Qi Ying (应琦)

411B Dwight Look Engineering Building (DLEB)

Zachry Department of Civil and Environmental Engineering

Texas A&M University

College Station, Texas 77845-3136

Phone: 979-845-9709

Email: qying@civil.tamu.edu

Ph.D. Environmental Engineering, 2004. University of California, Davis
B.S. Environmental Engineering, 2000. Tsinghua University

According to the World Health Organization, 90% of the world’s population is breathing contami-nated air, and 4.2 million people worldwide die prematurely every year due to ambient air pollution. Air pollution also causes reduced crop yields by 5-12% globally, with an estimated economic loss of up to 20 billion dollars per year. My research focuses on developing and applying science-based data-driven modeling tools to elucidate the impacts of emissions from human activities on air quality so that local and state governments can make informed regulatory decisions to reduce air pollution and protect human health. My current research focuses on the following three areas:

mechanistic modeling of air pollution in urban and regional scales with a particular focus on source apportionment
multiscale modeling of criteria and toxic air pollutants for exposure assessment in epidemiology studies
modeling secondary organic aerosol and their chemical constituents in chemical transport models

In my spare time (don’t have much recently), I play table tennis and chess. I also like to play with single chip computers. Recently, I have been working on an Apple II+ emulator which runs on the Arduino DUE. You can see a demonstration of the emulator on YouTube here.

selected publications

Assessment of sectoral NOx emission reductions during COVID-19 lockdown using combined satellite and surface observations and source-oriented model simulations

Kang, Mingjie, Zhang, Jie, Cheng, Zhen, Guo, Song, Su, Fangcheng, Hu, Jianlin, Zhang, Hongliang, and Ying, Qi

Geophysical research letters, Jan 2022

Abs Doi

Abstract Large emission reductions of anthropogenic nitrogen oxides (NOx) due to the coronavirus disease 2019 (COVID-19) lockdown policies in China have been extensively reported since the outbreak, while assessments of sectoral emission changes during that period are still limited. In this study, a source-oriented community multiscale air quality (CMAQ) model was applied to quantify NO2 concentrations from major emission sectors. A new optimization approach was employed to obtain the sectorial emission reductions using satellite and ground-level observations as constraints. The optimized emissions significantly improved the model performance of NO2 during the lockdown period. February NOx emission changes varied with regions and sectors, with relatively larger reductions in transportation (286.6 kt) and industrial sources (260.1 kt). The maximum amount of NOx emission reduction occurred in the North China Plain (230.6 kt). Our work presents a quick and reliable technique for assessing sector-specific emission changes due to short-term emission control policies.
Modeling secondary organic aerosol tracers and tracer-to-SOA ratios for monoterpenes and sesquiterpenes Using a chemical transport model

Zhang, Jie, He, Xiao, Ding, Xiang, Yu, Jian Zhen, and Ying, Qi

Environmental science & technology, Jan 2022

Abs Doi

The community multiscale air quality (CMAQ) model was modified to simulate secondary organic aerosol (SOA) formation from five explicit (α-pinene, β-pinene, d-limonene, Δ3-carene, and sabinene) and one lumped monoterpene (MT) species and sesquiterpenes (SQTs). The contribution of each oxidation pathway [including OH, O3, NO3, and O(3P)] was explicitly tracked in the SOA module. Three MT SOA tracers (pinic acid, PA; pinonic acid, PNA; and 3-methyl-1,2,3-butanetricarboxylic acid, MBTCA) and one SQT SOA tracer (β-caryophyllinic acid, BCARYA) were modeled to assess the tracer-to-SOA ratios (fSOA) for ambient SOA estimation. Good model performance for BCARYA and MBTCA and reasonable agreement between model predictions and observations of PA and PNA were achieved. The modeled daily fSOA showed significant variations, suggesting that using the chamber-derived constant fSOA could lead to large errors in estimating terpene SOA. Among the four tracers, MBTCA and BCARYA were more appropriate for tracking MT and SQT SOA due to their nonvolatility. Their fSOA values mainly depend on the organic aerosol loading and could be approximated using simple power-law equations. In addition, equations directly linking the tracer concentrations to the corresponding SOA concentrations were proposed and could lead to good SOA estimations. This work provides new insights into the formation of the key MT and SQT SOA tracers and would allow better assessments of the biogenic emissions to regional and global aerosol burden.
Estimation of Aromatic Secondary Organic Aerosol Using a Molecular Tracer-A Chemical Transport Model Assessment

Zhang, Jie, He, Xiao, Gao, Yaqin, Zhu, Shuhui, Jing, Shengao, Wang, Hongli, Yu, Jian Zhen, and Ying, Qi

Environmental science & technology, Oct 2021

Abs Doi

A modified community multiscale air quality model, which can simulate the regional distributions of 2,3-dihydroxy-4-oxopentanoic acid (DHOPA), a marker species for monoaromatic secondary organic aerosol (SOA), was applied to assess the applicability of using the DHOPA to aromatic SOA mass ratio (f(SOA)) from smog chamber experiments to estimate aromatic SOA during a three-week wintertime air quality campaign in urban Shanghai. The modeled daily DHOPA concentrations based on the chamber-derived mass yields agree well with the organic marker field measurements (R = 0.79; MFB = 0.152; and MFE = 0.440). Two-thirds of the DHOPA are from the oxidation of ARO1 (lumped less-reactive aromatic species; mostly toluene), with the rest from ARO2 (lumped more-reactive aromatic species; mostly xylenes). Modeled DHOPA is mainly in the particle phase under ambient organic aerosol (OA) loading but could exhibit significant gas-particle partitioning when a higher estimation of the DHOPA vapor pressure is used. The modeled f(SOA) shows a strong dependence on the OA loading when only semivolatile aromatic SOA components are included in the f(SOA) calculations. However, this OA dependence becomes weaker when non-volatile oligomers and dicarbonyl SOA products are considered. A constant f(SOA) value of similar to 0.002 is determined when all aromatic SOA components are included, which is a factor of 2 smaller than the commonly applied chamber-based fSOA value of 0.004 for toluene. This model-derived f(SOA) value does not show much spatial variation and is not sensitive to alternative estimates of DHOPA vapor pressures and SOA yields, and thus provides an appropriate scaling factor to assess aromatic SOA from DHOPA measurements. This result helps refine the quantification of SOA attributable to monoaromatic hydrocarbons in urban environments and thereby facilitates the evaluation of control measures targeting these specific precursors.
Study of Secondary Organic Aerosol Formation from Chlorine Radical-Initiated Oxidation of Volatile Organic Compounds in a Polluted Atmosphere Using a 3D Chemical Transport Model

Choi, Min Su, Qu, Xionghui, Zhang, Jie, Wang, Shuxiao, Li, Xinghua, Sun, Yele, Chen, Jianmin, and Ying, Qi

Environmental science & technology, Nov 2020

Abs Doi

The impact of chlorine (Cl) chemistry on the formation of secondary organic aerosol (SOA) during a severe wintertime air pollution episode is investigated in this study. The Community Multiscale Air Quality (CMAQ) model v5.0.1 with a modified SAPRC-11 gas-phase mechanism and heterogeneous reactions for reactive chlorine species is updated to include the formation of chlorine radical (Cl-center dot)- initiated SOA (Cl-SOA) from aromatic compounds, terpenes, and isoprene. Reported SOA yield data on Cl-SOA formation from environmental chamber studies are used to derive the mass yield and volatility data for the two-product equilibrium-partitioning model. The heterogeneous reaction of particulate chloride (pCl(-)) leads to a significant increase in the Cl-center dot and hydroxyl radical (OH) concentrations throughout the domain. Monthly Cl-SOA concentrations range from 0.7 to 3.0 mu g m(-3), with increasing anthropogenic Cl emissions leading to higher Cl-SOA concentrations. Indirectly, this also leads to an increase of monthly SOA by up to 2.5-3.0 g mu m(-3) from the traditional OH oxidation pathways as well as the surface uptake of glyoxal and methylglyoxal. Increased OH concentrations, however, do not always lead to higher overall SOA concentrations in the entire domain. High OH reduces the lifetime of glyoxal/methylglyoxal (GLY/MGLY), making them less available to form SOA. In the Sichuan Basin (SCB) and part of Southwest China where high O-3 concentrations meet high pCl emissions, a higher Cl-center dot/OH ratio leads to net O-3 loss from the Cl-center dot + O-3 reaction, thus reducing SOA formation from the O-3 oxidation of volatile organic compounds (VOCs). Also, the competition between Cl-center dot and OH for VOCs could lead to lower overall SOA because the molar yields of the semivolatile products in Cl-VOC reactions are lower than their OH + VOC reaction counterparts. When Cl-center dot concentrations are further increased with higher emissions of Cl, precursor gases can be depleted and become the limiting factor in SOA formation. This study reveals the direct and indirect impacts of chlorine chemistry on SOA in polluted winter conditions, which are greatly affected by the Cl emissions, the ambient O-3 level, and the availability of SOA precursors.
Attribution of Tropospheric Ozone to NOx and VOC Emissions: Considering Ozone Formation in the Transition Regime

Wang, Peng, Chen, Yuan, Hu, Jianlin, Zhang, Hongliang, and Ying, Qi

Environmental science & technology, Feb 2019

Abs Doi

An improved three-regime (3R) O-3 attribution technique for O-3 source apportionment in regional chemical transport models is developed to divide the entire range of VOC-NOx-O-3 formation sensitivity to VOC-limited, transition, and NOx-limited regimes based on the value of a regime indicator R. The threshold R values to mark the start(R-ts) and end (R-te) of the transition regime are defined at the point where O-3-NOx sensitivity turns from negative to positive and where O-3-NOx sensitivity is ten times higher than O-3-VOC sensitivity, respectively. R-ts and R-te are determined using NO and VOC sensitivity simulations in a box model with a modified SAPRC-11 mechanism. For the widely used indicator ration R = (P-H2O2 +P-ROOH)/PHNO3, which is based on the production rates of H2O2, HNO3 and organic hydroperoxides (ROOH), the recommended Rts and Rte values are 0.047 and 5.142, respectively. Parameterized attribution functions, depending only on the values of R, are developed to apportion modeled in situ O-3 formation in the transition regime to NO and VOCs. The new 3R and the traditional two-regime (2R) schemes are incorporated into the Community Multiscale Air Quality (CMAQ) model to quantify NOx and VOC contributions to regional O-3 concentrations in China in August 2013. The 3R approach predicts approximately 5-10 ppb and up to 15 ppb higher NOx contributions to 8 h O-3 in in the North China Plain, the Yangtze River Delta and the Pearl River Delta than the 2R approach. The big differences in O-3 attribution between 2R and 3R can have significant policy implications for air pollution emission controls.
Modeling Atmospheric Age Distribution of Elemental Carbon Using a Regional Age-Resolved Particle Representation Framework

Zhang, Hongliang, Guo, Hao, Hu, Jianlin, Ying, Qi, and Kleemani, Michael J.

Environmental science & technology, Jan 2019

Abs Doi

The aging process of soot particles has significant implications when estimating their impacts on air quality and climate. In this study, the source-oriented University of California at Davis/California Institute of Technology model with externally mixed aerosol representation is expanded to track the age distribution of elemental carbon (EC) in Southeast Texas. EC with the age of 0-3 h (i.e., emitted less than 3 h ago) accounted for similar to 70-90% of the total in urban Houston and 20-40% in rural areas of southeast Texas in August 2000. Significant diurnal variations in the mean age of EC are predicted, with higher contributions from fresh particles during the morning and early evening due to increased traffic emission and reduced atmospheric mixing. Spatially, the mean age of EC decreases with proximity to major sources. Ground-level EC with the age >6 h is less than 20% of the first age group over land, and background EC accounts for the majority over the Gulf of Mexico. Differences in EC spatial distribution indicate that age distribution could have regional impact on aerosol optical and hygroscopic properties, and thus potentially affect cloud formation and radiation balance. Appropriately accounting for the differential properties due to age distribution is needed to better evaluate aerosol direct and indirect effects.
Premature Mortality Attributable to Particulate Matter in China: Source Contributions and Responses to Reductions

Hu, Jianlin, Huang, Lin, Chen, Mindong, Liao, Hong, Zhang, Hongliang, Wang, Shuxiao, Zhang, Qiang, and Ying, Qi

Environmental science & technology, Sep 2017

Abs Doi

Excess mortality (Delta Mort) in China due to exposure to ambient fine particulate matter with aerodynamic diameter <= 2.5 mu m (PM2.5) was determined using an ensemble prediction of annual average PM2.5 in 2013 by the community multiscale air quality (CMAQ) model with four emission inventories and observation data fusing. Estimated Delta Mort values due to adult ischemic heart disease, cerebrovascular disease, chronic obstructive pulmonary disease, and lung cancer are 0.30, 0.73, 0.14, and 0.13 million in 2013, respectively, leading to a total Delta Mort of 1.3 million. Source-oriented CMAQ modeling determined that industrial and residential sources were the two leading sources of Delta Mort, contributing to 0.40 (30.5%) and 0.28 (21.7%) million deaths, respectively. Additionally, secondary ammonium ion from agriculture, secondary organic aerosol, and aerosols from power generation were responsible for 0.16, 0.14, and 0.13 million deaths, respectively. A 30% ?Mort reduction in China requires an average of 50% reduction of PM2.5 throughout the country and a reduction by 62%, 50%, and 38% for the Beijing-Tianjin-Hebei, Jiangsu-Zhejiang-Shanghai, and Pearl River Delta regions, respectively. Reducing PM2.5 to the CAAQS grade II standard of 35 mu g m(-3) would only lead to a small reduction in mortality, and a more stringent standard of <15 mu g m(-3) would be needed for more remarkable reduction of Delta Mort.
Estimating population exposure to ambient polycyclic aromatic hydrocarbon in the United States - Part I: Model development and evaluation

Zhang, Jie, Li, Jingyi, Wang, Peng, Chen, Gang, Mendola, Pauline, Sherman, Seth, and Ying, Qi

Environment international, Feb 2017

Abs Doi

PAHs (polycyclic aromatic hydrocarbons) in the environment are of significant concern due to their negative impact on human health. PAH measurements at the air toxics monitoring network stations alone are not sufficient to provide a complete picture of ambient PAH levels or to allow accurate assessment of public exposure in the United States. In this study, speciation profiles for PAHs were prepared using data assembled from existing emission profile data bases, and the Sparse Matrix Operator Kernel Emissions (SMOKE) model was used to generate the gridded national emissions of 16 priority PAHs in the US. The estimated emissions were applied to simulate ambient concentration of PAHs for January, April, July and October 2011, using a modified Community Multiscale Air Quality (CMAQ) model (v5.0.1) that treats the gas and particle phase partitioning of PAHs and their reactions in the gas phase and on particle surface. Predicted daily PAH concentrations at 61 air toxics monitoring sites generally agreed with observations, and averaging the predictions over a month reduced the overall error. The best model performance was obtained at rural sites, with an average mean fractional bias (MFB) of -0.03 and mean fractional error (MFE) of 0.70. Concentrations at suburban and urban sites were underestimated with overall MFB = -0.57 and MFE = 0.89. Predicted PAH concentrations were highest in January with better model performance (MFB = 0.12, MFE = 0.69; including all sites), and lowest in July with worse model performance (MFB = 0.90, MFE = 1.08). Including heterogeneous reactions of several PAHs with O-3 on particle surface reduced the over-prediction bias in winter, although significant uncertainties were expected due to relative simple treatment of the heterogeneous reactions in the current model. (C) 2016 Elsevier Ltd. All rights reserved.
One-year simulation of ozone and particulate matter in China using WRF/CMAQ modeling system

Hu, Jianlin, Chen, Jianjun, Ying, Qi, and Zhang, Hongliang

Atmospheric chemistry and physics, Aug 2016

Abs Doi

China has been experiencing severe air pollution in recent decades. Although an ambient air quality monitoring network for criteria pollutants has been constructed in over 100 cities since 2013 in China, the temporal and spatial characteristics of some important pollutants, such as particulate matter (PM) components, remain unknown, limiting further studies investigating potential air pollution control strategies to improve air quality and associating human health outcomes with air pollution exposure. In this study, a yearlong (2013) air quality simulation using the Weather Research and Forecasting (WRF) model and the Community Multi-scale Air Quality (CMAQ) model was conducted to provide detailed temporal and spatial information of ozone (O-3), total PM2.5, and chemical components. Multi-resolution Emission Inventory for China (MEIC) was used for anthropogenic emissions and observation data obtained from the national air quality monitoring network were collected to validate model performance. The model successfully reproduces the O-3 and PM2.5 concentrations at most cities for most months, with model performance statistics meeting the performance criteria. However, overprediction of O-3 generally occurs at low concentration range while underprediction of PM2.5 happens at low concentration range in summer. Spatially, the model has better performance in southern China than in northern China, central China, and Sichuan Basin. Strong seasonal variations of PM2.5 exist and wind speed and direction play important roles in high PM2.5 events. Secondary components have more boarder distribution than primary components. Sulfate (SO42-), nitrate (NO3-), ammonium (NH4+ ) , and primary organic aerosol (POA) are the most important PM2.5 components. All components have the highest concentrations in winter except secondary organic aerosol (SOA). This study proves the ability of the CMAQ model to reproduce severe air pollution in China, identifies the directions where improvements are needed, and provides information for human exposure to multiple pollutants for assessing health effects.
Significant Contributions of Isoprene to Summertime Secondary Organic Aerosol in Eastern United States

Ying, Qi, Li, Jingyi, and Kota, Sri Harsha

Environmental science & technology, Jul 2015

Abs Doi

A modified SAPRC-11 (S11) photochemical mechanism with more detailed treatment of isoprene oxidation chemistry and additional secondary organic aerosol (SOA) formation through surface-Controlled, reactive uptake of dicarbonyls, isoprene epoxydiol and rnethacrylic acid epoxide was incorporated in tie Community Multiscale Air Quality Model (CMAQ) to quantitatively determine contributions of isoprene to Summertime ambient SOA concentrations in the eastern United States. The modified model utilizes a precursor-origin resolved approach to determine secondary glyoxal and methylglyoxal produced by oxidation Of isoprene and other major volatile organic compounds (VOCs). Predicted OC concentrations show good agreement with field measurements without significant bias (MFB similar to 0.07 and MFE similar to 0.50), and predicted SOA reproduces observed day-to-day and diurnal variation of Oxygenated Organic Aerosol (OOA) determined by an aerosol mass spectrometer (AMS) at two locations in Houston, Texas. On average, isoprene SOA accounts for 55.5% of total predicted near-surface SOA in the eastern U.S, followed by aromatic compounds (13.2%), sesquiterpenes (13.0%) and monoterpenes (10.9%). Aerosol surface uptake of isoprene-generated glyoxal, methylglyoxal and epoxydiol accounts for approximately 83% of total isoprene SOA or more than 45% of total SOA. A domain wide reduction of NO emissions by 40% leads to a slight decrease of domain average SOA by 3.6% and isoprene SOA by approximately 2.6%. Although most of the isoprene SOA component concentrations are decreased, SOA from isoprene epoxydiol is increased by similar to 16%.
Source apportionment of formaldehyde during TexAQS 2006 using a source-oriented chemical transport model

Zhang, Hongliang, Li, Jingyi, Ying, Qi, Guven, Birnur Buzcu, and Olaguer, Eduardo P.

Journal of geophysical research-atmospheres, Feb 2013

Abs Doi

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 [HRM-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. Citation: Zhang, H., J. Li, Q. Ying, B. B. Guven, and E. P. Olaguer (2013), Source apportionment of formaldehyde during TexAQS 2006 using a source-oriented chemical transport model, J. Geophys. Res. Atmos., 118, 1525-1535, doi:10.1002/jgrd.50197.
Source contributions to the regional distribution of secondary particulate matter in California

Ying, Qi, and Kleeman, MJ

Atmospheric environment, Feb 2006

Abs Doi

Source contributions to PM2.5 nitrate, sulfate and ammonium ion concentrations in California’s San Joaquin Valley (SJV) (4-6 January 1996) and South Coast Air Basin (SoCAB) surrounding Los Angeles (23-25 September 1996) were predicted using a three-dimensional source-oriented Eulerian air quality model. The air quality model tracks the formation of PM2.5 nitrate, sulfate and ammonium ion from primary particles and precursor gases emitted from different sources though a mathematical simulation of emission, chemical reaction, gas-to-particle conversion, transport and deposition. The observed PM2.5 nitrate, sulfate and ammonium ion concentrations, and the mass distribution of nitrate, sulfate and ammonium ion as a function of particle size have been successfully reproduced by the model simulation. Approximately 45-57% of the PM2.5 nitrate and 34-40% of the PM2.5 ammonium ion in the SJV is formed from precursor gaseous species released from sources upwind of the valley. In the SoCAB, approximately 83% of the PM2.5 nitrate and 82% of the PM2.5 ammonium ion is formed from precursor gaseous species released from sources within the air basin. In the SJV, transportation related sources contribute approximately 24-30% of the PM2.5 nitrate (diesel engines similar to 13.5-17.0%, catalyst equipped gasoline engines similar to 10.2-12.8% and non-catalyst equipped gasoline engines similar to 0.3-0.4%). In the SoCAB, transportation related sources directly contribute to approximately 67% of the PM2.5 nitrate (diesel engines 34.6%, noncatalyst equipped gasoline engine 4.7% and catalyst equipped gasoline engine 28.1%). PM2.5 ammonium ion concentrations in the SJV were dominated by area (including animal) NH3 sources (16.7-25.3%), soil (7.2-10.9%), fertilizer NH3 sources (11.4-17.3%) and point NH3 sources (14.3-21.7%). In the SoCAB, ammonium ion is mainly associated with animal sources (28.2%) and catalyst equipped gasoline engines (16.2%). In both regions, the majority of the relatively low PM2.5 sulfate (< 5 mu g m(-3)) is associated with upwind sources. Most of the locally generated sulfate is emitted from diesel engines and high-sulfur fuel combustion processes in both modeling domains. Emissions control programs should target the sources listed above to reduce PM2.5 concentrations. (c) 2005 Elsevier Ltd. All rights reserved.