A daily emission inventory based on multi-source data and its impact on air quality simulations over Jiangsu province
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The development of temporally resolved emission inventories represents a persistent challenge in atmospheric modeling, as conventional approaches relying on monthly profiles systematically mask the dynamic nature of anthropogenic activities that drive air pollution formation. This study constructed a novel daily-scale emission inventory for Jiangsu Province in 2022 through the integration of multi-source environmental big data. This study first accurately quantifies regional air pollutant emissions based on extensive point source data and improved emission source quantification methodology. Our inventory quantifies total anthropogenic emissions at 728 Gg (NO X ), 179 Gg (SO 2 ), 248 Gg (PM 2.5 ), 1179 Gg (VOCs), and 377 Gg (NH 3 ), while crucially capturing the nuanced daily variations in emission patterns that emerge from the complex interplay of human activities. Implementation in the WRF-CMAQ modeling system demonstrated that this enhanced temporal resolution reduces NME for PM 2.5 and O 3 by 2–26% across representative months (January, April, July, October) compared to simulations using the traditional temporal profile. The daily-resolved profiles proved particularly valuable in capturing complex pollution episodes that traditional methods failed to reproduce, including the O 3 increase during April’s COVID-19 restrictions and the underestimation in O 3 simulation resulting from unrepresented emission control measures. This research establishes that refined temporal allocation is essential for accurately simulating chemical regimes and non-linear pollution formation processes, providing approach for targeted air quality management and effective evaluation of short-term emission control strategies.
