“…We also found that the BC concentration collected from Hemu village was mainly attributed to residential and transportation emission sources. These results are essentially consistent with previous suggestions by Yang and others (2019). Fig.…”
Light-absorbing impurities (LAIs, e.g. black carbon (BC), organic carbon (OC), mineral dust (MD)) deposited on snow cover reduce albedo and accelerate its melting. Northern Xinjiang (NX) is an arid and semi-arid inland region, where snowmelt leads to frequent floods that have been a serious threat to local ecological security. There is still a lack of quantitative assessments of the effects of LAIs on snowmelt in the region. This study investigates spatial variations of LAIs in snow and its effect on snow albedo, radiative forcing (RF) and snowmelt across NX. Results showed that concentrations of BC, OC (only water-insoluble OC), MD ranged from 32 to 8841 ng g−1, 77 to 8568 ng g−1 and 0.46 to 236 µg g−1, respectively. Weather Research and Forecasting Chemistry model suggested that residential emission was the largest source of BC. Snow, Ice, and Aerosol Radiative modelling showed that the average contribution of BC and MD to snow albedo reduction was 17 and 3%, respectively. RF caused by BC significantly exceeded RF caused by MD. In different scenarios, changes in snow cover duration (SCD) caused by BC and MD decreased by 1.36 ± 0.61 to 6.12 ± 3.38 d. Compared with MD, BC was the main dominant factor in reducing snow albedo and SCD across NX.
“…We also found that the BC concentration collected from Hemu village was mainly attributed to residential and transportation emission sources. These results are essentially consistent with previous suggestions by Yang and others (2019). Fig.…”
Light-absorbing impurities (LAIs, e.g. black carbon (BC), organic carbon (OC), mineral dust (MD)) deposited on snow cover reduce albedo and accelerate its melting. Northern Xinjiang (NX) is an arid and semi-arid inland region, where snowmelt leads to frequent floods that have been a serious threat to local ecological security. There is still a lack of quantitative assessments of the effects of LAIs on snowmelt in the region. This study investigates spatial variations of LAIs in snow and its effect on snow albedo, radiative forcing (RF) and snowmelt across NX. Results showed that concentrations of BC, OC (only water-insoluble OC), MD ranged from 32 to 8841 ng g−1, 77 to 8568 ng g−1 and 0.46 to 236 µg g−1, respectively. Weather Research and Forecasting Chemistry model suggested that residential emission was the largest source of BC. Snow, Ice, and Aerosol Radiative modelling showed that the average contribution of BC and MD to snow albedo reduction was 17 and 3%, respectively. RF caused by BC significantly exceeded RF caused by MD. In different scenarios, changes in snow cover duration (SCD) caused by BC and MD decreased by 1.36 ± 0.61 to 6.12 ± 3.38 d. Compared with MD, BC was the main dominant factor in reducing snow albedo and SCD across NX.
“…Our ML model even managed to surpass the performance of the CTM simulations of BC by adding more physically relevant variables. Previous applications of numerical model simulations of BC estimation construction provided an R 2 ≈ 0.37-0.65 [23][24][25][26][27]. The lower performance of the CTM models compared with our ML algorithms might be due to the limited number of BC ground-level databases in many CTM studies to be used for evaluation and validation purposes.…”
Section: Supplementary Materialsmentioning
confidence: 94%
“…Previous research shows that the quantity and impacts of aerosol components in a specific receptor region could be controlled by various factors, including their spatial dependence on primary sources, the global variation and dispersion of emissions, meteorological factors that influence source-receptor pathways, and the physic-chemical properties of the aerosol that affects the mixing state and interactions with other species [18]. Studies using the global aerosol climate models [19][20][21][22] and chemical transport models [23][24][25][26][27] have also been evaluated to quantify long-term high-resolution aerosol speciation concentrations in various parts of the world. However, these modeling approaches often suffer from significant biases (R 2 ≈ 0.4-0.6 in the studies mentioned above) and uncertainties, particularly in capturing the spatial and temporal variability of aerosols with sufficient horizontal resolutions and accuracy, as research suggests that the performance of these models could greatly rely on the parameterization scheme uncertainties as well as the emission inventories [28].…”
Tremendous efforts have been made to construct large-scale estimates of aerosol components. However, Black Carbon (BC) estimates over large spatiotemporal scales are still limited. We proposed a novel approach utilizing machine-learning techniques to estimate BC on a large scale. We leveraged a comprehensive gridded BC emission database and auxiliary variables as inputs to train various machine learning (ML) models, specifically a Random Forest (RF) algorithm, to estimate high spatiotemporal BC concentration over China. Different ML algorithms have been applied to a large number of potential datasets and detailed variable importance and sensitivity analysis have also been carried out to explore the physical relevance of variables on the BC estimation model. RF algorithm showed the best performance compared with other ML models. Good predictive performance was observed for the training cases (R2 = 0.78, RMSE = 1.37 μgm−3) and test case databases (R2 = 0.77, RMSE = 1.35 μgm−3) on a daily time scale, illustrating a significant improvement compared to previous studies with remote sensing and chemical transport models. The seasonal variation of BC distributions was also evaluated, with the best performance observed in spring and summer (R2 ≈ 0.7–0.76, RMSE ≈ 0.98–1.26 μgm−3), followed by autumn and winter (R2 ≈ 0.7–0.72, RMSE ≈ 1.37–1.63 μgm−3). Variable importance and sensitivity analysis illustrated that the BC emission inventories and meteorology showed the highest importance in estimating BC concentration (R2 = 0.73, RMSE = 1.88 μgm−3). At the same time, albedo data and some land cover type variables were also helpful in improving the model performance. We demonstrated that the emission-based ML model with an appropriate auxiliary database (e.g., satellite and reanalysis datasets) could effectively estimate the spatiotemporal BC concentrations at a large scale. In addition, the promising results obtained through this approach highlight its potential to be utilized for the assessment of other primary pollutants in the future.
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