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Atmosphere
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PM2.5 Predictions in the USA
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PM2.5 Predictions in the USA

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

Deadline for manuscript submissions: closed (28 February 2021) | Viewed by 7344

Special Issue Editors

Dr. Jianping Huang

E-Mail Website
Guest Editor
IMSG@NOAA/NCEP/EMC, College Park, MD 20740, USA
Interests: tropospheric ozone; planetary boundary layer dynamics; air quality predictions
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

Special Issue Information

Dear Colleagues,

High concentrations of particulate matter with an aerodynamic diameter of 2.5 micrometers or less (PM2.5) are of great concern due to their human health impact, visibility impairment, and environmental damage. Accurate forecasts of PM2.5 are difficult due in part to the complexity of its emission sources and chemical composition, as well as uncertainties associated with its eventual fate within the atmosphere. Ongoing research aims to characterize and constrain these key processes, improving PM2.5 forecasts and enabling citizens and stakeholders to better mitigate the negative effects of pollution episodes.

To highlight these efforts, we invite you to submit your research related to PM2.5 Predictions in the USA for publication in a special issue dedicated to the topic. This issue aims to collect and disseminate recent research papers on current scientific advances, applications, and challenges related to PM2.5 forecasts in the US, including (but not limited to) topics such as wildfire emissions, wind-blown dust, secondary aerosol formation, surface and satellite measurements and their applications, forecast challenges over complex terrain and coastal regions, meteorological impacts, planetary boundary layer dynamics, data assimilation, machine learning techniques, and air quality model development, evaluation, and bias correction.

Dr. Jianping Huang
Dr. Patrick C Campbell
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 2400 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

  • PM2.5
  • air quality forecast
  • wildfire emission
  • dust
  • data assimilation
  • secondary aerosol
  • model development and evaluation
  • planetary boundary layer

Published Papers (3 papers)

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Research

26 pages, 8712 KiB  
Article
Development and Assessment of Spatially Continuous Predictive Algorithms for Fine Particulate Matter in New York State
by Sam Lightstone, Barry Gross, Fred Moshary and Paulo Castillo
Atmosphere 2021, 12(3), 315; https://doi.org/10.3390/atmos12030315 - 28 Feb 2021
Cited by 4 | Viewed by 1619
Abstract
Health risks connected with fine particulate matter (PM2.5) pollutants are well documented; increased risks of asthma, heart attack and heart failure are a few of the effects associated with PM2.5. Accurately forecasting PM2.5 is crucial for state agencies [...] Read more.
Health risks connected with fine particulate matter (PM2.5) pollutants are well documented; increased risks of asthma, heart attack and heart failure are a few of the effects associated with PM2.5. Accurately forecasting PM2.5 is crucial for state agencies directed to devise State Implementation Plans (SIPS) to deal with National Ambient Air Quality Standards (NAAQS) exceedances. In previous work, we explored the application of multi-temporal data-driven neural networks (NNs) to forecasting PM2.5. Our work showed that under different input conditions, the NN approach achieves higher forecasting scores for local (12 km) resolution when compared to the other Chemical Transport Model forecast models, such as the Community Multi-Scale Air Quality system (CMAQ). Critical to our approach was the inclusion of prior PM2.5 concentrations, retrieved from ground monitoring stations, as part of the input dataset for the NN. The NN approach can provide high-level forecasting accuracy; however, because of the dependency on ground monitoring stations, the forecast coverage is sparse. Here, we extend our previous station-specific efforts by forecasting hourly PM2.5 values that are spatially continuous through the use of a deep neural network (DNN). The DNN approach combines spatial Kriging with additional local source variables to interpolate the measured PM2.5 concentrations across non-station locations. These interpolated PM2.5 values are used as inputs in the original forecasting NN. Cross-validation testing, using all New York State AirNow PM2.5 stations, showed that this forecast approach achieves accurate results, with a regression coefficient (R2) of 0.59, and a root mean square error (RMSE) of 2.22μgm3. Additionally, herein we demonstrate the usefulness of this approach on specific temporal events where significant dynamics of PM2.5 were observed; particularly, we show that even bias-corrected CMAQ forecasts do not track these transients and our NN method. Full article
(This article belongs to the Special Issue PM2.5 Predictions in the USA)
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23 pages, 6446 KiB  
Article
Description and Evaluation of the Fine Particulate Matter Forecasts in the NCAR Regional Air Quality Forecasting System
by Rajesh Kumar, Piyush Bhardwaj, Gabriele Pfister, Carl Drews, Shawn Honomichl and Garth D’Attilo
Atmosphere 2021, 12(3), 302; https://doi.org/10.3390/atmos12030302 - 26 Feb 2021
Cited by 7 | Viewed by 2833
Abstract
This paper describes a quasi-operational regional air quality forecasting system for the contiguous United States (CONUS) developed at the National Center for Atmospheric Research (NCAR) to support air quality decision-making, field campaign planning, early identification of model errors and biases, and support the [...] Read more.
This paper describes a quasi-operational regional air quality forecasting system for the contiguous United States (CONUS) developed at the National Center for Atmospheric Research (NCAR) to support air quality decision-making, field campaign planning, early identification of model errors and biases, and support the atmospheric science community in their research. This system aims to complement the operational air quality forecasts produced by the National Oceanic and Atmospheric Administration (NOAA), not to replace them. A publicly available information dissemination system has been established that displays various air quality products, including a near-real-time evaluation of the model forecasts. Here, we report the performance of our air quality forecasting system in simulating meteorology and fine particulate matter (PM2.5) for the first year after our system started, i.e., 1 June 2019 to 31 May 2020. Our system shows excellent skill in capturing hourly to daily variations in temperature, surface pressure, relative humidity, water vapor mixing ratios, and wind direction but shows relatively larger errors in wind speed. The model also captures the seasonal cycle of surface PM2.5 very well in different regions and for different types of sites (urban, suburban, and rural) in the CONUS with a mean bias smaller than 1 µg m−3. The skill of the air quality forecasts remains fairly stable between the first and second days of the forecasts. Our air quality forecast products are publicly available at a NCAR webpage. We invite the community to use our forecasting products for their research, as input for urban scale (<4 km), air quality forecasts, or the co-development of customized products, just to name a few applications. Full article
(This article belongs to the Special Issue PM2.5 Predictions in the USA)
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21 pages, 3997 KiB  
Article
Estimation of PM2.5 Concentrations in New York State: Understanding the Influence of Vertical Mixing on Surface PM2.5 Using Machine Learning
by Wei-Ting Hung, Cheng-Hsuan (Sarah) Lu, Stefano Alessandrini, Rajesh Kumar and Chin-An Lin
Atmosphere 2020, 11(12), 1303; https://doi.org/10.3390/atmos11121303 - 30 Nov 2020
Cited by 6 | Viewed by 2187
Abstract
In New York State (NYS), episodic high fine particulate matter (PM2.5) concentrations associated with aerosols originated from the Midwest, Mid-Atlantic, and Pacific Northwest states have been reported. In this study, machine learning techniques, including multiple linear regression (MLR) and artificial neural [...] Read more.
In New York State (NYS), episodic high fine particulate matter (PM2.5) concentrations associated with aerosols originated from the Midwest, Mid-Atlantic, and Pacific Northwest states have been reported. In this study, machine learning techniques, including multiple linear regression (MLR) and artificial neural network (ANN), were used to estimate surface PM2.5 mass concentrations at air quality monitoring sites in NYS during the summers of 2016–2019. Various predictors were considered, including meteorological, aerosol, and geographic predictors. Vertical predictors, designed as the indicators of vertical mixing and aloft aerosols, were also applied. Overall, the ANN models performed better than the MLR models, and the application of vertical predictors generally improved the accuracy of PM2.5 estimation of the ANN models. The leave-one-out cross-validation results showed significant cross-site variations and were able to present the different predictor-PM2.5 correlations at the sites with different PM2.5 characteristics. In addition, a joint analysis of regression coefficients from the MLR model and variable importance from the ANN model provided insights into the contributions of selected predictors to PM2.5 concentrations. The improvements in model performance due to aloft aerosols were relatively minor, probably due to the limited cases of aloft aerosols in current datasets. Full article
(This article belongs to the Special Issue PM2.5 Predictions in the USA)
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