Optical properties of vehicular brown carbon emissions: Road tunnel and chassis dynamometer tests

A comprehensive OBD data analysis framework: Identification and factor analysis of high-emission heavy-duty vehicles

On-Board Diagnostic (OBD) systems enable real-time monitoring of NOx emissions from heavy-duty diesel vehicles (HDDVs). However, few studies have focused on the root cause analysis of these emissions using OBD data. To address this gap, this study proposes an integrated analysis framework for HDDV NOx emissions that combines data processing, high-emission vehicle identification, and emission cause analysis. The framework employs a fuel-based window method to identify high-emission vehicles, while binning and machine learning techniques trace the causes of NOx emissions. A case study is conducted using data from 32 vehicles sourced from Tianjin On-Board Diagnostic Platform. Of these, five vehicles were identified as high emitters. A machine learning model was trained for each vehicle, with a detailed analysis conducted on three of them. The analysis involves a preliminary investigation of vehicle emissions status, followed by bin analysis to initially identify the causes of emissions. Finally, machine learning analysis is conducted, including the generation of individual conditional expectation (ICE) plots and multivariable partial dependence plots (PDPs), serving as a supplement to bin analysis when it cannot effectively pinpoint the causes of high emissions. This approach effectively uncovers the underlying factors within OBD big data. Using the analysis framework, we discover the identified causes of high NOx emissions were uneven heating of the Selective Catalytic Reduction (SCR) system and prolonged idling and high-power operation, catalyst degradation at 200-250 °C, and SCR system failure before 425 °C. The proposed framework offers a clear approach for identifying the causes of NOx emissions, aiding policymakers in implementing effective NOx control strategies for HDDVs.

A European aerosol phenomenology - 9: Light absorption properties of carbonaceous aerosol particles across surface Europe

Carbonaceous aerosols (CA), composed of black carbon (BC) and organic matter (OM), significantly impact the climate. Light absorption properties of CA, particularly of BC and brown carbon (BrC), are crucial due to their contribution to global and regional warming. We present the absorption properties of BC (bAbs,BC) and BrC (bAbs,BrC) inferred using Aethalometer data from 44 European sites covering different environments (traffic (TR), urban (UB), suburban (SUB), regional background (RB) and mountain (M)). Absorption coefficients showed a clear relationship with station setting decreasing as follows: TR > UB > SUB > RB > M, with exceptions. The contribution of bAbs,BrC to total absorption (bAbs), i.e. %AbsBrC, was lower at traffic sites (11-20 %), exceeding 30 % at some SUB and RB sites. Low AAE values were observed at TR sites, due to the dominance of internal combustion emissions, and at some remote RB/M sites, likely due to the lack of proximity to BrC sources, insufficient secondary processes generating BrC or the effect of photobleaching during transport. Higher bAbs and AAE were observed in Central/Eastern Europe compared to Western/Northern Europe, due to higher coal and biomass burning emissions in the east. Seasonal analysis showed increased bAbs, bAbs,BC, bAbs,BrC in winter, with stronger %AbsBrC, leading to higher AAE. Diel cycles of bAbs,BC peaked during morning and evening rush hours, whereas bAbs,BrC, %AbsBrC, AAE, and AAEBrC peaked at night when emissions from household activities accumulated. Decade-long trends analyses demonstrated a decrease in bAbs, due to reduction of BC emissions, while bAbs,BrC and AAE increased, suggesting a shift in CA composition, with a relative increase in BrC over BC. This study provides a unique dataset to assess the BrC effects on climate and confirms that BrC can contribute significantly to UV-VIS radiation presenting highly variable absorption properties in Europe.

Urban road BC emissions of LDGVs: Machine learning models using OBD/PEMS data

Urban Black Carbon (BC) emissions from light-duty gasoline vehicles (LDGVs) are challenging to quantify in real-world settings. This study employed a Portable Emission Measurement System (PEMS) to assess BC emissions from five LDGVs on urban roads. We also developed five machine learning (ML) models based on On-Board Diagnostics (OBD) data to predict BC emissions. Among these, the Random Forest (RF) model consistently demonstrates the best ability to predict BC emissions across all tested LDGVs, with R2 values exceeding 0.6. Integrating OBD-based ML models within vehicles could enable real-time BC monitoring and aid emission reduction strategies. We observed a strong correlation between BC emissions and engine parameters, such as engine speed and load (R2 values between 0.5 and 0.9). Furthermore, China VI standard-compliant LDGVs showed minor differences in BC emissions across urban road types. Vehicles equipped with gasoline direct injection (GDI) engines registered BC emission factors (EFs) of 0.141 ± 0.038 mg/km, an increase of 23.7% compared to their port fuel injection (PFI) counterparts, which averaged 0.114 ± 0.049 mg/km.

Variability in molecular composition and optical absorption of atmospheric brown carbon aerosols in two contrasting urban areas of China

Atmospheric brown carbon (BrC) aerosols were investigated at two urban sites in southern (Hefei) and northern (Shijiazhuang) China during summer and winter of 2019-2020 to explore regional variability in their compositional and optical properties. Organic matter in ambient PM2.5 samples were characterized at molecular level using ultrahigh performance liquid chromatography coupled with a diode array detector and an Orbitrap mass spectrometer. Although the molecular composition of organic aerosols varied substantially over different ambient environments, they were mainly composed by CHO and CHON species in positive ionization mode while CHO and CHOS species in negative mode. The mass absorption coefficients of BrC aerosols at wavelength range 250-450 nm were relatively higher for winter samples in both cities and for Shijiazhuang samples in both seasons, partly attributed to the higher concentration levels of anthropogenic air pollutants in these environments. The absorption Ångström exponents further revealed that BrC aerosols in winter seasons and in Shijiazhuang had a greater capacity of absorption at shorter wavelengths. A total of 26 BrC species with strong absorption were unambiguously identified from different environments, which mainly consisted of CHO, CHON, and CHN species and had higher degrees of unsaturation and lower degrees of oxidation. The presence and abundance of these BrC species varied dynamically across the seasons and cities, with a greater number of species presented in the winter of Shijiazhuang. The BrC species together contributed 12-26 % in the total absorbance of light-absorbing organic components at 250-450 nm. This study highlights the regional differences in BrC properties influenced by the sources and atmospheric processes, which should be taken into account to assess their climate impacts.