Mobile Source Emissions Research Articles

Sensitivity analysis of the near-road dispersion model RLINE - An evaluation at Detroit, Michigan

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.

Emission modelling of light-duty vehicles in India using the revamped VSP-based MOVES model: The case study of Hyderabad

US-EPA’s MOVES is the new generation mobile source emissions model, which is built on Vehicle Specific Power based assumptions and that makes it suitable to apply anywhere in the world. In this paper, we have successfully modified MOVES model for application in Hyderabad, India. As the model’s default underlying “Federal Test Procedure-based Driving Cycle” cannot represent India’s driving conditions, we have used “Modified Indian Driving Cycle” and local light-duty vehicle-specific driving cycles to revise the emission rates. On average, based on deterioration rate comparison, the emission rates in India are 9.54, 8.37 and 9.45 times higher than the default US emission rates, for CO, HC and NOx, respectively. Based on the results analysis and background information from other studies, the faster degradation of local vehicles are due to different local operating conditions like worse traffic congestion/slower vehicle speeds and local road conditions. The project-level dispersion modeling-based validation results showed high R2 values of 0.656 and 0.648 for CO and NOx, when our newer emission rates were used. Based on available literature, this is the first attempt that tried to revamp the VSP-based emission model, MOVES, for Indian context. In this study, the real-world traffic operational data was used to replace the fundamental parameters in the MOVES model and this research can be used as a reference for MOVES application in India as it provides all the necessary details to revise the emission rates.

Relative Contribution of Different Source Categories to Ozone Exceedances in the Houston-Galveston-Brazoria Area

The goal of this study is to analyze the relative contribution of different emission source categories to ozone in the Houston-Galveston-Brazoria (HGB) area of Texas. Emission Processing System (EPS3) is used to prepare the emission files for five different source combination cases (Base case, Biogenic, Area + Biogenic, Mobile + Biogenic, Low-level Point + Biogenic). These emission files are used to perform photochemical modeling with Comprehensive Air Quality Model with Extensions (CAMx), and the results are analyzed with Visual Environment for Rich Data Interpretation (VERDI) tool. The daily maximum ozone concentrations and individual contributions of the source categories were analyzed over a 15-day study period between June 1-15, 2012, at three locations (University of Houston-Sugarland, Bayland Park and Conroe). Biogenic sources contributed an average of 49.7% ± 12.8%, 43.1% ± 12.0%, and 39.9% ± 9.28% at Sugarland, Bayland Park and Conroe sites respectively, indicating the significance of isoprene emissions from the vegetation in northeast Houston. On peak ozone days, contribution of Mobile + Biogenic source category averages about 80.1% ± 12.6%, 79.9% ± 6.50%, and 75.9% ± 10.9% at Sugarland, Bayland Park and Conroe sites respectively, indicating the dominance of mobile source NOX emissions and the necessity for regulatory focus on mobile source emissions control.

Development of a Non-Road Mobile Source Emissions Inventory for Tianjin

Based on the collected data of activity level in 2015, a non-road mobile source emissions inventory for Tianjin was developed following the recommended method from the Compilation Guide for a Vehicle Emissions Inventory (trial edition) released by the Ministry of Environmental Protection. In 2015, the emissions of CO, hydrocarbons (HC), nitrogen oxides (NOx), particulate matter (PM) and SO2 from non-road mobile sources in Tianjin were 6.15×103 t, 2.45×103 t, 2.90×104 t, 1.45×103 t, and 1.37×104 t, respectively. Marine sources were identified as the largest non-road mobile source contributor accounting for 73.66% of total emissions, followed by non-road mobile machinery (21.66%). Emissions from civil aircraft and locomotives were relatively lower. Generally, emissions from non-road mobile sources increase from March (spring) to November and decrease from the beginning and the end of the year (winter). Emissions are mainly distributed in suburban districts where there is flourishing agriculture, downtown areas with dense populations, urban construction areas, ports, railway lines, and airports. Uncertainty in the inventory results is mainly due to the lack of key data, such as local emission factors and actual activity levels.

National- to port-level inventories of shipping emissions in China

Shipping in China plays a global role, and has led worldwide maritime transportation for the last decade. However, without taking national or local port boundaries into account, it is impossible to determine the responsibility that each local authority has on emission controls, nor compare them with land-based emissions to determine the priority for controlling these emissions. In this study, we provide national- to port-level inventories for China. The results show that in 2013, the total emissions of CO, non-methane volatile organic compounds (NMVOCs), nitrogen oxides (NOx), particulate matter (PM), SO2 and CO2 were 0.0741 ± 0.0004 Tg∙yr−1, 0.0691 ± 0.0004 Tg∙yr−1, 1.91 ± 0.01 Tg∙yr−1, 0.164 ± 0.001 Tg∙yr−1, 1.30 ± 0.01 Tg∙yr−1 and 86.3 ± 0.3 Tg∙yr−1 in China, respectively. By providing high-resolution spatial distribution maps of these emissions, we identify three hotspots, centered on the Bohai Rim Area, the Yangtze River Delta and Pearl River Delta. These three hotspots account for 8% of the ocean area evaluated in this study, but contribute around 37% of total shipping emissions. Compared with on-road mobile source emissions, NOx and PM emissions from ships are equivalent to about 34% and 29% of the total mobile vehicle emissions in China. Moreover, this study provides detailed emission inventories for 24 ports in the country, which also greatly contributes to our understanding of global shipping emissions, given that eight of these ports rank within the top twenty of the port league table. Several ports in China suffer emissions 12–147 times higher than those at Los Angeles port. The ports of Ningbo-Zhou Shan, Shanghai, Hong Kong and Dalian dominate the port-level inventories, with individual emissions accounting for 28%–31%, 10%–14%, 10%–12% and 8%–14% of total emissions, respectively.

Characterizing the effect of traffic density on ambient CO, NO2, and PM2.5 in Tehran, Iran: an hourly land-use regression model

Economic development and population growth in Tehran has increased demand for goods movement and road transportation. This trend has increased mobile source emissions and citizens’ exposure, imposing severe health risks. We used the framework of Land-Use Regression models to quantify the effect of real-time traffic, within buffer distance, on hourly exposure of CO, NO2, and PM2.5. Effect of residential, industrial, and governmental land-use in addition to the effect of pollution sources, such as construction sites and airport were assessed on air pollution. Meteorological variables were also employed to adjust between day variations. R-squared of the models were 38, 27, and 38% for CO, NO2, and PM2.5, respectively. Time-activity diaries can be combined with the developed LUR models to provide detailed exposure estimation for epidemiological studies. Furthermore, capability of simulating short-term effect of different traffic scenarios enables city administrations to evaluate policies aiming to diminish pollution levels, prior to applying them.

A web-based screening tool for near-port air quality assessments

The Community model for near-PORT applications (C-PORT) is a screening tool with an intended purpose of calculating differences in annual averaged concentration patterns and relative contributions of various source categories over the spatial domain within about 10 km of the port. C-PORT can inform decision-makers and concerned citizens about local air quality due to mobile source emissions related to commercial port activities. It allows users to visualize and evaluate different planning scenarios, helping them identify the best alternatives for making long-term decisions that protect community health and sustainability. The web-based, easy-to-use interface currently includes data from 21 seaports primarily in the Southeastern U.S., and has a map-based interface based on Google Maps. The tool was developed to visualize and assess changes in air quality due to changes in emissions and/or meteorology in order to analyze development scenarios, and is not intended to support or replace any regulatory models or programs.

Air quality modeling for accountability research: Operational, dynamic, and diagnostic evaluation

Photochemical grid models play a central role in air quality regulatory frameworks, including in air pollution accountability research, which seeks to demonstrate the extent to which regulations causally impacted emissions, air quality, and public health. There is a need, however, to develop and demonstrate appropriate practices for model application and evaluation in an accountability framework. We employ a combination of traditional and novel evaluation techniques to assess four years (2001-02, 2011-12) of simulated pollutant concentrations across a decade of major emissions reductions using the Community Multiscale Air Quality (CMAQ) model. We have grouped our assessments in three categories: Operational evaluation investigates how well CMAQ captures absolute concentrations; dynamic evaluation investigates how well CMAQ captures changes in concentrations across the decade of changing emissions; diagnostic evaluation investigates how CMAQ attributes variability in concentrations and sensitivities to emissions between meteorology and emissions, and how well this attribution compares to empirical statistical models. In this application, CMAQ captures O3 and PM2.5 concentrations and change over the decade in the Eastern United States similarly to past CMAQ applications and in line with model evaluation guidance; however, some PM2.5 species—EC, OC, and sulfate in particular—exhibit high biases in various months. CMAQ-simulated PM2.5 has a high bias in winter months and low bias in the summer, mainly due to a high bias in OC during the cold months and low bias in OC and sulfate during the summer. Simulated O3 and PM2.5 changes across the decade have normalized mean bias of less than 2.5% and 17%, respectively. Detailed comparisons suggest biased EC emissions, negative wintertime SO42− sensitivities to mobile source emissions, and incomplete capture of OC chemistry in the summer and winter. Photochemical grid model-simulated O3 and PM2.5 responses to emissions and meteorologically across the decade match results from receptor-based, statistical regression models. PM2.5 sensitivities to mobile source emissions in the summertime have decreased substantially, but wintertime mobile sensitives remain largely unchanged because decreases in negative SO42− sensitivities match decreases in positive sensitivities from other constituents. Similarly, NOX emissions have led to decreased summertime O3 and increased wintertime O3 because of opposite sensitivities. Overall, results show that emissions reductions improved air quality across the domain and remain a viable option for improving future air quality.

Evaluating the Performance of a Newly Developed Carbon Capture Device for Mobile Emission Sources

In the present study, a new carbon capture device that can be carried on-board vehicles has been developed and tested. The developed device uses absorption and adsorption methods of postcombustion CO2 capture. Sodium hydroxide (NaOH) pellets and calcium hydroxide Ca(OH)2 have been used as solvents and sorbents in the device. The CO2 capture efficiency has been evaluated at a wide range of operating conditions. The results showed that the higher the concentration of the solvent, the higher the capture efficiency, i.e., w 100% capture efficiency, being obtained at full saturation of NaOH. In addition, the increase in the solution temperature increases the capture efficiency up to 50 °C. Design of the gas distributer in the device has also a notable effect on CO2 capture. It was found that solvent prepared with seawater can provide high capture efficiency over a wide range of operation, but in general, it has a lower capture efficiency than that prepared by tap water. Moreover, solvents prepared by NaOH have a superior CO2 capture efficiency over those prepared by Ca(OH)2. For the adsorption technique, a 50% NaOH and 50% Ca(OH) mixture by mass has provided the highest capture efficiency compared with each sorbent when used alone.

Damage monitoring on carbonate stones: Field exposure tests contributing to pollution impact evaluation in two Italian sites

During the last decades, many studies have been carried out on environmental monitoring in specific sites aiming at their protection and conservation; however, researches focused on the direct implications, in terms of quantitative evaluation of stone deterioration, of these monitoring actions are still scarce.This experimental work aims at monitoring the degradation processes affecting historical buildings constituted by carbonate stones. Specifically, specimens of Carrara marble and two limestones largely used in the Sicilian Baroque architecture, namely Noto and Comiso stones, were exposed outdoor in two Italian sites (Catania and Palermo), which are characterized by different environmental conditions. The field exposure test lasted two years. Both the substrates and the deposited particulate collected at the end of the exposure underwent several analytical investigations including: i) optical microscopy, mercury intrusion porosimetry, colorimetric analysis and roughness analysis, for the characterization of stone substrates before exposure; ii) ion chromatography (IC), infrared spectroscopy analysis (FT-IR) and inductively coupled plasma mass spectrometry (ICP-MS), for the characterization of the particulate deposited on stone surfaces. The obtained results highlighted blackening and yellowing processes of the carbonate substrates, chiefly on those specimens exposed in Palermo where such processes were more noticeable. Furthermore, the high concentration of sulphates and heavy metals detected (mainly in Palermo site) pointed out that both cities are mainly interested by mobile emission sources such as vehicular traffic. Additionally, analyses demonstrated the key-role of intrinsic features of the lithotypes in the degradation processes. In this regard, the higher porosity and surface roughness of Noto stone explains the major deposition of particulate with respect to the other two examined carbonate rocks.

Modeling spatial patterns of link-based PM2.5 emissions and subsequent human exposure in a large canadian metropolitan area

Understanding the relationship between mobile source emissions and subsequent human exposure is crucial for emissions control. Determining this relationship over space is fundamental to improve the accuracy and precision of public policies. In this study, we evaluated the spatial patterns of link-based PM2.5 emissions and subsequent human exposure in a large Canadian metropolitan area - the Greater Toronto and Hamilton Area (GTHA). This study was performed in three stages. First, we estimated vehicle emissions using transportation models and emission simulators. Then we evaluated human exposure to PM2.5 emissions using the Intake fraction (iF) approach. Finally, we applied geostatistical methods to assess spatial patterns of vehicle emissions and subsequent human exposure based on three prospective goals: i) classification of emissions (Global Moran's I test), ii) level of emission exposure (Getis-Ord General G test), and; iii) location of emissions (Anselin Local Moran's I). Our results showed that passenger vehicles accounted for the highest total amount of PM2.5 emissions, representing 57% emissions from all vehicles. Examining only the emissions from passenger vehicles, on average, each person in the GTHA inhales 2.58 × 10−3 ppm per day. Accounting the emissions from buses and trucks, on average each person inhales 0.12 × 10−3 and 1.91 × 10−3 ppm per day, respectively. For both PM2.5 emissions and human exposure using iF approach, our analysis showed Moran's Index greater than 0 for all vehicle categories, suggesting the presence of significant clusters (p-value <0.01) in the region. Our study indicates that air pollution control policy must be developed for the whole region, because of the spatial distribution of housing and businesses centers and inter-connectivity of transportation networks across the region, where a policy cannot simply be based on a municipal or other boundaries.

Investigating the applicability of ADAPTS activity-based model in air quality analysis

The growing recognition of negative impacts of vehicular emissions on global warming and public health is one of the main factors that led transportation policymakers to devise congestion/pollution mitigation strategies. However, in order to attain the best perception of potential results of implementing these policies, it would be essential to understand the response of people in terms of changing their travel behaviors. Activity-based travel demand models provide comprehensive platforms to simulate individuals’ travel behavior at very fine scale and examine the impact of various policies on population. This paper explores the applicability of activity-based models in evaluating the small- and large-scale environmental consequences of transportation policies. To provide a holistic and policy sensitive platform for estimating mobile source emissions, we integrate an activity-based travel demand model with a mesoscopic dynamic traffic assignment (DTA) module and a microscopic emission simulator. Accordingly, the ADAPTS model is extended with an agent-based DTA component, which allows for dynamic interaction between the demand model and network simulator. Then, the US EPA’s mobile source emission model, MOVES, is leveraged to estimate emissions produced by passenger cars. The main challenge in developing such an integrated framework is compatibility of the data among its different components. The performance of this framework is analyzed through two county- and project-level case studies in Chicago metropolitan area. In the county-level analysis, the base-year replication results show a drastic rise in emissions during peak periods, that is, around 14.8% and 27.2% of total daily NOx emission are produced during morning and evening rush hours, respectively. Further, as the project-level analysis, a capacity improvement in a critical ramp in the network is implemented in the DTA model. The simulation results show 24% reduction in VHT and 2.3% increase in VMT in 10-h simulation period in that link.

Accountability assessment of regulatory impacts on ozone and PM2.5 concentrations using statistical and deterministic pollutant sensitivities

Since the 1990 Clean Air Act Amendments, the USA has seen dramatic decreases in air pollutant emissions from a wide variety of source sectors, which have led to changes in pollutant concentrations: both up and down. Multiple stakeholders, including policy-makers, industry, and public health professionals, seek to quantify the benefits of regulations on air pollution and public health, a major focus of air pollution accountability research. Two methods, one empirical, the other based on a chemical transport model (CTM), are used to calculate the sensitivities of ozone (O3) and particulate matter with diameters less than 2.5 μ m (PM2.5) to electricity-generating unit (EGU) and mobile source emissions. Both methods are applied to determine impacts of controls on daily concentrations (which are important in assessing acute health responses to air pollution), accounting for nonlinear, meteorologically, and emission-dependent responses of pollutant concentrations. The statistical method separates contributions of nearby EGU, regional EGU, and mobile source emissions on ambient city-center concentrations. Counterfactual emissions, an estimate of emissions under a scenario where no new controls were implemented on local EGU sources after 1995, regional EGUs after 1997, and mobile sources after 1993, are combined with these sensitivities to estimate counterfactual concentrations that represent what daily air quality in Atlanta, GA would have been had controls not been implemented and other emissions-reducing actions not been taken. Regulatory programs are linked with reduced peak summertime O3, but have had little effect on annual median concentrations at the city-center monitoring site, and led to increases in pollutant levels under less photochemically-active conditions. The empirical method and the CTM method found similar relationships between ozone concentrations and ozone sensitivity to anthropogenic emissions. Compared to the counterfactual between 2010 and 2013, the number of days on which O3 (PM2.5) concentrations exceeded 60 p p b (12.0 μ g m −3) was reduced from 396 to 200 (1391 to 222). In 2013, average daily ambient O3 and PM2.5 concentrations were reduced by 1.0 p p b (2 %) and 9.9 μ g m −3 (48 %), respectively, and fourth highest maximum daily average 8-h O3 was reduced by 14 p p b. Comparison of model-derived sensitivities to those derived using empirical methods show coherence, but some important differences, such as the O3 concentration where the sensitivity to NOx emissions changes sign.

Air Quality Impacts of Electrifying Vehicles and Equipment Across the United States.

U.S.-wide air quality impacts of electrifying vehicles and off-road equipment are estimated for 2030 using 3-D photochemical air quality model and detailed emissions inventories. Electrification reduces tailpipe emissions and emissions from petroleum refining, transport, and storage, but increases electricity demand. The Electrification Case assumes approximately 17% of light duty and 8% of heavy duty vehicle miles traveled and from 17% to 79% of various off-road equipment types considered good candidates for electrification is powered by electricity. The Electrification Case raises electricity demand by 5% over the 2030 Base Case but nitrogen oxide (NOx) emissions decrease by 209 thousand tons (3%) overall. Emissions of other criteria pollutants also decrease. Air quality benefits of electrification are modest, mostly less than 1 ppb for ozone and 0.5 μg m-3 for fine particulate matter (PM2.5), but widespread. The largest reductions for ozone and PM occur in urban areas due to lower mobile source emissions. Electrifying off-road equipment yields more benefits than electrifying on-road vehicles. Reduced crude oil imports and associated marine vessel emissions cause additional benefits in port cities. Changes in other gas and PM emissions, as well as impacts on acid and nutrient deposition, are discussed.

Contributions of wood smoke and vehicle emissions to ambient concentrations of volatile organic compounds and particulate matter during the Yakima wintertime nitrate study

AbstractA multiple linear regression (MLR) chemical mass balance model was applied to data collected during an air quality field experiment in Yakima, WA, during January 2013 to determine the relative contribution of residential wood combustion (RWC) and vehicle emissions to ambient pollutant levels. Acetonitrile was used as a chemical tracer for wood burning and nitrogen oxides (NOx) as a chemical tracer for mobile sources. RWC was found to be a substantial source of gas phase air toxics in wintertime. The MLR model found RWC primarily responsible for emissions of formaldehyde (73%), acetaldehyde (69%), and black carbon (55%) and mobile sources primarily responsible for emissions of carbon monoxide (CO; 83%), toluene (81%), C2‐alkylbenzenes (81%), and benzene (64%). When compared with the Environmental Protection Agency's 2011 winter emission inventory, the MLR results suggest that the contribution of RWC to CO emissions was underestimated in the inventory by a factor of 2. Emission ratios to NOx from the MLR model agreed to within 25% with wintertime emission ratios predicted from the Motor Vehicle Emissions Simulator (MOVES) 2010b emission model for Yakima County for all pollutants modeled except for CO, C2‐alkylbenzenes, and black carbon. The MLR model results suggest that MOVES was overpredicting mobile source emissions of CO relative to NOx by a factor of 1.33 and black carbon relative to NOx by about a factor of 3.

An emission processing system for air quality modelling in the Mexico City metropolitan area: Evaluation and comparison of the MOBILE6.2-Mexico and MOVES-Mexico traffic emissions.

This article describes the High-Elective Resolution Modelling Emission System for Mexico (HERMES-Mex) model, an emission processing tool developed to transform the official Mexico City Metropolitan Area (MCMA) emission inventory into hourly, gridded (up to 1km2) and speciated emissions used to drive mesoscale air quality simulations with the Community Multi-scale Air Quality (CMAQ) model. The methods and ancillary information used for the spatial and temporal disaggregation and speciation of the emissions are presented and discussed. The resulting emission system is evaluated, and a case study on CO, NO2, O3, VOC and PM2.5 concentrations is conducted to demonstrate its applicability. Moreover, resulting traffic emissions from the Mobile Source Emission Factor Model for Mexico (MOBILE6.2-Mexico) and the MOtor Vehicle Emission Simulator for Mexico (MOVES-Mexico) models are integrated in the tool to assess and compare their performance. NOx and VOC total emissions modelled are reduced by 37% and 26% in the MCMA when replacing MOBILE6.2-Mexico for MOVES-Mexico traffic emissions. In terms of air quality, the system composed by the Weather Research and Forecasting model (WRF) coupled with the HERMES-Mex and CMAQ models properly reproduces the pollutant levels and patterns measured in the MCMA. The system's performance clearly improves in urban stations with a strong influence of traffic sources when applying MOVES-Mexico emissions. Despite reducing estimations of modelled precursor emissions, O3 peak averages are increased in the MCMA core urban area (up to 30ppb) when using MOVES-Mexico mobile emissions due to its VOC-limited regime, while concentrations in the surrounding suburban/rural areas decrease or increase depending on the meteorological conditions of the day. The results obtained suggest that the HERMES-Mex model can be used to provide model-ready emissions for air quality modelling in the MCMA.

Uneven distribution of inorganic pollutants in marine air originating from ocean-going ships

The distribution of mass, water-soluble inorganic salts and mineral elements of size-segregated aerosols (PM1, PM2.5-1 and PM10-2.5), precursor gaseous pollutants, black carbon, and nanoparticles (10–300 nm size range) at the Southern Bight of the North Sea has been studied. The concentrations of air pollutants peaked over shipping lanes, open-water anchorage areas and frequently navigated waters, due to the presence of mobile emission sources. A considerable decrease in air pollutant levels was seen when diverting from these marine areas towards remote or coastal banks. These findings showed the rapid dispersion of pollutants in the marine air. The nano-aerosol count, originating from ocean-going ships, peaked at lower average aerodynamic diameters (e.g., ≈28 nm) than those, observed from low-displacement vessels (45–50 nm, e.g., for fishing boats). The average diameter of nano-PM depended also on weather conditions, e.g., it was higher (≈50 nm) in air of higher humidity.

Using of Extended Kalman Filter for Mobile Target Tracking in the Passive Air Based Radar System

In the report the problem of processing of signals from radio emission source in the passive air based radar system is considered. Algorithm of optimal non-linear filtering for purposes of mobile non-weaving radio emission source tracking on the basis of angle measurements is observed. Simulation modelling of considered algorithm is realized. According to the results of modelling conclusions of efficiency and validity of these algorithms are made.

Ex-post environmental and traffic assessment of a speed reduction strategy in Madrid's inner ring-road

Since urban traffic is a major source of CO2 and NOx emissions, cities play a key role averting climate change and combating air pollution. Most researchers agree on the need of designing comprehensive mitigation strategies instead of applying isolated measures. Nevertheless, it is important to understand the specific impact and scope of each measure to look for the most effective synergies among them. In 2004, the Madrid City Council launched a plan to re-design its inner ring-road to move traffic out of the city centre. For safety reasons the planned speed limit for the full-renovated South-West section was finally reduced from 90km/h to 70km/h. Besides contributing to traffic safety, this strategy could also be seen as positive to the environment due to the associated reduced fuel consumption and lower emissions. However, lower speed limits have lower rates of community acceptance due to its impact on average travel times at the individual level. This paper conducts an ex-post evaluation of this speed reduction strategy to explore its environmental and traffic performance impacts. The results support the thesis that, in this velocity range, lower speed limits present important opportunities for reducing GHG and air pollution in the section affected by the measure, without substantially altering traffic performance. The implementation of the new speed limit policy produces a 14.4% and 16.4% reduction in CO2 and NOx emissions respectively, while global travel time remains virtually constant and the saturation rate decreases slightly. Besides, this cost-effective measure reveals great potential to reduce air pollution in highly populated urban areas located next to urban highways. This work provides local policy makers and city managers with useful insights regarding potential co-benefits of traffic optimization and speed reduction management to reduce mobile source emissions in urban environments.

Ambient air quality measurements from a continuously moving mobile platform: Estimation of area-wide, fuel-based, mobile source emission factors using absolute principal component scores

We have applied the absolute principal component scores (APCS) receptor model to on-road, background-adjusted measurements of NOx, CO, CO2, black carbon (BC), and particle number (PN) obtained from a continuously moving platform deployed over nine afternoon sampling periods in Seattle, WA. Two Varimax-rotated principal component features described 75% of the overall variance of the observations. A heavy-duty vehicle feature was correlated with black carbon and particle number, whereas a light-duty feature was correlated with CO and CO2. NOx had moderate correlation with both features. The bootstrapped APCS model predictions were used to estimate area-wide, average fuel-based emission factors and their respective 95% confidence limits. The average emission factors for NOx, CO, BC and PN (14.8, 18.9, 0.40 g/kg, and 4.3 × 1015 particles/kg for heavy duty vehicles, and 3.2, 22.4, 0.016 g/kg, and 0.19 × 1015 particles/kg for light-duty vehicles, respectively) are consistent with previous estimates based on remote sensing, vehicle chase studies, and recent dynamometer tests. Information on the spatial distribution of the concentrations contributed by these two vehicle categories relative to background during the sampling period was also obtained.