Underestimation of biomass burning contribution to PM2.5 due to its chemical degradation based on hourly measurements of organic tracers: A case study in the Yangtze River Delta (YRD) region, China

Li, Qing; Zhang, Kun; Li, Rui; Yang, Liumei; Yi, Yannan; Liu, Zhiqiang; Zhang, Xiaojuan; Feng, Jialiang; Wang, Qiongqiong; Wu, Wei; Huang, Ling; Wang, Yangjun; Wang, Shunyao; Chen, Hui; Chan, Andy; Latif, Mohd Talib; Ooi, Maggie Chel Gee; Manomaiphiboon, Kasemsan; Yu, Jianzhen; Li, Li

doi:10.1016/j.scitotenv.2023.162071

Cited by 6 publications

(2 citation statements)

References 68 publications

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“…However, on 11 December 2018, the carbon dioxide concentration was 419 ppmv, which is 8.69 ppmv higher than the expected value of 409 ppmv, resulting in a residual. This indicated an abnormally increased concentration compared to the expected value, possibly influenced by non-periodic meteorological factors, which may be related to extreme weather conditions, posing a challenge for accurate predictions of YRD_XCO 2 concentrations [48,49]. Considering that CO 2 concentration is influenced by various factors, including time, weather, vegetation, elevation, and semantic information, we selected time information, meteorological input parameters, vegetation parameters, elevation information, and semantic data as important variables for model prediction.…”

Section: Data Analysis 231 Seasonal Analysismentioning

confidence: 99%

An Interpolation and Prediction Algorithm for XCO2 Based on Multi-Source Time Series Data

Hu,

Zhang,

Feng

et al. 2024

Remote Sensing

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Carbon satellites are an important observation tool for analyzing ground carbon emission. From the perspective of the Earth’s scale, the spatiotemporal sparse characteristics of raw data observed from carbon satellite requires the accurate interpolation of data, and based on only this work, people predict future carbon emission trends and formulate appropriate management and conservation strategies. The existing research work has not fully considered the close correlation between data and seasons, as well as the characteristics accumulated over a long time scale. In this paper, firstly, by employing extreme random forests and auxiliary data, we reconstruct a daily average CO2 dataset at a resolution of 0.25°, and achieve a validated determination coefficient of 0.92. Secondly, introducing technologies such as Time Convolutional Networks (TCN), Channel Attention Mechanism (CAM), and Long Short-Term Memory networks (LSTM), we conduct atmospheric CO2 concentration interpolation and predictions. When conducting predictive analysis for the Yangtze River Delta region, we train the model by using quarterly data from 2016 to 2020; the correlation coefficient in summer is 0.94, and in winter it is 0.91. These experimental data indicate that compared to other algorithms, this algorithm has a significantly better performance.

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Section: Data Analysis 231 Seasonal Analysismentioning

confidence: 99%

An Interpolation and Prediction Algorithm for XCO2 Based on Multi-Source Time Series Data

Hu,

Zhang,

Feng

et al. 2024

Remote Sensing

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“…To track BB emissions and aid SA, various chemical tracers, such as potassium (K + ), levoglucosan, mannosan and galactosan, are used in numerous studies carried out in Southeast Asia (Simoneit 2002, Adam et al 2021. Levoglucosan and K + are both typical tracers for BB that are widely employed to understand the source origin of particulates (Kaushal et al 2018, Shi et al 2019a, Devaprasad et al 2023, Li et al 2023. Moreover, compared to the typical inorganic tracer K + , the organic tracers in BB emissions are more source-specific (Simoneit 2002).…”

Section: Gaseous Emissionsmentioning

confidence: 99%

Air pollution from biomass burning in India

Tripathi,

Yadav,

Sharma

2024

Environ. Res. Lett.

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Air Pollution is the most difficult challenge India is facing today and anthropogenic biomass burning (ABB) is one of the major and least understood sources leading to serious health and climate implications by affecting air quality, visibility, atmospheric chemistry, the Earth’s radiation budget, and biogeochemical cycling. Scientific understanding on the composition, chemistry, and regional dynamics of BB emissions is still limited constraining the implementation of appropriate research and policy interventions. The broad categories, associated complex emissions and spatio-temporal variations of ABB in India are discussed here. Major types of ABB prevalent year-round all over India include BB for cooking, heating purpose and open waste burning. Moreover, crop residue burning (CRB) in Indo-Gangetic Plain (IGP) during pre (April-May) and post-monsoon (October-November) seasons; CRB in South-India during January-February; forest fires in Central India and slash & burn agriculture in North-East India during March-May are the other dominant sources active during specific months and regions. Over the IGP, CRB along with other episodic ABB events and stagnant meteorology result in severe air quality and poor visibility (< ~300 m) with reported daily mean PM2.5 concentration shooting upto 15 times higher than Central Pollution Control Board standards. When compared with wheat residue burning, higher fire counts and emissions are reported during paddy residue burning events. During winters, BB contribution to 37% of aerosol oxidative potential in Delhi indicates its health implications. Meta-analysis of data obtained from representative source apportionment studies on PM10 shows >24% BB contribution in Assam, Ahmedabad, Kanpur, and Visakhapatnam; 20-24% in Patiala and Kolkata; 10-14% in Delhi. In PM2.5, BB contribution of >24% in Ahmedabad and Agra; 15-19% in Delhi, Mumbai, and Kolkata is reported while in PM1, BB contributed 19-24% in Kanpur, Patiala, and Assam and <10% in Delhi. Observed spatio-temporal variations in ABB sources and composition over India call for region specific solutions through airshed-based management.

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Source apportionment of fine particulate matter in middle Indo-Gangetic Plain by coupled radiocarbon –molecular organic tracer method

Linthoingambi Devi,

Kumar,

Chandra Yadav

et al. 2024

Atmospheric Pollution Research

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Underestimation of biomass burning contribution to PM2.5 due to its chemical degradation based on hourly measurements of organic tracers: A case study in the Yangtze River Delta (YRD) region, China

Cited by 6 publications

References 68 publications

An Interpolation and Prediction Algorithm for XCO2 Based on Multi-Source Time Series Data

An Interpolation and Prediction Algorithm for XCO2 Based on Multi-Source Time Series Data

Air pollution from biomass burning in India

Source apportionment of fine particulate matter in middle Indo-Gangetic Plain by coupled radiocarbon –molecular organic tracer method

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