Atmospheric Oxidation Capacity Elevated during 2020 Spring Lockdown in Chengdu, China: Lessons for Future Secondary Pollution Control.

This study presents the measurement of photochemical precursors during the lockdown period from January 23, 2020, to March 14, 2020, in Chengdu in response to the coronavirus (COVID-19) pandemic. To derive the lockdown impact on air quality, the observations are compared to the equivalent periods in the last 2 years. An observation-based model is used to investigate the atmospheric oxidation capacity change during lockdown. OH, HO2, and RO2 concentrations are simulated, which are elevated by 42, 220, and 277%, respectively, during the lockdown period, mainly due to the reduction in nitrogen oxides (NOx). However, the radical turnover rates, i.e., OH oxidation rate L(OH) and local ozone production rate P(O3), which determine the secondary intermediates formation and O3 formation, only increase by 24 and 48%, respectively. Therefore, the oxidation capacity increases slightly during lockdown, which is partly attributed to unchanged alkene concentrations. During the lockdown, alkene ozonolysis seems to be a significant radical primary source due to the elevated O3 concentrations. This unique data set during the lockdown period highlights the importance of controlling alkene emission to mitigate secondary pollution formation in Chengdu and may also be applicable in other regions of China given an expected NOx reduction due to the rapid transformation to electrified fleets in the future.

Nitroxyl donating and visualization with a coumarin-based fluorescence probe.

Nitroxyl (HNO), the single-electron reduction product of nitric oxide (NO), has attracted great interest in the treatment of congestive heart failure in clinical trials. In this paper, we describe the first coumarin-based compound N-hydroxy-2-oxo-2H-chromene-6-sulfonamide (CD1) as a dualfunctional HNO donor, which can release both an HNO signaling molecule and a fluorescent reporter. Under physiological conditions (pH 7.4 and 37 °C), the CD1 HNO donor can readily decompose with a half-life of ∼90 min. The corresponding stoichiometry HNO from the CD1 donor was confirmed using both Vitamin B12 and phosphine compound traps. In addition to HNO releasing, specifically, the degradation product 2-oxo-2H-chromene-6-sulfinate (CS1) was generated as a fluorescent marker during the decomposition. Therefore, the HNO amount released in situ can be accurately monitored through fluorescence generation. As compared to the CD1 donor, the fluorescence intensity increased by about 4.9-fold. The concentration limit of detection of HNO releasing was determined to be ∼0.13 µM according to the fluorescence generation of CS1 at physiological conditions. Moreover, the bioimaging of the CD1 donor was demonstrated in the cell culture of HeLa cells, where the intracellular fluorescence signals were observed, inferring the site of HNO release. Finally, we anticipate that this novel coumarin-based CD1 donor opens a new platform for exploring the biology of HNO.

Urban air pollution evaluation in downtown streets of a medium-sized Latin American city using AERMOD dispersion model.

The transport sector is considered the largest contributor of air pollutants in urban areas, mainly on-road vehicles, affecting the environment and human health. Bahía Blanca is a medium-sized Latin American city, with high levels of traffic in the downtown area during peak hours. In this regard, it is necessary to analyze air pollution using an air quality model considering that there are no air pollutant measurements in the central area. Furthermore, this type of study has not been carried out in the region and since the city is expected to grow, it is necessary to evaluate the current situation in order to make effective future decisions. In this sense, the AERMOD model (US-EPA version) and the RLINE source type were used in this work. This study analyzes the variations of pollutant concentrations coming from mobile sources in Bahía Blanca's downtown area, particularly carbon monoxide (CO) and nitrogen oxides (NOx) during the period Jul-2020 to Jun-2022. It is interesting to note the results show the maximum concentration values detected are not directly associated with maximum levels of vehicle flow or emission rates, which highlights the importance of meteorological parameters in the modeling. In addition, alternative scenarios are proposed and analyzed from a sustainable approach. Regarding the scenario analysis, it can be concluded that diesel vehicles have a large influence on NOx emissions. Moreover, restrictions as strict as those proposed for a Low Emission Zone would be less applicable in the city than alternative temporary measures that modify traffic at peak hours.

Experimental assessment of pollutant emissions from residential fuel cells and comparative benchmark analysis.

Energy transition currently brings focus on fuel cell micro-combined heat and power (mCHP) systems for residential uses. The two main technologies already commercialized are the Proton Exchange Membrane Fuel Cells (PEMFCs) and Solid Oxide Fuel Cells (SOFCs). The pollutant emissions of one system of each technology have been tested with a portable probe both in laboratory and field-test configurations. In this paper, the nitrogen oxides (NOx), sulphur dioxide (SO2), and carbon monoxide (CO) emission levels are compared to other combustion technologies such as a recent Euro 6 diesel automotive vehicle, a classical gas condensing boiler, and a gas absorption heat pump. At last, a method of converting the concentration of pollutants (in ppm) measured by the sensors into pollutant intensity per unit of energy (in mg/kWh) is documented and reported. This allows for comparing the pollutant emissions levels with relevant literature, especially other studies conducted with other measuring sensors. Both tested residential fuel cell technologies fed by natural gas can be considered clean regarding SO2 and NOx emissions. The CO emissions can be considered quite low for the tested SOFC and even nil for the tested PEMFC. The biggest issue of natural gas fuel cell technologies still lies in the carbon dioxide (CO2) emissions associated with the fossil fuel they consume. The gas absorption heat pump however shows worse NOx and CO levels than the classical gas condensing boiler. At last, this study illustrates that the high level of hybridization between a fuel cell and a gas boiler may be responsible for unexpected ON/OFF cycling behaviours and therefore prevent both sub-systems from operating as optimally and reliably as they would have as standalone units.

Association between air pollution and skin cutaneous melanoma: A Mendelian randomization study.

There has been a consistent and notable increase in the global prevalence of skin cutaneous melanoma (SKCM). Although genetic factors are closely associated with the occurrence and development of melanoma, the potential influence of environmental factors cannot be overlooked. The existing literature lacks a definitive consensus on the correlation between air pollution and the incidence rate of SKCM. This study seeks to investigate the causal relationship between air pollution, specifically focusing on particulate matter (PM) 2.5, PM2.5-10, PM10, and nitrogen oxides, and the risk of SKCM. A 2-sample Mendelian randomization (MR) method was applied, utilizing extensive publicly accessible genome-wide association studies summary datasets within European populations. The primary analytical method employed was the inverse variance weighted method. Supplementary methods, including the weighted median model, MR-Egger, simple model, and weighted model, were chosen to ensure robust analysis. Heterogeneity assessment was conducted using Cochran's Q test. To identify potential pleiotropy, both MR-Egger regression and the MR-PRESSO global test were employed. Additionally, a sensitivity analysis was performed using the leave-one-out method. The analysis revealed no statistically significant association between air pollution and SKCM risk, with specific findings as follows: PM2.5 (P = .485), PM2.5-10 (P = .535), PM10 (P = .136), and nitrogen oxides (P = .745). While some results exhibited heterogeneity, all findings demonstrated an absence of pleiotropy. This study did not find substantive evidence supporting a causal relationship between air pollution and the risk of SKCM within European populations. The comprehensive MR analysis, encompassing various pollutants, suggests that environmental factors such as air pollution may not be significant contributors to the development of SKCM.

Evaluation of laboratory and environmental exposure systems for protein modification upon gas pollutants and environmental factors.

Chemical modifications of proteins induced by ambient ozone (O3) and nitrogen oxides (NOx) are of public health concerns due to their potential to trigger respiratory diseases. The laboratory and environmental exposure systems have been widely used to investigate their relevant mechanism in the atmosphere. Using bovine serum albumin (BSA) as a model protein, we evaluated the two systems and aimed to reduce the uncertainties of both the reactants and products in the corresponding kinetic study. In the laboratory simulation system, the generated gaseous pollutants showed negligible losses. Ten layers of BSA were coated on the flow tube with protein extraction recovery of 87.4%. For environmental exposure experiment, quartz fiber filter was selected as the upper filter with low gaseous O3 (8.0%) and NO2 (1.7%) losses, and cellulose acetate filter was appropriate for the lower filter with protein extraction efficiency of 95.2%. The protein degradation process was observed without the exposure to atmospheric oxidants and contributed to the loss of protein monomer mass fractions, while environmental factors (e.g., molecular oxygen and ultraviolet) may cause greater protein monomer losses. Based on the evaluation, the study exemplarily applied the two systems to protein modification and both showed that O3 promotes the protein oligomerization and nitration, while increased temperature can accelerate the oligomerization and increased relative humidity can inhibit the nitration in the environmental exposure samples. The developed laboratory and environmental systems are suitable for studying protein modifications formed under different atmospheric conditions. A combination of the two will further reveal the actual mechanism of protein modifications.

Improving photochemical indicators for attributing ozone sensitivities in source apportionment analysis.

Comprehensive Air Quality Model with extensions (CAMx)-Decoupled Direct Method (DDM) simulations of first-order ozone (O3) sensitivity to nitrogen oxides (NOx) and volatile organic compounds (VOCs) emissions were performed and combined with modelled [Formula: see text] ratios to obtain a range of thresholds for determining O3-sensitivity regimes for different areas of China. Utilising the new threshold ranges for photochemical indicators, the method for determining O3 formation in the Ozone Source Apportionment Technology (OSAT) module within CAMx was improved by a dynamically varied threshold of [Formula: see text] ratio. The O3 concentration contributions in the newly added transition regime were apportioned to NOx and VOCs emissions in proportion to the relationship between the [Formula: see text] ratio and first-order O3 sensitivity. The source contributions of O3 concentrations from different emission sectors from June to September 2019 were compared using the original and improved CAMx-OSAT. The results showed that the O3 concentration contributions changed significantly in the NOx-limited regime, with a maximum decrease of 21.89%, while the contributions increased by up to 7.57% in the VOC-limited regime, and were within 15 µg/m3 in the transition regime. The modified OSAT module enabled a more sophisticated attribution of O3 to precursor emissions and may have far-reaching implications for informing O3 pollution control policy.

Mitigating air pollution benefits multiple sustainable development goals in China.

Achieving the United nations 2030 Sustainable Development Goals (SDGs) remains a significant challenge, necessitating urgent and prioritized strategies. Among the various challenges, air pollution continues to pose one of the most substantial threats to the SDGs due to its widespread adverse effects on human health and ecosystems. However, the connections between air pollution and the SDGs have often been overlooked. This study reveals that out of the 169 SDG targets, 71 are adversely impacted by air pollution, while only 6 show potential positive effects. In China, two major atmospheric nitrogen pollutants, ammonia and nitrogen oxides, resulted in an economic loss of 400 billion United States Dollar (USD) in 2020, which could be reduced by 33% and 34% by 2030, respectively. It would enhance the progress towards SDGs in China by 14%, directly contributing to the achievement of SDGs 1 to 6 and 11 to 15. This improvement is estimated to yield overall benefits totaling 119 billion USD, exceeded the total implementation cost of 82 billion USD with ammonia as the preferential mitigation target. This study underscores the importance of robust scientific evidence in integrated policies aimed at aligning improvements in environmental quality with the priorities of sustainable development.

Regulatory Insights for On-Board Monitoring of Vehicular NOx Emission Compliance.

Nitrogen oxide (NOx) emissions from heavy-duty diesel vehicles (HDDVs) have adverse effects on human health and the environment. On-board monitoring (OBM), which can continuously collect vehicle performance and NOx emissions throughout the operation lifespan, is recognized as the core technology for future vehicle in-use compliance, but its large-scale application has not been reported. Here, we utilized OBM data from 22,520 HDDVs in China to evaluate their real-world NOx emissions. Our findings showed that China VI HDDVs had a 73% NOx emission reduction compared with China V vehicles, but a considerable proportion still faced a significant risk of higher NOx emissions than the corresponding limits. The unsatisfactory efficiency of the emission treatment system under disadvantageous driving conditions (e.g., low speed or ambient temperature) resulted in the incompliance of NOx emissions, especially for utility vehicles (sanitation/garbage trucks). Furthermore, the observed intertrip and seasonal variability of NOx emissions demonstrated the need for a long-term continuous monitoring protocol instead of instantaneous evaluation for the OBM. With both functions of emission monitoring and malfunction diagnostics, OBM has the potential to accurately verify the in-use compliance status of large-scale HDDVs and discern the responsibility of high-emitting activities from manufacturers, vehicle operators, and driving conditions.

Air quality on UK diesel and hybrid trains.

Concentrations of particulate matter (PM10, PM2.5), ultrafine (UFP), particle number (PNC), black carbon (BC), nitrogen dioxide (NO2) and nitrogen oxides (NOX) were measured in train carriages on diesel and bi-mode trains on inter-city and long-distance journeys in the United Kingdom (UK) using a high-quality mobile measurement system. Air quality on 15 different routes was measured using highly-time resolved data on a total of 119 journeys during three campaigns in winter 2020 and summer 2021; this included 13 different train classes. Each journey was sampled 4-10 times with approximatively 11,000 min of in-train concentrations in total. Mean-journey concentrations were 7.552 µg m-3 (PM10); 3.936 µg m-3 (PM2.5); 333-11,300 # cm-3 (PNC); 225-9,131 # cm-3 (UFP); 0.6-11 µg m-3 (BC); 28-201 µg m-3 (NO2); and 130-3,456 µg m-3 (NOX). The impact of different factors on in-train concentrations was evaluated. The presence of tunnels was the factor with the largest impact on the in-train particle concentrations with enhancements by a factor of 40 greater than baseline for BC, and a factor 6 to 7 for PM and PNC. The engine fuel mode was the factor with the largest impact on NO2 with enhancements of up to 14-times larger when the train run on diesel compared to the times running on electric on hybrid trains. Train classes with an age < 10 years observed the lowest in-train PM, BC and NOX concentrations reflecting improvements in aspects of rail technology in recent years. Air quality on UK diesel trains is higher than ambient concentrations but has lower PM2.5 and PNC than most other transport modes, including subway systems, diesel and petrol cars. This paper adds significantly to the evidence on exposure to poor air quality in transport micro-environments and provides the industry and regulatory bodies with reference-grade measurements on which to establish in-train air quality guidelines.

Optimizing a twin-chamber system for direct ozone production rate measurement.

High Ozone Production Rate (OPR) leads to O3 pollution episodes and adverse human health outcomes. OPR observation (Obs-OPR) and OPR modelling (Mod-OPR) have been obtained from observed and modelled peroxy radicals and nitrogen oxides. However, discrepancies between them remind of an imperfect understanding of O3 photochemistry. Direct measurement of OPR (Mea-OPR) by a twin-chamber system emerges. Herein, we optimized Mea-OPR design, i.e., minimizing the chamber surface area to volume ratio (S/V) to 9.8 m-1 from 18 m-1 and the dark uptake coefficient of O3 to 9.9 × 10-9 from 7.1 × 10-8 in the literature. In addition, control experiments further revealed and quantified a photo-enhanced O3 uptake, and therefore recommended an essential correction of Mea-OPR. We finally characterized a measurement uncertainty of ±38% and a detection limit of 3.2 ppbv h-1 (3SD), which suggested that Mea-OPR would be sensitive enough to measure OPR in urban or suburban environments. Further application of this system in urban Beijing during the Beijing 2022 Olympic Winter Games recorded a noontime OPR of 7.3 (±3.3, 1SD) ppbv h-1. These observational results added up to our confidence in future field application of Mea-OPR, to facilitate pollution control policy evaluation and to shed light on O3 photochemistry puzzle.

Air Pollution Inequality in the Denver Metroplex and its Relationship to Historical Redlining.

Prior studies have shown that people of color (POC) in the United States are exposed to higher levels of pollution than non-Hispanic White people. We show that the city of Denver, Colorado, displays similar race- and ethnicity-based air pollution disparities by using a combination of high-resolution satellite data, air pollution modeling, historical demographic information, and areal apportionment techniques. TROPOMI NO2 columns and modeled PM2.5 concentrations from 2019 are higher in communities subject to redlining. We calculated and compared Spearman coefficients for pollutants and race at the census tract level for every city that underwent redlining to contextualize the disparities in Denver. We find that the location of polluting infrastructure leads to higher populations of POC living near point sources, including 40% higher Hispanic and Latino populations. This influences pollution distribution, with annual average PM2.5 surface concentrations of 6.5 µg m-3 in census tracts with 0-5% Hispanic and Latino populations and 7.5 µg m-3 in census tracts with 60-65% Hispanic and Latino populations. Traffic analysis and emission inventory data show that POC are more likely to live near busy highways. Unequal spatial distribution of pollution sources and POC have allowed for pollution disparities to persist despite attempts by the city to rectify them. Finally, we identify the core causes of the pollution disparities to provide direction for remediation.

Evaluating combustion kinetics and quantifying fuel-N conversion tendency of shoe manufacturing waste.

Combustion is an effective and cost-efficient thermochemical conversion method for solid waste, showing promise for the resource utilization of shoe manufacturing waste (SMW). However, SMW is generally composed of different components, which can lead to unstable combustion and excessive pollutant emissions, especially NOx. To date, combustion characteristics, reaction mechanism and fuel nitrogen (fuel-N) conversion of different SMW components remain unclear. In this work, the combustion behavior of typical SMW components combustion was investigated using Thermogravimetric coupled with Fourier transform infrared spectrum (TG-FTIR). A simplified single-step reaction mechanism was proposed according to the temperature interval to estimate reaction mechanism of SMW. Additionally, the relationship between fuel-N conversion tendency and fuel properties was established. The results indicate that the values for the comprehensive combustion performance index (S) and flammability index (C) range from 1.65 to 0.44 and 3.98 to 1.37, respectively. This demonstrates the significant variability in combustion behavior among different SMW components. Cardboard, leather and sponge have higher values of S and C, suggesting a better ignition characteristic and a stable combustion process. During the combustion of SMW, nitrogen oxides (NO and N2O) are the main nitrogen-containing compounds in the flue gases, with NO being the major contributor, accounting for over 82.97 % of the nitrogen oxides. NO has a negative correlation with nitrogen content, but it is opposite for N2O, HCN and NH3. Furthermore, the conversion of NO, N2O and NH3 is proportional to logarithmic values of O/N, while its conversion to HCN is proportional to logarithmic values of VM/N. These findings facilitate the prediction of the fuel-N conversion of solid waste combustion. This work might shed light on combustion optimization and in-situ pollutant emission control in solid waste combustion.

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 machine learning-based ensemble model for estimating diurnal variations of nitrogen oxide concentrations in Taiwan.

Air pollution is inextricable from human activity patterns. This is especially true for nitrogen oxide (NOx), a pollutant that exists naturally and also as a result of anthropogenic factors. Assessing exposure by considering diurnal variation is a challenge that has not been widely studied. Incorporating 27 years of data, we attempted to estimate diurnal variations in NOx across Taiwan. We developed a machine learning-based ensemble model that integrated hybrid kriging-LUR, machine-learning, and an ensemble learning approach. Hybrid kriging-LUR was performed to select the most influential predictors, and machine-learning algorithms were applied to improve model performance. The three best machine-learning algorithms were suited and reassessed to develop ensemble learning that was designed to improve model performance. Our ensemble model resulted in estimates of daytime, nighttime, and daily NOx with high explanatory powers (Adj-R2) of 0.93, 0.98, and 0.94, respectively. These explanatory powers increased from the initial model that used only hybrid kriging-LUR. Additionally, the results depicted the temporal variation of NOx, with concentrations higher during the daytime than the nighttime. Regarding spatial variation, the highest NOx concentrations were identified in northern and western Taiwan. Model evaluations confirmed the reliability of the models. This study could serve as a reference for regional planning supporting emission control for environmental and human health.

NOx emissions prediction in diesel engines: a deep neural network approach.

The reduction of various nitrogen oxide (NOx) emissions from diesel engines is an important environmental issue due to their negative impact on air quality and public health. Selective catalytic reduction (SCR) has emerged as an effective technology to mitigate NOx emissions, but predicting the performance of SCR systems remains a challenge due to the complex chemistry involved. In this study, we propose using DNN models to predict NOx emission reductions in SCR systems. Four types of datasets were created; each consisted of five variables as inputs. We evaluated the models using experimental data collected from a diesel engine equipped with an SCR system. Our results indicated that the deep neural network (DNN) model produces precise estimates for exhaust gas temperature, NOx concentration, and De-NOx efficiency. Moreover, inclusion of additional input features, such as engine speed and temperature, improved the prediction accuracy of the DNN model. The mean absolute error (MAE) values for these parameters were 3.1 °C, 3.04 ppm, and 3.65%, respectively. Furthermore, the R-squared coefficient of determination values for the estimates were 0.912, 0.983, and 0.905, respectively. Overall, this study demonstrates the potential of using DNNs to accurately predict NOx emissions from diesel engines and provides insights into the impact of input features on the performance of the model.

Decreasing Production and Potential Urban Explosion of Nighttime Nitrate Radicals amid Emission Reduction Efforts.

Nighttime oxidation by nitrate (NO3) radicals has important ramifications on nocturnal aerosol formation and hence the climate and human health. Nitrate radicals are produced by the reaction of NO2 and O3. Despite large decreases in anthropogenic emissions of nitrogen oxides (NOx = NO + NO2), a previous study found significant increases in NO3 production (PNO3) from 2014 to 2019 in China, in contrast to decreasing trends in the U.S. and Europe. Using the summer observations from 2014 to 2022, we analyze the interannual variability of nocturnal PNO3 using a systematic framework, in which PNO3 is diagnosed as a function of odd oxygen (Ox = O3 + NO2) and the NO2/O3 ratio. We did not find an increase of PNO3 from 2014 to 2022 in China due to a continuous decrease in the NO2/O3 ratio, although PNO3 is modulated by the variation in Ox. Using in situ observations obtained in Beijing in 2007, we demonstrate the potential for an upsurge resembling an "explosion" in urban nighttime NO3 radicals amid emission reduction efforts.

Improved Spatial Resolution in Modeling of Nitrogen Oxide Concentrations in the Los Angeles Basin.

The extent to which emission control technologies and policies have reduced anthropogenic NOx emissions from motor vehicles is large but uncertain. We evaluate a fuel-based emission inventory for southern California during the June 2021 period, coinciding with the Re-Evaluating the Chemistry of Air Pollutants in CAlifornia (RECAP-CA) field campaign. A modified version of the Fuel-based Inventory of Vehicle Emissions (FIVE) is presented, incorporating 1.3 km resolution gridding and a new light-/medium-duty diesel vehicle category. NOx concentrations and weekday-weekend differences were predicted using the WRF-Chem model and evaluated using satellite and aircraft observations. Model performance was similar on weekdays and weekends, indicating appropriate day-of-week scaling of NOx emissions and a reasonable distribution of emissions by sector. Large observed weekend decreases in NOx are mainly due to changes in on-road vehicle emissions. The inventory presented in this study suggests that on-road vehicles were responsible for 55-72% of the NOx emissions in the South Coast Air Basin, compared to the corresponding fraction (43%) in the planning inventory from the South Coast Air Quality Management District. This fuel-based inventory suggests on-road NOx emissions that are 1.5 ± 0.4, 2.8 ± 0.6, and 1.3 ± 0.7 times the reference EMFAC model estimates for on-road gasoline, light- and medium-duty diesel, and heavy-duty diesel, respectively.

Electrification-Enhanced Low-Temperature NOx Storage-Reduction on Pt and K Co-Supported Antimony-Doped Tin Oxides.

NOx storage-reduction (NSR), a promising approach for removing NOx pollutants from diesel vehicles, remains elusive to cope with the increasingly lower exhaust temperatures (especially below 250 °C). Here, we develop a conceptual electrified NSR strategy, where electricity with a low input power (0.5-4 W) is applied to conductive Pt and K co-supported antimony-doped tin oxides (Pt-K/ATO), with C3H6 as a reductant. The ignition temperature for 10% NOx conversion is nearly 100 °C lower than that of the traditional thermal counterpart. Furthermore, reducing the power in the fuel-lean period relative to that in the fuel-rich period increases the maximum energy efficiency by 23%. Electrically driven release of lattice oxygen is revealed to play vital roles in multiple steps in NSR, including NO adsorption, desorption, and reduction, for improved NSR activity. This work provides an electrification strategy for developing high-activity NSR catalysis utilizing electricity onboard hybrid vehicles.

Spatial-temporal distribution characteristics of pollutants of heavy-duty diesel vehicles in urban road networks: a case study of Kunming City.

With the continuous promotion of urbanization in China, the economic level of small and medium-sized cities has been further improved. The transportation industry is crucial in promoting urban-rural integration and construction. Still, motor vehicle emissions also bring air pollution problems to cities, with heavy-duty diesel vehicle emissions severely impacting the urban environment. This study used a bottom-up approach to analyze the spatial emission characteristics of heavy-duty diesel vehicles under different road types in Kunming, a typical medium-sized city in China. A high-resolution emission inventory (1 km × 1 km) of heavy-duty diesel vehicles was developed using the vehicle emission inventory model (VEIN) and ArcGIS, and the vehicle emission standards were determined by the Weibull survival rate curve. The VEIN emission model was optimized using a velocity correction curve. The results showed that heavy-duty vehicles had a more significant impact on the emissions during the morning and evening peak hours, with low emission levels during the day and high emission levels at night and early morning. The total daily emissions of carbon monoxide (CO), hydrocarbons (HC), nitrogen oxides (NOx), and particulate matter (PM10 and PM2.5) from heavy-duty diesel vehicles in Motorway, Trunk, Primary, Secondary, and Tertiary were 14.44 tons, 5.26 tons, 4.78 tons, 7.02 tons, and 3.83 tons, respectively. China III heavy-duty diesel vehicles mainly contributed to CO, HC, NOx, and PM emissions. This study can be used as an essential reference for controlling the exhaust emissions of HDDVs in Kunming.