Samantha Frucht
Throughout the year, dust from the Sahara Desert blows across the Atlantic Ocean to the Caribbean, reaching maximum concentrations during the summer. This project is focused on a single dust storm, nicknamed “Godzilla”, that occurred in June 2020. The dust cloud reduced air quality to unhealthy levels for several days in the Caribbean and in the Southeastern United States while attracting widespread popular attention for its unusually high concentration. We use newly available measurements of dust surface concentration from the Interagency Monitoring of Protected Visual Environments (IMPROVE) network to determine the extent to which the June 2020 dust storm was anomalous compared to prior summer events. For each IMPROVE station, the average soil dust concentration and standard deviation were calculated over the full length of the record and compared to values in June 2020. Along with the observations, the NASA Goddard Institute for Space Studies Earth System ModelE2.1 (ModelE) nudged toward winds from the MERRA2 reanalysis was used to analyze dust transport over the Atlantic Ocean. Seven years of daily model output were used to calculate climatological 5-day averages within each June. During the Godzilla event, IMPROVE data showed an uptick in soil dust concentration over most of the Southeastern U.S. that was several standard deviations above the June average. Oddly, the Virgin Islands station in the Caribbean and the Everglades station in Florida did not show that the Godzilla event was exceptional. For comparison to the Virgin Islands record, AERONET Aerosol Optical Depth (AOD) was analyzed just downwind at three stations in Puerto Rico. Maximum AOD during June 2020 exceeded the mean by over eight standard deviations suggesting that there was a similar anomaly over the Virgin Islands. IMPROVE stations only take measurements once every three days and the Virgin Islands failed to report concentration around the time of peak AOD at nearby Puerto Rico, possibly missing the event. Over the Everglades, the model suggests that the absence of a significant anomaly was due to the trajectory of the dust cloud. Model transport showed dust channeling into the Gulf of Mexico and circulating into the Southeastern U.S. from the west, missing southern Florida. ModelE data illustrated that dust over the Atlantic Ocean during the event was over six standard deviations above the mean, indicating that dust transport was strong. These results show that ModelE driven by the observed transport simulated unusually high dust concentration downwind of the Sahara as observed during the June 2020 dust event.
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Julie Christopoulos
During 2020, the United States endured unseasonably higher fire activities in the western region compared to previous years. Throughout the period August 1st to October 31st, the western United States experienced its first known “Gigafire,” which burned more than 7.8 million acres of land. Wildfires are known to generate a substantial amount of O3 precursors, however, capturing the degree of O3 production from wildfires becomes complicated due to the wildfire emissions complexity, nonlinearity in plume photochemistry, aerosol radiative effects, mixing with external air masses, and additional plume dynamics. This work aims to identify the sources of O3 forecast uncertainty by the Community Multiscale Air Quality (CMAQ) Model and understand impacts on air quality during the 2020 Gigafire. We focus on the peak of the fire season, August 1st to October 31st, 2020, and utilize CMAQ V5.3.1 to examine two scenarios, one incorporating fires, CMAQFire, and one without, CMAQNoFire . A model evaluation of O3 and its precursors (NOx, CO, VOCS) is performed with in-situ measurements obtained from the AirNow and AQS networks. An analysis of O3 concentrations surrounding the fire region illustrates the capability of the model to successfully simulate downwind O3 increases; however, O3 remains underestimated close to the fire source. Evaluation of precursors in proximity to the fire source reveals model overestimates of NO2 and further underestimates of CO. Finally, we quantify the contribution of wildfire emissions to surface O3 production using the difference of simulated O3 fields between CMAQFire and CMAQNoFire runs.
Jiajun Gu, Jintao Gu, and K. Max Zhang
Distributed air quality sensor networks have been established in many cities in the world to continuously monitor concentrations of various air pollutants detrimental to human health. There is great interest in developing new analytical techniques capable of generating useful insights from sensor networks. In this study, we applied data-driven techniques, including machine-learning modeling and network analysis, to predict the spatial and temporal trends of NO2 and PM2.5 concentrations and identify local emission sources using data from the Breathe London project, where more than 100 low-cost air quality sensor pods were installed on lamp posts and buildings throughout the region of Greater London, England. During the network analysis, we defined the general temporal trends of pollutant concentrations driven by the regional phenomena among the sensor network and used them as reference to identify the local anomalous concentrations and the associated local drivers. In parallel, we implemented machine learning models to predict the spatial and temporal trends of pollutants using meteorological and land-use features. While the machine-learning models can effectively capture the general trends, we found that the poor performances were often indicative of local emission sources, consistent with those in the network analysis. A major accomplishment of this study is the identification of the influence of local emissions sources that have been poorly characterized in the emission inventories such as cooking and unpaved surfaces. Better understanding these emission sources will help inform new policy that can improve pollution and empower local communities to take local actions to address their air pollution problems.
