Both observations and modeling studies have demonstrated the long-range inter-continental transport of pollutants. Changes in emission patterns over different regions of the world are likely to exacerbate the impacts of long-range pollutant transport on background pollutant levels in another region, which may then impact the attainment of local air quality standards. Additionally, increased concerns of climate impacts on regional and local ecosystem disciplines have driven the need to utilize outputs from global models into regional modeling systems with different temporal and spatial scales. In such applications, downscaling approaches have been used to link the two modeling systems, with different physical and dynamical characteristics, to bridge the gap between global and local effects. This session seeks papers that discuss key issues related to the consistent coupling of atmospheric physical and chemical processes on local-to-global scales and related modeling applications. Topics of interest in this session include:
- Hemispheric chemistry transport models
- Multi-scale climate and air quality modeling applications
- Earth System Modeling and Feedbacks
- Dynamical and statistical downscaling
- Model evaluation and inter-comparison
Also, evaluation of air quality modeling systems (including meteorological and emissions models) is a key to verify the integrity of such modeling systems for various applications at various spatial and temporal resolutions. Abstracts are invited that present results of model evaluation studies, with emphasis on new techniques for model evaluation. Session topics include:
- Diagnostic tools
- Analyses and comparisons with data from measurement networks
- Process evaluation, including dynamic evaluation
- Sensitivity of air quality models to meteorological inputs
Presentations
Sharmin Akter
Mastooreh Ameri
Presentation: 2528Investigating the influences of dust storms on precipitation in Iran using WRF-CHEM model
Download Presentation
| Download Extended Abstract
Investigating the influences of dust storms on precipitation in Iran using WRF-CHEM modelMastooreh Ameria, Khosro Ashrafia, Sarmad Ghaderb, Mohammad Amin Mirrezaeia
a School of Environment, College of Engineering, University of Tehran, Tehran, Iran
b Space Physics Department, Institute of Geophysics, University of Tehran, Tehran, Iran
Aerosols affect the climate system in various ways, including direct reflection, absorbing incoming or outgoing short or long-wave radiation, or affecting cloud albedo and precipitation processes. Sand and dust storms are common meteorological hazards in arid and semi-arid regions. In this study, the main focus is on the role of dust storms on Iran's precipitation patterns. Dust storms originating from the west and southwest of Iran have the ability to affect weather through the changes in atmospheric humidity. These impacts are addressed using the Weather Research and Forecasting model coupled with chemistry (WRF-Chem 3.6.1). First, by comparing the Air Quality Monitoring stations of 30 provinces of Iran, Synoptic data of the capital cities and satellite maps of Aqua/AIS and MEYYA-2 satellites, three four-day periods of dust storm events were selected which coincide with rainfall. Also, the extensive distribution of precipitation through the domain was considered in determining the spatial-temporal precipitation patterns. Moreover, WRF physics and precipitation parameters have been chosen based on the appropriate schemes for Iran. In order to improve the accuracy of the simulation, the latest modification of microphysics and Cumulus parametrization schemes that have great importance for precipitation predictions has been applied. Second, the simulation variables such as temperature, pressure, wind speed and direction were validated by station reports. There was a reasonable positive correlation between simulated and observed data. The results of WRF-CHEM simulations depicted that in most diagrams, there is often a decline in the amount of precipitation, especially at the end of dust storm periods, in comparison to the WRF run series. Considering the aerosol-cloud feedbacks, the mean cumulative precipitation for the dust episodes during March, April and May were changed respectively from 12.45 mm to 12.84 mm, 31 mm to 30.86 mm and 8.79 mm to 7.78 mm. Also, the highest anomaly in total cumulative precipitation values was 0.8 mm, which was observed in the last day of May episode. Besides, the predicted PM10 and PM2.5 values in western and southwestern regions such as Ahvaz, Zabol and Genaveh were less than observed values, which is acceptable, as these regions are exposed to high levels of dust storms. Moreover, at the early stages of the dust storm, there is a decline in values of precipitation caused by radiative forcing and humidity reduction and then, rainfall rises due to indirect effects including, CCN activation. Finally, the results revealed that aerosols play a pivotal role in precipitation patterns and the dominant impact is reducing the rainfall amounts, especially in the regions which are severely affected by dust storms.
Shih Ying Chang
Presentation: 2472TDep Measurement Model Fusion (MMF) method to fuse modeled and measured air quality data to estimate total deposition with Python geoprocessing
Download Presentation
TDep Measurement Model Fusion (MMF) method to fuse modeled and measured air quality data to estimate total deposition with Python geoprocessingShih Ying Chang1, Nathan Pavlovic1, Greg Beachley2, Melissa Puchalski2, and Christopher Rogers3
1 Sonoma Technology, Inc., Petaluma, CA
2 U.S. Environmental Protection Agency, Washington, D.C.
3 Wood Environment & Infrastructure Solutions, Inc., Jacksonville, FL.
To assess spatial and temporal trends in annual atmospheric deposition, the Total Deposition Science Committee (TDep) under the National Atmospheric Deposition Program (NADP) developed a measurement-model fusion (MMF) method to estimate fluxes of total, wet, and dry deposition of sulfur, nitrogen, base cations, and chloride. The TDep method provides interpolated maps of annual deposition from the year 2000 to the present, using a modeling approach that fuses the Community Multiscale Air Quality (CMAQ) model data with data from ambient air quality and wet deposition monitoring sites. Due to its large spatial coverage, fine spatial resolution (i.e. 4 x 4 km2), and multiple data sources, the TDep approach involves complex geoprocessing steps that require an organized and iterable implementation to support ongoing data production and future process enhancements. In an effort to modernize and streamline the method and facilitate the development of new features, a new modularized Python geoprocessing TDep application was developed. The TDep dry deposition estimates are constructed by combining measurement-adjusted, CMAQ-modeled dry deposition fluxes with "measured" dry deposition fluxes cast as the product of spatially resolved CMAQ-modeled deposition velocities and ambient pollutant concentrations measured in the Clean Air Status and Trends Network (CASTNET). The TDep wet deposition estimates are constructed by first fusing an annual modeled precipitation field (Parameter-elevation Regression on Independent Slopes Model; PRISM) with precipitation data measured by NADP networks (National Trends Network; NADP/NTN and Mercury Deposition Network). The fused precipitation grid is combined with annually aggregated NADP/NTN precipitation chemistry measurements to calculate the wet deposition. Wet and dry deposition grids are combined to estimate the total annual deposition of pollutants. In this presentation, we will focus on the methodology of the TDep MMF method and the development and structure of the updated Python geoprocessing application. The developed Python framework breaks down the TDep approach into six modules: (1) data ingestion, (2) data interpolation, (3) bias adjustment for modeled data, (4) data fusion, (5) data aggregation, and (6) product export. Through the modularized framework, the Python application has greatly reduced the complexity of the codebase and will support streamlined processing. The new TDep framework will facilitate future improvements to the TDep MMF method, such as inclusion of satellite data or CMAQ-modeled wet deposition to improve the accuracy of interpolation for locations without available monitoring sites.
Wanying CHEN
Presentation: 2532Source apportionment of ozone under different synoptic patterns in the Pearl River Delta region
Download Presentation
| Download Extended Abstract
Source apportionment of ozone under different synoptic patterns in the Pearl River Delta region
Wanying CHEN, Yiang CHEN, Xingcheng LU, Jimmy C.H. FUNG*
In recent years, with the effective control of fine particulate matter (PM2.5) by the government, the concentration of PM2.5 in the Pearl River Delta (PRD) region has decreased gradually. However, the pollution problem characterized by a high concentration of ozone is sequentially emerging. In the case of relatively stable emissions, the meteorological condition is an essential factor affecting ozone pollution. Therefore, exploring the contribution of various source regions and source categories to the O3 concentration under different synoptic patterns is an integral part of regional atmospheric environmental research. In this work, the Comprehensive Air Quality Model with extension (CAMx) modeling system with Ozone Source Apportionment Technology (OSAT) module was applied to analyze the influence of particular synoptic patterns, including sea high pressure, equalizing pressure field and subtropical high pressure, on ozone concentration and source contribution covering the PRD region. The results showed that the concentration of ozone increased under three meteorological conditions. Under distinctive synoptic patterns, the emissions outside PRD have the most significant contribution invariably. Preceded by the cross-boundary transport, mobile and biogenic sources attribute to the highest contribution under subtropical high pressure and sea high pressure, respectively. With the effect of the subtropical high pressure, the local contribution in the PRD region increased significantly, especially in Guangzhou (+7.9%) and Huizhou (+6.3%), compared with the monthly mean contribution. As the equalizing pressure field changed the direction of the southernly prevailing wind in April, the contribution of emissions outside the PRD increased by 6.3%, compared with the monthly contribution. Our results indicated that collaborative emission control measures should be strengthened with the surrounding area. Combined with the meteorological situation, controlling the endogenous emission in the PRD plays a pivotal role in preventing O3 pollution.
James East
Tugce Erdem
Christina Feng Chang
Presentation: 2459On the Sensitivity of a Machine Learning-Based Model to Predict Chlorophyll- Using Multi-Media Modeling Environmental Predictors
Download Presentation
On the Sensitivity of a Machine Learning-Based Model to Predict Chlorophyll- Using Multi-Media Modeling Environmental PredictorsChristina Feng Chang1, Marina Astitha1, Valerie Garcia2, Chunling Tang2, Penny Vlahos3, David Wanik4, Jesse Bash2
1Department of Civil and Environmental Engineering, University of Connecticut, Storrs, CT 06269, USA
2National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC 27711, USA
3Deparment of Marine Sciences, University of Connecticut, Groton, CT 06340, USA
4Department of Operations and Information Management, Stamford, CT 06901, USA
In the past, we have introduced the concept of using multi-media modeling and machine learning (ML) to assess environmental variables (predictors) that affect water quality by using Lake Erie as a case study. Since then, we have improved our water quality model by utilizing outputs from updated versions of the numerical prediction models. This time, we will focus on the influence of each set of predictors (meteorological, air quality, hydrological and agricultural) by varying the usage of the ML-based model inputs through various sensitivity tests. In-situ chlorophyll- (chlor- ) measurements, which serve as a proxy for harmful algal blooms (HABs), are provided by the Lake Erie Committee Forage Task Group (LEC FTG) for the 2002-2012 period. Meteorological weather variables from WRF, hydrological variables from VIC, nitrogen deposition from CMAQ, and agricultural management practice variables from EPIC for the 11-year period are used to fit a random forest ML model to predict chlor- concentrations. We discuss the importance of explanatory variables that originate from these individual modeling systems, and analyze the contribution of each covariate in the model to better understand the occurrence of high chlor- concentrations. As HABs and hypoxia continue to threaten water bodies across the nation, lessons learned from developing and testing this ML-based approach can be used to tackle water quality problems in other lakes or coastal areas and inform policy decisions.
Lucas Henneman
Christian Hogrefe
Shannon Koplitz
Yukui Li
Presentation: 2558Source Contributions to Ozone in Connecticut
Download Presentation
Source Contributions to Ozone in Connecticut
Yukui Li, UConn
According to American Lung Association's "State of the Air 2020" report, every county in Connecticut continued to earn an F for ozone in spite of a slight decrease in the number of the most unhealthy days. There is an urgent need to lower ozone concentrations in Connecticut.
We model the point sources contributing to ozone concentrations in Connecticut in 2016 using both the Comprehensive Air Quality Model with Extensions (CAMx) and the Community Multiscale Air Quality Modeling System (CMAQ) and their respective source apportionment tools: Ozone Source Apportionment Tools (CAMx-OSAT) and Integrated Source Apportionment Method (CMAQ-ISAM). For consistency, we use emissions and meteorological inputs based on the US EPA's 2016beta platform for both models using a domain covering the full United States, southern Canada, and northern Mexico.
We model the contributions from nine major sources in the region with the goal of narrowing down the top contributing sources. These sources include Roseton Generating, Arthur Kill Generating Station, Bowline Generating Station, East River Generating Station, 74th Street Station, Astoria Gas Turbine Power, Astoria Generating Station, Ravenswood Generating Station, E F Barrett Power Station, Northport Generating Station, Holtsville Facility, and Bergen Generating Station (New Jersey). As a first estimate of the impact of these sources, instead of tracking the emissions specifically from the source, we track the contribution from all sources within the grid cell. This provides a quicker approach to narrow down the major contributors. We quantify the impact of these high emitting sources to average and peak ozone concentrations in Connecticut from April-October 2016. This will aid in the development of effective, targeted ozone policies to improve the air quality in Connecticut.
CAMx-OSAT shows that the boundary conditions and remaining sources (i.e. those not explicitly tracked) account for over 99% of the average ozone concentration between June and August of 2016. The nine selected source grids combined contribute less than one half a percent of the average ozone concentration.
Thalia Alejandra Montejo Barato
Mike Moran
Heather Simon
Nash Skipper
Pradeepa Vennam