Modeling air quality during the California Regional PM10/PM2.5 Air Quality Study (CRPAQS) using the UCD/CIT source-oriented air quality model – Part I. Base case model results

Ying, Qi; Lu, Jin; Allen, Pope; Livingstone, P. L.; Kaduwela, A. P.; Kleeman, Michael J.

doi:10.1016/j.atmosenv.2008.05.064

Cited by 54 publications

(61 citation statements)

References 28 publications

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“…Simulations for each scenario described in Section 3.2 were completed at both 4 km and 8 km horizontal resolution. Coarser spatial resolution greatly increased the speed of the simulation with little loss of accuracy under the meteorological conditions used in the present study (Ying et al, 2008).…”

Section: Air Quality Modelingmentioning

confidence: 94%

“…Emissions were split into separate files based on eight broad source categories: gasoline engines, diesel engines, wood smoke, food cooking, natural gas combustion, high-sulfur fuel combustion, crustal material including road dust, and other primary sources. These source-oriented emissions inventories were fed into the UCD source-oriented three dimensional photochemical air quality grid model (Kleeman and Cass, 2001;Ying et al, 2008;Ying and Kleeman, 2009). The UCD air quality model was configured with seven vertical layers that varied in thickness from 35 m at the surface up to 500 m at the top of the domain (5 km).…”

Section: Air Quality Modelingmentioning

confidence: 99%

“…The meteorological observations were used to generate a set of diagnostic meteorological data as described by Ying et al (2008). Meteorology was selected during a three week stagnation episode between .…”

Section: Air Quality Modelingmentioning

confidence: 99%

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Influence of regional development policies and clean technology adoption on future air pollution exposure

Hixson

Mahmud

et al. 2010

Atmospheric Environment

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a b s t r a c tFuture air pollution emissions in the year 2030 were estimated for the San Joaquin Valley (SJV) in central California using a combined system of land use, mobile, off-road, stationary, area, and biogenic emissions models. Four scenarios were developed that use different assumptions about the density of development and level of investment in transportation infrastructure to accommodate the expected doubling of the SJV population in the next 20 years. Scenario 1 reflects current land-use patterns and infrastructure while scenario 2 encouraged compact urban footprints including redevelopment of existing urban centers and investments in transit. Scenario 3 allowed sprawling development in the SJV with reduced population density in existing urban centers and construction of all planned freeways. Scenario 4 followed currently adopted land use and transportation plans for the SJV. The air quality resulting from these urban development scenarios was evaluated using meteorology from a winter stagnation event that occurred on December 15th, 2000 to January 7th 2001. Predicted base-case PM2.5 mass concentrations within the region exceeded 35 mg m À3 over the 22-day episode. Compact growth reduced the PM2.5 concentrations by w1 mg m À3 relative to the base-case over most of the SJV with the exception of increases (w1 mg m À3 ) in urban centers driven by increased concentrations of elemental carbon (EC) and organic carbon (OC).Low-density development increased the PM2.5 concentrations by 1e4 mg m À3 over most of the region, with decreases (0.5e2 mg m À3 ) around urban areas. Population-weighted average PM2.5 concentrations were very similar for all development scenarios ranging between 16 and 17.4 mg m À3 . Exposure to primary PM components such as EC and OC increased 10e15% for high density development scenarios and decreased by 11e19% for low-density scenarios. Patterns for secondary PM components such as nitrate and ammonium ion were almost exactly reversed, with a 10% increase under low-density development and a 5% decrease under high density development. The increased human exposure to primary pollutants such as EC and OC could be predicted using a simplified analysis of population-weighted primary emissions. Regional planning agencies should develop thresholds of population-weighted primary emissions exposure to guide the development of growth plans. This metric will allow them to actively reduce the potential negative impacts of compact growth while preserving the benefits.

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Section: Air Quality Modelingmentioning

confidence: 94%

Section: Air Quality Modelingmentioning

confidence: 99%

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Influence of regional development policies and clean technology adoption on future air pollution exposure

Hixson

Mahmud

et al. 2010

Atmospheric Environment

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“…To get more accurate pollutant predictions at shorter timescales would require more accurate representation of emissions, meteorological conditions, and atmospheric chemistry at these time scales. Many intensive studies that manually corrected input data have focused on high temporal resolution for short periods (generally less than 1 month), such as the California Regional PM 10 / PM 2.5 Air Quality Study (Ying et al, 2008). It is currently impractical to carry out such efforts for a ∼ 10-year modeling period in which there are a large number of special events that are not represented by automated meteorology and emissions models.…”

Section: Discussionmentioning

confidence: 99%

“…The host air quality model employed in the current study is based on the Eulerian source-oriented University of California, Davis/California Institute of Technology (UCD/CIT) chemical transport model Held et al, 2004;Held et al, 2005;Hixson et al, 2010Hixson et al, , 2012Hu et al, 2012Hu et al, , 2010Kleeman and Cass, 2001;Kleeman et al, 1997Kleeman et al, , 2007Mysliwiec and Kleeman, 2002;Rasmussen et al, 2013;Ying et al, 2008;Ying et al, 2007;Ying and Kleeman, 2006;Zhang and Ying, 2010). The UCD/CIT model includes a complete description of atmospheric transport, deposition, chemical reaction, and gas-particle transfer.…”

Section: Air Quality Model Descriptionmentioning

confidence: 99%

Long-term particulate matter modeling for health effect studies in California – Part 1: Model performance on temporal and spatial variations

Zhang

Ying

et al. 2015

Atmos. Chem. Phys.

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Abstract. For the first time, a ∼ decadal (9 years from 2000 to 2008) air quality model simulation with 4 km horizontal resolution over populated regions and daily time resolution has been conducted for California to provide air quality data for health effect studies. Model predictions are compared to measurements to evaluate the accuracy of the simulation with an emphasis on spatial and temporal variations that could be used in epidemiology studies. Better model performance is found at longer averaging times, suggesting that model results with averaging times ≥ 1 month should be the first to be considered in epidemiological studies. The UCD/CIT model predicts spatial and temporal variations in the concentrations of O 3 , PM 2.5 , elemental carbon (EC), organic carbon (OC), nitrate, and ammonium that meet standard modeling performance criteria when compared to monthly-averaged measurements. Predicted sulfate concentrations do not meet target performance metrics due to missing sulfur sources in the emissions. Predicted seasonal and annual variations of PM 2.5 , EC, OC, nitrate, and ammonium have mean fractional biases that meet the model performance criteria in 95, 100, 71, 73, and 92 % of the simulated months, respectively. The base data set provides an improvement for predicted population exposure to PM concentrations in California compared to exposures estimated by central site monitors operated 1 day out of every 3 days at a few urban locations. Uncertainties in the model predictions arise from several issues. Incomplete understanding of secondary organic aerosol formation mechanisms leads to OC bias in the model results in summertime but does not affect OC predictions in winter when concentrations are typically highest. The CO and NO (species dominated by mobile emissions) results reveal temporal and spatial uncertainties associated with the mobile emissions generated by the EMFAC 2007 model. The WRF model tends to overpredict wind speed during stagnation events, leading to underpredictions of high PM concentrations, usually in winter months. The WRF model also generally underpredicts relative humidity, resulting in less particulate nitrate formation, especially during winter months. These limitations must be recognized when using data in health studies. All model results included in the current manuscript can be downloaded free of charge at http: //faculty.engineering.ucdavis.edu/kleeman/.

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PM2.5 co-benefits of climate change legislation part 1: California’s AB 32

2012

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Modeling air quality during the California Regional PM10/PM2.5 Air Quality Study (CRPAQS) using the UCD/CIT source-oriented air quality model – Part I. Base case model results

Cited by 54 publications

References 28 publications

Influence of regional development policies and clean technology adoption on future air pollution exposure

Influence of regional development policies and clean technology adoption on future air pollution exposure

Long-term particulate matter modeling for health effect studies in California – Part 1: Model performance on temporal and spatial variations

PM2.5 co-benefits of climate change legislation part 1: California’s AB 32

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