Seasonal Variation and Source Apportionment of Inorganic and Organic Components in PM2.5: Influence of Organic Markers Application on PMF Source Apportionment

PM2.5 samples collected over a 1-year period in a Chinese megacity were analyzed for organic carbon (OC), elemental carbon (EC), water soluble ions, elements and organic markers such as polycyclic aromatic hydrocarbons (PAHs), hopanes, steranes, and n-alkanes. In order to study the applicability of organic markers in source apportionment, this study analyzes the relationship between organic and inorganic components, and four scenarios were implemented by incorporating different combinations of organic and inorganic tracers. A positive correlation of SO42− with 4 rings PAHs can prove that coal burning directly emits a portion of sulfate. A positive correlation of NO3− with 5-7 rings PAHs are found, implying collective impacts from the vehicle source. The concentrations of OC and EC positively correlate with the 5-7 rings PAHs and Cu and Zn, which proves that part of Cu and Zn comes from vehicle emissions. Five factors were identified by incorporating only conventional components, including secondary source (SS, 30%), urban fugitive dust (UFD, 14%), cement dust (CD, 4%), traffic source (TS, 19%) and coal combustion (CC, 14%). Six factors were identified by incorporating conventional components and PAHs, including SS (28%), UFD (15%), CD (4%), CC (13%), gasoline vehicles (GV, 12%) and diesel vehicles (DV, 10%). Eight factors were identified by incorporating conventional components, PAHs, hopanes, and n-alkanes, including SS (26%), UFD (17%), CD (3%), GV (14%), DV (8%), immature coal combustion (ICC, 5%), mature coal combustion (MCC, 10%) and biogenic source (BS, 1%).

A multi-pronged approach to strengthen diesel vehicle emission monitoring

China is constructing an vehicle emission monitoring system, aimed at combining remote OBD, periodic inspections, remote sensing and roadside checks. In this study, the exhaust emissions from diesel vehicles were investigated and analysed.

Biodiesel-Alkaline Transesterification Process for Methyl Ester Production

The world needs to increase renewable and alternative fuel sources such as Biomass, Bioethanol, and Biodiesel to compete with fossil fuels. Biodiesel is an important renewable fuel source since it can be used in regular diesel vehicles without requiring engine modifications. Conventional biodiesel production takes around 90 min of reaction time. A longer reaction time is not suitable for commercial production. Furthermore, traditional products such as oil react with biodiesel methoxide to produce a maximum of 90% biodiesel yield. As the catalyst is not involved with the reaction, pure methanol and methoxide (methanol with KOH catalyst) are separately added to the system to enhance the pre-reaction step. By changing the methanol to methoxide ratio, biodiesel is produced, and yield is calculated. The highest yield, which is 95%, is obtained with a 5:15% methanol to methoxide ratio. The total reaction time with the new experimental procedure is only 20 min. That is a significant reduction by saving operating costs such as energy consumption. Produced biodiesel show similar properties to that of standard biodiesel.

Prediction of Transient NOx Emission from Diesel Vehicles Based on Deep-Learning Differentiation Model with Double Noise Reduction

For diesel engines, accurate prediction of NOx (Nitrogen Oxides) emission plays an essential role in virtual NOx sensor development and engine design under situations of actual road driving. However, due to the randomness and uncertainty in the driving process of diesel vehicles, it is difficult to make predictions about NOx emissions. In order to solve this problem, this paper proposes differential models for noise reductions of NOx emissions in time series. First, according to the internal fluctuation of time series, use SSA (Singular Spectrum Analysis) to reduce the noises of the original time series; second, use ICEEMDAN (Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) to decompose the noise-reducing data into several relatively stable subsequences; third, use the sample entropy to calculate the complexity of each subsequence, and divide the sequences into high-frequency ones and low-frequency ones; finally, use GRU (Gated Recurrent Unit) to complete the prediction of high-frequency sequences and SVR (Support Vector Regression) for the prediction of low-frequency sequences. To obtain the final models, integrate the prediction results of the subsequences. Make comparisons with five single models, SSA single-processing models, and ICEEMDAN single-processing models. The experimental results show that the proposed model can predict the instantaneous NOx emissions of diesel engines better than the single model and the model processed by SSA, and the differentiated model can effectively improve the execution speed of the model.

Features Importance Analysis of Diesel Vehicles’ NOx and CO2 Emission Predictions in Real Road Driving Based on Gradient Boosting Regression Model

In order to have an accurate and fast prediction of the artificial intelligence (AI) model, the choice of input features is at least as important as the choice of model. The effect of input features selection on the emission models of light diesel vehicles driven on real roads was investigated in this paper. The gradient boosting regression (GBR) model was used to train and to predict the emissions of nitrogen oxide (NOx), carbon dioxide (CO2), and the fuel consumption of real driving diesel vehicles in urban scenarios, the suburbs, and on highways. A portable emissions measurement system (PEMS) system was used to collect data of vehicles as well as environmental conditions. The vehicle was run on two routes. The model was trained with the first route data and was used to predict the emissions of the second route. There were ten features related to the NOx model and nine features associated with the CO2 model. The importance of each feature was sorted, and a different number of features were used as input to train the models. The best NOx model had the coefficient of determination (R2) values of 0.99, 0.99, and 0.99 in each driving pattern (urban, suburbs, and highways). Predictions of the second route had the R2 values of 0.88, 0.89, and 0.96 respectively. The best CO2 model had the R2 values of 0.98, 0.99, and 0.99 in each driving pattern, respectively. Predictions of the second route had the R2 values are 0.79, 0.82, and 0.83, respectively. The most important features for the NOx model are mass air flow rate (g/s), exhaust flow rate (m3/min), and CO2 (ppm), while the important features for the CO2 model are exhaust flow rate (m3/min) and mass air flow rate (g/s). It is noted that the regression models based on the top three features may give predictions very close to the measured data.

Impact of the Mobility Restrictions in the Palestinian Territory on the Population and the Environment

This paper analyzes the mobility restrictions in the Palestinian territory on the population and the environment. The literature review shows a scientific concern for this issue, with an emphasis on describing mobility barriers and the severe conditions experienced by the population due to these barriers as well as the impact of mobility restrictions on employment opportunities. On the other hand, the literature review also shows a deficit in quantitative analysis of the effects of mobility restrictions on the environment, particularly on energy consumption and greenhouse gas emissions. This paper aims to fill this gap through a quantitative analysis by including data collection about mobility restrictions, using network analysis to determine the impact of these restrictions on inter-urban mobility, and analysis of the resulting energy consumption and CO2 emissions. The results show that mobility restrictions induce a general increase in energy consumption and CO2 emissions. The average value of this increase is about 358% for diesel vehicles and 275% for gasoline vehicles.

A New Approach for Static NOx Measurement in PTI

NOx emissions in vehicles are currently only controlled through the homologation process. There is a lack of knowledge to assess and control real NOx emissions of vehicles reliably. Even if vehicles in EU-27 are subject to Periodical Technical Inspection (PTI), NOx are not among the pollutants currently being controlled. For PTIs, tests need to be simple, quick, inexpensive, representative, and accurate. Ideally, tests need to be carried out under static conditions, without the need for a power bench or complex equipment. In this paper, a new approach for measuring NOx in PTI is proposed. The method has been developed and validated at a PTI Spanish station to ensure feasibility and repeatability. This method is based on the relationship between the “% engine load” value and exhaust NOx concentration at idle engine speed. Starting from the state of minimum possible power demand in a vehicle (idling and without any consumption), a load state with an average 98% increase in engine power demand is generated by connecting elements of the vehicle’s equipment. The relationship between power demand (through the “% engine load” value) and NOx concentration is then analyzed. The quality and representativity of this relationship have been checked with a p-value lower than 0.01. The method has been compared with a different NOx measurement technique, based on the simulation on a test bench and the ASM 2050 cycle, showing better performance in terms of repeatability and representativeness. The “% engine load” dispersion with the new approach is 7%, which ensures the reliability and repeatability of the method. The results show that the proposed method could be a valuable tool in PTI to detect high NOx emitting vehicles and to obtain information from the diesel vehicles fleet.