal movement characteristics of pedestrians to simultaneously consider the location and value of the pedestrian particulate generation source and their exposure streamline to particulate matter; the CFD model is used to analyze the airflow field and particulate matter concentration field in detail. To comprehensively consider the differences in the spatial concentration distribution of particulate matter caused by the time-varying characteristics of the airflow organization state in subway stations, surrogate models reflecting the nonlinear relationship between simulated and measured data are trained to perform accurate pedestrian exposure calculations. The actual measurement data proves the validity of the simulation and calculation methods, and the difference between the calculated and experimental values of the exposure is only about 5%.

“We threw away the stones”: a mixed method evaluation of a simple cookstove intervention in Malawi

Background:Exposure to air pollution is responsible for a substantial burden of respiratory disease globally. Household air pollution from cooking using biomass is a major contributor to overall exposure in rural low-income settings. Previous research in Malawi has revealed how precarity and food insecurity shape individuals’ daily experiences, contributing to perceptions of health. Aiming to avoid a mismatch between research intervention and local context, we introduced a simple cookstove intervention in rural Malawi, analysing change in fine particulate matter (PM2.5) exposures, and community perceptions.Methods:Following a period of baseline ethnographic research, we distributed‘chitetezo mbaula’, locally made clay cookstoves, to all households (n=300) in a rural Malawian village. Evaluation incorporated village-wide participant observation and concurrent exposure monitoring using portable PM2.5monitors at baseline and follow-up (three months post-intervention). Qualitative data were thematically analysed. Quantitative analysis of exposure data included pre-post intervention comparisons, with datapoints divided into cooking and non-cooking (‘baseline’) periods. Findings were integrated at the interpretation stage, using a convergent design mode of synthesis.Results:Individual exposure monitoring pre- and post-cookstove intervention involved a sample of 18 participants (15 female; mean age 43). Post-intervention PM2.5exposures (median 9.9μg/m3[interquartile range: 2.2–46.5]) were not significantly different to pre-intervention (11.8μg/m3[3.8–44.4]); p=0.71. On analysis by activity, ‘baseline’ exposures were found to be reduced post-intervention (from 8.2μg/m3[2.5–22.0] to 4.6μg/m3[1.0–12.6]; p=0.01). Stoves were well-liked and widely used by residents as substitutes for previous cooking methods (mainly three-stone fires). Most cited benefits related to fuel saving and shorter cooking times.Conclusions:The cookstove intervention had no impact on cooking-related PM2.5exposures. A significant reduction in baseline exposures may relate to reduced smouldering emissions. Uptake and continued use of the stoves was high amongst community members, who preferred using the stoves to cooking over open fires.

Source-resolved variability of fine particulate matter and human exposure in an urban area

Abstract. Increasing the resolution of chemical transport model (CTM) predictions in urban areas is important to capture sharp spatial gradients in atmospheric pollutant concentrations and better inform air quality and emissions controls policies that protect public health. The chemical transport model PMCAMx (Particulate Matter Comprehensive Air quality Model with Extensions) was used to assess the impact of increasing model resolution on the ability to predict the source-resolved variability and population exposure to PM2.5 at 36×36, 12×12, 4×4, and 1×1 km resolutions over the city of Pittsburgh during typical winter and summer periods (February and July 2017). At the coarse resolution, county-level differences can be observed, while increasing the resolution to 12×12 km resolves the urban–rural gradient. Increasing resolution to 4×4 km resolves large stationary sources such as power plants, and the 1×1 km resolution reveals intra-urban variations and individual roadways within the simulation domain. Regional pollutants that exhibit low spatial variability such as PM2.5 nitrate show modest changes when increasing the resolution beyond 12×12 km. Predominantly local pollutants such as elemental carbon and primary organic aerosol have gradients that can only be resolved at the 1×1 km scale. Contributions from some local sources are enhanced by weighting the average contribution from each source by the population in each grid cell. The average population-weighted PM2.5 concentration does not change significantly with resolution, suggesting that extremely high resolution PM2.5 predictions may not be necessary for effective urban epidemiological analysis at the county level.

Associations between PM2.5 exposure and infant growth: A mediation analysis of oral microbiota

BackgroundPrevious studies have linked growth retardation with ambient fine particulate matter (PM2.5) exposure. However, few studies explored such association from the perspective of microbiota, such as oral microbiota. We aimed to identify the potential role of oral microbiota in the links between PM2.5 exposure and infant growth. MethodsBaseline information of 335 recruited mother-child pairs was collected by structured questionnaires. Growth indicators (weight, length) of one-year-old infants were abstracted from medical records when they had physical examination and corresponding z scores were calculated. 16S rRNA gene amplicon sequencing was performed to assess oral microbiota of infants and co-abundance groups (CAGs) were further calculated. We assessed PM2.5 levels by inverse distance weighting (IDW). Generalized linear regression and mediation analysis were performed to determine associations between PM2.5 exposure, oral microbiota and growth indicators. ResultsPer 10 μg m−3 increment of PM2.5 in the period of 10th month–examination was associated with decreased length z score (β = −1.97, 95%CI: −3.83, −0.11). Oral microbiota correlated with weight z score and body mass index (BMI) z score was identified by Spearman correlation analysis. CAG4 was statistically associated with increased weight z score (β = 3.40, 95%CI: 0.29, 6.51) and BMI z score (β = 5.44, 95%CI: 1.00, 9.87). Several bacteria in the level of genus and CAG associated with PM2.5 exposure were additionally identified (P < 0.05). Mediation analysis revealed that PM2.5 in the period of birth−3rd month impacted the z scores of weight and BMI by altering relative abundance of Megasphaera (P < 0.05). ConclusionPM2.5 exposure from 10th to 12th month after birth could retard infant linear growth. PM2.5 might impact oral microbiota of one-year-old infants. Growth-related bacteria and CAGs were identified. Megasphaera might function as mediator between PM2.5 exposure during birth−3rd month and infant z scores of weight and BMI.

A Bayesian Multi-Outcome Analysis of Fine Particulate Matter and Cardiorespiratory Hospitalizations.

Short-term fine particulate matter (PM2.5) exposure is positively associated with acute cardiovascular and respiratory events. Understanding whether this association varies across specific cardiovascular and respiratory conditions has important biologic, clinical, and public health implications. We conducted a time-stratified case-crossover study of hospitalizations from 2000 through 2014 among United States Medicare beneficiaries aged 65+. The outcomes were hospitalizations with any of 57 cardiovascular and 32 respiratory discharge diagnoses. We estimated associations with two-day moving average PM2.5 as a piecewise linear term with a knot at PM2.5 = 25 g/m3. We used Multi-Outcome Regression with Tree-structured Shrinkage (MOReTreeS) to identify de novo groups of related diseases such that PM2.5 associations are: (1) similar within outcome groups; but (2) different between outcome groups. We adjusted for temperature, humidity, and individual-level characteristics. We introduce an R package, moretrees. Our dataset included 16,007,293 cardiovascular and 8,690,837 respiratory hospitalizations. Of 57 cardiovascular diseases, 51 were grouped and positively associated with PM2.5. We observed a stronger positive association for heart failure, which formed a separate group. We observed negative associations for groups containing the outcomes other aneurysm and intracranial hemorrhage. Of 32 respiratory outcomes, 31 were grouped and were positively associated with PM2.5. Influenza formed a separate group with a negative association. We used a new statistical approach, MOReTreeS, to uncover variation in the association between short-term PM2.5 exposure and hospitalizations for cardiovascular and respiratory causes controlling for patient characteristics, time trends, and environmental confounders.

Maternal exposure to fine particulate matter and preterm birth and low birth weight in Africa.

Fine particulate matter (PM2.5) exposure has been reported to adversely affect birth outcomes, but the evidence is limited, particularly in low- and middle-income countries (LMICs). We assessed the associations between maternal PM2.5 exposure and low birth weight (LBW) and preterm birth (PTB) in Africa. We used standard Demographic and Health Surveys (DHS) data (2005-2015) from 15 countries in Africa to conduct a cross-sectional study. The study population was composed of 131,594 births with detailed information on maternal and household variables. LBW was defined as a birth weight of<2500g after 37weeks, and PTB was defined as live birth occurring before 37weeks of gestation. Average exposure to PM2.5 during pregnancy was estimated using satellite-based models. Multivariable logistic regression models were constructed, and analyses of data by region (Western, Eastern, Central, and Southern Africa) and data stratified by potential effect modifiers were conducted. A total of 13,214 (10%) LBW and 4,377 (3.3%) PTB cases were identified. An interquartile range (IQR) (33.9μg/m3) increase in PM2.5 during pregnancy was associated with increased odds of LBW and PTB, with odds ratios (ORs) of 1.28 (95% CI: 1.23, 1.34) and 1.08 (95% CI: 1.01, 1.16), respectively. Region-specific analyses revealed significant associations between PM2.5 and LBW in all regions, and significant associations between PM2.5 and PTB in Western and Southern Africa. Subgroup analyses revealed that the association between PM2.5 and LBW was present in all subgroups, and stronger associations were observed in female infants, while the association between PM2.5 and PTB was larger in subgroups of older individuals living in urban areas. This multicountry study in Africa demonstrated significant associations between maternal exposure to PM2.5 and higher odds of LBW and PTB. Our findings may facilitate air quality control strategies that address adverse birth outcomes in LMICs.

Postnatal exposure to PM2.5 and weight trajectories in early childhood.

Background:Inconsistent evidence has assessed the impact of air pollution exposure on children’s growth trajectories. We investigated the role of 90-day average postnatal fine particulate matter (PM2.5) exposures by estimating the magnitude of effects at different ages, and the change in child weight trajectory by categories of exposure.Methods:We obtained weight values from electronic health records at each hospital visit (males = 1859, females = 1601) from birth to 6 years old children recruited into the Boston-based Children’s HealthWatch cohort (2009–2014). We applied mixed models, adjusting for individual and maternal confounders using (1) varying-coefficient models allowing for smooth non-linear interaction between age and PM2.5, (2) factor-smooth interaction between age and PM2.5 quartiles. Additionally, we stratified by sex and low birthweight (LBW) status (≤2500 g).Results:Using varying-coefficient models, we found that PM2.5 significantly modified the association between age and weight in males, with a positive association in children younger than 3 years and a negative association afterwards. In boys, for each 10 µg/m3 increase in PM2.5 we found a 2.6% increase (95% confidence interval = 0.8, 4.6) in weight at 1 year of age and a −0.6% (95% confidence interval = −3.9, 2.9) at 5 years. We found similar but smaller changes in females, and no differences comparing growth trajectories across quartiles of PM2.5. Most of the effects were in LBW children and null for normal birthweight children.Conclusions:This study suggests that medium-term postnatal PM2.5 may modify weight trajectories nonlinearly in young children, and that LBW babies are more susceptible than normal-weight infants.

Oxygen sensors mediated HIF-1α accumulation and translocation: A pivotal mechanism of fine particles-exacerbated myocardial hypoxia injury

Epidemiological studies have demonstrated a strong association of ambient fine particulate matter (PM2.5) exposure with the increasing mortality by ischemic heart disease (IHD), but the involved mechanisms remain poorly understood. Herein, we found that the chronic exposure of real ambient PM2.5 led to the upregulation of hypoxia-inducible factor-1 alpha (HIF-1α) protein in the myocardium of mice, accompanied by obvious myocardial injury and hypertrophy. Further data from the hypoxia-ischemia cellular model indicated that PM2.5-induced HIF-1α accumulation was responsible for the promotion of myocardial hypoxia injury. Moreover, the declined ATP level due to the HIF-1α-mediated energy metabolism remodeling from β-oxidation to glycolysis had a critical role in the PM2.5-increased myocardial hypoxia injury. The in-depth analysis delineated that PM2.5 exposure decreased the binding of prolyl hydroxylase domain 2 (PHD2) and HIF-1α and subsequent ubiquitin protease levels, thereby leading to the accumulation of HIF-1α. Meanwhile, factor-inhibiting HIF1 (FIH1) expression was down-regulated by PM2.5, resulting in the enhanced translocation of HIF-1α to the nucleus. Overall, our study provides valuable insight into the regulatory role of oxygen sensor-mediated HIF-1α stabilization and translocation in PM-exacerbated myocardial hypoxia injury, we suggest this adds significantly to understanding the mechanisms of haze particles-caused burden of cardiovascular disease.

Tempol Preserves Endothelial Progenitor Cells in Male Mice with Ambient Fine Particulate Matter Exposure.

Ambient fine particulate matter (PM) exposure associates with an increased risk of cardiovascular diseases (CVDs). Major sex differences between males and females exist in epidemiology, pathophysiology, and outcome of CVDs. Endothelial progenitor cells (EPCs) play a vital role in the development and progression of CVDs. PM exposure-induced reduction of EPCs is observed in male, not female, mice with increased reactive oxygen species (ROS) production and oxidative stress. The lung is considered an important source of ROS in mice with PM exposure. The aim of the present study was to investigate the sex differences in pulmonary superoxide dismutase (SOD) expression and ROS production, and to test the effect of SOD mimic Tempol on the populations of EPCs in mice with PM exposure. Both male and female C57BL/6 mice (8–10 weeks) were exposed to intranasal PM or vehicle for 6 weeks. Flow cytometry analysis demonstrated that PM exposure significantly decreased the levels of EPCs (CD34+/CD133+) in both blood and bone marrow with increased ROS production in males, but not in females. ELISA analysis showed higher levels of serum IL-6 and IL-1βin males than in females. Pulmonary expression of the antioxidant enzyme SOD1 was significantly decreased in males after PM exposure, but not in females. Administration of the SOD mimic Tempol in male mice with PM exposure attenuated the production of ROS and inflammatory cytokines, and preserved EPC levels. These data indicated that PM exposure-induced reduction of EPC population in male mice may be due to decreased expression of pulmonary SOD1 in male mice.

Long-term exposure to fine particulate matter modifies the association between physical activity and hypertension incidence

BackgroundThe trade-off between the benefits of regular physical activity (PA) and the potentially detrimental effects of augmented exposure to air pollution in highly polluted regions remains unclear. This study aimed to examine whether ambient fine particulate matter (PM2.5) exposure modified the impacts of PA volume and intensity on hypertension risk. MethodsWe included 54,797 participants without hypertension at baseline in a nationwide cohort of the Prediction for Atherosclerotic Cardiovascular Disease Risk in China (China-PAR) project. PA volume and intensity were assessed by questionnaire, and high-resolution (1 km ×1 km) PM2.5 estimates were generated using a satellite-based model. ResultsDuring 413,516 person-years of follow-up, 12,100 incident hypertension cases were identified. PM2.5 significantly modified the relationship between PA and hypertension incidence (pinteraction < 0.001). Increased PA volume was negatively associated with incident hypertension in the low PM2.5 stratum (<59.8 μg/m3, ptrend < 0.001), with a hazard ratio of 0.81 (95% confidence interval (95%CI): 0.74–0.88) when comparing the fourth with the first quartile of PA volume. However, the health benefits were not observed in the high PM2.5 stratum (≥59.8 μg/m3, ptrend = 0.370). Moreover, compared with light PA intensity, vigorous intensity was related to a 20% (95%CI: 9%–29%) decreased risk of hypertension for participants exposed to low PM2.5, but a 17% (95%CI: 4%–33%) increased risk for those with high PM2.5 levels. ConclusionPA was associated with a reduced risk of hypertension only among participants with low PM2.5 exposure. Our findings recommended regular PA to prevent hypertension in less polluted regions and reinforced the importance of air quality improvement.

Long-term effects of PM2.5 components on incident dementia in the northeastern United States

Growing evidence has linked long-term fine particulate matter (PM2.5) exposure to neurological disorders. Less is known about the individual effects of PM2.5 components. A population-based cohort study investigated the association between long-term (1-year average) exposure to PM2.5 components and dementia incidence among the elderly population (age, ≥65 years) in the United States. We used data from the Medicare Chronic Conditions Warehouse and a high-resolution PM2.5 components dataset of the northeastern United States (2000–2017). We identified dementia diagnoses from patients’ hospital and medical insurance records and carried out Cox proportional hazards regression to investigate their association with PM2.5 components. Among ∼2 million participants, 15.1% developed dementia. From the single-pollutant models, hazard ratios per interquartile range increase were 1.10 (95% confidence interval [CI]: 1.09–1.11) for black carbon, 1.08 (95% CI: 1.07, 1.10) for inorganic nitrate, 1.03 (95% CI: 1.02, 1.04) for organic matter, 1.13 (95% CI: 1.11, 1.15) for sulfate, 1.07 (95% CI: 1.06, 1.07) for soil particles, and 1.04 (95% CI: 1.03, 1.05) for sea salt. Increase in exposure to black carbon and sulfate per interquartile range had the strongest associations with dementia incidence. Penalized spline models indicated that dementia incidence increased linearly with elevated black carbon concentrations, whereas the incidence of dementia was only elevated significantly following sulfate concentrations above ∼2 μg/m3. Our study suggests that long-term exposure to PM2.5 components is significantly associated with increased dementia incidence and that different components have different neurotoxicity. Reduction of PM2.5 emissions, especially for main sources of black carbon and sulfate, may reduce the burden of dementia in the aging United States population.

The Canadian Optimized Statistical Smoke Exposure Model (Canossem): A Machine Learning Approach to Estimate National Daily Fine Particulate Matter (Pm2.5) Exposure

The Canadian Optimized Statistical Smoke Exposure Model (Canossem): A Machine Learning Approach to Estimate National Daily Fine Particulate Matter (Pm2.5) Exposure

A Cross-Sectional Analysis of Ambient Fine Particulate Matter (Pm2.5) Exposure and Haemoglobin Levels in Children Aged Under 5 Years Living in 36 Countries

A Cross-Sectional Analysis of Ambient Fine Particulate Matter (Pm2.5) Exposure and Haemoglobin Levels in Children Aged Under 5 Years Living in 36 Countries

EFFECT OF FINE PARTICULATE MATTER EXPOSURE ON TRIGGERING DEPRESSIVE-LIKE RESPONSE AND TOTAL BODY WEIGHT IN C57BL/6J MICE

Objective: To study the effect of fine particulate matter exposure in instigating depressive-like response and total body weight changes in C57BL/6J mice.&#x0D; Study Design: Laboratory-based experimental study.&#x0D; Place and Duration of Study: Anatomy Department, Army Medical College, Rawalpindi, in collaboration with the Military Hospital, Rawalpindi and the National Institute of Health (NIH), Islamabad, from Jun to Sep 2020.&#x0D; Methodology: Thirty male and female C57BL/6 mice, 8 weeks of age, weighing 37 ± 2 gm were obtained from NIH, Islamabad. They were retained in separate cages in the animal house of NIH under standard laboratory conditions. The animals were divided into two groups, 15 mice in each group (8 males and 7 females) Group A was marked as control, received regular diet and water ad libitum. Group B (experimental group) received dynamic inhalation of 3 mg/m³ fine particles (soot) through air circulation for 6h/d for 12 weeks, in a plastic cabin measuring 2x2x2 feet fitted with two small fans for evenly distribution of PM. The initial and final weight of animals was recorded, and a tail suspension test was performed to assessdepression in form of immobility.&#x0D; Result: The experimental group after exposure to fine particulate matter showed decreased activity and behavioural deficits with p-value &lt;0.001 and loss in total body weight as compared to the control group with a p-value &lt;0.001.&#x0D; Conclusion: Fine particulate matter has a significant effect on behaviour and cognition. It also causes a decrease in the total body weight of animals due to stress and agitation.

Sunlight Exposure, Sun-Protective Behavior, and Anti-Citrullinated Protein Antibody Positivity: A General Population-Based Study in Quebec, Canada.

To examine associations between sunlight exposure and anti-citrullinated protein antibodies (ACPAs) using general population data in Quebec, Canada. A random sample of 7,600 individuals (including 786 subjects who were ACPA positive and 201 self-reported rheumatoid arthritis [RA] cases) from the CARTaGENE cohort was studied cross-sectionally. All subjects were nested in 4 census metropolitan areas, and mixed-effects logistic regression models were used to calculate odds ratios (ORs) and 95% confidence intervals (95% CIs) for ACPA positivity related to sunlight exposure, adjusting for sun-block use, industrial fine particulate matter (PM2.5 ) exposures, smoking, age, sex, French Canadian ancestry, and family income. We also performed sensitivity analyses excluding subjects with RA, defining ACPA positivity by higher titers, and stratifying by age and sex. The adjusted ORs and 95% CIs did not suggest conclusive associations between ACPA and sunlight exposure or sun-block use, but robust positive relationships were observed between industrial PM2.5 emissions and ACPA (OR 1.19 per μg/m3 [95% CI 1.03-1.36] in primary analyses). We did not see clear links between ACPA and sunlight exposure or sun-block use, but we did note positive associations with industrial PM2.5 . Future studies of sunlight and RA (or ACPA) should take air pollution exposures into account.

A high resolution spatiotemporal fine particulate matter exposure assessment model for the contiguous United States

Currently available nationwide prediction models for fine particulate matter (PM2.5) lack prediction confidence intervals and usually do not describe cross validated model performance at different spatiotemporal resolutions and extents. We used 41 different spatiotemporal predictors, including data on land use, meteorology, aerosol optical density, emissions, wildfires, population, traffic, and spatiotemporal indicators to train a machine learning model to predict daily averages of PM2.5 concentrations at 0.75 sq km resolution across the contiguous United States from 2000 through 2020. We utilized a generalized random forest model that allowed us to generate asymptotically-valid prediction confidence intervals and took advantage of its usefulness as an ensemble learner to quickly and cheaply characterize leave-one-location-out CV model performance for different temporal resolutions and geographic regions. Using a variable importance metric, we selected 8 predictors that were able to accurately predict daily PM2.5, with an overall leave-one-location-out cross validated median absolute error of 1.20 μgm3, an R2 of 0.84, and confidence interval coverage fraction of 95%. When considering aggregated temporal windows, the model achieved leave-one-location-out cross validated median absolute errors of 0.99, 0.76, 0.63, and 0.60 μgm3 for weekly, monthly, annual, and all-time exposure assessments, respectively. We further describe the model's cross validated performance at different geographic regions in the United States, finding that it performs worse in the Western half of the country where there are less monitors. The code and data used to create this model are publicly available and we have developed software packages to be used for exposure assessment. This accurate exposure assessment model will be useful for epidemiologists seeking to study the health effects of PM2.5 across the continental United States, while possibly considering exposure estimation accuracy and uncertainty specific to the the spatiotemporal resolution and extent of their study design and population.

Development of an alveolar chip model to mimic respiratory conditions due to fine particulate matter exposure

• Development of an alveolar chip that can reliably and efficiently assess the harmful effects of PMs across different particle sizes ranges and compositions. • Optimized chip setup using PC-PET to provide long term stability. • Good testing performance to mimic the respiratory conditions associated with different types of PMs.