As the demand for the monitoring of VOCs increases, various unpowered colorimetric sensors are being developed, but the performance evaluation method of the developed sensors has not been systematically established. In this study, the device, experimental process, and data calculation methods for the performance evaluation of the colorimetric sensors were proposed. An aluminum chamber (70W× 128 L × 40 mm H) was designed to expose the sensor to a constant concentration of VOCs. In addition, an experimental apparatus was devised to evaluate the effect of environmental factors (temperature and humidity) affecting the ability of the sensor to detect VOCs. To calculate the color change value of the sensor corresponding to the concentration of VOCs, the ‘peak wavelength method’ that analyzes the wavelength of the highest intensity for high-concentration VOCs and the ‘spectral centroid method’ using a weighted arithmetic average for low-concentration VOCs were used. As a result of evaluating the ability of the colorimetric sensor to detect VOCs, which was made of polydimethylsiloxane (PMDS) by the method proposed in this study, the wavelength change values (bandgap shift) of the sensor for 1,000 ppm of benzene, toluene, oxylene, and acetone were 0.898 nm, 2.304 nm, 5.775 nm, and 0.249 nm, respectively. The precision was calculated by repeatedly measuring the sensing ability of the sensor 5 times for each type of VOCs. The precision of the sensor responses to benzene, toluene, o-xylene, and acetone were 15.23%, 7.84%, 4.14%, and 30.00% RSD, respectively. The method proposed in this study can be used to evaluate the performance of various types of VOCs colorimetric sensors.

In this study, we identified heavy rain damage and rainfall characteristics for each region, and proposed Hazard-Triggering rainfall according to heavy rain damage scale focused on Gyeonggi-do. We classified the damage scale into three groups (total damage, over 100 million won, over 1 billion won) to identify the characteristics of heavy rain damage, and we determined criteria of the rainfall class for each rainfall variable (maximum rainfalls for the durations of 1, 3, 6, 12 hours) to identify the rainfall characteristics. We calculated the cumulative probability of heavy rain damage based on the rain criteria mentioned above to establish the Hazard-Triggering rainfall according to the heavy rainfall damage scale. Using the results, we establish the Hazard-Triggering rainfall for each rain variable according to heavy rain damage. Finally, this study calculated the assessment indicator (F1-Score) for classification performance to test the performance of the Hazard-Triggering rainfall. As the results, the classification performance of the Hazard-Triggering rainfall which proposed in this study was 11%, 30%, 10% higher than the criteria by KMA (Korea Meteorological Administration).