Particulate matter (PM) has recently been considered one of the most harmful air pollutants to public health. Plants have been known to degrade and deposit particle pollutants with epicuticular wax (EW), and this capacity can be influenced by environmental conditions including relative humidity (RH). The present study examined the effects of RH on EW generation and PM deposition upon leaf surfaces within Asplenium nidus ‘Avis’. The plants were treated in growth chambers with two levels of RH (low: 30% - 40% and high: 80% - 90%) for a period of four weeks, and subsequently exposed to a 30 μg･m-3 concentration of TiO2 particles as a PM resource for 72 hours. The EW ultrastructure on the leaf surface was observed as the thin films type, which was not morphologically changed in the condition of low or high RH treatment. For four weeks of RH treatment, the fresh weight and leaf area per plant were not significant between low and high RH treatment, while dry weight was significantly higher in the high RH condition. We also found that greater amounts of EW per fresh weight, dry weight and leaf area were generated in high RH. However, the total amounts of PM deposition (surface PM + in-wax PM) of the plants were higher within the low RH treatment with a higher proportion of surface PM. In contrast the proportion of in-wax PM was 15% higher within the high RH. These results suggest that EW generation is affected by air humidity and that proportion of PM deposition in the EW layer were influenced by the amount of total wax load.

콘크리트 내부의 습도분포와 변화를 예측하는 것은 현장에서 콘크리트의 양생품질을 향상시키기 위하여 필수적이다 콘크리트의 습도는 주로 표면습도를 경계조건을 사용하는 수치해석을 통하여 예측된다. 하지만, 표면습도를 정확히 측정하기 어려우므로 거의 모든 수치해석에 표면습도 대신 대기습도를 사용하여 왔다. 본 논문에서는 표면습도를 정확하게 측정할 수 있는 방법을 제시하고 일련의 실내실험을 통하여 측정된 대기습도와 콘크리트의 내부 및 표면습도를 보여준다. 이와는 별개의 실험을 통하여 타설 직후의 콘크리트가 낮은 습도를 나타내는 원인을 조사하였다. 측정된 습도를 이용하여 콘크리트의 표면습도를 예측할 수 있는 모형을 개발하였으며 추가적인 실험을 통하여 모형의 유효성을 검증하였다.

This study evaluates the quality of surface air temperature, relative humidity, and precipitation detection observed by 22 internet of thing (IoT)-based mini-weather stations in Seoul in 2020 summer. The automatic weather station (AWS) closest to each IoT-based station is used as reference. The IoT-based observations show surface air temperature and relative humidity are about 0.2-4.0°C higher and about -1--22% lower than the AWS observations, respectively. However, they exhibit temporal variability similar to the AWS observations on both diurnal and daily time scales, with daily correlations greater than 0.90 for temperature and 0.82 for relative humidity. Given these strong linear relationships, it show that temperature and relative humidity biases can be effectively corrected by applying a simple bias correction method. For IoT-based precipitation detection, we found that precipitation conductivity value (PCV) during precipitation events is well separated from that during non-precipitation events, providing a basis for distinguishing precipitation events from non-precipitation events. When the PCV threshold is set to 250 for precipitation detection, the highest critical success index and the bias score index close to one, suitable for operational precipitation detection, are obtained. These results demonstrate that IoT-based mini-weather stations can successfully measure surface air temperature, relative humidity, and precipitation detection with appropriate bias corrections.