Key Takeaways & Executive Findings
- •• • In 2022, Hebei's non-road mobile sources emitted 76.1×10^3 t CO, 20.6×10^3 t HC, 164.0×10^3 t NOx, 8.5×10^3 t PM2.5, 9.0×10^3 t PM10, and 2.4×10^3 t SO2, with agricultural machinery contributing >60% to CO, HC, PM2.5, and PM10, and railway locomotives contributing 50.9% to NOx. • • Spatial analysis identified Tangshan (21.3%), Shijiazhuang (15.7%), Cangzhou (11.6%), and Handan (11.6%) as the top four emitting cities, guiding targeted regional emission control strategies. • • Scenario analysis for 2030 shows that updating emission standards could reduce NOx and PM10 by ~35.0%, while phasing out old machinery could cut CO by 36.0%, and both electrification and phase-out scenarios significantly reduce HC emissions. • • The study provides a high-resolution emission inventory for Hebei, enabling policy-makers to prioritize control measures for agricultural machinery and railway locomotives, which are the dominant sources of multiple pollutants.
Abstract
Based on the 2022 activity data of non-road mobile sources in Hebei Province, this study employed the emission factor method recommended by the Guidelines to estimate emissions of CO, HC, NOx, PM2.5, PM10, and SO2. A comprehensive emission inventory was established, followed by spatial and uncertainty analyses. Scenario analysis, aligned with the 14th Five-Year Plan policies, was used to project emissions for 2030. The results indicate that non-road mobile sources in Hebei emitted 76.1×10^3 t of CO, 20.6×10^3 t of HC, 164.0×10^3 t of NOx, 8.5×10^3 t of PM2.5, 9.0×10^3 t of PM10, and 2.4×10^3 t of SO2. Agricultural machinery was the dominant contributor to CO, HC, PM2.5, and PM10, accounting for over 60.0% of CO emissions. Railway locomotives were the primary source of NOx, contributing 50.9%. For SO2, agricultural machinery and railway locomotives contributed 39.0% and 44.4%, respectively. The highest emitting cities were Tangshan (21.3%), Shijiazhuang (15.7%), Cangzhou (11.6%), and Handan (11.6%). Ship emissions were concentrated in Tangshan Port; civil aviation emissions were mainly in Shijiazhuang, Tangshan, Qinhuangdao, and Handan; railway emissions were distributed in Shijiazhuang, Baoding, and Handan. Under the updated emission standard scenario, NOx and PM10 emissions in 2030 could be reduced by approximately 35.0%. The phase-out of old machinery yielded the largest reduction in CO (36.0%), while both electrification and phase-out scenarios significantly impacted HC emissions.
1. Introduction
Non-road mobile sources, including agricultural machinery, railway locomotives, ships, and civil aviation aircraft, are significant contributors to atmospheric pollution in China. However, their emission inventories are often less characterized compared to on-road vehicles, leading to uncertainties in regional air quality modeling and policy formulation. In Hebei Province, a major industrial and agricultural hub, the lack of a comprehensive non-road emission inventory has hindered the development of effective emission reduction strategies. Existing studies have focused on other regions, such as the Pearl River Delta and Yangtze River Delta, but the unique source composition and activity patterns in Hebei necessitate a dedicated assessment.
This study addresses this gap by constructing a detailed emission inventory for non-road mobile sources in Hebei for the base year 2022, using the emission factor method recommended by national guidelines. By integrating activity data from multiple sources and applying spatial analysis, the study identifies key emission hotspots and source contributions. Furthermore, scenario analysis based on the 14th Five-Year Plan policies provides projections for 2030, offering critical insights into the potential effectiveness of different control measures. The findings not only support local air quality management but also contribute to the broader understanding of non-road emission characteristics in northern China.
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WANG Hongyu, ZHAO Yingfan, XU Ruiguang, PEI Boni, WANG Yucong, WANG Litao, WANG Qing, LIU Jingyun, LIU Yan, JIANG Zhiwen, ZHANG Yanjie, LI Ruikang (2026). Emission Inventory and Scenario Prediction of Non-Road Mobile Sources in Hebei Province. Environmental Chemistry. https://doi.org/10.7524/j.issn.0254-6108.2024112102
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Frequently Asked Questions
What are the dominant source categories for NOx emissions in Hebei's non-road mobile sources, and what are their contribution rates?
Railway locomotives are the dominant source of NOx, contributing 50.9% of total NOx emissions. This is followed by agricultural machinery, which also contributes significantly to other pollutants. The high contribution from railway locomotives suggests that targeted measures for this sector, such as electrification or stricter emission standards, could yield substantial NOx reductions.
How do the 2030 scenario projections for NOx and PM10 compare under the updated emission standard scenario?
Under the updated emission standard scenario, both NOx and PM10 emissions are projected to be reduced by approximately 35.0% by 2030. This indicates that implementing stricter emission standards for non-road mobile sources can be highly effective in mitigating these pollutants, which are critical for meeting air quality targets.
Which cities in Hebei have the highest non-road mobile source emissions, and what are their respective shares?
The four cities with the highest emissions are Tangshan (21.3%), Shijiazhuang (15.7%), Cangzhou (11.6%), and Handan (11.6%). These cities should be prioritized for emission control policies, as they collectively account for over 60% of the total emissions from non-road mobile sources in the province.
What is the spatial distribution of ship and civil aviation emissions in Hebei?
Ship emissions are mainly concentrated in Tangshan Port, reflecting the port's high activity. Civil aviation emissions are distributed across Shijiazhuang, Tangshan, Qinhuangdao, and Handan, corresponding to the locations of major airports. This spatial heterogeneity underscores the need for localized emission reduction strategies.
How does the phase-out of old machinery scenario impact CO and HC emissions compared to other scenarios?
The phase-out of old machinery scenario results in the largest reduction in CO emissions, with a reduction potential of 36.0%. For HC emissions, both the electrification scenario and the phase-out scenario have significant impacts. This suggests that replacing older, less efficient equipment with newer, cleaner technologies is a key strategy for reducing CO and HC emissions from non-road mobile sources.
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