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.

Exposure and health risk assessment of indoor volatile organic compounds in a medical university.

Many volatile organic compounds (VOCs) are used for experiments at universities, and most of them contain benzene, toluene, ethylbenzene, xylene, and an extraction solvent of dichloromethane. This study aimed to investigate the indoor concentrations of these five compounds in different locations on campus and to evaluate possible health risks for faculty members and students in a medical university. We selected 10 locations as sampling sites to conduct 4-h monitoring sessions on weekdays each season during 2019-2020. We used a 6-liter canister to collect air samples and analyzed these five VOCs via gas chromatography with a flame ionization detector. Monte Carlo simulation was performed to evaluate the carcinogenic and noncarcinogenic risks of these five VOCs. We found that dichloromethane was the most highly detected compound (median: 621.07 µg/m3; range: 44.01-8523.91 µg/m3), and the Department of Medicine had the highest concentration of the total of these VOCs among all of the locations (median: 5595.29 µg/m3; range: 1565.67-7398.66 µg/m3). The median carcinogenic risks of dichloromethane and benzene were 6.36 × 10-5 (95% confidence interval [CI]: 6.83 × 10-6-7.37 × 10-4) and 5.47 × 10-6 (95% CI: 4.03 × 10-7-2.42 × 10-5), respectively, for faculty members, and the lower risks of 3.14 × 10-5 (95% CI: 3.39 × 10-6-3.64 × 10-4) and 2.69 × 10-6 (95% CI: 1.97 × 10-7-1.19 × 10-5) were estimated for the students. The chronic noncarcinogenic risks of four VOCs were less than one, except for dichloromethane with a median hazard index of 1.92 (95% CI: 2.11 × 10-1-2.22 × 101). This study observed the spatial variation in the concentrations of the total of five VOCs and dichloromethane. The carcinogenic risks were classified as being at the possible level, and the noncarcinogenic risk of dichloromethane was greater than the acceptable level. Increasing local exhaust ventilation during the experiment and reducing the using amount of dichloromethane are recommended actions to reduce VOCs exposures in the medical university.

Noise Induced Hearing Loss and Tinnitus-New Research Developments and Remaining Gaps in Disease Assessment, Treatment, and Prevention.

Long-term noise exposure often results in noise induced hearing loss (NIHL). Tinnitus, the generation of phantom sounds, can also result from noise exposure, although understanding of its underlying mechanisms are limited. Recent studies, however, are shedding light on the neural processes involved in NIHL and tinnitus, leading to potential new and innovative treatments. This review focuses on the assessment of NIHL, available treatments, and development of new pharmacologic and non-pharmacologic treatments based on recent studies of central auditory plasticity and adaptive changes in hearing. We discuss the mechanisms and maladaptive plasticity of NIHL, neuronal aspects of tinnitus triggers, and mechanisms such as tinnitus-associated neural changes at the cochlear nucleus underlying the generation of tinnitus after noise-induced deafferentation. We include observations from recent studies, including our own studies on associated risks and emerging treatments for tinnitus. Increasing knowledge of neural plasticity and adaptive changes in the central auditory system suggest that NIHL is preventable and transient abnormalities may be reversable, although ongoing research in assessment and early detection of hearing difficulties is still urgently needed. Since no treatment can yet reverse noise-related damage completely, preventative strategies and increased awareness of hearing health are essential.