Wintertime Heavy Haze Episodes in Northeast China Driven by Agricultural Fire Emissions

Abstract

Heavy haze events occur frequently over northeast China during the winter, despite the successful implementation of the Clean Air Act, which primarily targets fossil fuel sources, in recent years. Agricultural fires have been suggested as one of the main causes of these haze episodes. However, their regional contribution to fine particulate matter (PM 2.5 ) pollution has not been systematically evaluated. In this study, we use the GEOS-Chem model to investigate the role of agricultural fires in heavy haze episodes from December 2018 to March 2019 in Heilongjiang province. Our results show significant discrepancies between the simulated and observed PM 2.5 concentrations during severe haze pollution days. By increasing agricultural fire emissions in the GFED4s inventory by a factor of ∼23, we are able to better replicate the haze episodes in the model, indicating a severe under-representation of agricultural fires in the inventory. Furthermore, the baseline simulation overestimates the observed black carbon to organic carbon ratio in Harbin, suggesting a biased emission ratio specified in the inventory. Our assessment underscores that agriculture fires constitute the main cause of the extreme haze episodes during the study period, and a strictly implemented ban would improve air quality with substantial health benefits.

Comment on egusphere-2024-1260

“…A limitation of this approach was the missing of fires due to satellite overpass timing (Uranishi et al, 2019), which was also the case for the Global Fire Emissions Database (GFED), another commonly-used open burning inventory based on burned areas (Konovalov et al, 2018;Chen et al, 2023). Previous studies suggested that the underestimation of open burning emissions by FINN or GFED could be considerable, e.g., by a factor of as high as above 20 (Xie et al, 2024). Given the massive agricultural sector in Harbin and surrounding areas (e.g., the Harbin-Changchun metropolitan area), we suggest that the uncertainties of open burning inventories merit particular attention for the modeling studies in this region.…”

Spatial-temporal patterns of anthropogenic and biomass burning contributions on air pollution and mortality burden changes in India from 1995 to 2014

“…Zhang et al (2021b) revealed that model performance with global CEDS inventory tends to predict lower bias for surface PM 2.5 and higher bias for surface O 3 compared with a regional emission inventory (MEIC) in China due to disparities in spatial allocation. Xie et al (2024) also highlighted a significant underestimation of agricultural fires in the inventory. Moreover, the uncertainty from health functions ranging from the choice of the exposure-response functions (Ostro et al, 2018;Giani et al, 2020) and the uncertainties of the baseline mortality rates both have different impacts on human health (Lelieveld et al, 2015;Pozzer et al, 2023).…”