Exposure and health risk assessment of volatile organic compounds among drivers and passengers in long-distance buses.

Exposure to volatile organic compounds (VOCs) such as benzene, toluene, ethylbenzene, xylene, and formaldehyde from long-distance buses has been reported to adversely affect human health. This study investigates the concentrations of these five VOCs and evaluates their health risks to drivers and passengers on board. Ten trips from Taipei to Taichung were performed during the warm and cold seasons of 2021-2022. Two locations inside the bus were established to collect air samples by a 6-liter canister for drivers and passengers. Exposure concentrations of benzene, toluene, ethylbenzene, and xylene were analyzed via gas chromatography with a flame ionization detector and the formaldehyde concentration was monitored using a formaldehyde meter. Subsequently, a Monte Carlo simulation was conducted to evaluate the carcinogenic and non-carcinogenic risks of the five VOCs. Formaldehyde emerged as the highest detected compound (9.06 ± 3.77 µg/m3), followed by toluene (median: 6.11 µg/m3; range: 3.86-14.69 µg/m3). In particular, formaldehyde was identified to have the significantly higher concentration during non-rush hours (10.67 ± 3.21 µg/m3) than that during rush hours (7.45 ± 3.41 µg/m3) and during the warm season (10.71 ± 2.97 µg/m3) compared with that during the cold season (7.41 ± 4.26 µg/m3). Regarding non-carcinogenic risks to drivers and passengers, the chronic hazard indices for these five VOCs were under 1 to indicate an acceptable risk. In terms of carcinogenic risk, the median risks of benzene and formaldehyde for drivers were 2.88 × 10-6 (95% confidence interval [CI]: 2.11 × 10-6 - 5.13 × 10-6) and 1.91 × 10-6 (95% CI: 4.54 × 10-7 - 3.44 × 10-6), respectively. In contrast, the median carcinogenic risks of benzene and formaldehyde for passengers were less than 1 × 10-6 to present an acceptable risk. This study suggests that benzene and formaldehyde may present carcinogenic risks for drivers. Moreover, the non-carcinogenic risk for drivers and passengers is deemed acceptable. We recommended that the ventilation frequency be increased to mitigate exposure to VOCs in long-distance buses.

Application of artificial intelligence algorithms and low-cost sensors to estimate respirable dust in the workplace.

The Internet of Things (IoT) and low-cost sensor technology have become common tools for environmental exposure monitoring; however, their application in measuring respirable dust (RD) in the workplace remains limited. This study aimed to develop a predictive model for RD using artificial intelligence (AI) algorithms and low-cost sensors and subsequently assess its validity using a standard sampling approach. Various low-cost sensors were combined into an RD sensor module and mounted on a portable aerosol monitor (GRIMM 11-D) for two weeks. AI algorithms were used to capture data per minute over 14 days to establish predictive RD models. The best-fitting model was validated using an aluminum cyclone equipped with an air pump and polytetrafluoroethylene filters to sample the 8-hour RD for 5 days at an aircraft manufacturing company. This module was continuously monitored for two weeks to evaluate its stability. The RD concentration measured by GRIMM 11-D in a general outdoor environment over two weeks was 28.1 ± 16.1 µg/m3 (range: 2.4-85.3 µg/m3). Among the various established models, random forest regression was observed to have the best prediction capacity (R2 = 0.97 and root mean square error = 2.82 µg/m3) in comparison to the other 19 methods. Field-based validation revealed that the predicted RD concentration (35.9 ± 4.1 µg/m3, range: 32.7-42.9 µg/m3) closely approximated the results obtained by the traditional method (38.1 ± 8.9 µg/m3, range: 28.1-52.5 µg/m3), and a strong positive Spearman correlation was observed between the two (rs = 0.70). The average bias was -2.2 µg/m3 and the precision was 5.8 µg/m3, resulting in an accuracy of 6.2 µg/m3 (94.2 %). Data completeness was 99.7 % during the continuous two-week monitoring period. The developed sensor module of RD exhibited excellent predictive performance and good data stability that can be applied to exposure assessments in occupational epidemiological studies.

Fast and Facile Synthesis of 4-Nitrophenyl 2-Azidoethylcarbamate Derivatives from N-Fmoc-Protected α-Amino Acids as Activated Building Blocks for Urea Moiety-Containing Compound Library.

A fast and facile synthesis of a series of 4-nitrophenyl 2-azidoethylcarbamate derivatives as activated urea building blocks was developed. The N-Fmoc-protected 2-aminoethyl mesylates derived from various commercially available N-Fmoc-protected α-amino acids, including those having functionalized side chains with acid-labile protective groups, were directly transformed into 4-nitrophenyl 2-azidoethylcarbamate derivatives in 1 h via a one-pot two-step reaction. These urea building blocks were utilized for the preparation of a series of urea moiety-containing mitoxantrone-amino acid conjugates in 75-92% yields and parallel solution-phase synthesis of a urea compound library consisted of 30 members in 38-70% total yields.