Obscured Contribution of Oxygenated Intermediate-Volatility Organic Compounds to Secondary Organic Aerosol Formation from Gasoline Vehicle Emissions.

Secondary organic aerosol (SOA) formation from gasoline vehicles spanning a wide range of emission types was investigated using an oxidation flow reactor (OFR) by conducting chassis dynamometer tests. Aided by advanced mass spectrometric techniques, SOA precursors, including volatile organic compounds (VOCs) and intermediate/semivolatile organic compounds (I/SVOCs), were comprehensively characterized. The reconstructed SOA produced from the speciated VOCs and I/SVOCs can explain 69% of the SOA measured downstream of an OFR upon 0.5-3 days' OH exposure. While VOCs can only explain 10% of total SOA production, the contribution from I/SVOCs is 59%, with oxygenated I/SVOCs (O-I/SVOCs) taking up 20% of that contribution. O-I/SVOCs (e.g., benzylic or aliphatic aldehydes and ketones), as an obscured source, account for 16% of total nonmethane organic gas (NMOG) emission. More importantly, with the improvement in emission standards, the NMOG is effectively mitigated by 35% from China 4 to China 6, which is predominantly attributed to the decrease of VOCs. Real-time measurements of different NMOG components as well as SOA production further reveal that the current emission control measures, such as advances in engine and three-way catalytic converter (TWC) techniques, are effective in reducing the "light" SOA precursors (i.e., single-ring aromatics) but not for the I/SVOC emissions. Our results also highlight greater effects of O-I/SVOCs to SOA formation than previously observed and the urgent need for further investigation into their origins, i.e., incomplete combustion, lubricating oil, etc., which requires improvements in real-time molecular-level characterization of I/SVOC molecules and in turn will benefit the future design of control measures.

Particulate matter forecast and prediction in Curitiba using machine learning.

Introduction: Air quality is directly affected by pollutant emission from vehicles, especially in large cities and metropolitan areas or when there is no compliance check for vehicle emission standards. Particulate Matter (PM) is one of the pollutants emitted from fuel burning in internal combustion engines and remains suspended in the atmosphere, causing respiratory and cardiovascular health problems to the population. In this study, we analyzed the interaction between vehicular emissions, meteorological variables, and particulate matter concentrations in the lower atmosphere, presenting methods for predicting and forecasting PM2.5. Methods: Meteorological and vehicle flow data from the city of Curitiba, Brazil, and particulate matter concentration data from optical sensors installed in the city between 2020 and 2022 were organized in hourly and daily averages. Prediction and forecasting were based on two machine learning models: Random Forest (RF) and Long Short-Term Memory (LSTM) neural network. The baseline model for prediction was chosen as the Multiple Linear Regression (MLR) model, and for forecast, we used the naive estimation as baseline. Results: RF showed that on hourly and daily prediction scales, the planetary boundary layer height was the most important variable, followed by wind gust and wind velocity in hourly or daily cases, respectively. The highest PM prediction accuracy (99.37%) was found using the RF model on a daily scale. For forecasting, the highest accuracy was 99.71% using the LSTM model for 1-h forecast horizon with 5 h of previous data used as input variables. Discussion: The RF and LSTM models were able to improve prediction and forecasting compared with MLR and Naive, respectively. The LSTM was trained with data corresponding to the period of the COVID-19 pandemic (2020 and 2021) and was able to forecast the concentration of PM2.5 in 2022, in which the data show that there was greater circulation of vehicles and higher peaks in the concentration of PM2.5. Our results can help the physical understanding of factors influencing pollutant dispersion from vehicle emissions at the lower atmosphere in urban environment. This study supports the formulation of new government policies to mitigate the impact of vehicle emissions in large cities.

Dataset of vehicle chase measurements in real-world subfreezing winter conditions.

This dataset comprises thorough measurements of light-duty vehicles emissions conducted in Siilinjärvi and Kuopio, Finland, during February 2021, using a mobile laboratory. The measurements focused on subfreezing conditions to capture emissions nuances during cold weather. Measurements were carried out on minimally trafficked roads to diminish external disturbances. The dataset includes a large number of variables from gas and particle emissions. Gaseous emissions of CO, CO2, and NOx were measured. Measured variables of particle emissions were number concentration (CPC), size distribution (ELPI+), black carbon concentration (AE33), and chemical composition (SP-AMS). A total of six light-duty vehicles were investigated, featuring three diesel and three gasoline engines. The measurements incorporated three distinct drive scenarios: subfreezing-cold start, preheated-cold start (utilizing either electrical or fuel-operated auxiliary heaters), and hot start (where a vehicle engine has reached the optimal temperature through prior driving). Each drive type was replicated twice, resulting in six driven rounds per vehicle and 36 rounds in total. Additionally, daily background measurements were conducted by following the same route without chasing a specific vehicle. Meteorological conditions during the measurements were representative of winter in Finland, with outside temperatures ranging from -9 °C to -28 °C. The effect of weather conditions on the measurements were minimal. Only a minor effect was due to the occasional snowfall, especially on the last day when the road surface was snowy, and the car being chased lifted the snow from the road surface. We didn't recognize other factors, such as high wind speeds or major road dust emissions, that could have affected the measurement results. This dataset serves as a valuable resource for comparing emissions under diverse environmental conditions, particularly in real-life winter settings where data are scarce. Furthermore, it provides an opportunity for meta-analysis of emission factors from various passenger vehicle types. The dataset's richness and specificity make it a valuable contribution to the understanding of winter-time vehicular emissions.

Estimating regional CO2 and NOx emissions from road transport using real-world data-based emission factors in Korea.

The average-speed emission model (Speed-based model), a widely used and simple method of calculating road vehicle emissions, offers easy accessibility by expressing emissions as a function of average speed. However, there are limitations in expressing emissions generated through complex mechanisms simply as a function of speed. Real-world driving tests using a portable emission measurement system can incorporate the impact of vehicle driving load on emissions. In this study, we analyzed real-world emissions data from 94 light-duty vehicles and developed time-based emission factors depending on vehicle speed and vehicle-specific power (VSP). We also propose a speed-VSP based model to estimate regional CO2 and NOx emissions by combining time-based emission factors and vehicle operating times. The speed-based model and Speed-VSP based model exhibit a 44% difference in NOx emissions and a 29% difference in CO2 emission. In a comparison of the two models against RDE test results, the speed-VSP based model achieved high accuracy in predicting NOx and CO2 emissions with a lower root mean square error (RMSE). Specifically, for NOx emissions predictions, the speed-VSP based model achieved an RMSE of 122-270 mg/km, while the speed-based model showed a much higher RMSE of 435-476 mg/km. For CO2 emissions predictions, the speed-VSP based model achieved an RMSE of 34-56 mg/km, while the speed-based model showed a much higher RMSE of 36-72 mg/km. The results of this study present an opportunity to reassess and improve conventional method of measuring and evaluating emissions from road transport.

Updating vehicle VOCs emissions characteristics under clean air actions in a tropical city of China.

In the context of clean air actions in China, vehicle emission limits have been continuously tightened, which has facilitated the reduction of volatile organic compounds (VOCs) emissions. However, the characteristics of VOC emissions from vehicles with strict emission limits are poorly understood. This study investigated the VOC emission characteristics from vehicles under the latest standards based on tunnel measurements, and identified future control strategies for vehicle emissions. The results showed that the highest percentage of VOCs from vehicle consisted of alkanes (80.9 %), followed by aromatics (15.8 %) and alkenes (3.1 %). Alkanes had the most significant ozone formation potential due to their high concentrations, in contrast to the aromatics that have been dominant in previous studies. The measured fleet-average VOC emission factor was 71.3 mg·km-1, including tailpipe emissions of 39.6 mg·km-1 and evaporative emissions of 31.7 mg·km-1. The VOC emission factors of the subgroups were obtained. The emission of evaporated VOCs accounted for 44.5 % of the total vehicle VOC emissions, which have increased substantially from previous studies. In addition, the emission characteristics of vehicles that are under the latest emission threshold values have changed significantly, and the mixing ratio of toluene/benzene (T/B) has been updated to 3:1. This study updates the VOCs emission factors of vehicles under clean air actions and highlights the future mitigation policies should focus on reducing evaporative VOC emissions.

Features of the highway road network that generate or retain tyre wear particles.

The environmental accumulation of microplastics poses a formidable global challenge, with tyre wear particles (TWPs) emerging as major and potentially harmful contributors to this particulate pollution. A critical pathway for TWPs to aquatic environments is via road drainage. While drainage assets are employed worldwide, their effectiveness in retaining microplastics of highly variable densities (TWP ~ 1-2.5 g cm3) remains unknown. This study examines their ability to impede the transfer of TWPs from the UK Strategic Road Network (SRN) to aquatic ecosystems. Samples were collected from the influent, effluent and sediments of three retention ponds and three wetlands. The rate of TWP generation is known to vary in response to vehicle speed and direction. To ascertain the significance of this variability, we further compared the mass of TWPs in drainage from curved and straight sections of the SRN across eight drainage outfalls. Pyrolysis gas chromatography-mass spectrometry (Py-GC-MS) was used to quantify tyre wear using benzothiazole as a molecular marker for TWPs (with an internal standard benzothiazole-D4). Tyre wear was present in drainage from the SRN at concentrations of 2.86 ± 6 mg/L and was found within every sample analysed. Drainage from curved sections of the SRN contained on average a 40% greater TWP mass than straight sections but this was not significant. The presence of wetlands and retention ponds generally led to a reduction in TWP mass (74.9% ± 8.2). This effect was significant for retention ponds but not for wetlands; most probably due to variability among sites and sampling occasions. Similar drainage assets are used on a global scale; hence our results are of broad relevance to the management of TWP pollution.

Chemodiversity of organic nitrogen emissions from light-duty gasoline vehicles is governed by engine displacements and driving speed.

Organic nitrogen emissions from light-duty gasoline vehicles (LDGVs) is believed to play a pivotal role in atmospheric particulate matter (PM) in urban environments. Here, the characterization of organic nitrogen emitted by LDGVs with varying engine displacements at different speed phases was analyzed using a Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) at molecular level. For the LDGV with small engine displacements, the nitrogen-containing organic (CHON) compounds exhibit higher abundance, molecular weight, oxygen content and aromaticity in the extra-high-speed phase. Conversely, for the LDGV with big engine displacements, more CHON compounds with elevated abundance, molecular weight, oxygen content and aromaticity were observed in the low-speed phase. Our study assumed that the formation of CHON compounds emitted from LDGVs is mainly the oxidation reaction during fuel combustion, so the potential precursor-product pairs related to oxidation process were used to study the degree of combustion reaction. The results show that the highest proportion of oxidation occurs during extra-high-speed phase for LDGV with small engine displacements, and during low-speed phase for LDGV with big engine displacements. These results offer a novel perspective for comprehending the mechanism behind vehicle emissions formation and contribute valuable insights for crafting effective air pollution regulations.

Nitrogen isotopic characteristics of aerosol ammonium in a Chinese megacity indicate the reduction from vehicle emissions during the lockdown period.

The role of agricultural versus vehicle emissions in urban atmospheric ammonia (NH3) remains unclear. The lockdown due to the outbreak of COVID-19 provided an opportunity to assess the role of source emissions on urban NH3. Concentrations and δ15N of aerosol ammonium (NH4+) were measured before (autumn in 2017) and during the lockdown (summer, autumn, and winter in 2020), and source contributions were quantified using SIAR. Despite the insignificant decrease in NH4+ concentrations, significantly lower δ15N-NH4+ was found in 2020 (0.6 ± 1.0‰ in PM2.5 and 1.4 ± 2.1‰ in PM10) than in 2017 (15.2 ± 6.7‰ in PM2.5), which indicates the NH3 from vehicle emissions has decreased by∼50% during the lockdown while other source emissions are less affected. Moreover, a reversed seasonal pattern of δ15N-NH4+ during the lockdown in Changsha has been revealed compared to previous urban studies, which can be explained by the dominant effect of non-fossil fuel emissions due to the reductions of vehicle emissions during the lockdown period. Our results highlight the effects of lockdown on aerosol δ15N-NH4+ and the importance of vehicle emissions to urban atmospheric NH3, providing conclusive evidence that reducing vehicle NH3 emissions could be an effective strategy to reduce PM2.5 in Chinese megacities.

Evaluating real-world emissions from in-use buses and taxis using on-road remote sensing.

Assessing real-world emissions from buses and taxis is vital to comprehend their impact on urban air quality. Such vehicles differ significantly from the majority of the fleet owing to their higher mileage rates. However, few studies have focused on specifically assessing the emissions from this segment of the vehicle fleet. In this context, this study evaluated the real-world emissions of nitrogen oxides (NOx) from in-use buses and taxis in Dublin, Ireland, using crossroad remote sensing technology. The remote sensing system was deployed at strategic locations throughout the city to capture on-road emissions from passing vehicles. The collected data included vehicle related information such as emission standard, make, and mileage, and pollutants including NOx. Based on this data, analysis was aimed to understand the impact of Euro emission standard, ambient temperature, mileage, and make of the vehicle on NOx emissions. The results reveal that the average emissions from taxis reduce by 37% from Euro 5 to Euro 6b, and average emissions from Euro 6 buses are 87% lower compared to Euro 5. The trends in emission factors (EFs) of buses and taxis were similar during summer and winter sampling. Moreover, on comparing the emissions from the top five taxi manufacturers, different trends in the emission factors were observed. Finally, the study found that the effect of vehicle mileage on emissions was unclear for both buses and taxis. In any case, these findings provide valuable insights into the real-world emission performance of the existing fleet of buses and taxis in Dublin and highlight the need for targeted measures to reduce emissions from these vehicles. The results can assist policymakers and urban planners in formulating evidence-based strategies to improve air quality in Dublin and other cities facing similar challenges.

Enhancing vehicular emissions monitoring: A GA-GRU-based soft sensors approach for HDDVs.

Vehicle emissions have a serious impact on urban air quality and public health, so environmental authorities around the world have introduced increasingly stringent emission regulations to reduce vehicle exhaust emissions. Nowadays, PEMS (Portable Emission Measurement System) is the most widely used method to measure on-road NOx (Nitrogen Oxides) and PN (Particle Number) emissions from HDDVs (Heavy-Duty Diesel Vehicles). However, the use of PEMS requires a lot of workforce and resources, making it both costly and time-consuming. This study proposes a neural network based on a combination of GA (Genetic Algorithm) and GRU (Gated Recurrent Unit), which uses CC (Pearson Correlation Coefficient) to determine and simplify OBD (On-board Diagnosis) data. The GA-GRU model is trained under three real driving conditions of HDDVs, divided by vehicle driving parameters, and then embedded as a soft sensor in the OBD system to monitor real-time emissions of NOx and PN within the OBD system. This research addresses the existing research gap in the development of soft sensors specifically designed for NOx and PN emission monitoring. In this study, it is demonstrated that the described soft sensor has excellent R2 values and outperforms other conventional models. This research highlights the ability of the proposed soft sensor to eliminate outliers accurately and promptly while consistently tracking predictions throughout the vehicle's lifetime. This method is a groundbreaking update to the vehicle's OBD system, permanently adding monitoring data to the vehicle's OBD, thus fundamentally improving the vehicle's self-monitoring capabilities.

A comparison of vehicle emissions control strategies for smart cities.

Many studies have shown that air quality in cities is affected due to emissions of carbon from vehicles. As a result, policymakers (e.g., municipalities) intensely search for new ways to reduce air pollution due to its relation to health diseases. With this concern, connected vehicle technologies can leverage alternative on-road emissions control policies. The present investigation studies the impact on air pollution by (i) updating vehicles' routes to avoid pollution exposure (route choice policy), (ii) updating vehicles' speed limits (speed control policy), and (iii) considering electric vehicles (EVs). Vehicles are informed in advance about route conditions (i.e., on-road emissions) using the vehicular network. We found that by updating vehicle routes, 7.43% less CO emissions are produced within the evaluated region. Also, we find no evidence of significant emissions reductions in the case of limiting vehicles' speed. Lastly, with 30% of EV penetration, safe CO emissions levels are reached.

[The facility design and operation effect of control vehicle emissions hazard in test section of automobile assembly workshop].

The hazard of vehicle emissions mainly come from the four wheel positioning, drum test and vehicle emissions test sections in automobile assembly workshop, which can lead to abnormal hemoglobin and hepatic insufficiency in workers. We researched on preventing toxic gases technologies for the vehicle emissions generated by these three sections, designed the ventilation facilities, and then detected and evaluated the operation effect, thereby improving the working environment, ensuring the occupational health of workers, and providing scientific basis for the control of vehicle emissions hazards.

The impact on passenger car emissions associated with the promotion and demise of diesel fuel.

The promotion and growth in the use of diesel fuel in passenger cars in the UK and Europe over the past two decades led to considerable adverse air quality impacts in urban areas and more widely. In this work, we construct a multi-decade analysis of passenger car emissions in the UK based on real driving emissions data. An important part of the study is the use of extensive vehicle emission remote sensing data covering multiple measurement locations, time periods, environmental conditions and consisting of over 600,000 measurements. These data are used to consider two scenarios: first, that diesel fuel use was not promoted in the early 2000s for climate mitigation reasons, and second, that there was not a dramatic decline in diesel fuel use following the Dieselgate scandal. The strong growth of diesel fuel use coincided with a time when diesel NOx emissions were high and, conversely, the strong decrease of diesel fuel use coincided with a time when diesel vehicle after-treatment systems for NOx control were effective. We estimate that the growth in diesel car use in the UK results in excess NOx emissions of 721 kt over a three decade period; equivalent to over 7 times total annual passenger car NOx emissions and greater than total UK NOx emissions of 681.8 kt in 2021 and with an associated damage cost of £5.875 billion. However, the sharp move away from diesel fuel post-Dieselgate only reduced NOx emissions by 41 kt owing to the effectiveness of modern diesel aftertreatment systems.

Occupational exposures of firefighting and urinary tract cancer risk among men in the Norwegian Fire Departments Cohort.

OBJECTIVES: Increased risks of bladder cancer and mesothelioma were the strongest evidence for the recent reclassification of firefighting as carcinogenic (Group 1) by the International Agency for Research on Cancer. Our study aim was to develop indicators for specific firefighting exposures and examine associations with urinary tract cancer (UTC), including bladder cancer. METHODS: We developed indicators for exposure from employment at a fire department or in firefighting jobs, to fire and smoke, and to diesel exhaust for men in the Norwegian Fire Departments Cohort (n=4250). Incident UTC cases were obtained from the Cancer Registry of Norway (1960-2021). Poisson regression was used to estimate incidence rate ratios (IRR) with cumulative exposures grouped into tertiles (reference: lowest exposed tertile) with 0-year, 10-year and 15-year lagging of exposures. RESULTS: During 125 090 person-years of follow-up, there were 76 cases of UTC. IRRs were mostly non-significantly increased in the middle tertile and at or below 1 in the highest tertile for total duration of employment, number of fires attended and fire exposure score with and without lags. In the middle tertile for diesel exhaust exposure, UTC risk was elevated over twofold with 10-year (IRR 2.27, 95% CI 1.22 to 4.20) and 15- year (2.21, 1.18 to 4.16) lags, and near 1 in the highest tertile. Findings for bladder cancer were similar to those for UTC. CONCLUSIONS: Dose-response associations between the exposure indicators and UTC were not observed. Future studies using the indicators with more cases are needed.

Methylsiloxanes from Vehicle Emissions Detected in Aerosol Particles.

Methylsiloxanes have gained growing attention as emerging pollutants due to their toxicity to organisms. As man-made chemicals with no natural source, most research to date has focused on volatile methylsiloxanes from personal care or household products and industrial processes. Here, we show that methylsiloxanes can be found in primary aerosol particles emitted by vehicles based on aerosol samples collected in two tunnels in São Paulo, Brazil. The aerosol samples were analyzed with thermal desorption-proton transfer reaction-mass spectrometry (TD-PTR-MS), and methylsiloxanes were identified and quantified in the mass spectra based on the natural abundance of silicon isotopes. Various methylsiloxanes and derivatives were found in aerosol particles from both tunnels. The concentrations of methylsiloxanes and derivatives ranged 37.7-377 ng m-3, and the relative fractions in organic aerosols were 0.78-1.9%. The concentrations of methylsiloxanes exhibited a significant correlation with both unburned lubricating oils and organic aerosol mass. The emission factors of methylsiloxanes averaged 1.16 ± 0.59 mg kg-1 of burned fuel for light-duty vehicles and 1.53 ± 0.37 mg kg-1 for heavy-duty vehicles. Global annual emissions of methylsiloxanes in vehicle-emitted aerosols were estimated to range from 0.0035 to 0.0060 Tg, underscoring the significant yet largely unknown potential for health and climate impacts.

Comparing sources of carbonaceous aerosols during haze and nonhaze periods in two northern Chinese cities.

Radiocarbon (14C), stable carbon isotope (13C), and levoglucosan in PM2.5 were measured in two northern Chinese cities during haze events and nonhaze periods in January 2019, to ascertain the sources and their differences in carbonaceous aerosols between the two periods. The contribution of primary vehicle emissions (17.8 ± 3.7%) to total carbon in Beijing during that haze event was higher than that of primary coal combustion (7.3 ± 4.2%), and it increased significantly (7.1%) compared to the nonhaze period. The contribution of primary vehicle emissions (4.1 ± 2.8%) was close to that of primary coal combustion (4.3 ± 3.3%) during the haze event in Xi'an, and the contribution of primary vehicle emissions decreased by 5.8% compared to the nonhaze period. Primary biomass burning contributed 21.1 ± 10.5% during the haze event in Beijing and 40.9 ± 6.6% in Xi'an (with an increase of 3.3% compared with the nonhaze period). The contribution of secondary fossil fuel sources to total secondary organic carbon increased by 29.2% during the haze event in Beijing and by 18.4% in Xi'an compared to the nonhaze period. These results indicate that specific management measures for air pollution need to be strengthened in different Chinese cities in the future, that is, controlling vehicle emissions in Beijing and restricting the use of coal and biomass fuels in winter in Xi'an.

Updating On-Road Vehicle Emissions for China: Spatial Patterns, Temporal Trends, and Mitigation Drivers.

Vehicle emissions in China have been decoupled from rapid motorization owing to comprehensive control strategies. China's increasingly ambitious goals for better air quality are calling for deep emission mitigation, posing a need to develop an up-to-date emission inventory that can reflect the fast-developing policies on vehicle emission control. Herein, large-sample vehicle emission measurements were collected to update the vehicle emission inventory. For instance, ambient temperature correction modules were developed to depict the remarkable regional and seasonal emission variations, showing that the monthly emission disparities for total hydrocarbon (THC) and nitrogen oxide (NOX) in January and July could be up to 1.7 times in northern China. Thus, the emission ratios of THC and NOX can vary dramatically among various seasons and provinces, which have not been considered well by previous simulations regarding the nonlinear atmospheric chemistry of ozone (O3) and fine particulate matter (PM2.5) formation. The new emission results indicate that vehicular carbon monoxide (CO), THC, and PM2.5 emissions decreased by 69, 51, and 61%, respectively, during 2010-2019. However, the controls of NOX and ammonia (NH3) emissions were not as efficient as other pollutants. Under the most likely future scenario (PC [1]), CO, THC, NOX, PM2.5, and NH3 emissions were anticipated to reduce by 35, 36, 35, 45, and 4%, respectively, from 2019 to 2025. These reductions will be expedited with expected decreases of 56, 58, 74, 53, and 51% from 2025 to 2035, which are substantially promoted by the massive deployment of new energy vehicles and more stringent emission standards. The updated vehicle emission inventory can serve as an important tool to develop season- and location-specific mitigation strategies of vehicular emission precursors to alleviate haze and O3 problems.

Traffic assignment in urban transportation network problem with emission constraints in China: a cooperative game theory.

Traffic assignment in urban transport planning is the process of allocating traffic flows in a network. Traditionally, traffic assignment can reduce travel time or travel costs. As the number of vehicles increases and congestion causes increased emissions, environmental issues in transportation are gaining more and more attention. The main objective of this study is to address the issue of traffic assignment in urban transport networks under an abatement rate constraint. A traffic assignment model based on cooperative game theory is proposed. The influence of vehicle emissions is incorporated into the model. The framework consists of two parts. First, the performance model predicts travel time based on the Wardrop traffic equilibrium principle, which reflects the system travel time. No travelers can experience a lower travel time by unilaterally changing their path. Second, the cooperative game model gives link importance ranking based on the Shapley value, which measures the average marginal utility contribution of links of the network to all possible link coalitions that include the link, and assigns traffic flow based on the average marginal utility contribution of a link with system vehicle emission reduction constraints. The proposed model shows that traffic assignment with emission reduction constraints allows more vehicles in the network with an emission reduction rate of 20% than traditional models.

Measuring the route topography impact on real driving emissions based on neural network models.

Route topography is an important test boundary of real driving emission (RDE) tests. However, the RDE test boundaries, such as atmospheric environment, driver behavior, route topography, and traffic congestion, are random, uncertain, and completely coupled. It is difficult to know to what extent route topography can determine on-road emissions, especially in a region with hilly topography. In this regard, the neural network predictor importance algorithms were proposed to measure the importance of the route topography test boundary. Based on tens of thousands of data window samples from the RDE tests in Chongqing, factor analysis was performed to reduce the data dimensionality and eliminate information overlap, and neural network models were established to predict pollutant emissions and calculate the relative importance of input variables. The results show that route topography is comparable to trip dynamics for on-road emissions but the importance of the route topography test boundary is not fully appreciated in the existing RDE regulation, making mountain cities suffer from severe vehicle emissions that are not effectively controlled.

Temporal-spatial dynamic characteristics of vehicle emissions on intercity roads in Guangdong Province based on vehicle identity detection data.

Estimating intercity vehicle emissions precisely would benefit collaborative control in multiple cities. Considering the variability of emissions caused by vehicles, roads, and traffic, the 24-hour change characteristics of air pollutants (CO, HC, NOX, PM2.5) on the intercity road network of Guangdong Province by vehicle categories and road links were revealed based on vehicle identity detection data in real-life traffic for each hour in July 2018. The results showed that the spatial diversity of emissions caused by the unbalanced economy was obvious. The vehicle emissions in the Pearl River Delta region (PRD) with a higher economic level were approximately 1-2 times those in the non-Pearl River Delta region (non-PRD). Provincial roads with high loads became potential sources of high emissions. Therefore, emission control policies must emphasize the PRD and key roads by travel guidance to achieve greater reduction. Gasoline passenger cars with a large proportion of traffic dominated morning and evening peaks in the 24-hour period and were the dominant contributors to CO and HC emissions, contributing more than 50% in the daytime (7:00-23:00) and higher than 26% at night (0:00-6:00). Diesel trucks made up 10% of traffic, but were the dominant player at night, contributed 50%-90% to NOX and PM2.5 emissions, with a marked 24-hour change rule of more than 80% at night (23:00-5:00) and less than 60% during daytime. Therefore, targeted control measures by time-section should be set up on collaborative control. These findings provide time-varying decision support for variable vehicle emission control on a large scale.