Predicting Atmospheric Particle Phase State Using an Explainable Machine Learning Approach Based on Particle Rebound Measurements.

The particle phase state plays a vital role in the gas-particle partitioning, multiphase reactions, ice nucleation activity, and particle growth in the atmosphere. However, the characterization of the atmospheric phase state remains challenging. Herein, based on measured aerosol chemical composition and ambient relative humidity (RH), a machine learning (ML) model with high accuracy (R2 = 0.952) and robustness (RMSE = 0.078) was developed to predict the particle rebound fraction, f, which is an indicator of the particle phase state. Using this ML model, the f of particles in the urban atmosphere was predicted based on seasonal average aerosol chemical composition and RH. Regardless of seasons, aerosols remain in the liquid state of mid-high latitude cities in the northern hemisphere and in the semisolid state over semiarid regions. In the East Asian megacities, the particles remain in the liquid state in spring and summer and in the semisolid state in other seasons. The effects of nitrate, which is becoming dominant in fine particles in several urban areas, on the particle phase state were evaluated. More nitrate led the particles to remain in the liquid state at an even lower RH. This study proposed a new approach to predict the particle phase state in the atmosphere based on RH and aerosol chemical composition.

Sources, size-resolved deposition in the human respiratory tract and health risks of submicron black carbon in urban atmosphere in Pearl River Delta, China.

Black carbon (BC) has a significantly negative impact on air quality, climate and human health. Here we investigated the sources and health effects of BC in urban area of the Pearl River Delta (PRD) based on online data measured by Aerodyne soot particle high-resolution time of flight aerosol mass spectrometer (SP-AMS). In urban PRD, BC particles mainly came from vehicle emissions especially heavy-duty vehicle exhausts (contributing 42.9 % of total BC mass concentration), long-range transport (27.6 %), and aged biomass combustion emissions (22.3 %). Indicated by source analysis using simultaneous aethalometer data, BC associated with local secondary oxidation and transport may also be originated from fossil fuel combustion, especially traffic sources in urban and surrounding areas. Size-resolved BC mass concentrations provided by SP-AMS, for the first time to our best knowledge, were used to calculate BC deposition in the human respiratory tract (HRT) of different populations (children, adults, and the elderly) by the Multiple-Path Particle Dosimetry (MPPD) model. We found that submicron BC was deposited more in the pulmonary (P) region (49.0-53.2 % of the total BC deposition dose), while less in the tracheobronchial (TB, 35.6-37.2 %) and head (HA, 11.2-13.8 %) regions. Adults suffered the highest BC deposition (1.19 µg day-1) than the elderly (1.09 µg day-1) and children (0.25 µg day-1). BC deposition rate was greater at night (especially 18:00-24:00) than during the daytime. The maximum deposition in the HRT was found for BC particles around 100 nm, mainly in deeper respiratory regions (TB and P), which may cause more serious health effects. Adults and the elderly group are confronted with the notable carcinogenic risk of BC in the urban PRD, up to 29 times higher than the threshold. Our study emphasizes the need to control BC pollution in the urban area, especially nighttime vehicle emissions.

Historically understanding the spatial distributions of particle surface area concentrations over China estimated using a non-parametric machine learning method.

A non-parametric ensemble model was proposed to estimate the long-term (2015-2019) particle surface area concentrations (SA) over China for the first time on basis of a vilification dataset of measured particle number size distribution. This ensemble model showed excellent cross-validation R2 value (CV R2 = 0.83) as well as a relatively low root-mean-square error (RMSE = 195.0 µm2/cm3). No matter in which year, considerable spatial heterogeneity of SA was found over China with higher SA in Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), and Middle Lower Reaches of Yangtze River (MLYR). From 2015 to 2019, SA significantly decreased in representative city clusters. The reduction rates were 140.1 µm2·cm-3·a-1 in BTH, 110.7 µm2·cm-3·a-1 in Pearl River Delta (PRD), 105.2 µm2·cm-3·a-1 in YRD, and 92.4 µm2·cm-3·a-1 in Sichuan Basin (SCB), respectively. Even though such quick reduction, high SA (ranged from ~800 µm2/cm3 to ~1750 µm2/cm3) during the heavy pollution period (PM2.5 > 75 µg/m3) still existed in the above-mentioned city clusters and may provide rich reaction vessels for multiphase chemistry. A dichotomy of enhanced annual 4th maximum daily 8-h average O3 concentrations (4MDA8 O3) and decreased SA during summertime was found in Shanghai, a representative city of YRD. In Chengdu (SCB), increased 4MDA8 O3 concentration was associated with a synchronous increase of SA from 2017 to 2019. Differently, 4MDA8 O3 concentrations enhanced in Beijing (BTH) and Guangzhou (PRD), while not significant for SA before 2018. This work will greatly deepen our understanding of the historical variation and spatial distributions of SA over China.

[Characteristics of PM2.5 Pollution in Beijing During the Historical Period of the 2022 Olympic Winter Games].

Based on the dataset derived from January to March between 2015 and 2021 in Beijing, the PM2.5 pollution characteristics and its potential source regions during the historical period of the Beijing 2022 Olympic Winter Games and Paralympic Winter Games were investigated. From 2015 to 2018, both the number of severely polluted days (daily average ρ(PM2.5)>75 µg·m-3) and the average PM2.5 concentrations during severe pollution episodes decreased significantly in the period of January to March. While, neither variable has changed obviously since 2018. On average, severely polluted days occurred 23 times in each year between 2018 and 2021 during the period of January to March, and the average of ρ(PM2.5) was approximately 120.0 µg·m-3 during such polluted days. From January to March in 2015-2021, the severely polluted event with more than 5 consecutive polluted days occurred 2-3 times in each year, and the severest one lasted 8 d. During the historical period of the Beijing 2022 Olympic Winter Games, severely polluted days took place 2-9 d every year. The large quantities of fireworks during the Spring Festival maybe one of important primary sources of the PM2.5. The number of severely polluted days during the historical period of the Paralympic Winter Games ranged from 1 to 5 d, except for 2021 with 9 d owing to the frequent stagnant weather condition. The PM2.5 chemical composition was dominated by secondary species on severely polluted days during the historical period of the Beijing 2022 Olympic Winter Games and Paralympic Winter Games. Nitrate accounted for 46% of the measurable chemical components of PM2.5 during severe pollution events in 2020, which was remarkably higher than that during clean days in the same year (11%). The mass fraction of SO42- ranged from 12% to 19% in 2018-2020, indicating that the contribution of sulfate was much less, but cannot be ignored. The main potential source regions of PM2.5 in Beijing during the period concerned in this study were central and western Inner Mongolia, Hebei Province, Tianjin City, Shanxi Province, Shaanxi Province, central and western Shandong Province, and northern Henan Province.