Background: A few studies have investigated the interaction between exposure to road traffic noise, air pollutants, and cardiovascular disease (CVD), but their results were inconsistent. This cross-sectional study investigated whether road traffic noise, particulate matter with dynamic diameter less than 10 μm (PM₁₀) and nitrogen dioxides (NO₂) exposure were independently associated with the risk of CVD. Methods: We recruited 663 volunteers who had been living near main roads for more than three years in 2008. Information concerning the subjects’ home addresses was combined with noise measurements at 42 locations and annual average of air pollutants from 2 monitoring stations to estimate individual exposure. Multivariate logistic regression was used to calculate the odds ratio (OR) for diagnosed CVD, adjusting for potential confounders and co-exposure. Results: Only per 5-dBA increase in road traffic noise was significantly associated with elevated risk of CVD (adjusted OR = 2.23, 95% confidence interval (CI) = 1.26–3.93) in the single-exposure models. Such association was aggravated (adjusted OR = 2.96, 95% CI = 1.41–6.23) after adjustment for total traffic and PM₁₀ or NO₂ in the two-exposure models. Conclusions: Road traffic noise exposure may be associated with the increasing prevalence of CVD. No synergistic association was observed between co-exposure to noise and air pollutants and the risk of CVD.

This study describes the performance evaluation of an energy management system, based on electricity consumption, for a Gen 6 Thin Film Transistor Liquid Crystal Display (TFT-LCD) panel plant. Of the various production lines and facility systems, the array system and the compressed dry air consumed the most electricity of 21.8% and 19.8%, respectively, while the public utility used only 1.6% of the total electricity. The baseline electricity consumptions were correlated well (R² ≥ 0.77) to the monthly average wet-bulb temperatures of ambient air and the panel yield rates, which were determined by the product yield over the equipment available time index. After implementing the energy saving projects, the energy conservation performance was determined using a three-parameter change-point regression model incorporated with the panel yield rates. The post-retrofit monthly savings of the total electricity consumption for the panel manufacture were 5.35%–10.36%, with the efficiency of the electricity performance revealing an upswing trend following the implementation of the energy management system.

This study aimed to measure personal exposure to sulfuric acid in the electroplating industry to establish a predictive model and test its validation. We collected indoor air parameters and related information from four electroplating plants. Silica gel sorbents were used to collect air samples using high-performance ion chromatography. We collected air samples from three plants (i.e., Plant B, Plant C, and Plant D) and applied multiple linear regressions to build a predictive model. Eight samples collected from the fourth plant (i.e., Plant A) were used to validate the model. A total of 41 samples were collected with a mean of 25.0 ± 9.8 μg/m³ (range 12.1–51.7 μg/m³) in this study, including Plant A (8 samples, 17.5 ± 2.8 μg/m³, 13.0–22.0 μg/m³), Plant B (11 samples, 36.5 ± 9.7 μg/m³, 23.1–51.7 μg/m³), Plant C (11 samples, 16.4 ± 1.7 μg/m³, 12.1–17.8 μg/m³), and Plant D (11 samples, 27.4 ± 1.7 μg/m³, 24.1–29.9 μg/m³). Plant B was significantly higher in sulfuric acid than the other plants. Workers from the electroplating process plants were exposed to sulfuric acid at 29.0 ± 11.5 μg/m³. The predictive model for personal exposure to sulfuric acid fit the data well (r² = 0.853; adjusted r² = 0.837) and had an accuracy of 5.52 μg/m³ (bias ± precision; 4.98 ± 2.38 μg/m³), validated by the personal sampling of the fourth plant. This study observed that sulfuric acid exposure was lower than the permissible exposure level of 1000 μg/m³ in Taiwan and the United States, and only two samples were lower than the European Union standard of 50 μg/m³. The developed model can be applied in epidemiological studies to predict personal exposure to sulfuric acid in plants using electroplating.