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 (NH3) 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 NH3 emissions over East Asia using the Cross-track Infrared Sounder (CRIS) satellite for July, August, and September 2019. Compared to the emissions, the revised NH3 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. The enhancement in NH3 emissions resulted in a remarkable increase in concentrations of NH3 by 5 ppb. in July and September, there is an increase in ammonium (NH4+) and nitrate (NO3-) concentrations by 5 µg/m3, particularly in the NCP region, while in August, both NH4+ and NO3- concentrations exhibit a decrease. For sulfate (SO42-), in August and September, the concentrations decreased over most regions of China and Taiwan, as a result of the production of ammonium sulfate; increased concentrations of SO42-, however, were simulated over South Korea, Japan, and the southern region of Chengdu, caused by higher relative humidity (RH). In contrast, during the month of July, our simulations showed an increase in SO42- concentrations over most regions of China. To gain a more comprehensive understanding, we defined a sulfur conversion ratio ( [Formula: see text] ), which explains how changes in sulfur in the gas phase affect changes in sulfate concentrations. A subsequent sensitivity analysis performed in this study indicated the same relationship between changes in ammonia and its effect on inorganic fine particulate matter (PM2.5). This study highlights the challenge of controlling and managing inorganic PM2.5 and indicates that reducing the emissions of air pollutants do not necessarily lead to a reduction in their concentrations.

Quantifying the premature mortality and economic loss from wildfire-induced PM2.5 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. Environmental Protection Agency CMAQ model under two cases: with and without wildfire emissions. This study then assessed the health impacts and economic values attributable to PM2.5 from fires. Results indicated that wildfires could lead annually to 4000 cases of premature mortality in the U.S., corresponding to $36 billion losses. Regions with high concentrations of fire-induced PM2.5 were in the west (e.g., Idaho, Montana, and northern California) and Southeast (e.g., Alabama, Georgia). Metropolitan areas located near fire sources, exhibited large health burdens, such as Los Angeles (119 premature deaths, corresponding to $1.07 billion), Atlanta (76, $0.69 billion), and Houston (65, $0.58 billion). Regions in the downwind of western fires, although experiencing relatively low values of fire-induced PM2.5, showed notable health burdens due to their large population, such as metropolitan areas of New York (86, $0.78 billion), Chicago (60, $0.54 billion), and Pittsburgh (32, $0.29 billion). Results suggest that impacts from wildfires are substantial, and to mitigate these impacts, better forest management and more resilient infrastructure would be needed.

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. Using the CNN architecture, we bias-correct several meteorological parameters calculated by the WRF model for all of 2018. We train the CNN model with a four-year history (2014-2017) to investigate the patterns in WRF biases and then reduce these biases in simulations for surface wind speed and direction, precipitation, relative humidity, surface pressure, dewpoint temperature, and surface temperature. The WRF data, with a spatial resolution of 27 km, cover South Korea. We obtain ground observations from the Korean Meteorological Administration station network for 93 weather station locations. The results indicate a noticeable improvement in WRF simulations in all station locations. The average of annual index of agreement for surface wind, precipitation, surface pressure, temperature, dewpoint temperature, and relative humidity of all stations is 0.85 (WRF:0.67), 0.62 (WRF:0.56), 0.91 (WRF:0.69), 0.99 (WRF:0.98), 0.98 (WRF:0.98), and 0.92 (WRF:0.87), respectively. While this study focuses on South Korea, the proposed approach can be applied for any measured weather parameters at any location.

The sensitivities of ozone and PM2.5 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 PM2.5 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 PM2.5 concentrations for June 2019. The use of up-to-date LAI and GVF products resulted in monthly mean LAI differences ranging from -56.20% to 96.81% over the study domain. The increase in LAI resulted in the differences in hourly mean ozone and PM2.5 concentrations over inland areas ranging from 0.27 ppbV to -7.17 ppbV and 0.89 µg/m3 to -2.65 µg/m3, and the differences of those over the adjacent sea surface ranging from 0.69 ppbV to -2.86 ppbV and 3.41 µg/m3 to -7.47 µg/m3. The decreases in inland ozone and PM2.5 concentrations were mainly the results of dry deposition accelerated by increases in LAI, which outweighed the ozone and PM2.5 formations via BVOC-driven chemistry. Some inland regions showed further decreases in PM2.5 concentrations due to reduced reactions of PM2.5 precursors with hydroxyl radicals depleted by BVOCs. The reductions in sea surface ozone and PM2.5 concentrations were accompanied by the reductions in those in upwind inland regions, which led to less ozone and PM2.5 inflows. The results suggest the importance of the selective use of vegetation parameters for air quality modeling.

Changes in the ozone chemical regime over the contiguous United States inferred by the inversion of NOx and VOC emissions using satellite observation.

To investigate changes in the ozone (O3) 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 (NO2) 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 (NOx) and non-methane volatile organic compounds (NMVOCs) emissions mitigate the discrepancy between satellite and modeled columns: the inversion suggested 2.33-2.84 (1.07-1.34) times higher NOx over the CONUS (over urban regions) and 0.28-0.81 times fewer NMVOCs emissions over the southeastern United States. The model-derived HCHO/NO2 column ratio shows gradual spatial changes in the O3 production regime near urban cores relative to previously defined threshold values representing NOx and VOC sensitive conditions. We also found apparent shifts from the NOx-saturated regime to the transition regime (or the transition regime to the NOx-limited regime) over the major cities in the western United States. In contrast, rural areas, especially in the east-southeastern United States, exhibit a decreased HCHO/NO2 column ratio by -1.30 ± 1.71 with a reduction in HCHO column primarily driven by meteorology, becoming sensitive to VOC emissions. Results show that incorporating satellite observations into numerical modeling could help policymakers implement appropriate emission control policies for O3 pollution.

A comprehensive study of the COVID-19 impact on PM2.5 levels over the contiguous United States: A deep learning approach.

We investigate the impact of the COVID-19 outbreak on PM2.5 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 PM2.5 levels over the contiguous U.S. in March-May 2019 and 2020, and leveraging a deep convolutional neural network, we find a correlation coefficient, an index of agreement, a mean absolute bias, and a root mean square error of 0.90 (0.90), 0.95 (0.95), 1.34 (1.24) µg/m3, and 2.04 (1.87) µg/m3, respectively. Results from Google Community Mobility Reports and estimated PM2.5 concentrations show a greater reduction of PM2.5 in regions with larger decreases in human mobility and those in which individuals remain in their residential areas longer. The relationship between vehicular PM2.5 (i.e., the ratio of vehicular PM2.5 to other sources of PM2.5) emissions and PM2.5 reductions (R = 0.77) in various regions indicates that regions with higher emissions of vehicular PM2.5 generally experience greater decreases in PM2.5. While most of the urban environments ⸺ Washington DC, New York, Boston, Chicago, Los Angeles, Houston, Dallas, Philadelphia, Detroit, and Seattle ⸺ show a decrease in PM2.5 levels by 21.1%, 20.7%, 18.5%, 8.05%, 3.29%, 3.63%, 6.71%, 4.82%, 13.5%, and 7.73%, respectively, between March-May of 2020 and 2019, Phoenix shows a 5.5% increase during the same period. Similar to their PM2.5 reductions, Washington DC, New York, and Boston, compared to other cities, exhibit the highest reductions in human mobility and the highest vehicular PM2.5 emissions, highlighting the great impact of human activity on PM2.5 changes in eleven regions. Moreover, compared to changes in meteorological factors, changes in pollutant concentrations, including those of black carbon, organic carbon, SO2, SO4, and especially NO2, appear to have had a significantly greater impact on PM2.5 changes during the study period.

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., CMAQ) can forecast 24 to 48 h in advance. In this study, we develop a modeling system based on a convolutional neural network (CNN) model that is not only fast but covers a temporal period of two weeks with a resolution as small as a single hour for 255 stations. The CNN model uses meteorology from the Weather Research and Forecasting model (processed by the Meteorology-Chemistry Interface Processor), forecasted air quality from the Community Multi-scale Air Quality Model (CMAQ), and previous 24-h concentrations of various measurable air quality parameters as inputs and predicts the following 14-day hourly surface ozone concentrations. The model achieves an average accuracy of 0.91 in terms of the index of agreement for the first day and 0.78 for the fourteenth day, while the average index of agreement for one day ahead prediction from the CMAQ is 0.77. Through this study, we intend to amalgamate the best features of numerical modeling (i.e., fine spatial resolution) and a deep neural network (i.e., computation speed and accuracy) to achieve more accurate spatio-temporal predictions of hourly ozone concentrations. Although the primary purpose of this study is the prediction of hourly ozone concentrations, the system can be extended to various other pollutants.

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 inclusion of the direct effect of aerosols enhanced its strength. On foggy days, we find dominant changes of aerosols at an altitude of 150-200 m over the Yellow Sea resulted by the production through aqueous chemistry (~12.36% and ~3.08% increases in sulfate and ammonium) and loss via the wet deposition process (~-2.94% decrease in nitrate); we also find stronger wet deposition of all species occurring in PBL. Stagnant conditions associated with reduced air temperature caused by the direct effect of aerosols enhanced aerosol chemistry, especially in coastal regions, and it exceeded the loss of nitrate. The transport of air pollutants affected by sea fog extended to a much broader region. Our findings show that the Yellow Sea acts as not only a path of long-range transport but also as a sink and source of air pollutants. Further study should investigate changes in the impact of sea fog on air quality in conjunction with changes in the concentrations of aerosols and the climate.

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.g., wind field, temperature) and air pollution concentrations (NO x and ozone) from the previous day. The model is trained for predicting next-day, 24-hour ozone concentrations. We acquired meteorological and air pollution data from 2014 to 2017 from the Texas Commission on Environmental Quality (TCEQ). For 19 of the 21 stations in the study, results show that the yearly index of agreement (IOA) is above 0.85, confirming the acceptable accuracy of the CNN model. The results also show the model performed well, even for stations with varying monthly trends of ozone concentrations (specifically CAMS-012, located in El-Paso, and CAMS-013, located in Fort Worth, both with IOA=0.89). In addition, to ensure that the model was robust, we tested it on stations where fewer meteorological variables are monitored. Although these stations have fewer input features, their performance is similar to that of other stations. However, despite its success at capturing daily trends, the model mostly underpredicts the daily maximum ozone, which provides a direction for future study and improvement. As this model predicts ozone concentrations 24 h in advance with greater accuracy and computationally fewer resources, it can serve as an early warning system for individuals susceptible to ozone and those engaging in outdoor activities.

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: 0.12, R: 0.77, IOA: 0.69, MAE: 0.08). The inclusion of the direct effect of aerosols produces the best model performance (RMSE: 0.10, R: 0.86, IOA: 0.72, MAE: 0.07). AOD values were increased as much as 0.15, which is associated with an average reduction in solar radiation of -31.39 W/m2, a planetary boundary layer height (-104.70 m), an air temperature (-0.58 °C), and a surface wind speed (-0.07 m/s) over land. In addition, concentrations of major gaseous and particulate pollutants at the surface (SO2, NO2, NH3, SO 4 2 - , NO 3 - , NH 4 + , PM2.5) increase by 7.87 - 34% while OH concentration decreases by -4.58 %. Changes in meteorology and air quality appear to be more significant in high-aerosol loading areas. The integrated process rate analysis shows decelerated vertical transport, resulting in an accumulation of air pollutants near the surface and the amount of nitrate, which is higher than that of sulfate because of its response to reduced temperature. We conclude that constraining aerosol concentrations using geostationary satellite data is a prerequisite for quantifying the impact of aerosols on meteorology and air quality.

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 (86.2%) were identified as being of the Salmonella Typhimurium serotype, followed by Salmonella Derby (6 of 94, 6.4%), Salmonella 4,[5],12:i:- (4 of 94, 4.3%), Salmonella Enteritidis (2 of 94, 2.1%), and Salmonella Brandenburg (1 of 94, 1.1%). The majority of Salmonella Typhimurium isolates were resistant to tetracycline (92.6%), followed by streptomycin (88.9%) and ampicillin (80.2%). Overall, 96.3% of Salmonella Typhimurium isolates showed multidrug-resistant phenotypes and commonly harbored the resistance genes blaTEM (64.9%), flo (32.8%), aadA (55.3%), strA (58.5%), strB (58.5%), sulII (53.2%), and tetA (61.7%). The pulsed-field gel electrophoresis analysis of 45 Salmonella Typhimurium isolates from individual farms revealed 27 distinct patterns that formed one major and two minor clusters in the dendrogram analysis, suggesting that most of the isolates (91.1%) from diseased pigs were genetically related. These findings can assist veterinarians in the selection of appropriate antimicrobial agents to combat Salmonella Typhimurium infections in pigs. Furthermore, they highlight the importance of continuous surveillance of antimicrobial resistance and genetic status in Salmonella Typhimurium for the detection of emerging resistance trends.

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). Among them, isolates of Arthrobacter were detected only in samples from sites with no human impact, while isolates affiliated with Enterobacteriaceae were detected only in samples from sites with strong human impact. Bacterial isolates included in Actinobacteria and Bacteroidetes were frequently isolated from pristine sites and showed low maximum growth temperature and enzyme secretion. Bacterial isolates included in Gammaproteobacteria were more frequently isolated from sites with stronger human impact and showed high maximum growth temperature and enzyme secretion. Ecotypic differences were not evident among isolates of Janthinobacterium lividum, Pseudomonas fluorescens, and Pseudomonas veronii, which were frequently isolated from sites with different degrees of anthropogenic effect.

Tyroservatide inhibits the growth of human hepatocarcinoma in nude mice / 中华肿瘤杂志

<p><b>OBJECTIVE</b>To investigate the inhibitory effects of tyroservatide and its amino acid mixture on growth of hepatocarcinoma.</p><p><b>METHODS</b>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. The effects of tyroservatide on tumor cells in nude mice were assessed by immunohistochemical staining of proliferating cell nuclear antigen (PCNA), electron microscopic observation of ultrastructure, and apoptosis of tumor cells using terminal deoxynucleotidyl transferase biotin-dUTP nick end labeling (TUNEL).</p><p><b>RESULTS</b>Tyroservatide significantly inhibited the growth of human hepatocarcinoma in nude mice, with an inhibiting rate more than 60%. But the mixture of amino acid did not show a significant inhibitory effect on the tumor growth. Tyroservatide also induced apoptosis of tumor cells and decreased the expression of PCNA in tumor cells.</p><p><b>CONCLUSION</b>Tyroservatide may significantly inhibit the growth of human hepatocarcinoma in nude mice by inducing apoptosis and inhibiting proliferation of tumor cells.</p>