Role of Aerosols in Spring Blooms in the Central Yellow Sea During the COVID-19 Lockdown by China

Abstract

Reduced amounts of aerosols blowing into the Yellow Sea (YS), owing to the temporary lockdown of factories in China during COVID-19, resulted in a 15% decrease in spring chlorophyll-a concentration (CHL) in March 2020 compared to its mean March values from 2003 to 2021. Particularly, the effect of land-based AOD is insignificant compared with that of atmospheric aerosols flowing into the YS, as indicated by the currents and wind directions. Hence, the main objective of this study was to understand the relationship between atmospheric aerosols and CHL by quantitatively considering relevant environmental changes using a Random Forest (RF) algorithm. Various input physical forcing variables to RF were employed, including aerosol optical depth (AOD), sea surface temperature (SST), mixed layer depth (MLD), wind divergence (WD), and total precipitation (TP). From the RF-based analysis, we estimated the relative contribution of each physical forcing variable to the difference in CHL during and after the COVID-19 lockdown period. The sensitivity of the RF model to changes in aerosol levels indicated positive effects of increased amounts of aerosols during spring blooms. Additionally, we calculated the quantitative contribution of aerosols to CHL changes. When SST was warmer and TP was lower than their climatology in March 2020, CHL increased by 0.22 mg m-3 and 0.02 mg m-3, respectively. Conversely, when MLD became shallower and AOD was lower than their climatology, CHL decreased as much as 0.01 mg m-3 and 0.20 mg m-3. Variations in WD caused no significant change in CHL. Overall, the specific estimations for reduced spring blooms were caused by a reduction in aerosols during the COVID-19 lockdown period. Furthermore, the RF developed in this study can be used to examine CHL changes and the relative role of significant environmental changes in biological blooms in the ocean for any normal year.

Coastal environmental changes after the Saemangeum seawall construction

“…The randomness in the RF model plays a critical role in preventing overfitting and enhancing the prediction performance. The model was constructed by setting two essential parameters: the number of single decision trees (N trees ) and the number of features (M try ) (Baek et al, 2022). Each tree randomly selects a subset of the dataset and chooses the best-split function from the selected subset to split the nodes (Park et al, 2020).…”

“…Thus, N trees and M try are significant parameters in the RF model. In this study, we employed an empirical trial to set N trees to 52 single decision trees, whereas M try was set to five, based on the convention (Baek et al, 2022). Five variables (predictors) were used as inputs for the RF model.…”

“…Finally, the PD plots help interpret the prediction results of the models because they visualize the interactions between variables and explain the model results (Friedman, 2001;. In particular, PD plots show how the model's prediction reacts to changes in the values of one or two variables (Baek et al, 2022). Thus, we can identify how a variable affects the model's prediction and understand its operational mechanisms.…”

“…The PD plots for set S were calculated by designating a specific variable of X S and averaging all values of X c , which are the other variables. For further details, refer to Baek et al (2022).…”