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.

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.

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.

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.

Trends in onroad transportation energy and emissions.

Globally, 1.3 billion on-road vehicles consume 79 quadrillion BTU of energy, mostly gasoline and diesel fuels, emit 5.7 gigatonnes of CO2, and emit other pollutants to which approximately 200,000 annual premature deaths are attributed. Improved vehicle energy efficiency and emission controls have helped offset growth in vehicle activity. New technologies are diffusing into the vehicle fleet in response to fuel efficiency and emission standards. Empirical assessment of vehicle emissions is challenging because of myriad fuels and technologies, intervehicle variability, multiple emission processes, variability in operating conditions, and varying capabilities of measurement methods. Fuel economy and emissions regulations have been effective in reducing total emissions of key pollutants. Real-world fuel use and emissions are consistent with official values in the United States but not in Europe or countries that adopt European standards. Portable emission measurements systems, which uncovered a recent emissions cheating scandal, have a key role in regulatory programs to ensure conformity between "real driving emissions" and emission standards. The global vehicle fleet will experience tremendous growth, especially in Asia. Although existing data and modeling tools are useful, they are often based on convenience samples, small sample sizes, large variability, and unquantified uncertainty. Vehicles emit precursors to several important secondary pollutants, including ozone and secondary organic aerosols, which requires a multipollutant emissions and air quality management strategy. Gasoline and diesel are likely to persist as key energy sources to mid-century. Adoption of electric vehicles is not a panacea with regard to greenhouse gas emissions unless coupled with policies to change the power generation mix. Depending on how they are actually implemented and used, autonomous vehicles could lead to very large reductions or increases in energy consumption. Numerous other trends are addressed with regard to technology, emissions controls, vehicle operations, emission measurements, impacts on exposure, and impacts on public health. IMPLICATIONS: Without specific policies to the contrary, fossil fuels are likely to continue to be the major source of on-road vehicle energy consumption. Fuel economy and emission standards are generally effective in achieving reductions per unit of vehicle activity. However, the number of vehicles and miles traveled will increase. Total energy use and emissions depend on factors such as fuels, technologies, land use, demographics, economics, road design, vehicle operation, societal values, and others that affect demand for transportation, mode choice, energy use, and emissions. Thus, there are many opportunities to influence future trends in vehicle energy use and emissions.

Factors affecting variability in PM2.5 exposure concentrations in a metro system.

The objectives of this study were to: (1) evaluate PM2.5 inflow to metro train cabins when doors open at stations; (2) assess the spatial and temporal variability in PM2.5 exposure concentration; and (3) quantify the relationship between in-cabin concentration versus outdoor and non-ambient PM2.5. We measured in-cabin PM2.5 concentrations using portable monitors at the door-side and center of a train cabin simultaneously on a Hong Kong metro line. In addition, platform and in-cabin pollutant concentrations near a train door were simultaneously measured. Short-term spikes in PM2.5 concentrations typically occur near train doors when doors open, related to inflow of ambient air aboveground and tunnel air underground. In-cabin PM2.5 exposure concentrations are typically lower away from the doors when the doors open. PM2.5 concentrations inside train cabins and on station platform operating above-ground are more influenced, compared to underground, by outdoor PM2.5. Moreover, non-ambient sources contribute approximately 50% of train in-cabin and station platform PM2.5 concentrations during underground operation. The results help more accurately quantify commuting PM2.5 exposure on a metro system, and can be used to improve population-based exposure simulation models.

Comparison of real-world and certification emission rates for light duty gasoline vehicles.

U.S. light duty vehicles are subject to the U.S. Environmental Protection Agency (EPA) emission standards. Emission compliance is determined by certification testing of selected emissions from representative vehicles on standard driving cycles using chassis dynamometers. Test results are also used in many emission inventories. The dynamometer based emission rates are adjusted to provide the certification levels (CL), which must be lower than the standards for compliance. Although standard driving cycles are based on specific observations of real-world driving, they are not necessarily real-world representative. A systematic comparison of the real-world emission rates of U.S. light duty gasoline vehicles (LDGVs) versus CL, and emission standards has not been previously reported. The purpose of this work is to compare regulatory limits (both CLs and emission standards) and the real-world emissions of LDGVs. The sensitivity of the comparisons to cold start emission was assessed. Portable Emission Measurement Systems (PEMS) were used to measure hot stabilized exhaust emissions of 122 LDGVs on a specified 110 mile test route. Cold start emissions were measured with PEMS for a selected vehicle sample of 32 vehicles. Emissions were measured for carbon dioxide (CO2), carbon monoxide (CO), hydrocarbons (HC) and nitrogen oxides (NOx). For each vehicle, a Vehicle Specific Power (VSP) modal emission rate model was developed. The VSP modal rates were weighted by the standard driving cycles and real-world driving cycles to estimate the respective cycle average emission rates (CAERs). Measured vehicles were matched with certification test vehicles for comparison. For systematic trends in comparison, vehicles were classified into four groups based on the Tier 1 and Tier 2 emission regulation, and the vehicle type such as passenger car and passenger truck. Depending on the cycle-pollutant and the vehicle groups, hot stabilized CAERs are on average either statistically significantly higher than or significantly not different from the CLs, with the exception of CO on the US06 cycle, for which real-world rates are lower than CLs. Compared to the emission standards, hot stabilized CAERs are on average significantly lower. However, comparisons of CAERs and standards are sensitive to cold start emissions. For some combinations of pollutants and vehicle groups, cold start inclusive CAERs are higher than the corresponding CLs and as high as the standards. The CLs, which are based on standard driving cycles, tend to underestimate real-world emission rates. Therefore, emission inventory estimates using certification test results are potentially underestimated.

Real-world fuel use and gaseous emission rates for flex fuel vehicles operated on E85 versus gasoline.

Flex fuel vehicles (FFVs) typically operate on gasoline or E85, an 85%/15% volume blend of ethanol and gasoline. Differences in FFV fuel use and tailpipe emission rates are quantified for E85 versus gasoline based on real-world measurements of five FFVs with a portable emissions measurement system (PEMS), supplemented chassis dynamometer data, and estimates from the Motor Vehicle Emission Simulator (MOVES) model. Because of inter-vehicle variability, an individual FFV may have higher nitrogen oxide (NOx) or carbon monoxide (CO) emission rates on E85 versus gasoline, even though average rates are lower. Based on PEMS data, the comparison of tailpipe emission rates for E85 versus gasoline is sensitive to vehicle-specific power (VSP). For example, although CO emission rates are lower for all VSP modes, they are proportionally lowest at higher VSP. Driving cycles with high power demand are more advantageous with respect to CO emissions, but less advantageous for NOx. Chassis dynamometer data are available for 121 FFVs at 50,000 useful life miles. Based on the dynamometer data, the average difference in tailpipe emissions for E85 versus gasoline is -23% for NOx, -30% for CO, and no significant difference for hydrocarbons (HC). To account for both the fuel cycle and tailpipe emissions from the vehicle, a life cycle inventory was conducted. Although tailpipe NOx emissions are lower for E85 versus gasoline for FFVs and thus benefit areas where the vehicles operate, the life cycle NOx emissions are higher because the NOx emissions generated during fuel production are higher. The fuel production emissions take place typically in rural areas. Although there are not significant differences in the total HC emissions, there are differences in HC speciation. The net effect of lower tailpipe NOx emissions and differences in HC speciation on ozone formation should be further evaluated. IMPLICATIONS: Reported comparisons of flex fuel vehicle (FFV) tailpipe emission rates for E85 versus gasoline have been inconsistent. To date, this is the most comprehensive evaluation of available and new data. The large range of inter-vehicle variability illustrates why prior studies based on small sample sizes led to apparently contradictory findings. E85 leads to significant reductions in tailpipe nitrogen oxide (NOx) and carbon monoxide (CO) emission rates compared with gasoline, indicating a potential benefit for ozone air quality management in NOx-limited areas. The comparison of FFV tailpipe emissions between E85 and gasoline is sensitive to power demand and driving cycles.

Characterization of PM2.5 exposure concentration in transport microenvironments using portable monitors.

Recently, portable monitors have been increasingly used to quantify air pollutant concentrations at high spatiotemporal resolution. A sampling campaign was conducted to measure the fine particulate matter (PM2.5) and carbon monoxide (CO) exposure concentrations in transport microenvironments (TMEs) in Hong Kong in January and June 2015 using TSI DustTrak and Q-Trak portable monitors. The objectives were to: (1) calibrate DustTrak and Q-Trak; (2) evaluate variability between seasons and microenvironments; (3) estimate indoor/outdoor relationships; and (4) determine minimum sample size. Calibration equations, obtained through side-by-side measurement against stationary reference methods in winter and summer, were applied to correct the measured PM2.5 data set. In general, PM2.5 concentrations in all TMEs were significantly higher in winter than in summer. The mean PM2.5 concentration in winter was lower for underground sections of the Mass Transit Railway (MTR) metro system (31 µg/m3) than for other TMEs, whereas in summer TMEs had mean PM2.5 concentrations in the range of 10-15 µg/m3, with above-ground MTR train as an exception, at 23 µg/m3. PM2.5 concentrations measured in TMEs were strongly correlated with nearby air quality monitoring stations (AQMSs) measurements in winter, but in summer there was little correlation. The minimum sample size estimates varied more among TMEs in summer versus winter because of the differences in PM2.5 concentration distributions related to changes in ambient PM2.5 concentrations and ventilation practices. This study provides a feasible protocol on the calibration and application of portable monitors in TME air quality measurement and develops a method for estimating minimum sample size.

Effect of Biodiesel Fuels on Real-World Emissions of Passenger Locomotives.

Few data are available regarding the effect of biodiesel on exhaust emission rates of two-stroke engines used in many passenger locomotives. Using a portable emissions measurement system (PEMS), duty cycle average nitrogen oxides (NOx), hydrocarbons (HC), carbon monoxide (CO), particulate matter (PM), and carbon dioxide (CO2) emission rates were measured for three locomotives operating on ultra-low sulfur diesel (ULSD) and soy-based B10, B20, and B40 biodiesel blends. Measurements were conducted in the rail yard (RY) and over-the-rail (OTR) during passenger service. Compared to ULSD, B20 biodiesel had statistically significant average emission rate reductions in the RY of 58% for CO, 45% for PM, and 6% CO2 and OTR of 59% for HC, 50% for CO, 26% for PM, and 5% for CO2. The average differences in NOx emission rates for both the RY and OTR, and HC in the RY, were not statistically significant. The OTR findings typically agreed qualitatively with the RY findings; however, OTR provides a better basis for estimating the real-world impact of fuel switching. The results indicate substantial potential to reduce in-use locomotive emissions for existing older locomotives, with the exception of NOx.

Sequential Measurement of Intermodal Variability in Public Transportation PM2.5 and CO Exposure Concentrations.

A sequential measurement method is demonstrated for quantifying the variability in exposure concentration during public transportation. This method was applied in Hong Kong by measuring PM2.5 and CO concentrations along a route connecting 13 transportation-related microenvironments within 3-4 h. The study design takes into account ventilation, proximity to local sources, area-wide air quality, and meteorological conditions. Portable instruments were compacted into a backpack to facilitate measurement under crowded transportation conditions and to quantify personal exposure by sampling at nose level. The route included stops next to three roadside monitors to enable comparison of fixed site and exposure concentrations. PM2.5 exposure concentrations were correlated with the roadside monitors, despite differences in averaging time, detection method, and sampling location. Although highly correlated in temporal trend, PM2.5 concentrations varied significantly among microenvironments, with mean concentration ratios versus roadside monitor ranging from 0.5 for MTR train to 1.3 for bus terminal. Measured inter-run variability provides insight regarding the sample size needed to discriminate between microenvironments with increased statistical significance. The study results illustrate the utility of sequential measurement of microenvironments and policy-relevant insights for exposure mitigation and management.

Comparison of Over-the-Rail and Rail Yard Measurements of Diesel Locomotives.

Locomotive prime mover engine emission rates are typically measured at steady-state for discrete throttle notches using an engine dynamometer weighted by a standard duty cycle. However, this method may not represent real-world locomotive emissions. A method for in-use measurement of passenger locomotives, using a portable emissions measurement system (PEMS), was developed to estimate duty cycle average emission rates. We conducted 48 measurements of one-way trips between Raleigh and Charlotte, NC, on 7 locomotives and 18 sets of measurements in the rail yard (RY). Real-world duty cycles differed from those used for regulatory analyses, leading to statistically significant lower cycle average NOx and HC emission rates. Compared to RY measurements, notch average NOx emission rates measured over-the-rail (OTR) at the highest two notch settings were, on average, 19% lower for four locomotives. At the highest notch, OTR CO2 emission rates were, on average, 12% lower than RY rates for five locomotives. For a more accurate representation of real-world emission rates, OTR measurements are preferred. However, using steady-state notch average RY emission rates and standard duty cycles may be tolerable for some applications. OTR versus RY cycle average emission rates typically differed by less than 10%.