Performance of a Thermodynamic Model for Predicting Inorganic Aerosols in the Southeastern U.S.

Atmosphere · 2022 · 11 citations

• Environmental science
• Thermodynamics
• Atmospheric sciences

Fine particulate matter (i.e., PM2.5) has gained intensive attention due to its adverse health and visibility degradation effects. As a significant fraction of atmospheric PM2.5, secondary inorganic PM2.5 may be formed through the gas-phase ammonia (NH3) and particle-phase ammonium (NH4+) partitioning. While partitioning of NH3-NH4+ may be simulated using a thermodynamic equilibrium model, disagreement between model predictions and measurements have been realized. In addition, the applicability of the model under different conditions has not been well studied. This research aims to investigate the applicability of a thermodynamic equilibrium model, ISORROPIA II, under different atmospheric conditions and geographic locations. Based upon the field measurements at the Southeastern Aerosol Research and Characterization (SEARCH) network, the performance of ISORROPIA II was assessed under different temperature (T), relative humidity (RH), and model setups in urban and rural locations. The impact of organic aerosol (OA) on the partitioning of NH3-NH4+ was also evaluated. Results of this research indicate that the inclusion of non-volatile cations (NVCs) in the model input is necessary to improve the model performance. Under high T (>10 °C) and low RH (<60%) conditions, ISORROPIA II tends to overpredict nitric acid (HNO3) concentration and underpredict nitrate (NO3−) concentration. The predominance of one phase of semi-volatile compound leads to low accuracy in the model prediction of the other phase. The model with stable and metastable setups may also perform differently under different T-RH conditions. Metastable model setup might perform better under high T (>10 °C) and low RH (<60%) conditions, while stable model setup might perform better under low T (<5 °C) conditions. Both model setups have consistent performance when RH is greater than 83%. Future studies using ISORROPIA II for the prediction of NH3-NH4+ partitioning should consider the inclusion of NVCs, the under/over prediction of NO3−/HNO3, the selection of stable/metastable model setups under different T-RH conditions, and spatiotemporal variations of inorganic PM2.5 chemical compositions.

Partitioning of NH3-NH4+ in the Southeastern U.S.

Atmosphere · 2021 · 4 citations

• Environmental science
• Environmental chemistry
• Chemistry

The formation of inorganic fine particulate matter (i.e., iPM2.5) is controlled by the thermodynamic equilibrium partitioning of NH3-NH4+. To develop effective control strategies of PM2.5, we aim to understand the impacts of changes in different precursor gases on iPM2.5 concentrations and partitioning of NH3-NH4+. To understand partitioning of NH3-NH4+ in the southeastern U.S., responses of iPM2.5 to precursor gases in four seasons were investigated using field measurements of iPM2.5, precursor gases, and meteorological conditions. The ISORROPIA II model was used to examine the effects of changes in total ammonia (gas + aerosol), total sulfuric acid (aerosol), and total nitric acid (gas + aerosol) on iPM2.5 concentrations and partitioning of NH3-NH4+. The results indicate that reduction in total H2SO4 is more effective than reduction in total HNO3 and total NH3 to reduce iPM2.5 especially under NH3-rich condition. The reduction in total H2SO4 may change partitioning of NH3-NH4+ towards gas-phase and may also lead to an increase in NO3− under NH3-rich conditions, which does not necessarily lead to full neutralization of acidic gases (pH < 7). Thus, future reduction in iPM2.5 may necessitate the coordinated reduction in both H2SO4 and HNO3 in the southeastern U.S. It is also found that the response of iPM2.5 to the change in total H2SO4 is more sensitive in summer than winter due to the dominance of SO42− salts in iPM2.5 and the high temperature in summer. The NH3 emissions from Animal Feeding Operations (AFOs) at an agricultural rural site (YRK) had great impacts on partitioning of NH3-NH4+. The Multiple Linear Regression (MLR) model revealed a strong positive correlation between cation-NH4+ and anions-SO42− and NO3−. This research provides an insight into iPM2.5 formation mechanism for the advancement of PM2.5 control and regulation in the southeastern U.S.

Spatial and temporal variations of atmospheric chemical condition in the Southeastern U.S.

Atmospheric Research · 2020 · 4 citations

• Environmental science
• Atmospheric sciences
• Environmental chemistry

Spatial and temporal variations of PM2.5 mass closure and inorganic PM2.5 in the Southeastern U.S.

Environmental Science and Pollution Research · 2019-09-13 · 16 citations

Dynamic correlation multivariate stochastic volatility with latent factors

Statistica Neerlandica · 2017-08-10 · 4 citations

Modeling the correlation structure of returns is essential in many financial applications. Considerable evidence from empirical studies has shown that the correlation among asset returns is not stable over time. A recent development in the multivariate stochastic volatility literature is the application of inverse Wishart processes to characterize the evolution of return correlation matrices. Within the inverse Wishart multivariate stochastic volatility framework, we propose a flexible correlated latent factor model to achieve dimension reduction and capture the stylized fact of ‘correlation breakdown’ simultaneously. The parameter estimation is based on existing Markov chain Monte Carlo methods. We illustrate the proposed model with several empirical studies. In particular, we use high‐dimensional stock return data to compare our model with competing models based on multiple performance metrics and tests. The results show that the proposed model not only describes historic stylized facts reasonably but also provides the best overall performance.

Bayesian inference for generalized extreme value distribution with Gaussian copula dependence

arXiv (Cornell University) · 2017-03-02 · 1 citations

Dependent generalized extreme value (dGEV) models have attracted much attention due to the dependency structure that often appears in real datasets. To construct a dGEV model, a natural approach is to assume that some parameters in the model are time-varying. A previous study has shown that a dependent Gumbel process can be naturally incorporated into a GEV model. The model is a nonlinear state space model with a hidden state that follows a Markov process, with its innovation following a Gumbel distribution. Inference may be made for the model using Bayesian methods, sampling the hidden process from a mixture normal distribution, used to approximate the Gumbel distribution. Thus the response follows an approximate GEV model. We propose a new model in which each marginal distribution is an exact GEV distribution. We use a variable transformation to combine the marginal CDF of a Gumbel distribution with the standard normal copula. Then our model is a nonlinear state space model in which the hidden state equation is Gaussian. We analyze this model using Bayesian methods, and sample the elements of the state vector using particle Gibbs with ancestor sampling (PGAS). The PGAS algorithm turns out to be very efficient in solving nonlinear state space models. We also show our model is flexible enough to incorporate seasonality.

Biometric validation of a virtual reality-based psychomotor test for motor skill training

Assistive Technology · 2016-03-24 · 6 citations

Psychomotor tests have been applied in clinical therapy and laboratory research as tools for evaluating motor and cognitive skills. Some studies have developed computerized versions of such tests using virtual reality (VR) systems with haptic interface controls. These systems allow for increased flexibility in test delivery and accuracy in performance assessment. In this study, a VR-based computer simulation of the block design (BD) test (a standardized psychomotor task as part of an adult IQ test) was developed and compared with the physical version of the test. Performance was evaluated based on four types of muscle activation collected using electromyography (EMG), time spent in completing the task, and subjective ratings of workload. Results verified the VR-based task as physically comparable to the conventional BD test. The validated computerized psychomotor task may be applied for both experimental and clinical use in future studies.

Electromyography (EMG) as a Tool for Computerized Psychomotor Test Validation

Advances in intelligent systems and computing · 2016-01-01 · 1 citations

Major ionic compositions of fine particulate matter in an animal feeding operation facility and its vicinity

Journal of the Air & Waste Management Association · 2014-10-20 · 14 citations

Animal feeding operations (AFOs) produce particulate matter (PM) and gaseous pollutants. Investigation of the chemical composition of PM2.5 inside and in the local vicinity of AFOs can help to understand the impact of the AFO emissions on ambient secondary PM formation. This study was conducted on a commercial egg production farm in North Carolina. Samples of PM2.5 were collected from five stations, with one located in an egg production house and the other four located in the vicinity of the farm along four wind directions. The major ions of NH4+, Na+, K+, SO42−, Cl−, and NO3− were analyzed using ion chromatography (IC). In the house, the mostly abundant ions were SO42−, Cl−, and K+. At ambient stations, SO42−, and NH4+ were the two most abundant ions. In the house, NH4+, SO42−, and NO3− accounted for only 10% of the PM2.5 mass; at ambient locations, NH4+, SO42−, and NO3− accounted for 36–41% of the PM2.5 mass. In the house, NH4+ had small seasonal variations indicating that gas-phase NH3 was not the only major force driving its gas–particle partitioning. At the ambient stations, NH4+ had the highest concentrations in summer. In the house, K+, Na+, and Cl− were highly correlated with each other. In ambient locations, SO42− and NH4+ had a strong correlation, whereas in the house, SO42− and NH4+ had a very weak correlation. Ambient temperature and solar radiation were positively correlated with NH4+ and SO42−. This study suggests that secondary PM formation inside the animal house was not an important source of PM2.5. In the vicinity, NH3 emissions had greater impact on PM2.5 formation. ImplicationsThe chemical composition of PM2.5 inside and in the local vicinity of AFOs showed the impact of the AFO emissions on ambient secondary PM2.5 formation, and the fate and transport of air pollutants associated with AFOs. The results may help to manage in-house animal facility air quality, and to develop regional air quality control strategies and policies, especially in animal agriculture-concentrated areas.

On the degrees of freedom in MCMC-based Wishart models for time series data

Statistics & Probability Letters · 2014-12-21 · 3 citations