High spatiotemporal resolution vehicular emission inventory in Beijing-Tianjin-Hebei and its surrounding areas (BTHSA) during 2000-2020, China

Annual dynamics of global remote industrial heat sources dataset from 2012 to 2021

The spatiotemporal distribution of industrial heat sources (IHS) is an important indicator for assessing levels of energy consumption and air pollution. Continuous, comprehensive, dynamic monitoring and publicly available datasets of global IHS (GIHS) are lacking and urgently needed. In this study, we built the first long-term (2012-2021) GIHS dataset based on the density-based spatiotemporal clustering method using multi-sources remote sensing data. A total of 25,544 IHS objects with 19 characteristics are identified and validated individually using high-resolution remote sensing images and point of interest (POI) data. The results show that the user's accuracy of the GIHS dataset ranges from 90.95% to 93.46%, surpassing other global IHS products in terms of accuracy, omission rates, and granularity. This long-term GIHS dataset serves as a valuable resource for understanding global environmental changes and making informed policy decisions. Its availability contributes to filling the gap in GIHS data and enhances our knowledge of global-scale industrial heat sources.

High spatiotemporal resolution ammonia emission inventory from typical industrial and agricultural province of China from 2000 to 2020

As a typical industrial and agricultural province, Shandong is one of China's most seriously air-polluted regions. One comprehensive ammonia emission inventory with a high spatial resolution (1 km × 1 km) for 136 county-level administrative divisions in Shandong from 2000 to 2020 is developed based on county-level activity data with the corrected and updated emission factors of seventy-seven subcategories. Annual ammonia emissions decrease from 1003.3 Gg in 2000 to 795.9 Gg in 2020, with an annual decrease rate of 1.2 %. Therein, the ammonia emissions associated with livestock and farmland ecosystems in 2020 account for 50.8 % and 32.9 % of the provincial total ammonia emission, respectively. Laying hen and wheat are the livestock and crop with the highest ammonia emissions, accounting for 23.3 % and 36.3 % of ammonia emissions from livestock and the application of synthetic fertilizers, respectively. Furthermore, waste treatment, humans and vehicles are the top three ammonia emission sources in urban areas, accounting for 5.0 %, 4.7 % and 1.3 % of total ammonia emissions, respectively. The spatial distribution of grids with high ammonia emissions is consistent with the distribution of intensive farms. Significant emission intensity areas mainly concentrate in western Shandong (e.g., Caoxian of Heze, Qihe of Dezhou, Yanggu of Liaocheng, Liangshan of Jining) due to the large area of arable land and the high levels of agricultural activity. Overall, prominent seasonal variability characteristics of ammonia emission are observed. Ammonia emissions tend to be high in summer and low in winter, and the August to January-emission ratio is 5.6. The high temperature and fertilization for maize are primarily responsible for Shandong's increase in ammonia emissions in summer. Finally, the validity of the estimates is further evaluated using uncertainty analysis and comparison with previous studies. This study can provide information to determine preferentially effective PM2.5 control strategies.

Database of emission factors of volatile organic compound (VOC) species in motor vehicle exhaust in China

The synergistic strategy for fine particulate matter (PM2.5) and O3 pollution prevention and control has emerged as a pivotal approach in combating air pollution. Volatile organic compounds (VOCs) serve as crucial precursors to both O3 and secondary organic aerosols (SOAs), with motor vehicles representing one of their significant sources. In this study, a standard for establishing a database of VOC species emission factors for motor vehicles was developed, and a database containing 134 VOC species was constructed through field tests and literature surveys. The VOC emissions of light-duty gasoline passenger vehicles (LDGPVs) comprised primarily alkanes and aromatics. The VOC emissions of light-duty diesel trucks (LDDTs) comprised mostly alkanes. Regarding low-speed trucks, 3-wheel vehicles, medium-duty diesel trucks (MDDTs) and heavy-duty diesel trucks (HDDTs), their VOC emissions comprised mainly oxygenated volatile organic compounds (OVOCs). The update of emission standards resulted in a reduction in VOC species emission factors while altering the composition of VOCs. Attention should be directed toward isopentane, benzene and dichloromethane emitted by LDGPVs, dodecane, undecane, ethene and propene emitted by LDDTs, and acetaldehyde emitted by HDDTs. VOC species originating from LDGPVs were more dispersed than those originating from LDDTs and HDDTs. In addition, variations in VOC species were observed among motor vehicles with different fuel types. Toluene, ethene, benzene, m,p-xylene, isopentane, hexanal, ethyne and 1,2,4-trimethylbenzene were the predominant VOC species emitted by gasoline vehicles. Diesel vehicles emitted mainly dodecane, formaldehyde, propene, undecane, acetaldehyde, ethene, decane and benzene. The results could enhance our comprehension of the emission characteristics of VOC species originating from motor vehicles and provide data support and a scientific foundation for achieving synergistic PM2.5 and O3 pollution prevention and control.

Driving factors and clustering analysis of expressway vehicular CO2 emissions in Guizhou Province, China

Expressways are essential for intercounty trips of passenger travel and freight mobility, which are also an important source of vehicular CO2 emissions in transportation sector. This study takes the expressway system of Guizhou Province as the research objective, and establishes the multi-year expressway vehicular CO2 emission inventories at the county level from 2011 to 2019. We employ the extended STIRPAT model incorporating ridge regression to identify driving factors from six different aspects, and then utilize the affinity propagation cluster method to conduct the differentiation research by dividing Guizhou's counties into four clusters. Based upon clustering analysis, localized and targeted policies are formulated for each cluster to reduce expressway vehicular CO2 emissions. The results indicate that generally: (1) Guizhou's expressway vehicular CO2 emissions manifest a continuously upward trend during 2011-2019. Small-duty passenger vehicle (SDV), light-duty truck (LDT), and heavy-duty truck (HDT) contribute to the largest CO2 emissions in eight vehicle types. (2) GDP and population are the foremost two positive driving factors, followed by urbanization rate and expressway length. The proportion of secondary industry is also a positive driver, but that of tertiary industry exhibits an opposite effect. (3) Regional disparity exists in four county clusters of Guizhou Province. Efficient policies are proposed, such as improving the layout and infrastructure of transportation hubs, promoting multimodal integration, and implementing industrial upgrading as per regional advantages. Sustainable expressway vehicular CO2 emission reduction is realized from both the source of industry and low-carbon modes of transport.

Spatial-temporal distribution characteristics of pollutants of heavy-duty diesel vehicles in urban road networks: a case study of Kunming City

With the continuous promotion of urbanization in China, the economic level of small and medium-sized cities has been further improved. The transportation industry is crucial in promoting urban-rural integration and construction. Still, motor vehicle emissions also bring air pollution problems to cities, with heavy-duty diesel vehicle emissions severely impacting the urban environment. This study used a bottom-up approach to analyze the spatial emission characteristics of heavy-duty diesel vehicles under different road types in Kunming, a typical medium-sized city in China. A high-resolution emission inventory (1 km × 1 km) of heavy-duty diesel vehicles was developed using the vehicle emission inventory model (VEIN) and ArcGIS, and the vehicle emission standards were determined by the Weibull survival rate curve. The VEIN emission model was optimized using a velocity correction curve. The results showed that heavy-duty vehicles had a more significant impact on the emissions during the morning and evening peak hours, with low emission levels during the day and high emission levels at night and early morning. The total daily emissions of carbon monoxide (CO), hydrocarbons (HC), nitrogen oxides (NOx), and particulate matter (PM10 and PM2.5) from heavy-duty diesel vehicles in Motorway, Trunk, Primary, Secondary, and Tertiary were 14.44 tons, 5.26 tons, 4.78 tons, 7.02 tons, and 3.83 tons, respectively. China III heavy-duty diesel vehicles mainly contributed to CO, HC, NOx, and PM emissions. This study can be used as an essential reference for controlling the exhaust emissions of HDDVs in Kunming.