Multi-pollutant emission characteristics of non-road construction equipment based on real-world measurement

Single particle mass spectral signatures from on-road and non-road vehicle exhaust particles and their application in refined source apportionment using deep learning

With advances in vehicle emission control technology, updating source profiles to meet the current requirements of source apportionment has become increasingly crucial. In this study, on-road and non-road vehicle particles were collected, and then the chemical compositions of individual particles were analyzed using single particle aerosol mass spectrometry. The data were grouped using an adaptive resonance theory neural network to identify signatures and establish a mass spectral database of mobile sources. In addition, a deep learning-based model (DeepAerosolClassifier) for classifying aerosol particles was established. The objective of this model was to accomplish source apportionment. During the training process, the model achieved an accuracy of 98.49 % for the validation set and an accuracy of 93.36 % for the testing set. Regarding the model interpretation, ideal spectra were generated using the model, verifying its accurate recognition of the characteristic patterns in the mass spectra. In a practical application, the model performed hourly source apportionment at three specific field monitoring sites. The effectiveness of the model in field measurement was validated by combining traffic flow and spatial information with the model results. Compared with other machine learning methods, our model achieved highly automated source apportionment while eliminating the need for feature selection, and it enables end-to-end operation. Thus, in the future, it can be applied in refined and online source apportionment of particulate matter.

Data-driven method for estimating emission factors of multiple pollutants from excavators based on portable emission measurement system and online driving characteristic identification

This study aims to explore the factors that influence the emission characteristics of multiple pollutants from non-road mobile machinery (NRMM) under real-world conditions and to establish a data-driven method for calculating accurate emission factors. This research focused on NRMM excavators meeting the third-stage emission standards and identified the actual work characteristics of 108 excavators in different scenarios based on a self-developed testing system for 368,000 h. Additionally, a portable emission testing system (PEMS) was used to study the instantaneous emission characteristics under different driving styles and modes for 10 EC210 excavators with the largest engineering construction inventory. The results showed that the average time proportions of idling, working, and moving modes for excavators were 21 %, 66 %, and 13 %, respectively. The results also revealed that the instantaneous emission rates of multiple pollutants varied significantly under different driving styles and modes. Driving style affected the hydraulic pump power change rate through hydraulic pilot pressure, and engine load surge caused turbocharger response delay and in-cylinder combustion deterioration, which affected pollutant emissions. Driving mode affected the emission characteristics of idling, high-speed idling, moving, and working modes of excavators through the external characteristics corresponding to the engine speed gear set. The data-driven method for calculating emission factors differed from the traditional method for most indicators to varying degrees. In terms of fuel-based emission factors (EFfs), except for the EFfNOx indicator, which was 7.859 % higher than the traditional method, the other three indicators were significantly lower than the traditional method. In terms of power-based emission factors (EFps), except for EFpPM and EFpPN, the other two indicators were much higher than the traditional method. EFpCO and EFpNOx were 7.93 % and 20.332 % higher than the traditional method, respectively. It is recommended to use the data-driven method based on the actual driving data distribution to provide scientific support for accurately establishing the emission inventory.

Refined mass absorption cross-section of black carbon from typical non-road mobile machinery in China based on real-world measurements

Non-road mobile machinery (NRMM) is becoming a more prominent contribution of black carbon (BC), and mass absorption cross-section (MAC) as an essential parameter to characterize the BC optical property is still not clear. In this study, we explored the impacts of key factors on the MAC of BC based on real-world measurements from 41 typical NRMM. We characterized the organic carbon (OC) and elemental carbon (EC), and found MAC values of BC from NRMM increase as the OC/EC mass ratios increase, since the OC coating can enhance BC light absorption. With more stringent emission standards, the MAC values of all tested NRMM show a significant decreasing trend. Meanwhile, we found the absorption coefficients obtained by filter-based (bfilter) and in-situ-based (bin-situ) methods present good correlation for NRMM in this study, but bfilter are significantly higher than bin-situ when bfilter are above 40,000 Mm-1. Furthermore, we have refined the MAC values under different emission standards, and recommended a more appropriate MAC value (11.5 ± 3.4 m2/g) of NRMM at 550 nm wavelength, which is 1.5 times of the MAC value (7.5 m2/g) commonly used in previous studies. Our results will be indispensable for accurate BC quantification from NRMM and climate radiative effects prediction.

Emission inventory and air quality impact of non-road construction equipment in different emission stages

Non-road construction equipment (NRCE) is an important source of air pollution, and it is crucial to fully understand the impact of NRCE on atmospheric PM2.5 and O3 pollution. However, systematic assessment of the impact of NRCE emissions on the atmosphere is lacking, especially with the latest implementation of the Stage IV Standard, and current research progress is insufficient for the development of effective control measures. This study estimated NRCE emission inventories at different emission standard stages and their impact on the atmosphere, using the "2 + 26" cities as the case study area. The results showed that the total NRCE emissions of CO, NOx, VOC, and PM2.5 were 387, 418, 82, and 24 kt in 2015 and 319, 262, 62, and 15 kt in 2020 and are predicted to be 270, 226, 48, and 10 kt in 2025, respectively. Simulation results showed that the contributions of NRCE to NO3-, NO2, PM2.5, and O3 were 16.7 %, 18.9 %, 7.7 %, and 8.2 % in 2015 to 13.6 %, 18.4 %, 6.5 %, and 6.7 % in 2020, respectively. In both 2015 and 2020, NRCE emissions in southern cities showed greater impacts on the average concentrations in the "2 + 26" cities than those in northern cities. The contributions of local NRCE emissions to local PM2.5 and O3 concentrations in the 28 cities ranged from 30 %-59 % and 13 %-39 %, respectively. The O3 sensitivity estimated by the HDDM illustrated that nonlinear characteristics highlighted the importance of coordinated control of NOx and VOC and can inspire development of post-processing technology and electricity substitution. The belt-like area connecting Zhengzhou to Beijing showed higher exposure concentrations of PM2.5 and O3, and the concentration exposure in urban areas was much higher than that in the rural and other areas. The environmental impact assessment of NRCE emissions can provide guidance for its management and development.

Real-World Emission Characteristics of Full-Volatility Organics Originating from Nonroad Agricultural Machinery during Agricultural Activities

Nonroad agricultural machinery (NRAM) emissions constitute a significant source of air pollution in China. Full-volatility organics originating from 19 machines under 6 agricultural activities were measured synchronously. The diesel-based emission factors (EFs) for full-volatility organics were 4.71 ± 2.78 g/kg fuel (average ± standard deviation), including 91.58 ± 8.42% volatile organic compounds (VOCs), 7.94 ± 8.16% intermediate-volatility organic compounds (IVOCs), 0.28 ± 0.20% semivolatile organic compounds (SVOCs), and 0.20 ± 0.16% low-volatility organic compounds (LVOCs). Full-volatility organic EFs were significantly reduced by stricter emission standards and were the highest under pesticide spraying activity. Our results also demonstrated that combustion efficiency was a potential factor influencing full-volatility organic emissions. Gas-particle partitioning in full-volatility organics could be affected by multiple factors. Furthermore, the estimated secondary organic aerosol formation potential based on measured full-volatility organics was 143.79 ± 216.80 mg/kg fuel and could be primarily attributed to higher-volatility-interval IVOCs (bin₁₂-bin₁₆ contributed 52.81 ± 11.58%). Finally, the estimated emissions of full-volatility organics from NRAM in China (2021) were 94.23 Gg. This study provides first-hand data on full-volatility organic EFs originating from NRAM to facilitate the improvement of emission inventories and atmospheric chemistry models.

Real-world emission characteristics of semivolatile/intermediate-volatility organic compounds originating from nonroad construction machinery in the working process

Detailed emission characterization of semivolatile/intermediate-volatility organic compounds (S/IVOCs) originating from nonroad construction machines (NRCMs) remains lacking in China. Twenty-one NRCMs were evaluated with a portable emission measurement system in the working process. Gas phase S/IVOCs were collected by Tenax TA tubes and analyzed via thermal desorption-gas chromatography-mass spectrometry (TD-GC-MS). Particle phase S/IVOCs were collected by quartz filters and analyzed via GC-MS. The average emission factors (EFs) for fuel-based total (gas + particle phase) IVOCs and SVOCs of the assessed NRCMs were 221.45 ± 194.60 and 11.68 ± 10.67 mg/kg fuel, respectively. Compared to excavators, the average IVOC and SVOC EFs of loaders were 1.32 and 1.55 times higher, respectively. Compared to the working mode, the average IVOC EFs under the moving mode (only moving forward or backward) were 1.28 times higher. The IVOC and SVOC EFs for excavators decreased by 69.06% and 38.37%, respectively, from China II to China III. These results demonstrate the effectiveness of emission control regulations. In regard to individual NRCMs, excavators and loaders were affected differently by emission standards. The volatility distribution demonstrated that IVOCs and SVOCs were dominated by gas- and particle-phase compounds, respectively. The mode of operation also affected S/IVOC gas-particle partitioning. Combined with previous studies, the mechanical type significantly affected the volatility distribution of IVOCs. IVOCs from higher volatile fuels are more distributed in the high-volatility interval. The total secondary organic aerosol (SOA) production potential was 104.36 ± 79.67 mg/kg fuel, which originated from VOCs (19.98%), IVOCs (73.87%), and SVOCs (6.15%). IVOCs were a larger SOA precursor than VOCs and SVOCs. In addition, normal (n-) alkanes were suitably correlated with IVOCs, which may represent a backup solution to quantify IVOC EFs. This work provides experimental data support for the refinement of the emission characteristics and emission inventories of S/IVOCs originating from NRCMs.