Application of an adjoint neighborhood‐scale chemistry transport model to the attribution of primary formaldehyde at Lynchburg Ferry during TexAQS II

Olaguer, Eduardo P.

doi:10.1002/jgrd.50406

Cited by 19 publications

(26 citation statements)

References 33 publications

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“…Some peak HCHO concentrations may have been linked to transient emission events of HCHO that were not reported to TCEQ. More recently, Olaguer [2012] used an adjoint neighborhood scale 3D chemical transport model to show that the highest concentrations of HCHO observed at Lynchburg Ferry are best explained by primary HCHO emissions in the area rather than by long-range transport of secondary HCHO or exclusively by secondary HCHO formed from local olefin emissions. Thus, underprediction by the CMAQ model implies that some primary emissions of HCHO might be incorrectly represented in the current inventory near this location.…”

Section: Modeling Results Of Hchomentioning

confidence: 99%

“…This source-oriented technique has been previously applied in the CMAQ model to study regional source contributions to O 3 [Ying and Krishnan, 2010;Zhang and Ying, 2011a], secondary inorganic aerosol and secondary organic aerosol [Zhang and Ying, 2011b;2012]. The source apportionment technique for primary and secondary HCHO is described in greater detail below.…”

Section: Model Descriptionmentioning

confidence: 99%

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Source apportionment of formaldehyde during TexAQS 2006 using a source‐oriented chemical transport model

Zhang

Ying

et al. 2013

JGR Atmospheres

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[1] In this study, a source-oriented version of the Community Multiscale Air Quality (CMAQ) model was developed and used to quantify the contributions of five major local emission source types in Southeast Texas (vehicles, industry, natural gas combustion, wildfires, biogenic sources), as well as upwind sources, to regional primary and secondary formaldehyde (HCHO) concentrations. Predicted HCHO concentrations agree well with observations at two urban sites (the Moody Tower [MT] site at the University of Houston and the Haden Road #3 site operated by Texas Commission on Environmental Quality). However, the model underestimates concentrations at an industrial site (Lynchburg Ferry). Throughout most of Southeast Texas, primary HCHO accounts for approximately 20-30% of total HCHO, while the remaining portion is due to secondary HCHO (30-50%) and upwind sources (20-50%). Biogenic sources, natural gas combustion, and vehicles are important sources of primary HCHO in the urban Houston area, respectively, accounting for 10-20%, 10-30%, and 20-60% of total primary HCHO. Biogenic sources, industry, and vehicles are the top three sources of secondary HCHO, respectively, accounting for 30-50%, 10-30%, and 5-15% of overall secondary HCHO. It was also found that over 70% of PAN in the Houston area is due to upwind sources, and only 30% is formed locally. The model-predicted source contributions to HCHO at the MT generally agree with source apportionment results obtained from the Positive Matrix Factorization (PMF) technique.

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Section: Modeling Results Of Hchomentioning

confidence: 99%

Section: Model Descriptionmentioning

confidence: 99%

Source apportionment of formaldehyde during TexAQS 2006 using a source‐oriented chemical transport model

Zhang

Ying

et al. 2013

JGR Atmospheres

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“…In particular, we wish to infer and quantify the emission sources that explain observed transient peaks in ambient benzene concentrations during BEE-TEX. Our analysis is based on the HARC three-dimensional (3D) microscale Eulerian air quality model that has been documented and evaluated based on field measurements in several publications (Olaguer, 2011(Olaguer, , 2012a(Olaguer, , 2012b(Olaguer, , 2013Olaguer et al, 2013), including previous mobile PTR-MS measurements of benzene outside a refinery in Texas City, Texas .…”

Section: Journal Of the Air And Wastementioning

confidence: 99%

Source attribution and quantification of benzene event emissions in a Houston ship channel community based on real-time mobile monitoring of ambient air

Olaguer

Erickson

Wijesinghe

et al. 2015

Journal of the Air & Waste Management Association

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“…The HARC model, driven by winds from the QUIC model, then minimized a cost function related to the difference between the concentration predictions of the forward model and the HCHO observations to infer emissions from each unit in the facility, using permit data as first-guess emission estimates. The underlying technique is known as four-dimensional (4D) variational data assimilation, the HARC model implementation of which was discussed in detail by Olaguer (2013) and Olaguer et al (2013). Figure 2 shows the wind flow around the facility simulated by the QUIC urban wind model, as well as air concentrations of HCHO inferred by the HARC model based on inverse model-optimized emissions from the facility's compressor engines and glycol reboilers.…”

Section: Microscale Modelingmentioning

confidence: 99%

“…This model, which we refer to as the Houston Advanced Research Center (HARC) model, has been documented and evaluated based on field observations in several publications (Olaguer, 2011(Olaguer, , 2012a(Olaguer, , 2012b(Olaguer, , 2013Buzcu Guven et al, 2013;Olaguer et al, 2013).…”

Section: Microscale Modelingmentioning

confidence: 99%

Updated methods for assessing the impacts of nearby gas drilling and production on neighborhood air quality and human health

Olaguer

Erickson

Wijesinghe

et al. 2015

Journal of the Air & Waste Management Association

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An explosive growth in natural gas production within the last decade has fueled concern over the public health impacts of air pollutant emissions from oil and gas sites in the Barnett and Eagle Ford shale regions of Texas. Commonly acknowledged sources of uncertainty are the lack of sustained monitoring of ambient concentrations of pollutants associated with gas mining, poor quantification of their emissions, and inability to correlate health symptoms with specific emission events. These uncertainties are best addressed not by conventional monitoring and modeling technology, but by increasingly available advanced techniques for real-time mobile monitoring, microscale modeling and source attribution, and real-time broadcasting of air quality and human health data over the World Wide Web. The combination of contemporary scientific and social media approaches can be used to develop a strategy to detect and quantify emission events from oil and gas facilities, alert nearby residents of these events, and collect associated human health data, all in real time or near-real time. The various technical elements of this strategy are demonstrated based on the results of past, current, and planned future monitoring studies in the Barnett and Eagle Ford shale regions.Implications: Resources should not be invested in expanding the conventional air quality monitoring network in the vicinity of oil and gas exploration and production sites. Rather, more contemporary monitoring and data analysis techniques should take the place of older methods to better protect the health of nearby residents and maintain the integrity of the surrounding environment.PAPER HISTORY

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Application of an adjoint neighborhood‐scale chemistry transport model to the attribution of primary formaldehyde at Lynchburg Ferry during TexAQS II

Cited by 19 publications

References 33 publications

Source apportionment of formaldehyde during TexAQS 2006 using a source‐oriented chemical transport model

Source apportionment of formaldehyde during TexAQS 2006 using a source‐oriented chemical transport model

Source attribution and quantification of benzene event emissions in a Houston ship channel community based on real-time mobile monitoring of ambient air

Updated methods for assessing the impacts of nearby gas drilling and production on neighborhood air quality and human health

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