In US cities, the concentrations of many air pollutants have been observed, modeled, and inferred to be higher in neighborhoods where residents are primarily people of color and have lower household incomes (e.g.,

The objective of this study was to assess experimentally the potential impact of anthropogenic pH perturbation (ApHP) on concentrations of dimethyl sulfide (DMS) and dimethylsulfoniopropionate (DMSP), as well as processes governing the microbial cycling of sulfur compounds. A summer planktonic community from surface waters of the Lower St. Lawrence Estuary was monitored in microcosms over 12 days under three pCO2 targets: 1 × pCO2 (775 µatm), 2 × pCO2 (1,850 µatm), and 3 × pCO2 (2,700 µatm). A mixed phytoplankton bloom comprised of diatoms and unidentified flagellates developed over the course of the experiment. The magnitude and timing of biomass buildup, measured by chlorophyll a concentration, changed in the 3 × pCO2 treatment, reaching about half the peak chlorophyll a concentration measured in the 1 × pCO2 treatment, with a 2-day lag. Doubling and tripling the pCO2 resulted in a 15% and 40% decline in average concentrations of DMS compared to the control. Results from 35S-DMSPd uptake assays indicated that neither concentrations nor microbial scavenging efficiency of dissolved DMSP was affected by increased pCO2. However, our results show a reduction of the mean microbial yield of DMS by 34% and 61% in the 2 × pCO2 and 3 × pCO2 treatments, respectively. DMS concentrations correlated positively with microbial yields of DMS (Spearman’s ρ = 0.65; P < 0.001), suggesting that the impact of ApHP on concentrations of DMS in diatom-dominated systems may be strongly linked with alterations of the microbial breakdown of dissolved DMSP. Findings from this study provide further empirical evidence of the sensitivity of the microbial DMSP switch under ApHP. Because even small modifications in microbial regulatory mechanisms of DMSP can elicit changes in atmospheric chemistry via dampened efflux of DMS, results from this study may contribute to a better comprehension of Earth’s future climate.

The COVID-19 pandemic and ensuing lockdown of many US States resulted in rapid changes to motor vehicle traffic and their associated emissions. This presents a challenge for air quality modelling and forecasting during this period, in that transportation emission inventories need to be updated in near real-time. Here, we update the previously developed fuel-based inventory of vehicle emissions (FIVE) to account for changes due to COVID-19 lockdowns. We first construct a 2020 business-as-usual (BAU) case inventory and adjust the emissions for a COVID-19 case using monthly fuel sales information. We evaluate cellular phone-based mobility data products (Google COVID-19 Community Mobility, Apple COVID-19 Mobility Trends) in comparison to embedded traffic monitoring sites in four US cities. We find that mobility datasets tend to overestimate traffic reductions in April 2020 (i.e. lockdown period), while fuel sales adjustments are more similar to changes observed by traffic monitors; for example, mobility-based methods for scaling emissions result in an approximately two-times greater estimate of on-road nitrogen oxide (NO x ) reductions in April 2020 than we find using a fuel-based method. Overall, FIVE estimates a 20%–25% reduction in mobile source NO x emissions in April 2020 versus BAU, and a smaller 6%–7% drop by July. Reductions in April showed considerable spatial heterogeneity, ranging from 6% to 39% at the state level. Similar decreases are found for carbon monoxide (CO) and volatile organic compounds. Decreases to mobile source NO x emissions are expected to lower total US anthropogenic emissions by 9%–12% and 3%–4% in April and July, respectively, with larger relative impacts in urban areas. Changes to diurnal and day-of-week patterns of light- and heavy-duty vehicular traffic are evaluated and found to be relatively minor. Beyond the applicability to modelling air quality in 2020, this work also represents a methodology for quickly updating US transportation inventories and for calibrating mobility-based estimates of emissions.