Comparison of the Vertical PM2.5 Distributions according to Atmospheric Stability Using a Drone during Open Burning Events

Oh, Hyo-Sook; Lee, Seung-Hyeop; Choi, Dongwon; Kwak, Kyung-Hwan

doi:10.5572/kosae.2020.36.1.108

Cited by 4 publications

(5 citation statements)

References 19 publications

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“…Biological combustion such as incineration can be interpreted to be the cause of high local concentration of PM 2.5 . This is considered consistent with the results of previous studies that have reported it to be one of the factors influencing the occurrence of PM 2.5 and shown a rather low ranking for PM 10 [31]. On the other hand, in PM 10 , total mileage per year (XT5) ranked second, but this factor ranked slightly lower in PM 2.5 .…”

Section: Risk Detectorsupporting

confidence: 92%

“…In South Korea, quantitative emissions statistics from various sectors are currently provided by the Atmospheric Policy Support System (CAPSS) of the Ministry of Environment. Furthermore, as studies proposing the problem of micro-dust generation due to incineration are gradually being discussed, open burning performed in agricultural activities or the incineration ratio among domestic waste disposal methods, for example, can be used as variables [31].…”

Section: Influencing Factors Of Particulate Matter Distributionmentioning

confidence: 99%

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Spatial-Temporal Characteristics and Influencing Factors of Particulate Matter: Geodetector Approach

Mun

Jung

2022

Land

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In 2019, South Korea’s Framework Act on The Management of Disasters and Safety was revised to include respirable particulate matter as a social disaster. Urban air pollution, especially particulate matter pollution, has been a serious threat to socioeconomic development and public health. In order to address this problem, strong climate crisis response strategies and policies to improve urban air quality are necessary. Therefore, it is of great importance to assess the frequency of urban air pollution occurrence and its influencing factors. The objective of this study is to develop consistent methodologies for the construction of an index system and for assessing the influencing factors of urban particulate matter pollution based on population, social welfare, land use, environmental, transportation, and economic governance considerations. We applied the local indicators of spatial association and geographical detector methods, and 35 influencing factors were selected to assess their influence on urban air pollution occurrence in 229 cities and counties in South Korea. The results indicated the spatial pattern of the particulate matter concentration in these locations showed strong spatial correlation, and it was confirmed that there was a difference in distribution according to the season. As a result of the analysis of influencing factors, it was found that environment and land use characteristics were the main influencing factors for PM10 and PM2.5. The explanatory power between the two influencing factors of particulate matter was greater than that of a single influencing factor. In addition, most influencing factors resulted in both positive and negative effects on urban fine particulate matter pollution. The interaction relationship of all factors showed a strong action effect in the case of both PM10 and PM2.5, so it was confirmed that all influencing factors were interdependent. In particular, the findings proved that combining the two factors would have a more pronounced effect on particulate matter than when they were independent. We confirmed the significant results for the factors affecting particulate matter. This study offers suggestions on reducing urban air pollution occurrence that can be used to provide a basis and reference for the government to form policies on urban air pollution control in cities and counties.

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Section: Risk Detectorsupporting

confidence: 92%

Section: Influencing Factors Of Particulate Matter Distributionmentioning

confidence: 99%

Spatial-Temporal Characteristics and Influencing Factors of Particulate Matter: Geodetector Approach

Mun

Jung

2022

Land

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“…The drone used in this study was an octocopter drone (DJI A2) with rotary wings that can hover at a certain altitude. The total width and height are 105 and 50 cm, respectively, and the flight time is approximately 10 min with a carrying capacity of 2 kg or more [ 33 ]. Meteorological (Imet-XQ 2 for air temperature (Temp) and relative humidity (RH)), particulate matter (AM520 for PM 2.5 and MA200 for BC), and gaseous (Aeroqual 500 for O 3 ) sensors were used for drone measurements.…”

Section: Methodsmentioning

confidence: 99%

Assessing 3-D Spatial Extent of Near-Road Air Pollution around a Signalized Intersection Using Drone Monitoring and WRF-CFD Modeling

Lee

Kwak

2020

IJERPH

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In this study, we have assessed the three-dimensional (3-D) spatial extent of near-road air pollution around a signalized intersection in a densely populated area using collaborating methodologies of stationary measurements, drone monitoring, and atmospheric dispersion modeling. Stationary measurement data collected in the roadside apartment building showed a substantial effect of emitted pollutants, such as nitrogen oxides (NOx), black carbon (BC), and ultrafine particles (UFPs), especially during the morning rush hours. Vertical drone monitoring near the road intersection exhibited a steeper decreasing trend with increasing altitude for BC concentration rather than for fine particulate matter (PM2.5) concentration below the apartment building height. Atmospheric NOx dispersion was simulated using the weather research and forecasting (WRF) and computational fluid dynamics (CFD) models for the drone measurement periods. Based on the agreement between the measured BC and simulated NOx concentrations, we concluded that the air pollution around the road intersection has adverse effects on the health of residents living within the 3-D spatial extent within at least 120 m horizontally and a half of building height vertically during the morning rush hours. The comparability between drone monitoring and WRF-CFD modeling can further guarantee the identification of air pollution hotspots using the methods.

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“…The rotary-wing UAV used for the observation was an octocopter UAV with a total width of 1050 mm, a height of 500 mm, and a maximum carrying capacity of 2 kg (Kpro System, Seoul, Korea) [9]. The maximum flight time of the fully equipped UAV is 12 min.…”

Section: Tower and Uav Observationsmentioning

confidence: 99%

“…Nocturnal low-level jet and temperature inversion are well-known examples of wind and thermal phenomena, respectively, which occur in the lower ABL [6,7]. In addition, air pollutants frequently accumulate within the lower ABL close to emission sources when the atmosphere is stable [8,9]. The variations in the vertical structure of lower ABL are predominantly attributed to the heating of the earth's surface due to solar insolation, topography, land cover types, vegetation, anthropogenic heat, surface obstacles, etc.…”

Section: Introductionmentioning

confidence: 99%

Daytime Evolution of Lower Atmospheric Boundary Layer Structure: Comparative Observations between a 307-m Meteorological Tower and a Rotary-Wing UAV

et al. 2020

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A 307-m tall meteorological tower was used to evaluate meteorological observation data obtained using a rotary-wing unmanned aerial vehicle (UAV). A comparative study between the tower and UAV observations was conducted during the daytime (06:00 to 19:00 local time (LT)) in the summer of 2017 (16–18thAugust). Hourly vertical profiles of air temperature, relative humidity, black carbon (BC), and ozone (O3) concentrations were obtained for up to 300 m height. Statistical metrics for evaluating the accuracy of UAV observations against the tower observation showed positive (potential temperature) and negative (relative humidity) biases, which were within acceptable ranges. The daytime evolution of the lower atmospheric boundary layer (ABL) was successfully captured by the hourly UAV observations. During the early morning, a large vertical slope of potential temperature was observed between 100 and 140 m, corresponding to the stable ABL height. The large vertical slope coincided with the large differences in BC and O3 concentrations between altitudes below and above the height. The transition from stable to convective ABL was observed at 10–11 LT, indicated by the ABL height higher than 300 m in the convective ABL. Finally, we provide several recommendations to reduce uncertainties of UAV observation.

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Comparison of the Vertical PM2.5 Distributions according to Atmospheric Stability Using a Drone during Open Burning Events

Cited by 4 publications

References 19 publications

Spatial-Temporal Characteristics and Influencing Factors of Particulate Matter: Geodetector Approach

Spatial-Temporal Characteristics and Influencing Factors of Particulate Matter: Geodetector Approach

Assessing 3-D Spatial Extent of Near-Road Air Pollution around a Signalized Intersection Using Drone Monitoring and WRF-CFD Modeling

Daytime Evolution of Lower Atmospheric Boundary Layer Structure: Comparative Observations between a 307-m Meteorological Tower and a Rotary-Wing UAV

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