scholarly journals Simulating Performance of CHIMERE on a Late Autumnal Dust Storm over Northern China

2019 ◽  
Vol 11 (4) ◽  
pp. 1074
Author(s):  
Siqi Ma ◽  
Xuelei Zhang ◽  
Chao Gao ◽  
Quansong Tong ◽  
Aijun Xiu ◽  
...  
Keyword(s):  
Air Quality ◽  
Dust Storm ◽  
Dust Emission ◽  
Northern China ◽  
Distribution Patterns ◽  
Gansu Province ◽  
Sandy Land ◽  
Air Quality Model ◽  
Quality Model ◽  
Air Quality Forecast

The accurate forecasting of dust emission and transport is a societal demand worldwide as dust pollution is part of many health, economic, and environment issues, which significantly impact sustainable development. The dust forecasting ability of present air quality forecast systems is mainly focused on spring dust events in East Asia, but further improvement may be needed as there is still difficulty in forecasting autumn dust activities, such as failing to predict the serious dust storm that occurred on 25 to 26 November 2018. In this study, a state-of-the-art air quality model, CHIMERE, with three coupled dust schemes was introduced for the first time to simulate the dust emissions during this event to qualitatively and quantitatively validate its dust simulating performance over Northern China. The model results reported that two of the three dust schemes were able to capture the dust emission source located in Gansu Province and reproduce the easterly dust transport path, showing moderately close agreement in the horizontal and vertical distribution patterns with the ground-based and satellite observations. The simulated PM10 concentration had a better relationship with the observed values with a correlation coefficient up to 0.96, while it was lower in the transported areas. Meanwhile, the simulations also presented incorrect dust emission positions such as in areas between the Hulun Buir sandy land and Horqin sandy land. Our results indicate that CHIMERE exhibits reasonably good performance regarding its dust simulation and forecast ability over this area, and its application would help to improve the dust analysis and forecast abilities in Northern China.

scholarly journals Simulation of a Severe Sand and Dust Storm Event in March 2021 in Northern China: Dust Emission Schemes Comparison and the Role of Gusty Wind

Atmosphere ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 108
Author(s):  
Jikang Wang ◽  
Bihui Zhang ◽  
Hengde Zhang ◽  
Cong Hua ◽  
Linchang An ◽  
...  
Keyword(s):  
Dust Storm ◽  
Dust Emission ◽  
Northern China ◽  
Pm10 Concentration ◽  
Storm Event ◽  
Air Quality Model ◽  
Wind Model ◽  
Dust Source ◽  
Source Regions ◽  
Sand And Dust Storm

Northern China experienced a severe sand and dust storm (SDS) on 14/15 March 2021. It was difficult to simulate this severe SDS event accurately. This study compared the performances of three dust-emission schemes on simulating PM10 concentration during this SDS event by implementing three vertical dust flux parameterizations in the Comprehensive Air-Quality Model with Extensions (CAMx) model. Additionally, a statistical gusty-wind model was implemented in the dust-emission scheme, and it was used to quantify the gusty-wind contribution to dust emissions and peak PM10 concentration. As a result, the LS scheme (Lu and Shao 1999) produced the minimum errors for peak PM10 concentrations, the MB scheme (Marticorena and Bergametti 1995) underestimated the PM10 concentrations by 70–90%, and the KOK scheme (Kok et al. 2014) overestimated PM10 concentrations by 10–50% in most areas. The gusty-wind model could reasonably reproduce the probability density function of 2-min wind speeds. There were 5–40% more dust-emission flux and 5–40% more peak PM10 concentrations generated by the gusty wind than the hourly wind in the dust-source regions. The increase of peak PM10 concentration caused by gusty wind in the non-dust-source regions was higher than in the dust-source regions, with 10–50%. Implementing the gusty-wind model could help improve the LS scheme’s performance in simulating PM10 concentrations of this severe SDS event. More work is still needed to investigate the reliability of the gusty-wind model and LS scheme on various SDS events.

New Categorical Metrics for Air Quality Model Evaluation

2007 ◽  
Vol 46 (4) ◽  
pp. 549-555 ◽  
Author(s):  
Daiwen Kang ◽  
Rohit Mathur ◽  
Kenneth Schere ◽  
Shaocai Yu ◽  
Brian Eder
Keyword(s):  
Air Quality ◽  
False Alarm ◽  
Regional Scale ◽  
Central Line ◽  
Threshold Concentration ◽  
Air Quality Model ◽  
Quality Model ◽  
False Alarm Ratio ◽  
Air Quality Forecast ◽  
Success Index

Abstract Traditional categorical metrics used in model evaluations are “clear cut” measures in that the model’s ability to predict an “exceedance” is defined by a fixed threshold concentration and the metrics are defined by observation–forecast sets that are paired both in space and time. These metrics are informative but limited in evaluating the performance of air quality forecast (AQF) systems because AQF generally examines exceedances on a regional scale rather than a single monitor. New categorical metrics—the weighted success index (WSI), area hit (aH), and area false-alarm ratio (aFAR)—are developed. In the calculation of WSI, credits are given to the observation–forecast pairs within the observed exceedance region (missed forecast) or the forecast exceedance region (false alarm), depending on the distance of the points from the central line (perfect observation–forecast match line or 1:1 line on scatterplot). The aH and aFAR are defined by matching observed and forecast exceedances within an area (i.e., model grid cells) surrounding the observation location. The concept of aH and aFAR resembles the manner in which forecasts are usually issued. In practice, a warning is issued for a region of interest, such as a metropolitan area, if an exceedance is forecast to occur anywhere within the region. The application of these new categorical metrics, which are supplemental to the traditional counterparts (critical success index, hit rate, and false-alarm ratio), to the Eta Model–Community Multiscale Air Quality (CMAQ) forecast system has demonstrated further insight into evaluating the forecasting capability of the system (e.g., the new metrics can provide information about how the AQF system captures the spatial variations of pollutant concentrations).

scholarly journals What Can We Expect from Data Assimilation for Air Quality Forecast? Part II: Analysis with a Semi-Real Case

2019 ◽  
Vol 36 (7) ◽  
pp. 1433-1448 ◽  
Author(s):  
Bertrand Bessagnet ◽  
Laurent Menut ◽  
Florian Couvidat ◽  
Frédérik Meleux ◽  
Guillaume Siour ◽  
...  
Keyword(s):  
Air Quality ◽  
Data Assimilation ◽  
Observational Data ◽  
Vertical Direction ◽  
Pollutant Emissions ◽  
Air Quality Model ◽  
Quality Model ◽  
Initial State ◽  
Air Quality Forecast ◽  
Maximum Benefit

AbstractAssimilation of observational data from ground stations and satellites has been identified as a technique to improve air quality model results. This study is an evaluation of the maximum benefit expected from data assimilation in chemical transport models. Various tests are performed under real meteorological conditions; the injection of various subsets of “simulated observational data” at the initial state of a forecasting period is analyzed in terms of benefit on selected criteria. This observation dataset is generated by a simulation with perturbed input data. Several criteria are defined to analyze the simulations leading to the definition of a “tipping time” to compare the behavior of simulations. Assimilating three-dimensional data instead of ground observations clearly adds value to the forecast. For the studied period and considering the expected best favorable data assimilation experiment, the maximum benefit is higher for particulate matter (PM) with tipping times exceeding 80 h; for ozone (O3) the gain is on average around 30 h. Assimilating O3 concentrations with a delta calculated on the first level and propagated over the vertical direction provides better results on O3 mean concentrations when compared with the expected best experiment corresponding to the injection of the O3 “observations” 3D dataset, but for maximum O3 concentrations the opposite behavior is observed. If data assimilation of secondary pollutant concentrations provides an improvement, assimilation of primary pollutant emissions can have beneficial impacts when compared with an assimilation of concentrations, after several days on secondary pollutants like O3 or nitrate concentrations and more quickly for the emitted primary pollutants. An assimilation of ammonia concentrations has slightly better performances on nitrate, ammonium, and PM concentrations relative to the assimilation of nitrogen or sulfur dioxides.

Air Quality Forecast Model Based on Model Products of CMAQ in Fuzhou City, China

2012 ◽  
Vol 518-523 ◽  
pp. 1442-1450
Author(s):  
Bin Bin Chen ◽  
Kai Yang ◽  
Chang Cheng Lin ◽  
Wen Lin ◽  
Hong Wang ◽  
...  
Keyword(s):  
Air Quality ◽  
Air Pollutants ◽  
Forecast Model ◽  
Observation Data ◽  
Air Quality Model ◽  
Quality Model ◽  
Forecasting Accuracy ◽  
Air Quality Forecast ◽  
Index Level ◽  
Daily Forecast

Based on the forecast products of Community Multiscale Air Quality Model (CMAQ), observational data of air pollutants and the conventional meteorological observation data of surface from January 2007 to June 2010 in Fuzhou City, Fujian Province, China, the weather systems influencing on Fuzhou City are divided into 7 weather patterns, including continental high, subtropical high, shear, warm sectors convergence, upper trough, typhoon and tropical convergence. According to the forecast method of combination of dynamic and statistic, using multiple linear stepwise regression method, the forecasting models of daily pollutant concentration under different weather systems are established. The confidence level of the equations achieves 0.000 and so the models are statistically significant. Using the models, the prediction effect of the concentration of different air pollutants in Fuzhou City from July to December, 2010 is tested by back substitution. The forecasting accuracy on contamination index level of PM10 reaches to 71.3%, and the forecasting accuracy on SO2和NO2 both reaches to 100%. The comprehensive score of air quality daily forecast in the city comes to 88.8 points on average.

scholarly journals A Dynamic Dust Emission Allocation Method and Holiday Profiles Applied to Emission Processing for Improving Air Quality Model Performance

2019 ◽  
Vol 19 (11) ◽  
pp. 2531-2542
Author(s):  
Guanglin Jia ◽  
Zhijiong Huang ◽  
Yuanqian Xu ◽  
Zhuangmin Zhong ◽  
Qinge Sha ◽  
...  
Keyword(s):  
Air Quality ◽  
Dust Emission ◽  
Model Performance ◽  
Air Quality Model ◽  
Quality Model ◽  
Allocation Method ◽  
Emission Allocation

Ammonia measurements from space with the Cross-track Infrared Sounder (CrIS): characteristics and applications

2020 ◽  
Author(s):  
Mark Shephard ◽  
Chris McLinden ◽  
Enrico Dammers ◽  
Shailesh Kharol ◽  
Karen Cady-Pereira ◽  
...  
Keyword(s):  
Air Quality ◽  
North America ◽  
Dry Deposition ◽  
Forecast Model ◽  
Satellite Observations ◽  
Spatiotemporal Variability ◽  
Air Quality Model ◽  
Quality Model ◽  
Air Quality Forecast ◽  
The Impact

<p>Satellite data are helping to fill monitoring gaps in order to better inform decision makers and assess the impact of ammonia-related policies.  Presented is an overview demonstrating the current capabilities of the ammonia (NH<sub>3</sub>) data product derived from the CrIS satellite instrument for monitoring, air quality forecast model evaluation, dry deposition estimates, and emissions estimates.  This includes examples of daily, seasonal, and annual observations of CrIS ammonia that demonstrate the spatiotemporal variability of ammonia globally. These results further demonstrate the ability of CrIS to observe regional changes in ammonia concentrations, such as spring maximum values over agricultural regions from the fertilizing of crops.  Also shown is the importance contribution of wildfires, especially in regions where there is little or no agriculture sources, such as the northern latitudes in North America during summer.  Initial comparisons of CrIS NH<sub>3</sub> satellite observations with air quality model simulations show that while there is general agreement on the spatial distribution of the anthropogenic hotspots, some areas are markedly different.  Some key findings are that dry deposition estimates of NH<sub>3</sub> and NO<sub>2</sub> from CrIS and the Ozone Monitoring Instrament (OMI), respectively, indicate that the NH<sub>3</sub> dominates over most regions across North America. Their 2013 annual ratio shows NH<sub>3</sub> accounting for ~82% and ~55 % of the combined reactive nitrogen dry deposition from these two species over Canada and the U.S.  Furthermore, we show the use of CrIS satellite observations to estimate annual and seasonal emissions over Concentrated Animal Feeding Operations (CAFOs).  These results are used to evaluate the seasonal and temporal emissions profiles used in bottom-up inventories over an agriculture hotspot, which are often underreported</p>

Use of a Dynamic Air Quality Model to Reduce the Uncertainties of Seasonal and Annual Atmospheric Depositions for Mercury TMDLs

2005 ◽  
Vol 2005 (3) ◽  
pp. 1393-1414
Author(s):  
Kuo-Liang Lai ◽  
Janet Kremer ◽  
Susan Sciarratta ◽  
R. Dwight Atkinson ◽  
Tom Myers
Keyword(s):  
Air Quality ◽  
Air Quality Model ◽  
Quality Model ◽  
Atmospheric Depositions

scholarly journals Assessment of Emission Reduction and Meteorological Change in PM2.5 and Transport Flux in Typical Cities Cluster during 2013–2017

2021 ◽  
Vol 13 (10) ◽  
pp. 5685
Author(s):  
Panbo Guan ◽  
Hanyu Zhang ◽  
Zhida Zhang ◽  
Haoyuan Chen ◽  
Weichao Bai ◽  
...  
Keyword(s):  
Air Quality ◽  
Action Plan ◽  
Influence Factors ◽  
Pollution Prevention ◽  
Weather Conditions ◽  
Yangtze River Delta ◽  
Control Measures ◽  
Joint Control ◽  
Air Quality Model ◽  
Quality Model

Under the Air Pollution Prevention and Control Action Plan (APPCAP) implemented, China has witnessed an air quality change during the past five years, yet the main influence factors remain relatively unexplored. Taking the Beijing-Tianjin-Hebei (BTH) and Yangtze River Delta (YRD) regions as typical cluster cities, the Weather Research Forecasting (WRF) and Comprehensive Air Quality Model with Extension (CAMx) were introduced to demonstrate the meteorological and emission contribution and PM2.5 flux distribution. The results showed that the PM2.5 concentration in BTH and YRD significantly declined with a descend ratio of −39.6% and −28.1%, respectively. For the meteorological contribution, those regions had a similar tendency with unfavorable conditions in 2013–2015 (contribution concentration 1.6–3.8 μg/m3 and 1.1–3.6 μg/m3) and favorable in 2016 (contribution concentration −1.5 μg/m3 and −0.2 μg/m3). Further, the absolute value of the net flux’s intensity was positively correlated with the degree of the favorable/unfavorable weather conditions. When it came to emission intensity, the total net inflow flux increased, and the outflow flux decreased significantly across the border with the emission increasing. In short: the aforementioned results confirmed the effectiveness of the regional joint emission control and provided scientific support for the proposed effective joint control measures.

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