Identifying emissions hotspots and strategies to reduce real-world fuel use and emissions for passenger rail: A spatially resolved approach.

The objectives of this work are to model spatially resolved passenger locomotive fuel use and emission rates, locate emissions hotspots, and identify strategies to reduce trip train fuel use and emissions. Train fuel use and emission rates, speed, acceleration, track grade, and track curvature were quantified based on over-the-rail measurements, using portable emission measurement systems, for diesel and biodiesel passenger rail service on the Amtrak-operated Piedmont route. Measurements included 66 one-way trips and 12 combinations of locomotives, consists, and fuels. A locomotive power demand (LPD) based emissions model was developed based on the physics of resistive forces opposing train motion, taking into account factors such as speed, acceleration, track grade, and curvature. The model was applied to locate spatially-resolved locomotive emissions hotspots on a passenger rail route, and also identify train speed trajectories with low trip fuel use and emissions. Results show that acceleration, grade, and drag are the major resistive forces affecting LPD. Hotspot track segments have 3 to 10 times higher emission rates than non-hotspot segments. Real-world trajectories are identified that reduce trip fuel use and emissions by 13 % to 49 % compared to the average. Strategies for reducing trip fuel use and emissions include dispatching energy-efficient and low-emitting locomotives, using a 20 % blend of biodiesel, and operating on low-LPD trajectories. Implementing these strategies will not only decrease trip fuel use and emissions but reduce the number and intensity of hotspots and, thus, lowering the potential for exposure to train-generated pollution near railroad tracks. This work provides insights on reducing railroad energy use and emissions, which would lead to a more sustainable and environmental-friendly rail transportation system.

In Flight Performance of the Far Ultraviolet Instrument (FUV) on ICON.

The NASA Ionospheric Connection Explorer (ICON) was launched in October 2019 and has been observing the upper atmosphere and ionosphere to understand the sources of their strong variability, to understand the energy and momentum transfer, and to determine how the solar wind and magnetospheric effects modify the internally-driven atmosphere-space system. The Far Ultraviolet Instrument (FUV) supports these goals by observing the ultraviolet airglow in day and night, determining the atmospheric and ionospheric composition and density distribution. Based on the combination of ground calibration and flight data, this paper describes how major instrument parameters have been verified or refined since launch, how science data are collected, and how the instrument has performed over the first 3 years of the science mission. It also provides a brief summary of science results obtained so far.

Microenvironment Tracker (MicroTrac) model to estimate time-location of individuals for air pollution exposure assessments: model evaluation using smartphone data.

BACKGROUND: A critical aspect of air pollution exposure assessments is determining the time spent in various microenvironments (ME), which can have substantially different pollutant concentrations. We previously developed and evaluated a ME classification model, called Microenvironment Tracker (MicroTrac), to estimate time of day and duration spent in eight MEs (indoors and outdoors at home, work, school; inside vehicles; other locations) based on input data from global positioning system (GPS) loggers. OBJECTIVE: In this study, we extended MicroTrac and evaluated the ability of using geolocation data from smartphones to determine the time spent in the MEs. METHOD: We performed a panel study, and the MicroTrac estimates based on data from smartphones and GPS loggers were compared to 37 days of diary data across five participants. RESULTS: The MEs were correctly classified for 98.1% and 98.3% of the time spent by the participants using smartphones and GPS loggers, respectively. SIGNIFICANCE: Our study demonstrates the extended capability of using ubiquitous smartphone data with MicroTrac to help reduce time-location uncertainty in air pollution exposure models for epidemiologic and exposure field studies.

Characterizing Determinants of Near-Road Ambient Air Quality for an Urban Intersection and a Freeway Site.

INTRODUCTION: Near-road ambient air pollution concentrations that are affected by vehicle emissions are typically characterized by substantial spatial variability with respect to distance from the roadway and temporal variability based on the time of day, day of week, and season. The goal of this work is to identify variables that explain either temporal or spatial variability based on case studies for a freeway site and an urban intersection site. The key hypothesis is that dispersion modeling of near-road pollutant concentrations could be improved by adding estimates or indices for site-specific explanatory variables, particularly related to traffic. Based on case studies for a freeway site and an urban intersection site, the specific aims of this project are to (1) develop and test regression models that explain variability in traffic-related air pollutant (TRAP) ambient concentration at two near-roadway locations; (2) develop and test refined proxies for land use, traffic, emissions and dispersion; and (3) prioritize inputs according to their ability to explain variability in ambient concentrations to help focus efforts for future data collection and model development.The key pollutants that are the key focus of this work include nitrogen oxides (NOx), carbon monoxide (CO), black carbon (BC), fine particulate matter (PM2.5; PM ≤ 2.5 µm in aerodynamic diameter), ultrafine particles (UFPs; PM ≤ 0.1 µm in aerodynamic diameter), and ozone (O3). NOx, CO, and BC are tracers of vehicle emissions and dispersion. PM2.5 is influenced by vehicle table emissions and regional sources. UFPs are sensitive to primary vehicle emissions. Secondary particles can form near roadways and on regional scales, influencing both PM2.5 and UFP concentrations. O3 concentrations are influenced by interaction with NOx near the roadway. Nitrogen dioxide (NO2), CO, PM2.5, and O3 are regulated under the National Ambient Air Quality Standards (NAAQS) because of demonstrated health effects. BC and UFPs are of concern for their potential health effects. Therefore, these pollutants are the focus of this work. METHODS: The methodological approach includes case studies for which variables are identified and assesses their ability to explain either temporal or spatial variability in pollutant ambient concentrations. The case studies include one freeway location and one urban intersection. The case studies address (1) temporal variability at a fixed monitor 10 meters from a freeway; (2) downwind concentrations perpendicular to the same location; (3) variability in 24-hour average pollutant concentrations at five sites near an urban intersection; and (4) spatiotemporal variability along a walking path near that same intersection.The study boundary encompasses key factors in the continuum from vehicle emissions to near-road exposure concentrations. These factors include land use, transportation infrastructure and traffic control, vehicle mix, vehicle (traffic) flow, on-road emissions, meteorology, transport and evolution (transformation) of primary emissions, and production of secondary pollutants, and their resulting impact on measured concentrations in the near-road environment. We conducted field measurements of land use, traffic, vehicle emissions, and near-road ambient concentrations in the vicinity of two newly installed fixed-site monitors. One is a monitoring station jointly operated by the U.S. Environmental Protection Agency (U.S. EPA) and the North Carolina Department of Environmental Quality (NC DEQ) on I-40 between Airport Boulevard and I-540 in Wake County, North Carolina. The other is a fixed-site monitor for measuring PM2.5 at the North Carolina Central University (NCCU) campus on E. Lawson Street in Durham, North Carolina. We refer to these two locations as the freeway site and the urban site, respectively. We developed statistical models for the freeway and urban sites. RESULTS: We quantified land use metrics at each site, such as distances to the nearest bus stop. For the freeway site, we quantified lane-by-lane total vehicle count, heavy vehicle (HV) count, and several vehicle-activity indices that account for distance from each lane to the roadside monitor. For the urban site, we quantified vehicle counts for all 12 turning movements through the intersection. At each site, we measured microscale vehicle tailpipe emissions using a portable emission measurement system.At the freeway site, we measured the spatial gradient of NOx, BC, UFPs, and PM, quantified particle size distributions at selected distances from the roadway and assessed partitioning of particles as a function of evolving volatility. We also quantified fleet-average emission factors for several pollutants.At the urban site, we measured daily average concentrations of nitric oxide (NO), NOx, O3, and PM2.5 at five sites surrounding the intersection of interest; we also measured high resolution (1-second to 10-second averages) concentrations of O3, PM2.5, and UFPs along a pedestrian transect. At both sites, the Research LINE-source (R-LINE) dispersion model was applied to predict concentration gradients based on the physical dispersion of pollution.Statistical models were developed for each site for selected pollutants. With variables for local wind direction, heavy-vehicle index, temperature, and day type, the multiple coefficient of determination (R2) was 0.61 for hourly NOx concentrations at the freeway site. An interaction effect of the dispersion model and a real-time traffic index contributed only 24% of the response variance for NOx at the freeway site. Local wind direction, measured near the road, was typically more important than wind direction measured some distance away, and vehicle-activity metrics directly related to actual real-time traffic were important. At the urban site, variability in pollutant concentrations measured for a pedestrian walk-along route was explained primarily by real-time traffic metrics, meteorology, time of day, season, and real-world vehicle tailpipe emissions, depending on the pollutant. The regression models explained most of the variance in measured concentrations for BC, PM, UFPs, NO, and NOx at the freeway site and for UFPs and O3 at the urban site pedestrian transect. CONCLUSIONS: Among the set of candidate explanatory variables, typically only a few were needed to explain most of the variability in observed ambient concentrations. At the freeway site, the concentration gradients perpendicular to the road were influenced by dilution, season, time of day, and whether the pollutant underwent chemical or physical transformations. The explanatory variables that were useful in explaining temporal variability in measured ambient concentrations, as well as spatial variability at the urban site, were typically localized real-time traffic-volume indices and local wind direction. However, the specific set of useful explanatory variables was site, context (e.g., next to road, quadrants around an intersection, pedestrian transects), and pollutant specific. Among the most novel of the indicators, variability in real-time measured tailpipe exhaust emissions was found to help explain variability in pedestrian transect UFP concentrations. UFP particle counts were very sensitive to real-time traffic indicators at both the freeway and urban sites. Localized site-specific data on traffic and meteorology contributed to explaining variability in ambient concentrations. HV traffic influenced near-road air quality at the freeway site more so than at the urban site. The statistical models typically explained most of the observed variability but were relatively simple. The results here are site-specific and not generalizable, but they are illustrative that near-road air quality can be highly sensitive to localized real-time indicators of traffic and meteorology.

New Insights Into the Substorm Initiation Sequence From the Spatio-Temporal Development of Auroral Electrojets.

In the present study we examine three substorm events, Events 1-3, focusing on the spatio-temporal development of auroral electrojets (AEJs) before auroral breakup. In Events 1 and 2, auroral breakup was preceded by the equatorward motion of an auroral form, and the ground magnetic field changed northward and southward in the west and east of the expected equatorward flow, respectively. Provided that these magnetic disturbances were caused by local ionospheric Hall currents, this feature suggests that the equatorward flow turned both eastward and westward as it reached the equatorward part of the auroral oval. The auroral breakup took place at the eastward-turning and westward-turning branches in Events 1 and 2, respectively, and after the auroral breakup, the westward AEJ enhanced only on the same side of the flow demarcation meridian. The zonal flow divergence is considered as an ionospheric manifestation of the braking of an earthward flow burst in the near-Earth plasma sheet and subsequent dawnward and duskward turning. Therefore, in Events 1 and 2, the auroral breakup presumably mapped to the dawnward and duskward flow branches, respectively. Moreover, for Event 3, we do not find any pre-onset auroral or magnetic features that can be associated with an equatorward flow. These findings suggest that the braking of a pre-onset earthward flow burst itself is not the direct cause of substorm onset, and therefore, the wedge current system that forms at substorm onset is distinct from the one that is considered to form as a consequence of the flow braking.

Dynamic interplay between metal nanoparticles and oxide support under redox conditions.

The dynamic interactions between noble metal particles and reducible metal-oxide supports can depend on redox reactions with ambient gases. Transmission electron microscopy revealed that the strong metal-support interaction (SMSI)-induced encapsulation of platinum particles on titania observed under reducing conditions is lost once the system is exposed to a redox-reactive environment containing oxygen and hydrogen at a total pressure of ~1 bar. Destabilization of the metal-oxide interface and redox-mediated reconstructions of titania lead to particle dynamics and directed particle migration that depend on nanoparticle orientation. A static encapsulated SMSI state was reestablished when switching back to purely oxidizing conditions. This work highlights the difference between reactive and nonreactive states and demonstrates that manifestations of the metal-support interaction strongly depend on the chemical environment.

Characterizing Fuel Use and Emission Hotspots for a Diesel-Operated Passenger Rail Service.

Spatially varying diesel locomotive fuel use and emission rates (FUERs) are needed to accurately quantify local emission hotspots and their health impacts. However, existing locomotive FUER data are typically not spatially resolved or representative of real-world locomotive operation. Therefore, existing data are of limited use in quantifying the spatial variability in real-world FUERs. The objectives of this work are to quantify spatial variability in locomotive FUERs and identify factors differentiating hotspots from non-hotspots. FUERs were measured based on real-world measurements conducted for the Piedmont passenger rail service using a portable emission measurement system. FUERs were quantified based on 0.25 mile track segments on the Piedmont route. Hotspots were defined as segments in the top quintile of segment-average FUERs. On average, hotspots contributed 40-50% to trip fuel use and emissions. Hotspots were typically associated with low-to-medium speed, and high acceleration and grade. In contrast, non-hotspots were associated with high speed, and low acceleration and grade. Hotspots were typically located near populated areas and, thus, may exacerbate air pollutant exposure. The method demonstrated here can be applied to other passenger train services to assess key trends in hotspot locations and factors that explain the occurrence of hotspots.

A massive rock and ice avalanche caused the 2021 disaster at Chamoli, Indian Himalaya.

On 7 February 2021, a catastrophic mass flow descended the Ronti Gad, Rishiganga, and Dhauliganga valleys in Chamoli, Uttarakhand, India, causing widespread devastation and severely damaging two hydropower projects. More than 200 people were killed or are missing. Our analysis of satellite imagery, seismic records, numerical model results, and eyewitness videos reveals that ~27 × 106 cubic meters of rock and glacier ice collapsed from the steep north face of Ronti Peak. The rock and ice avalanche rapidly transformed into an extraordinarily large and mobile debris flow that transported boulders greater than 20 meters in diameter and scoured the valley walls up to 220 meters above the valley floor. The intersection of the hazard cascade with downvalley infrastructure resulted in a disaster, which highlights key questions about adequate monitoring and sustainable development in the Himalaya as well as other remote, high-mountain environments.

Sensitivity of light duty vehicle tailpipe emission rates from simplified portable emission measurement systems to variation in engine volumetric efficiency.

Light-duty gasoline vehicle (LDGV) tailpipe emission rates can be quantified based on pollutant concentrations measured using portable emission measurement systems (PEMS). Emission rates depend on exhaust flow. For simplified and micro-PEMS, exhaust flow is inferred from engine mass air flow (MAF) and air-to-fuel ratio. For many LDGVs, MAF is broadcast via the on-board diagnostic (OBD) interface. For some vehicles, only indirect indicators of MAF are broadcast. In such cases, MAF can be estimated using the speed-density method (SDM). The SDM requires an estimate of the engine volumetric efficiency (VE), which is the ratio of actual to theoretical MAF. VE is affected by intra-vehicle variability in the engine load and inter-vehicle variability in engine characteristics (e.g., the type of valvetrain). The suitability of SDM-based estimates of MAF in conjunction with simplified and micro-PEMS has not been adequately evaluated. Therefore, the objectives are to: (1) quantify VE accounting for intra- and inter-vehicle variability; and (2) evaluate the accuracy of SDM-based vehicle emission rate estimation approaches. Seventy-seven naturally-aspirated LDGVs were measured using PEMS. For each vehicle, VE was estimated using three approaches: (1) constant VE calibrated to actual fuel use; (2) average estimates of VE for Vehicle Specific Power modes imputed from OBD data; and (3) modeled VE using multilinear regression (MLR). The MLR models were developed based on engine load and engine characteristics. The best model was selected based on various statistical diagnostics. When engines were under load, variability in VE was most sensitive to variations in engine load. During idling, VE differed between engines depending on engine characteristics. The constant and modeled VE estimation approaches enable the accurate estimation of microscale and mesoscale emission rates, with errors typically within ±10% compared to values imputed from OBD data. Thus, accurate emission rates can be obtained from simplified and micro-PEMS. Implications: Simplified and micro portable emission measurement systems (PEMS) enable widespread measurement of vehicle exhaust emission. As a cost saving measure, they estimate exhaust flow indirectly rather than via measurement, typically based on engine mass air flow (MAF). For some vehicles, MAF is not reported by the on-board diagnostic (OBD) system but can be inferred from other reported variables and volumetric efficiency (VE). However, VE is typically proprietary. Methods demonstrated here for estimating VE enable accurate quantification of emission rates, thereby enabling use of these PEMS for policy-relevant applications such as technology assessments, trends analysis, and emissions inventories.

Combined effects of increased O3 and reduced NO2 concentrations on short-term air pollution health risks in Hong Kong.

The reduction of NOx emissions in a VOC-limited region can lead to an increase of the local O3 concentration. An evaluation of the net health effects of such pollutant changes is therefore important to ascertain whether the emission control measures effectively improve the overall protection of public health. In this study, we use a short-term health risk (added health risk or AR) model developed for the multi-pollutant air quality health index (AQHI) in Hong Kong to examine the overall health impacts of these pollutant changes. We first investigate AR changes associated with NO2 and O3 changes, followed by those associated with changes in all four AQHI pollutants (NO2, O3, SO2, and particulate matter (PM)). Our results show that for the combined health effects of NO2 and O3 changes, there is a significant reduction in AR in urban areas with dense traffic, but no statistically significant changes in other less urbanized areas. The increase in estimated AR for higher O3 concentrations is offset by a decrease in the estimated AR for lower NO2 concentrations. In areas with dense traffic, the reduction in AR as a result of decreased NO2 is substantially larger than the increase in AR associated with increased O3. When additionally accounting for the change in ambient SO2 and PM, we found a statistically significant reduction in total AR everywhere in Hong Kong. Our results show that the emission control measures resulting in NO2, SO2, and PM reductions over the past decade have effectively reduced the AR over Hong Kong, even though these control measures may have partially contributed to an increase in O3 concentrations. Hence, efforts to reduce NOx, SO2, and PM should be continued.

First ICON-FUV Nighttime NmF2 and hmF2 Comparison to Ground and Space-Based Measurements.

The Far Ultra Violet (FUV) ultraviolet imager onboard the NASA-ICON mission is dedicated to the observation and study of the ionosphere dynamics at mid and low latitudes. We compare O+ density profiles provided by the ICON FUV instrument during nighttime with electron density profiles measured by the COSMIC-2 constellation (C2) and ground-based ionosondes. Co-located simultaneous observations are compared, covering the period from November 2019 to July 2020, which produces several thousands of coincidences. Manual scaling of ionogram sequences ensures the reliability of the ionosonde profiles, while C2 data are carefully selected using an automatic quality control algorithm. Photoelectron contribution coming from the magnetically conjugated hemisphere is clearly visible in FUV data around solstices and has been filtered out from our analysis. We find that the FUV observations are consistent with the C2 and ionosonde measurements, with an average positive bias lower than 1 × 1011 e/m3. When restricting the analysis to cases having an NmF2 value larger than 5 × 1011 e/m3, FUV provides the peak electron density with a mean difference with C2 of 10%. The peak altitude, also determined from FUV observations, is found to be 15 km above that obtained from C2, and 38 km above the ionosonde value on average.

Real-world activity, fuel use, and emissions of heavy-duty compressed natural gas refuse trucks.

Over 50% of new refuse truck sales have been compressed natural gas (CNG). Compared to diesel, CNG is less expensive on diesel gallon equivalent (dge) basis. This study quantifies the real-world fuel use and tailpipe exhaust emissions from three front- and three side-loader refuse trucks, each with a spark ignition CNG engine, three-way catalyst, and similar gross weight. Measurements were made at 1 Hz using a portable emissions measurement system (PEMS). Inter-cycle and inter-vehicle variability is quantified. Effect of vehicle weight was analyzed and comparisons were made with MOVES predicted cycle average emission rates. In total, about 220,000 s of data covering 490 miles of operation were recorded. The average fuel economy was 1.9 miles per dge. On average the trucks spent 53% of time in idle, which includes trash collection activity. The average speeds were 10 mph and 5 mph, for front- and side-loader trucks, respectively. Overall, compared to side-loader trucks, front-loader trucks had 55% better fuel economy and 60% lower emission rates. Compared to diesel trucks, CNG truck cycle average NOx and PM emission rates, at 1.2 g/mile and 0.006 g/mile respectively, were substantially lower while CO and HC rates, at 29 g/mile and 6 g/mile respectively, were considerably higher. Fuel use and CO2 emissions rates increased by 10% due to increase in truck weight during trash collection, while CO emissions rates increased by up to 30%. Compared to measured values, MOVES estimated cycle average fuel use and CO2 emissions were 25% lower, CO emissions are 70% lower, and NOx emissions were 200% higher. Results from this study can be used to improve solid waste life cycle and tailpipe emission factor models and, when combined with previous studies on diesel refuse trucks, evaluate the effect on fuel use and emissions from adoption of CNG refuse trucks.

Geospatial Variation of Real-World Tailpipe Emission Rates for Light-Duty Gasoline Vehicles.

Spatial variability in real-world on-road tailpipe light-duty gasoline vehicle nitrogen oxides, hydrocarbon, carbon monoxide, and carbon dioxide emission rates, the locations of emissions hotspots, and factors that explain spatial variability are quantified. A sample of 205 vehicles were measured on four predefined round-trip study routes using Portable Emission Measurement Systems. The trips on each route were divided into segments, averaging 1/4 mile in length. Segment-average emission rates were estimated based on measured 1 Hz emission rates. Emission hotspots are defined as segments with ≥90th percentile of segment-average emission rates. The hotspots have average emission rates 2-4 times greater, depending on the pollutant, than other segments. Hotspots are of heterogeneous characteristics including road attributes and vehicle activity metrics. For example, some hotspots were on arterial roads with an upstream signalized intersection and positive road grade, whereas some hotspots were on interstates with positive grade. Vehicle activity metrics, including average vehicle specific power and relative positive acceleration, help identify the hotspots. To reliably identify a fleet-average hotspot, data are needed for at least 36-130 vehicles, depending on the pollutant.

Indoor Exposure to Ambient Particles and Its Estimation Using Fixed Site Monitors.

Ambient PM2.5 concentrations measured at fixed site monitors (FSM) are often biased with respect to exposure concentrations because of spatial variability and infiltration. Based on comparison of ambient concentrations from 14 FSMs and of exposure concentrations measured indoors and outdoors at two schools in Hong Kong for winter and summer seasons, the magnitude and sources of exposure error based on using FSMs as a surrogate for exposure are quantified. An approach for bias correcting surrogate exposure estimates from FSMs is demonstrated. The approach is based on a proximity factor (PF) that accounts for differences in spatial locations, proximity to emissions and deviation from dominant wind direction, and an infiltration factor (IF) that varies by season. The combination of the PF and IF reduce bias in mean school exposure estimates from ±90% to ±20%. Bias in exposure estimates from using FSMs as surrogates tend to be smaller for which the exposure site and FSM are aligned with wind direction, have similar sampling height, and are in close proximity. The methodology demonstrated to assess concordance between FSMs and exposure measurement sites can be applied more broadly to help reduce exposure error, which may help to interpret seasonal variations in health estimates.

An evaluation of fetal heart rate characteristics associated with neonatal encephalopathy: a case-control study.

OBJECTIVE: We sought to identify fetal heart rate (FHR) characteristics that are associated with neonatal encephalopathy (NE). DESIGN: Retrospective case-control study. SETTING: A single medical centre in Shanghai, China, 2006-2015. SAMPLE: Women delivering a singleton, non-anomalous infant at ≥36 weeks' gestation diagnosed with NE (cases, n = 109) were compared with a group of women with unaffected infants (controls, n = 233). METHODS: Two physicians blinded to the outcome independently reviewed FHR tracings during the last 30 minutes of tracing prior to delivery. FHR characteristics were compared in the two groups and multivariable logistic regression was used to adjust for confounding. MAIN OUTCOME MEASURES: Adjusted odds ratio (aOR) and 95% confidence interval (CI) for the presence of specific FHR categories and characteristics. RESULTS: Category II FHR tracings were observed in 89% of women prior to delivery and were not independently associated with NE. Notably, a category III FHR was observed in 17.4% of women in the NE group compared with 0.9% of women in the control group (aOR 44.99, 95% CI 7.23-279.97). Bradycardia, minimal/absent variability, late decelerations and prolonged decelerations were independently associated with NE, whereas accelerations were protective. Similar findings were found when the cases were limited to NE with arterial cord pH <7.1 and in a subgroup analysis of women with category II tracings. CONCLUSIONS: Category III tracings, while infrequent, are not uncommon prior to delivery among fetuses who develop NE. In contrast, most FHR tracings are category II prior to delivery; however, individual FHR characteristics within this category are associated with NE. FUNDING: This research was supported by the Interdisciplinary Programme of Shanghai Jiao Tong University. TWEETABLE ABSTRACT: Category III tracings are not uncommon prior to delivery among fetuses who develop neonatal encephalopathy.