A study of vertical distribution patterns of PM2.5 concentrations based on ambient monitoring with unmanned aerial vehicles: A case in Hangzhou, China

Peng, Zhong-Ren; Wang, Dongsheng; Wang, Zhanyong; Gao, Ya; Lü, Sijia

doi:10.1016/j.atmosenv.2015.10.074

Cited by 124 publications

(64 citation statements)

References 26 publications

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“…PBLH is a critical factor affecting the vertical dilution of air pollutants, and decreasing PBLH could trap anthropogenic pollutants emitted from the Earth's surface, causing high concentrations of air pollutants [37][38][39]. Unfortunately, PBLH was not observed during the study period, and the effects of PBLH on PM concentrations can not be examined in this study.…”

Section: Effects Of Meteorological Parameters On Pm 25 and Pm 25-10mentioning

confidence: 78%

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Potential Sources and Formations of the PM2.5 Pollution in Urban Hangzhou

Wang

et al. 2016

Atmosphere

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Continuous measurements of meteorological parameters, gaseous pollutants, particulate matters, and the major chemical species in PM 2.5 were conducted in urban Hangzhou from 1 September to 30 November 2013 to study the potential sources and formations of PM 2.5 pollution. The average PM 2.5 concentration was 69 µg¨m´3,~97% higher than the annual concentration limit in the national ambient air quality standards (NAAQS) of China. Relative humidity (RH) and wind speed (WS) were two important factors responsible for the increase of PM 2.5 concentration, with the highest value observed under RH of 70%-90%. PM 2.5 was in good correlation with both NO 2 and CO, but not with SO 2 , and the potential source contribution function (PSCF) results displayed that local emissions were important potential sources contributing to the elevated PM 2.5 and NO 2 in Hangzhou. Thus, local vehicle emission was suggested as a major contribution to the PM 2.5 pollution. Concentrations of NO 2 and CO significantly increased in pollution episodes, while the SO 2 concentration even decreased, implying local emission rather than region transport was the major source contributing to the formation of pollution episodes. The sum of SO 4 2´, NO 3´, and NH 4 + accounted for~50% of PM 2.5 in mass in pollution episodes and the NO 3´/ EC ratios were significantly elevated, revealing that the formation of secondary inorganic species, particularly NO 3´, was an important contributor to the PM 2.5 pollution in Hangzhou. This study highlights that controlling local pollution emissions was essential to reduce the PM 2.5 pollution in Hangzhou, and the control of vehicle emission in particular should be further promoted in the future.

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Section: Effects Of Meteorological Parameters On Pm 25 and Pm 25-10mentioning

confidence: 78%

“…The occurrence of low WS (<2.0 m·s −1 ) was up to ~85% in Hangzhou (Figure 3b). Thus, local anthropogenic emission could easily trigger pollution episodes [37].…”

Section: Effects Of Meteorological Parameters On Pm 25 and Pm 25-10mentioning

confidence: 99%

Potential Sources and Formations of the PM2.5 Pollution in Urban Hangzhou

Wang

et al. 2016

Atmosphere

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“…The low operational altitude of UAS surveys results in the generation of ultra-high resolution data [5], while their reduced physical size allows their rapid deployment, improving their capability to exploit limited windows of opportunity [6]. Furthermore, the low cost of platforms and navigation systems together with the variety of available sensors make the UAS suitable to be employed in many situations where a traditional vehicle (i.e., airplane) would be too expensive to justify its use.…”

Section: Introductionmentioning

confidence: 99%

Improving Tree Species Classification Using UAS Multispectral Images and Texture Measures

Gini

Sona

Ronchetti

et al. 2018

IJGI

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This paper focuses on the use of ultra-high resolution Unmanned Aircraft Systems (UAS) imagery to classify tree species. Multispectral surveys were performed on a plant nursery to produce Digital Surface Models and orthophotos with ground sample distance equal to 0.01 m. Different combinations of multispectral images, multi-temporal data, and texture measures were employed to improve classification. The Grey Level Co-occurrence Matrix was used to generate texture images with different window sizes and procedures for optimal texture features and window size selection were investigated. The study evaluates how methods used in Remote Sensing could be applied on ultra-high resolution UAS images. Combinations of original and derived bands were classified with the Maximum Likelihood algorithm, and Principal Component Analysis was conducted in order to understand the correlation between bands. The study proves that the use of texture features produces a significant increase of the Overall Accuracy, whose values change from 58% to 78% or 87%, depending on components reduction. The improvement given by the introduction of texture measures is highlighted even in terms of User's and Producer's Accuracy. For classification purposes, the inclusion of texture can compensate for difficulties of performing multi-temporal surveys.

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“…On 8-9 December 2015, the meteorological center of China meteorological administration used unmanned helicopters to detect the vertical variation of aerosol mass concentration in Fangshan, Beijing [28]. Peng Z R used the mobile sensor carried by the UAV to monitor PM2.5 concentration in Hangzhou [29]. UAVs can be used for aerial-pollutant measurements, but there are several limitations to existing UAV PM monitoring systems: first, the purpose of most previous studies [30,31] is mainly to integrate sensors into the UAV to acquire, store, and display data in real time.…”

Section: Introductionmentioning

confidence: 99%

Unmanned Aerial Vehicle-Borne Sensor System for Atmosphere-Particulate-Matter Measurements: Design and Experiments

Wang

Han

Zhang

et al. 2019

Sensors

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An unmanned aerial vehicle (UAV) particulate-matter (PM) monitoring system was developed that can perform three-dimensional stereoscopic observation of PM2.5 and PM10 in the atmosphere. The UAV monitoring system was mainly integrated by modules of data acquisition and processing, wireless data transmission, and global positioning system (GPS). Particularly, in this study, a ground measurement-control subsystem was added that can display and store collected data in real time and set up measurement scenarios, data-storage modes, and system sampling frequency as needed. The UAV PM monitoring system was calibrated via comparison with a national air-quality monitoring station; the data of both systems were highly correlated. Since rotation of the UAV propeller affects measured PM concentration, this study specifically tested this effect by setting up another identical monitoring system fixed at a tower as reference. The UAV systems worked simultaneously to collect data for comparison. A correction method for the propeller disturbance was proposed. Averaged relative errors for the PM2.5 and PM10 concentrations measured by the two systems were 6.2% and 6.6%, respectively, implying that the UAV system could be used for monitoring PM in an atmosphere environment.

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A study of vertical distribution patterns of PM2.5 concentrations based on ambient monitoring with unmanned aerial vehicles: A case in Hangzhou, China

Cited by 124 publications

References 26 publications

Potential Sources and Formations of the PM2.5 Pollution in Urban Hangzhou

Potential Sources and Formations of the PM2.5 Pollution in Urban Hangzhou

Improving Tree Species Classification Using UAS Multispectral Images and Texture Measures

Unmanned Aerial Vehicle-Borne Sensor System for Atmosphere-Particulate-Matter Measurements: Design and Experiments

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