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Special Issue "Improving Air Quality Predictions and Assessment across Scales"

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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 6618

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Special Issue Editors

Dr. Patrick C. Campbell

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
Special Issues, Collections and Topics in MDPI journals

Dr. Barry D. Baker

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

Dr. Daiwen Kang

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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Atmosphere is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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 (5 papers)

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Research

Open AccessArticle

Examining the Impact of Dimethyl Sulfide Emissions on Atmospheric Sulfate over the Continental U.S.

and

Atmosphere 2023, 14(4), 660; https://doi.org/10.3390/atmos14040660 - 31 Mar 2023

Viewed by 604

Abstract

We examined the impact of dimethylsulfide (DMS) emissions on sulfate concentrations over the continental U.S. by using the Community Multiscale Air Quality (CMAQ) model version 5.4 and performing annual simulations without and with DMS emissions for 2018. DMS emissions enhance sulfate not only over seawater but also over land, although to a lesser extent. On an annual basis, the inclusion of DMS emissions increase sulfate concentrations by 36% over seawater and 9% over land. The largest impacts over land occur in California, Oregon, Washington, and Florida, where the annual mean sulfate concentrations increase by ~25%. The increase in sulfate causes a decrease in nitrate concentration due to limited ammonia concentration, especially over seawater, and an increase in ammonium concentration with a net effect of increased inorganic particles. The largest sulfate enhancement occurs near the surface (over seawater), and the enhancement decreases with altitude, diminishing to 10–20% at an altitude of ~5 km. Seasonally, the largest enhancement in sulfate over seawater occurs in summer, and the lowest in winter. In contrast, the largest enhancements over land occur in spring and fall due to higher wind speeds that can transport more sulfate from seawater into land. Full article

(This article belongs to the Special Issue Improving Air Quality Predictions and Assessment across Scales)

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Open AccessArticle

Updating and Evaluating Anthropogenic Emissions for NOAA’s Global Ensemble Forecast Systems for Aerosols (GEFS-Aerosols): Application of an SO₂ Bias-Scaling Method

Partha S. Bhattacharjee

Steven J. Smith

Daniel Tong

and

Youhua Tang

Atmosphere 2023, 14(2), 234; https://doi.org/10.3390/atmos14020234 - 25 Jan 2023

Cited by 1 | Viewed by 924

Abstract

We updated the anthropogenic emissions inventory in NOAA’s operational Global Ensemble Forecast for Aerosols (GEFS-Aerosols) to improve the model’s prediction of aerosol optical depth (AOD). We used a methodology to quickly update the pivotal global anthropogenic sulfur dioxide (SO₂) emissions using a speciated AOD bias-scaling method. The AOD bias-scaling method is based on the latest model predictions compared to NASA’s Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA2). The model bias was subsequently applied to the CEDS 2019 SO₂ emissions for adjustment. The monthly mean GEFS-Aerosols AOD predictions were evaluated against a suite of satellite observations (e.g., MISR, VIIRS, and MODIS), ground-based AERONET observations, and the International Cooperative for Aerosol Prediction (ICAP) ensemble results. The results show that transitioning from CEDS 2014 to CEDS 2019 emissions data led to a significant improvement in the operational GEFS-Aerosols model performance, and applying the bias-scaled SO₂ emissions could further improve global AOD distributions. The biases of the simulated AODs against the observed AODs varied with observation type and seasons by a factor of 3~13 and 2~10, respectively. The global AOD distributions showed that the differences in the simulations against ICAP, MISR, VIIRS, and MODIS were the largest in March–May (MAM) and the smallest in December–February (DJF). When evaluating against the ground-truth AERONET data, the bias-scaling methods improved the global seasonal correlation (r), Index of Agreement (IOA), and mean biases, except for the MAM season, when the negative regional biases were exacerbated compared to the positive regional biases. The effect of bias-scaling had the most beneficial impact on model performance in the regions dominated by anthropogenic emissions, such as East Asia. However, it showed less improvement in other areas impacted by the greater relative transport of natural emissions sources, such as India. The accuracies of the reference observation or assimilation data for the adjusted inputs and the model physics for outputs, and the selection of regions with less seasonal emissions of natural aerosols determine the success of the bias-scaling methods. A companion study on emission scaling of anthropogenic absorbing aerosols needs further improved aerosol prediction. Full article

(This article belongs to the Special Issue Improving Air Quality Predictions and Assessment across Scales)

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Open AccessArticle

Decadal Application of WRF/Chem under Future Climate and Emission Scenarios: Impacts of Technology-Driven Climate and Emission Changes on Regional Meteorology and Air Quality

and

Atmosphere 2023, 14(2), 225; https://doi.org/10.3390/atmos14020225 - 21 Jan 2023

Viewed by 912

Abstract

This work presents new climate and emissions scenarios to investigate changes on future meteorology and air quality in the U.S. Here, we employ a dynamically downscaled Weather Research and Forecasting model coupled with chemistry (WRF/Chem) simulations that use two Intergovernmental Panel on Climate Change scenarios (i.e., A1B and B2) integrated with explicitly projected emissions from a novel Technology Driver Model (TDM). The projected 2046–2055 emissions show widespread reductions in most gas and aerosol species under both TDM/A1B and TDM/B2 scenarios over the U.S. The WRF/Chem simulations show that under the combined effects of the TDM/A1B climate and emission changes, the maximum daily average 8-h ozone (MDA8 h O₃) increases by ~3 ppb across the U.S. mainly due to widespread increases in near-surface temperature and background methane concentrations, with some contributions from localized TDM emission changes near urban centers. For the TDM/B2 climate and emission changes, however, the MDA8 h O₃ is widely decreased, except near urban centers where the relative TDM emission changes and O₃ formation regimes leads to increased O₃. The number of O₃ exceedance days (i.e., MDA8 h O₃ > 70 ppb) for the entire domain is significantly reduced by a grid cell maximum of up to 43 days (domain average ~0.5 days) and 62 days (domain average ~2 days) for the TDM/A1B and TDM/B2 scenarios, respectively, while in the western U.S., larger O₃ increases lead to increases in nonattainment areas, especially for the TDM/A1B scenario. The combined effects of climate and emissions (for both A1B and B2 scenarios) will lead to widespread decreases in the daily 24-h average (DA24 h) PM_2.5 concentrations, especially in the eastern U.S. (max decrease up to 93 µg m⁻³). The PM_2.5 changes are dominated by decreases in anthropogenic emissions for both the TDM/A1B and TDM/B2 scenarios, with secondary effects on decreasing PM_2.5 from climate change. The number of PM_2.5 exceedance days (i.e., DA24 h PM_2.5 > 35 µg m⁻³) is significantly reduced over the eastern U.S. under both TDM/A1B and B2 scenarios, which suggests that both climate and emission changes may synergistically lead to decreases in PM_2.5 nonattainment areas in the future. Full article

(This article belongs to the Special Issue Improving Air Quality Predictions and Assessment across Scales)

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Open AccessArticle

Assessing the Impact of Lightning NO_x Emissions in CMAQ Using Lightning Flash Data from WWLLN over the Contiguous United States

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Atmosphere 2022, 13(8), 1248; https://doi.org/10.3390/atmos13081248 - 06 Aug 2022

Viewed by 1309

Abstract

Comparison of lightning flash data from the National Lightning Detection Network (NLDN) and from the World Wide Lightning Location Network (WWLLN) over the contiguous United States (CONUS) for the 2016–2018 period reveals temporally and spatially varying flash rates that would influence lightning NO_x (LNO_x) production due to known detection efficiency differences especially during summer months over land (versus over ocean). However, the lightning flash density differences between the two networks show persistent seasonal patterns over geographical regions (e.g., land versus ocean). Since the NLDN data are considered to have higher accuracy (lightning detection with >95% efficiency), we developed scaling factors for the WWLLN flash data based on the ratios of WWLLN to NLDN flash data over time (months of year) and space. In this study, sensitivity simulations using the Community Multiscale Air Quality (CMAQ) model are performed utilizing the original data sets (both NLDN and WWLLN) and the scaled WWLLN flash data for LNO_x production over the CONUS. The model performance of using the different lightning flash datasets for ambient O₃ and NO_x mixing ratios that are directly impacted by LNO_x emissions and the wet and dry deposition of oxidized nitrogen species that are indirectly impacted by LNO_x emissions is assessed based on comparisons with ground-based observations, vertical profile measurements, and satellite products. During summer months, the original WWLLN data produced less LNO_x emissions (due to its lower lightning detection efficiency) compared to the NLDN data, which resulted in less improvement in model performance than the simulation using NLDN data as compared to the simulation without any LNO_x emissions. However, the scaled WWLLN data produced LNO_x estimates and model performance comparable with the NLDN data, suggesting that scaled WWLLN may be used as a substitute for the NLDN data to provide LNO_x estimates in air quality models when the NLDN data are not available (e.g., due to prohibitive cost or lack of spatial coverage). Full article

(This article belongs to the Special Issue Improving Air Quality Predictions and Assessment across Scales)

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Open AccessArticle

Particulate Matter and Ammonia Pollution in the Animal Agricultural-Producing Regions of North Carolina: Integrated Ground-Based Measurements and Satellite Analysis

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Atmosphere 2022, 13(5), 821; https://doi.org/10.3390/atmos13050821 - 17 May 2022

Cited by 2 | Viewed by 1954

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 (PM_2.5) 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 PM_2.5 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 PM_2.5 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, PM_2.5 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, PM_2.5 chemical composition analysis suggests that ammonium is a major component of the PM_2.5 aerosol. Full article

(This article belongs to the Special Issue Improving Air Quality Predictions and Assessment across Scales)

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