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Abstract Traditional statistical methods (TSM) and machine learning (ML) methods have been widely used to separate the effects of emissions and meteorology on air pollutant concentrations, while their performance compared to the chemistry transport model has been less fully investigated. Using the Community Multiscale Air Quality Model (CMAQ) as a reference, a series of experiments was conducted to comprehensively investigate the performance of TSM (e.g., multiple linear regression and Kolmogorov–Zurbenko filter) and ML (e.g., random forest and extreme gradient boosting) approaches in quantifying the effects of emissions and meteorology on the trends of fine particulate matter (PM2.5) during 2013−2017. Model performance evaluation metrics suggested that the TSM and ML methods can explain the variations of PM2.5 with the highest performance from ML. The trends of PM2.5 showed insignificant differences (p > 0.05) for both the emission-related ( $${{\rm{PM}}}_{2.5}^{{\rm{EMI}}}$$ PM 2.5 EMI ) and meteorology-related components between TSM, ML, and CMAQ modeling results. $${{\rm{PM}}}_{2.5}^{{\rm{EMI}}}$$ PM 2.5 EMI estimated from ML showed the least difference to that from CMAQ. Considering the medium computing resources and low model biases, the ML method is recommended for weather normalization of PM2.5. Sensitivity analysis further suggested that the ML model with optimized hyperparameters and the exclusion of temporal variables in weather normalization can further produce reasonable results in emission-related trends of PM2.5.

Abstract The result of Aethalometer model to black carbon (BC) source apportionment is highly determined by the absorption Ångström exponent (α) of aerosols from fossil fuel combustion (α ff) and wood burning (α wb). A method using hourly measured potassium to calculate the α ff and α wb values was developed in this study. Results showed that the optimal α ff and α wb were 1.09 and 1.79 for the whole dataset. The optimal α values in the diurnal resolution were also calculated with α ff and α wb varied in 1.02 –1.19 and 1.71–1.90, respectively. Using the dynamic α values, the Pearson correlation coefficient between BC and potassium from wood burning substantially improved compared to the results derived from the fixed α values. The method developed in this study is expected to provide more reasonable BC source identification results, which are helpful for air quality, climate, and human health modeling studies.

Abstract The less improvement of ambient visibility suspects the government’s efforts on alleviating PM2.5 pollution. The COVID-19 lockdown reduced PM2.5 and increased visibility in Wuhan. Compared to pre-lockdown period, the PM2.5 concentration decreased by 39.0 μg m−3, dominated by NH4NO3 mass reduction (24.8 μg m−3) during lockdown period. The PM2.5 threshold corresponding to visibility of 10 km (PTV10) varied in 54–175 μg m−3 and an hourly PM2.5 of 54 μg m−3 was recommended to prevent haze occurrence. The lockdown measures elevated PTV10 by 9–58 μg m−3 as the decreases in PM2.5 mass scattering efficiency and optical hygroscopicity. The visibility increased by 107%, resulted from NH4NO3 extinction reduction. The NH4NO3 mass reduction weakened its mutual promotion with aerosol water and increased PM2.5 deliquescence humidity. Controlling TNO3 (HNO3 + NO3 −) was more effective to reduce PM2.5 and improve visibility than NH x (NH3 + NH4 +) unless the NH x reduction exceeded 11.7–17.5 μg m−3.

Abstract Morphology, composition, and mixing state of individual particles emitted from crop residue, wood, and solid waste combustion in a residential stove were analyzed using transmission electron microscopy (TEM). Our study showed that particles from crop residue and apple wood combustion were mainly organic matter (OM) in smoldering phase, whereas soot-OM internally mixed with K in flaming phase. Wild grass combustion in flaming phase released some Cl-rich-OM/soot particles and cardboard combustion released OM and S-rich particles. Interestingly, particles from hardwood (pear wood and bamboo) and softwood (cypress and pine wood) combustion were mainly soot and OM in the flaming phase, respectively. The combustion of foam boxes, rubber tires, and plastic bottles/bags in the flaming phase released large amounts of soot internally mixed with a small amount of OM, whereas the combustion of printed circuit boards and copper-core cables emitted large amounts of OM with Br-rich inclusions. In addition, the printed circuit board combustion released toxic metals containing Pb, Zn, Sn, and Sb. The results are important to document properties of primary particles from combustion sources, which can be used to trace the sources of ambient particles and to know their potential impacts in human health and radiative forcing in the air.