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Vittaforma corneae is an obligate intracellular fungus and can cause human ocular microsporidiosis. Although accumulating reports of V. corneae causing keratoconjunctivitis in both healthy and immunocompromised persons have been published, little is known about the organism's occurrence in aquatic environments. Limitations in detection sensitivity have meant a large sampling volume is required to detect the pathogen up to now, which is problematic. A recent study in Taiwan has shown that some individuals suffering from microsporidial keratitis (MK) were infected after exposure to the pathogen at a hot spring. As a consequence of this, a survey and analysis of environmental V. corneae present in hot springs became an urgent need. In this study, sixty water samples from six hot spring recreation areas around Taiwan were analyzed. One liter of water from each sample site was filtered to harvest the fungi. The positive samples were detected using a modified nested PCR approach followed by sequencing using specific SSU rRNA gene primer pairs for V. corneae. In total fifteen V. corneae-like isolates were identified (25.0% of sites). Among them, six isolates, which were collected from recreational areas B, C and D, were highly similar to known V. corneae keratitis strains from Taiwan and other countries. Furthermore, five isolates, which were collected from recreation areas A, C, E and F, were very similar to Vittaforma-like diarrhea strains isolated in Portugal. Cold spring water tubs and public foot bath pools had the highest detection rate (50%), suggesting that hot springs might be contaminated via untreated water sources. Comparing the detection rate across different regions of Taiwan, Taitung, which is in the east of the island, gave the highest positive rate (37.5%). Statistical analysis showed that outdoor/soil exposure and a high heterotrophic plate count (HPC) were risk factors for the occurrence of V. corneae. Our findings provide empirical evidence supporting the need for proper control and regulations at hot spring recreational waters in order to avoid health risks from this pathogen. Finally, we have developed a small volume procedure for detecting V. corneae in water samples and this has proved to be very useful., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

In this study, the presence of Legionella in major water reservoirs of Taiwan was examined with respect to seasonal variation, geographical variation, and water quality parameters using TaqMan real-time qPCR. Water samples were collected quarterly at 19 reservoirs in Taiwan between November 2012 and August 2013. The detection rate for Legionella was 35.5% (27/76), and Legionella was detected in all seasons. The Legionella concentration was relatively high in spring and summer, reaching 3.86 × 10(8) and 7.35 × 10(8) cells/L, respectively. By sampling the area, Legionella was detected at a higher proportion in reservoirs in the northern and southern areas, and the difference was consistent in all seasons. Significant association was found between detection of Legionella and various water quality parameters, including conductivity, chlorophyll a, and dissolved oxygen (Mann-Whitney U test, P < 0.05). Results of Spearman rank test showed negative correlation for Legionella detection with pH (P = 0.030, R = -0.497) and dissolved oxygen (P = 0.007, R = -0.596) in fall and positive correlation with Carlson's trophic state index (P = 0.049, R = 0.457) in spring. The identified species included Legionella pneumophila and Legionella drancourtii. The detection of Legionella in reservoirs was indicative of a potential public health risk and should be further evaluated.

In order to detect the presence/absence of Acanthamoeba along with geographical variations, water quality variations and seasonal change of Acanthamoeba in Taiwan was investigated by 18S ribosomal RNA (rRNA) gene TaqMan quantitative real-time PCR. Samples were collected quarterly at 19 drinking water reservoir sites from November 2012 to August 2013. Acanthamoeba was detected in 39.5 % (30/76) of the water sample, and the detection rate was 63.2 % (12/19) from samples collected in autumn. The average concentration of Acanthamoeba was 3.59 × 10(4) copies/L. For geographic distribution, the detection rate for Acanthamoeba at the northern region was higher than the central and southern regions in all seasons. Results of Spearman rank test revealed that heterotrophic plate count (HPC) had a negative correlation (R = -0.502), while dissolved oxygen (DO) had a positive correlation (R = 0.463) in summer. Significant differences were found only between the presence/absence of Acanthamoeba and HPC in summer (Mann-Whitney U test, P < 0.05). T2 and T4 genotypes of Acanthamoeba were identified, and T4 was the most commonly identified Acanthamoeba genotypes. The presence of Acanthamoeba in reservoirs presented a potential public health threat and should be further examined.

High-technology industries have grown continuously in Taiwan and elsewhere in the world. Volatile organic compounds (VOCs) comprise the highest percentage of emissions in these industries. The objectives of this study were to identify VOC sources and to apportion their contributions by using a three-step approach. These included estimating concentration distributions, performing principal component analysis (PCA), and mapping concentration contours. The results showed that the dominant compound groups were aromatic and aliphatic compounds. The PCA resolved four emission sources: vehicular traffic, industrial solvents, waste water plants, and cleaning/degreasing agents. Spatial distributions showed that concentrations of vehicular traffic-related compounds (benzene and isooctane) were highest at the entrances to the science park, and strongly related to traffic volume, and that the emissions of industry-related compounds (xylene and ethylbenzene) were closest to the associated sources. This study provided an accurate, practical and efficient method of characterizing emission sources in an industrial complex., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

The objectives of this study are to investigate the volatile organic compound (VOC) distribution using passive samplers and to assess the resulting health risks in a high-tech science industrial park. With the advantages of passive sampling techniques, long-term and wide-area samples are collected. The results show TVOC concentrations in summer, fall, winter, and spring are 7.14 ± 5.66 ppb, 18.17 ± 5.81 ppb, 10.30 ± 3.54 ppb, and 14.56 ± 4.53 ppb, respectively; those on weekdays and weekends are 14.36 ± 6.80 ppb and 9.87 ± 4.86 ppb, respectively; and those in industrial and residential zones are 12.97 ± 0.39 ppb and 11.13 ± 0.68 ppb, respectively. Based on concentration variations, and benzene, toluene, ethylbenzene, and xylene ratios, we can resolve the source origins. Health risks are assessed based on the resulting concentrations. In the case of non-cancer chronic effects, long-term exposure to these concentrations does not support there is a risk of adverse health effects. However, potential cancer risks of exposure to these concentrations may occur, especially to carbon tetrachloride and benzene. By applying this study's procedures, information on VOC concentration distribution, source identification, and health assessment can be obtained and they are applicable to similar studies.

A total of 357 water samples were collected from a public beach in northern Taiwan during beach season, and the densities of enterococci were analyzed by Enterolert methods. The mean enterococci level was 356 MPN/100 ml and ranged from <10 to 2,005 MPN/100 ml, which was classified as high contamination level according to the WHO water quality guideline (95 percentile >1,000 MPN/100 ml). Most of the deteriorated water quality conditions occurred during rainfall. By excluding data from the rain days, the overall beach water quality would be considered in the moderate contamination level (95 percentile 200-1,000 MPN/100 ml). Among the selected microbiological parameters tested, the densities of total coliforms and enterococci exhibited the highest correlation (r = 0.449, p = 0.009), followed by the concentrations of total coliforms and fecal coliforms (r = 0.403, p = 0.02). Nonetheless, no significant correlation was found between enterococci and fecal coliform levels (r = 0.197, p = 0.271).