Pollutant transport among California regions

Angevine, W. M.; Brioude, J.; McKeen, S. A.; Holloway, J. S.; Lerner, B. M.; Goldstein, Allen H.; Guha, Apratim; Andrews, A. E.; Nowak, John B.; Evan, Stéphanie; Fischer, M. L.; Gilman, J. B.; Bon, D.

doi:10.1002/jgrd.50490

Cited by 30 publications

(23 citation statements)

References 37 publications

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“…The height of the boundary layer may vary with wind speed (Oke, 1987), influencing the concentration and vertical profile of pollutants. The boundary layer not only controls transport and location of pollutants and aerosols but also their concentrations would be different in variable boundary layer structures (Angevine et al, 2013). …”

Section: Methodsmentioning

confidence: 99%

A new hybrid spatio-temporal model for estimating daily multi-year PM2.5 concentrations across northeastern USA using high resolution aerosol optical depth data

Kloog¹,

Chudnovsky²,

Just³

et al. 2014

Atmospheric Environment

279

246

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Background The use of satellite-based aerosol optical depth (AOD) to estimate fine particulate matter (PM2.5) for epidemiology studies has increased substantially over the past few years. These recent studies often report moderate predictive power, which can generate downward bias in effect estimates. In addition, AOD measurements have only moderate spatial resolution, and have substantial missing data. Methods We make use of recent advances in MODIS satellite data processing algorithms (Multi-Angle Implementation of Atmospheric Correction (MAIAC), which allow us to use 1 km (versus currently available 10 km) resolution AOD data. We developed and cross validated models to predict daily PM2.5 at a 1×1km resolution across the northeastern USA (New England, New York and New Jersey) for the years 2003–2011, allowing us to better differentiate daily and long term exposure between urban, suburban, and rural areas. Additionally, we developed an approach that allows us to generate daily high-resolution 200 m localized predictions representing deviations from the area 1×1 km grid predictions. We used mixed models regressing PM2.5 measurements against day-specific random intercepts, and fixed and random AOD and temperature slopes. We then use generalized additive mixed models with spatial smoothing to generate grid cell predictions when AOD was missing. Finally, to get 200 m localized predictions, we regressed the residuals from the final model for each monitor against the local spatial and temporal variables at each monitoring site. Results Our model performance was excellent (mean out-of-sample R2=0.88). The spatial and temporal components of the out-of-sample results also presented very good fits to the withheld data (R2=0.87, R2=0.87). In addition, our results revealed very little bias in the predicted concentrations (Slope of predictions versus withheld observations = 0.99). Conclusion Our daily model results show high predictive accuracy at high spatial resolutions and will be useful in reconstructing exposure histories for epidemiological studies across this region.

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Section: Methodsmentioning

confidence: 99%

A new hybrid spatio-temporal model for estimating daily multi-year PM2.5 concentrations across northeastern USA using high resolution aerosol optical depth data

Kloog¹,

Chudnovsky²,

Just³

et al. 2014

Atmospheric Environment

279

246

View full text Add to dashboard Cite

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“…The WRF simulations were carried out using a configuration similar to the pollution transport simulations of Angevine et al (2013), with a horizontal resolution of 12 km, temporal resolution of 1 hour and 50 vertical levels. The sensitivity of back trajectories to the temporal and spatial resolution of meteorological data is discussed in Draxler (1987).…”

Section: The Weather Research and Forecasting Model (Wrf)mentioning

confidence: 99%

Particulate Pollution in the Sydney Region: Source Diagnostics and Synoptic Controls

Crawford

Griffiths

Cohen

et al. 2016

Aerosol Air Qual. Res.

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Airborne particulate matter (PM 2.5 ) was sampled at Richmond and Liverpool, located in the Sydney Basin, Australia, and ion beam analysis was used to obtain the elemental composition. Using self-organising maps to classify synoptic weather systems, it was found that high PM 2.5 concentrations were associated with high pressure systems located to the east of the sampling sites. The highest median sulfur was associated with weak synoptic conditions and high soil dust days were more often associated with frontal systems.To investigate the effect of local flows in the Sydney Basin, the Weather Research and Forecasting model (WRF) was used to generate meteorological data of 12 km resolution. A comparison was made between back trajectories generated using the higher-resolution WRF data, the 0.5° by 0.5° Climate Forecast System data and the 1° by 1° Global Data Assimilation System data. It was found that for high soil dust days, there were small differences between the different back trajectories. However, under weak synoptic conditions (high sulfur days), the back trajectories generated from higher resolution data showed larger variations over a 24 hr period. This was attributed to the meandering of local winds and seabreezes.Lower altitude back trajectories, generated from low resolution data, passed more often over the power stations located on the western side of the Great Dividing Range (while the sampling sites are on the east). This demonstrates the need for higher resolution meteorological data for generating low altitude back trajectories when the source and receptor are separated by hilly terrain.In estimating the number of high sulfur days for which a power station was crossed, there was up to 20% difference at Liverpool and up to 10% difference at Richmond, between back trajectories starting at different altitudes and generated from meteorological data of three different resolutions.

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“…For this purpose the Advanced Research WRF (WRF hereafter) model version 3.5.1 (Skamarock et al, 2005;Skamarock and Klemp, 2008) was run with boundary-conditions from the half-degree Climate Forecast System (CFS) and CFSv2 (Saha et al, 2010(Saha et al, , 2013) with a change-over in 2011. Our configuration is similar to that used by Angevine et al (2013) and verification against surface observations confirmed that the WRF model was an improvement on using CFS or ERA-interim directly.…”

Section: Wind Speed and Direction Estimatesmentioning

confidence: 53%

Using Radon-222 as an Indicator of Atmospheric Mixing Depth in ME-2 for PM2.5 Source Apportionment

Crawford

Chambers

Cohen

et al. 2015

Aerosol Air Qual. Res.

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We isolated diurnal timescale contributions to a 6-year hourly radon record and incorporated them in ME-2 as a proxy for changes in atmospheric mixing depth in an attempt to improve the source apportionment of fine atmospheric particulate matter (PM 2.5 ). Results from this radon-based implementation of ME-2 are directly compared with the more traditional ME-2 implementation where wind speed is used, as a proxy for changes in mixing depth. The radon-based version more accurately reproduced daily PM 2.5 source contributions, as evidenced by better correlations with the results from the corresponding bi-linear model. The versions of ME-2 employed in this study were modified to account for calm wind conditions separately, and a recently updated solution approach was adopted.Source apportionment for the radon-based ME-2 implementation was most successful for the finer, primary emissions (Smoke, Autos, Industry) that are more easily suspended and whose concentrations are more directly tied to changes in atmospheric mixing depth. Incorporation of the diurnal radon signal in ME-2 improved the estimated source strength distributions of the Smoke, Autos and Industry sources with respect to the township of Muswellbrook. It also resulted in a more consistent anti-correlation between these 3 source types and atmospheric mixing depth than for the wind speed case. These results confirm that near surface radon concentration is more closely tied to atmospheric mixing depth (and therefore pollutant concentrations) than wind speed.The measurement site for this study is a small township in a rural setting, with nearby power stations and open-cut coal mines. Consequently, the distribution and characteristics of anthropogenic aerosol sources are very different than for a typical urban or industrial setting. This is reflected in lower correlation between the multi-linear models and the corresponding bi-linear models, indicating that the performance of multi-linear models is affected by the nature of the distribution of sources.

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Pollutant transport among California regions

Cited by 30 publications

References 37 publications

A new hybrid spatio-temporal model for estimating daily multi-year PM2.5 concentrations across northeastern USA using high resolution aerosol optical depth data

A new hybrid spatio-temporal model for estimating daily multi-year PM2.5 concentrations across northeastern USA using high resolution aerosol optical depth data

Particulate Pollution in the Sydney Region: Source Diagnostics and Synoptic Controls

Using Radon-222 as an Indicator of Atmospheric Mixing Depth in ME-2 for PM2.5 Source Apportionment

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