Improving Air Quality Predictions and Assessment across Scales

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Air Quality".

Deadline for manuscript submissions: closed (20 May 2023) | Viewed by 4124

Special Issue Editors


E-Mail Website1 Website2
Guest Editor
1. Center for Spatial Information Science and Systems, George Mason University, Fairfax, VA, 22030, USA
2. National Oceanic and Atmospheric Administration (NOAA) Air Resources Laboratory Affiliate, College Park, MD, 20740, USA
Interests: atmospheric composition and deposition; multimedia surface fluxes and emissions; air quality predictions; coupled model development and applications; research and consulting
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E-Mail Website
Guest Editor
National Oceanic and Atmospheric Administration (NOAA) Air Resources Laboratory, College Park, MD, 20740, USA
Interests: atmospheric composition and deposition; severe weather induced; dust emissions; coupled model development and application; air quality predictions

E-Mail Website
Guest Editor
United States Environmental Protection Agency, Research Triangle Park, NC, 27711, USA
Interests: atmospheric composition and process modeling; air quality predictions; model evaluation and applications; natural and biogenic emissions; data assimilation

Special Issue Information

Dear Colleagues,

The presence of air pollutants, such as ground-level ozone and fine particulate matter (PM2.5), has prominent impacts on human, ecosystem, and crop health, and thus it is critical to improve air quality assessments and predictions across scales. For example, the Global Burden of Disease Study 2019 attributes approximately 4.51 million deaths each year to outdoor air pollution. In response to this concern about air pollution, there have been significant reductions in anthropogenic emissions over the last decades in many parts of the world, thus leading to relatively “cleaner” atmospheric conditions in some regions. Consequently, more emphasis has been placed on understanding the roles of natural emissions, such as nitric oxide (NO), from soil and lightning; sulfur dioxide (SO2) and carbon dioxide (CO2), from volcanic eruptions; and biogenic volatile organic compounds (BVOCs) from vegetation, windblown dust, and biomass-burning sources. Numerous world regions have experienced events leading to significantly worsened air quality conditions, including extreme wildfires or windblown dust outbreaks.   

To highlight such efforts in the scientific community, we are inviting the submission of research papers that investigate improved methods, applications, and evaluations of air quality assessments and predictions across scales. These papers may use either (or both) observations or models; new modeling approaches developed to improve predictions and forecasting of air quality through improved inputs, process development, or novel inline to postprocessing methods are also highly encouraged. Papers that delve into the interplay between anthropogenic and natural source emissions and how they affect atmospheric composition and air quality are also encouraged. Finally, papers using novel measurement techniques, observations, and analysis/statistical methods to evaluate air quality model predictions across scales are welcome.

Dr. Patrick C. Campbell
Dr. Barry D. Baker
Dr. Daiwen Kang
Guest Editors

Manuscript Submission Information

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Keywords

  • ozone and PM2.5 Pollution
  • air quality predictions and forecasting
  • anthropogenic and natural emissions
  • wildfire emissions
  • windblown dust emissions
  • lightning nitric oxide emissions
  • model development and evaluation
  • observational analysis

Published Papers (1 paper)

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Research

16 pages, 3785 KiB  
Article
Particulate Matter and Ammonia Pollution in the Animal Agricultural-Producing Regions of North Carolina: Integrated Ground-Based Measurements and Satellite Analysis
by Rebecca Wiegand, William H. Battye, Casey Bray Myers and Viney P. Aneja
Atmosphere 2022, 13(5), 821; https://doi.org/10.3390/atmos13050821 - 17 May 2022
Cited by 2 | Viewed by 2922
Abstract
Intensive animal agriculture is an important part of the US and North Carolina’s (NC’s) economy. Large emissions of ammonia (NH3) gas emanate from the handling of animal wastes at these operations contributing to the formation of fine particulate matter (PM2.5 [...] Read more.
Intensive animal agriculture is an important part of the US and North Carolina’s (NC’s) economy. Large emissions of ammonia (NH3) gas emanate from the handling of animal wastes at these operations contributing to the formation of fine particulate matter (PM2.5) around the state causing a variety of human health and environmental effects. The objective of this research is to provide the relationship between ammonia, aerosol optical depth and meteorology and its effect on PM2.5 concentrations using satellite observations (column ammonia and aerosol optical depth (AOD)) and ground-based meteorological observations. An observational-based multiple linear regression model was derived to predict ground-level PM2.5 during the summer months (JJA) from 2008–2017 in New Hanover County, Catawba County and Sampson County. A combination of the Cumberland and Johnston County models for the summer was chosen and validated for Duplin County, NC, then used to predict Sampson County, NC, PM2.5 concentrations. The model predicted a total of six 24 h exceedances over the nine-year period. This indicates that there are rural areas of the state that may have air quality issues that are not captured for a lack of measurements. Moreover, PM2.5 chemical composition analysis suggests that ammonium is a major component of the PM2.5 aerosol. Full article
(This article belongs to the Special Issue Improving Air Quality Predictions and Assessment across Scales)
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