Biomass burning injects many different gases and aerosols into the atmosphere that could have a harmful effect on air quality, climate, and human health. In this study, a comprehensive biomass burning emission inventory including domestic and in-field straw burning, firewood burning, livestock excrement burning, and forest and grassland fires is presented, which was developed for mainland China in 2012 based on county-level activity data, satellite data, and updated source-specific emission factors (EFs). The emission inventory within a 1 × 1 km² grid was generated using geographical information system (GIS) technology according to source-based spatial surrogates. A range of key information related to emission estimation (e.g. province-specific proportion of domestic and in-field straw burning, detailed firewood burning quantities, uneven temporal distribution coefficient) was obtained from field investigation, systematic combing of the latest research, and regression analysis of statistical data. The established emission inventory includes the major precursors of complex pollution, greenhouse gases, and heavy metal released from biomass burning. The results show that the emissions of SO₂, NO_x, PM₁₀, PM_2.5, NMVOC, NH₃, CO, EC, OC, CO₂, CH₄, and Hg in 2012 are 336.8 Gg, 990.7 Gg, 3728.3 Gg, 3526.7 Gg, 3474.2 Gg, 401.2 Gg, 34 380.4 Gg, 369.7 Gg, 1189.5 Gg, 675 299.0 Gg, 2092.4 Gg, and 4.12 Mg, respectively. Domestic straw burning, in-field straw burning, and firewood burning are identified as the dominant biomass burning sources. The largest contributing source is different for various pollutants. Domestic straw burning is the largest source of biomass burning emissions for all the pollutants considered, except for NH₃, EC (firewood), and NO_x (in-field straw). Corn, rice, and wheat represent the major crop straws. The combined emission of these three straw types accounts for 80 % of the total straw-burned emissions for each specific pollutant mentioned in this study. As for the straw burning emission of various crops, corn straw burning has the largest contribution to all of the pollutants considered, except for CH₄; rice straw burning has highest contribution to CH₄ and the second largest contribution to other pollutants, except for SO₂, OC, and Hg; wheat straw burning is the second largest contributor to SO₂, OC, and Hg and the third largest contributor to other pollutants. Heilongjiang, Shandong, and Henan provinces located in the north-eastern and central-southern regions of China have higher emissions compared to other provinces in China. Gridded emissions, which were obtained through spatial allocation based on the gridded rural population and fire point data from emission inventories at county resolution, could better represent the actual situation. High biomass burning emissions are concentrated in the areas with more agricultural and rural activity. The months of April, May, June, and October account for 65 % of emissions from in-field crop residue burning, while, regarding EC, the emissions in January, February, October, November, and December are relatively higher than other months due to biomass domestic burning in heating season. There are regional differences in the monthly variations of emissions due to the diversity of main planted crops and climatic conditions. Furthermore, PM_2.5 component results showed that OC, Cl⁻, EC, K⁺, NH₄⁺, elemental K, and SO₄²⁻ are the main PM_2.5 species, accounting for 80 % of the total emissions. The species with relatively high contribution to NMVOC emission include ethylene, propylene, toluene, mp-xylene, and ethyl benzene, which are key species for the formation of secondary air pollution. The detailed biomass burning emission inventory developed by this study could provide useful information for air-quality modelling and could support the development of appropriate pollution-control strategies.

In the summer of 2017, measurements of ozone (O₃) and its precursors were carried out at an urban site in Jinan, a central city in the North China Plain (NCP). A continuous O₃ pollution event was captured during 4–11 August, with the maximum hourly O₃ mixing ratio reaching 154.1 ppbv. Model simulation indicated that local photochemical formation and regional transport contributed 14.0±2.3 and 18.7±4.0 ppbv h⁻¹, respectively, to the increase in O₃ during 09:00–15:00 LT (local time) in this event. For local O₃ formation, the calculated OH reactivities of volatile organic compounds (VOCs) and carbon monoxide (CO) were comparable between O₃ episodes and non-episodes (p>0.05), so was the OH reactivity of nitrogen oxides (NO_x). However, the ratio of OH reactivity of VOCs and CO to that of NO_x increased from 2.0±0.4 s⁻¹ s¹ during non-episodes to 3.7±0.7 s⁻¹ s¹ during O₃ episodes, which resulted in the change in the O₃ formation mechanism from the VOC-limited regime before the O₃ pollution event to the transitional regime during the event. Correspondingly, the simulated local O₃ production rate during the event (maximum: 21.3 ppbv h⁻¹) was markedly higher than that before the event (p<0.05) (maximum: 16.9 ppbv h⁻¹). Given that gasoline and diesel exhaust made large contributions to the abundance of O₃ precursors and the O₃ production rate, constraint on vehicular emissions is the most effective strategy to control O₃ pollution in Jinan. The NCP has been confirmed as a source region of tropospheric O₃, where the shift in regimes controlling O₃ formation like the case presented in this study can be expected across the entire region, due to the substantial reductions of NO_x emissions in recent years.

Constructing a highly resolved comprehensive emission dataset for China is challenging due to limited availability of refined information for parameters in a unified bottom-up framework. Here, by developing an integrated modeling framework, we harmonized multi-source heterogeneous data, including several up-to-date emission inventories at national and regional scales and for key species and sources in China to generate a 0.1° resolution inventory for 2017. By source mapping, species mapping, temporal disaggregation, spatial allocation, and spatial–temporal coupling, different emission inventories are normalized in terms of source categories, chemical species, and spatiotemporal resolutions. This achieves the coupling of multi-scale, high-resolution emission inventories with the Multi-resolution Emission Inventory for China (MEIC), forming the high-resolution INTegrated emission inventory of Air pollutants for China (INTAC). We find that INTAC provides more accurate representations for emission magnitudes and spatiotemporal patterns. In 2017, China's emissions of sulfur dioxide (SO₂), nitrous oxides (NO_x), carbon monoxide (CO), non-methane volatile organic compounds (NMVOCs), ammonia (NH₃), PM₁₀ and PM_2.5 (particulate matter), black carbon (BC), and organic carbon (OC) were 12.3, 24.5, 141.0, 27.9, 9.2, 11.1, 8.4, 1.3, and 2.2 Tg, respectively. The proportion of point source emissions for SO₂, PM₁₀, NO_x, and PM_2.5 increases from 7 %–19 % in MEIC to 48 %–66 % in INTAC, resulting in improved spatial accuracy, especially mitigating overestimations in densely populated areas. Compared with MEIC, INTAC reduces mean biases in simulated concentrations of major air pollutants by 2–14 µg m⁻³ across 74 cities, compared against ground observations. The enhanced model performance by INTAC is particularly evident at finer-grid resolutions. Our new dataset is accessible at http://meicmodel.org.cn/intac (last access: 15 April 2024) and https://doi.org/10.5281/zenodo.10459198 (Wu et al., 2024), and it will provide a solid data foundation for fine-scale atmospheric research and air-quality improvement.