Variability in Observation-based Onroad Emission Constraints from a Near-road Environment

This study uses Las Vegas near-road measurements of carbon monoxide (CO) and nitrogen oxides (NOx) to test the consistency of onroad emission constraint methodologies. We derive commonly used CO to NOx ratios (ΔCO:ΔNOx) from cross-road gradients and from linear regression using ordinary least squares (OLS) regression and orthogonal regression. The CO to NOx ratios are used to infer NOx emission adjustments for a priori emissions estimates from EPA's MOtor Vehicle Emissions Simulator (MOVES) model assuming unbiased CO. The assumption of unbiased CO emissions may not be appropriate in many circumstances but was implemented in this analysis to illustrate the range of NOx scaling factors that can be inferred based on choice of methods and monitor distance alone. For the nearest road estimates (25m), the cross-road gradient and ordinary least squares (OLS) agree with each other and are not statistically different from the MOVES-based emission estimate while ΔCO:ΔNOx from orthogonal regression is significantly higher than the emitted ratio from MOVES. Using further downwind measurements (i.e., 115m and 300m) increases OLS and orthogonal regression estimates of ΔCO:ΔNOx but not cross-road gradient ΔCO:ΔNOx. The inferred NOx emissions depend on the observation-based method, as well as the distance of the measurements from the roadway and can suggest either that MOVES NOx emissions are unbiased or that they should be adjusted downward by between 10% and 47%. The sensitivity of observation-based ΔCO:ΔNOx estimates to the selected monitor location and to the calculation method characterize the inherent uncertainty of these methods that cannot be derived from traditional standard-error based uncertainty metrics.

Mobile Source Contributions to Ambient Ozone and Particulate Matter in 2025

The contribution of precursor emissions from 17 mobile source sectors to ambient ozone and fine particulate matter levels across the U.S. were evaluated, using the CAMx photochemical model, to identify which mobile source sectors are projected to have the largest impacts on air pollution in 2025. Both onroad and nonroad sectors contribute considerably to projected air pollution across much of the country. Summer ozone season ozone contributions between 2 and 5 ppb, which are among the highest levels presented on the maps of mobile source sectors, are largely found in the southeast United States from the onroad sectors, most notably light-duty and heavy-duty vehicles, and along the coastline from the Category 3 (C3) marine sector. Annual average PM2.5 contributions between 0.5 to 0.9 μg/m3, which are among the highest levels presented on the maps of mobile source sectors, are found throughout the Midwest and along portions of the east and west coast from onroad sectors as well as nonroad diesel and rail sectors. Additionally, contributions of precursor emissions to ambient ozone and PM2.5 levels were evaluated to understand the range of impacts from precursors in the various mobile source sectors. For most mobile source sectors, in most locations, NOX emissions contributed more to ozone than VOC emissions, and secondary PM2.5 contributed more to ambient PM2.5 than primary PM2.5. The largest ozone levels on the maps showing contributions from mobile source NOX emissions tended to be between 2 and 5 ppb, while the largest ozone levels on the maps showing contributions from mobile source VOC emissions tended to be between 0.9 and 2 ppb, except for southern California where ozone contributions from VOC emissions from onroad light duty vehicles were between 2 and 5 ppb. The largest contributions to ambient PM2.5 on the maps showing primary and secondary contributions from mobile source sectors tended to be between 0.1 and 0.5 μg/m3. The contribution from primary PM2.5 extended over localized areas (urban-scale) and the contribution from secondary PM2.5 extended over more regional (multi-state) areas.

Characterizing CO and NO y Sources and Relative Ambient Ratios in the Baltimore Area Using Ambient Measurements and Source Attribution Modeling

Modeled source attribution information from the Community Multiscale Air Quality model was coupled with ambient data from the 2011 Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality Baltimore field study. We assess source contributions and evaluate the utility of using aircraft measured CO and NO _y relationships to constrain emission inventories. We derive ambient and modeled ΔCO:ΔNO _y ratios that have previously been interpreted to represent CO:NO _y ratios in emissions from local sources. Modeled and measured ΔCO:ΔNO _y are similar; however, measured ΔCO:ΔNO _y has much more daily variability than modeled values. Sector-based tagging shows that regional transport, on-road gasoline vehicles, and nonroad equipment are the major contributors to modeled CO mixing ratios in the Baltimore area. In addition to those sources, on-road diesel vehicles, soil emissions, and power plants also contribute substantially to modeled NO _y in the area. The sector mix is important because emitted CO:NO _x ratios vary by several orders of magnitude among the emission sources. The model-predicted gasoline/diesel split remains constant across all measurement locations in this study. Comparison of ΔCO:ΔNO _y to emitted CO:NO _y is challenged by ambient and modeled evidence that free tropospheric entrainment, and atmospheric processing elevates ambient ΔCO:ΔNO _y above emitted ratios. Specifically, modeled ΔCO:ΔNO _y from tagged mobile source emissions is enhanced 5-50% above the emitted ratios at times and locations of aircraft measurements. We also find a correlation between ambient formaldehyde concentrations and measured ΔCO:ΔNO _y suggesting that secondary CO formation plays a role in these elevated ratios. This analysis suggests that ambient urban daytime ΔCO:ΔNO _y values are not reflective of emitted ratios from individual sources.

Evaluation of the temporal variations of air quality in Taipei City, Taiwan, from 1994 to 2003

Data collected from the five air-quality monitoring stations established by the Taiwan Environmental Protection Administration in Taipei City from 1994 to 2003 are analyzed to assess the temporal variations of air quality. Principal component analysis (PCA) is adopted to convert the original measuring pollutants into fewer independent components through linear combinations while still retaining the majority of the variance of the original data set. Two principal components (PCs) are retained together explaining 82.73% of the total variance. PC1, which represents primary pollutants such as CO, NO(x), and SO(2), shows an obvious decrease over the last 10 years. PC2, which represents secondary pollutants such as ozone, displays a yearly increase over the time period when a reduction of primary pollutants is obvious. In order to track down the control measures put forth by the authorities, 47 days of high PM(10) concentrations caused by transboundary transport have been eliminated in analyzing the long-term trend of PM(10) in Taipei City. The temporal variations over the past 10 years show that the moderate peak in O(3) demonstrates a significant upward trend even when the local primary pollutants have been well under control. Monthly variations of PC scores demonstrate that primary pollution is significant from January to April, while ozone increases from April to August. The results of the yearly variations of PC scores show that PM(10) has gradually shifted from a strong correlation with PC1 during the early years to become more related to PC2 in recent years. This implies that after a reduction of primary pollutants, the proportion of secondary aerosols in PM(10) may increase. Thus, reducing the precursor concentrations of secondary aerosols will be an effective way to lower PM(10) concentrations.

Prediction of tropospheric ozone concentrations by using the design system approach

Data on the concentrations of non-methane hydrocarbons (NMHC), nitrogen oxide (NO), nitrogen dioxide (NO2), carbon monoxide (CO), and meteorological parameters (air temperature and solar radiation) were used to predict the concentration of tropospheric ozone using the Design-Ease software. These data were collected on hourly basis over a 12-month period. Sampling of the data was conducted automatically. The effect of the NMHC, NO, NO2,CO, temperature and solar radiation variables in predicting ozone concentrations was examined under two scenarios: (i) when NO is included with the absence of NO2; and (ii) when NO2 is addressed with the absence of NO. The results of these two scenarios were validated against ozone actual data. The predicted concentration of ozone in the second scenario (i.e., when NO2 is addressed) was in better agreement with the real observations. In addition, the paper indicated that statistical models of hourly surface ozone concentrations require interactions and non-linear relationships between predictor variables in order to accurately capture the ozone behavior.

Receptor modeling application framework for particle source apportionment

Receptor models infer contributions from particulate matter (PM) source types using multivariate measurements of particle chemical and physical properties. Receptor models complement source models that estimate concentrations from emissions inventories and transport meteorology. Enrichment factor, chemical mass balance, multiple linear regression, eigenvector. edge detection, neural network, aerosol evolution, and aerosol equilibrium models have all been used to solve particulate air quality problems, and more than 500 citations of their theory and application document these uses. While elements, ions, and carbons were often used to apportion TSP, PM10, and PM2.5 among many source types, many of these components have been reduced in source emissions such that more complex measurements of carbon fractions, specific organic compounds, single particle characteristics, and isotopic abundances now need to be measured in source and receptor samples. Compliance monitoring networks are not usually designed to obtain data for the observables, locations, and time periods that allow receptor models to be applied. Measurements from existing networks can be used to form conceptual models that allow the needed monitoring network to be optimized. The framework for using receptor models to solve air quality problems consists of: (1) formulating a conceptual model; (2) identifying potential sources; (3) characterizing source emissions; (4) obtaining and analyzing ambient PM samples for major components and source markers; (5) confirming source types with multivariate receptor models; (6) quantifying source contributions with the chemical mass balance; (7) estimating profile changes and the limiting precursor gases for secondary aerosols; and (8) reconciling receptor modeling results with source models, emissions inventories, and receptor data analyses.