First top-down diurnal adjustment to NO emissions inventory in Asia informed by the Geostationary Environment Monitoring Spectrometer (GEMS) tropospheric NO columns.

Pioneering the use of the Geostationary Environment Monitoring Spectrometer's (GEMS) observation data in air quality modeling, we adjusted Asia's NO emissions inventory by leveraging the instrument's unprecedented sampling frequency. GEMS tropospheric NO columns served as top-down constraints, guiding our Bayesian inversion to constrain NO emissions in Asia during spring 2022. This enabled the model to better capture the diurnal variation in NO emissions, such as its morning rush hour peak, particularly when more retrievals were available each day, improving the simulation accuracy to a certain extent.

A case study of surface ozone source contributions in the Seoul metropolitan area using the adjoint of CMAQ.

To quantitatively investigate the transboundary behaviors and source attributions of ozone (O) and its precursor species over East Asia, we utilize the adjoint technique in the CMAQ modeling system (the CMAQ adjoint). Our focus is on the Seoul Metropolitan Area (SMA) in South Korea, which is the receptor region of this study. We examine the contributions of both local and transported emissions to an O exceedance episode observed on June 3, 2019, estimating up to four days in advance.

Constraining East Asia ammonia emissions through satellite observations and iterative Finite Difference Mass Balance (iFDMB) and investigating its impact on inorganic fine particulate matter.

Uncertainty in ammonia (NH) emissions causes the inaccuracy of fine particulate matter simulations, which is associated with human health. To address this uncertainty, in this work, we employ the iterative finite difference mass balance (iFDMB) technique to revise NH emissions over East Asia using the Cross-track Infrared Sounder (CRIS) satellite for July, August, and September 2019. Compared to the emissions, the revised NH emissions show an increase in China, particularly in the North China Plain (NCP) region, corresponding to agricultural land use in July, August, and September and a decrease in South Korea in September.

Quantifying the premature mortality and economic loss from wildfire-induced PM in the contiguous U.S.

Emissions from wildfires worsen air quality and can adversely impact human health. This study utilized the fire inventory from NCAR (FINN) as wildfire emissions, and performed air quality modeling of April-October 2012, 2013, and 2014 using the U.S.

The sensitivities of ozone and PM concentrations to the satellite-derived leaf area index over East Asia and its neighboring seas in the WRF-CMAQ modeling system.

Vegetation plays an important role as both a sink of air pollutants via dry deposition and a source of biogenic VOC (BVOC) emissions which often provide the precursors of air pollutants. To identify the vegetation-driven offset between the deposition and formation of air pollutants, this study examines the responses of ozone and PM concentrations to changes in the leaf area index (LAI) over East Asia and its neighboring seas, using up-to-date satellite-derived LAI and green vegetation fraction (GVF) products. Two LAI scenarios that examine (1) table-prescribed LAI and GVF from 1992 to 1993 AVHRR and 2001 MODIS products and (2) reprocessed 2019 MODIS LAI and 2019 VIIRS GVF products were used in WRF-CMAQ modeling to simulate ozone and PM concentrations for June 2019.

Changes in the ozone chemical regime over the contiguous United States inferred by the inversion of NO and VOC emissions using satellite observation.

To investigate changes in the ozone (O) chemical production regime over the contiguous United States (CONUS) with accurate knowledge of concentrations of its precursors, we applied an inverse modeling technique with Ozone Monitoring Instrument (OMI) tropospheric nitrogen dioxide (NO) and total formaldehyde (HCHO) retrieval products in the summers of 2011, 2014, and 2017, years in which United States National Emission Inventory were based. The inclusion of dynamic chemical lateral boundary conditions and lightning-induced nitric oxide emissions significantly account for the contribution of background sources in the free troposphere. Satellite-constrained nitrogen oxide (NO) and non-methane volatile organic compounds (NMVOCs) emissions mitigate the discrepancy between satellite and modeled columns: the inversion suggested 2.

A comprehensive study of the COVID-19 impact on PM levels over the contiguous United States: A deep learning approach.

We investigate the impact of the COVID-19 outbreak on PM levels in eleven urban environments across the United States: Washington DC, New York, Boston, Chicago, Los Angeles, Houston, Dallas, Philadelphia, Detroit, Phoenix, and Seattle. We estimate daily PM levels over the contiguous U.S.

A Deep Convolutional Neural Network Model for Improving WRF Simulations.

Advancements in numerical weather prediction (NWP) models have accelerated, fostering a more comprehensive understanding of physical phenomena pertaining to the dynamics of weather and related computing resources. Despite these advancements, these models contain inherent biases due to parameterization of the physical processes and discretization of the differential equations that reduce simulation accuracy. In this work, we investigate the use of a computationally efficient deep learning (DL) method, the convolutional neural network (CNN), as a postprocessing technique that improves mesoscale Weather Research and Forecasting (WRF) one-day simulation (with a 1-h temporal resolution) outputs.

A novel CMAQ-CNN hybrid model to forecast hourly surface-ozone concentrations 14 days in advance.

Issues regarding air quality and related health concerns have prompted this study, which develops an accurate and computationally fast, efficient hybrid modeling system that combines numerical modeling and machine learning for forecasting concentrations of surface ozone. Currently available numerical modeling systems for air quality predictions (e.g.

Role of sea fog over the Yellow Sea on air quality with the direct effect of aerosols.

In this study, we investigate the impact of sea fog over the Yellow Sea on air quality with the direct effect of aerosols for the entire year of 2016. Using the WRF-CMAQ two-way coupled model, we perform four model simulations with the up-to-date emission inventory over East Asia and dynamic chemical boundary conditions provided by hemispheric model simulations. During the spring of 2016, prevailing westerly winds and anticyclones caused the formation of a temperature inversion over the Yellow Sea, providing favorable conditions for the formation of fog.

The impact of the direct effect of aerosols on meteorology and air quality using aerosol optical depth assimilation during the KORUS-AQ campaign.

To quantify the impact of the direct aerosol effect accurately, this study incorporated the Geostationary Ocean Color Imager (GOCI) aerosol optical depth (AOD) into a coupled meteorology-chemistry model. We designed three model simulations to observe the impact of AOD assimilation and aerosol feedback during the KORUS-AQ campaign (May - June 2016). By assimilating the GOCI AOD with high temporal and spatial resolutions, we improve the statistics from the comparison AOD and AERONET data (RMSE: 0.

Using a deep convolutional neural network to predict 2017 ozone concentrations, 24 hours in advance.

In this study, we use a deep convolutional neural network (CNN) to develop a model that predicts ozone concentrations 24 h in advance. We have evaluated the model for 21 continuous ambient monitoring stations (CAMS) across Texas. The inputs for the CNN model consist of meteorology (e.

Characterization and Antimicrobial Resistance of Salmonella Typhimurium Isolates from Clinically Diseased Pigs in Korea.

This study investigated the prevalence of Salmonella enterica serovar and antimicrobial resistance in Salmonella Typhimurium isolates from clinically diseased pigs collected from 2008 to 2014 in Korea. Isolates were also characterized according to the presence of antimicrobial resistance genes and pulsed-field gel electrophoresis patterns. Among 94 Salmonella isolates, 81 (86.

Cultured bacterial diversity and human impact on alpine glacier cryoconite.

The anthropogenic effect on the microbial communities in alpine glacier cryoconites was investigated by cultivation and physiological characterization of bacteria from six cryoconite samples taken at sites with different amounts of human impact. Two hundred and forty seven bacterial isolates were included in Actinobacteria (9%, particularly Arthrobacter), Bacteroidetes (14%, particularly Olleya), Firmicutes (0.8%), Alphaproteobacteria (2%), Betaproteobacteria (16%, particularly Janthinobacterium), and Gammaproteobacteria (59%, particularly Pseudomonas).

[Tyroservatide inhibits the growth of human hepatocarcinoma in nude mice].

Objective: To investigate the inhibitory effects of tyroservatide and its amino acid mixture on growth of hepatocarcinoma.

Methods: Hepatocarcinoma in nude mice was induced by implantation of cells of human hepatocarcinoma cell line BEL-7402. The inhibition of hepatocarcinoma growth was determined by calculating the tumor volume and measuring the tumor weight.