Simulating the Degree of Oxidation in Atmospheric Organic Particles

Modeled ratios of organic mass to organic carbon (OM/OC) and oxygen to carbon (nO/nC) in organic particulate matter are presented across the US for the first time and evaluated extensively against ambient measurements. The base model configuration systematically underestimates OM/OC ratios during winter and summer months. Model performance is greatly improved by applying source-specific OM/OC ratios to the primary organic aerosol (POA) emissions and incorporating a new parametrization to simulate oxidative aging of POA in the atmosphere. These model improvements enable simulation of urban-scale gradients in OM/OC with values in urban areas as much as 0.4 lower than in the surrounding regions. Modeled OM/OC and nO/nC ratios in January range from 1.4 to 2.0 and 0.2 to 0.6, respectively. In July, modeled OM/OC and nO/nC ratios range from 1.4 to 2.2 and 0.2 to 0.8, respectively. Improved model performance during winter is attributed entirely to our application of source-specific OM/OC ratios to the inventory. During summer, our treatment of oxidative aging also contributes to improved performance. Advancements described in this paper are codified in the latest public release of the Community Multiscale Air Quality model, CMAQv5.0

Diagnostic Model Evaluation for Carbonaceous PM_2.5 Using Organic Markers Measured in the Southeastern U.S.

Summertime concentrations of fine particulate carbon in the southeastern United States are consistently underestimated by air quality models. In an effort to understand the cause of this error, the Community Multiscale Air Quality model is instrumented to track primary organic and elemental carbon contributions from fifteen different source categories. The model results are speciated using published source profiles and compared with ambient measurements of 100 organic markers collected at eight sites in the Southeast during the 1999 summer. Results indicate that modeled contributions from vehicle exhaust and biomass combustion, the two largest sources of carbon in the emission inventory, are unbiased across the region. In Atlanta, good model performance for total carbon (TC) is attributed to compensating errors:  overestimation of vehicle emissions with underestimations of other sources. In Birmingham, 35% of the TC underestimation can be explained by deficiencies in primary sources. Cigarette smoke and vegetative detritus are not in the inventory, but contribute less than 3% of the TC at each site. After the model results are adjusted for source-specific errors using the organic-marker measurements, an average of 1.6 μgC m-3 remain unexplained. This corresponds to 26−38% of ambient TC concentrations at urban sites and up to 56% at rural sites. The most likely sources of unexplained carbon are discussed

Seasonal and Regional Variations of Primary and Secondary Organic Aerosols over the Continental United States: Semi-Empirical Estimates and Model Evaluation

Seasonal and regional variations of primary (OCpri) and secondary (OCsec) organic carbon aerosols across the continental United States for the year 2001 were examined by a semi-empirical technique using observed OC and elemental carbon (EC) data from 142 routine monitoring sites in mostly rural locations across the country, coupled with the primary OC/EC ratios, obtained from a chemical transport model (i.e., Community Multiscale Air Quality (CMAQ) model). This application yields the first non-mechanistic estimates of the spatial and temporal variations in OCpri and OCsec over an entire year on a continental scale. There is significant seasonal and regional variability in the relative contributions of OCpri and OCsec to OC. Over the continental United States, the median OCsec concentrations are 0.13, 0.36, 0.63, 0.44, and 0.42 μg C m-3 in winter (DJF), spring (MAM), summer (JJA), fall (SON), and annual, respectively, making 21, 44, 51, 42, and 43% contributions to OC, respectively. OCpri exceeds OCsec in all seasons except summer. Regional analysis shows that the southeastern region has the highest concentration of OCpri (annual median = 1.35 μg C m-3), whereas the central region has the highest concentration of OCsec (annual median = 0.76 μg C m-3). The mechanistic OCsec estimates from the CMAQ model were compared against the independently derived semi-empirical OCsec estimates. The results indicate that the mechanistic model reproduced the monthly medians of the semi-empirical OCsec estimates well over the northeast, southeast, midwest, and central regions in all months except the summer months (June, July, and August), during which the modeled regional monthly medians were consistently lower than the semi-empirical estimates. This indicates that the CMAQ model is missing OCsec formation pathways that are important in the summer

Seasonal and Regional Variations of Primary and Secondary Organic Aerosols over the Continental United States: Semi-Empirical Estimates and Model Evaluation

Seasonal and regional variations of primary (OCpri) and secondary (OCsec) organic carbon aerosols across the continental United States for the year 2001 were examined by a semi-empirical technique using observed OC and elemental carbon (EC) data from 142 routine monitoring sites in mostly rural locations across the country, coupled with the primary OC/EC ratios, obtained from a chemical transport model (i.e., Community Multiscale Air Quality (CMAQ) model). This application yields the first non-mechanistic estimates of the spatial and temporal variations in OCpri and OCsec over an entire year on a continental scale. There is significant seasonal and regional variability in the relative contributions of OCpri and OCsec to OC. Over the continental United States, the median OCsec concentrations are 0.13, 0.36, 0.63, 0.44, and 0.42 μg C m-3 in winter (DJF), spring (MAM), summer (JJA), fall (SON), and annual, respectively, making 21, 44, 51, 42, and 43% contributions to OC, respectively. OCpri exceeds OCsec in all seasons except summer. Regional analysis shows that the southeastern region has the highest concentration of OCpri (annual median = 1.35 μg C m-3), whereas the central region has the highest concentration of OCsec (annual median = 0.76 μg C m-3). The mechanistic OCsec estimates from the CMAQ model were compared against the independently derived semi-empirical OCsec estimates. The results indicate that the mechanistic model reproduced the monthly medians of the semi-empirical OCsec estimates well over the northeast, southeast, midwest, and central regions in all months except the summer months (June, July, and August), during which the modeled regional monthly medians were consistently lower than the semi-empirical estimates. This indicates that the CMAQ model is missing OCsec formation pathways that are important in the summer

To What Extent Can Biogenic SOA be Controlled?

The implicit assumption that biogenic secondary organic aerosol (SOA) is natural and can not be controlled hinders effective air quality management. Anthropogenic pollution facilitates transformation of naturally emitted volatile organic compounds (VOCs) to the particle phase, enhancing the ambient concentrations of biogenic secondary organic aerosol (SOA). It is therefore conceivable that some portion of ambient biogenic SOA can be removed by controlling emissions of anthropogenic pollutants. Direct measurement of the controllable fraction of biogenic SOA is not possible, but can be estimated through 3-dimensional photochemical air quality modeling. To examine this in detail, 22 CMAQ model simulations were conducted over the continental U.S. (August 15 to September 4, 2003). The relative contributions of five emitted pollution classes (i.e., NOx, NH3, SOx, reactive non methane carbon (RNMC) and primary carbonaceous particulate matter (PCM)) on biogenic SOA were estimated by removing anthropogenic emissions of these pollutants, one at a time and all together. Model results demonstrate a strong influence of anthropogenic emissions on predicted biogenic SOA concentrations, suggesting more than 50% of biogenic SOA in the eastern U.S. can be controlled. Because biogenic SOA is substantially enhanced by controllable emissions, classification of SOA as biogenic or anthropogenic based solely on VOC origin is not sufficient to describe the controllable fraction

Predicting the Effects of Nanoscale Cerium Additives in Diesel Fuel on Regional-Scale Air Quality

Diesel vehicles are a major source of air pollutant emissions. Fuel additives containing nanoparticulate cerium (nCe) are currently being used in some diesel vehicles to improve fuel efficiency. These fuel additives also reduce fine particulate matter (PM_2.5) emissions and alter the emissions of carbon monoxide (CO), nitrogen oxides (NO_<i>x</i>), and hydrocarbon (HC) species, including several hazardous air pollutants (HAPs). To predict their net effect on regional air quality, we review the emissions literature and develop a multipollutant inventory for a hypothetical scenario in which nCe additives are used in all on-road and nonroad diesel vehicles. We apply the Community Multiscale Air Quality (CMAQ) model to a domain covering the eastern U.S. for a summer and a winter period. Model calculations suggest modest decreases of average PM_2.5 concentrations and relatively larger decreases in particulate elemental carbon. The nCe additives also have an effect on 8 h maximum ozone in summer. Variable effects on HAPs are predicted. The total U.S. emissions of fine-particulate cerium are estimated to increase 25-fold and result in elevated levels of airborne cerium (up to 22 ng/m³), which might adversely impact human health and the environment

Identifying Waste Burning Plumes Using High-Resolution Satellite Imagery and Machine Learning: A Case Study in the Maldives

A rapid increase in municipal solid waste generation has far outpaced resources to manage waste in many developing countries, resulting in the burning of trash in designated landfills or public places, the release of harmful air pollutants, and exposure of nearby populations. While some governments have recently banned open burning at municipal facilities, monitoring the success of mitigation strategies has been challenging due to the lack of adequate air pollution monitoring methodologies. To address this, we have developed a machine learning approach that utilizes high-resolution (3 m/pixel) satellite imagery and applied the methodology to detect plumes of smoke from waste burning on Thilafushi in the Maldives. We employed an image classification and semantic segmentation model based on a pretrained convolutional neural network to identify and locate plumes within images. Our approach achieved an average intersection over union (overlap) of 0.70 between visually identified plumes and the machine learning output as well as a pixel-level classification accuracy of 96.3% on our holdout testing data. Our results demonstrate the potential of machine learning models in detecting plumes from sources where measurements are not available, including wildfires, coal-fired power plants, and industrial plumes, as well as in tracking the progress of mitigation strategies

Evaluation of an Air Quality Model for the Size and Composition of Source-Oriented Particle Classes

Air quality model predictions of the size and composition of atmospheric particle classes are evaluated by comparison with aerosol time-of-flight mass spectrometry (ATOFMS) measurements of single-particle size and composition at Long Beach and Riverside, CA, during September 1996. The air quality model tracks the physical diameter, chemical composition, and atmospheric concentration of thousands of representative particles from different emissions classes as they are transported from sources to receptors while undergoing atmospheric chemical reactions. In the model, each representative particle interacts with a common gas phase but otherwise evolves separately from all other particles. The model calculations yield an aerosol population, in which particles of a given size may exhibit different chemical compositions. ATOFMS data are adjusted according to the known particle detection efficiencies of the ATOFMS instruments, and model predictions are modified to simulate the chemical sensitivities and compositional detection limits of the ATOFMS instruments. This permits a direct, semiquantitative comparison between the air quality model predictions and the single-particle ATOFMS measurements to be made. The air quality model accurately predicts the fraction of atmospheric particles containing sodium, ammonium, nitrate, carbon, and mineral dust, across all particle sizes measured by ATOFMS at the Long Beach site, and in the coarse particle size range (Da ≥ 1.8 μm) at the Riverside site. Given that this model evaluation is very likely the most stringent test of any aerosol air quality model to date, the model predictions show impressive agreement with the single-particle ATOFMS measurements

A Field-Based Approach for Determining ATOFMS Instrument Sensitivities to Ammonium and Nitrate

Aerosol time-of-flight mass spectrometry (ATOFMS) instruments measure the size and chemical composition of individual particles in real-time. ATOFMS chemical composition measurements are difficult to quantify, largely because the instrument sensitivities to different chemical species in mixed ambient aerosols are unknown. In this paper, we develop a field-based approach for determining ATOFMS instrument sensitivities to ammonium and nitrate in size-segregated atmospheric aerosols, using tandem ATOFMS-impactor sampling. ATOFMS measurements are compared with collocated impactor measurements taken at Riverside, CA, in September 1996, August 1997, and October 1997. This is the first comparison of ion signal intensities from a single-particle instrument with quantitative measurements of atmospheric aerosol chemical composition. The comparison reveals that ATOFMS instrument sensitivities to both and decline with increasing particle aerodynamic diameter over a 0.32−1.8 μm calibration range. The stability of this particle size dependence is tested over the broad range of fine particle concentrations (PM1.8 = 17.6 ± 2.0−127.8 ± 1.8 μg m-3), ambient temperatures (23−35 °C), and relative humidity conditions (21−69%), encountered during the field experiments. This paper describes a potentially generalizable methodology for increasing the temporal and size resolution of atmospheric aerosol chemical composition measurements, using tandem ATOFMS-impactor sampling

Emissions Inventory of PM_2.5 Trace Elements across the United States

This paper presents the first National Emissions Inventory (NEI) of fine particulate matter (PM2.5) that includes the full suite of PM2.5 trace elements (atomic number >10) measured at ambient monitoring sites across the U.S. PM2.5 emissions in the NEI were organized and aggregated into a set of 84 source categories for which chemical speciation profiles are available (e.g., Unpaved Road Dust, Agricultural Soil, Wildfires). Emission estimates for ten metals classified as Hazardous Air Pollutants (HAP) were refined using data from a recent HAP NEI. All emissions were spatially gridded, and U.S. emissions maps for dozens of trace elements (e.g., Fe, Ti) are presented for the first time. Nationally, the trace elements emitted in the highest quantities are silicon (3.8 × 105 ton/yr), aluminum (1.4 × 105 ton/yr), and calcium (1.3 × 105 ton/yr). Our chemical characterization of the PM2.5 inventory shows that most of the previously unspeciated emissions are comprised of crustal elements, potassium, sodium, chlorine, and metal-bound oxygen. This work also reveals that the largest PM2.5 sources lacking specific speciation data are off-road diesel-powered mobile equipment, road construction dust, marine vessels, gasoline-powered boats, and railroad locomotives