Monsoon Season Quantitative Assessment of Biomass Burning Clear-Sky Aerosol Radiative Effect at Surface by Ground-Based Lidar Observations in Pulau Pinang, Malaysia in 2014

Lolli, Simone; Khor, Wei Ying; MatJafri, M. Z.; Lim, H. S.

doi:10.3390/rs11222660

Cited by 50 publications

(37 citation statements)

References 52 publications

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“…Table S4 shows the cited references. The extreme values for LR@355 are 21 sr (Müller et al, 2005) and 130 sr (Tesche et al, 2011), while for LR@532 they are 26 sr (Müller et al, 2005) and 147 sr (Mariano et al, 2010). For EAE we have 0 (Müller et al, 2005) and 2.4 (Giannakaki et al, 2016).…”

Section: Calculation Of the Mean Optical Properties And Intensive Parmentioning

confidence: 99%

Biomass burning events measured by lidars in EARLINET – Part 1: Data analysis methodology

et al. 2020

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Abstract. The methodology of analysing the biomass burning events recorded in the database of the European Aerosol Research Lidar Network in the framework of the Aerosol, Clouds and Trace Gases Research Infrastructure is presented. The period of 2008–2017 was chosen to analyse all of the events stored in the database under the Forest Fire category for a total of 14 stations available. The data provided ranged from complete datasets (particle backscatter, extinction and linear depolarization ratio profiles) to single profiles (particle backscatter coefficient profile). Smoke layers geometry was evaluated and the mean optical properties within each layer were computed. The back-trajectory technique was used to double-check the source of all pollution layers. The biomass burning layers were identified by taking into account the presence of the fires along the back trajectory. The biomass burning events are analysed by the means of the intensive parameters. The analysis was structured in three directions: (I) common biomass burning source (fire) recorded by at least two stations, (II) long-range transport from North America, and (III) analysis over four geographical regions (south-eastern Europe, north-eastern Europe, central Europe, and south-western Europe). Based on back-trajectory calculations and fire locations, the lidar measurements can be labelled either as measurements of a “single fire” or “mixed fires” (case I), measurements of North American fires, or measurements of mixed North American and local fires (case II). The histogram of the fire locations reveals the smoke sources for each region. For each region, statistics on intensive parameters are performed. The source origin of the intensive parameters is categorized based on the continental origin of the air mass (European, African, Asian, North American, or a combination of them). The methodology presented here is meant to provide a perspective to explore a large number of lidar data and deliver novel approaches to analyse the intensive parameters based on the assigned biomass burning sources. A thorough consideration of all potential fire sources reveals that most of the time the lidar measurements characterize the smoke from a mixture of fires. A comprehensive discussion of all the results (based on the intensive parameters and the source locations) will be given in a companion paper submitted to the ACP EARLINET special issue.

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Section: Calculation Of the Mean Optical Properties And Intensive Parmentioning

confidence: 99%

Biomass burning events measured by lidars in EARLINET – Part 1: Data analysis methodology

et al. 2020

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“…were analyzed. Meanwhile, a trajectory analysis combined with a high resolution emission inventory may help us understand the advection of pollutants [53]. In this study, column AOD data were used to investigate the relationship with the surface PM 2.5 massive concentrations.…”

Section: Discussionmentioning

confidence: 99%

Similarities and Differences in the Temporal Variability of PM2.5 and AOD Between Urban and Rural Stations in Beijing

Song

et al. 2020

Remote Sensing

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Surface particulate matter with an aerodynamic diameter of <2.5 μm (PM2.5) and column-integrated aerosol optical depth (AOD) exhibits substantial diurnal, daily, and yearly variabilities that are regionally dependent. The diversity of these temporal variabilities in urban and rural areas may imply the inherent mechanisms. A novel time-series analysis tool developed by Facebook, Prophet, is used to investigate the holiday, seasonal, and inter-annual patterns of PM2.5 and AOD at a rural station (RU) and an urban station (UR) in Beijing. PM2.5 shows a coherent decreasing tendency at both stations during 2014–2018, consistent with the implementation of the air pollution action plan at the end of 2013. RU is characterized by similar seasonal variations of AOD and PM2.5, with the lowest values in winter and the highest in summer, which is opposite that at UR with maximum AOD, but minimum PM2.5 in summer and minimum AOD, but maximum PM2.5 in winter. During the National Day holiday (1–7 October), both AOD and PM2.5 holiday components regularly shift from negative to positive departures, and the turning point generally occurs on October 4. AODs at both stations steadily increase throughout the daytime, which is most striking in winter. A morning rush hour peak of PM2.5 (7:00–9:00 local standard time (LST)) and a second peak at night (23:00 LST) are observed at UR. PM2.5 at RU often reaches minima (maxima) at around 12:00 LST (19:00 LST), about four hours later (earlier) than UR. The ratio of PM2.5 to AOD (η) shows a decreasing tendency at both stations in the last four years, indicating a profound impact of the air quality control program. η at RU always begins to increase about 1–2 h earlier than that at UR during the daytime. Large spatial and temporal variations of η suggest that caution should be observed in the estimation of PM2.5 from AOD.

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“…Therefore, here we focus on respiratory droplet spread rather than aerosol matter. We do note, however, that aerosol spread is an important aspect of transmission, and can spread over large areas over time as displayed by larger scale studies that analyze atmospheric aerosol behavior [ 7 , 8 ]. In order to develop and inform best practices for personal protective equipment, various simulations of respiratory and aerosolized droplet dispersion have been conducted.…”

Section: Introductionmentioning

confidence: 99%

Assessing the Physiological Relevance of Cough Simulators for Respiratory Droplet Dispersion

Patel

Yim

Garg

et al. 2020

JCM

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Various breathing and cough simulators have been used to model respiratory droplet dispersion and viral droplets, in particular for SARS-CoV-2 modeling. However, limited data are available comparing these cough simulations to physiological breathing and coughing. In this study, three different cough simulators (Teleflex Mucosal Atomization Device Nasal (MAD Nasal), a spray gun, and GloGermTM MIST) that have been used in the literature were studied to assess their physiologic relevance. Droplet size, velocity, dispersion, and force generated by the simulators were measured. Droplet size was measured with scanning electron microscopy (SEM). Slow-motion videography was used to 3D reconstruct and measure the velocity of each simulated cough. A force-sensitive resistor was used to measure the force of each simulated cough. The average size of droplets from each cough simulator was 176 to 220 µm. MAD Nasal, the spray gun, and GloGermTM MIST traveled 0.38 m, 0.89 m, and 1.62 m respectively. The average velocities for the MAD Nasal, spray gun, and GloGermTM MIST were 1.57 m/s, 2.60 m/s, and 9.27 m/s respectively, and all yielded a force of <0.5 Newtons. GloGermTM MIST and the spray gun most closely resemble physiological coughs and breathing respectively. In conclusion, none of the simulators tested accurately modeled all physiologic characteristics (droplet size, 3-D dispersion velocity, and force) of a cough, while there were various strengths and weaknesses of each method. One should take this into account when performing simulations with these devices.

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Monsoon Season Quantitative Assessment of Biomass Burning Clear-Sky Aerosol Radiative Effect at Surface by Ground-Based Lidar Observations in Pulau Pinang, Malaysia in 2014

Cited by 50 publications

References 52 publications

Biomass burning events measured by lidars in EARLINET – Part 1: Data analysis methodology

Biomass burning events measured by lidars in EARLINET – Part 1: Data analysis methodology

Similarities and Differences in the Temporal Variability of PM2.5 and AOD Between Urban and Rural Stations in Beijing

Assessing the Physiological Relevance of Cough Simulators for Respiratory Droplet Dispersion

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