An Observational Constraint of VOC Emissions for Air Quality Modeling Study in the Pearl River Delta Region

• Published in: Journal of geophysical research. Atmospheres 2023-06, Vol.128 (11), p.n/a
• Main Authors: Zhou, Beining, Guo, Hai, Zeren, Yangzong, Wang, Yu, Lyu, Xiaopu, Wang, Boguang, Wang, Hongli
• Format: Article
• Language: English
• Subjects: Air pollution; Air quality; Air quality measurements; air quality modeling; Air quality models; Anthropogenic factors; Atmospheric chemistry; Atmospheric models; Bias; Chemical composition; Chemical reactions; Cities; Constraints; Diurnal variations; Emission analysis; Emission inventories; Emission measurements; Emissions; Geophysics; Human influences; Modelling; Organic compounds; Outdoor air quality; Ozone; Pollution control; Pollution research; Pollution studies; Rivers; Secondary aerosols; Simulation; South China; Spatial variations; VOC emissions; VOC measurements; VOCs; Volatile organic compounds; WRF‐CMAQ
• ISSN: 2169-897X; 2169-8996
• DOI: 10.1029/2022JD038122

Volatile organic compounds (VOCs) have crucial influences on atmospheric chemistry. Accurate quantification of the VOC emissions is critical for air pollution research, especially when applying to air quality models. However, current bottom‐up emission inventories have biases, making observational constraints of VOC emissions necessary. We conducted concurrent VOC measurements in the Pearl River Delta (PRD) region during the summer of 2018 and found large discrepancies in the spatiotemporal variations of VOCs between observations and model simulations when using the priori VOC emission inventory (Multi‐resolution Emission Inventory for China). The normalized biases of total VOC concentrations ranged from −55% to 85% across the PRD cities in the study period. To improve the simulations, we constrained the anthropogenic VOC emissions based on their measured concentrations. The observation‐constrained VOC emissions showed clear diurnal variations and resolved the spatially‐concentrated priori emissions by reducing the high emissions by 15%–36% in the central PRD cities while elevating the sparse emissions in other cities. After employing the observation‐constrained VOC emissions, the model better reproduced the spatiotemporal variations of VOCs in the PRD region, alleviating the biases to −13%–13%. Furthermore, simulations of peak ozone (O3) concentrations were amended to reduce the mean normalized bias by 5%–12% on high O3 days. Our work has effectively combined VOC field measurements with air quality modeling to achieve better simulations of VOCs and O3. Besides, the observational‐constrained emissions are flexible for studying short‐term emission changes and their subsequent impacts on air quality. Plain Language Summary Air quality models are important tools for studying the physical and chemical processes of air pollution. Model simulations rely heavily on the input emission profiles. Despite efforts to establish accurate and model‐ready emission inventories, biases persist, which further affects model results. Volatile organic compounds (VOCs) emitted from various anthropogenic sources are key precursors to ozone (O3) and secondary organic aerosols. In this study, we constructed an observational constraint to validate current VOC emission inventories against observations in nine cities. Constrained emission profiles revealed more pronounced spatiotemporal variations in VOC emissions across cities. Furthermore, since VOC emissions were constrained by obs