Search NASA⌕ Search

Engineering topics

Tong, Daniel

Publications and source records attributed to Tong, Daniel.

9 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Updating and Evaluating Anthropogenic Emissions for NOAA’s Global Ensemble Forecast Systems for Aerosols (GEFS-Aerosols): Application of an SO 2 Bias-Scaling Method

We updated the anthropogenic emissions inventory in NOAA’s operational Global Ensemble Forecast for Aerosols (GEFS-Aerosols) to improve the model’s prediction of aerosol optical depth (AOD). We used a methodology to quickly update the pivotal global anthropogenic sulfur dioxide (SO 2 ) emissions using a speciated AOD bias-scaling method. The AOD bias-scaling method is based on the latest model predictions compared to NASA’s Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA2). The model bias was subsequently applied to the CEDS 2019 SO 2 emissions for adjustment. The monthly mean GEFS-Aerosols AOD predictions were evaluated against a suite of satellite observations (e.g., MISR, VIIRS, and MODIS), ground-based AERONET observations, and the International Cooperative for Aerosol Prediction (ICAP) ensemble results. The results show that transitioning from CEDS 2014 to CEDS 2019 emissions data led to a significant improvement in the operational GEFS-Aerosols model performance, and applying the bias-scaled SO 2 emissions could further improve global AOD distributions. The biases of the simulated AODs against the observed AODs varied with observation type and seasons by a factor of 3~13 and 2~10, respectively. The global AOD distributions showed that the differences in the simulations against ICAP, MISR, VIIRS, and MODIS were the largest in March–May (MAM) and the smallest in December–February (DJF). When evaluating against the ground-truth AERONET data, the bias-scaling methods improved the global seasonal correlation (r), Index of Agreement (IOA), and mean biases, except for the MAM season, when the negative regional biases were exacerbated compared to the positive regional biases. The effect of bias-scaling had the most beneficial impact on model performance in the regions dominated by anthropogenic emissions, such as East Asia. However, it showed less improvement in other areas impacted by the greater relative transport of natural emissions sources, such as India. The accuracies of the reference observation or assimilation data for the adjusted inputs and the model physics for outputs, and the selection of regions with less seasonal emissions of natural aerosols determine the success of the bias-scaling methods. A companion study on emission scaling of anthropogenic absorbing aerosols needs further improved aerosol prediction.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

A Multi-Analysis Approach for Estimating Regional Health Impacts from the 2017 Northern California Wildfires

In the evening of October 8 and early hours of October 9, 2017, high winds in Northern California downed trees and power lines, igniting some of the most devastating wildfires the state had seen, and within hours unhealthy air quality impacted millions of people. We simulated these air quality conditions using fire detection information from the MODIS, VIIRS, and GOES-16 ABI satellite instruments, and applying a set of three WRF–CMAQ simulations, one data fusion, and three machine learning methods. We investigated using the 5-min available GOES-16 fire detection data to simulate timing of fire activity to allocate emissions hourly for the WRF-CMAQ air quality modeling system. Interestingly, this approach did not necessarily improve results compared to the baseline case, which used a default time profile. However, this approach was key to simulating the initial 12-hr explosive fire activity and smoke impacts. The WRF-CMAQ simulations compared well with observational data for the October 8-15 time period and tended to overestimate concentrations October 16-20. To improve these results, we applied three machine learning algorithms. We also had a unique opportunity to evaluate results with temporary monitors deployed specifically for wildfires, and performance was markedly different. For example, at the permanent monitoring locations, the WRF-CMAQ simulations had a Pearson correlation of 0.65, and the data fusion approach improved this (Pearson correlation = 0.95), while at the temporary monitor locations across the WRF-CMAQ, data fusion, and machine learning datasets, the best Pearson correlation was 0.5. The data fusion and machine learning results were biased low and WRF-CMAQ results were biased high. Finally, we applied the optimized PM2.5 exposure estimate in a short-term exposure-response function. Total estimated mortality attributable to PM2.5 exposure during the smoke episode was 83 (95% confidence interval: 0, 196) with 47% of these deaths attributable to wildland fire smoke.

O'Neill, Susan↗
DOE OSTI2021

Shaping the Future of Science: COVID‐19 Highlighting the Importance of GeoHealth

Abstract From the heated debates over the airborne transmission of the novel coronavirus to the abrupt Earth system changes caused by the sudden lockdowns, the dire circumstances resulting from the coronavirus disease 2019 (COVID‐19) pandemic have brought the field of GeoHealth to the forefront of visibility in science and policy. The pandemic has inadvertently provided an opportunity to study how human response has impacted the Earth system, how the Earth system may impact the pandemic, and the capacity of GeoHealth to inform real‐time policy. The lessons learned throughout our responses to the COVID‐19 pandemic are shaping the future of GeoHealth.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2019

Investigating Meteorological Conditions Associated with Valley Fever Outbreaks in Southwestern United States

According to CDC, a sharp increase in reported Valley fever cases (Coccidioidomycosis) has been found between 2009 and 2012. Environmental conditions play an important role for Valley fever outbreaks. For example, dust storms can significantly increase the amount of the fungus Coccidioides in the air and associated strong winds can transport the fungus to other areas. In last year’s AGU session, we reported a preliminary study to investigate hydrometeorological conditions and their connection with dust storm activities in southwestern United States. We found wind is a major contributing factor for the seasonal variation of dust storm activities. Interannual variation of the regional hydrometeorological conditions are closely linked to the large-scale environment such as the Pacific Decadal Oscillation (PDO). Strong winds are linked with a number of weather events such cold front passages, thunderstorms that produce downbursts and strong winds, the Santa Ana winds, etc. In this presentation, we will report the results of our latest investigation on meteorological conditions associated with Valley fever outbreaks in southwestern United States, using NLDAS (North American Land Data Assimilation System), GPM (Global Precipitation Measurement), and MERRA-2 (Modern Era Retrospective-analysis for Research and Applications, Version 2) hourly datasets, from the NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC).

Liu, Zhong↗
NASA NTRS2018

Satellite and Reanalysis Air Quality Data and Services at NASA GES DISC for Public Health Study

Outbreaks of infectious diseases and health can be influenced by airborne and water-borne pollutants. Furthermore, air and water quality are associated with climate variability, industrialization, land use and land cover change, and water resource management. It is therefore crucial to understand environment-disease connections with existing long-term observed and modeled data, particularly for development of early warning systems for infectious disease outbreaks. The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) (https://disc.gsfc.nasa.gov) archives large volumes of global environment data that are useful for research and applications regarding environmental factors and public health. Examples of air quality measurements are: Daily satellite remotely sensed data, including Aerosol Index (AI), O3, SO2, CO, and NO2 from Aura/OMI (October 2014 to present), and OMPS-NPP (January 2012 to present, currently research data products only) Hourly and monthly reanalysis modeled data, including PM2.5, O3, CO, SO2, BC, dust, AOD, and aerosol types from MERRA-2 (January 1980 to present) Examples of surface meteorology and land surface measurements are: hourly, daily, and monthly satellite precipitation from TRMM (December 1997 to March 2015) 30-minute and monthly satellite precipitation from GPM (March 2014 to present); Hourly and monthly modeled surface meteorology and land surface condition from MERRA-2 (January 1980 to present) and land surface assimilation models (January 1948 to present), including precipitation, surface temperature, relative humidity, wind, and soil moisture.This presentation will give an overview of relevant environmental data at the NASA GES DISC. Through a number of use cases, such as dust events and active fires, we will introduce data services that assist in finding the right data, enable visualization and analysis of the data online, and allow downloading of data in user-preferred format.

remote sensing data↗
NASA NTRS2016

Impact of the 2008 Global Recession on Air Quality over the United States: Implications for Surface Ozone Levels from Changes in NOx Emissions

Satellite and ground observations detected large variability in nitrogen oxides (NOx) during the 2008 economic recession, but the impact of the recession on air quality has not been quantified. This study combines observed NOx trends and a regional chemical transport model to quantify the impact of the recession on surface ozone (O3) levels over the continental United States. The impact is quantified by simulating O3 concentrations under two emission scenarios: business-as-usual (BAU) and recession. In the BAU case, the emission projection from the Cross-State Air Pollution Rule is used to estimate the would-be NOx emission level in 2011. In the recession case, the actual NO2 trends observed from Air Quality System ground monitors and the Ozone Monitoring Instrument on the Aura satellite are used to obtain realistic changes in NOx emissions. The model prediction with the recession effect agrees better with ground O3 observations over time and space than the prediction with the BAU emission. The results show that the recession caused a 12ppbv decrease in surface O3 concentration over the eastern United States, a slight increase (0.51ppbv) over the Rocky Mountain region, and mixed changes in the Pacific West. The gain in air quality benefits during the recession, however, could be quickly offset by the much slower emission reduction rate during the post-recession period.

NOx emissions↗
NASA NTRS2011

The Increasing Use of Remote Sensing Data in Studying the Climatological Impacts on Public Health

One of the more fortunate outcomes of the capture and transformation of remote sensing data into applied information is their usefulness and impacts to better understanding climatological impacts on public health. Today, with petabytes of remote sensing data providing global coverage of climatological parameters, public health research and policy decision makers have an unprecedented (and growing) data record that relates the effects of climatic parameters, such as rainfall, heat, soil moisture, etc. to incidences and spread of disease, as well as predictive modeling. In addition, tools and services that specifically serve public health researchers and respondents have grown in response to needs of the these information users.

Kempler, Steven↗