Enhanced commercial cooking inventories from the city scale through normalized emission factor dataset and big data

Pengchuan Lin; Jian Gao; Yisheng Xu; James J Schauer; Jiaqi Wang; Wanqing He; Lei Nie

doi:10.1016/j.envpol.2022.120320

Enhanced commercial cooking inventories from the city scale through normalized emission factor dataset and big data

Environ Pollut. 2022 Dec 15:315:120320. doi: 10.1016/j.envpol.2022.120320. Epub 2022 Sep 30.

Authors

Pengchuan Lin¹, Jian Gao², Yisheng Xu¹, James J Schauer³, Jiaqi Wang¹, Wanqing He⁴, Lei Nie⁴

Affiliations

¹ State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China.
² State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China. Electronic address: gaojian@craes.org.cn.
³ Environmental Chemistry and Technology Program, University of Wisconsin-Madison, Madison, WI, 53706, USA; Wisconsin State Laboratory of Hygiene, University of Wisconsin-Madison, Madison, WI, 53718, USA.
⁴ Beijing Key Laboratory of Urban Atmospheric Volatile Organic Compounds Pollution Control and Application, Beijing Municipal Research Institute of Eco-Environmental Protection, Beijing, 100037, China.

PMID: 36191795
DOI: 10.1016/j.envpol.2022.120320

Abstract

Cooking emission inventories always have poor spatial resolutions when applying with traditional methods, making their impacts on ambient air and human health remain obscure. In this study, we created a systematic dataset of cooking emission factors (CEFs) and applied it with a new data source, cooking-related point of interest (POI) data, to build up highly spatial resolved cooking emission inventories from the city scale. Averaged CEFs of six particulate and gaseous species (PM, OC, EC, NMHC, OVOCs, VOCs) were 5.92 ± 6.28, 4.10 ± 5.50, 0.05 ± 0.05, 22.54 ± 20.48, 1.56 ± 1.44, and 7.94 ± 6.27 g/h normalized in every cook stove, respectively. A three-field CEF index containing activity and emission factor species was created to identify and further build a connection with cooking-related POI data. A total of 95,034 cooking point sources were extracted from Beijing, as a study city. In downtown areas, four POI types were overlapped in the central part of the city and radiated into eight distinct directions from south to north. Estimated PM/VOC emissions caused by cooking activities in Beijing were 4.81/9.85 t per day. A 3D emission map showed an extremely unbalanced emission density in the Beijing region. Emission hotspots were seen in Central Business District (CBD), Sanlitun, and Wangjing in Chaoyang District and Willow and Zhongguancun in Haidian District. PM/VOC emissions could be as high as 16.6/42.0 kg/d in the searching radius of 2 km. For PM, the total emissions were 417.4, 389.0, 466.9, and 443.0 t between Q1 and Q4 2019 in Beijing, respectively. The proposed methodology is transferrable to other Chinese cities for deriving enhanced commercial cooking inventories and potentially highlighting the further importance of cooking emissions on air quality and human health.

Keywords: Beijing; Cooking emission factors; Dataset; Emission inventory; Point of interest data.

MeSH terms

Air Pollutants* / analysis
Big Data
Cities
Cooking / methods
Environmental Monitoring
Humans
Particulate Matter / analysis
Volatile Organic Compounds* / analysis

Substances

Air Pollutants
Particulate Matter
Volatile Organic Compounds