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研究生:翁歆喻
研究生(外文):SHIN-YU WENG
論文名稱:微粒感測器性能評估
論文名稱(外文):Performance Evaluation of Particulate Matter Sensors
指導教授:陳志傑陳志傑引用關係
指導教授(外文):Chih-Chieh Chen
口試委員:林文印蕭大智黃盛修林志威
口試委員(外文):Wen-Yinn LinTa-Chih HsiaoSheng-Hsiu HuangChih-Wei Lin
口試日期:2020-07-06
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:環境與職業健康科學研究所
學門:醫藥衛生學門
學類:公共衛生學類
論文種類:學術論文
論文出版年:2020
畢業學年度:108
語文別:中文
論文頁數:82
中文關鍵詞:低成本微粒感測器採樣效率吸入效率耐久性測試微粒堆積穿戴式微粒偵測器吸入效率微粒物質衝擊器衝擊器負載
外文關鍵詞:low-cost PM sensorsampling efficiencyaspiration efficiencydurability testparticle depositionwearable particle detectoraspiration efficiencyparticle matterimpactorimpactor loading
DOI:10.6342/NTU202001564
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第一章
近年來物聯網空氣盒子的環境濃度監測越來越普及,其監測讀值大多標榜可以針對空氣中的PM2.5。為了瞭解其感測器讀值是否為PM2.5,因此本研究旨在了解低成本微粒感測器採樣流率及探討不同粒徑大小的微粒進入感測器時的吸入效率及傳輸效率,同時也探究微粒大小、濃度與感測器擺放方式對讀值之影響,冀希提出最佳採樣流率與擺置方位。
本研究使用三款主動式微粒感測器,為攀藤科技粉塵感測器。三款感測器均使用雷射光散射理論偵測微粒濃度,微粒的粒徑偵測範圍為0.3 ~ 10 μm。實驗挑戰氣膠採用氯化鈉微粒,經超音波霧化器通過輻射源經帶電中和到乾燥箱的方式,產生穩定特定大小的微粒及濃度,並利用氣動微粒分析儀進行量測。
三款微粒感測器A到C出廠的採樣流量分別為1.5、0.8及0.7 L/min。隨著進氣口表面風速增加,三款感測器的進氣口在微粒氣動粒徑5 μm以上的吸入效率會下降。針對攀藤科技的三款微粒感測器,採樣流率分別為1.5、1.5、0.5 L/min時,其採樣效率最佳,高於此流量會有慣性衝擊損失,低於此流量則會有重力沈降損失。為了避免慣性衝擊造成之損失,針對A款之感測器結構進行改造,使得進氣及出氣口成一直管道,使用出廠設定的採樣流量1.5 L/min,其採樣效率較改造前好。耐久性測試方面,使用不同濃度進行,感測器濃度會受微粒沉積於感測元件而影響,當挑戰微粒濃度為8 mg/m3時,感測元件朝上擺放,經一周後監測值下降約7%,此結果顯示感測元件的擺置應避開微粒沈積的位置。感測器濃度監測之準確度,本研究產生了三種氣動粒徑進行分析,產生相同粒徑不同濃度的環境,四款感測器在氣動粒徑5 μm之微粒,其監測結果皆會有低估的情形,A到C款感測器讀值與標準濃度相比,誤差百分比趨近於100%。而在氣動粒徑1微米之微粒,質量濃度低於70 μg/m3與標準濃度相比誤差可小於10%。使用不同光學特性的微粒物質氯化鈉及亞甲藍,產生相同微粒濃度15 μg/m3,氯化鈉量測結果與標準質量濃度高估約13%,而亞甲藍方面則低估約33%。
攀藤科技的三款感測器在特定採樣流率下有最佳的採樣效率,而改造後的感測器採樣效率較先前好。感測器採樣時的擺放方位會影響監測讀值的變化。建議未來設計感測器時,應多加考量感測器的內部構造,以減少微粒損失。

第二章
微粒感測器被廣泛使用在室內外環境的微粒濃度監測上,具有微粒數目分布的測值及環境濃度量測的功能。在職業衛生的應用上為了能更準確評估工作者危害暴露情形,在個人穿戴式微粒偵測器研發上提供了可呼吸性微粒及短時間暴露濃度等資訊。然而對於穿戴式微粒偵測器的性能研究尚未充足,因此本研究旨在了解偵測器採樣流率及探討不同粒徑大小的微粒進入感測器時的吸入效率,同時也探究衝擊器的操作型態、微粒種類與濃度對讀值之影響。
本研究主要評估Nanozen的DustCount兩款穿戴式微粒偵測器,系列8899及9000,兩款儀器分別可量測0.5 ~ 10 m及0.3 ~ 20 m的粒徑範圍。測試系統中使用超音波霧化器來產生微米級氯化鈉及亞甲藍微粒作為挑戰氣膠。在儀器的準確度評估與所產生的標準質量濃度相比,而衝擊器評估方面調整不同採樣流率(0.5 - 1.5 L/min),利用氣動微粒分析儀進行量測上下游之數目濃度與粒徑分布,以符合不同截取粒徑的衝擊器。
產生氯化鈉及亞甲藍微粒,儀器量測值與標準質量濃度相比均有低估的情形,又亞甲藍微粒的量測結果比氯化鈉低約80%。在兩款衝擊器的分徑上,系列9000的PM2.5、PM4.0及PM10衝擊器須將採樣流率調整成0.8、0.6、0.7 L/min才能符合截取粒徑在2.5、4.0及10 μm。對於衝擊器的負載使用系列8899衝擊器,產生21.6 mg/m3的質量濃度經80分鐘的負載後,截取粒徑4.0 μm的穿透率會下降約10%。
關於儀器準確度,DustCount系列的偵測器出廠前是透過聚苯乙烯乳膠微粒(PSL)及亞利桑那粉塵(Arizona dust)進行校正,針對不同微粒物質監測應考量其微粒特性。在衝擊器的使用上,系列9000的三種衝擊器分徑效率,使用原廠採樣流率(1 L/min)無法達到預期截取粒徑,須降低採樣流率,以符合截取粒徑在2.5、4及10 μm。
Section 1
In recent years, the environmental concentration monitoring of the Internet of Things air box has become more and more popular, and most of its monitoring readings can be advertised as PM2.5 in the air. In order to understand whether the sensor reading is PM2.5, This study aims to understand the sampling flow rate of low-cost particulate matter sensors, and to explore the aspiration efficiency and transmission efficiency of particles of different particle sizes entering the sensor, as well as the effects of particle size and concentration.
In this study, three active PM sensors were used, which were Plantower technology dust sensors. All three sensors use laser light scattering theory to detect particle concentration, and the particle size detection range is 0.3 ~ 10 μm. The experimental measurement of the particles used sodium chloride solution. The particles were neutralized by a radiation source to via the dilution air to stably generate the concentrations. In the evaluation of sampling efficiency, different sampling flow rate are adjusted for different types of sensors. The sampling efficiency is performed using an aerodynamic particle sizer to understand the flow of three particle sensors for different sampling flow rate.
The sampling flow rates of the three particle sensors A to C are 1.5, 0.8 and 0.7 L/min, respectively. As the velocity on the surface of the air inlet increases, the aspiration efficiency of the air inlet of the three sensors at particle aerodynamic diameters of more than 5 μm will decrease. The transmission efficiency is the best when the sampling flow rate is 1.5, 1.5, and 0.5 L/min. Over this flow, there will be inertial impact loss, and below this flow, there will be loss of gravitational settling. In order to avoid the loss caused by inertial impact, the sensor structure of model A was modified so that the air intake and air outlet are in line, and the factory-set sampling flow rate of 1.5 L/min is used. For the durability test, different concentrations are used. The sensor concentration will be affected by the particles deposited on the sensing element. When the particle concentration is 8 mg/m3, the sensing element is placed upwards, and the monitoring value decay about 7% after a week, this result shows that the placement of the sensing element should avoid the position where the particles are deposited. The accuracy of sensor concentration monitoring, this study produced three aerodynamic particle sizes for analysis. In the environment with the same particle size and different concentration, the monitoring results of the four sensors in the aerodynamic particle size of 5 μm will all be underestimated. The readings of the A to C sensors are compared with the standard concentration, and the error percentage close to 100%. For particles with aerodynamic particle size of 1 μm, the mass concentration are less than 70 μg/m3 and the error can be less than 10% compared with the standard concentration.
The three sensors of Plantower Technology have the best sampling efficiency at a specific sampling flow rate, and the modified sensor has better than before. The direction of the sensor during sampling will affect the changes in the monitored readings. It is recommended that when designing the sensor in the future, more consideration should be given to the internal structure of the sensor to decrease the loss of particles.
Section 2
The particle sensor is widely used for monitoring the concentration of particles in indoor and outdoor environments. In the application of occupational health, in order to be able to more accurately assess the hazard exposure of workers, information on respirable dust and short-term exposure limit is provided in the development of personal wearable particle detectors. However, research on the performance of wearable particle detectors is not sufficient, so this study aims to understand the sampling flow rate of the detector and explore the aspiration efficiency of particles of different particle sizes, and also explore the operation of the impactor.
This research mainly evaluates Nanozen's DustCount two wearable particle detectors, series 8899 and 9000, and the two detectors can measure the range of 0.5 ~ 10 μm and 0.3 ~ 20 μm, respectively. The experimental measurement of the particles used sodium chloride and methylene blue. The particles were neutralized by a radiation source to via the dilution air to stably generate the concentrations. Impactors evaluation are adjusted for different sampling flow rates (0.5-1.5 L/min), and the measurement is performed using an aerodynamic particle sizer (APS).
Sodium chloride and methylene blue particles are produced, and the measurement value of the instrument is underestimated compared with the standard mass concentration. The measurement result of methylene blue particles is about 80% lower than sodium chloride. In the diameter of the two impactors, the PM2.5, PM4.0 and PM10 impactors of the series 9000 must adjust the sampling flow rate to 0.8, 0.6, 0.7 L/min to meet the cut-size of 2.5, 4.0 and 10 μm. For impactor loading, a series of 8899 impactor are used. After loading 21.6 mg/m3 for 80 minutes, the penetration rate of the cut-size of 4.0 μm will decrease by about 10%.
Regarding the accuracy of the instrument, the detectors of the DustCount series are calibrated by polystyrene latex particles (PSL) and Arizona dust before leaving the factory. The particle characteristics should be considered for different particulate matters monitoring. In the use of impactors, the seperation efficiency of three impactors in series 9000, using the default setting sampling flow rate (1 L/min) could not achieve the expected cut-size. To meet the cut-sizes of 2.5, 4 and 10 μm, the sampling flow rate must be reduced.
第一章目錄
表目錄 6
圖目錄 7
摘要 8
Abstract 9
一、前言 11
1.1研究背景 11
1.2 研究目的 12
二、文獻探討 12
2.1 低成本微粒感測器特性探討 12
2.2 低成本微粒感應器量測效能與不同參數間的影響 13
2.2.1微粒粒徑 13
2.2.2微粒濃度 14
2.2.3微粒物質 14
2.2.4環境溫度與濕度 15
2.2.5進口吸入風速 15
2.3 各國微粒感測器驗證方法 15
三、研究方法 16
3.1標準微粒產生系統的建置 16
3.2標準微粒產生系統的粒徑控制 17
3.3標準微粒產生系統的質量濃度控制 17
3.4低成本微粒感測器實驗室性能測試 18
3.4.1低成本微粒感測器的選擇 18
3.4.2低成本微粒感測器微型風扇風量及採樣流率量測 18
3.4.3低成本微粒感測器的評估 19
四、結果與討論 19
4.1 PM1.0、PM2.5、PM10讀值準確性 19
4.2 測試微粒粒徑與濃度影響 21
4.3 不同進口風速影響 21
4.4 不同路徑影響 22
4.5 高環境濃度影響 23
4.6 不同擺放方向影響 24
4.7 不同微粒尺寸負載影響 24
4.8 測試微粒種類影響 25
五、結論與建議 26
六、參考文獻 27

第二章目錄
表目錄 50
圖目錄 51
摘要 52
Abstract 53
一、前言 55
1.1研究背景 55
1.2研究目的 56
二、文獻探討 56
2.1 具分徑器的微粒監偵測設備探討 56
2.2 微粒偵測器量測性能與不同因素之影響 57
2.3 衝擊器採樣效率探討 58
三、研究方法 59
3.1標準微粒產生系統的建置 59
3.2 穿戴式微粒偵測器實驗室性能測試 59
3.2.1 穿戴式微粒偵測器的選擇 60
3.2.2 穿戴式微粒偵測器性能評估 60
3.2.3 衝擊器性能評估 61
四、結果與討論 61
4.1 穿戴式微粒偵測器評估結果 62
4.2 進口吸入效率 62
4.3 測試微粒種類影響 62
4.4 衝擊器評估結果 63
4.4.1 衝擊器塗油與未塗油影響 64
4.4.2 衝擊板負載影響 64
4.4.3 衝擊器測試微粒粒徑分布影響 65
五、結論與建議 65
六、參考文獻 66
第一章
Ahn, K. H., H. Lee, H. D. Lee, S. C. Kim. 2020. Extensive evaluation and classification of low‐cost dust sensors in laboratory using a newly developed test method. Indoor air 30:137-146. doi.
Austin, E., I. Novosselov, E. Seto, M. G. Yost. 2015. Laboratory evaluation of the shinyei ppd42ns low-cost particulate matter sensor. PLOS ONE 10:e0137789. doi: 10.1371/journal.pone.0137789.
Badura, M., P. Batog, A. Drzeniecka-Osiadacz, P. Modzel. 2018. Optical particulate matter sensors in pm2.5 measurements in atmospheric air. E3S Web Conf. 44:00006. doi.
Budde, M., T. Müller, B. Laquai, N. Streibl, A. Schwarz, G. Schindler, T. Riedel, M. Beigl, A. Dittler. 2018. Suitability of the low-cost sds011 particle sensor for urban pm-monitoring.
Bulot, F. M. J., S. J. Johnston, P. J. Basford, N. H. C. Easton, M. Apetroaie-Cristea, G. L. Foster, A. K. R. Morris, S. J. Cox, M. Loxham. 2019. Long-term field comparison of multiple low-cost particulate matter sensors in an outdoor urban environment. Sci Rep-Uk 9. doi.
Castell, N., F. R. Dauge, P. Schneider, M. Vogt, U. Lerner, B. Fishbain, D. Broday, A. Bartonova. 2017. Can commercial low-cost sensor platforms contribute to air quality monitoring and exposure estimates? Environment International 99:293-302. doi: https://doi.org/10.1016/j.envint.2016.12.007.
Chan, W.-H. 2012. Characterization of vibrating mesh aerosol generators. doi.
Chen, B., R. A. Fletcher, Y. Cheng. 2011. Calibration of aerosol instruments, 449-478.
Chien, C.-H., A. Theodore, C.-Y. Wu, Y.-M. Hsu, B. Birky. 2016. Upon correlating diameters measured by optical particle counters and aerodynamic particle sizers. Journal of Aerosol Science 101:77-85. doi.
Curto, A., D. Donaire-Gonzalez, J. Barrera-Gomez, J. D. Marshall, M. J. Nieuwenhuijsen, G. A. Wellenius, C. Tonne. 2018. Performance of low-cost monitors to assess household air pollution. Environ Res 163:53-63. doi: 10.1016/j.envres.2018.01.024.
Đorđević, D., J. Đuričić-Milanković, A. Pantelić, S. Petrović, A. Gambaro. 2020. Coarse, fine and ultrafine particles of sub-urban continental aerosols measured using an 11-stage berner cascade impactor. Atmospheric Pollution Research 11:499-510. doi.
Gao, Y., S. Lai, S.-C. Lee, P. S. Yau, Y. Huang, Y. Cheng, T. Wang, Z. Xu, C. Yuan, Y. Zhang. 2015. Optical properties of size-resolved particles at a hong kong urban site during winter. Atmospheric Research 155:1-12. doi.
He, M., N. Kuerbanjiang, S. Dhaniyala. 2019. Performance characteristics of the low-cost plantower pms optical sensor. Aerosol Sci Tech:1-11. doi.
Jayaratne, R., X. Liu, K.-H. Ahn, A. Asumadu-Sakyi, G. Fisher, J. Gao, A. Mabon, M. Mazaheri, B. Mullins, M. Nyaku. 2020. Low-cost pm2. 5 sensors: An assessment of their suitability for various applications. Aerosol Air Qual Res 20:520-532. doi.
Jayaratne, R., X. Liu, P. Thai, M. Dunbabin, L. Morawska. 2018. The influence of humidity on the performance of a low-cost air particle mass sensor and the effect of atmospheric fog. Atmospheric Measurement Techniques 11:4883-4890. doi.
Kelly, K. E., J. Whitaker, A. Petty, C. Widmer, A. Dybwad, D. Sleeth, R. Martin, A. Butterfield. 2017. Ambient and laboratory evaluation of a low-cost particulate matter sensor. Environ Pollut 221:491-500. doi.
Kousaka, Y., K. Okuyama, M. Shimada, K. Ohshima, T. Hase. 1989. Performance of a nebulizer for standard aerosol particle generation. Earozoru Kenkyu 4:294-302. doi: 10.11203/jar.4.294.
Kumar, P., A. N. Skouloudis, M. Bell, M. Viana, M. C. Carotta, G. Biskos, L. Morawska. 2016. Real-time sensors for indoor air monitoring and challenges ahead in deploying them to urban buildings. Sci Total Environ 560-561:150-159. doi: 10.1016/j.scitotenv.2016.04.032.
Li, J. 2019. Recent advances in low-cost particulate matter sensor: Calibration and application. doi.
Li, J. Y. and P. Biswas. 2017. Optical characterization studies of a low-cost particle sensor. Aerosol Air Qual Res 17:1691-1704. doi.
Lin, Z., Z. Zhang, L. Zhang, J. Tao, R. Zhang, J. Cao, S. Fan, Y. Zhang. 2014. An alternative method for estimating hygroscopic growth factor of aerosol light-scattering coefficient: A case study in an urban area of guangzhou, south china. Atmospheric Chemistry & Physics 14. doi.
Liu, B. Y. H. and K. W. Lee. 1975. An aerosol generator of high stability. American Industrial Hygiene Association Journal 36:861-865. doi: 10.1080/0002889758507357.
Liu, B. Y. H. and D. Y. H. Pui. 1974. A submicron aerosol standard and the primary, absolute calibration of the condensation nuclei counter. Journal of Colloid and Interface Science 47:155-171. doi: https://doi.org/10.1016/0021-9797(74)90090-3.
Lyamani, H., F. Olmo, L. Alados-Arboledas. 2008. Light scattering and absorption properties of aerosol particles in the urban environment of granada, spain. Atmospheric Environment 42:2630-2642. doi.
Manikonda, A., N. Zíková, P. K. Hopke, A. R. Ferro. 2016. Laboratory assessment of low-cost pm monitors. Journal of Aerosol Science 102:29-40. doi: https://doi.org/10.1016/j.jaerosci.2016.08.010.
Mercer, T. T. 1973a. Aerosol technology in hazard evaluation. United States: Academic Press, Inc.
Mercer, T. T. 1973b. Production and characterization of aerosols. Archives of internal medicine 131:39-50. doi.
Morawska, L., P. K. Thai, X. Liu, A. Asumadu-Sakyi, G. Ayoko, A. Bartonova, A. Bedini, F. Chai, B. Christensen, M. Dunbabin, J. Gao, G. S. W. Hagler, R. Jayaratne, P. Kumar, A. K. H. Lau, P. K. K. Louie, M. Mazaheri, Z. Ning, N. Motta, B. Mullins, M. M. Rahman, Z. Ristovski, M. Shafiei, D. Tjondronegoro, D. Westerdahl, R. Williams. 2018. Applications of low-cost sensing technologies for air quality monitoring and exposure assessment: How far have they gone? Environ Int 116:286-299. doi: 10.1016/j.envint.2018.04.018.
Mukherjee, A., L. G. Stanton, A. R. Graham, P. T. Roberts. 2017. Assessing the utility of low-cost particulate matter sensors over a 12-week period in the cuyama valley of california. Sensors-Basel 17. doi.
OZLER, S. 2018. Field applications of low-cost air quality monitors for pm 2.5 studies. doi.
Pawar, H. and B. Sinha. 2020. Humidity, density, and inlet aspiration efficiency correction improve accuracy of a low-cost sensor during field calibration at a suburban site in the north-western indo-gangetic plain (nw-igp). Aerosol Sci Tech. doi.
Rai, A. C., P. Kumar, F. Pilla, A. N. Skouloudis, S. Di Sabatino, C. Ratti, A. Yasar, D. Rickerby. 2017. End-user perspective of low-cost sensors for outdoor air pollution monitoring. Sci Total Environ 607-608:691-705. doi: 10.1016/j.scitotenv.2017.06.266.
Sakhnini, N. 2018. Mycitymeter wearable: Measuring the environmental risk factors for cognitive impairment in older adults, in Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers, 1793-1797.
Sayahi, T., A. Butterfield, K. E. Kelly. 2018. Long-term field evaluation of the plantower pms low-cost particulate matter sensors. Environmental Pollution. doi: https://doi.org/10.1016/j.envpol.2018.11.065.
Sayahi, T., A. Butterfield, K. E. Kelly. 2019. Long-term field evaluation of the plantower pms low-cost particulate matter sensors. Environ Pollut 245:932-940. doi.
Schieweck, A., E. Uhde, T. Salthammer, L. C. Salthammer, L. Morawska, M. Mazaheri, P. Kumar. 2018. Smart homes and the control of indoor air quality. Renewable and Sustainable Energy Reviews 94:705-718. doi: https://doi.org/10.1016/j.rser.2018.05.057.
Shanghai One-xin Electronics Technology Co., L. 2016. Digital universal particle concentration sensor. doi.
Sousan, S., K. Koehler, G. Thomas, J. H. Park, M. Hillman, A. Halterman, T. M. Peters. 2016. Inter-comparison of low-cost sensors for measuring the mass concentration of occupational aerosols. Aerosol Sci Technol 50:462-473. doi: 10.1080/02786826.2016.1162901.
Thomas, J. W. 1967. Particle loss in sampling conduits, in Assessment of Airborne Radioactivity. Proceedings of a Symposium on Instruments and Techniques for the Assessment of Airborne Radioactivity in Nuclear Operations.
Tiele, A., S. Esfahani, J. Covington. 2018. Design and development of a low-cost, portable monitoring device for indoor environment quality. Journal of Sensors 2018:14. doi: 10.1155/2018/5353816.
Tien, C. P., C. H. Chen, W. Y. Lin, C. S. Liu, K. J. Liu, M. Hsiao, Y. C. Chang, S. C. Hung. 2019. Ambient particulate matter attenuates sirtuini and augments srebp1-pir axis to induce human pulmonary fibroblast inflammation: Molecular mechanism of microenvironment associated with copd. Aging-Us 11:4654-4671. doi.
Wang, K., F. E. Chen, W. Au, Z. Zhao, Z. L. Xia. 2018. Evaluating the feasibility of a personal particle exposure monitor in outdoor and indoor microenvironments in shanghai, china. International journal of environmental health research:1-12. doi: 10.1080/09603123.2018.1533531.
Wang, Y., J. Li, H. Jing, Q. Zhang, J. Jiang, P. Biswas. 2015. Laboratory evaluation and calibration of three low-cost particle sensors for particulate matter measurement. Aerosol Sci Tech 49:1063-1077. doi.
William, C. and Z. HINDS. 1999. Aerosol technology: Properties, behavior, and measurement of airborne particles: WILEY-BLACKWELL.
Williams, R., A. Kaufman, T. Hanley, J. Rice, S. Garvey. 2014. Evaluation of field-deployed low cost pm sensors. US Environmental Protection Agency. doi.
Xing, Y. F., Y. H. Xu, M. H. Shi, Y. X. Lian. 2016. The impact of pm2.5 on the human respiratory system. J Thorac Dis 8:E69-E74. doi.
Zamora, M. L., F. L. Z. Xiong, D. Gentner, B. Kerkez, J. Kohrman-Glaser, K. Koehler. 2019. Field and laboratory evaluations of the low-cost plantower particulate matter sensor. Environ Sci Technol 53:838-849. doi.
Zanobetti, A., M. Franklin, P. Koutrakis, J. Schwartz. 2009. Fine particulate air pollution and its components in association with cause-specific emergency admissions. Environ Health-Glob 8. doi.
Zanobetti, A. and J. Schwartz. 2006. Air pollution and emergency admissions in boston, ma. J Epidemiol Commun H 60:890-895. doi.
Zimmerman, N., A. A. Presto, S. P. Kumar, J. Gu, A. Hauryliuk, E. S. Robinson, A. L. Robinson, R. Subramanian. 2018. A machine learning calibration model using random forests to improve sensor performance for lower-cost air quality monitoring. Atmospheric Measurement Techniques 11. doi.
Zuidema, C., L. V. Stebounova, S. Sousan, G. Thomas, K. Koehler, T. M. Peters. 2019. Sources of error and variability in particulate matter sensor network measurements. Journal of occupational and environmental hygiene 16:564-574. doi.

第二章
Brouwer, D. H., J. H. Gijsbers, M. W. Lurvink. 2004. Personal exposure to ultrafine particles in the workplace: Exploring sampling techniques and strategies. Annals of Occupational Hygiene 48:439-453. doi.
Castell, N., F. R. Dauge, P. Schneider, M. Vogt, U. Lerner, B. Fishbain, D. Broday, A. Bartonova. 2017. Can commercial low-cost sensor platforms contribute to air quality monitoring and exposure estimates? Environment international 99:293-302. doi.
Chakrabarti, B., P. M. Fine, R. Delfino, C. Sioutas. 2004. Performance evaluation of the active-flow personal dataram pm2. 5 mass monitor (thermo anderson pdr-1200) designed for continuous personal exposure measurements. Atmospheric Environment 38:3329-3340. doi.
Chen, C.-C. and S.-H. Huang. 1999. Shift of aerosol penetration in respirable cyclone samplers. American Industrial Hygiene Association Journal 60:720-729. doi.
Đorđević, D., J. Đuričić-Milanković, A. Pantelić, S. Petrović, A. Gambaro. 2020. Coarse, fine and ultrafine particles of sub-urban continental aerosols measured using an 11-stage berner cascade impactor. Atmospheric Pollution Research 11:499-510. doi.
Fisher, J. A., M. C. Friesen, S. Kim, S. J. Locke, Y. Kefelegn, J. Y. Wong, P. S. Albert, R. R. Jones. 2019. Sources of variability in real-time monitoring data for fine particulate matter: Comparability of three wearable monitors in an urban setting. Environmental Science & Technology Letters 6:222-227. doi.
Gao, Y., S. Lai, S.-C. Lee, P. S. Yau, Y. Huang, Y. Cheng, T. Wang, Z. Xu, C. Yuan, Y. Zhang. 2015. Optical properties of size-resolved particles at a hong kong urban site during winter. Atmospheric Research 155:1-12. doi.
Hering, S. V. 1995. Impactors, cyclones, and other inertial and gravitational collectors. Air sampling instruments for evaluation of atmospheric contaminants 8:279-321. doi.
Hinds, W. C., W.-C. V. Liu, J. R. FROINES. 1985. Particle bounce in a personal cascade impactor: A field evaluation. American Industrial Hygiene Association Journal 46:517-523. doi.
Janssen, N. A., G. Hoek, B. Brunekreef, H. Harssema, I. Menswik, A. Zuidhof. 1998. Personal sampling of particles in adults: Relation among personal, indoor, and outdoor air concentrations. American journal of epidemiology 147:537-547. doi.
Jayaratne, R., X. Liu, K.-H. Ahn, A. Asumadu-Sakyi, G. Fisher, J. Gao, A. Mabon, M. Mazaheri, B. Mullins, M. Nyaku. 2020. Low-cost pm2. 5 sensors: An assessment of their suitability for various applications. Aerosol and Air Quality Research 20:520-532. doi.
Karlsson, H. L., J. Gustafsson, P. Cronholm, L. Möller. 2009. Size-dependent toxicity of metal oxide particles—a comparison between nano-and micrometer size. Toxicology letters 188:112-118. doi.
Lin, Z., Z. Zhang, L. Zhang, J. Tao, R. Zhang, J. Cao, S. Fan, Y. Zhang. 2014. An alternative method for estimating hygroscopic growth factor of aerosol light-scattering coefficient: A case study in an urban area of guangzhou, south china. Atmospheric Chemistry & Physics 14. doi.
Liu, B. Y. H. and K. W. Lee. 1975. An aerosol generator of high stability. American Industrial Hygiene Association Journal 36:861-865. doi: 10.1080/0002889758507357.
Liu, H.-Y., P. Schneider, R. Haugen, M. Vogt. 2019. Performance assessment of a low-cost pm2. 5 sensor for a near four-month period in oslo, norway. Atmosphere 10:41. doi.
Lyamani, H., F. Olmo, L. Alados-Arboledas. 2008. Light scattering and absorption properties of aerosol particles in the urban environment of granada, spain. Atmospheric Environment 42:2630-2642. doi.
Marple, V. A. and K. Willeke. 1976. Inertial impactors: Theory, design and use. Fine Particles: Aerosol Generation, Measurement, Sampling, and Analysis:412-446. doi.
Pak, S. S., B. Y. Liu, K. L. Rubow. 1992. Effect of coating thickness on particle bounce in inertial impactors. Aerosol Science and Technology 16:141-150. doi.
Price, H. D., B. Stahlmecke, R. Arthur, H. Kaminski, J. Lindermann, E. Däuber, C. Asbach, T. A. Kuhlbusch, K. A. Berube, T. P. Jones. 2014. Comparison of instruments for particle number size distribution measurements in air quality monitoring. Journal of Aerosol Science 76:48-55. doi.
Rao, A. and K. Whitby. 1978. Non-ideal collection characteristics of inertial impactors—i. Single-stage impactors and solid particles. Journal of Aerosol Science 9:77-86. doi.
Sousan, S., K. Koehler, G. Thomas, J. H. Park, M. Hillman, A. Halterman, T. M. Peters. 2016. Inter-comparison of low-cost sensors for measuring the mass concentration of occupational aerosols. Aerosol Sci Technol 50:462-473. doi: 10.1080/02786826.2016.1162901.
Tsai, C.-J. and Y.-H. Cheng. 1995. Solid particle collection characteristics on impaction surfaces of different designs. Aerosol Science and Technology 23:96-106. doi.
Ueberham, M. and U. Schlink. 2018. Wearable sensors for multifactorial personal exposure measurements–a ranking study. Environment international 121:130-138. doi.
Wang, K., F. E. Chen, W. Au, Z. H. Zhao, Z. L. Xia. 2019. Evaluating the feasibility of a personal particle exposure monitor in outdoor and indoor microenvironments in shanghai, china. Int J Environ Heal R 29:209-220. doi.
Wang, Y., J. Li, H. Jing, Q. Zhang, J. Jiang, P. Biswas. 2015. Laboratory evaluation and calibration of three low-cost particle sensors for particulate matter measurement. Aerosol Sci Tech 49:1063-1077. doi.
Xing, Y. F., Y. H. Xu, M. H. Shi, Y. X. Lian. 2016. The impact of pm2.5 on the human respiratory system. J Thorac Dis 8:E69-E74. doi.
Yao, M. and G. Mainelis. 2007. Analysis of portable impactor performance for enumeration of viable bioaerosols. Journal of occupational and environmental hygiene 4:514-524. doi.
鍾俊彬 and 蔡春進. 2000. 凹槽慣性衝擊器的理論研究.
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