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    <title>Transport Research International Documentation (TRID)</title>
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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
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    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>Transport Research International Documentation (TRID)</title>
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      <title>Clean Air in Cities: Impact of the Layout of Buildings in Urban Areas on Pedestrian Exposure to Ultrafine Particles From Traffic</title>
      <link>https://trid.trb.org/View/2606530</link>
      <description><![CDATA[Traffic-related pollutant concentrations are typically much higher in near-roadway microenvironments, and pedestrian and resident exposures to air pollutants can be substantially increased by the short periods of time spent on and near roadways. The design of the built environment plays a critical role in the dispersion of pollutants at street level; after normalizing for traffic, differences of a factor of ~5 have been observed between urban neighborhoods with different built environment characteristics. The authors examined the effects of different built environment designs on the concentrations of street-level ultrafine particles (UFP) at the scale of several blocks using the Quick Urban and Industrial Complex (QUIC) numerical modeling system. The model was capable of reasonably reproducing the complex ensemble mean 3D air flow patterns and pollutant concentrations in urban areas at fine spatial scale. The authors evaluated the effects of several built environment designs, changing building heights and spacing while holding total built environment volumes constant. The authors found that ground-level open space reduces street-level pollutant concentrations. Holding volume/surface area constant, tall buildings clustered together with larger open spaces between buildings resulted in substantially lower pollutant concentrations than buildings in rows. Buildings arranged on a ‘checkerboard’ grid with smaller contiguous open spaces, a configuration with some open space on one of the sides of the roadway at all locations, resulted in the lowest average concentrations for almost all wind directions. Rows usually prohibit mixing for perpendicular and oblique wind directions, even when there are large spaces between them, and clustered buildings have some areas where buildings border both sides of the roadways, inhibiting mixing. The model results suggest that pollutant concentrations drop off rapidly with height in the first 10 m or so above the roadways. In addition, the simulated vertical concentration profiles show a moderate elevated peak at the roof levels of the shorter buildings within the area. Model limitations and suggestions both for urban design are both discussed.]]></description>
      <pubDate>Mon, 08 Dec 2025 15:19:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/2606530</guid>
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    <item>
      <title>Evaluating AERMOD With Measurements From a Major U.S. Airport Located on a Shoreline</title>
      <link>https://trid.trb.org/View/2210250</link>
      <description><![CDATA[The impact of airport operations on air quality is a key public health concern for the population surrounding an airport. Air pollution regulations require the assessment of this impact using dispersion models. Modeling dispersion of aircraft-related sources poses challenges because of the large number and variety of airport sources, which include aircraft, ground operation vehicles, and traffic in and out of the airport, most of which are mobile. Emissions from aircraft sources are transient, buoyant, and occur at different heights from the ground. Quantifying these emissions as well as modeling the governing processes is challenging. An added complexity occurs when the airport is situated near a shoreline where meteorological conditions are far from being spatially uniform. These features that characterize the dispersion of airport emissions are being incorporated into the AERMOD model in this paper. This paper examines the impact of shoreline meteorology and urban effects on dispersion by comparing model estimates of SO₂ with corresponding measurements made during a field study conducted at the Los Angeles International Airport (LAX) during winter and summer of 2012 at all the four core sites (Air Quality site (AQ), Community North (CN), Community East (CE), and Community South (CS)) as a part of the LAX Air Quality Source Apportionment Study (AQSAS). The authors modified outputs from AERMOD's meteorological preprocessor AERMET to account for (1) the formation of the internal boundary layer that is formed when stable air from the ocean flows onto the warmer land surface of the airport, and (2) urban roughness effects on winds flowing from Los Angeles, east of the airport. Simulations with unmodified AERMET yielded concentrations that were substantially higher than the concentrations at AQ and CS and much lower than those at CN and CE. Model performance improved when AERMOD used the modified meteorology. The fraction of model estimates within a factor of two of the observations improved from 34 to 84% at the CS site and CE site, by up to 79% in winter season whereas in summer, FAC2 values are almost comparable at all the sites. The ratio of robust highest modeled values to measured values improved from 7.72 to 2.53 and 4.92 to 1.94 in winter and summer seasons respectively.]]></description>
      <pubDate>Mon, 17 Jul 2023 09:13:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2210250</guid>
    </item>
    <item>
      <title>Are average speed emission functions scale-free?</title>
      <link>https://trid.trb.org/View/1899473</link>
      <description><![CDATA[Although emission models have been designed using vehicle data over driving cycles of a few minutes, they are often applied at large scale to estimate total emission (inventories). In between, there is a range of scales in use in traffic and environmental studies (road sections, sub-areas, etc.). Coupling a traffic microsimulation with COPERT emission factors at different scales reveals scaling biases. We compare network fuel consumption (FC) and nitrogen oxide (NOx) emissions resulting from emission calculations based on different spatial decompositions. The results show that for an area of Paris covering 3 km2, the differences due to the aggregation scale for emissions range from 5 to 17% depending on the pollutant, spatial partitioning and traffic conditions. These discrepancies can be reduced using a distance-weighted mean speed, which is not a scale-consistent definition of mean travel speed. They can almost be cancelled by using a correction term derived analytically in this paper, thus consistency can be guaranteed between emissions assessed at different scales. Finally, a case study shows that it is possible to evaluate FC and NOx emissions on a large-scale network from a sample of traffic data (probes), and obtain the corrective term to be applied to remove scaling bias. The most critical step is the accurate estimation of the total travel distance. The gaps were successfully reduced to a maximum of 8% in congestion for a penetration rate of about 20%.]]></description>
      <pubDate>Tue, 21 Dec 2021 16:46:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/1899473</guid>
    </item>
    <item>
      <title>Aviation NOx-Induced CH₄ Effect: Fixed Mixing Ratio Boundary Conditions versus Flux Boundary Conditions</title>
      <link>https://trid.trb.org/View/1875796</link>
      <description><![CDATA[Atmospheric chemistry-climate models are often used to calculate the effect of aviation nitrogen oxides (NOx) emissions on atmospheric ozone (O₃) and methane (CH₄). Due to the long (∼10 yr) atmospheric lifetime of methane, model simulations must be run for long time periods, typically for more than 40 simulation years, to reach steady-state if using CH₄ emission fluxes. Because of the computational expense of such long runs, studies have traditionally used specified CH₄ mixing ratio lower boundary conditions (BCs) and then applied a simple parameterization based on the change in CH₄ lifetime between the control and NOx-perturbed simulations to estimate the change in CH₄ concentration induced by NOx emissions. In this parameterization a feedback factor (typically a value of 1.4) is used to account for the feedback of CH₄ concentrations on its lifetime. Modeling studies comparing simulations using CH₄ surface fluxes and fixed mixing ratio BCs are used to examine the validity of this parameterization. The latest version of the Community Earth System Model (CESM), with the CAM5 atmospheric model, was used for this study. Aviation NOx emissions for 2006 were obtained from the AEDT (Aviation Environmental Design Tool) global commercial aircraft emissions. Results show a 31.4 ppb change in CH₄ concentration when estimated using the parameterization and a 1.4 feedback factor, and a 28.9 ppb change when the concentration was directly calculated in the CH₄ flux simulations. The model calculated value for CH₄ feedback on its own lifetime agrees well with the 1.4 feedback factor. Systematic comparisons between the separate runs indicated that the parameterization technique overestimates the CH₄ concentration by 8.6%. Therefore, it is concluded that the estimation technique is good to within ∼10% and decreases the computational requirements in the simulations by nearly a factor of 8.]]></description>
      <pubDate>Mon, 20 Sep 2021 14:52:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/1875796</guid>
    </item>
    <item>
      <title>The Impact of NOx Emissions From Lightning on the Production of Aviation-Induced Ozone</title>
      <link>https://trid.trb.org/View/1872014</link>
      <description><![CDATA[Due to the non-linear nature of ozone production in the troposphere, ozone production as a function of aviation nitrogen oxide (NOx = NO + NO₂) emissions varies based on the background NOx levels. Of the several different sources of background NOx in the atmosphere, NOx from lightning (LNOx) contributes a substantial amount of NOx to the upper troposphere and has an effect on the ozone production efficiency, even though the LNOx source still has significant uncertainty. In this study, CAM5, the atmospheric component of the Community Earth System Model (CESM), was used to study the effect of uncertainties in NOx emissions from lightning on the production of aviation-induced ozone. Three sensitivity studies were analyzed with varying LNOx values of 3.7, 5, and 7.4 TgN/yr, representing the best current range estimates for LNOx. Results show a decrease in the aviation-induced ozone production rate and radiative forcing (RF) as LNOx increases. This is tied to the decreased ozone production under NOx saturated conditions. The ozone production per unit of NOx emission from lightning ranges from 2.38 TgO₃/TgN for the case with 3.7 TgN from lightning to 0.97 TgO₃/TgN for the case with 7.4 TgN from lightning. Similarly, the O₃ RF decreases from 43.9 mW/m² for the 3.7 TgN/yr case to 34.3 mW/m² for 7.4 TgN/yr case. Understanding the current sensitivity of aviation-induced ozone production to the LNOx strength is important for reducing the uncertainty in ozone production from aviation NOx emissions.]]></description>
      <pubDate>Mon, 30 Aug 2021 14:46:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/1872014</guid>
    </item>
    <item>
      <title>Aviation Impact on Air Quality Present Day and Mid-century Simulated in the Community Atmosphere Model (CAM)</title>
      <link>https://trid.trb.org/View/1872012</link>
      <description><![CDATA[The projected increase in global air traffic raises concerns about the potential impact aviation emissions have on climate and air quality. Previous studies have shown that aircraft non-landing and take-off (non-LTO) emissions (emitted above 1 km) can affect surface air quality by increasing concentrations of ozone (O₃) and fine particles (PM₂.₅). Here, the authors examine the global impacts of aviation non-LTO emissions on surface air quality for present day and mid-century (2050) using the Community Atmosphere Model with Chemistry, version 5 (CAM5). An important update in CAM5 over previous versions is the modal aerosol module (MAM), which provides a more accurate aerosol representation. Additionally the authors evaluate of the aviation impact at mid-century with two fuel scenarios, a fossil fuel (SC1) and a biofuel (Alt). Monthly-mean results from the present day simulations show a northern hemisphere (NH) mean surface O₃ increase of 1.3 ppb (2.7% of the background) and a NH maximum surface PM₂.₅ increase of 1.4 μg/m³ in January. Mid-century simulations show slightly greater surface O₃ increases (mean of 1.9 ppb (4.2%) for both scenarios) and greater PM₂.₅ increases (maximum of 3.5 μg/m³ for SC1 and 2.2 μg/m³ for Alt). While these perturbations do not significantly increase the frequency of extreme air quality events (increase is less than 1.5%), they do contribute to the background concentrations of O₃ and PM₂.₅, making it easier for urban areas to surpass these standards.]]></description>
      <pubDate>Mon, 30 Aug 2021 14:46:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/1872012</guid>
    </item>
    <item>
      <title>Air Pollution and Early Deaths in the United States. Part II: Attribution of PM2.5 Exposure to Emissions Species, Time, Location and Sector</title>
      <link>https://trid.trb.org/View/1748802</link>
      <description><![CDATA[Combustion emissions constitute the largest source of anthropogenic emissions in the US, and lead to the degradation of air quality and human health. In Part I the authors computed the population fine particulate matter (PM₂.₅) exposure and number of early deaths caused by emissions from six major sectors: electric power generation, industry, commercial and residential activities, road transportation, marine transportation and rail transportation. In Part II the authors attribute exposure and early deaths to sectors, emissions species, time of emission, and location of emission. The authors apply a long-term adjoint sensitivity analysis and calculate the four dimensional sensitivities (time and space) of PM₂.₅ exposure with respect to each emissions species. Epidemiological evidence is used to relate increased population exposure to premature mortalities. This is the first regional application of the adjoint sensitivity analysis method to characterize long-term air pollution exposure. (A global scale application has been undertaken related to intercontinental pollution.) The authors find that for the electric power generation sector 75% of the attributable PM₂.₅ exposure is due to SO₂ emissions, and 80% of the annual impacts are attributed to emissions from April to September. In the road transportation sector, 29% of PM₂.₅ exposure is due to NOₓ emissions and 33% is from ammonia (NH₃), which is a result of emissions after-treatment technologies. The authors estimate that the benefit of reducing NH₃ emissions from road transportation is ∼20 times that of NOₓ per unit mass. 75% of the road transportation ammonia impacts occur during the months October to March. The authors publicly release the sensitivity matrices computed, noting their potential use as a rapid air quality policy assessment tool.]]></description>
      <pubDate>Wed, 25 Nov 2020 10:11:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/1748802</guid>
    </item>
    <item>
      <title>Evolution of Sectoral Emissions and Contributions to Mortality From Particulate Matter Exposure in the Asia-Pacific Region Between 2010 and 2015</title>
      <link>https://trid.trb.org/View/1748778</link>
      <description><![CDATA[Anthropogenic emissions in the Asia-Pacific region have changed rapidly in recent years due to increasing industrialization and mobility, as well as the implementation of emission abatement controls. These changes are likely to have affected the region's burden of premature mortalities associated with exposure to fine particulate matter (PM₂₋₅). However, the contribution of each sector and effectiveness of different policy measures has not yet been quantified. As such, this study estimates changes in regional anthropogenic emissions by sector between 2010 and 2015. These changes are combined with an existing high-resolution emissions inventory to estimate emissions for the year 2015. Using a chemistry-transport model, we then estimate (i) the total contribution of each sector to premature mortality in 2015; and (ii) the effects of changes in each sector's contribution to total PM₂₋₅-driven premature mortalities between 2010 and 2015. We estimate that globally, 2,030,000 (95% CI: 1,770,000 to 2,280,000) PM₂₋₅-driven premature mortalities are attributable to anthropogenic emissions in 2015 in Asia-Pacific with the top three sources being the agricultural, industrial, and residential sectors. Between 2010 and 2015, excluding the effects of changes in population distribution or other social factors, sustained growth in economic activity led to an estimated 99,000 (95% CI: 81,000 to 130,000) additional premature mortalities annually, primarily across India, Indonesia, and Bangladesh. Simultaneously, changes such as electrification of railroads and newly-introduced abatement measures, e.g. China's Action Plan on the Prevention and Control of Air Pollution, region-wide adoption of Euro IV/V/VI-compliant road vehicles, and implementation of fuel quality standards, resulted in an estimated total reduction of 76,000 (95% CI: 65,000 to 100,000) annual premature mortalities, primarily across East Asia, including China and Japan. These opposing drivers add to a net change of an additional 22,000 (95% CI: 12,000 to 33,000) PM₂₋₅-driven annual premature mortalities between 2010 and 2015 associated with anthropogenic combustion emissions in Asia-Pacific.]]></description>
      <pubDate>Tue, 24 Nov 2020 15:58:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/1748778</guid>
    </item>
    <item>
      <title>CO₂-equivalent emissions from European passenger vehicles in the years 1995–2015 based on real-world use: Assessing the climate benefit of the European “diesel boom”</title>
      <link>https://trid.trb.org/View/1704702</link>
      <description><![CDATA[A comprehensive overview is provided evaluating direct real-world CO₂ emissions of both diesel and petrol cars newly registered in Europe between 1995 and 2015. Before 2011, European diesel cars emitted less CO₂ per kilometre than petrol cars, but since then there is no appreciable difference in per-km CO₂ emissions between diesel and petrol cars. Real-world CO₂ emissions of diesel cars have not declined appreciably since 2001, while the CO₂ emissions of petrol cars have been stagnant since 2012. When adding black carbon related CO₂-equivalents, such as from diesel cars without particulate filters, diesel cars were discovered to have had much higher climate relevant emissions until the year 2001 when compared to petrol cars. From 2001 to 2015 CO₂-equivalent emissions from new diesel cars and petrol cars were hardly distinguishable. Lifetime use phase CO₂-equivalent emissions of all European passenger vehicles were modelled for 1995–2015 based on three scenarios: the historic case, another scenario freezing percentages of diesel cars at the low levels from the early 1990s (thus avoiding the observed “boom” in new diesel registrations), and an advanced mitigation scenario based on high proportions of petrol hybrid cars and cars burning gaseous fuels. The difference in CO₂-equivalent emissions between the historical case and the scenario avoiding the diesel car boom is only 0.4%. The advanced mitigation scenario would have been able to achieve a 3.4% reduction in total CO₂-equivalent emissions over the same time frame. The European diesel car boom appears to have been ineffective at reducing climate-warming emissions from the European transport sector.]]></description>
      <pubDate>Mon, 15 Jun 2020 10:44:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/1704702</guid>
    </item>
    <item>
      <title>Real-world European driving cycles, for measuring pollutant emissions from high- and low-powered cars</title>
      <link>https://trid.trb.org/View/1681551</link>
      <description><![CDATA[Pollutant emissions from cars are usually measured on a test bench using driving cycles. However, the use of one unique set of driving cycles to test all cars can be seen as a weak point of emission estimation, as vehicles could conceivably be tested differently depending on their performance levels and usage characteristics. A specific study was then conducted to characterize driving conditions and vehicle usage as a function of vehicle categories, as well as to derive driving cycles specially designed for high- and low-powered cars which have significantly different driving conditions. Pollutant emissions were measured on a sample of 30 passenger cars, using on the one hand the three real-world ARTEMIS driving cycles (urban, rural road and motorway), representative of European driving, and on the other hand specific driving cycles. The comparison of the resulting aggregated emissions demonstrates that the usual test procedure (i.e. with a unique set of driving cycles) can lead to strong differences in emissions, particularly for the most recent vehicle categories.]]></description>
      <pubDate>Tue, 28 Jan 2020 16:13:57 GMT</pubDate>
      <guid>https://trid.trb.org/View/1681551</guid>
    </item>
    <item>
      <title>A tunnel study to validate motor vehicle emission prediction software in Australia</title>
      <link>https://trid.trb.org/View/1632616</link>
      <description><![CDATA[]]></description>
      <pubDate>Tue, 25 Jun 2019 09:23:05 GMT</pubDate>
      <guid>https://trid.trb.org/View/1632616</guid>
    </item>
    <item>
      <title>Air pollution abatement performances of green infrastructure in open road and built-up street canyon environments: a review</title>
      <link>https://trid.trb.org/View/1471105</link>
      <description><![CDATA[]]></description>
      <pubDate>Fri, 16 Jun 2017 11:30:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/1471105</guid>
    </item>
    <item>
      <title>Effect of measurement protocol on organic aerosol measurements of exhaust emissions from gasoline and diesel vehicles</title>
      <link>https://trid.trb.org/View/1460311</link>
      <description><![CDATA[Exhaust emissions of semi-volatile organic compounds (SVOC) from passenger vehicles are usually estimated only for the particle phase via the total particulate matter measurements. However, they also need to be estimated for the gas phase, as they are semi-volatile. To better estimate SVOC emission factors of passenger vehicles, a measurement campaign using a chassis dynamometer was conducted with different instruments: (1) a constant volume sampling (CVS) system in which emissions were diluted with filtered air and sampling was performed on filters and polyurethane foams (PUF) and (2) a Dekati Fine Particle Sampler (FPS) in which emissions were diluted with purified air and sampled with on-line instruments (PTR-ToF-MS, HR-ToF-AMS, MAAP, CPC). Significant differences in the concentrations of organic carbon (OC) measured by the instruments are observed. The differences can be explained by sampling artefacts, differences between (1) the time elapsed during sampling (in the case of filter and PUF sampling) and (2) the time elapsed from emission to measurement (in the case of on-line instruments), which vary from a few seconds to 15 min, and by the different dilution factors. To relate elapsed times and measured concentrations of OC, the condensation of SVOC between the gas and particle phases is simulated with a dynamic aerosol model. The simulation results allow us to understand the relation between elapsed times and concentrations in the gas and particle phases. They indicate that the characteristic times to reach thermodynamic equilibrium between gas and particle phases may be as long as 8 min. Therefore, if the elapsed time is less than this characteristic time to reach equilibrium, gasphase SVOC are not at equilibriumwith the particle phase and a larger fraction of emitted SVOC will be in the gas phase than estimated by equilibrium theory, leading to an underestimation of emitted OC if only the particle phase is considered or if the gas-phase SVOC are estimated by equilibrium theory. Current European emission inventories for passenger cars do not yet estimate gas-phase SVOC emissions, although they may represent 60% of total emitted SVOC (gas þ particle phases).]]></description>
      <pubDate>Fri, 17 Mar 2017 10:36:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/1460311</guid>
    </item>
    <item>
      <title>PAH, BTEX, carbonyl compound, black-carbon, NO2 and ultrafine particle dynamometer bench emissions for Euro 4 and Euro 5 diesel and gasoline passenger cars</title>
      <link>https://trid.trb.org/View/1460310</link>
      <description><![CDATA[Although implementing Diesel particulate filters (DPF) and other novel aftertreatment technologies makes it possible to achieve significant reductions in particle mass emissions, it may induce the release of ultrafine particles and emissions of many other unregulated compounds. This paper focuses on (i) ultrafine particles, black carbon, BTEX, PAH, carbonyl compounds, and NO2 emissions from Euro 4 and Euro 5 Diesel and gasoline passenger cars, (ii) the influence of driving conditions (e.g., cold start, urban, rural and motorway conditions), and (iii) the impact of additive and catalysed DPF devices on vehicle emissions. Chassis dynamometer tests were conducted on four Euro 5 vehicles and two Euro 4 vehicles: gasoline vehicles with and without direct injection system and Diesel vehicles equipped with additive and catalysed particulate filters. The results showed that compared to hot-start cycles, cold-start urban cycles increased all pollutant emissions by a factor of two. The sole exception was NO2, which was reduced by a factor of 1.3e6. Particulate and black carbon emissions from the gasoline engines were significantly higher than those from the Diesel engines equipped with DPF. Moreover, the catalysed DPF emitted about 3e10 times more carbonyl compounds and particles than additive DPF, respectively, during urban driving cycles, while the additive DPF vehicles emitted 2 and 5 times more BTEX and carbonyl compounds during motorway driving cycles. Regarding particle number distribution, the motorway driving cycle induced the emission of particles smaller in diameter (mode at 15 nm) than the urban cold-start cycle (mode at 80e100 nm). The results showed a clear positive correlation between particle, black carbon, and BTEX emissions, and a negative correlation between particles and NO2.]]></description>
      <pubDate>Fri, 17 Mar 2017 10:36:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/1460310</guid>
    </item>
    <item>
      <title>Impact of aftertreatment device and driving conditions on black carbon, particle and NO2 emissions for Euro 5 DPF vehicles</title>
      <link>https://trid.trb.org/View/1395460</link>
      <description><![CDATA[Vehicle pollution constitutes the major source of gaseous and particulate air pollution in urban areas. To limit particle mass emissions, and comply with European standards, diesel particle filters (DPF) have been largely used. However, certain after treatment devices increase ultrafine particle emission, and also affect NO2 emission. The first results show the importance of the cold start for the recent vehicles and that the frequency and duration of the DPF regeneration is a key point for particle emissions. In this poster two types of DPF are studied: catalyzed DPF and additive DPF.]]></description>
      <pubDate>Tue, 19 Jan 2016 11:56:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/1395460</guid>
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