Background: On April 11th, 2023, the My Way Trading (MWT) recycling facility in Richmond, Indiana caught fire, mandating the evacuation of local residents and necessitating the U.S. Environmental Protection Agency (EPA) to conduct air monitoring. The EPA detected elevated levels of plastic combustion-related air pollutants, including hydrogen cyanide and benzene.
Objective: We aimed to identify these and other volatile organic compounds (VOCs) present as well as to identify the potential hazard of each compound for various human health effects.
Methods: To identify the VOCs, we conducted air monitoring at sites within and bordering the evacuation zone using proton transfer reaction mass spectrometry (PTR-MS) and non-targeted analysis (NTA). To facilitate risk assessment of the emitted VOCs, we used the EPA Hazard Comparison Dashboard.
Results: We identified 46 VOCs, within and outside the evacuation zone, with average detection levels above local background levels measured in Middletown, OH. Levels of hydrogen cyanide and 4 other VOCs were at least 1.8-fold higher near the incidence site in comparison to background levels and displayed unique temporal and spatial patterns. The 46 VOCs identified had the highest hazardous potential for eye and skin irritation, with approximately 45% and 39%, respectively, of the VOCs classified as high and very high hazards for these endpoints. Notably, all detected VOC levels were below the hazard thresholds set for single VOC exposures; however, hazard thresholds for exposure to VOC mixtures are currently unclear.
Impact: This study serves as a proof-of-concept that PTR-MS coupled with NTA can facilitate rapid identification and hazard assessment of VOCs emitted following anthropogenic disasters. Furthermore, it demonstrates that this approach may augment future disaster responses to quantify additional VOCs present in complex combustion mixtures.
Background: When experimentally determined dislodgeable foliar residue (DFR) values are not available, regulatory agencies use conservative default DFR values as a first-tier approach to assess post-application dermal exposures to plant protection products (PPPs). These default values are based on a limited set of field studies, are very conservative, and potentially overestimate exposures from DFRs.
Objective: Use Random Forest to develop classification and regression-type ensemble models to predict DFR values after last application (DFR0) by considering experimentally-based variability due to differences in physical and chemical properties of PPPs, agronomic practices, crop type, and climatic conditions.
Methods: Random Forest algorithm was used to develop in-silico ensemble DFR0 prediction models using more than 100 DFR studies from Corteva AgriscienceTM. Several variables related to the active ingredient (a.i.) that was applied, crop, and climate conditions at the time of last application were considered as model parameters.
Results: The proposed ensemble models demonstrated 98% prediction accuracy that if a DFR0 is predicted to be less than the European Food Safety Authority (EFSA) default DFR0 value of 3 µg/cm2/kg a.i./ha, it is highly indicative that the measured DFR value will be less than the default if the study is conducted. If a value is predicted to be larger than or equal to the EFSA default, the model has an 83% prediction accuracy.
Impact statement: This manuscript is expected to have significant impact globally as it provides: A framework for incorporating in silico DFR data into worker exposure assessment, A roadmap for a tiered approach for conducting re-entry exposure assessment, and A proof of concept for using existing DFR data to provide a read-across framework that can easily be harmonized across all regulatory agencies to provide more robust assessments for PPP exposures.
Background: Characterizing the spatial distribution of PM2.5 species concentrations is challenging due to the geographic sparsity of the stationary monitoring network. Recent advances have enabled valid estimation of PM2.5 species concentrations using satellite remote sensing data for use in epidemiologic studies.
Objective: In this study, we used satellite-based estimates of ambient PM2.5 species concentrations to estimate associations with birth weight and preterm birth in California.
Methods: Daily 24 h averaged ground-level PM2.5 species concentrations of organic carbon, elemental carbon, nitrate, and sulfate were estimated during 2005-2014 in California at 1 km resolution. Birth records were linked to ambient pollutant exposures based on maternal residential zip code. Linear regression and Cox regression were conducted to estimate the effect of 1 µg/m3 increases in PM2.5 species concentrations on birth weight and preterm birth.
Results: Analyses included 4.7 million live singleton births having a median 28 days with exposure measurements per pregnancy. In single pollutant models, the observed changes in mean birth weight (per 1 µg/m3 increase in speciated PM2.5 concentrations) were: organic carbon -3.12 g (CI: -4.71, -1.52), elemental carbon -14.20 g (CI: -18.76, -9.63), nitrate -5.51 g (CI: -6.79, -4.23), and sulfate 9.26 g (CI: 7.03, 11.49). Results from multipollutant models were less precise due to high correlation between pollutants. Associations with preterm birth were null, save for a negative association between sulfate and preterm birth (Hazard Ratio per 1 µg/m3 increase: 0.973 CI: 0.958, 0.987).
Geographical and meteorological factors have been reported to influence the prevalence of echinococcosis, but there’s a lack of indicator system and model.
To provide further insight into the impact of geographical and meteorological factors on AE prevalence and establish a theoretical basis for prevention and control.
Principal component and regression analysis were used to screen and establish a three-level indicator system. Relative weights were examined to determine the impact of each indicator, and five mathematical models were compared to identify the best predictive model for AE epidemic levels.
By analyzing the data downloaded from the China Meteorological Data Service Center and Geospatial Data Cloud, we established the KCBIS, including 50 basic indicators which could be directly obtained online, 15 characteristic indicators which were linear combination of the basic indicators and showed a linear relationship with AE epidemic, and 8 key indicators which were characteristic indicators with a clearer relationships and fewer mixed effects. The relative weight analysis revealed that monthly precipitation, monthly cold days, the difference between negative and positive temperature anomalies, basic air temperature conditions, altitude, the difference between positive and negative atmospheric pressure anomalies, monthy extremely hot days, and monthly fresh breeze days were correlated with the natural logarithm of AE prevalence, with sequential decreases in their relative weights. The multinomial logistic regression model was the best predictor at epidemic levels 1, 3, 5, and 6, whereas the CART model was the best predictor at epidemic levels 2, 4, and 5.
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