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

Elevated airborne radioactivity downwind of a Colorado oil refinery

Airborne radioactivity from fossil fuel production systems is poorly characterized, but a recent study showed elevated ambient levels with proximity to oil and gas production wells. Here, we report year-long, high temporal resolution monitoring results of airborne alpha radioactivity from both radon gas and radon progeny attached to particulates immediately northeast of an oil refinery in Commerce City, Colorado, USA, in an environmental justice community of concern. Gas and particle-associated radioactivity contributed nearly evenly to the total alpha radioactivity. Total radioactivity levels of 30-40 Bq m-3 were 2-3 times higher than background levels (~10-15 Bq m-3) when winds were light and southwesterly, suggesting the refinery as the geographic origin. Furthermore, elevated airborne radioactivity tracked most closely with the light hydrocarbon and natural gas tracer ethane. Thus, the data imply natural gas as the radon emission carrier. Our findings are unique and suggest a need for further investigations of radon emissions from oil and gas infrastructure such as natural gas processing plants, compressor stations, petrochemical plants, and oil refineries that process oil and natural gas from unconventional production.Implications: Regulatory agencies currently do not mandate or conduct monitoring of radioactivity releases and public exposure from petroleum industry air emissions. This study reports elevated radioactivity from radon gas and nonvolatile radon decay products attached to particulate matter, at about 2-3 times above background levels in proximity to Colorado's largest oil refinery. Observations were within an environmental justice community of concern that experiences well above-average exposure to many other harmful atmospheric pollutants, suggesting potential adverse health effects from this cumulative exposure. Our findings offer actionable insights for policymakers, industry stakeholders, and affected communities alike.

Airborne particle radioactivity during desert dust days in Cyprus

Mediterranean countries are often affected by desert dust storms, which have significant effects on the environment and public health. We compared airborne particle radioactivity levels during desert dust and non-dust days in Cyprus. Gross α- and β-radioactivity from Total Suspended Particle (TSP) samples, collected at two urban routine monitoring stations in Limassol and Nicosia, were available for the period 2017-2020 and 2008-2020, respectively. Radionuclides 137Cs and 40K, from TSP samples, were also available from a semi-industrial monitoring station in Nicosia during 2008-2020. Information on desert dust presence, dust origin, particulate matter (PM) levels, and solar activity (KP index and solar sunspot numbers - SSN) were also obtained. We used linear regression models adjusting for seasonality and long-term trends, and solar activity to assess the effect of dust storms on TSP gross α- and β-, and 137Cs and 40K radioactivity levels. Gross α- and β-radioactivity, and 137Cs and 40K radioactivity levels were significantly higher on days with desert dust compared to days characterized with no influence of desert dust. Levels of gross α- and β-radioactivity during dust days were higher when dust originated from the Middle East deserts than from the Sahara Desert. The same trend was observed for the ratios 137Cs to 40K and 137Cs to PM10. Conversely, ratios of TSP gross α- and β-radioactivity to PM10 were significantly lower during desert dust days in comparison to days without dust influence. This study suggests that desert dust increase both TSP gross α- and β-radioactivity, as well as 137Cs and 40K radioactivity levels. Further studies should clarify the contribution of anthropogenic and other natural sources to the emission or transportation of particles radioactivity, to better mitigate future exposures.

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.

Direct Quantification of Attogram Levels of Strontium-90 in Microscale Biosamples Using Isotope Dilution-Thermal Ionization Mass Spectrometry Assisted by Quadrupole Energy Filtering

Although thermal ionization mass spectrometry (TIMS) has been employed for the high-precision analysis of isotope ratios, direct quantification of artificial mono-nuclide in the environment is difficult by even using isotope dilution (ID) due to the coexistence of the great magnitude of natural stable nuclides or isobars. In traditional TIMS and ID-TIMS, a sufficient amount of stable Sr doped on a filament is required to realize a stable and adequate ion-beam intensity (i.e., thermally ionized beams). However, the background noise (BGN) at m/z 90, detected by an electron multiplier, disturbs ⁹⁰Sr analysis at low concentration levels due to peak tailing of a significant ⁸⁸Sr ion beam dependent on the ⁸⁸Sr-doping amount. Here, TIMS assisted by quadruple energy filtering was successfully employed for the direct quantification of attogram levels of an artificial monoisotopic radionuclide strontium-90 (⁹⁰Sr) in microscale biosamples. Direct quantification was achieved by integrating the ID quantification of natural Sr and simultaneous ⁹⁰Sr/⁸⁶Sr isotope ratio analysis. Additionally, the measurement amount calculated by the combination of the ID and intercalibration was corrected for the net result amount of ⁹⁰Sr by subtracting dark noise and the detected amount derived from the survived ⁸⁸Sr, which are equivalent with the BGN intensity at m/z 90. Background correction revealed that the detection limits were in the range of 6.15 × 10^-2-3.90 × 10^-1 ag (0.31-1.95 μBq), depending on the concentration of natural Sr in a 1 μL sample, and the quantification of 0.98 ag (5.0 μBq) of ⁹⁰Sr in 0-300 mg/L of natural Sr was successful. This method could analyze small sample quantities (1 μL), and the quantitative results were verified against authorized radiometric analysis techniques. Furthermore, the amount of ⁹⁰Sr in actual teeth was successfully quantified. This method will be a powerful tool for measuring ⁹⁰Sr in the measurement of micro-samples, which are required to assess and understand the degree of internal radiation exposure.

Diagnosing the Model Bias in Simulating Daily Surface Ozone Variability Using a Machine Learning Method: The Effects of Dry Deposition and Cloud Optical Depth

Machine learning methods are increasingly used in air quality studies to predict air pollution levels, while few applied them to diagnose and improve the underlying mechanisms controlling air pollution represented in chemical transport models (CTMs). Here, we use the random forest (RF) method to diagnose high biases of surface daily maximum 8 h average (MDA8) ozone concentrations in the GEOS-Chem CTM evaluated against measurements from the nationwide monitoring network in summer 2018 over China. The feature importance results show that cloud optical depth (COD), relative humidity, and precipitation are the top three factors affecting CTM high biases. Such results indicate that the high ozone biases in summer over China mainly occur on wet/cloudy days (∼40% biased high), while biases on dry/clear days are small (within 5%). We link the important features with model parameterizations and variables, identifying model underestimates in the dry deposition velocity and COD on wet/cloudy days. By accounting for the enhanced dry deposition on wet plant cuticles and using satellite observation constrained COD, we find that CTM high ozone biases can be halved with an improved agreement in the temporal variability, highlighting the effects of dry deposition and COD on ozone, as suggested by the RF outcomes.

Synergistic Effects of Particle Radioactivity (Gross β Activity) and Particulate Matter ≤2.5 μm Aerodynamic Diameter on Cardiovascular Disease Mortality

Background Although the effects of fine particulate matter (particulate matter ≤2.5 μm aerodynamic diameter [PM2.5]) on cardiovascular disease (CVD) morbidity and mortality are well established, little is known about the CVD health effects of particle radioactivity. In addition, there are still questions about which of the PM2.5 physical, chemical, or biological properties are mostly responsible for its toxicity. Methods and Results We investigated the association between particle radioactivity, measured as gross β activity from highly resolved spatiotemporal predictions, and mortality for CVD, myocardial infarction, stroke, and all-cause nonaccidental mortality in Massachusetts (2001-2015). Within both difference-in-differences model and generalized linear mixed model frameworks, we fit both single-exposure and 2-exposure models adjusting for PM2.5 and examined the interaction between PM2.5 and gross β activity. We found significant associations between gross β activity and PM2.5 and each mortality cause. Using difference-in-differences and adjusting for PM2.5, we found the highest associations with myocardial infarction (rate ratio, 1.16 [95% CI, 1.08-1.24]) and stroke (rate ratio, 1.11 [95% CI, 1.04-1.18]) for an interquartile range increase (0.055 millibecquerels per cubic meter) in gross β activity. We found a significant positive interaction between PM2.5 and gross β activity, with higher associations between PM2.5 and mortality at a higher level of gross β activity. We also observed that the associations varied across age groups. The results were comparable between the 2 statistical methods also with and without adjusting for PM2.5. Conclusions This is the first study that, using highly spatiotemporal predictions of gross β-activity, provides evidence that particle radioactivity increases CVD mortality and enhances PM2.5 CVD mortality. Therefore, particle radioactivity can be an important property of PM2.5 that must be further investigated. Addressing this important question can lead to cost-effective air-quality regulations.

Ambient PM gross β-activity and glucose levels during pregnancy

BACKGROUND

Exposure to ionizing radiation has been associated with insulin resistance and type 2 diabetes. In light of recent work showing an association between ambient particulate matter (PM) gross β-activity and gestational diabetes mellitus (GDM) among pregnant women, we examined pregnancy glucose levels in relation to PM gross β-activity to better understand this pathway.

METHODS

Our study included 103 participants receiving prenatal care at Beth Israel Deaconess Medical Center in Boston, MA. PM gross β-activity was obtained from US Environmental Protection Agency's RadNet program monitors, and blood glucose levels were obtained from the non-fasting glucose challenge test performed clinically as the first step of the 2-step GDM screening test. For each exposure window we examined (i.e., moving average same-day, one-week, first-trimester, and second-trimester PM gross β-activity), we fitted generalized additive models and adjusted for clinical characteristics, socio-demographic factors, temporal variables, and PM with an aerodynamic diameter ≤ 2.5 μm (PM_2.5). Subgroup analyses by maternal age and by body mass index were also conducted.

RESULTS

An interquartile range increase in average PM gross β-activity during the second trimester of pregnancy was associated with an increase of 17.5 (95% CI: 0.8, 34.3) mg/dL in glucose concentration. Associations were stronger among younger and overweight/obese participants. Our findings also suggest that the highest compared to the lowest quartile of one-week exposure was associated with 17.0 (95% CI: - 4.0, 38.0) mg/dL higher glucose levels. No associations of glucose were observed with PM gross β-activity during same-day and first-trimester exposure windows. PM_2.5 was not associated with glucose levels during any exposure window in our data.

CONCLUSIONS

Exposure to higher levels of ambient PM gross β-activity was associated with higher blood glucose levels in pregnant patients, with implications for how this novel environmental factor could impact pregnancy health.

Measurement of the gross alpha activity of the fine fractions of road dust and near-roadway ambient particle matter

Traffic-related air pollution, including direct exhaust emissions and road dust (RD), impacts individuals living near busy roads. We recently conducted a study to investigate the sources and composition of tailpipe and non-tailpipe traffic emissions, where we collected and analyzed samples of ambient air fine particulate matter (PM_2.5) and fine RD (RD_2.5) at different distances from major roadways. We analyzed a subset of the samples, including those collected at the roadside and local background, for their alpha activity level. Subsequently, we investigated whether there is a distance-related decay in the alpha activity in RD_2.5 or PM_2.5 similar to those observed for traffic-related species in PM_2.5 and RD_2.5. We found that the alpha activity of ambient air PM_2.5 (Bq/mg) was more than an order of magnitude higher than the activity level of the corresponding RD_2.5 sample, suggesting that PM_2.5 may be more toxic than RD_2.5. Using mixed-effects regression models, we found that ambient PM_2.5 alpha activity was significantly higher during the cold months than during warm months, and that the background was higher than the roadside (though not significantly). In contrast, the RD_2.5 alpha activity was significantly higher at the background site compared to the roadside but was not significantly affected by season. In addition to sampling position, both Zn and elemental carbon (EC) were significant predictors of RD_2.5 alpha activity. In addition, the roadside RD_2.5 activity levels were found to be higher at highways as compared to secondary roads. While traffic-related emissions do not appear to be significant sources of either ambient PM_2.5 or RD_2.5 alpha activity, the RD_2.5 results suggest that traffic-related particles may contribute to RD_2.5 alpha-activity. Implications: Many studies have reported the effects of traffic-related particulate matter (PM) on human health, and there is growing interest in the health effects of exposure to environmental PM alpha activity. This is the first study to report on the alpha activity of road dust (RD) or near-roadway ambient PM. We found that the alpha activity of ambient PM is twenty times higher than RD, suggesting that ambient PM may be more toxic. In PM and RD, the alpha activities were higher at background sites than at the roadside, indicating that traffic-related emissions are not a significant source of particulate radioactivity.