H. Oliver Gao

Public Health Costs of Primary PM2.5 and Inorganic PM2.5 Precursor Emissions in the United States

Current methods of estimating the public health effects of emissions are computationally too expensive or do not fully address complex atmospheric processes, frequently limiting their applications to policy research. Using a reduced-form model derived from tagged chemical transport model (CTM) simulations, we present PM2.5 mortality costs per tonne of inorganic air pollutants with the 36 km × 36 km spatial resolution of source location in the United States, providing the most comprehensive set of such estimates comparable to CTM-based estimates. Our estimates vary by 2 orders of magnitude. Emission-weighted seasonal averages were estimated at

Diesel Particulate Matter Number Emissions: Evaluation of Existing Modal Emission Modeling Approaches

88,000-130,000/t PM2.5 (inert primary),

Accounting for Exhaust Gas Transport Dynamics in Instantaneous Emission Models via Smooth Transition Regression

14,000-24,000/t SO2,

Using Cellular Communication Networks To Detect Air Pollution

3,800-14,000/t NOx, and

Functional convolution models

23,000-66,000/t NH3. The aggregate social costs for year 2005 emissions were estimated at

Modeling On-Road Particle Number Emissions from a Hybrid Diesel–Electric Bus: Exploratory Econometric Analysis

1.0 trillion dollars. Compared to other studies, our estimates have similar magnitudes and spatial distributions for primary PM2.5 but substantially different spatial patterns for precursor species where secondary chemistry is important. For example, differences of more than a factor of 10 were found in many areas of Texas, New Mexico, and New England states for NOx and of California, Texas, and Maine for NH3. Our method allows for updates as emissions inventories and CTMs improve, enhancing the potential to link policy research to up-to-date atmospheric science.

Public health costs accounting of inorganic PM2.5 pollution in metropolitan areas of the United States using a risk-based source-receptor model

Recent health and exposure studies have indicated that emissions and air quality standards of particulate matters (PM) numbers could be beneficial since they account for the more harmful ultrafine particles that have negligible mass. This study examines existing modal emission modeling approaches [e.g., polynomial regressions, fuel rate-based approach, and vehicle specific power (VSP)-based approach] in order to evaluate their ability of predicting diesel particle number concentration emitted from a transit diesel bus. This is a necessary and useful first step to quantifying vehicular emissions in terms of PM number. The existing modeling approaches were applied to and then the technique of bootstrapping was used on the onboard measurement data of PM number concentrations emitted from a diesel transit bus. The predictive ability of the models with respect to particle number concentration is assessed by examining the statistical significance of the key regressors used in those approaches and the adjusted R-squared values of the models. Results indicate less than satisfying predictive capability of the examined approaches for PM number emission that behaves in a different manner from gaseous emissions. The study, however, demonstrates that vehicle operating factors such as VSP, speed, and acceleration are significant in explaining the variability of transient PM number concentration measurements from the bus.

Energy Consumption and Emission Rates of Highway Mowing Activities

Collecting and analyzing high frequency emission measurements has become very usual during the past decade as significantly more information with respect to formation conditions can be collected than from regulated bag measurements. A challenging issue for researchers is the accurate time-alignment between tailpipe measurements and engine operating variables. An alignment procedure should take into account both the reaction time of the analyzers and the dynamics of gas transport in the exhaust and measurement systems. This paper discusses a statistical modeling framework that compensates for variable exhaust transport delay while relating tailpipe measurements with engine operating covariates. Specifically it is shown that some variants of the smooth transition regression model allow for transport delays that vary smoothly as functions of the exhaust flow rate. These functions are characterized by a pair of coefficients that can be estimated via a least-squares procedure. The proposed models can be adapted to encompass inherent nonlinearities that were implicit in previous instantaneous emissions modeling efforts. This article describes the methodology and presents an illustrative application which uses data collected from a diesel bus under real-world driving conditions.

Exposure to fine particle mass and number concentrations in urban transportation environments of New York City

Accurate real time monitoring of atmospheric conditions at ground level is vital for hazard warning, meteorological forecasting, and various environmental applications required for public health and safety. However, conventional monitoring facilities are costly and often insufficient, for example, since they are not representative of the larger space and are not deployed densely enough in the field. There have been numerous scientific works showing the ability of commercial microwave links that comprise the data transmission infrastructure in cellular communication networks to monitor hydrometeors as a potential complementary solution. However, despite the large volume of research carried out in this emerging field during the past decade, no study has shown the ability of the system to provide critical information regarding air quality. Here we reveal the potential for identifying atmospheric conditions prone to air pollution by detecting temperature inversions that trap pollutants at ground level. The technique is based on utilizing standard signal measurements from an existing cellular network during routine operation.

Day of week effects on diurnal ozone/NOx cycles and transportation emissions in Southern California

This article considers the application of functional data analysis methods to modelling particulate matter emission profiles from dynamometer experiments. In particular the functional convolution model is introduced as an extension of the distributed lag model to functional (smooth and continuous) observations. We present a penalized ordinary least squares estimator for the model and a novel bootstrap procedure to provide pointwise confidence regions for the estimated convolution functions. The model is illustrated on the Coordinating Research Council E55/59 study of diesel truck emissions.