Coal and biomass burning as major emissions of NOX in Northeast China: Implication from dual isotopes analysis of fine nitrate aerosols

Zhao, Zhu‐Yu; Cao, Fang; Fan, Mei‐Yi; Zhang, Wenqi; Zhai, Xiao-Yao; Wang, Qian; Zhang, Yanlin

doi:10.1016/j.atmosenv.2020.117762

Cited by 41 publications

(20 citation statements)

References 69 publications

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“…The presence of NH4NO3 aerosols in biomass burning plumes, has also been reported previously (Paulot et al, 2017;Zhao et al, 2020).…”

Section: Oil Combustionsupporting

confidence: 77%

Insight into PM2.5 Sources by Applying Positive Matrix Factorization (PMF) at an Urban and Rural Site of Beijing

Srivastava¹,

Xu²,

Vu³

et al. 2021

Preprint

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Abstract. This study presents the source apportionment of PM2.5 performed by PMF on data collected at an urban (Institute of Atmospheric Physics – IAP) and a rural site (Pinggu-PG) in Beijing as part of the Atmospheric Pollution and Human Health in a Chinese megacity (APHH-Beijing) field campaigns. The campaigns were carried out from 9th November to 11th December 2016 and 22nd May to 24th June 2017. The PMF included both organic and inorganic species, and a seven-factor output provided the most reasonable solution for the PM2.5 source apportionment. These factors are interpreted to be traffic emissions, biomass burning, road dust, soil dust, coal combustion, oil combustion and secondary inorganics. Major contributors to PM2.5 mass were secondary inorganics (22–24 %), biomass burning (30–36 %), and coal combustion (20–21 %) sources during the winter period at both sites. Secondary inorganics (48 %), road dust (20 %) and coal combustion (17 %) showed the highest contribution during summer at PG, while PM2.5 particles were mainly composed of soil dust (35 %) and secondary inorganics (40 %) at IAP. Despite this, factors that were resolved based on metal signatures were not fully resolved and indicate a mixing of two or more sources. PMF results were also compared with sources resolved from another receptor model (i.e. CMB) and PMF performed on other measurements (i.e. online and offline aerosol mass spectrometry (AMS)) and showed a good agreement for some but not all sources. The biomass burning factor in PMF may contain aged aerosols as a good correlation was observed between biomass burning and oxygenated fractions (r2 = 0.6–0.7) from AMS. The PMF failed to resolve some sources identified by the CMB and AMS, and appears to overestimate the dust sources. A comparison with earlier PMF source apportionment studies from the Beijing area highlights the very divergent findings from application of this method.

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“…The presence of NH4NO3 aerosols in biomass burning plumes, has also been reported previously (Paulot et al, 2017;Zhao et al, 2020).…”

Section: Oil Combustionsupporting

confidence: 77%

Insight into PM2.5 Sources by Applying Positive Matrix Factorization (PMF) at an Urban and Rural Site of Beijing

Srivastava¹,

Xu²,

Vu³

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…Tropospheric NO 2 column density was selected as one of the input variable candidates to account for the presence of scattering-dominant aerosols. Nitrate aerosols, which are one of the scattering-dominant aerosols, are produced by the oxidation of nitrogen oxides (NOx) [35][36][37][38]. NOx is produced by all combustion sources including vehicular emissions, coal combustion, biomass burning, and industrial sources [38][39][40][41][42].…”

Section: Satellite Input Variable Candidatesmentioning

confidence: 99%

“…Nitrate aerosols, which are one of the scattering-dominant aerosols, are produced by the oxidation of nitrogen oxides (NOx) [35][36][37][38]. NOx is produced by all combustion sources including vehicular emissions, coal combustion, biomass burning, and industrial sources [38][39][40][41][42]. TROPOMI SO 2 column density in the planetary boundary layer is reported to have a product accuracy of about 50% or more due to surface albedo or SO 2 vertical profile shape [43].…”

Section: Satellite Input Variable Candidatesmentioning

confidence: 99%

A First Approach to Aerosol Classification Using Space-Borne Measurement Data: Machine Learning-Based Algorithm and Evaluation

2021

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A new method was developed for classifying aerosol types involving a machine-learning approach to the use of satellite data. An Aerosol Robotic NETwork (AERONET)-based aerosol-type dataset was used as a target variable in a random forest (RF) model. The contributions of satellite input variables to the RF-based model were quantified to determine an optimal set of input variables. The new method, based on inputs of satellite variables, allows the classification of seven aerosol types: pure dust, dust-dominant mixed, pollution-dominant mixed aerosols, and pollution aerosols (strongly, moderately, weakly, and non-absorbing). The performance of the model was statistically evaluated using AERONET data excluded from the model training dataset. Model accuracy for classifying the seven aerosol types was 59%, improving to 72% for four types (pure dust, dust-dominant mixed, strongly absorbing, and non-absorbing). The performance of the model was evaluated against an earlier aerosol classification method based on the wavelength dependence of single-scattering albedo (SSA) and fine-mode-fraction values from AERONET. Typical wavelength dependences of SSA for individual aerosol types are consistent with those obtained for aerosol types by the new method. This study demonstrates that an RF-based model is capable of satellite aerosol classification with sensitivity to the contribution of non-spherical particles.

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“…These two pollutants also produce negative synergistic effects with particulate matter and SO 2 in many diseases in which their relative risks (RRs) are statistically significant and the highest [8]. Although there are some natural emissions of NOx, the majority of emissions are from anthropogenic sources [9], in which coal and biomass are regarded as the major sources of emissions in northeastern China [10], and urban-traffic NO 2 has increased the COVID-19 pandemic fatality rate [11]. O 3 pollution is also serious due to its close relationship with NOx.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of O3 and NOx Pollution Based on People’s Perception

Jiang¹,

Zhu²,

Jiang³

et al. 2021

Pol. J. Environ. Stud.

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Coal and biomass burning as major emissions of NOX in Northeast China: Implication from dual isotopes analysis of fine nitrate aerosols

Cited by 41 publications

References 69 publications

Insight into PM<sub>2.5</sub> Sources by Applying Positive Matrix Factorization (PMF) at an Urban and Rural Site of Beijing

Insight into PM<sub>2.5</sub> Sources by Applying Positive Matrix Factorization (PMF) at an Urban and Rural Site of Beijing

A First Approach to Aerosol Classification Using Space-Borne Measurement Data: Machine Learning-Based Algorithm and Evaluation

Evaluation of O<sub>3</sub> and NOx Pollution Based on People’s Perception

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Coal and biomass burning as major emissions of NOX in Northeast China: Implication from dual isotopes analysis of fine nitrate aerosols

Cited by 41 publications

References 69 publications

Insight into PM&lt;sub&gt;2.5&lt;/sub&gt; Sources by Applying Positive Matrix Factorization (PMF) at an Urban and Rural Site of Beijing

Insight into PM&lt;sub&gt;2.5&lt;/sub&gt; Sources by Applying Positive Matrix Factorization (PMF) at an Urban and Rural Site of Beijing

A First Approach to Aerosol Classification Using Space-Borne Measurement Data: Machine Learning-Based Algorithm and Evaluation

Evaluation of O<sub>3</sub> and NOx Pollution Based on People’s Perception

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Product

Resources

About

Insight into PM<sub>2.5</sub> Sources by Applying Positive Matrix Factorization (PMF) at an Urban and Rural Site of Beijing

Insight into PM<sub>2.5</sub> Sources by Applying Positive Matrix Factorization (PMF) at an Urban and Rural Site of Beijing