Regionally differentiated responses of chlorophyll-a concentrations to reduced human activity during COVID-19 lockdown in the San Francisco Bay area.

The COVID-19 lockdown created a unique opportunity to study the impact of reduced human activities on water quality. This study aimed to explore how changes in human activities, specifically reduced traffic emissions, influenced water quality in the San Francisco Bay Area from 2019 to 2021. Using chlorophyll-a (Chl-a) concentration as an indicator of water quality and NO₂ concentration as a proxy for traffic emissions, we analyzed the effects of reduced emissions on water quality across different regions of the Bay. This study combined traffic flow, satellite remote sensing, and meteorological data. Pearson correlation analysis was used to assess the time-lag effects between NO₂ and Chl-a concentrations. Additionally, a random forest regression model was applied to analyze the drivers of Chl-a concentrations across different regions. The findings revealed that NO₂'s impact on Chl-a exhibited significant spatial heterogeneity: positive correlations increased in the Upper Bay region, a general positive correlation was observed in the Central Bay region, the Coastal Zone shifted from positive to negative correlations, and there was no significant correlation in the Lower South Bay region. The random forest regression analysis demonstrated that the main drivers of Chl-a varied by region. In Upper Bay and Central Bay, NO₂ concentration was the most significant driver of Chl-a, whereas temperature was the primary influence in the Coastal Zone and Lower South Bay. These findings offer insights into the spatially variable impacts of traffic emissions on water quality and suggest strategies for targeted management of water quality in urban estuarine environments.