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Abstract Sea surface temperature (SST) has been increasing since industrialization with rising greenhouse gases. However, a warming hole exists in the North Atlantic where SST has cooled by 0.4 K/century during 1900–2017. It has been argued that this cooling is due to a slowdown of the Atlantic Meridional Overturning Circulation (AMOC), and subpolar North Atlantic SST has thus been utilized to estimate AMOC variability. We assess the robustness of subpolar North Atlantic SST as a proxy for AMOC strength under historical forcing, abrupt quadrupling of CO₂, and a medium future emissions pathway, finding that AMOC's fingerprint on SST depends upon forcing scenarios. AMOC is important in warming hole development during significant warming periods, although SST may introduce uncertainties for AMOC reconstruction in stabilized regimes due to diverse forcing mechanisms and decadal variability. Our results caution against using SST alone as a proxy for AMOC variability—both on paleoclimatic and contemporary time scales. 

Abstract. While the importance of carbon cycling in estuaries is increasingly recognized, the role of benthic macrofauna remains poorly quantified due to limited spatial and temporal resolution in biomass measurements. Here, we ask: (1) To what extent do benthic macrofauna contribute to estuarine carbon cycling via respiration and calcification? and (2) How well can routinely collected environmental variables predict their biomass? We analyzed data from 8128 benthic samples collected from the Chesapeake Bay between 1995 and 2022 and estimated associated carbon fluxes using empirical relationships. We then used generalized additive models to relate observed and modeled environmental variables to the biomass. Biomass was highest in the upper mainstem of the Bay (Upper Bay) and upper Potomac River Estuary, the largest tidal tributary of the Bay. In the Upper Bay, benthic macrofauna respired 18 %–45 % of the estimated organic carbon supply. Calcification-driven alkalinity reduction reached 6.31 ± 2.84 mol m−2 yr−1 in the Potomac River Estuary, aligning with prior estimates of alkalinity sinks in the tributary and highlighting the potential importance of calcifying fauna in alkalinity dynamics. Estimated CO2 production in the Upper Bay from benthic respiration and calcification (151 g C m−2 yr−1) also exceeded observed air–sea CO2 fluxes (74.5 g C m−2 yr−1). Generalized additive models revealed that low salinity, moderate dissolved oxygen, and elevated nitrate best predicted high-biomass zones, with the three predictors explaining 52 % of biomass deviance. These predictive relationships offer a pathway to estimate macrofaunal biomass and associated carbon fluxes in systems where direct biomass measurements are sparse. Our findings demonstrate that benthic macrofauna play a substantial and spatially structured role in estuarine carbon cycling. Incorporating their contributions into estuarine biogeochemical models will improve predictions of ecosystem responses to environmental and anthropogenic change. 

Introduction:Traditional methods to estimate exposure to PM_2.5(particulate matter with less than 2.5 µm in diameter) have typically relied on limited regulatory monitors and do not consider human mobility and travel. However, the limited spatial coverage of regulatory monitors and the lack of consideration of mobility limit the ability to capture actual air pollution exposure. Methods:This study aims to improve traditional exposure assessment methods for PM_2.5by incorporating the measurements from a low-cost sensor network (PurpleAir) and regulatory monitors, an automated machine learning modeling framework, and big human mobility data. We develop a monthly-aggregated hourly land use regression (LUR) model based on automated machine learning (AutoML) and assess the model performance across eight metropolitan areas within the US. Results:Our results show that integrating low-cost sensor with regulatory monitor measurements generally improves the AutoML-LUR model accuracy and produces higher spatial variation in PM_2.5concentration maps compared to using regulatory monitor measurements alone. Feature importance analysis shows factors highly correlated with PM_2.5concentrations, including satellite aerosol optical depth, meteorological variables, vegetation, and land use. In addition, we incorporate human mobility data on exposure estimates regarding where people visit to identify spatiotemporal hotspots of places with higher risks of exposure, emphasizing the need to consider both visitor numbers and PM_2.5concentrations when developing exposure reduction strategies. Discussion:This research provides important insights for further public health studies on air pollution by comprehensively assessing the performance of AutoML-LUR models and incorporating human mobility into considering human exposure to air pollution.