Impacts of springtime biomass burning in the northern Southeast Asia on marine organic aerosols over the Gulf of Tonkin, China

2018 ◽  
Vol 237 ◽  
pp. 285-297 ◽  
Author(s):  
Lishan Zheng ◽  
Xiaoyang Yang ◽  
Senchao Lai ◽  
Hong Ren ◽  
Siyao Yue ◽  
...  
2012 ◽  
Vol 12 (2) ◽  
pp. 1083-1100 ◽  
Author(s):  
W. Trivitayanurak ◽  
P. I. Palmer ◽  
M. P. Barkley ◽  
N. H. Robinson ◽  
H. Coe ◽  
...  

Abstract. We use a nested version of the GEOS-Chem global 3-D chemistry transport model to better understand the composition and variation of aerosol over Borneo and the broader Southeast Asian region in conjunction with aircraft and satellite observations. Our focus on Southeast Asia reflects the importance of this region as a source of reactive organic gases and aerosols from natural forests, biomass burning, and food and fuel crops. We particularly focus on July 2008 when the UK BAe-146 research aircraft was deployed over northern Malaysian Borneo as part of the ACES/OP3 measurement campaign. During July 2008 we find using the model that Borneo (defined as Borneo Island and the surrounding Indonesian islands) was a net exporter of primary organic aerosol (42 kT) and black carbon aerosol (11 kT). We find only 13% of volatile organic compound oxidation products partition to secondary organic aerosol (SOA), with Borneo being a net exporter of SOA (15 kT). SOA represents approximately 19% of the total organic aerosol over the region. Sulphate is mainly from aqueous-phase oxidation (68%), with smaller contributions from gas-phase oxidation (15%) and advection into the regions (14%). We find that there is a large source of sea salt, as expected, but this largely deposits within the region; we find that dust aerosol plays only a relatively small role in the aerosol burden. In contrast to coincident surface measurements over Northern Borneo that find a pristine environment with evidence for substantial biogenic SOA formation we find that the free troposphere is influenced by biomass burning aerosol transported from the northwest of the Island and further afield. We find several transport events during July 2008 over Borneo associated with elevated aerosol concentrations, none of which coincide with the aircraft flights. We use MODIS aerosol optical depths (AOD) data and the model to put the July campaign into a longer temporal perspective. We find that Borneo is where the model has the least skill at reproducing the data, where the model has a negative bias of 76% and only captures 14% of the observed variability. This model performance reflects the small-scale island-marine environment and the mix of aerosol species, with the model showing more skill at reproducing observed AOD over larger continental regions such as China where AOD is dominated by one aerosol type. The model shows that AOD over Borneo is approximately evenly split between organic and sulphate aerosol with sea salt representing 10–20% during May–September; we find a similar breakdown over continental Southeast Asia but with less sea salt aerosol and more dust aerosol. In contrast, East China AOD is determined mainly by sulphate aerosol and a seasonal source of dust aerosol, as expected. Realistic sensitivity runs, designed to test our underlying assumptions about emissions and chemistry over Borneo, show that model AOD is most sensitive to isoprene emissions and organic gas-phase partitioning but all fail to improve significantly upon the control model calculation. This emphasises the multi-faceted dimension of the problem and the need for concurrent and coordinated development of BVOC emissions, and BVOC chemistry and organic aerosol formation mechanisms.


2010 ◽  
Vol 44 (22) ◽  
pp. 8453-8459 ◽  
Author(s):  
Yoshiteru Iinuma ◽  
Olaf Böge ◽  
Ricarda Gräfe ◽  
Hartmut Herrmann

Author(s):  
Krishna Prasad Vadrevu ◽  
Toshimasa Ohara ◽  
Christopher Justice

2019 ◽  
Vol 19 (24) ◽  
pp. 15247-15270 ◽  
Author(s):  
Jianhui Jiang ◽  
Sebnem Aksoyoglu ◽  
Imad El-Haddad ◽  
Giancarlo Ciarelli ◽  
Hugo A. C. Denier van der Gon ◽  
...  

Abstract. Source apportionment of organic aerosols (OAs) is of great importance to better understand the health impact and climate effects of particulate matter air pollution. Air quality models are used as potential tools to identify OA components and sources at high spatial and temporal resolution; however, they generally underestimate OA concentrations, and comparisons of their outputs with an extended set of measurements are still rare due to the lack of long-term experimental data. In this study, we addressed such challenges at the European level. Using the regional Comprehensive Air Quality Model with Extensions (CAMx) and a volatility basis set (VBS) scheme which was optimized based on recent chamber experiments with wood burning and diesel vehicle emissions, and which contains more source-specific sets compared to previous studies, we calculated the contribution of OA components and defined their sources over a whole-year period (2011). We modeled separately the primary and secondary OA contributions from old and new diesel and gasoline vehicles, biomass burning (mostly residential wood burning and agricultural waste burning excluding wildfires), other anthropogenic sources (mainly shipping, industry and energy production) and biogenic sources. An important feature of this study is that we evaluated the model results with measurements over a longer period than in previous studies, which strengthens our confidence in our modeled source apportionment results. Comparison against positive matrix factorization (PMF) analyses of aerosol mass spectrometric measurements at nine European sites suggested that the modified VBS scheme improved the model performance for total OA as well as the OA components, including hydrocarbon-like (HOA), biomass burning (BBOA) and oxygenated components (OOA). By using the modified VBS scheme, the mean bias of OOA was reduced from −1.3 to −0.4 µg m−3 corresponding to a reduction of mean fractional bias from −45 % to −20 %. The winter OOA simulation, which was largely underestimated in previous studies, was improved by 29 % to 42 % among the evaluated sites compared to the default parameterization. Wood burning was the dominant OA source in winter (61 %), while biogenic emissions contributed ∼ 55 % to OA during summer in Europe on average. In both seasons, other anthropogenic sources comprised the second largest component (9 % in winter and 19 % in summer as domain average), while the average contributions of diesel and gasoline vehicles were rather small (∼ 5 %) except for the metropolitan areas where the highest contribution reached 31 %. The results indicate the need to improve the emission inventory to include currently missing and highly uncertain local emissions, as well as further improvement of VBS parameterization for winter biomass burning. Although this study focused on Europe, it can be applied in any other part of the globe. This study highlights the ability of long-term measurements and source apportionment modeling to validate and improve emission inventories, and identify sources not yet properly included in existing inventories.


Atmosphere ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 330 ◽  
Author(s):  
Manousos Ioannis Manousakas ◽  
Kalliopi Florou ◽  
Spyros N. Pandis

Fine particulate matter (PM) originates from various emission sources and physicochemical processes. Quantification of the sources of PM is an important step during the planning of efficient mitigation strategies and the investigation of the potential risks to human health. Usually, source apportionment studies focus either on the organic or on the inorganic fraction of PM. In this study that took place in Patras, Greece, we address both PM fractions by combining measurements from a range of on- and off-line techniques, including elemental composition, organic and elemental carbon (OC and EC) measurements, and high-resolution Aerosol Mass Spectrometry (AMS) from different techniques. Six fine PM2.5 sources were identified based on the off-line measurements: secondary sulfate (34%), biomass burning (15%), exhaust traffic emissions (13%), nonexhaust traffic emissions (12%), mineral dust (10%), and sea salt (5%). The analysis of the AMS spectra quantified five factors: two oxygenated organic aerosols (OOA) factors (an OOA and a marine-related OOA, 52% of the total organic aerosols (OA)), cooking OA (COA, 11%) and two biomass burning OA (BBOA-I and BBOA-II, 37% in total) factors. The results of the two methods were synthesized, showcasing the complementarity of the two methodologies for fine PM source identification. The synthesis suggests that the contribution of biomass burning is quite robust, but that the exhaust traffic emissions are not due to local sources and may also include secondary OA from other sources.


2009 ◽  
Vol 9 (21) ◽  
pp. 8573-8585 ◽  
Author(s):  
Q. Wang ◽  
M. Shao ◽  
Y. Zhang ◽  
Y. Wei ◽  
M. Hu ◽  
...  

Abstract. Fine particles (PM2.5, i.e., particles with an aerodynamic diameter of ≤2.5 μm) were collected from the air in August 2005, August–September 2006, and January–February 2007, in Beijing, China. The chemical compositions of particulate organic matter in the ambient samples were quantified by gas chromatography/mass spectrometry. The dominant compounds identified in summertime were n-alkanoic acids, followed by dicarboxylic acids and sugars, while sugars became the most abundant species in winter, followed by polycyclic aromatic hydrocarbons, n-alkanes, and n-alkanoic acids. The contributions of seven emission sources (i.e., gasoline/diesel vehicles, coal burning, wood/straw burning, cooking, and vegetative detritus) to particulate organic matter in PM2.5 were estimated using a chemical mass balance receptor model. The model results present the seasonal trends of source contributions to organic aerosols. Biomass burning (straw and wood) had the highest contribution in winter, followed by coal burning, vehicle exhaust, and cooking. The contribution of cooking was the highest in summer, followed by vehicle exhaust and biomass burning, while coal smoke showed only a minor contribution to ambient organic carbon.


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