Brown carbon light absorption over an urban environment in northern peninsular Southeast Asia

Pani, Shantanu Kumar; Lin, Neng Huei; Griffith, Stephen M.; Chantara, Somporn; Lee, Chung‐Te; Thepnuan, Duangduean; Tsai, Ying I.

doi:10.1016/j.envpol.2021.116735

Cited by 53 publications

(22 citation statements)

References 104 publications

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“…6 from a global chemical transport model (GEOS-Chem) coupled with the rapid radiative transfer model for GCMs (RRTMG) models to study contributions of BC and BrC to light apsorption (Zhang et al, 2021). Contributions of BC and BrC were 60-67% and 33-40%, respectively, at 400 nm, consistent with the observation by Pani et al (2021). Instantaneous radiative forcing of carbonaceous PM2.5 in Chiang Mai, during the dry season, were about 4-9 times higher than those in rainy seasons.…”

Section: Accepted Manuscriptsupporting

confidence: 71%

“…Recently, not only black carbon (BC) or EC, but also brown carbon (BrC), which is a fraction of OC, have been identified as light-absorbing carbonaceous aerosols in PM2.5 (Pani et al, 2018;Pani et al, 2020;Pani et al, 2021;Tao et al, 2020;Zhang et al, 2021). Radiative forcing (RF) of biomass burning aerosols during dry season in Chiang Mai was -45.3 to -103.4 at the surface and -1.7 to +6.2 W/m 2 at top of atmosphere corresponding to low -high biomass burning emissions events respectively (Pani et al, 2018).…”

Section: Accepted Manuscriptmentioning

confidence: 99%

“…biomass burning, cooking and vehicles exhaust, and generally absorb solar radiation, whereas SOC mainly originates from photochemical reaction and scatters it (Safai et al, 2014). However, BrC, which is a fraction of OC, has recently been found to strongly absorb light in the ultraviolet and short visible wavelength regions (Pani et al, 2018;Pani et al, 2020;Pani et al, 2021). The effective carbon ratio (ECR) was estimated to understand the relation between carbonaceous aerosols and climate impact (Safai et al, 2014;Ramirez et al, 2018):…”

Section: Effective Carbon Ratio (Ecr)mentioning

confidence: 99%

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Influence of Meteorological Conditions and Fire Hotspots on PM0.1 in Northern Thailand during Strong Haze Episodes and Carbonaceous Aerosol Characterization

Sresawasd

Chetiyanukornkul

Suriyawong

et al. 2021

Aerosol Air Qual. Res.

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Northern Thailand has long been severely affected by haze from biomass burning containing fine and ultrafine aerosols in the dry period. The carbonaceous PM0.1 comprising elemental carbon (EC) and organic carbon (OC) collected during the haze and non-haze periods in Chiang Mai, Thailand was investigated. The PM0.1 levels during the haze periods were about 3 times higher than the non-haze periods, a significant increase. PM0.1 concentration was strongly correlated with atmospheric relative humidity and the number of forest fire hotspots. Carbonaceous aerosol characteristics in PM0.1 were analyzed with the thermal/optical transmittance (TOT) method

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Section: Accepted Manuscriptsupporting

confidence: 71%

Section: Accepted Manuscriptmentioning

confidence: 99%

Section: Effective Carbon Ratio (Ecr)mentioning

confidence: 99%

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Influence of Meteorological Conditions and Fire Hotspots on PM0.1 in Northern Thailand during Strong Haze Episodes and Carbonaceous Aerosol Characterization

Sresawasd

Chetiyanukornkul

Suriyawong

et al. 2021

Aerosol Air Qual. Res.

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“…The descriptive statistics of the measured variables that are used in SVR and RF models for all stations in Malaysia are summarized in Table 1. The columnar AOD 500 over the Malaysian sites during 2018-2019 exhibits a mean of 0.69, which is above the median value 0.46 due to many episodic aerosol events with AODs above 2, representing thick smoke plumes from extensive fires in Indonesia and Indochina [10,132,133]. The measured PM 2.5 concentrations at the 65 examined sites follow a similar distribution with a higher mean (21.9 µg m −3 ) than median (17.1 µg m −3 ) and a maximum value of 230 µg m −3 (Table 1, Figure 3).…”

Section: Descriptive Statisticsmentioning

confidence: 91%

Evaluation of Machine Learning Models for Estimating PM2.5 Concentrations across Malaysia

et al. 2021

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Southeast Asia (SEA) is a hotspot region for atmospheric pollution and haze conditions, due to extensive forest, agricultural and peat fires. This study aims to estimate the PM2.5 concentrations across Malaysia using machine-learning (ML) models like Random Forest (RF) and Support Vector Regression (SVR), based on satellite AOD (aerosol optical depth) observations, ground measured air pollutants (NO2, SO2, CO, O3) and meteorological parameters (air temperature, relative humidity, wind speed and direction). The estimated PM2.5 concentrations for a two-year period (2018–2019) are evaluated against measurements performed at 65 air-quality monitoring stations located at urban, industrial, suburban and rural sites. PM2.5 concentrations varied widely between the stations, with higher values (mean of 24.2 ± 21.6 µg m−3) at urban/industrial stations and lower (mean of 21.3 ± 18.4 µg m−3) at suburban/rural sites. Furthermore, pronounced seasonal variability in PM2.5 is recorded across Malaysia, with highest concentrations during the dry season (June–September). Seven models were developed for PM2.5 predictions, i.e., separately for urban/industrial and suburban/rural sites, for the four dominant seasons (dry, wet and two inter-monsoon), and an overall model, which displayed accuracies in the order of R2 = 0.46–0.76. The validation analysis reveals that the RF model (R2 = 0.53–0.76) exhibits slightly better performance than SVR, except for the overall model. This is the first study conducted in Malaysia for PM2.5 estimations at a national scale combining satellite aerosol retrievals with ground-based pollutants, meteorological factors and ML techniques. The satisfactory prediction of PM2.5 concentrations across Malaysia allows a continuous monitoring of the pollution levels at remote areas with absence of measurement networks.

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“…On the other hand, OC sources potentially originate from processes that involve chemical reactions of hydrocarbons [14]. There is a contrast between the effects of OC and EC on the climate; EC is involved in the global warming effect due to its strong light-absorption property [15,16], whereas OC is responsible for cooling the atmosphere mainly because it reflects solar radiation [17]; however, some recent publications have reported that some OC (a newly emerged phrase: brown carbon) can significantly absorb light in the region of 300-400 nm and could hinder and oppose the general cooling action [18][19][20][21]. In terms of health effects, it has been suggested that increased mortality rates and respiratory diseases are related to OC and EC content exposure [22][23][24][25][26][27][28][29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

Organic and Elemental Carbon in the Urban Background in an Eastern Mediterranean City

Hussein

Bakri

et al. 2022

Atmosphere

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The Mediterranean region is an important area for air pollution as it is the crossroads between three continents; therefore, the concentrations of atmospheric aerosol particles are influenced by emissions from Africa, Asia, and Europe. Here we concentrate on an eleven-month time series of the ambient concentration of organic carbon (OC) and elemental carbon (EC) between May 2018–March 2019 in Amman, Jordan. Such a dataset is unique in Jordan. The results show that the OC and EC annual mean concentrations in PM2.5 samples were 5.9 ± 2.8 µg m–3 and 1.7 ± 1.1 µg m–3, respectively. It was found that the majority of OC and EC concentrations were within the fine particle fraction (PM2.5). During sand and dust storm (SDS) episodes OC and EC concentrations were higher than the annual means; the mean values during these periods were about 9.6 ± 3.5 µg m–3 and 2.5 ± 1.2 µg m–3 in the PM2.5 samples. Based on this, the SDS episodes were identified to be responsible for an increased carbonaceous aerosol content as well as PM2.5 and PM10 content, which may have direct implications on human health. This study encourages us to perform more extensive measurements during a longer time period and to include an advanced chemical and physical characterization for urban aerosols in the urban atmosphere of Amman, which can be representative of other urban areas in the region.

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Brown carbon light absorption over an urban environment in northern peninsular Southeast Asia

Cited by 53 publications

References 104 publications

Influence of Meteorological Conditions and Fire Hotspots on PM0.1 in Northern Thailand during Strong Haze Episodes and Carbonaceous Aerosol Characterization

Influence of Meteorological Conditions and Fire Hotspots on PM0.1 in Northern Thailand during Strong Haze Episodes and Carbonaceous Aerosol Characterization

Evaluation of Machine Learning Models for Estimating PM2.5 Concentrations across Malaysia

Organic and Elemental Carbon in the Urban Background in an Eastern Mediterranean City

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