摘要(英) |
In recent years, people pay more and more attention to air pollution, which is particularly enthusiastic discussion of particles matter (PM). PM spread everywhere in our daily life. Sea salt, exhaust gas emissions from industries, vehicle emissions and raised dust, are the main reasons of the formation of PM. PM has high environmental variability. It is far less enough to rely on the government set up by the environmental monitoring network. Because of the limitation of the expensive costs of the instruments and the difficulties of the setting of the stations, reducing the size and cost of the instrument is the main purpose to improve the observation density. There are many PM sensors with low-cost portability, but most PM sensors are calibrated indoor. This time we will test the feasibility of low-cost sensors in outdoor observation.
This work mainly uses Shinyei PPD42NS use with Arduino Uno microcontroller board for PM observation. PPD42NS is PM sensor based on light scattering, the sensor installation location at the National Central University of the Science Building#2, the observation time is 2016/03/23 to 2017/03/06 for 349 days. In this work, the correlation analysis was using the real-time data of the EPA Zhongli and Pingzhen station, and the meteorological data of the NCU station. We will be divided into four for the temperature, relative humidity, wind direction, rainfall to discuss. The results show that the temperature change has no significant effect. The relative humidity has a great influence. In high relative humidity (RH> 95%), the sensor will output abnormally. Rainfall will cause the sensor to malfunction. When the east or north wind occurred, lower value appeared for the shelter of the building. |
參考文獻 |
Austin E, Novosselov I, Seto E, Yost MG (2015) Laboratory Evaluation of the Shinyei PPD42NS Low-Cost Particulate Matter Sensor. PLoS ONE 10(9): e0137789.
Gao, M., Cao, J., & Seto, E. (2015). A distributed network of low-cost continuous reading sensors to measure spatiotemporal variations of PM2. 5 in Xi′an, China. Environmental pollution, 199, 56-65.
Holstius, D. M., Pillarisetti, A., Smith, K. R., & Seto, E. (2014). Field calibrations of a low-cost aerosol sensor at a regulatory monitoring site in California. Atmospheric Measurement Techniques, 7(4), 1121-1131.
Karlsson, H. L., Gustafsson, J., Cronholm, P., and Möller, L. (2009). Size-Dependent Toxicity of Metal Oxide Particles—A Comparison BetweenNano-and Micrometer Size. Toxicol. Lett., 188:112–118
Kelly, K. E., Whitaker, J., Petty, A., Widmer, C., Dybwad, A., Sleeth, D., ... & Butterfield, A. (2017). Ambient and laboratory evaluation of a low-cost particulate matter sensor. Environmental Pollution, 221, 491-500.
Mead, M. I., Popoola, O. A. M., Stewart, G. B., Landshoff, P., Calleja, M., Hayes, M., ... & Lewis, A. (2013). The use of electrochemical sensors for monitoring urban air quality in low-cost, high-density networks. Atmospheric Environment, 70, 186-203.
Schubert, E. Fred, Thomas Gessmann, and Jong Kyu Kim. Light emitting diodes. John Wiley & Sons, Inc., 2005.
Steinle, S., Reis, S., Sabel, C. E., Semple, S., Twigg, M. M., Braban, C. F., ... & Wu, H. (2015). Personal exposure monitoring of PM 2.5 in indoor and outdoor microenvironments. Science of the Total Environment, 508, 383-394.
Wang, Y., Li, J., Jing, H., Zhang, Q., Jiang, J., & Biswas, P. (2015). Laboratory evaluation and calibration of three low-cost particle sensors for particulate matter measurement. Aerosol Science and Technology, 49(11), 1063-1077.
高解析度空氣污染物擴散模擬模式的發展; Development of a High-Resolution Wind Model for Atmospheric Pollutant Dispersion Simulation. 2010. PhD Thesis. 國立中央大學.
全球大氣二氧化碳商用貨輪觀測平台之發展; Development of ship-based platforms for atmospheric carbon dioxide measurements. 2010. PhD Thesis. 國立中央大學. |