This study investigated the effects of weather conditions, air pollutants, and the air quality index (AQI) on daily cases of COVID-19 in the Bangkok Metropolitan Region (BMR). In this research, we collected data from January 1 to March 30, 2020 (90 days). This study used secondary data of meteorological and air pollutant parameters obtained from the Pollution Control Department of the Ministry of Natural Resources and Environment as well as daily confirmed COVID-19 case data in the BMR obtained from the official webpage of the Department of Disease Control, Ministry of Public Health, Thailand. We employed descriptive statistics, and Spearman and Kendall rank correlation tests were used to investigate the associations of weather variables, air pollutants, AQI with daily confirmed COVID-19 cases. Our findings indicate that CO, NO 2 , SO 2 , O 3 PM 10 , PM 2.5 , AQI have a significantly negative association with daily confirmed COVID-19 cases in the BMR, whereas meteorological parameters such as temperature, relative humidity (RH), absolute humidity (AH) and wind speed (WS) showed significant positive associations with daily confirmed COVID-19 cases in the BMR. Our study is a useful supplement to encourage regulatory bodies to promote environmental strategies, as air pollution regulation could be a sustainable policy for mitigating the harmful effects of air pollutants. Furthermore, this study provides new insights into the relationship between daily meteorological factors, AQI, and air pollutants and daily confirmed COVID-19 cases in the BMR. These data may provide useful information to the public health authorities and decision makers in Thailand, as well as to the World Health Organization (WHO), in order to set proper strategic aimed at reducing the impact of the COVID-19. Future studies concerning SARS-CoV-2 and other viruses should investigate the possibility of infectious droplet dispersion in indoor and outdoor air during and after the epidemic outbreak.

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Exposure to respirable crystalline silica (RCS) reportedly induces chronic lung injury. We investigated the association between RCS exposure and two biomarkers of the effect, plasma club cell protein 16 (CC16) and heme oxygenase-1 (HO-1) levels, in stone-carving workers. Fifty-seven exposed workers (EWs) and 20 unexposed workers (UWs) were enrolled onto the study. Cumulative exposure to RCS was individually estimated using a filter-based gravimetric method. The plasma CC16 and HO-1 levels were determined using commercial kits. The 8-h time-weighted average for RCS concentration in the EW was significantly greater than this concentration in the UW ( p < 0.001). The health risk characterization for RCS exposure expressed as a hazard quotient (HQ) indicated that crystalline silica might be a risk factor where there is chronic exposure (HQ = 4.48). The EW group presented a significant decrease in CC16 and an increase in HO-1 levels in comparison to the UW group ( p < 0.001). In addition, we found a significant association between RCS concentration and plasma CC16 only. Therefore, our findings representing a significant decrease in CC16 in the plasma of stone-carving workers and this biological marker were significantly associated with RCS concentration. Our data indicated that CC16 might be a suitable biomarker to use to predict the health risk to stone-carving workers of exposure to RCS.

Objective: Noise pollution is an unwanted phenomenon that affects human health and can lead to occupational hearingloss in exposed workers. The stone-mortar industry is one of the processes which can create a noise hazard. This studyaimed to explore the factors associated with occupational hearing loss among stone-mortar workers in Phayao Province, Northern Thailand.Material and Methods: A cross-sectional study was conducted 27 stone-mortar workers who were interviewed with a questionnaire. Pure-tone hearing thresholds were measured using audiometry. The data were analyzed using Mann-Whitney U test, Spearman’s rank correlation test and Kruskal-Wallis test and Multiple linear regression analysis.Results: The study found a significant difference between age and high frequency hearing loss in both right and left ears (p-values 0.024 and 0.049, respectively). There were significant correlations between working hours per day and high frequency hearing loss in both right and left ears (p-values 0.030 and 0.042, respectively). Multiple linear regression analysis found increasing age was associated with high frequency hearing loss in both right and left ears (p-values 0.033 and 0.017, respectively) after adjusting for number of years worked, working hours per day, and use of personal protective equipment as random variables.Conclusion: All stone-mortar factories produce noise pollution. Therefore, the local policy makers should emphasize reducing noise pollution from stone factories and surveillance of occupational hearing loss to improve the quality of life of the people who work in such factories.