Abstract
The vertical distribution of fine particulate matter (PM2.5) is a vital link in understanding the transport and evolution of haze. However, existing ground stations cannot provide sufficient vertical observations of PM2.5, especially at fine scales regarding space and time. This study deployed a six-rotor unmanned aerial vehicle (UAV) equipped with portable monitors to observe the vertical distributions of PM2.5 and meteorological parameters within 1000 m lower troposphere. By comparing with ground-based monitoring station and tethered balloon platform for PM2.5 measurements, the UAV was improved and then used to perform a field observation experiment in the Qingpu district of Shanghai, China. The UAV-based observations showed a decreasing vertical profile of PM2.5 in the experimental day, with a decrease of more than 50% at 0–1000 m height. PM2.5 had a vertical pattern that declined rapidly after 700 m in the afternoon, but the morning PM2.5 had a rapid decline from 200 to 500 m compared with other height intervals in this period. A temperature inversion at a lower height in the morning blocked newly formed PM2.5 at ground to disperse upward, and PM2.5 above the temperature inversion was composed of residuals in last night. The temperature inversion gradually climbed up in the afternoon, which was beneficial to the dispersion of near-ground PM2.5. The difference of relative humidity above and below 700 m height implies different geographical origins that were well identified and explained by a cluster analysis. This study generally highlights the significance of using a lightweight UAV to understand air pollution and governance environments in the urban area.
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References
Altstädter B, Platis A, Wehner B, Scholtz A, Wildmann N, Hermann M, Käthner R, Baars H, Bange J, Lampert A (2015) ALADINA: an unmanned research aircraft for observing vertical and horizontal distributions of ultrafine particles within the atmospheric boundary layer. Atmos Meas Tech 8:1627–1639
Alvarado M, Gonzalez F, Erskine P, Cliff D, Heuff D (2017) A methodology to monitor airborne PM10 dust particles using a small unmanned aerial vehicle. Sensors 17:343
Axisa D, Defelice TP (2016) Modern and prospective technologies for weather modification activities: a look at integrating unmanned aircraft systems. Atmos Res 178–179:114–124
Brady JM, Stokes MD, Bonnardel J, Bertram TH (2016) Characterization of a quadrotor unmanned aircraft system for aerosol-particle-concentration measurements. Environ Sci Technol 50:1376–1383
Corrigan CE, Roberts GC, Ramana MV, Kim D, Ramanathan V (2008) Capturing vertical profiles of aerosols and black carbon over the Indian Ocean using autonomous unmanned aerial vehicles. Atmos Chem Phys 8:737–747
Ding G, Chan C, Gao Z, Yao W, Li Y, Cheng X, Meng Z, Yu H, Wong K, Wang S, Miao Q (2005) Vertical structures of PM10 and PM2.5 and their dynamical character in low atmosphere in Beijing urban areas. Sci China Ser D 48(s2):38–54
Ester M, Kriegel HP, Sander J, Xu X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the 2nd international conference on knowledge discovery and data mining, pp 226–231
Haas PY, Balistreri C, Pontelandolfo P, Triscone G, Pekoz H, Pignatiello A (2014) Development of an unmanned aerial vehicle UAV for air quality measurement in urban areas. In: 32nd AIAA applied aerodynamics conference; American institute of aeronautics and astronautics: Reston, Virginia, pp 1–9
Harrison WA, Lary DJ, Nathan BJ, Moore AG (2015) Using remote control aerial vehicles to study variability of airborne particulates. Air Soil Water Res 8:43–51
He T, Hou L, Lü B, Liu Y (2013) Study of accuracy of lidar inversion PM2.5 concentration. Chin J Lasers 40(1):206–211
Hemingway BL, Frazier AE, Elbing BR, Jacob JD (2017) Vertical sampling scales for atmospheric boundary layer measurements from small unmanned aircraft systems (sUAS). Atmosphere 8(9):176
Huang XHH, Bian Q, Ng WM, Louie PKK, Yu JZ (2014) Characterization of PM2.5 major components and source investigation in suburban Hong Kong: a one year monitoring study. Aerosol Air Qual Res 14:237–250
Hyslop NP (2009) Impaired visibility: the air pollution people see. Atmos Environ 43:182–195
Li W, Shi Z, Zhang D, Zhang X, Li P, Feng Q, Yuan Q, Wang W (2012) Haze particles over a coal-burning region in the China Loess Plateau in winter: three flight missions in December 2010. J Geophys Res 117(D12):D12306
Li J, Fu Q, Huo J, Wang D, Yang W, Bian Q, Duan Y, Zhang Y, Pan J, Lin Y, Huang K, Bai Z, Wang SH, Fu JS, Louie PKK (2015) Tethered balloon-based black carbon profiles within the lower troposphere of Shanghai in the 2013 East China smog. Atmos Environ 123:327–338
Li XB, Wang DS, Lu QC, Peng ZR, Lu SJ, Li B, Li C (2017) Three-dimensional investigation of ozone pollution in the lower troposphere using an unmanned aerial vehicle platform. Environ Pollut 224:107–116
Li X, Wang D, Lu Q, Peng Z, Wang Z (2018) Investigating vertical distribution patterns of lower tropospheric PM2.5 using unmanned aerial vehicle measurements. Atmos Environ 173:62–71
Li C, Wang Z, Li B, Peng ZR, Fu Q (2019) Investigating the relationship between air pollution variation and urban form. Build Environ 147:559–568
Liao XN, Zhang XL, Wang YC, Liu WD, Du J, Zhao LH (2014) Comparative analysis on meteorological condition for persistent haze cases in summer and winter in Beijing. Environ Sci 35(6):2031
Lu WZ, He HD, Dong LY (2011) Performance assessment of air quality monitoring networks using principal component analysis and cluster analysis. Build Environ 46:577–583
Lu WZ, Xue Y, He HD (2014) Detrended fluctuation analysis of particle number concentrations on roadsides in Hong Kong. Build Environ 82:580–587
Pan L, Xu J, Tie X, Mao X, Gao W, Chang L (2019) Long-term measurements of planetary boundary layer height and interactions with PM2.5 in Shanghai, China. Atmos Pollut Res 10:989–996
Pedregosa F, Varoquaux G, Gramfort A et al (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825–2830
Peng ZR, Wang D, Wang Z, Gao Y, Lu S (2015) A study of vertical distribution patterns of PM2.5 concentrations based on ambient monitoring with unmanned aerial vehicles: a case in Hangzhou, China. Atmos Environ 123:357–369
Platis A, Altstädter B, Wehner B, Wildmann N, Lampert A, Hermann M, Birmili W, Bange J (2016) An observational case study on the influence of atmospheric boundary-layer dynamics on new particle formation. Bound Layer Meteorol 158:67–92
Pui DYH, Chen SC, Zuo Z (2014) PM2.5 in China: measurements, sources, visibility and health effects, and mitigation. Particuology 13:1–26
Quan J, Gao Y, Zhang Q, Tie X, Cao J, Han S, Meng J (2013) Particuology Evolution of planetary boundary layer under different weather conditions, and its impact on aerosol concentrations. Particuology 11:34–40
Ramanathan V, Carmichael G (2008) Global and regional climate changes due to black carbon. Nat Geosci 1(4):221–227
Ramanathan V, Ramana MV, Roberts G, Kim D, Corrigan C, Chung C, Winker D (2007) Warming trends in Asia amplified by brown cloud solar absorption. Nature 448(7153):575–578
Renard JB, Dulac F, Berthet G et al (2016) Loac: a small aerosol optical counter/sizer for ground-based and balloon measurements of the size distribution and nature of atmospheric particles—part 2: first results from balloon and unmanned aerial vehicle flights. Atmos Meas Tech Discuss 9(8):3673–3686
Rolph GD (2018) Real-time environmental applications and display system (READY). http://ready.arl.noaa.gov. Accessed 5 Mar 2018
Schuyler TJ, Guzman MI (2017) Unmanned aerial systems for monitoring trace tropospheric gases. Atmosphere 8:206
Schwartz J, Dockery DW, Neas LM (1996) Is daily mortality associated specifically with fine particles? J Air Waste Manag Assoc 46:1996
Song Y, Wang X, Maher BA, Li F, Xu C, Liu X, Sun X, Zhang Z (2016) The spatial-temporal characteristics and health impacts of ambient fine particulate matter in China. J Clean Prod 112:1312–1318
Stein AF, Draxler RR, Rolph GD, Stunder BJB, Cohen MD, Ngan F (2015) Noaa’s hysplit atmospheric transport and dispersion modeling system. Bull Am Meteorol Soc 96:2059–2077
Tao Z, Wang Z, Yang S, Shan H, Ma X, Zhang H, Zhao S, Liu D, Xie C, Wang Y (2016) Profiling the PM2.5 mass concentration vertical distribution in the boundary layer. Atmos Meas Tech 9:1369–1376
Toledo D, Córdoba-Jabonero C, Gil-Ojeda M (2014) Cluster analysis: a new approach applied to lidar measurements for atmospheric boundary layer height estimation. J Atmos Ocean Technol 31:422–436
TSI Inc. (2016) SidePak personal aerosol monitor AM510. http://www.tsi.com/SIDEPAK-Personal-Aerosol-Monitor-AM510/. Accessed 20 June 2018
Villa TF, Salimi F, Morton K, Morawska L, Gonzalez F (2016a) Development and validation of a UAV based system for air pollution measurements. Sensors 16:2202
Villa TF, Gonzalez F, Miljievic B, Ristovski ZD, Morawska L (2016b) An overview of small unmanned aerial vehicles for air quality measurements: present applications and future prospectives. Sensors 16:1072
Villa TF, Jayaratne ER, Gonzalez LF, Morawsk L (2017) Determination of the vertical profile of particle number concentration adjacent to a motorway using an unmanned aerial vehicle. Environ Pollut 230:134–142
Wang W, Ma J, Hatakeyama S, Liu X, Chen Y, Takami A, Ren L, Geng C (2008) Aircraft measurements of vertical ultrafine particles profiles over Northern China Coastal Areas during dust storms in 2006. Atmos Environ 42(22):5715–5720
Wang SH, Welton EJ, Holben BN et al (2015a) Vertical distribution and columnar optical properties of springtime biomass-burning aerosols over Northern Indochina during 2014 7-SEAS campaign. Aerosol Air Qual Res 15:2037–2050
Wang Z, Lu F, He HD, Lu QC, Wang D, Peng ZR (2015b) Fine-scale estimation of carbon monoxide and fine particulate matter concentrations in proximity to a road intersection by using wavelet neural network with genetic algorithm. Atmos Environ 104:264–272
Wang Z, He HD, Lu F, Lu QC, Peng ZR (2015c) Hybrid model for prediction of carbon monoxide and fine particulate matter concentrations near a road intersection. Transp Res Rec 2503:29–38
Wang HL, Qiao LP, Lou SR, Zhou M, Ding AJ, Huang HY, Chen JM, Wang Q, Tao SK, Chen CH, Li L, Huang C (2016) Chemical composition of PM2.5 and meteorological impact among three years in urban Shanghai. China J Clean Prod 112:1302–1311
Wang Z, Lu QC, He HD, Wang D, Gao Y, Peng ZR (2017a) Investigation of the spatiotemporal variation and influencing factors on fine particulate matter and carbon monoxide concentrations near a road intersection. Front Earth Sci 11(1):1–13
Wang Z, Cai M, Peng ZR, Gao Y (2017b) Spatiotemporal distributions of roadside PM2.5 and CO concentrations based on mobile observations. China Environ Sci 37:4428–4434 (in Chinese)
Wang Z, Wang D, Peng ZR, Cai M, Fu Q, Wang D (2018a) Performance assessment of a portable nephelometer for outdoor particle mass measurement. Environ Sci Process Impact 20:370–383
Wang Z, Zhong S, He HD, Peng ZR, Cai M (2018b) Fine-scale variations in PM2.5 and black carbon concentrations and corresponding influential factors at an urban road intersection. Build Environ 141:215–225
Witte B, Singler R, Bailey S (2017) Development of an unmanned aerial vehicle for the measurement of turbulence in the atmospheric boundary layer. Atmosphere 8:195
Zhang J, Ji Y, Zhao J, Zhao J (2017a) Optimal location of a particulate matter sampling head outside an unmanned aerial vehicle. Particuology 32:153–159
Zhang K, Wang D, Bian Q, Duan Y, Zhao M, Fei D, Xiu G, Fu Q (2017b) Tethered balloon-based particle number concentration, and size distribution vertical profiles within the lower troposphere of Shanghai. Atmos Environ 154:141–150
Acknowledgments
This work was partially supported by the National Key R&D Program of China (No. 2016YFC0200500), the National Natural Science Foundation of China (No. 41701552), and the Science and Technology Project of Guangzhou, China (No. 201803030032). The authors thank the NOAA Air Resources Laboratory (ARL) for providing the HYSPLIT transport and dispersion model and the READY Web site (http://www.ready.noaa.gov) used in this study. The authors also declare no conflict of interest.
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Wang, D., Wang, Z., Peng, ZR. et al. Using unmanned aerial vehicle to investigate the vertical distribution of fine particulate matter. Int. J. Environ. Sci. Technol. 17, 219–230 (2020). https://doi.org/10.1007/s13762-019-02449-6
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DOI: https://doi.org/10.1007/s13762-019-02449-6