Potential air quality benefits from increased solar photovoltaic electricity generation in the Eastern United States

2018 ◽  
Vol 175 ◽  
pp. 65-74 ◽  
Author(s):  
David Abel ◽  
Tracey Holloway ◽  
Monica Harkey ◽  
Arber Rrushaj ◽  
Greg Brinkman ◽  
...  
Keyword(s):  
United States ◽  
Air Quality ◽  
Electricity Generation ◽  
Solar Photovoltaic ◽  
Eastern United States

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.

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 Impact of lightning-NO on Eastern United States photochemistry during the summer of 2006 as determined using the CMAQ model

2011 ◽  
Vol 11 (6) ◽  
pp. 17699-17757 ◽  
Author(s):  
D. J. Allen ◽  
K. E. Pickering ◽  
R. W. Pinder ◽  
B. H. Henderson ◽  
K. W. Appel ◽  
...  
Keyword(s):  
United States ◽  
Air Quality ◽  
Wet Deposition ◽  
Surface Ozone ◽  
Convective Precipitation ◽  
Eastern United States ◽  
Ozone Monitoring Instrument ◽  
Monitoring Instrument ◽  
Ozone Monitoring ◽  
The Impact

Abstract. A lightning-nitrogen oxide (NO) algorithm is developed for the regional Community Multiscale Air Quality Model (CMAQ) and used to evaluate the impact of lightning-NO emissions (LNOx) on tropospheric photochemistry over the Eastern United States during the summer of 2006. The scheme assumes flash rates are proportional to the model convective precipitation rate but then adjusts the flash rates locally to match monthly average observations. Over the Eastern United States, LNOx is responsible for 20–25 % of the tropospheric nitrogen dioxide (NO2) column. This additional NO2 reduces the low-bias of simulated NO2 columns with respect to satellite-retrieved Dutch Ozone Monitoring Instrument NO2 (DOMINO) columns from 41 to 14 %. It also adds 10–20 ppbv to upper tropospheric ozone and 1.5–4.5 ppbv to 8-h maximum surface layer ozone, although, on average, the contribution of LNOx to surface ozone is 1–2 ppbv less on poor air quality days. Biases between modeled and satellite-retrieved tropospheric NO2 columns vary greatly between urban and rural locations. In general, CMAQ overestimates columns at urban locations and underestimates columns at rural locations. These biases are consistent with in situ measurements that also indicate that CMAQ has too much NO2 in urban regions and not enough in rural regions. However, closer analysis suggests that most of the differences between modeled and satellite-retrieved urban to rural ratios are likely a consequence of the horizontal and vertical smoothing inherent in columns retrieved by the Ozone Monitoring Instrument (OMI). Within CMAQ, LNOx increases wet deposition of nitrate by 50 % and total deposition of nitrogen by 11 %. This additional deposition reduces the magnitude of the CMAQ low-bias in nitrate wet deposition with respect to National Atmospheric Deposition monitors to near zero. In order to obtain an upper bound on the contribution of uncertainties in chemistry to upper tropospheric NOx low biases, sensitivity calculations with updated chemistry were run for the time period of the Intercontinental Chemical Transport Experiment (INTEX-A) field campaign (summer 2004). After adjusting for possible interferences in NO2 measurements and averaging over the entire campaign, these updates reduced 7–9 km biases from 32 to 17 % and 9–12 km biases from 57 to 46 %. While these changes lead to better agreement, a considerable NO2 low-bias remains in the uppermost troposphere.

scholarly journals Sensitivity of the Community Multiscale Air Quality (CMAQ) model v4.7 results for the eastern United States to MM5 and WRF meteorological drivers

2010 ◽  
Vol 3 (1) ◽  
pp. 169-188 ◽  
Author(s):  
K. W. Appel ◽  
S. J. Roselle ◽  
R. C. Gilliam ◽  
J. E. Pleim
Keyword(s):  
United States ◽  
Air Quality ◽  
Mesoscale Model ◽  
Model Performance ◽  
Wrf Model ◽  
Total Carbon ◽  
Eastern United States ◽  
Model Simulations ◽  
Mixing Ratios ◽  
Vegetation Fraction

Abstract. This paper presents a comparison of the operational performances of two Community Multiscale Air Quality (CMAQ) model v4.7 simulations that utilize input data from the 5th-generation Mesoscale Model (MM5) and the Weather Research and Forecasting (WRF) meteorological models. Two sets of CMAQ model simulations were performed for January and August 2006. One set utilized MM5 meteorology (MM5-CMAQ) and the other utilized WRF meteorology (WRF-CMAQ), while all other model inputs and options were kept the same. For January, predicted ozone (O3) mixing ratios were higher in the Southeast and lower Mid-west regions in the WRF-CMAQ simulation, resulting in slightly higher bias and error as compared to the MM5-CMAQ simulations. The higher predicted O3 mixing ratios are attributed to less dry deposition of O3 in the WRF-CMAQ simulation due to differences in the calculation of the vegetation fraction between the MM5 and WRF models. The WRF-CMAQ results showed better performance for particulate sulfate (SO42−), similar performance for nitrate (NO3−), and slightly worse performance for nitric acid (HNO3), total carbon (TC) and total fine particulate (PM2.5) mass than the corresponding MM5-CMAQ results. For August, predictions of O3 were notably higher in the WRF-CMAQ simulation, particularly in the southern United States, resulting in increased model bias. Concentrations of predicted particulate SO42− were lower in the region surrounding the Ohio Valley and higher along the Gulf of Mexico in the WRF-CMAQ simulation, contributing to poorer model performance. The primary causes of the differences in the MM5-CMAQ and WRF-CMAQ simulations appear to be due to differences in the calculation of wind speed, planetary boundary layer height, cloud cover and the friction velocity (u∗) in the MM5 and WRF model simulations, while differences in the calculation of vegetation fraction and several other parameters result in smaller differences in the predicted CMAQ model concentrations. The performance for SO42−, NO3− and NH4+ wet deposition was similar for both simulations for January and August.

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

2013 ◽  
Vol 6 (1) ◽  
pp. 1859-1899 ◽  
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 generally did well replicating the observed soil concentrations in the western United States; however the model consistently overestimated the observed soil concentrations in the eastern United States, regardless of season. The performance of the individual trace metals was generally good at the rural network sites, with relatively small biases for Fe, Al, Si and Ti throughout the year, while Ca, K and Mn were overestimated and Mg underestimated. For the urban 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, Ti and Si 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 emissions sources in the emissions inventory.

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