Marine Submicron Aerosols from the Gulf of Mexico: Polluted and Acidic with Rapid Production of Sulfate and Organosulfates.

We measured submicron aerosols (PM1) at a beachfront site in Texas in Spring 2021 to characterize the "background" aerosol chemical composition advecting into Texas and the factors controlling this composition. Observations show that marine "background" aerosols from the Gulf of Mexico were highly processed and acidic; sulfate was the most abundant component (on average 57% of total PM1 mass), followed by organic material (26%). These chemical characteristics are similar to those observed at other marine locations globally. However, Gulf "background" aerosols were much more polluted; the average non-refractory (NR-) PM1 mass concentration was 3-70 times higher than that observed in other clean marine atmospheres. Anthropogenic shipping emissions over the Gulf of Mexico explain 78.3% of the total measured "background" sulfate in the Gulf air. We frequently observed haze pollution in the air mass from the Gulf, with significantly elevated concentrations of sulfate, organosulfates, and secondary organic aerosol associated with sulfuric acid. Analysis suggests that aqueous oxidation of shipping emissions over the Gulf of Mexico by peroxides in the particles might potentially be an important pathway for the rapid production of acidic sulfate and organosulfates during the haze episodes under acidic conditions.

Using Kriging incorporated with wind direction to investigate ground-level PM2.5 concentration.

Conventional interpolation methods, such as spatial averaging, nearest neighbor, inverse distance weight and ordinary Kriging (OK); for estimating the spatial distribution of ground-level particulate matter (PM) data, do not account for the wind direction for estimating the spatial distribution of PM2.5. In this work, an interpolation algorithm, Win-OK accounting for the wind direction, is developed. In contrast to ordinary Kriging where all locations (irrespective of the wind direction) in the vicinity of a site is considered, the new algorithm (Win-OK) predicts the value at a certain location based on the measured values at locations upwind as determined by the wind direction. This new methodology, Win-OK is validated by applying it to analyze the hourly spatial distribution of ground-level PM2.5 concentrations during Chinese New Year and Chinese National Day in 2017 in Xinxiang city, China. The performance of OK and Win-OK are compared by using them to build PM2.5 concentration heat-maps. A "leave-one-out" cross validation methodology is used to calculate the root-mean-square error (RMSE) and standard deviation for evaluating both algorithms. The results show that OK sometimes gives an extremely high RMSE value using a Gaussian semi-variance model, and the standard deviation significantly deviates from the measured values. Win-OK was found to more accurately predict the PM2.5 spatial distribution in a specific sector. The performance of Win-OK is more stable than OK as established by comparing the calculated RMSE and standard deviation from predictions of both algorithms. Win-OK with a spherical semi-variance model is the most accurate method investigated here for deriving the spatial distribution of ground-level PM2.5. The new algorithm developed here could improve the prediction accuracy of PM2.5 spatial distribution by considering the effect of wind direction.