Vehicle Exhaust Emissions Research Articles

Optimum Material Selection for Battery Carrier System in Electric Buses

European Union has started to encourage countries to use public transportation in order to reduce air pollution and carbon emissions. Decreasing fossil fuel reserves make energy supply difficult. Vehicle exhaust emissions increase air pollution day by day. In this period when global warming is increasing in the global world, the transition to zero-emission electric vehicle production in public transportation has begun. Electric buses provide a clean, efficient and sustainable transportation alternative to diesel vehicles. One of the most important issues to consider in electric buses is the SoC (State of Charge) value of the battery, which is the range it will get from 0 to 100 when fully charged. Reducing weight in electric buses is one of the factors that increases the range value. For this reason, in the study, finite element analysis was carried out on the currently used battery carrier frame material EN10025 S355JR, alternatively aluminium 6061-T6, aluminium 7075-T6 and carbon fiber reinforced PEEK materials to lighten the vehicle weight. As a result of the analysis, 65.54% and 73.07% lightness were achieved with aluminum 6061-T6 and carbon fiber reinforced PEEK materials, respectively. When the analysis results were compared in terms of cost, manufacturability and lifetime, the most suitability for mass production was evaluated.

Variation characteristics, source analysis, and health risk assessment of carbonyl compounds in Zhangjiajie National Forest Park, China

This study presents a comprehensive analysis of carbonyls in the atmosphere of Zhangjiajie Forest from May 2016 to January 2017 following the Environmental Protection Agency (EPA) TO-11 A guidelines, including pollution characteristics, human health risks, and sources. The dominant carbonyl compounds were formaldehyde, acetaldehyde, and acetone, with average concentrations of 5.94, 1.48, and 4.24 μg/m3, respectively. A seasonal variation was observed in the total carbonyl compound concentrations in the order summer > autumn > spring > winter. The diurnal patterns showed that the concentration of the main carbonyl compounds peaked in the afternoon. The ratios of formaldehyde/acetaldehyde (C1/C2) and acetaldehyde/propionaldehyde (C2/C3) ranged from 2.82 to 5.66 and 0.76–3.14; this indicates that compared with anthropogenic sources, volatile organic compounds emitted from natural sources contributed more significantly to atmospheric carbonyl compounds in the Zhangjiajie Forest. Correlation analysis of the three main carbonyl compounds with common air pollutants revealed that formaldehyde and acetaldehyde have similar secondary sources for photochemical reactions. In autumn and winter, acetaldehyde and acetone were affected by fuel combustion and vehicle exhaust emissions. Reverse trajectory, potential source contribution function, and concentration-weighted trajectory analyses revealed that carbonyl compounds in the atmosphere of Zhangjiajie Forest were mainly polluted locally in spring and autumn and influenced by the urban air mass from the northeast in summer and winter. The health risk assessment showed that the lifetime carcinogenic risk of formaldehyde and acetaldehyde exceeded the acceptable level and that long-term exposure to this concentration poses serious carcinogenic risks. The findings of this study shed light on the characteristics of atmospheric carbonyl compound pollution in subtropical forest areas, and provide scientific basis for formulating pollution control measures.

DEVELOPMENT OF A PORTABLE MOTOR VEHICLE EMISSION TEST SYSTEM BASED ON ARDUINO WITH ANDROID INTERFACE

This study takes a comprehensive approach by proposing the design of an innovative emission test tool for motorized vehicles. The primary objective of this tool's design is to establish an alternative emission testing apparatus based on Arduino AT-Mega 2560, proficient in capturing vehicle exhaust emissions. The underlying methodology involves an in-depth investigation of various components, including the MQ2 and MQ7 sensors, microcontrollers, and supplementary sensors. This meticulous observational process aims to unravel the fundamental principles that govern the functionality of these components. Subsequently, the study advances to the prototyping phase, manifesting in the creation of an Android-based emission test system. This system capitalizes on the integration of Arduino programming and App Inventor technology. The integrated system is devised to facilitate sensor data acquisition. The empirical results of the tests indicate that the developed tool effectively measures hydrocarbon gas and carbon monoxide gas concentrations, yielding readings of 6.31% and 3.73%, respectively, under engine conditions ranging from 1500 to 3000 rpm with error in regions 1.4% and 5.1% compared to a commercial instrument. However, during the testing phase, certain challenges surfaced. Notably, the presence of water particles within the tool, coupled with the generation of heat due to the accommodated exhaust gases, increased the temperature within the tool's enclosure. Consequently, the sensors' temperature escalated, resulting in erratic sensor behavior and unstable readings. Nonetheless, a significant advantage of the proposed tool lies in its real-time data visualization capability, which is particularly accessible through Android smartphones. This feature enhances the immediacy of test results, facilitating prompt analysis and decision-making. In conclusion, this study lays the groundwork for an innovative emission testing tool that demonstrates promise in addressing the air quality degradation stemming from vehicular emissions.

Microstructure and chemical composition of deposited particulate matter from gasoline and diesel vehicle exhaust emissions

A comprehensive physicochemical characterization of transport-emitted aerosols containing in vehicle exhaust emissions derived from the combustion of fuels such as diesel, premium gasoline, and unleaded gasoline was performed in this study by employing a range of analytical techniques (Laser granulometry, X-ray Fluorescence (XRF), Fourier Transform Infrared (FTIR), X-ray diffraction (DRX), Scanning Electron Microscopy (SEM), and Thermogravimetry). The X-ray diffractogram of diesel (D) derived aerosols showed an amorphous structure while those of unleaded gasoline (UG) and premium gasoline (PG), showed amorphous crystalline phases. The chemical composition of D, PG and UG derived particles was dominated by aliphatic C-H groups of alkanes with relatively low C=O groups of carboxylic acids, ketones, aldehydes, esters, lactones, and sulphate (SO4 2-) inorganic salts. The nitrogen-containing functionality (NO3-) was specific to particles of PG and UG. Laser particle size analysis showed fine particle sizes (Range) generated from diesel exhausts, thus making them dangerous when inhaled, as they can penetrate deeply into the human airways and become incorporated into the blood stream damaging other viscera.

Health risk assessment of heavy metals in soils and crops in a mining area (Au-Ag-Cu-trona-oil et al.) of the Nanyang Basin, Henan Province, China

Heavy metal distribution in mining areas has always been a hot research topic due to the special environment of these areas. This study aims to explore the impact of heavy metal pollution on soils and crops in the study area, ensure the safety of local crops and the health of local residents, and provide a basis for the subsequent environmental restoration and the prevention and control of environmental pollution. Based on the analysis of the heavy metal concentrations in local soils and crops, the study investigated the spatial distribution, pollution degrees, and potential ecological risks of heavy metals in the farmland of a mining area in the southeastern Nanyang Basin, Henan province, China explored the sources of heavy metals and assessed the health risks caused by crop intake. The results of this study are as follows. The root soils of crops in the study area suffered heavy metal pollution to varying degrees. The degree of heavy metal pollution in maize fields is higher than that in wheat fields, and both types of fields suffer the most severe Cd pollution. Moreover, the root soils of different crops suffer compound pollution. The root soils in the maize fields suffer severe compound pollution at some sampling positions, whose distribution is similar to that of the mining area. Cd poses the highest potential ecological risks among all heavy metals, and the study area mainly suffers low and moderate comprehensive potential ecological risks. The principal component analysis (PCA) shows that the distribution of Zn, Cd, Pb, and As in soils of the study area is mainly affected by anthropogenic factors such as local mining activities; the distribution of Cr and Ni is primarily controlled by the local geological background; the distribution of Hg is mainly affected by local vehicle exhaust emissions, and the distribution of Cu is influenced by both human activities and the geological background. Different cereal crops in the study area are polluted with heavy metals dominated by Cd and Ni to varying degrees, especially wheat. As indicated by the health risk assessment results, the intake of maize in the study area does not pose significant human health risks; however, Cu has high risks to human health, and the compound heavy metal pollution caused by the intake of wheat in the study area poses risks to the health of both adults and children. Overall, the soils and crops in the study area suffer a high degree of heavy metal pollution, for which mining activities may be the main reason.©2023 China Geology Editorial Office.

A bilinear interpolation model for estimating commercial vehicles' fuel consumption and exhaust emissions

In this paper, an estimation model based on bilinear interpolation of data from the EMEP/EEA air pollutant emission inventory guidebook database regarding fuel consumption and exhaust emissions of commercial vehicles in different operating conditions is developed. Depending on vehicle's average speed, road slope and load factor, the model determines the fuel consumption and emissions for the observed vehicle type, category and emission standard. In order to validate the model, recordings on actual commercial vehicles were made in real operating conditions on two urban public transport bus lines in Belgrade. Comparison of the estimated and actual results from vehicle operation substantiate satisfactory validation of the model, making it suitable for logistics managers in operational and tactical planning as easy to use decision making tool allowing better sustainability and lower environmental footprint.

Polycyclic aromatic hydrocarbons in the sediments of highly polluted coastal area in the Red Sea: levels, spatial distribution, and risk assessment.

The southern part of the Jeddah coast faces a range of pollution challenges that can impact the water quality and ecosystem in the area. Pollution sources are represented mainly by treated wastewater (TWW), harbor activities, and atmospheric deposition from vehicle exhaustemissions. Polycyclic aromatic hydrocarbons (PAHs) are among the persistent organic pollutants that interfere with all environmental matrices and could cause humane mutagenic and carcinogenic effects. In the present study, 16 priority parent and 21 methylated PAHs (∑37PAHs) were assessed in the sediments of three hot spot coastal sites (Islamic Jeddah port, Al-Arbaeen, and Al-Shabab lagoons) to evaluate the factors affecting their spatial distribution, examine their probable sources and potential adverse effects. The total detected concentrations of ∑37PAHs ranged from 785.9 to 8359.8ng/g dw (average: 2296.3 ± 2017.3ng/g dw). The highest levels of ∑37PAHs were detected near TWW stations. The highest individual PAH congeners observed were phenanthrene, anthracene, and pyrene. PAH molecular diagnostic ratios pointed out different pyrogenic sources. In some specific stations, there was an overlap of petrogenic origin. The sediment samples contained high concentrations of methylated PAHs, with concentrations ranging from 558.42 to 6321.21ng/g dw and an average concentration of 1679.1 ± 1430.7ng/g dw. The sediment quality guidelines indicated that adverse biological effects are likely to occur at least at the two TWW disposal stations and the sediments in these stations are at risk. The values of the mutagenic equivalence quotient (MEQ) and toxic equivalence quotient (TEQ) of carcinogenic PAHs were recorded at 39.88 and 33.17ng/g, respectively.

Seasonal variations in concentrations of PM2.5 and tire wear particle of < 2.5 μm (TWP2.5) and polymeric components of PM2.5 at a bus stop

Air pollution is a global public health concern. Bus stops have heavy traffic and local emissions. PM2.5 was collected for 2 day at a bus stop each month for one year. Polymeric components in the PM2.5 samples were analyzed using pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS). Seasonal variations of the polymeric components, PM2.5 concentration, and concentration of tire wear particles <2.5 μm (TWP2.5) were investigated. Various polymeric components were found, including natural rubber (NR) and curing agents from TWPs, bitumen originating from asphalt pavement wear particles (APWPs), plant-related components (PRCs), and poly (ethylene-terephthalate) (PET) from PET fabric lint. The PM2.5 concentrations at the bus stop were 4–124% higher than those in the regional background, and the annual average PM2.5 concentration at the bus stop was more than 1.5 times higher than that in the regional background. Non-exhanst sources such as abrasion of tires, brakes, and asphalt pavements, vehicle exhaust emissions, and road dust resuspension accounted for the high PM2.5 concentrations. The PM2.5 concentrations during winter were higher than those in the other seasons. On average, the additional generation of PM2.5 at the bus stop was approximately 10 μg/m3, while that in the winter was approximately 18 μg/m3. The annual average TWP2.5 concentration at the bus stop was 1.28 μg/m3 (0.7–1.8 μg/m3), and the seasonal order was winter > fall > spring > summer. TWP2.5 is directly generated by the abrasion of tire tread through the friction with the road surface and also is produced from primary TRWPs by pressing and breaking them upon the contact with tires while driving vehicles. The annual average contribution of TWP2.5 to the PM2.5 concentration at the bus stop was 5.5% (1.6–14.0%).

Exhaust Emissions from Euro 6 Vehicles in WLTC and RDE—Part 2: Verification by Experimental Measurement

The subject of assessing exhaust emissions in real driving conditions has been relevant for a long time. Its introduction into approval tests focused attention on the comparative possibilities of tests performed on a chassis dynamometer and in road conditions. The article is a continuation of research on the possibilities of estimating emissions in the Real Driving Emission test based on emission data from Worldwide harmonized Light Vehicles Test Cycles. The first part discussed the possibility of comparing dynamic parameters in these tests, and the second part discussed the possibility of estimating road exhaust emissions. The work was done in two stages: the first stage involved the use of distance-specific emissions in individual parts of the WLTC test, and the second stage involved the use of exhaust emission rates as datasets divided into intervals defined by vehicle speed and acceleration. Comparative tests were performed for conventional vehicles (gasoline, diesel) and hybrid vehicles. A chassis dynamometer was used to carry out WLTC tests and PEMS equipment was used for the RDE tests. The exhaust gas components that had to be measured in road tests, namely: carbon monoxide, carbon dioxide, nitrogen oxides, and the number of particulate matter, were analyzed. Based on the data collected, parameters such as road emissions and the exhaust emissions rate were determined for each phase of the dynamometer test as well as the road test. Because of this, it was possible to compare the distance-specific exhaust emissions of each vehicle in the two emission tests. The comparison resulted in establishing that it is possible to estimate distance-specific exhaust emissions of conventional and hybrid vehicles in road test conditions, using only the results obtained in the approval test (for selected test phases). The research concluded that it is possible to estimate selected RDE test parameters based on the results obtained in the WLTC test for the tested vehicles.

Machine learning-based estimation of gaseous and particulate emissions using internally observable vehicle operating parameters

Measuring vehicular emissions is crucial for emission management and air quality control. However, conventional measurement equipment is costly and requires continuous maintenance. This study aims to address these challenges by introducing an Artificial Neural Network (ANN)-based methodology that using internally observable vehicle operating parameters to estimate emissions of particulate number (PN), nitrogen oxide (NOx), carbon monoxide (CO), and carbon dioxide (CO2). The research was conducted in three steps: (1) analyzing the correlations between vehicle operating parameters and exhaust emissions through emission map analysis, (2) developing and validating artificial neural networks (ANNs), and (3) examining the impact of vehicle driving conditions on emission predictions. The ANNs were rigorously validated across various operating and ambient conditions, including different temperatures, cold and warmed-up starts, and various driving scenarios. The prediction accuracy for CO2 is higher than other exhaust gases, with an error of <1% in all validation cases. The mean prediction error for NOx was 12.5%, with observed variations in accuracy depending on dynamic and thermal conditions. The ANN model accurately predicted the influence of thermal conditions on CO emissions, which exhibit a significant increase at lower temperatures.

Nitrogen dioxide (NO2) levels and health problems in street sellers around Universitas Negeri Gorontalo (UNG)

Air is the most critical factor for human survival, which must and must always be maintained for quality. If the air quality changes from normal, the air has been polluted. Transportation activities are one of the contributing factors to air pollution. UNG is one of the largest campuses in Gorontalo Province, which involves students in increasing the number of residents outside the area who have an impact on campus activities and an increase in the number of vehicles. In the UNG area, street vendors sell from 6 in the morning to midnight. This makes street vendors a group at risk of experiencing health problems due to exposure to NO2 from vehicle exhaust emissions. This type of research is observational with a descriptive study design. This research was conducted on Jalan Jendral Sudirman, around the UNG campus. The population in this study were all street vendors selling around the UNG campus, totaling 58 traders. The sampling technique uses the Total Sampling technique; the entire population is used as a sample. The results showed that the average concentration of NO2 around the UNG campus did not exceed the quality standards set by Government Regulation No. 22 of 2021, namely 0.2 mg/m3, and the most common health complaints experienced by respondents were coughing 43 people (74.1%), body aches and headaches 36 people (62.1%), runny nose 32 people (55.2%), sore throat 12 people (20.7%). Moreover, the least experienced was eye irritation in 2 people (3.4%). It is suggested that there should be monitoring of pollutant concentrations, and it is hoped that street vendors will use masks when selling and reduce selling time.

Quantifying the Nitrogen Sources and Secondary Formation of Ambient HONO with a Stable Isotopic Method.

Nitrous acid (HONO) is a reactive gas that plays an important role in atmospheric chemistry. However, accurately quantifying its direct emissions and secondary formation in the atmosphere as well as attributing it to specific nitrogen sources remains a significant challenge. In this study, we developed a novel method using stable nitrogen and oxygen isotopes (δ15N; δ18O) for apportioning ambient HONO in an urban area in North China. The results show that secondary formation was the dominant HONO formation processes during both day and night, with the NO2 heterogeneous reaction contributing 59.0 ± 14.6% in daytime and 64.4 ± 10.8% at nighttime. A Bayesian simulation demonstrated that the average contributions of coal combustion, biomass burning, vehicle exhaust, and soil emissions to HONO were 22.2 ± 13.1, 26.0 ± 5.7, 28.6 ± 6.7, and 23.2 ± 8.1%, respectively. We propose that the isotopic method presents a promising approach for identifying nitrogen sources and the secondary formation of HONO, which could contribute to mitigating HONO and its adverse effects on air quality.

Pengaruh Penggunaan Box Power Ignition terhadap Performa Sepeda Motor Injeksi

&lt;p&gt;&lt;em&gt;This study aims to (1) determine the effect of voltage variations on the power ignition box on the torque and power of the Honda Scoopy motorcycle in 2017. (2) determine the effect of variations in the voltage on the power ignition box on the fuel consumption of the Honda Scoopy motorcycle in 2017. (3) Knowing the effect of voltage variations on the power ignition box on exhaust emissions of the Honda Scoopy motorcycle in 2017. (4) Knowing the best voltage variations on the power ignition box are on power torque, fuel consumption, and exhaust emissions of the 2017 Honda Scoopy. Engine performance, including power torque, fuel consumption, and vehicle exhaust emissions, will be obtained by the maximum vehicle engine if the combustion system works optimally. One way to maximize the combustion system is using a power ignition box. The research method used is experimental. The research data was obtained using a sample of Honda Scoopy motorcycles in 2017. The independent variable used a power ignition box with voltage variations of 14.4 V, 14.7 V, 15.0 V, 15.3 V, 15.6 V, and 15 .9 V. The dependent variable is the power torque using a Dynamometer, fuel consumption using stationary test and road test on predetermined roads, and exhaust emissions using a Gas Analyzer. The results of this study are: (1) The maximum torque is obtained at a voltage variation of 15.6 V of 9.57 N.m, an increase of 8.5% from the standard state. The maximum power is obtained at the 15.6 V voltage variation of 6.63 HP, up to 8.5% from the standard state. (2) The maximum fuel consumption for the stationary test was obtained at a voltage variation of 14.7 V of 4.25 ml/min, decreased by 0.18 ml/min. The increase in average fuel consumption is at a value of 4.55 ml/min. The maximum fuel consumption for the road test was found at a voltage variation of 14.7 V of 30 m/ml increasing to 15 m from the standard state. The average increase in fuel consumption was 24.93 m/ml from standard conditions. (3) The maximum CO (carbon monoxide) of 0.3% at a voltage variation of 14.4 V, down 0.11% from standard conditions. The maximum HC (hydro carbon) is 16.00 ppm at a voltage variation of 15.0 V, down 15.33 ppm from standard conditions. Maximum CO2 (carbon dioxide) is 2.0% at a voltage variation of 15.9 V, down 0.10% from standard conditions. The maximum O2 (oxygen) is 9.25% at a voltage variation of 15.6 V, down 3.23% from standard conditions. The best configuration for the Box Power Ignition is at a voltage of 15.3 V.&lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;&lt;em&gt;Keywords:&lt;/em&gt;&lt;/strong&gt;&lt;em&gt; Honda Scoopy, Box Power Ignition, Torque and Power, Fuel Consumption, Exhaust Emissions.&lt;/em&gt;&lt;/p&gt;

Vehicle exhaust emissions: From peril to power

Vehicle exhaust emissions: From peril to power

Composition Characteristics of VOCs in the Atmosphere of the Beibei Urban District of Chongqing: Insights from Long-Term Monitoring

Reducing anthropogenic volatile organic compounds (VOCs) is the most effective way to mitigate O3 pollution, which has increased over the past decades in China. From 2012 to 2017, special stainless-steel cylinders were used to collect ambient air samples from the urban area of Beibei district, Chongqing. Three-step pre-concentration gas chromatography–mass spectrometry was used to detect the collected air samples. The composition, concentration, photochemical reactivity, and sources of VOCs in Beibei were analyzed. During the observation period, the annual average VOC concentration was 31.3 ppbv, which was at an intermediate range compared to other cities in China. Alkanes (36.8%) and aromatics (35.6%) were the most abundant VOC groups, followed by halo-hydrocarbons (14.4%) and alkenes (12.6%). The overall trend of seasonal distribution of VOC concentration was high in summer and autumn, and low in winter and spring, with a statistically significant difference between summer and winter concentrations. The ozone formation potential (OFP) showed that alkenes were the most active species, followed by aromatics and alkanes, and summer was the season with the highest OFP (131.6 ppbv). Three major emission sources were identified through principal component analysis (PCA), i.e., vehicle exhaust emissions (66.2%), fuel oil evaporation (24.8%), and industrial sources (9.0%). To ameliorate the air quality within the study area, concerted efforts should be directed towards curtailing traffic emissions and mitigating the release of alkenes, particularly emphasizing more stringent interventions during the summer season.

Characterization of photochemical losses of volatile organic compounds and their implications for ozone formation potential and source apportionment during summer in suburban Jinan, China

Volatile organic compounds (VOCs) undergo substantial photochemical losses during their transport from emission sources to receptor sites, resulting in serious implications for their source apportionment and ozone (O3) formation. Based on the continuous measurements of VOCs in suburban Jinan in August 2022, the effects of photochemical losses on VOC source contributions and O3 formation were evaluated in this study. The observed and initial concentrations of total VOCs (TVOC) were 12.0 ± 5.1 and 16.0 ± 7.4 ppbv, respectively. Throughout the observation period, alkenes had the most prominent photochemical losses (58.2%), followed by aromatic hydrocarbons (23.1%), accounting for 80.6% and 6.9% of the total losses, respectively. During high O3 episodes, the photochemical loss of VOCs was 6.9 times higher than that during the cleaning period. Alkene losses (exceeding 67.3%), specifically losses of isoprene, propylene, ethylene, and n-butene, dominated the total losses of VOCs during the O3 increase period. Eight sources of VOCs were identified by positive matrix factorization (PMF) based on the observed and initial concentration data (OC-PMF and IC-PMF, respectively). Concentrations of all emission sources in the OC-PMF were underestimated by 2.4%–57.1%. Moreover, the contribution of each emission source was over- or underestimated compared with that in case of the IC-PMF. The contributions of biogenic and motor vehicle exhaust emissions were underestimated by 5.3 and 2.8 percentage points, respectively, which was associated with substantial oxidation of the emitted high-reactive species. The contributions of coal/biomass burning and natural gas were overestimated by 2.4 and 3.9 percentage points, respectively, which were related to the emission of low-reactive species (acetylene, ethane, and propane). Based on our results, the photochemical losses of VOCs grossly affect their source apportionment and O3 formation. Thus, photochemical losses of VOCs must be thoroughly accounted to establish a precise scientific foundation for air-pollution control strategies.

A comprehensive observation on the pollution characteristics of peroxyacetyl nitrate (PAN) in Beijing, China

Peroxyacetyl nitrate (PAN) is a typical secondary photochemical product in the atmospheric environment with significant adverse effects on human health and plant growth. In this study, PAN and other pollutants, as well as meteorological conditions were observed intensively from August to September in 2022 at a typical urban sampling site in Beijing, China. The mean and maximum PAN concentrations during the observation period were 1.00 ± 0.97 ppb and 4.84 ppb, respectively. Severe photochemical pollution occurred during the observation period, with the mean PAN concentration about 3.1 times higher than that during the clean period. There was a good positive correlation between O3 and PAN, and their correlation was higher during the O3 exposure period than that during the clean period. The simulated results by box-model coupled with the Master Chemical Mechanism (MCM v3.3.1) showed that the O3-related reactions were the largest sources of OH radicals during O3 exposure period, which was conducive to the co-contamination of PAN and O3. Acetaldehyde (CH3CHO) and methylglyoxal (MGLY) were the largest OVOCs precursors of peroxyacetyl radicals (PA), with the contributions to the total PA generated by OVOCs about 67 % – 83 % and 17 % – 30 %, respectively. The reduction of emissions from liquefied petroleum gas (LPG) and solvent usage has the highest reduction effect on PAN and O3, followed by the control of gasoline vehicle exhaust emissions. This study deepens the understanding of the PAN photochemistry in urban areas with high O3 background conditions and the impact of anthropogenic activities on the photochemical pollution. Meanwhile, the findings of this study highlight the necessity of strengthening anthropogenic emissions control to effectively reduce the co-contamination of PAN and O3 in Beijing in the future.

Machine learning reveals the effects of drivers on PM2.5 and CO2 based on ensemble source apportionment method

Air pollution and climate change are currently common global concern issues. Understanding the main pollution sources of fine particulate matter (PM2.5) and carbon dioxide (CO2), and the drivers that accelerate their concentrations can provide novel insights into the co-benefits of air-quality improvement and climate change mitigation. Hence, in this study, we conducted source apportionment of PM2.5 and CO2, and obtained impacts of common sources on PM2.5-CO2 using an ensemble source apportionment method. Then, machine learning (ML) methods were utilized to study the effects of different drivers (including main pollution sources and meteorological factors) on PM2.5 and CO2. The results demonstrate that coal combustion (40%), vehicle exhaust (37%), and industrial emissions (23%) were the main common sources of PM2.5 and CO2. Controlling the combustion of fossil fuels and long-term vehicle electrification still hold significant importance for reducing pollutions and carbon emissions. The results of data-driven ML models show that temperature had the highest effect among meteorological factors, reflecting the seasonal changes of CO2 and PM2.5. The study provides a feasible framework for exploring co-benefits of clean air policies on greenhouse gas emissions based on online observation dataset.

Improvement of a Rapid Method of Detecting Gasoline Detergency Based on the Image Recognition.

The detergency of motor gasoline is closely related to vehicle exhaust emissions and fuel economy. This paper proposed an improved method for the rapid detection of gasoline detergency based on the deposit images of test gasoline on aluminum plates produced by a multichannel gasoline detergency simulation test (MGST). The detection algorithm system was structured to recognize the deposit plate images by computer vision based on the convolutional neural networks (CNNs). Compared with the traditional simulation test, the improved MGST method resulted in significant reductions in fuel consumption, cost, and test time. The performance of three transfer learning models (Inception-ResNet-V2, Inception-V3, and ResNet50-V2) and a customized CNN was evaluated in the detection algorithm system, and their detection accuracies reached 94, 94, 88, and 82%. Inception-RsNet-V2 was selected due to its higher accuracy and better robustness. Based on the model interpretation, it is evident that the model undergoes feature extraction from the sediment deposits on the deposit plate. Subsequently, it employed the acquired deposit features to accurately detect gasoline samples that failed to meet detergency standards. This approach was proved to be effective in enhancing the detection process and ensuring reliable results for gasoline detergency evaluation. It is beneficial to environmental protection regulators for managing market gasoline detergency and urban mobile source pollution. In addition, a deposit plate image database should be established to further improve the detection model performance during the environmental regulation.

HYDROGEN GENERATOR FOR INTERNAL COMBUSTION ENGINE

The test of the hydrogen generator on the internal combustion engine of the car is considered in the work. In the machine, HHO and fuel are jointly burned to reduce fuel consumption and exhaust emissions. Numerous studies prove the undeniable advantage of using a car using a hydrogen generator compared to traditional fuels. The use of a lean combustible mixture in internal combustion engines reduces the volume of exhaust gases and increases thermal efficiency. Experimental results show that by adding KOH with a concentration of (35-50 g/l) to the cell, fuel consumption can be reduced (8.4-43.6%), and exhaust pollutant emissions are reduced. Thus, adding hydrogen to an internal combustion engine can reduce fuel consumption and vehicle exhaust emissions.