From COVID-19 to future electrification: Assessing traffic impacts on air quality by a machine-learning model.

The large fluctuations in traffic during the COVID-19 pandemic provide an unparalleled opportunity to assess vehicle emission control efficacy. Here we develop a random-forest regression model, based on the large volume of real-time observational data during COVID-19, to predict surface-level NO2, O3, and fine particle concentration in the Los Angeles megacity. Our model exhibits high fidelity in reproducing pollutant concentrations in the Los Angeles Basin and identifies major factors controlling each species. During the strictest lockdown period, traffic reduction led to decreases in NO2 and particulate matter with aerodynamic diameters <2.5 µm by -30.1% and -17.5%, respectively, but a 5.7% increase in O3 Heavy-duty truck emissions contribute primarily to these variations. Future traffic-emission controls are estimated to impose similar effects as observed during the COVID-19 lockdown, but with smaller magnitude. Vehicular electrification will achieve further alleviation of NO2 levels.

Light absorption properties and radiative effects of primary organic aerosol emissions.

Organic aerosols (OAs) in the atmosphere affect Earth's energy budget by not only scattering but also absorbing solar radiation due to the presence of the so-called "brown carbon" (BrC) component. However, the absorptivities of OAs are not represented or are poorly represented in current climate and chemical transport models. In this study, we provide a method to constrain the BrC absorptivity at the emission inventory level using recent laboratory and field observations. We review available measurements of the light-absorbing primary OA (POA), and quantify the wavelength-dependent imaginary refractive indices (kOA, the fundamental optical parameter determining the particle's absorptivity) and their uncertainties for the bulk POA emitted from biomass/biofuel, lignite, propane, and oil combustion sources. In particular, we parametrize the kOA of biomass/biofuel combustion sources as a function of the black carbon (BC)-to-OA ratio, indicating that the absorptive properties of POA depend strongly on burning conditions. The derived fuel-type-based kOA profiles are incorporated into a global carbonaceous aerosol emission inventory, and the integrated kOA values of sectoral and total POA emissions are presented. Results of a simple radiative transfer model show that the POA absorptivity warms the atmosphere significantly and leads to ∼27% reduction in the amount of the net global average POA cooling compared to results from the nonabsorbing assumption.

Toward verifying fossil fuel CO2 emissions with the CMAQ model: motivation, model description and initial simulation.

UNLABELLED: Motivated by the question of whether and how a state-of-the-art regional chemical transport model (CTM) can facilitate characterization of CO2 spatiotemporal variability and verify CO2 fossil-fuel emissions, we for the first time applied the Community Multiscale Air Quality (CMAQ) model to simulate CO2. This paper presents methods, input data, and initial results for CO2 simulation using CMAQ over the contiguous United States in October 2007. Modeling experiments have been performed to understand the roles of fossil-fuel emissions, biosphere-atmosphere exchange, and meteorology in regulating the spatial distribution of CO2 near the surface over the contiguous United States. Three sets of net ecosystem exchange (NEE) fluxes were used as input to assess the impact of uncertainty of NEE on CO2 concentrations simulated by CMAQ. Observational data from six tall tower sites across the country were used to evaluate model performance. In particular, at the Boulder Atmospheric Observatory (BAO), a tall tower site that receives urban emissions from Denver CO, the CMAQ model using hourly varying, high-resolution CO2 fossil-fuel emissions from the Vulcan inventory and Carbon Tracker optimized NEE reproduced the observed diurnal profile of CO2 reasonably well but with a low bias in the early morning. The spatial distribution of CO2 was found to correlate with NO(x), SO2, and CO, because of their similar fossil-fuel emission sources and common transport processes. These initial results from CMAQ demonstrate the potential of using a regional CTM to help interpret CO2 observations and understand CO2 variability in space and time. The ability to simulate a full suite of air pollutants in CMAQ will also facilitate investigations of their use as tracers for CO2 source attribution. This work serves as a proof of concept and the foundation for more comprehensive examinations of CO2 spatiotemporal variability and various uncertainties in the future. IMPLICATIONS: Atmospheric CO2 has long been modeled and studied on continental to global scales to understand the global carbon cycle. This work demonstrates the potential of modeling and studying CO2 variability at fine spatiotemporal scales with CMAQ, which has been applied extensively, to study traditionally regulated air pollutants. The abundant observational records of these air pollutants and successful experience in studying and reducing their emissions may be useful for verifying CO2 emissions. Although there remains much more to further investigate, this work opens up a discussion on whether and how to study CO2 as an air pollutant.

VOCs and Odor Episodes near the German-Czech Border: Social Participation, Chemical Analyses and Health Risk Assessment.

People living on both sides of the German-Czech border are subject to episodes of odor air pollution. A joint German-Czech air sampling and risk assessment project was established to identify the substances responsible and their sources. Twenty-four volunteer study participants, 14 from the NW Czech Republic and 10 from Germany (Saxony) reported odors and collected canister samples during sampling periods in winter 2017 and 2018 and autumn 2018. Canister samples and passive samplers were analyzed for volatile organic compounds (VOCs) and passive samplers were analyzed for VOCs and carbonyls. OAVs (Odor Activity Values) and back trajectories were calculated with the aim of identifying the odor sources. Calculated OAVs were in excellent agreement with perceived smells close to an oil processing plant. Odorants identified in fifty canister samples during odor episodes and carbonyl measurements close to the edible oil processing plant were used for health evaluation. Odors reported by participants in Saxony frequently differed from those reported by participants in the Czech Republic. This suggests that certain sources of odor lying on either side of the border only affect that side and not the other with similar considerations regarding health effects. VOCs, including carbonyls, were also sampled at two relatively remote locations during winters of 2017 and 2018; two main sources of odorous compounds were identified at these sites. Analysis of samples taken at sampling sites shows that VOC air pollution and, to a lesser extent carbonyl pollution, originate from both industrial and local sources. Even though levels of sampled substances were not associated with acute effects at any site, long-term exposures to selected compounds could be cause for concern for carcinogenicity at some sites. Odors in Seiffen were associated with carcinogenic compounds in can samples. Although not necessarily representative of long-term exposures to the compounds studied, results such as these suggest that further study is needed to better quantify long-term exposure to potentially harmful compounds, and to either confirm or deny the existence of substantive health risk.

Associations between ozone and morbidity using the Spatial Synoptic Classification system.

BACKGROUND: Synoptic circulation patterns (large-scale tropospheric motion systems) affect air pollution and, potentially, air-pollution-morbidity associations. We evaluated the effect of synoptic circulation patterns (air masses) on the association between ozone and hospital admissions for asthma and myocardial infarction (MI) among adults in North Carolina. METHODS: Daily surface meteorology data (including precipitation, wind speed, and dew point) for five selected cities in North Carolina were obtained from the U.S. EPA Air Quality System (AQS), which were in turn based on data from the National Climatic Data Center of the National Oceanic and Atmospheric Administration. We used the Spatial Synoptic Classification system to classify each day of the 9-year period from 1996 through 2004 into one of seven different air mass types: dry polar, dry moderate, dry tropical, moist polar, moist moderate, moist tropical, or transitional. Daily 24-hour maximum 1-hour ambient concentrations of ozone were obtained from the AQS. Asthma and MI hospital admissions data for the 9-year period were obtained from the North Carolina Department of Health and Human Services. Generalized linear models were used to assess the association of the hospitalizations with ozone concentrations and specific air mass types, using pollutant lags of 0 to 5 days. We examined the effect across cities on days with the same air mass type. In all models we adjusted for dew point and day-of-the-week effects related to hospital admissions. RESULTS: Ozone was associated with asthma under dry tropical (1- to 5-day lags), transitional (3- and 4-day lags), and extreme moist tropical (0-day lag) air masses. Ozone was associated with MI only under the extreme moist tropical (5-day lag) air masses. CONCLUSIONS: Elevated ozone levels are associated with dry tropical, dry moderate, and moist tropical air masses, with the highest ozone levels being associated with the dry tropical air mass. Certain synoptic circulation patterns/air masses in conjunction with ambient ozone levels were associated with increased asthma and MI hospitalizations.

Sulfur monoxide dimer chemistry as a possible source of polysulfur in the upper atmosphere of Venus.

The abundance of SO dimers (SO)2 in the upper atmosphere of Venus and their implications for the enigmatic ultraviolet absorption has been investigated in several studies over the past few years. However, the photochemistry of sulfur species in the upper atmosphere of Venus is still not well understood and the identity of the missing ultraviolet absorber(s) remains unknown. Here we update an existing photochemical model of Venus' upper atmosphere by including the photochemistry of SO dimers. Although the spectral absorption profile of SO dimers fits the unknown absorber, their abundance is found to be too low for them to contribute significantly to the absorption. It is more likely that their photolysis and/or reaction products could contribute more substantively. Reactions of SO dimers are found to be important sources of S2O, and possibly higher order SnO species and polysulfur, Sn. All of these species absorb in the critical ultraviolet region and are expected to be found in both the aerosol and gas phase. indicating that in-situ high resolution aerosol mass spectrometry might be a useful technique for identifying the ultraviolet absorber on Venus.

Potential ozone impacts of excess NO2 emissions from diesel particulate filters for on- and off-road diesel engines.

This study considers potential impacts of increased use of diesel oxidation catalysts (DOCs) and catalyzed diesel particulate filters (DPFs) on ozone formation in the Dallas/ Fort Worth (DFW) area. There is concern that excess nitrogen dioxide (NO2) emissions from vehicles equipped with these devices could increase ambient ozone levels. The approach involved developing two scenarios for use of these devices, quantifying excess NO2 emissions in each scenario, and using a photochemical model to estimate the resulting ozone changes. In the "maximum penetration" scenario, DOC/DPF devices in a 2009 fleet of heavy-duty on-road trucks, school buses, and construction equipment were significantly increased by accelerating turnover of these vehicles and equipment to models that would require DOCs/DPFs. In the "realistic" scenario, current fractional usage of these devices was assessed for 2009. For both scenarios, excess NO2 emissions from DOCs/DPFs were estimated using U.S. Environmental Protection Agency's MOBILE6 and NONROAD emissions inventory modeling tools. The emissions analyses were used to adjust the DFW photochemical modeling emissions inventories and the Comprehensive Air Quality Model with extensions air quality model was rerun for the DFW area to determine the impact of these two scenarios on ozone formation. The maximum penetration scenario, which showed an overall reduction in oxides of nitrogen (NO(x)) because of the accelerated turnover of equipment to cleaner models, resulted in a net decrease in daily maximum 8-hr ozone of 4-5 parts per billion (ppb) despite the increase in NO2 emissions. The realistic scenario resulted in a small increase in daily maximum 8-hr ozone of less than 1 ppb for the DFW area. It was concluded that the excess NO2 emissions from DOC/DPF devices result in very small ozone impacts, particularly for the realistic scenario, in the DFW area. There are noticeable decreases in ozone for the maximum penetration scenario because NO(x) reductions associated with DOC/DPFs (i.e., accelerated fleet turnover) exert more influence than excess NO2.

Deciphering the role of radical precursors during the Second Texas Air Quality Study.

The Texas Environmental Research Consortium (TERC) funded significant components of the Second Texas Air Quality Study (TexAQS II), including the TexAQS II Radical and Aerosol Measurement Project (TRAMP) and instrumented flights by a Piper Aztec aircraft. These experiments called attention to the role of short-lived radical sources such as formaldehyde (HCHO) and nitrous acid (HONO) in increasing ozone productivity. TRAMP instruments recorded daytime HCHO pulses as large as 32 parts per billion (ppb) originating from upwind industrial activities in the Houston Ship Channel, where in situ surface monitors detected HCHO peaks as large as 52 ppb. Moreover, Ship Channel petrochemical flares were observed to produce plumes of apparent primary HCHO. In one such combustion plume that was depleted of ozone by large emissions of oxides of nitrogen (NOx), the Piper Aztec measured a ratio of HCHO to carbon monoxide (CO) 3 times that of mobile sources. HCHO from uncounted primary sources or ozonolysis of underestimated olefin emissions could significantly increase ozone productivity in Houston beyond previous expectations. Simulations with the CAMx model show that additional emissions of HCHO from industrial flares or mobile sources can increase peak ozone in Houston by up to 30 ppb. Other findings from TexAQS II include significant concentrations of HONO throughout the day, well in excess of current air quality model predictions, with large nocturnal vertical gradients indicating a surface or near-surface source of HONO, and large concentrations of nighttime radicals (approximately30 parts per trillion [ppt] HO2). HONO may be formed heterogeneously on urban canopy or particulate matter surfaces and may be enhanced by organic aerosol of industrial or motor vehicular origin, such as through conversion of nitric acid (HNO3). Additional HONO sources may increase daytime ozone by more than 10 ppb. Improving the representation of primary and secondary HCHO and HONO in air quality models could enhance the simulated effectiveness of control strategies.

A Measurement-Model Fusion Approach for Improved Wet Deposition Maps and Trends.

Air quality models provide spatial fields of wet deposition (WD) and dry deposition that explicitly account for the transport and transformation of emissions from thousands of sources. However, many sources of uncertainty in the air quality model including errors in emissions and meteorological inputs (particularly precipitation) and incomplete descriptions of the chemical and physical processes governing deposition can lead to bias and error in the simulation of WD. We present an approach to bias correct Community Multiscale Air Quality model output over the contiguous United States using observation-based gridded precipitation data generated by the Parameter-elevation Regressions on Independent Slopes Model and WD observations at the National Atmospheric Deposition Program National Trends Network sites. A cross-validation analysis shows that the adjusted annual accumulated WD for NO3 -, NH4 +, and SO4 2- from 2002 to 2012 has less bias and higher correlation with observed values than the base model output without adjustment. Temporal trends in observed WD are captured well by the adjusted model simulations across the entire contiguous United States. Consistent with previous trend analyses, WD NO3 - and SO4 2- are shown to decrease during this period in the eastern half of the United States, particularly in the Northeast, while remaining nearly constant in the West. Trends in WD of NH4 + are more spatially and temporally heterogeneous, with some positive trends in the Great Plains and Central Valley of CA and slightly negative trends in the south.

Spatial variability of PM2.5 in urban areas in the United States.

Data from the U.S. Environmental Protection Agency's Aerometric Information Retrieval System (now known as the Air Quality System) database for 1999 and 2000 have been used to characterize the spatial variability of concentrations of particulate matter with aerodynamic diameter < or = 2.5 microg (PM2.5) in 27 urban areas across the United States. Different measures were used to quantify the degree of uniformity of PM2.5 concentrations in the urban areas characterized. It was observed that PM2.5 concentrations varied to differing degrees in the urban areas examined. Analyses of several urban areas in the Southeast indicated high correlations between site pairs and spatial uniformity in concentration fields. Considerable spatial variation was found in other regions, especially in the West. Even within urban areas in which all site pairs were highly correlated, a variable degree of heterogeneity in PM2.5 concentrations was found. Thus, even though concentrations at pairs of sites were highly correlated, their concentrations were not necessarily the same. These findings indicate that the potential for exposure misclassification errors in time-series epidemiologic studies exists.

Regional variations in particulate matter composition and the ability of monitoring data to represent population exposures.

Epidemiologic studies have demonstrated that relative risks for mortality associated with ambient particulate matter (PM) concentrations vary with location in the U.S. with larger associations in both magnitude and strength observed in the East compared to the West. Two factors potentially contributing to the regional heterogeneity in PM-mortality associations observed are regional variations in PM composition and the ability of a single PM concentration estimate to represent the community-average exposure for an entire study area, which may lead to regional differences in exposure error. Variations in PM composition and the proportion of the population living in proximity to ambient monitors, an indicator of potential exposure error, are examined for the 20 most populated and 10 mid-size study areas included in the National Morbidity, Mortality and Air Pollution Study (NMMAPS). Clear differences in PM and in the proportion of the population living in proximity to ambient monitors are found for some of these cities. Differences in these exposure parameters may be interpreted more reasonably in terms of north-south differences compared to east-west differences, and may need to be considered when conducting future epidemiologic studies that aim to examine the factors that influence the regional variability in PM-mortality associations.

Source apportionment and spatial distributions of coarse particles during the Regional Air Pollution Study.

To identify the coarse particle sources and to estimate the variability in their contributions to coarse particle mass (CPM) concentrations across the St. Louis metropolitan area, positive matrix factorization (PMF) was applied to historic ambient coarse particle compositional data from 10 Regional Air Pollution Study/Regional Air Monitoring System (RAPS/RAMS) monitoring sites in St. Louis. Coarse particles in this study had aerodynamic sizes between 2.4 and 20 microm. The sources were qualitatively identified, and the source contributions were quantitatively estimated. Nine sources were identified for 8 of the 10 sampling sites (except rural sites 122 and 124) including soil, cement kiln/quarry, iron and steel, motor vehicle, incinerator, pigment plant primary/secondary lead smelter, zinc smelter, and copper production, respectively. At site 122, five sources were identified as soil, cement kiln/quarry, motor vehicle, incinerator, and zinc smelter. At site 124, six sources were identified as soil, cement kiln/quarry, motor vehicle, incinerator, primary/secondary lead smelter, and zinc smelter. Soil was the largest coarse particle source across the study area (6.15 microg/m3, 29.3%). Cement kiln/quarry, iron and steel, and motorvehicle sources were the other large contributions to the coarse particles mass (5.27 microg/m3, 25.1%; 3.53 microg/m3, 16.8%; 2.72 microg/m3, 12.9%). The results of this study suggest there can be significant potential for exposure misclassification in time-series epidemiologic studies when regressing health outcomes against source contributions if they were to be estimated at a single central monitoring site.

Spatial variability of fine particle mass, components, and source contributions during the regional air pollution study in St. Louis.

Community time-series epidemiology typically uses either 24-hour integrated particulate matter (PM) concentrations averaged across several monitors in a city or data obtained at a central monitoring site to relate PM concentrations to human health effects. If the day-to-day variations in 24-hour integrated concentrations differ substantially across an urban area (i.e., daily measurements at monitors at different locations are not highly correlated), then there is a significant potential for exposure misclassification in community time-series epidemiology. If the annual average concentration differs across an urban area, then there is a potential for exposure misclassification in epidemiologic studies that use annual averages (or multi-year averages) as an index of exposure across different cities. The spatial variability in PM2.5 (particulate matter < or = 2.5 microm in aerodynamic diameter), its elemental components, and the contributions from each source category at 10 monitoring sites in St. Louis, Missouri were characterized using the ambient PM2.5 compositional data set of the Regional Air Pollution Study (RAPS) based on the Regional Air Monitoring System (RAMS) conducted between 1975 and 1977. Positive matrix factorization (PMF) was applied to each ambient PM2.5 compositional data set to estimate the contributions from the source categories. The spatial distributions of components and source contributions to PM2.5 at the 10 sites were characterized using Pearson correlation coefficients and coefficients of divergence. Sulfur and PM2.5 are highly correlated elements between all of the site pairs Although the secondary sulfate is the most highly correlated and shows the smallest spatial variability, there is a factor of 1.7 difference in secondary sulfate contributions between the highest and lowest site on average. Motor vehicles represent the next most highly correlated source component. However, there is a factor of 3.6 difference in motor vehicle contributions between the highest and lowest sites. The contributions from point source categories are much more variable. For example, the contributions from incinerators show a difference of a factor of 12.5 between the sites with the lowest and highest contributions. This study demonstrates that the spatial distributions of elemental components of PM2.5 and contributions from source categories can be highly heterogeneous within a given airshed and thus, there is the potential for exposure misclassification when a limited number of ambient PM monitors are used to represent population-average ambient exposures.

Approaches to monitoring of air pollutants and evaluation of health impact produced by biomass burning

It highlights key types of health-related information useful in evaluating the potential health impacts of air pollution resulting from biomass fires, and provides an overview of general air monitoring approaches and preferred methods for monitoring ambient concentrations of selected key air pollutants useful in evaluating the effects of biomass fire emissions. Document in pdf format; Acrobat Reader required.