Indoor and ambient black carbon and fine particulate matter associations with blood biomarkers in COPD patients.

BACKGROUND: Systemic inflammation contributes to cardiovascular risk and chronic obstructive pulmonary disease (COPD) pathophysiology. Associations between systemic inflammation and exposure to ambient fine particulate matter (PM ≤ 2.5 µm diameter; PM2.5), and black carbon (BC), a PM2.5 component attributable to traffic and other sources of combustion, infiltrating indoors are not well described. METHODS: Between 2012 and 2017, COPD patients completed in-home air sampling over one-week intervals, up to four times (seasonally), followed by measurement of plasma biomarkers of systemic inflammation, C-reactive protein (CRP) and interleukin-6 (IL-6), and endothelial activation, soluble vascular adhesion molecule-1 (sVCAM-1). Ambient PM2.5, BC and sulfur were measured at a central site. The ratio of indoor/ambient sulfur in PM2.5, a surrogate for fine particle infiltration, was used to estimate indoor BC and PM2.5 of ambient origin. Linear mixed effects regression with a random intercept for each participant was used to assess associations between indoor and indoor of ambient origin PM2.5 and BC with each biomarker. RESULTS: 144 participants resulting in 482 observations were included in the analysis. There were significant positive associations between indoor BC and indoor BC of ambient origin with CRP [%-increase per interquartile range (IQR);95 % CI (13.2 %;5.2-21.8 and 11.4 %;1.7-22.1, respectively)]. Associations with indoor PM2.5 and indoor PM2.5 of ambient origin were weaker. There were no associations with IL-6 or sVCAM-1. CONCLUSIONS: In homes of patients with COPD without major sources of combustion, indoor BC is mainly attributable to the infiltration of ambient sources of combustion indoors. Indoor BC of ambient origin is associated with increases in systemic inflammation in patients with COPD, even when staying indoors.

Protocol for the air purification for eosinophilic COPD study (APECS): a randomised controlled trial of home air filtration by HEPA.

INTRODUCTION: Exposure to particulate matter (PM) pollution has been associated with lower lung function in adults with chronic obstructive pulmonary disease (COPD). Patients with eosinophilic COPD have been found to have higher levels of airway inflammation, greater responsiveness to anti-inflammatory steroid inhalers and a greater lung function response to PM pollution exposure compared with those with lower eosinophil levels. This study will evaluate if reducing home PM exposure by high-efficiency particulate air (HEPA) air filtration improves respiratory health in eosinophilic COPD. METHODS AND ANALYSIS: The Air Purification for Eosinophilic COPD Study (APECS) is a double-blinded randomised placebo-controlled trial that will enrol 160 participants with eosinophilic COPD living in the area of Boston, Massachusetts. Real and sham air purifiers will be placed in the bedroom and living rooms of the participants in the intervention and control group, respectively, for 12 months. The primary trial outcome will be the change in forced expiratory volume in 1 s (FEV1). Lung function will be assessed twice preintervention and three times during the intervention phase (at 7 days, 6 months and 12 months postrandomisation). Secondary trial outcomes include changes in (1) health status by St. George's Respiratory Questionnaire; (2) respiratory symptoms by Breathlessness, Cough and Sputum Scale (BCSS); and (3) 6-Minute Walk Test (6MWT). Inflammatory mediators were measured in the nasal epithelial lining fluid (NELF). Indoor PM will be measured in the home for the week preceding each study visit. The data will be analysed to contrast changes in outcomes in the intervention and control groups using a repeated measures framework. ETHICS AND DISSEMINATION: Ethical approval was obtained from the Institutional Review Board of Beth Israel Deaconess Medical Centre (protocol #2019P0001129). The results of the APECS trial will be presented at scientific conferences and published in peer-reviewed journals. TRIAL REGISTRATION: NCT04252235. Version: October 2023.

Indoor (residential) and ambient particulate matter associations with urinary oxidative stress biomarkers in a COPD cohort.

OBJECTIVES: Oxidative stress contributes to chronic obstructive pulmonary disease (COPD) pathophysiology. Associations between indoor (residential) exposure to particulate matter ≤2.5 µm in diameter (PM2.5) and one of its components, black carbon (BC), and oxidative stress are ill-defined. METHODS: Between 2012 and 2017, 140 patients with COPD completed in-home air sampling over one week intervals, followed by collection of urine samples to measure oxidative stress biomarkers, malondialdehyde (MDA), a marker of lipid peroxidation, and 8-hydroxy-2' -deoxyguanosine (8-OHdG), a marker of oxidative DNA damage. Ambient (central site) BC and PM2.5 were measured, and the ratio of indoor/ambient sulfur in PM2.5, a surrogate for residential ventilation and particle infiltration, was used to estimate indoor BC and PM2.5 of outdoor origin. Mixed effects linear regression models with a participant-specific random intercept were used to assess associations with oxidative biomarkers, adjusting for personal characteristics. RESULTS: There were positive associations (% increase per IQR; 95 % CI) of directly measured indoor BC with total MDA (6.96; 1.54, 12.69) and 8-OHdG (4.18; -0.67, 9.27), and similar associations with both indoor BC of outdoor origin and ambient BC. There were no associations with directly measured indoor PM2.5, but there were positive associations between indoor PM2.5 of outdoor origin and total MDA (5.40; -0.91, 12.11) and 8-OHdG (8.02; 2.14, 14.25). CONCLUSIONS: In homes with few indoor combustion sources, directly measured indoor BC, estimates of indoor BC and PM2.5 of outdoor origin, and ambient BC, were positively associated with urinary biomarkers of oxidative stress. This suggests that the infiltration of particulate matter from outdoor sources, attributable to traffic and other sources of combustion, promotes oxidative stress in COPD patients.

Personal air pollution exposure and metals in the nasal epithelial lining fluid of COPD patients.

Sampling of the nasal epithelial lining fluid is a potential method to assess exposure to air pollution within the respiratory tract among high risk populations. We investigated associations of short- and long-term particulate matter exposure (PM) and pollution-related metals in the nasal fluid of people with chronic obstructive pulmonary disease (COPD). This study included 20 participants with moderate-to-severe COPD from a larger study who measured long-term personal exposure to PM2.5 using portable air monitors and short-term PM2.5 and black carbon (BC) using in-home samplers for the seven days preceding nasal fluid collection. Nasal fluid was sampled from both nares by nasosorption, and inductively coupled plasma mass spectrometry was used to determine the concentration of metals with major airborne sources. Correlations of selected elements (Fe, Ba, Ni, Pb, V, Zn, Cu) were determined within the nasal fluid. Associations between personal long-term PM2.5 and seven day home PM2.5 and BC exposure and nasal fluid metal concentrations were determined by linear regression. Within nasal fluid samples, concentrations of vanadium and nickel (r = 0.8) and lead and zinc (r = 0.7) were correlated. Seven day and long-term PM2.5 exposure were both associated with higher levels of copper, lead, and vanadium in the nasal fluid. BC exposure was associated with higher levels of nickel in the nasal fluid. Levels of certain metals in the nasal fluid may serve as biomarkers of air pollution exposure in the upper respiratory tract.

Improved indoor air quality during desert dust storms: The impact of the MEDEA exposure-reduction strategies.

Desert dust storms (DDS) are natural events that impact not only populations close to the emission sources but also populations many kilometers away. Countries located across the main dust sources, including countries in the Eastern Mediterranean, are highly affected by DDS. In addition, climate change is expanding arid areas exacerbating DDS events. Currently, there are no intervention measures with proven, quantified exposure reduction to desert dust particles. As part of the wider "MEDEA" project, co-funded by LIFE 2016 Programme, we examined the effectiveness of an indoor exposure-reduction intervention (i.e., decrease home ventilation during DDS events and continuous use of air purifier during DDS and non-DDS days) across homes and/or classrooms of schoolchildren with asthma and adults with atrial fibrillation in Cyprus and Crete-Greece. Participants were randomized to a control or intervention groups, including an indoor intervention group with exposure reduction measures and the use of air purifiers. Particle sampling, PM10 and PM2.5, was conducted in participants' homes and/or classrooms, between 2019 and 2022, during DDS-free weeks and during DDS days for as long as the event lasted. In indoor and outdoor PM10 and PM2.5 samples, mass and content in main and trace elements was determined. Indoor PM2.5 and PM10 mass concentrations, adjusting for premise type and dust conditions, were significantly lower in the indoor intervention group compared to the control group (PM2.5-intervention/PM2.5-control = 0.57, 95% CI: 0.47, 0.70; PM10-intervention/PM10-control = 0.59, 95% CI: 0.49, 0.71). In addition, the PM2.5 and PM10 particles of outdoor origin were significantly lower in the intervention vs. the control group (PM2.5 infiltration intervention-to-control ratio: 0.49, 95% CI: 0.42, 0.58; PM10 infiltration intervention-to-control ratio: 0.68, 95% CI: 0.52, 0.89). Our findings suggest that the use of air purifiers alongside decreased ventilation measures is an effective protective measure that reduces significantly indoor exposure to particles during DDS and non-DDS in high-risk population groups.

Particle radioactivity from radon decay products and reduced pulmonary function among chronic obstructive pulmonary disease patients.

BACKGROUND: Radon (222Rn) decay products can attach to particles in the air, be inhaled, and potentially cause airway damage. RESEARCH QUESTION: Is short-term exposure to particle radioactivity (PR) attributable to radon decay products emitted from particulate matter ≤2.5 µm in diameter (PM2.5) associated with pulmonary function in chronic obstructive pulmonary disease (COPD) patients? STUDY DESIGN AND METHODS: In this cohort study, 142 elderly, predominantly male patients with COPD from Eastern Massachusetts each had up to 4 one-week long seasonal assessments of indoor (home) and ambient (central site) PR and PM2.5 over the course of a year (467 assessments). Ambient and indoor PR were measured as α-activity on archived PM2.5 filter samples. Ratios of indoor/ambient PR were calculated, with higher ratios representing PR from an indoor source of radon decay. We also considered a measure of outside air infiltration that could dilute the concentrations of indoor radon decay products, the indoor/ambient ratio of sulfur concentrations in PM2.5 filter samples. Spirometry pre- and post-bronchodilator (BD) forced expiratory volume in 1 s (FEV1) and forced vital capacity (FVC) were conducted following sampling. Generalized additive mixed models were adjusted for meteorologic variables, seasonality, and individual-level determinants of pulmonary function. We additionally adjusted for indoor PM2.5 and black carbon (BC). RESULTS: PR exposure metrics indicating radon decay product exposure from an indoor source were associated with a reduction in FEV1 and FVC. Patients in homes with high indoor PR (≥median) and low air infiltration (

Determinants of indoor carbonaceous aerosols in homes in the Northeast United States.

BACKGROUND: Little is known about sources of residential exposure to carbonaceous aerosols, which include black carbon (BC), the elemental carbon core of combustion particles, and organic compounds from biomass combustion (delta carbon). OBJECTIVE: Assess the impact of residential characteristics on indoor BC and delta carbon when known sources of combustion (e.g., smoking) are minimized. METHODS: Between November 2012-December 2014, 125 subjects (129 homes) in Northeast USA were recruited and completed a residential characteristics questionnaire. Every 3 months, participants received an automated sampler to measure fine particulate matter (PM2.5) in their home during a weeklong period (N = 371 indoor air samples) and were also questioned about indoor exposures. The samples were analyzed using a transmissometer at 880 nm (reflecting BC) and at 370 nm. The difference between the two wavelengths estimates delta carbon. Outdoor BC and delta carbon were measured using a central site aethalometer. RESULTS: Geometric mean indoor concentrations of BC and delta carbon (0.65 µg/m³ and 0.19 µg/m³, respectively), were greater than central site concentrations (0.53 µg/m³ and 0.02 µg/m³, respectively). Multivariable analysis showed that greater indoor concentrations of BC were associated with infrequent candle use, multi-family homes, winter season, lack of air conditioning, and central site BC. For delta carbon, greater indoor concentrations were associated with apartments, spring season, and central site concentrations. SIGNIFICANCE: In addition to outdoor central site concentrations, factors related to the type of housing, season, and home exposures are associated with indoor exposure to carbonaceous aerosols. Recognition of these characteristics should enable greater understanding of indoor exposures and their sources.

Estimation of fine particulate matter in an arid area from visibility based on machine learning.

BACKGROUND: The absence of air pollution monitoring networks makes it difficult to assess historical fine particulate matter (PM2.5) exposures for countries in the areas, such as Kuwait, which are severe impacted by desert dust and anthropogenic pollution. OBJECTIVE: We constructed an ensemble machine learning model to predict daily PM2.5 concentrations for regions lack of PM2.5 observations. METHODS: The model was constructed based on daily PM2.5, visibility, and other meteorological data collected at two sites in Kuwait. Then, our model was applied to predict the daily level of PM2.5 concentrations for eight airports located in Kuwait and Iraq from 2013 to 2020. RESULTS: As compared to traditional statistic models, the proposed machine learning methods improved the accuracy in using visibility to predict daily PM2.5 concentrations with a cross-validation R2 of 0.68. The predicted level of daily PM2.5 concentrations were consistent with previous measurements. The predicted average yearly PM2.5 concentration for the eight stations is 50.65 µg/m3. For all stations, the monthly average PM2.5 concentrations reached their maximum in July and their minimum in November. SIGNIFICANCE: These findings make it possible to retrospectively estimate daily PM2.5 exposures using the large-scale databases of historical visibility in regions with few particulate matter monitoring stations. IMPACT STATEMENT: The scarcity of air pollution ground monitoring networks makes it difficult to assess historical fine particulate matter exposures for countries in arid areas such as Kuwait. Visibility is closely related to atmospheric particulate matter concentrations and historical airport visibility records are commonly available in most countries. Our model make it possible to retrospectively estimate daily PM2.5 exposures using the large-scale databases of historical visibility in arid regions with few particulate matter ground monitoring stations. The product of such models can be critical for environmental risk assessments and population health studies.

Factors Influencing Classroom Exposures to Fine Particles, Black Carbon, and Nitrogen Dioxide in Inner-City Schools and Their Implications for Indoor Air Quality.

BACKGROUND: School classrooms, where students spend the majority of their time during the day, are the second most important indoor microenvironment for children. OBJECTIVE: We investigated factors influencing classroom exposures to fine particulate matter (PM2.5), black carbon (BC), and nitrogen dioxide (NO2) in urban schools in the northeast United States. METHODS: Over the period of 10 y (2008-2013; 2015-2019) measurements were conducted in 309 classrooms of 74 inner-city schools during fall, winter, and spring of the academic period. The data were analyzed using adaptive mixed-effects least absolute shrinkage and selection operator (LASSO) regression models. The LASSO variables included meteorological-, school-, and classroom-based covariates. RESULTS: LASSO identified 10, 10, and 11 significant factors (p<0.05) that were associated with indoor PM2.5, BC, and NO2 exposures, respectively. The overall variability explained by these models was R2=0.679, 0.687, and 0.621 for PM2.5, BC, and NO2, respectively. Of the model's explained variability, outdoor air pollution was the most important predictor, accounting for 53.9%, 63.4%, and 34.1% of the indoor PM2.5, BC, and NO2 concentrations. School-based predictors included furnace servicing, presence of a basement, annual income, building type, building year of construction, number of classrooms, number of students, and type of ventilation that, in combination, explained 18.6%, 26.1%, and 34.2% of PM2.5, BC, and NO2 levels, whereas classroom-based predictors included classroom floor level, classroom proximity to cafeteria, number of windows, frequency of cleaning, and windows facing the bus area and jointly explained 24.0%, 4.2%, and 29.3% of PM2.5, BC, and NO2 concentrations, respectively. DISCUSSION: The adaptive LASSO technique identified significant regional-, school-, and classroom-based factors influencing classroom air pollutant levels and provided robust estimates that could potentially inform targeted interventions aiming at improving children's health and well-being during their early years of development. https://doi.org/10.1289/EHP10007.

Air Pollution Exposure and Daily Lung Function in Chronic Obstructive Pulmonary Disease: Effect Modification by Eosinophil Level.

Rationale: Few studies have assessed personal exposure to pollutants and lung function among adults with chronic obstructive pulmonary disease (COPD). Blood eosinophil level may be a biomarker of airway inflammation and pollution susceptibility. Objectives: To evaluate if daily pollutant exposures are associated with lung function and if associations are modified by eosinophil level in COPD. Methods: We recruited 30 former smokers with moderate to severe COPD living in the Boston area and followed them for up to 4 nonconsecutive months in different seasons. Participants measured morning lung function and carried a portable air quality monitor daily. Previous-day exposure to pollutants (particulate matter ⩽2.5 µm in aerodynamic diameter, nitrogen dioxide [NO2], and ozone) were measured by portable and community monitors. We constructed multilevel linear mixed-effects models with random intercepts for person and observation month, adjusted for temperature, humidity, age, sex, race, height, weight, income, and season, to assess associations of previous-day pollutant exposure with lung function and effect modification by eosinophil count (<150/µl vs. ⩾150/µl). Results: A total of 3,314 observations with exposure and lung function data were collected. Each interquartile range (5.1 parts per billion [ppb])-higher previous-day personal exposure to NO2 was associated with an 11.3 ml (95% confidence interval [CI], -18.7 to -4.0) lower forced expiratory volume in 1 second (FEV1) and an 18.0 ml (95% CI, -32.0 to -4.2) lower forced vital capacity. Personal and community-level exposure to particulate matter ⩽2.5 µm in aerodynamic diameter and community-level NO2 were negatively associated with FEV1 among the 55.2% of participants with the higher eosinophil level (Pinteraction < 0.05). Conclusions: Our study highlights the need to address air pollution exposure among patients with COPD. Future research is needed to verify if eosinophil level is a biomarker for susceptibility to air pollution in COPD.

Effect of School Integrated Pest Management or Classroom Air Filter Purifiers on Asthma Symptoms in Students With Active Asthma: A Randomized Clinical Trial.

Importance: School and classroom allergens and particles are associated with asthma morbidity, but the benefit of environmental remediation is not known. Objective: To determine whether use of a school-wide integrated pest management (IPM) program or high-efficiency particulate air (HEPA) filter purifiers in the classrooms improve asthma symptoms in students with active asthma. Design, Setting, and Participants: Factorial randomized clinical trial of a school-wide IPM program and HEPA filter purifiers in the classrooms was conducted from 2015 to 2020 (School Inner-City Asthma Intervention Study). There were 236 students with active asthma attending 41 participating urban elementary schools located in the Northeastern US who were randomized to IPM by school and HEPA filter purifiers by classroom. The date of final follow-up was June 20, 2020. Interventions: The school-wide IPM program consisted of application of rodenticide, sealing entry points, trap placement, targeted cleaning, and brief educational handouts for school staff. Infestation was assessed every 3 months, with additional treatments as needed. Control schools received no IPM, cleaning, or education. Classroom portable HEPA filter purifiers were deployed and the filters were changed every 3 months. Control classrooms received sham HEPA filters that looked and sounded like active HEPA filter purifiers. Randomization was done independently (split-plot design), with matching by the number of enrolled students to ensure a nearly exact 1:1 student ratio for each intervention with 118 students randomized to each group. Participants, investigators, and those assessing outcomes were blinded to the interventions. Main Outcomes and Measures: The primary outcome was the number of symptom-days with asthma during a 2-week period. Symptom-days were assessed every 2 months during the 10 months after randomization. Results: Among the 236 students who were randomized (mean age, 8.1 [SD, 2.0] years; 113 [48%] female), all completed the trial. At baseline, the 2-week mean was 2.2 (SD, 3.9) symptom-days with asthma and 98% of the classrooms had detectable levels of mouse allergen. The results were pooled because there was no statistically significant difference between the 2 interventions (P = .18 for interaction). During a 2-week period, the mean was 1.5 symptom-days with asthma after use of the school-wide IPM program vs 1.9 symptom-days after no IPM across the school year (incidence rate ratio, 0.71 [95% CI, 0.38-1.33]), which was not statistically significantly different. During a 2-week period, the mean was 1.6 symptom-days with asthma after use of HEPA filter purifiers in the classrooms vs 1.8 symptom-days after use of sham HEPA filter purifiers across the school year (incidence rate ratio, 1.47 [95% CI, 0.79-2.75]), which was not statistically significantly different. There were no intervention-related adverse events. Conclusions and Relevance: Among children with active asthma, use of a school-wide IPM program or classroom HEPA filter purifiers did not significantly reduce symptom-days with asthma. However, interpretation of the study findings may need to consider allergen levels, particle exposures, and asthma symptoms at baseline. Trial Registration: ClinicalTrials.gov Identifier: NCT02291302.

Predictors of indoor radon levels in the Midwest United States.

Radon (Rn) is a natural and toxic radioactive gas that accumulates indoors, mainly in low-ventilated underground floors and basements. Several factors make prediction of indoor Rn exposure in enclosed spaces challenging. In this study, we investigated the influence of soil, geology, topography, atmospheric variables, radiation, urbanization, community economic well-being, and monthly and yearly variations on indoor Rn concentrations. We analyzed 7,515 monthly indoor Rn measurements in 623 zip codes from two U.S. States (Michigan and Minnesota) during 2005-2018 using a random forest model. Using Shapley Additive exPlanations (SHAP) values we investigated the contribution of each factor using variable importance and partial dependence plots. Factors that predict indoor Rn differed between states, with topographical, geological and soil composition being most influential. Cross-validated Pearson correlation between predictions and measurements was 0.68 (RMSE = 47.8 Bq/m3) in Minnesota, and 0.67 (RMSE = 52.5 Bq/m3) in Michigan. Our results underline the importance of soil structure for radon exposure, presumably due to strapped Rn in soil. The differences across states also suggest that Rn studies performing model development should consider geographical variables, along with other factors. As indoor Rn levels are multifactorial, an understanding of the factors that influence its emanation and build up indoors will help better assess spatial and temporal variations, which will be useful to improve prevention and mitigation control strategies.Implications: Radon exposure has become a year-round problem as people spend most of their time indoors. In North America, radon exposure is increasing over time and awareness related to its health effects remains low in the general population. Several factors make prediction of indoor radon exposure in enclosed spaces challenging. In this study, we used random forest to investigate the influence of factors on indoor radon in the Midwest United States. We found that topography, geology, and soil composition were the most influential factors on indoor radon levels. These results will help better assess spatial and temporal variations, which will further help better prevention and mitigation control strategies.

Predicting Monthly Community-Level Domestic Radon Concentrations in the Greater Boston Area with an Ensemble Learning Model.

Inhaling radon and its progeny is associated with adverse health outcomes. However, previous studies of the health effects of residential exposure to radon in the United States were commonly based on a county-level temporally invariant radon model that was developed using measurements collected in the mid- to late 1980s. We developed a machine learning model to predict monthly radon concentrations for each ZIP Code Tabulation Area (ZCTA) in the Greater Boston area based on 363,783 short-term measurements by Spruce Environmental Technologies, Inc., during the period 2005-2018. A two-stage ensemble-based model was developed to predict radon concentrations for all ZCTAs and months. Stage one included 12 base statistical models that independently predicted ZCTA-level radon concentrations based on geological, architectural, socioeconomic, and meteorological factors for each ZCTA. Stage two aggregated the predictions of these 12 base models using an ensemble learning method. The results of a 10-fold cross-validation showed that the stage-two model has a good prediction accuracy with a weighted R2 of 0.63 and root mean square error of 22.6 Bq/m3. The community-level time-varying predictions from our model have good predictive precision and accuracy and can be used in future prospective epidemiological studies in the Greater Boston area.

A two-year assessment of particulate air pollution and sources in Kuwait.

BACKGROUND: Kuwait and the Gulf region have a desert, hyper-arid and hot climate that makes outdoor air sampling challenging. The region is also affected by intense dust storms. Monitoring challenges from the harsh climate have limited data needed to inform appropriate regulatory actions to address air pollution in the region. OBJECTIVES: To compare gravimetric measurements with existing networks that rely on beta-attenuation measurements in a desert climate; determine the annual levels of PM2.5 and PM10 over a two-year period in Kuwait; assess compliance with air quality standards; and identify and quantify PM2.5 sources. METHODS: We custom-designed particle samplers that can withstand large quantities of dust without their inlet becoming overloaded. The samplers were placed in two populated residential locations, one in Kuwait City and another near industrial and petrochemical facilities in Ali Sabah Al-Salem (ASAS) to collect PM2.5 and PM10 samples for mass and elemental analysis. We used positive matrix factorization to identify PM2.5 sources and apportion their contributions. RESULTS: We collected 2339 samples during the period October 2017 through October 2019. The beta-attenuation method in measuring PM2.5 consistently exceeded gravimetric measurements, especially during dust events. The annual levels for PM2.5 in Kuwait City and ASAS were 41.6 ± 29.0 and 47.5 ± 27.6 µg/m3, respectively. Annual PM2.5 levels in Kuwait were nearly four times higher than the U.S. National Ambient Air Quality Standard. Regional pollution was a major contributor to PM2.5 levels in both locations accounting for 44% in Kuwait City and 46% in ASAS. Dust storms and re-suspended road dust were the second and third largest contributors to PM2.5, respectively. CONCLUSIONS: The premise that frequent and extreme dust storms make air quality regulation futile is dubious. In this comprehensive particulate pollution analysis, we show that the sizeable regional anthropogenic particulate sources warrant national and regional mitigation strategies to ensure compliance with air quality standards.

Sources of indoor PM2.5 gross α and ß activities measured in 340 homes.

Particle radioactivity (PR) exposure has been linked to adverse health effects. PR refers to the presence of α- and ß-emitting radioisotopes attached to fine particulate matter (PM2.5). This study investigated sources contributing to indoor PM2.5 gross α- and ß-radioactivity levels. We measured activity from long-lived radon progeny radionuclides from archived PM2.5 samples collected in 340 homes in Massachusetts during the period 2006-2010. We analyzed the data using linear mixed effects models and positive matrix factorization (PMF) analysis. Indoor PM2.5 gross α-activity levels were correlated with sulfur (S), iron (Fe), bromine (Br), vanadium (V), sodium (Na), lead (Pb), potassium (K), calcium (Ca), silicon (Si), zinc (Zn), arsenic (As), titanium (Ti), radon (222Rn) and black carbon (BC) concentrations (p <0.05). Indoor PM2.5 ß-activity was correlated with S, As, antimony (Sb), Pb, Br and BC. We identified four indoor PM2.5 sources: outdoor air pollution (62%), salt aerosol source (14%), fireworks and environmental tobacco smoke (7%) and indoor mixed dust (17%). Outdoor air pollution was the most significant contributor to indoor PM2.5 α- and ß-activity levels. The contributions of this source were during the summer months and when windows were open. Indoor mixed dust was also found to contribute to PM2.5 α-activity. PM2.5 α-activity was further associated with radon during winter months, showing radon's important role as an indoor source of ionizing radiation.

Estimating climate change-related impacts on outdoor air pollution infiltration.

BACKGROUND: Rising temperatures due to climate change are expected to impact human adaptive response, including changes to home cooling and ventilation patterns. These changes may affect air pollution exposures via alteration in residential air exchange rates, affecting indoor infiltration of outdoor particles. We conducted a field study examining associations between particle infiltration and temperature to inform future studies of air pollution health effects. METHODS: We measured indoor fine particulate matter (PM2.5) in Atlanta in 60 homes (810 sampling-days). Indoor-outdoor sulfur ratios were used to estimate particle infiltration, using central site outdoor sulfur concentrations. Linear and mixed-effects models were used to examine particle infiltration ratio-temperature relationships, based on which we incorporated projected meteorological values (Representative Concentration Pathways intermediate scenario RCP 4.5) to estimate particle infiltration ratios in 20-year future (2046-2065) and past (1981-2000) scenarios. RESULTS: The mean particle infiltration ratio in Atlanta was 0.70 ± 0.30, with a 0.21 lower ratio in summer compared to transition seasons (spring, fall). Particle infiltration ratios were 0.19 lower in houses using heating, ventilation, and air conditioning (HVAC) systems compared to those not using HVAC. We observed significant associations between particle infiltration ratios and both linear and quadratic models of ambient temperature for homes using natural ventilation and those using HVAC. Future temperature was projected to increase by 2.1 °C in Atlanta, which corresponds to an increase of 0.023 (3.9%) in particle infiltration ratios during cooler months and a decrease of 0.037 (6.2%) during warmer months. DISCUSSION: We estimated notable changes in particle infiltration ratio in Atlanta for different 20-year periods, with differential seasonal patterns. Moreover, when stratified by HVAC usage, increases in future ambient temperature due to climate change were projected to enhance seasonal differences in PM2.5 infiltration in Atlanta. These analyses can help minimize exposure misclassification in epidemiologic studies of PM2.5, and provide a better understanding of the potential influence of climate change on PM2.5 health effects.

Trends in particle matter and its elemental composition in Santiago de Chile, 2011 - 2018.

Daily fine (PM2.5) and coarse (PM10-2.5) particle matter (PM) samples collected at Parque O'Higgins station in downtown Santiago de Chile have been studied to find the trends in concentration from 1998 to 2018. Elemental concentration was obtained using X-ray fluorescence (XRF). Regression models from previous studies indicate that the PM2.5 and PM10-2.5 fractions have had a continuous decrease since 1988 mostly due to several policy control measures carried out over several decades. PM2.5 has decreased from 68.3 in 1988 to 27.6 µg/m3 in 2018 (60.4%). However, if only the last 8 years are considered (2011-2018), a leveling off can be observed in PM10-2.5 and PM2.5, which points to a change in the tendency. Cluster analysis of the elements in the fine and coarse fractions were identified to evaluate trends in the contributing sources. In the fine fraction, the mass contribution of crustal elements (Si, Al, Ca, and Fe) has remained stable in the last 8 years, and mass contribution of elements (Pb, Br, and Cl) associated to anthropogenic sources (traffic, wood burning) has also remained stable in the same period. For the coarse fraction, the contribution of one group of elements associated to crustal or anthropogenic sources has remained stable, and another group has decreased in the last 8 years. The leveling off can be ascribed to decreased rainfall during the last 8 years that have promoted soil dryness and resuspension of dust facilitated by wind or vehicular traffic. Mean temperatures have increased in the last 30 years, but have not contributed directly to the leveling of the concentration.Implications: Regression models indicate that the PM2.5 (fine) and PM10-2.5 (coarse) fractions at Parque O'Higgins station in Santiago de Chile have had a continuous decrease since 1988 mostly due to several policy control measures carried out over several decades. However, in the last 8 years (2011-2018), a leveling off can be observed in PM10-2.5 and PM2.5. X-ray fluorescence (XRF) analysis was performed in the fine fractions indicating that the mass contribution of crustal elements (Ca, Al, Si, Fe) to the fine fraction has remained stable. This phenomenon can be ascribed to decreased rainfall during the last 8 years that have promoted soil dryness and resuspension of dust facilitated by wind or vehicular traffic. The crustal elements in the coarse fraction have also remained stable.

Prenatal PM2.5 exposure and the risk of adverse births outcomes: Results from Project ELEFANT.

BACKGROUND: Studies investigating the impact of fine particulate matter (PM2.5) exposure during pregnancy upon adverse birth outcomes have primarily been performed in Western nations with low ambient PM2.5 levels. We examined associations between high levels of PM2.5 exposure during pregnancy and risk of adverse birth outcomes by timing and level of exposure in a Chinese population. METHODS: We analysed data from 10,738 live births within the Project ELEFANT study based in Tianjin, China. Personal mean daily PM2.5 exposures were estimated using data from 25 local monitoring sites across the city, used to compute the days exceeding 50, 100, 150, 200 and 250 µg/m3. Relative risk of pre-term birth (<37 weeks) and low birthweight (<2500 g) were estimated by generalized additive distributed lag models, adjusted for maternal age, sex, region, paternal smoking, parity, maternal occupation, season, temperature and dew point. RESULTS: A dose-response was exhibited for PM2.5 exposure and relative risk (RR) of adverse birth outcomes, with exposure in the second and third trimesters of pregnancy associated with greatest risk of adverse birth outcomes. The RRs of pre-term birth with exposures of >50, >150 and > 250 µg/m3 PM2.5 in the third trimester were 1.09 (95%CI: 1.03-1.16), 1.30 (1.09-1.54) and 2.73 (2.03-3.66) respectively. For low birthweight, exposures of >50, >150 and > 250 µg/m3 PM2.5 in the third trimester were associated with RRs of 0.99 (0.88-1.11), 1.37 (1.04-1.81) and 3.03 (1.75-5.23) respectively. CONCLUSIONS: Exposure to high levels of PM2.5 from the second trimester onwards was most strongly associated with increased risk of pre-term birth and low birthweight, with a dose-response relationship. Our data demonstrates the need to account for both level and timing of exposure in analysis of PM2.5-associated birth outcomes.

Measurements of Gross α- and ß-Activities of Archived PM2.5 and PM10 Teflon Filter Samples.

The adverse effects of ambient particulate matter (PM) on human health have been well demonstrated, but the underlying properties responsible for its toxicity are still unclear. We hypothesized that particulate radioactivity, which is due to the attachment of radioactive nuclides on particle surfaces, may be responsible for part of PM toxicity. We measured the gross α- and ß-activities for daily PM2.5 and PM10 filters collected at the Harvard Supersite in downtown Boston from 2005 to 2006 and calculated the radioactivities at the time of air sampling retrospectively based on a previously established formula. We examined the relationship between different radioactivities and compared our measurements to those measured at the Boston EPA RadNet Station. The results showed that the majority of PM10 radioactivity is associated with that of PM2.5 samples for both α-activity (98%) and ß-activity (83%). A strong linear relationship was observed between the α- and ß-activities for both PM2.5 [slope = 0.47 (±0.03); p-value < 0.0001] and PM10 [slope = 0.46 (±0.09); p-value < 0.0001] samples. Measurements at the Harvard Supersite and at EPA RadNet sites are highly correlated for both α-activities [slope = 0.17 (±0.02), p-value < 0.0001] and ß-activities [slope = 0.30 (±0.05), p-value < 0.0001]. Additionally, we identified several significant predictors for PM2.5 α-activities. This novel method we developed to measure α- and ß-activities from archived filters will make it possible to assess the retrospective particle radioactivity exposure for future epidemiological studies.