scholarly journals Seasonal and regional air quality and atmospheric deposition in the eastern United States

2007 ◽  
Vol 112 (D17) ◽  
Author(s):  
Joseph E. Sickles ◽  
Douglas S. Shadwick
Keyword(s):  
United States ◽  
Air Quality ◽  
Atmospheric Deposition ◽  
Eastern United States ◽  
Regional Air Quality

Regional Air Quality:  Local and Interstate Impacts of NOxand SO2Emissions on Ozone and Fine Particulate Matter in the Eastern United States

2007 ◽  
Vol 41 (13) ◽  
pp. 4677-4689 ◽  
Author(s):  
Michelle S. Bergin ◽  
Jhih-Shyang Shih ◽  
Alan J. Krupnick ◽  
James W. Boylan ◽  
James G. Wilkinson ◽  
...  
Keyword(s):  
United States ◽  
Particulate Matter ◽  
Air Quality ◽  
Fine Particulate Matter ◽  
Eastern United States ◽  
Fine Particulate ◽  
Regional Air Quality

scholarly journals Evidence for an increase in the ozone photochemical lifetime in the eastern United States using a regional air quality model

2015 ◽  
Vol 120 (24) ◽  
pp. 12778-12793 ◽  
Author(s):  
Daniel L. Goldberg ◽  
Timothy P. Vinciguerra ◽  
Kyle M. Hosley ◽  
Christopher P. Loughner ◽  
Timothy P. Canty ◽  
...  
Keyword(s):  
United States ◽  
Air Quality ◽  
Eastern United States ◽  
Air Quality Model ◽  
Quality Model ◽  
Regional Air Quality

scholarly journals Evaluation of dust and trace metal estimates from the Community Multiscale Air Quality (CMAQ) model version 5.0

2013 ◽  
Vol 6 (4) ◽  
pp. 883-899 ◽  
Author(s):  
K. W. Appel ◽  
G. A. Pouliot ◽  
H. Simon ◽  
G. Sarwar ◽  
H. O. T. Pye ◽  
...  
Keyword(s):  
United States ◽  
Air Quality ◽  
Trace Metals ◽  
Urban Areas ◽  
Temporal Distribution ◽  
Air Pollutant ◽  
Eastern United States ◽  
Air Quality Model ◽  
Quality Model ◽  
Model Version

Abstract. The Community Multiscale Air Quality (CMAQ) model is a state-of-the-science air quality model that simulates the emission, transformation, transport, and fate of the many different air pollutant species that comprise particulate matter (PM), including dust (or soil). The CMAQ model version 5.0 (CMAQv5.0) has several enhancements over the previous version of the model for estimating the emission and transport of dust, including the ability to track the specific elemental constituents of dust and have the model-derived concentrations of those elements participate in chemistry. The latest version of the model also includes a parameterization to estimate emissions of dust due to wind action. The CMAQv5.0 modeling system was used to simulate the entire year 2006 for the continental United States, and the model estimates were evaluated against daily surface-based measurements from several air quality networks. The CMAQ modeling system overall did well replicating the observed soil concentrations in the western United States (mean bias generally around ±0.5 μg m−3); however, the model consistently overestimated the observed soil concentrations in the eastern United States (mean bias generally between 0.5–1.5 μg m−3), regardless of season. The performance of the individual trace metals was highly dependent on the network, species, and season, with relatively small biases for Fe, Al, Si, and Ti throughout the year at the Interagency Monitoring of Protected Visual Environments (IMPROVE) sites, while Ca, K, and Mn were overestimated and Mg underestimated. For the urban Chemical Speciation Network (CSN) sites, Fe, Mg, and Mn, while overestimated, had comparatively better performance throughout the year than the other trace metals, which were consistently overestimated, including very large overestimations of Al (380%), Ti (370%) and Si (470%) in the fall. An underestimation of nighttime mixing in the urban areas appears to contribute to the overestimation of trace metals. Removing the anthropogenic fugitive dust (AFD) emissions and the effects of wind-blown dust (WBD) lowered the model soil concentrations. However, even with both AFD emissions and WBD effects removed, soil concentrations were still often overestimated, suggesting that there are other sources of errors in the modeling system that contribute to the overestimation of soil components. Efforts are underway to improve both the nighttime mixing in urban areas and the spatial and temporal distribution of dust-related emission sources in the emissions inventory.

scholarly journals Strong influence of 2000–2050 climate change on particulate matter in the United States: Results from a new statistical model

2016 ◽  
Author(s):  
Lu Shen ◽  
Loretta J. Mickley ◽  
Lee T. Murray
Keyword(s):  
Climate Change ◽  
United States ◽  
Particulate Matter ◽  
Air Quality ◽  
Statistical Model ◽  
The United States ◽  
Anthropogenic Sources ◽  
Meteorological Variables ◽  
Eastern United States ◽  
Low Cloud

Abstract. We use a statistical model to investigate the effect of 2000–2050 climate change on fine particulate matter (PM2.5) air quality across the contiguous United States. By applying observed relationships of PM2.5 and meteorology to the IPCC Coupled Model Intercomparision Project Phase 5 (CMIP5) archives, we bypass many of the uncertainties inherent in chemistry-climate models. Our approach uses both the relationships between PM2.5 and local meteorology as well as the synoptic circulation patterns, defined as the Singular Value Decomposition (SVD) pattern of the spatial correlations between PM2.5 and meteorological variables in the surrounding region. Using an ensemble of 17 GCMs under the RCP4.5 scenario, we project an increase of ~ 1 μg m−3 in annual mean PM2.5 in the eastern US and a decrease of 0.3–1.2 μg m−3 in the Intermountain West by the 2050s, assuming present-day anthropogenic sources of PM2.5. Mean summertime PM2.5 increases as much as 2–3 μg m−3 in the eastern United States due to faster oxidation rates and greater mass of organic carbon from biogenic emissions. Mean wintertime PM2.5 decreases by 0.3–3 μg m−3 over most regions in United States, likely due to the volatilization of ammonium nitrate. Our approach provides an efficient method to calculate the climate penalty or benefit on air quality across a range of models and scenarios. We find that current atmospheric chemistry models may underestimate or even fail to capture the strongly positive sensitivity of monthly mean PM2.5 to temperature in the eastern United States in summer, and may underestimate future changes in PM2.5 in a warmer climate. In GEOS-Chem, the underestimate in monthly mean PM2.5-temperature relationship in the East in summer is likely caused by overly strong negative sensitivity of monthly mean low cloud fraction to temperature in the assimilated meteorology (~ −0.04 K−1), compared to the weak sensitivity implied by satellite observations (±0.01 K−1). The strong negative dependence of low cloud cover on temperature, in turn, causes the modeled rates of sulfate aqueous oxidation to diminish too rapidly as temperatures rise, leading to the underestimate of sulfate-temperature slopes, especially in the South. Our work underscores the importance of evaluating the sensitivity of PM2.5 to its key controlling meteorological variables in climate-chemistry models on multiple timescales before they are applied to project future air quality.

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