Associations between gestational residential radon exposure and term low birthweight in Connecticut, USA.

BACKGROUND: Studies suggest biologic mechanisms for gestational exposure to radiation and impaired fetal development. We explored associations between gestational radon exposure and term low birthweight, for which evidence is limited. METHODS: We examined data for 68,159 singleton full-term births in Connecticut, USA, 2016-2018. Using a radon spatiotemporal model, we estimated ZIP code-level basement and ground-level exposures during pregnancy and trimesters for each participant's address at birth or delivery. We used logistic regression models, including confounders, to estimate odds ratios (ORs) for term low birth weight in four exposure quartiles (Q1 to Q4) with the lowest exposure group (Q1) as the reference. RESULTS: Exposure levels to basement radon throughout pregnancy (0.27-3.02 pCi/L) were below the guideline level set by the US Environmental Protection Agency (4 pCi/L). The ORs for term low birth weight in the second-highest (Q3; 1.01-1.33 pCi/L) exposure group compared to the reference (<0.79 pCi/L) group for basement radon during the first trimester was 1.22 (95% confidence interval [CI]: 1.02, 1.45). The OR in the highest (Q4; 1.34-4.43 pCi/L) quartile group compared to the reference group during the first trimester was 1.26 (95% CI: 1.05, 1.50). Risks from basement radon were higher for participants with lower income, lower maternal education levels, or living in urban regions. CONCLUSION: This study found increased term low birth weight risks for increases in basement radon. Results have implications for infants' health for exposure to radon at levels below the current national guideline for indoor radon concentrations and building remediations.

High precision polishing of aluminum alloy mirrors through a combination of magnetorheological finishing and chemical mechanical polishing.

After the aluminum alloy mirror machined by single point diamond turning (SPDT), the residual tool marks and surface accuracy of the aluminum alloy mirror cannot meet the requirements of visible or ultraviolet light system. In this study, a processing method combining magnetorheological finishing (MRF) and chemical mechanical polishing (CMP) is proposed to realize the polishing of aluminum alloy mirrors with high efficiency, high precision and high-quality. Firstly, the properties and composition of passivation layer after MRF were analyzed and the polishing performance of acidic, neutral and alkaline alumina polishing fluid on passivation layer were investigated based on the computer numerical control (CNC) polishing equipment. Based on the experimental results, a new acidic nano-silica polishing fluid which is suitable for the efficient and high-quality removal of passivation layers on aluminum alloy surfaces was developed. Finally, a combined approach of MRF-CMP was used to the directly polishing of a rapidly solidified aluminum mirror (RSA-6061) with a diameter of 100 mm after SPDT. With two iterative of MRF-CMP polishing in 220 minutes, the surface accuracy of the aluminum alloy mirror was improved from 0.1λ (λ=632.8 nm) to 0.024λ, and the surface roughness (Ra) decreased from 3.6 nm to 1.38 nm. The experiment results manifest that high precision, and high-quality aluminum alloy mirror can be achieved by MRF-CMP method with the new developed acid nano-silica polishing fluid and suitable MR polishing fluid. The research results will provide a new strategy for ultra-precision direct polishing of aluminum alloy mirrors and will also give the important technical support for the extensive use of aluminum alloy mirror in visible light and ultraviolet optical systems.

Tool mark prediction on the surface of large-aperture mirrors via magnetorheological finishing.

The magnetorheological finishing (MRF) of surfaces often results in tool mark errors. A prediction model can effectively guide subsequent processing, necessitating thorough research. To address this issue, this paper introduces an enhanced continuous tool influence function method. This method involves sub dwell time convolution with varying tool influence functions, enabling tool mark prediction. Numerical simulations demonstrate the proposed method's effectiveness, while the data size is estimated to confirm its economic properties. Subsequently, a MRF experiment was conducted, affirming the practicability through power spectral density evaluation. A fast algorithm is given to guide tool mark predictions on large-aperture mirrors fabrication engineering subjected to sub-aperture polishing.

Evolution mechanism of scratch removal based on the implementation of magnetorheological finishing.

Scratches on optical components induce laser damage and limit the increase in laser power. Magnetorheological finishing (MRF) is a highly deterministic optical manufacturing technology that can improve the surface roughness of optical components. Although MRF has exhibited significant potential for reducing subsurface damage and removing scratches, the principle and mechanism behind the scratch removal are not sufficiently understood. In this study, the theory of fluid mechanics is used to analyze the pressure, velocity, and particle trajectory distribution near a scratch. A physical model was developed for the differential removal of scratches at the bottom and surface of the optical components. The morphological evolution of the scratch was predicted during removal, and detailed experiments were performed to verify the effectiveness of the proposed model. The results indicate that scratches expand laterally rather than being completely removed. Furthermore, scratch removal efficiency is greater when the removal direction is perpendicular to the scratch rather than being parallel. This study offers an intrinsic perspective for a comprehensive understanding of the MRF technique used for scratch removal, which can be beneficial for removing scratches from aspherical optical systems.

Short-Term Increases in NO2 and O3 Concentrations during Pregnancy and Stillbirth Risk in the U.S.: A Time-Stratified Case-Crossover Study.

Associations between gaseous pollutant exposure and stillbirth have focused on exposures averaged over trimesters or gestation. We investigated the association between short-term increases in nitrogen dioxide (NO2) and ozone (O3) concentrations and stillbirth risk among a national sample of 116 788 Medicaid enrollees from 2000 to 2014. A time-stratified case-crossover design was used to estimate distributed (lag 0-lag 6) and cumulative lag effects, which were adjusted for PM2.5 concentration and temperature. Effect modification by race/ethnicity and proximity to hydraulic fracturing (fracking) wells was assessed. Short-term increases in the NO2 and O3 concentrations were not associated with stillbirth in the overall sample. Among American Indian individuals (n = 1694), a 10 ppb increase in NO2 concentrations was associated with increased stillbirth odds at lag 0 (5.66%, 95%CI: [0.57%, 11.01%], p = 0.03) and lag 1 (4.08%, 95%CI: [0.22%, 8.09%], p = 0.04) but not lag 0-6 (7.12%, 95%CI: [-9.83%, 27.27%], p = 0.43). Among participants living in zip codes within 15 km of active fracking wells (n = 9486), a 10 ppb increase in NO2 concentration was associated with increased stillbirth odds in single-day lags (2.42%, 95%CI: [0.37%, 4.52%], p = 0.02 for lag 0 and 1.83%, 95%CI: [0.25%, 3.43%], p = 0.03 for lag 1) but not the cumulative lag (lag 0-6) (4.62%, 95%CI: [-2.75%, 12.55%], p = 0.22). Odds ratios were close to the null in zip codes distant from fracking wells. Future studies should investigate the role of air pollutants emitted from fracking and potential racial disparities in the relationship between short-term increases in NO2 concentrations and stillbirth.

Dwell time for optical fabrication using the modified discrete convolution matrix method.

More accurate dwell time calculation methods are necessary to achieve superior error convergence in producing optically critical components. Although the discrete convolution matrix method finds widespread application, it still has approximate errors in the non-uniform discrete form of tool paths. To address this issue, this paper introduced a modified matrix elements method and presented the general Voronoi polygon area weight calculation forms under different tool path discretization forms. The mechanism is explained through analysis and the validity is verified by numerical simulation. The modified method significantly improved uniformity distribution and accuracy in computation of surface residuals. This improvement holds promise as a guiding principle for the fabrication of ultra-precision optical components.

Single-atom iron boosts electrochemiluminescence for ultrasensitive carcinoembryonic antigen detection.

A simple and ultrasensitive sandwich-type electrochemiluminescence (ECL) immunosensor has been developed using porous three-dimensional gold nanoparticles (Au NPs) iron(Fe)-zinc(Zn) metal-organic frameworks (Au NPs-FeZn-MOFs@luminol) as high-efficiency ECL signal probes with Fe single-atom catalysts (SACs) (Fe-N-C SACs) as potentially advanced coreaction accelerators and dissolved oxygen as a coreaction agent to realize an H2O2-free amplification method for detecting carcinoembryonic antigen (CEA). The cathodic ECL of luminol, which was usually negligible, increased first. Because the Fe-N-C SACs exhibited an outstanding catalytic performance and a unique electronic structure, different reactive oxygen species (ROS) were generated via the oxygen reduction reaction. ROS oxidized the luminol anions to luminol anion radicals, preventing the time-consuming luminol electrochemical oxidation. Furthermore, the luminol anion radicals generated in situ reacted with ROS to produce potent cathodic ECL emissions. The immunosensor exhibited favorable analytical accuracy (detection range: 0.1 pg mL-1 - 80 ng mL-1), and its detection limit for serum samples was 0.031 pg mL-1 (S/N = 3). Consequently, the proposed strategy offers a new approach for early screening of CEA.

Elastoplastic contact model of pitch-based rough surface and its polishing characteristics.

Fused silica glass is widely used in optical systems, including astronomical telescopes, laser systems, optical communications, and the semiconductor industry. At the same time, the surface quality of the fused silica directly determines the performance and precision of the system. In order to analyze the microscopic surface interaction based on the basis of tribology, a roughness contact model of pitch and fused silica glass surfaces was established. Analyze the performance parameters of contact materials, surface roughness, and the relationship between load and contact area. Pitch materials with a higher plasticity index have a larger elastoplastic contact area with the fused silica surface during the polishing process. The experimental results demonstrate that the surface quality of the polished fused silica improves as the plasticity index of the pitch material increases. At the same time, judging from the PSD curve results, the polished surface of the No. 55 pitch on the spatial-frequency band curve (100-101/mm) is significantly lower than the other two brands of pitch. Additionally, the Ra value of the workpiece surface roughness reaches 0.091 nm. The results of this study provide important theoretical guidance for achieving full-diameter, full-frequency ultra-smooth polishing of large-diameter complex curved surfaces.

Accurately predicting the tool influence function to achieve high-precision magnetorheological finishing using robots.

Industrial robots with six degrees-of-freedom have significant potential for use in optical manufacturing owing to their flexibility, low cost, and high space utilisation. However, the low trajectory accuracy of robots affects the manufacturing accuracy of optical components when combined with magnetorheological finishing (MRF). Moreover, general robot trajectory-error compensation methods cannot compensate for the running errors of large robots with high precision. To address this problem, a three-dimensional (3D) tool influence function (TIF) model based on inverse distance interpolation is developed in this study to accurately predict the TIF of different polishing gaps. A high-precision robot-MRF polishing strategy based on variable TIFs and surface shape accuracy of polished optics is proposed to achieve high-precision manufacturing without compensating for trajectory errors. Subsequently, the accuracy of a ϕ420 mm fused silica mirror is experimentally verified to be from 0.11 λ RMS to 0.013 λ RMS. This validates that the robot-MRF can achieve high-precision polishing without compensating for trajectory errors. Furthermore, the proposed model will promote the applications of industrial robots in optical manufacturing and will serve as a reference in the field of intelligent optical manufacturing.

Predicting Monthly Community-Level Radon Concentrations with Spatial Random Forest in the Northeastern and Midwestern United States.

In 1987, the United States Environmental Protection Agency recommended installing a mitigation system when the indoor concentration of radon, a well-known carcinogenic radioactive gas, is at or above 148 Bq/m3. In response, tens of millions of short-term radon measurements have been conducted in residential buildings over the past three decades either for disclosure or to initially evaluate the need for mitigation. These measurements, however, are currently underutilized to assess population radon exposure in epidemiological studies. Based on two relatively small radon surveys, Lawrence Berkeley National Laboratory developed a state-of-the-art national radon model. However, this model only provides coarse and invariant radon estimations, which limits the ability of epidemiological studies to accurately investigate the health effects of radon, particularly the effects of acute exposure. This study involved obtaining over 2.8 million historical short-term radon measurements from independent laboratories. With the use of these measurements, an innovative spatial random forest (SRF) model was developed based on geological, architectural, socioeconomical, and meteorological predictors. The model was used to estimate monthly community-level radon concentrations for ZIP Code Tabulation Areas (ZCTAs) in the northeastern and midwestern regions of the United States from 2001 to 2020. Via cross-validation, we found that our ZCTA-level predictions were highly correlated with observations. The prediction errors declined quickly as the number of radon measurements in a ZCTA increased. When ≥15 measurements existed, the mean absolute error was 24.6 Bq/m3, or 26.5% of the observed concentrations (R2 = 0.70). Our study demonstrates the potential of the large amount of historical short-term radon measurements that have been obtained to accurately estimate longitudinal ZCTA-level radon exposures at unprecedented levels of resolutions and accuracy.

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 (

Air Pollutants and Asthma Hospitalization in the Medicaid Population.

Rationale: Risk of asthma hospitalization and its disparities associated with air pollutant exposures are less clear within socioeconomically disadvantaged populations, particularly at low degrees of exposure. Objectives: To assess effects of short-term exposures to fine particulate matter (particulate matter with an aerodynamic diameter of ⩽2.5 µm [PM2.5]), warm-season ozone (O3), and nitrogen dioxide (NO2) on risk of asthma hospitalization among national Medicaid beneficiaries, the most disadvantaged population in the United States, and to test whether any subpopulations were at higher risk. Methods: We constructed a time-stratified case-crossover dataset among 1,627,002 hospitalizations during 2000-2012 and estimated risk of asthma hospitalization associated with short-term PM2.5, O3, and NO2 exposures. We then restricted the analysis to hospitalizations with degrees of exposure below increasingly stringent thresholds. Furthermore, we tested effect modifications by individual- and community-level characteristics. Measurements and Main Results: Each 1-µg/m3 increase in PM2.5, 1-ppb increase in O3, and 1-ppb increase in NO2 was associated with 0.31% (95% confidence interval [CI], 0.24-0.37%), 0.10% (95% CI, 0.05 - 0.15%), and 0.28% (95% CI, 0.24 - 0.32%) increase in risk of asthma hospitalization, respectively. Low-level PM2.5 and NO2 exposures were associated with higher risk. Furthermore, beneficiaries with only one asthma hospitalization during the study period or in communities with lower population density, higher average body mass index, longer distance to the nearest hospital, or greater neighborhood deprivation experienced higher risk. Conclusions: Short-term air pollutant exposures increased risk of asthma hospitalization among Medicaid beneficiaries, even at concentrations well below national standards. The subgroup differences suggested individual and contextual factors contributed to asthma disparities under effects of air pollutant exposures.

High-precision magnetorheological finishing based on robot by measuring and compensating trajectory error.

The 6-DOF industrial robot has wide application prospects in the field of optical manufacturing because of its high degrees of freedom, low cost, and high space utilisation. However, the low trajectory accuracy of robots will affect the manufacturing accuracy of optical components when the robots and magnetorheological finishing (MRF) are combined. In this study, aiming at the problem of the diversity of trajectory error sources of robot-MRF, a continuous high-precision spatial dynamic trajectory error measurement system was established to measure the trajectory error accurately, and a step-by-step and multistage iterations trajectory error compensation method based on spatial similarity was established to obtain a high-precision trajectory. The experimental results show that compared with the common model calibration method and general non-model calibration method, this trajectory error compensation method can achieve accurate compensation of the trajectory error of the robot-MRF, and the trajectory accuracy of the Z-axis is improved from PV > 0.2 mm to PV < 0.1 mm. Furthermore, the finishing accuracy of the plane mirror from 0.066λ to 0.016λ RMS and the finishing accuracy of the spherical mirror from 0.184λ RMS to 0.013λ RMS using the compensated robot-MRF prove that the robot-MRF has the ability of high-precision polishing. This promotes the application of industrial robots in the field of optical manufacturing and lays the foundation for intelligent optical manufacturing.

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.

Assessing mortality risk attributable to high ambient temperatures in Ahmedabad, 1987 to 2017.

BACKGROUND: Studies on high temperatures and mortality have not focused on underdeveloped tropical regions and have reported the associations of different temperature metrics without conducting model selection. METHODS: We collected daily mortality and meteorological data including ambient temperatures and humidity in Ahmedabad during summer, 1987-2017. We proposed two cross-validation (CV) approaches to compare semiparametric quasi-Poisson models with different temperature metrics and heat wave definitions. Using the fittest model, we estimated heat-mortality associations among general population and subpopulations. We also conducted separate analyses for 1987-2002 and 2003-2017 to evaluate temporal heterogeneity. FINDINGS: The model with maximum and minimum temperatures and without heat wave indicator gave the best performance. With this model, we found a substantial and significant increase in mortality rate starting from maximum temperature at 42 °C and from minimum temperature at 28 °C: 1 °C increase in maximum and minimum temperatures at lag 0 were associated with 9.56% (95% confidence interval [CI]: 6.64%, 12.56%) and 9.82% (95% CI: 6.33%, 13.42%) increase in mortality risk, respectively. People aged ≥65 years and lived in South residential zone where most slums were located, were more vulnerable. We observed flatter increases in mortality risk associated with high temperatures comparing the period of 2003-2017 to 1987-2002. INTERPRETATION: The analyses provided better understanding of the relationship of high temperatures with mortality in underdeveloped tropical regions and important implications in developing heat warning system for local government. The proposed CV approaches will benefit future scientific work.

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.

Assessing additive effects of air pollutants on mortality rate in Massachusetts.

BACKGROUND: We previously found additive effects of long- and short-term exposures to fine particulate matter (PM2.5), ozone (O3), and nitrogen dioxide (NO2) on all-cause mortality rate using a generalized propensity score (GPS) adjustment approach. The study addressed an important question of how many early deaths were caused by each exposure. However, the study was computationally expensive, did not capture possible interactions and high-order nonlinearities, and omitted potential confounders. METHODS: We proposed two new methods and reconducted the analysis using the same cohort of Medicare beneficiaries in Massachusetts during 2000-2012, which consisted of 1.5 million individuals with 3.8 billion person-days of follow-up. The first method, weighted least squares (WLS), leveraged large volume of data by aggregating person-days, which gave equivalent results to the linear probability model (LPM) method in the previous analysis but significantly reduced computational burden. The second method, m-out-of-n random forests (moonRF), implemented scaling random forests that captured all possible interactions and nonlinearities in the GPS model. To minimize confounding bias, we additionally controlled relative humidity and health care utilizations that were not included previously. Further, we performed low-level analysis by restricting to person-days with exposure levels below increasingly stringent thresholds. RESULTS: We found consistent results between LPM/WLS and moonRF: all exposures were positively associated with mortality rate, even at low levels. For long-term PM2.5 and O3, the effect estimates became larger at lower levels. Long-term exposure to PM2.5 posed the highest risk: 1 µg/m3 increase in long-term PM2.5 was associated with 1053 (95% confidence interval [CI]: 984, 1122; based on LPM/WLS methods) or 1058 (95% CI: 988, 1127; based on moonRF method) early deaths each year among the Medicare population in Massachusetts. CONCLUSIONS: This study provides more rigorous causal evidence between PM2.5, O3, and NO2 exposures and mortality, even at low levels. The largest effect estimate for long-term PM2.5 suggests that reducing PM2.5 could gain the most substantial benefits. The consistency between LPM/WLS and moonRF suggests that there were not many interactions and high-order nonlinearities. In the big data context, the proposed methods will be useful for future scientific work in estimating causality on an additive scale.

Enhancing the activity and thermal stability of a phthalate-degrading hydrolase by random mutagenesis.

Our previous work has reported that EstJ6 was a phthalate-degrading hydrolase. In the study, a random mutant library was constructed by two rounds of error-prone PCR, three mutants (ET1.1, ET2.1, and ET2.2) with enhanced hydrolytic activity against dibutyl phthalate (DBP) were obtained. The best mutant ET2.2, accumulated three amino acid substitutions (Thr91Met, Ala67Val, and Val249Ile) and exhibited 2.8-fold increase enzyme activity and 2.3-fold higher expression level. Meanwhile, compared with EstJ6, ET2.2 showed over 50% improvement in thermostability (at 50 °C for 1 h) and 1.2-fold increase in 50% methanol tolerance. Kinetic parameters analysis revealed that the Km value for ET2.2 decreased by 60% and the kcat/Km value increased by 166%. The molecular docking indicated that the shortening of hydrogen bond between Ser146-OH and DBP-CO, which may led to an increase in enzyme activity and catalytic efficiency, the enhancement of hydrophobicity of hydrophobic pocket was related to the improvement of organic solvents tolerance, and three hydrophobic amino acid substitutions Thr91Met, Ala67Val, and Val249Ile facilitated to improve the thermal stability and organic solvents tolerance. These results confirmed that random mutagenesis was an effective tool for improving enzyme properties and lay a foundation for practical applications of phthalate-degrading hydrolase in biotechnology and industrial fields.

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.

Exposure to Particle Beta Radiation in Greater Massachusetts and Factors Influencing Its Spatial and Temporal Variability.

Particle radioactivity is a property of airborne particles caused by the presence of naturally occurring or anthropogenic radionuclides. Recent studies have found associations between particle radioactivity and adverse health outcomes, including changes in blood pressure and lung function. However, the spatiotemporal distribution of particle radioactivity and factors influencing its variability have not been extensively studied. We address these knowledge gaps using measurements of gross beta activity, collected at seven Environmental Protection Agency (EPA) RadNet monitors located in and around Massachusetts. We apply back-trajectory analysis to identify prevailing air mass trajectories and find that these trajectories strongly influence seasonal trends in beta activity. We also evaluate the effects of different meteorological predictors on daily beta activity concentrations using a mixed-effect model. Important predictors of beta activity include air mass trajectories, temperature, and relative humidity. Finally, we create a series of random forest models to impute missing beta activity concentrations at each RadNet monitor for use in future health studies. This is the first study to analyze spatiotemporal trends in particle radioactivity using measurements from the EPA RadNet system.