Journal: Atmospheric environment (Oxford, England : 1994)
Global aviation operations contribute to anthropogenic climate change via a complex set of processes that lead to a net surface warming. Of importance are aviation emissions of carbon dioxide (CO2), nitrogen oxides (NOx), water vapor, soot and sulfate aerosols, and increased cloudiness due to contrail formation. Aviation grew strongly over the past decades (1960-2018) in terms of activity, with revenue passenger kilometers increasing from 109 to 8269 billion km yr-1, and in terms of climate change impacts, with CO2 emissions increasing by a factor of 6.8-1034 Tg CO2 yr-1. Over the period 2013-2018, the growth rates in both terms show a marked increase. Here, we present a new comprehensive and quantitative approach for evaluating aviation climate forcing terms. Both radiative forcing (RF) and effective radiative forcing (ERF) terms and their sums are calculated for the years 2000-2018. Contrail cirrus, consisting of linear contrails and the cirrus cloudiness arising from them, yields the largest positive net (warming) ERF term followed by CO2 and NOx emissions. The formation and emission of sulfate aerosol yields a negative (cooling) term. The mean contrail cirrus ERF/RF ratio of 0.42 indicates that contrail cirrus is less effective in surface warming than other terms. For 2018 the net aviation ERF is +100.9 mW (mW) m-2 (5-95% likelihood range of (55, 145)) with major contributions from contrail cirrus (57.4 mW m-2), CO2 (34.3 mW m-2), and NOx (17.5 mW m-2). Non-CO2 terms sum to yield a net positive (warming) ERF that accounts for more than half (66%) of the aviation net ERF in 2018. Using normalization to aviation fuel use, the contribution of global aviation in 2011 was calculated to be 3.5 (4.0, 3.4) % of the net anthropogenic ERF of 2290 (1130, 3330) mW m-2. Uncertainty distributions (5%, 95%) show that non-CO2 forcing terms contribute about 8 times more than CO2 to the uncertainty in the aviation net ERF in 2018. The best estimates of the ERFs from aviation aerosol-cloud interactions for soot and sulfate remain undetermined. CO2-warming-equivalent emissions based on global warming potentials (GWP* method) indicate that aviation emissions are currently warming the climate at approximately three times the rate of that associated with aviation CO2 emissions alone. CO2 and NOx aviation emissions and cloud effects remain a continued focus of anthropogenic climate change research and policy discussions.
We estimate future wildfire activity over the western United States during the mid-21(st) century (2046-2065), based on results from 15 climate models following the A1B scenario. We develop fire prediction models by regressing meteorological variables from the current and previous years together with fire indexes onto observed regional area burned. The regressions explain 0.25-0.60 of the variance in observed annual area burned during 1980-2004, depending on the ecoregion. We also parameterize daily area burned with temperature, precipitation, and relative humidity. This approach explains ~0.5 of the variance in observed area burned over forest ecoregions but shows no predictive capability in the semi-arid regions of Nevada and California. By applying the meteorological fields from 15 climate models to our fire prediction models, we quantify the robustness of our wildfire projections at mid-century. We calculate increases of 24-124% in area burned using regressions and 63-169% with the parameterization. Our projections are most robust in the southwestern desert, where all GCMs predict significant (p<0.05) meteorological changes. For forested ecoregions, more GCMs predict significant increases in future area burned with the parameterization than with the regressions, because the latter approach is sensitive to hydrological variables that show large inter-model variability in the climate projections. The parameterization predicts that the fire season lengthens by 23 days in the warmer and drier climate at mid-century. Using a chemical transport model, we find that wildfire emissions will increase summertime surface organic carbon aerosol over the western United States by 46-70% and black carbon by 20-27% at midcentury, relative to the present day. The pollution is most enhanced during extreme episodes: above the 84(th) percentile of concentrations, OC increases by ~90% and BC by ~50%, while visibility decreases from 130 km to 100 km in 32 Federal Class 1 areas in Rocky Mountains Forest.
The Iowa City Landfill in eastern Iowa, United States, experienced a fire lasting 18 days in 2012, in which a drainage layer of over 1 million shredded tires burned, generating smoke that impacted the surrounding metropolitan area of 130,000 people. This emergency required air monitoring, risk assessment, dispersion modeling, and public notification. This paper quantifies the impact of the fire on local air quality and proposes a monitoring approach and an Air Quality Index (AQI) for use in future tire fires and other urban fires. Individual fire pollutants are ranked for acute and cancer relative risks using hazard ratios, with the highest acute hazard ratios attributed to SO2, particulate matter, and aldehydes. Using a dispersion model in conjunction with the new AQI, we estimate that smoke concentrations reached unhealthy outdoor levels for sensitive groups out to distances of 3.1 km and 18 km at 24-h and 1-h average times, respectively. Modeled and measured concentrations of PM2.5 from smoke and other compounds such as VOCs and benzo[a]pyrene are presented at a range of distances and averaging times, and the corresponding cancer risks are discussed. Through reflection on the air quality response to the event, consideration of cancer and acute risks, and comparison to other tire fires, we recommend that all landfills with shredded tire liners plan for hazmat fire emergencies. A companion paper presents emission factors and detailed smoke characterization.
The availability of low-cost monitors marketed for use in homes has increased rapidly over the past few years due to the advancement of sensing technologies, increased awareness of urban pollution, and the rise of citizen science. The user-friendly packages can make them appealing for use in research grade indoor exposure assessments, but a rigorous scientific evaluation has not been conducted for many monitors on the open market, which leads to uncertainty about the validity of the data. Furthermore, many previous sensor studies were conducted for a relatively short period of time, which may not capture the changes this type of instrument may exhibit over time (known as sensor aging). We evaluated three monitors (AirVisual Pro, Speck, and AirThinx) in an occupied, non-smoking residence over a 12-month period in order to assess the sensors, the built-in calibrations, and the need for additional data to achieve high accuracy for long deployments. Two units of each type of monitor were evaluated in order to assess the precision between units, and a personal DataRAM (pDR-1200) with a filter was placed in the home for about 20% of the sampling period (e.g., about a week each month) to evaluate the accuracy over time. The average PM2.5 mass concentration from the periods of colocation with the pDR were 5.31 μg/m3 for the gravimetric-corrected pDR (hereafter pDR-corrected), 5.11 and 5.03 μg/m3 for the AirVisual Pro units, 13.58 and 22.68 μg/m3 for the Speck units, and 7.56 and 7.57 μg/m3 for the AirThinx units. The AirVisual Pros exhibited the best accuracy compared to the filter at about 86%, which was slightly better than the nephelometric component of the pDR compared to the filter weight (84%). The accuracies of the Speck (-174 and -405%) and AirThinx (42 and 40%) monitors were much lower. When the 1-minute averaged PM2.5 mass concentrations were categorized by air quality index (AQI), the pDR-corrected matched the AirVisual Pro, Speck, and AirThinx bins about 97, 40, and 87% of the time, respectively. The Pearson correlation coefficients (R2) between the unit pairs and the pDR were 0.90/0.90, 0.50/0.27, and 0.92/0.93 for the AirVisual Pro, Speck, and AirThinx units, respectively. The R2 between units of the same type were 0.99, 0.17, and 1.00 for the AirVisual Pro, Speck, and AirThinx, respectively. All of the monitors could achieve better accuracy by adding filter corrections and post-processing to correct for known biases in addition to the manufacturer’s correction routine. Monthly calibrations yielded the highest accuracies, but nearly as high of accuracies could be achieved with only one or two calibrations for the Air Visual Pro and the AirThinx for many applications. In general, this type of new low-cost monitor shows exciting potential for use in scientific research. However, only one of the three monitors exhibited high accuracy (within 20% of the true mass concentration) without any post processing or additional measurements, so an evaluation of each monitor is essential before the data can be used to confidently evaluate residential exposures.
A photochemical model platform for Hawaii, Puerto Rico, and Virgin Islands predicting O3, PM2.5, and regional haze would be useful to support assessments relevant for the National Ambient Air Quality Standards (NAAQS), Regional Haze Rule, and the Prevention of Significant Deterioration (PSD) program. These areas have not traditionally been modeled with photochemical transport models, but a reasonable representation of meteorology, emissions (natural and anthropogenic), chemistry, and deposition could support air quality management decisions in these areas. Here, a prognostic meteorological model (Weather Research and Forecasting) and photochemical transport (Community Multiscale Air Quality) model were applied for the entire year of 2016 at 27, 9, and 3 km grid resolution for areas covering the Hawaiian Islands and Puerto Rico/Virgin Islands. Model predictions were compared against surface and upper air meteorological and chemical measurements available in both areas. The vertical gradient of temperature, humidity, and winds in the troposphere was well represented. Surface layer meteorological model performance was spatially variable, but temperature tended to be underestimated in Hawaii. Chemically speciated daily average PM2.5 was generally well characterized by the modeling system at urban and rural monitors in Hawaii and Puerto Rico/Virgin Islands. Model performance was notably impacted by the wildfire emission methodology. Model performance was mixed for hourly SO2, NO2, PM2.5, and CO and was often related to how well local emissions sources were characterized. SO2 predictions were much lower than measurements at monitors near active volcanos on Hawaii, which was expected since volcanic emissions were not included in these model simulations. Further research is needed to assess emission inventory representation of these areas and how microscale meteorology influenced by the complex land-water and terrain interfaces impacts higher time resolution performance.
Black carbon (BC) is an important contributor to global particulate matter emissions. BC is associated with adverse health effects, and an important short-lived climate pollutant. Here, we describe a low cost method of analysis that utilizes images of PTFE filters taken with a digital camera to estimate BC content on filters. This method is compared with two existing optical methods for analyzing BC (Smokestain Reflectance and Hybrid Integrating Plate and Sphere System) as well as the standard chemical analysis method for determining elemental carbon (Thermal-Optical Reflectance). In comparisons of aerosol generated under controlled conditions (using an inverted diffusion flame burner to cover a range of mass loading and reflectance levels) (N=12) and in field samples collected from residential solid fuel combustion in China and India (N=50), the image-based method was found to correlate well (normalized RMSE <10% for all comparisons) with existing methods. A correlational analysis of field samples between the optical methods and Fourier-transform infrared spectroscopy indicated that the same functional groups were predominantly responsible for light attenuation in each optical method. This method offers reduced equipment cost, rapid analysis time, and is available at no cost, which may facilitate more measurement of BC where PM2.5 mass concentrations are already measured, especially in low income countries or other sampling efforts with limited resources.
Sulfate plays an important role in atmospheric haze in China, which has received considerable attention in recent years. Various types of parameterization methods and heterogeneous oxidation rates of SO2 have been used in previous studies. However, properly representing heterogeneous sulfate formation in air quality models remains a big challenge. In this study, we quantified the heterogeneous oxidation reaction using experimental results that approximate the haze conditions in China. Firstly, a series of experiments were conducted to investigate the heterogeneous uptake of SO2 with different relative humidity (RH) levels and the presence of NH3 and NO2 on natural dust surfaces. Then the uptake coefficients for heterogeneous oxidation of SO2 to sulfate at different RH under NH3 and NO2coexistence were parameterized based on the experimental results and implemented in the Community Multiscale Air Quality modeling system (CMAQ). Simulation results suggested that this new parameterization improved model performance by 6.6% in the simulation of wintertime sulfate concentrations for Beijing. The simulated maximum growth rate of SO42- during a heavy pollution period increased from 0.97 μg m-3 h-1 to 10.11 μg m-3 h-1. The heterogeneous oxidation of SO2 in the presence of NH3 contributed up to 23% of the sulfate concentration during heavy pollution periods.
In this study, we examine the oxidative potential of airborne particulate matter (PM) in Beirut, Lebanon, as influenced by dust events originating in the Sahara and Arabian deserts. Segregated fine (< 2.5 μm) and coarse (2.5-10 μm) PM samples collected during dust events, as well as during non-dust periods, were analyzed for chemical composition, and the in vitro alveolar macrophage (AM) assay was utilized to determine the oxidative potential of both types of samples. We performed Spearman rank-order correlation analysis between individual chemical components and the oxidative potential of PM to examine the impact of the changes in PM chemical composition due to the occurrence of dust events on overall PM oxidative potential. Our findings revealed that the oxidative potential of Beirut's urban PM during non-dust periods was much higher than during dust episodes for fine PM. Our findings also indicated that tracers of tailpipe emissions (i.e., elemental (EC) and organic carbon (OC)), non-tailpipe emissions (i.e., heavy metals including Cu, Zn, As, Cd, and Pb), and secondary organic aerosols (SOA) (i.e., water-soluble organic carbon, WSOC) were significantly associated with the oxidative potential of PM during dust days and non-dust periods. However, the contribution of desert dust aerosols to Beirut's indigenous PM composition did not exacerbate its oxidative potential, as indicated by the negative correlations between the oxidative potential of PM and the concentrations of crustal elements that were enriched during the dust days. This suggests that aerosols generated during Saharan and Arabian dust events pose no additional health risk to the population due to PM-triggered reactive oxygen species formation. These results significantly contribute to our understanding of the effects of desert dust aerosols on the composition and oxidative potential of PM in several countries throughout the entire Middle East region that are impacted by dust events in the Sahara and Arabian deserts.
The development of accurate and appropriate exposure metrics for health effect studies of traffic-related air pollutants (TRAPs) remains challenging and important given that traffic has become the dominant urban exposure source and that exposure estimates can affect estimates of associated health risk. Exposure estimates obtained using dispersion models can overcome many of the limitations of monitoring data, and such estimates have been used in several recent health studies. This study examines the sensitivity of exposure estimates produced by dispersion models to meteorological, emission and traffic allocation inputs, focusing on applications to health studies examining near-road exposures to TRAP. Daily average concentrations of CO and NOx predicted using the Research Line source model (RLINE) and a spatially and temporally resolved mobile source emissions inventory are compared to ambient measurements at near-road monitoring sites in Detroit, MI, and are used to assess the potential for exposure measurement error in cohort and population-based studies. Sensitivity of exposure estimates is assessed by comparing nominal and alternative model inputs using statistical performance evaluation metrics and three sets of receptors. The analysis shows considerable sensitivity to meteorological inputs; generally the best performance was obtained using data specific to each monitoring site. An updated emission factor database provided some improvement, particularly at near-road sites, while the use of site-specific diurnal traffic allocations did not improve performance compared to simpler default profiles. Overall, this study highlights the need for appropriate inputs, especially meteorological inputs, to dispersion models aimed at estimating near-road concentrations of TRAPs. It also highlights the potential for systematic biases that might affect analyses that use concentration predictions as exposure measures in health studies, e.g., to estimate health impacts.
The dithiothreitol (DTT) assay is widely used to measure the oxidative potential of particulate matter. Results are typically presented in mass-normalized units (e.g., pmols DTT lost per minute per microgram PM) to allow for comparison among samples. Use of this unit assumes that the mass-normalized DTT response is constant and independent of the mass concentration of PM added to the DTT assay. However, based on previous work that identified non-linear DTT responses for copper and manganese, this basic assumption (that the mass-normalized DTT response is independent of the concentration of PM added to the assay) should not be true for samples where Cu and Mn contribute significantly to the DTT signal. To test this we measured the DTT response at multiple PM concentrations for eight ambient particulate samples collected at two locations in California. The results confirm that for samples with significant contributions from Cu and Mn, the mass-normalized DTT response can strongly depend on the concentration of PM added to the assay, varying by up to an order of magnitude for PM concentrations between 2 and 34 μg mL(-1). This mass dependence confounds useful interpretation of DTT assay data in samples with significant contributions from Cu and Mn, requiring additional quality control steps to check for this bias. To minimize this problem, we discuss two methods to correct the mass-normalized DTT result and we apply those methods to our samples. We find that it is possible to correct the mass-normalized DTT result, although the correction methods have some drawbacks and add uncertainty to DTT analyses. More broadly, other DTT-active species might also have non-linear concentration-responses in the assay and cause a bias. In addition, the same problem of Cu- and Mn-mediated bias in mass-normalized DTT results might affect other measures of acellular redox activity in PM and needs to be addressed.