利用浊度计于2021年11月~2021年12月在深圳市开展颗粒物光散射在线观测,并同步利用相关在线仪器开展PM2.5及其化学组分观测,结合受体模型和可解释性机器学习模型探究深圳市秋冬季颗粒物散射的主要来源.结果显示,深圳市颗粒物525nm散射系数在观测期间平均值为119.6 1/Mm.相关性分析表明,颗粒物散射系数与相对湿度相关性低,与PM2.5相关性高(R2>0.8),其中,与PM2.5中NH4+、NO3-、m/z 44、OM、SO42-、黑碳的相关性在0.55~0.83,表明PM2.5中二次组分及黑碳是影响深圳市颗粒物散射的重要因素.基于相对湿度和受体模型识别的污染源,利用XGBoost模型建立了颗粒物散射系数预测模型并利用SHAP模型识别其主要来源,研究表明二次硝酸盐、二次硫酸盐、机动车排放和二次有机气溶胶是影响深圳市颗粒物散射系数的主要源类,燃煤和相对湿度也有一定贡献,生物质燃烧、船舶排放、工业排放、扬尘和建筑尘的影响小.
Abstract
High time resolution observations of scattering coefficient of particulate matter (PM) were conducted using turbidimeter, while PM2.5 and its chemical compositions were analysed using others corresponding online instruments from November 2021 to December 2021 in Shenzhen, China. Receptor model and interpretable machine learning models were employed to identify the major sources of PM scattering coefficient. The average PM scattering coefficient at 525nm was 119.6 1/Mm during the observation period in Shenzhen. Correlation analysis revealed that PM scattering coefficient had low correlations with relative humidity (RH) and high correlations with PM2.5 and its components, such as NH4+, NO3-, m/z 44, OM, SO42-, and black carbon, indicating the important influence of secondary components and black carbon in PM2.5 on PM scattering coefficient in Shenzhen. The XGBoost model was employed to predict the PM scattering coefficient based on RH and PM2.5 sources identified by the receptor model, and then the SHAP model was used to identify the major factors of PM scattering coefficient. The results indicated that secondary nitrate, secondary sulfate, vehicle emissions, and secondary organic aerosols were the major sources of PM scattering coefficient in Shenzhen during fall and winter, followed by coal combustion and RH. Biomass burning, ship emissions, industrial emissions, fugitive dust and construction dust had small contributions.
关键词
PM2.5 /
PMF模型 /
机器学习 /
来源解析 /
散射系数
Key words
machine learning /
PM2.5 /
PMF model /
scattering coefficient /
source apportionment
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基金
国家自然科学基金资助项目(42375101);深圳市科技计划项目(GXWD20201231165807007-20200808165742001)
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