Effects of biomass burning and photochemical oxidation on the black carbon mixing state and light absorption in summer season

Wang, Tiantian; Zhao, Gang; Tan, Tianyi; Yu, Ying; Tang, Rongzhi; Dong, Huabin; Chen, Shiyi; Li, Xin; Lu, Keding; Zeng, Limin; Gao, Yaqin; Wang, Hongli; Lou, Shengrong; Liu, Dantong; Hu, Min; Zhao, Chunsheng; Guo, Song

doi:10.1016/j.atmosenv.2021.118230

Cited by 15 publications

(4 citation statements)

References 63 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Tsagkaraki M. used CMB to analyze the source of atmospheric PM10 and PM2.5 in a certain place and concluded that urban dust, coal dust, and motor vehicle exhaust dust are the three major pollution sources of atmospheric PM10 and PM2.5 [14]. Wang T. analyzed the source of atmospheric PM10 in a place by using the CMB model and concluded that dust is the main source of PM10 [15]. Miszczak E. analyzed the sources of nonmethane hydrocarbons in the atmosphere of a place in 2006 by the PMF method and obtained that gasoline volatilization, automobile exhaust, combustion source, industrial emission, and plant emission are five possible sources of nonmethane hydrocarbons [16].…”

Section: Literature Reviewmentioning

confidence: 99%

Human Health Risk Prediction Method of Regional Atmospheric Environmental Pollution Sources Based on PMF and PCA Analysis under Artificial Intelligence Cloud Model

Zhang

Sun

Liu

et al. 2022

International Journal of Analytical Chemistry

View full text Add to dashboard Cite

In order to solve the problem that atmospheric particulate matter has become the primary pollutant with serious harm and complex sources in recent years, this paper proposes an accurate identification method of pollution sources based on a receptor model to obtain the contribution rate of each pollution source category. This method takes the 75-day measured environmental receptor data of an area under the artificial intelligence cloud model as the basic data, uses the normrnd () function to expand the receptor data, and uses the positive definite matrix factor analysis (PMF) and principal component analysis (PCA) models to verify the rationality of the data expansion. The results are as follows: the number of extended simulated receptor component spectra has a certain effect on the PCA analysis results, but the effect is smaller than the extended range. All relative errors are less than 14%, and the relative error is the smallest when the six simulated receptor component spectra are expanded, that is, the PCA analysis results of the expanded data are most consistent with the measured data; the number of expanded simulated receptor component spectra has a certain influence on the PMF analysis results. But the relative error is less than 40%. When extending the spectrum of six simulated receptor components, the relative error is the smallest, that is, the PMF analysis results of the extended data are most consistent with the measured data. It is proven that this method provides a more direct basis for the targeted treatment of pollution sources that are more harmful to human health.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Human Health Risk Prediction Method of Regional Atmospheric Environmental Pollution Sources Based on PMF and PCA Analysis under Artificial Intelligence Cloud Model

Zhang

Sun

Liu

et al. 2022

International Journal of Analytical Chemistry

View full text Add to dashboard Cite

show abstract

“…No entanto, esses produtos secundários são ainda mais difíceis de prever em virtude dos vários fatores envolvidos. (24)(25)(26)(27) A região Norte do Brasil não possui redes de monitoramento contínuo da qualidade do ar, (28) exceto por uma iniciativa pioneira estabelecida pela Universidade Federal do Acre, que passou a monitorar MP com diâmetro médio < 2,5 µm (MP 2,5 ) em 22 cidades do estado do Acre em 2019. (29) Os resultados mostraram que, durante a estação seca, a concentração média de MP 2,5 nos 22 municípios ficou em torno de 40 µg/m 3 , muitas vezes ultrapassando o limite recomendado pela OMS (média em 24 h de 25 µg/m 3 ).…”

Section: Emissões De Incêndios E Qualidade Do Arunclassified

“…Três mecanismos principais explicam os efeitos bioquímicos, fisiológicos e clínicos da exposição a partículas de poluição do ar. (27) Em primeiro lugar, as partículas inaladas podem reagir com os receptores neurais pulmonares e ativar o reflexo que está envolvido na comunicação química e elétrica entre o pulmão e o sistema nervoso. Os sinais de retorno do cérebro que viajam pelo sistema nervoso autônomo podem desencadear aumento dos níveis de pressão arterial e alterações do ritmo cardíaco.…”

Section: Poluição Do Ar E Efeitos Na Saúdeunclassified

Impact of exposure to smoke from biomass burning in the Amazon rain forest on human health

et al. 2021

View full text Add to dashboard Cite

This review study aimed to determine the relationship between exposure to smoke from biomass burning in the Amazon rain forest and its implications on human health in that region in Brazil. A nonsystematic review was carried out by searching PubMed, Google Scholar, SciELO, and EMBASE databases for articles published between 2005 and 2021, either in Portuguese or in English, using the search terms "biomass burning" OR "Amazon" OR "burned" AND "human health." The review showed that the negative health effects of exposure to smoke from biomass burning in the Amazon have been poorly studied in that region. There is an urgent need to identify effective public health interventions that can help improve the behavior of vulnerable populations exposed to smoke from biomass burning, reducing morbidity and mortality related to that exposure.

show abstract

“…A similar situation was observed in nine regional background sites in Europe with MAE values ranging from 4.55 to 26.2 m 2 /g (measured at 637 and 880 nm) (Zanatta et al, 2016). MAE reported in the previous studies varied dramatically depending on source types (e.g., vehicle emission, biomass burning, and secondary aerosol formation) (Liu et al, 2017;Sun et al, 2021;Wang et al, 2021). Compared with MAEs at three sites in Nanjing, our previous study found that high MAE value at an industrial site MAE was mainly attributed to the lensing effect by secondary organic aerosol (SOA) coating (Chen et al, 2019).…”

Section: Introductionmentioning

confidence: 90%

Study of Variations in Mass Absorption Efficiency of Elemental Carbon Influenced by Different Measurement Techniques and Vehicle Emission

Chen

Zhao

et al. 2022

Front. Environ. Sci.

View full text Add to dashboard Cite

An inter-comparison study of mass absorption efficiency (MAE) of elemental carbon (EC) by different sampling modes and measurements was conducted at the School of the Environment (SE) and Station for Observing Regional Processes of the Earth System (SORPES) in Nanjing from November 2015 to October 2016. Compared with offline sampling, the underestimation in MAE of online sampling was mainly due to the decreased optical attenuation (ATN) from the losing EC, and the difference in MAEs of the two types of sampling was greatly influenced by secondary organic aerosol (SOA) formation. Based on five temperature protocols, which include four NIOSH-derived protocols and one IMPROVE-A protocol, dependence of MAE on the temperature protocol was investigated. The main reason for the change in MAE estimation was the difference in EC determination. The result showed that low peak inert mode temperature (Tpeak) produced a small amount of pyrolysis carbon, and this carbon fraction was typically classified as organic carbon (OC), resulting in overestimation of EC and thereby underestimation of MAE. In order to study the influence of vehicle emission of highway on MAE values of EC, the simultaneous observation at SE and SORPES was conducted. The mean MAE of SE was 8.5% lower than that of SORPES. EC concentration was estimated to decrease by 0.13 μg/m3 with an increment distance of 100 m. Good correlation was found between the differential ATN for the two sites and the proportion of secondary organic carbon (SOC) at SORPES (R2 = 0.71). These results indicated that high MAE at SORPES was expected to be relevant with the dry deposition of EC from vehicle emission and the lensing effect by SOA coating.

show abstract

Effects of biomass burning and photochemical oxidation on the black carbon mixing state and light absorption in summer season

Cited by 15 publications

References 63 publications

Human Health Risk Prediction Method of Regional Atmospheric Environmental Pollution Sources Based on PMF and PCA Analysis under Artificial Intelligence Cloud Model

Human Health Risk Prediction Method of Regional Atmospheric Environmental Pollution Sources Based on PMF and PCA Analysis under Artificial Intelligence Cloud Model

Impact of exposure to smoke from biomass burning in the Amazon rain forest on human health

Study of Variations in Mass Absorption Efficiency of Elemental Carbon Influenced by Different Measurement Techniques and Vehicle Emission

Contact Info

Product

Resources

About