Toward a national emission inventory for the catering industry in China

Environmental Footprints in Food Services: A Scoping Review

The collective meals market generates significant revenue for the world economy. Food services are responsible for consuming large amounts of water and energy, as well as generating a substantial volume of waste, which is often improperly disposed of. Given the unchecked expansion of food services, the lack of proper management of environmental resources can undermine sustainability principles, posing a threat to future generations. This scoping review aimed to synthesize the existing scientific literature on carbon and water footprints in food services, describing the main methods and tools used and what strategies have been proposed to mitigate the high values of these footprints. The search for articles was performed on 6 June 2024 in seven electronic databases, using MeSH Terms and adaptations for each database from database inception. The search for local studies was complemented by a manual search in the list of references of the studies selected to compose this review. It included quantitative studies assessing footprints (water or carbon) in food services and excluded reviews, studies that reported footprints for diets, and protocols. A total of 2642 studies were identified, and among these, 29 were selected for this review. According to the findings, it was observed that meats, especially beef, contribute more to water and carbon footprint compared to other proteins. Mitigation strategies for the water footprint include promoting plant-based diets, menu changes, and awareness.

Impact of commercial cooking on urban PM2.5 and O3 with online data-assisted emission inventory

Commercial cooking (CC) is an intensive near-field source contributing to ambient PM_2.5 and O₃ concentration in urban areas. Compilation of CC emission inventory has been challenging due to the dynamic variation of the emission sector, which has resulted in data deficiencies including underestimated quantity and poor temporal-spatial resolution. In this study, we have developed a methodology that integrates existing emission statistics with online oil fumes monitoring (OOFM) data to create a highly spatiotemporally resolved emission inventory of CC. The new emission estimate differs from legacy inventory in emission quantity and temporal pattern. Using the emission data, the impacts of CC emission on local PM_2.5 and O₃ were evaluated using WRF-CMAQ and model-monitor data fusion tool of SMAT-CE in Shunde, China. The OOFM data-assisted emission inventory led to improved model performance for both model-predicted PM_2.5 and O₃ concentrations. The simulation results using the new inventory data showed that the CC emissions contributed 1.25±2 μg/m³ of PM_2.5, and accounted for 24±1 % of PM_2.5 concentration derived from local anthropogenic emissions. Moreover, a higher contribution of CC to PM_2.5 was predicted in areas with elevated CC emissions, while the contribution to O₃ was insignificant.

Description and evaluation of a newly developed emission inventory processing system (EMIPS)

Commonly available emission inventories are often incompatible with the input requirements of atmospheric chemistry models due to temporal and spatial resolution, pollutant types, etc. We present the Emission Inventory Processing System (EMIPS) version 1, an open-source and multi-scale atmospheric emission modeling framework that prepares specific emissions inputs for atmospheric chemistry models. EMIPS is a multifunctional and user-friendly system, coded in Jython and developed on the MeteoInfo software platform. It allows users to freely combine and process emission inventories to generate model-ready emissions data. The core functions of EMIPS include preprocessing, temporal allocation, spatial allocation, chemical speciation, and vertical allocation. We detail the implementation of each function in the body of this paper, and several examples are provided for illustration. The emission outputs obtained with EMIPS have been evaluated by simulating four pollutants (PM_2.5, PM₁₀, NO₂, and O₃) concentrations in January 2017 using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), and comparison of model results with the observations indicates the model can reproduce the temporal and spatial patterns of pollutants, suggesting that EMIPS is capable of supporting atmospheric chemistry modeling. We expect this work could help to improve air quality research and forecast.

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

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.

Inventory of Commercial Cooking Activities and Emissions in a Typical Urban Area in Greece

The pollutants emitted during meal preparation in restaurants deteriorate the air quality. Thus, it is an environmental issue that needs to be addressed, especially in areas where these activities are densely located. The purpose of this study is to examine the impact on air quality from commercial cooking activities by performing a qualitative and quantitative analysis of the related parameters. The area of interest is located in the southeastern Mediterranean (Greater Athens area in Greece). Due to the lack of the necessary activity information, a survey was conducted. Emissions from the fuel burnt during the cooking procedures were calculated and it was found that, overall, 940.1 tonnes are attributed to commercial cooking activities annually (generated by classical pollutants, heavy metals, particulates and polycyclic aromatic hydrocarbon emissions). Comparing the contribution of different sources to the pollutants emitted, it was found that commercial cooking is responsible for about 0.6%, 0.8% and 1.0% of the total CO, NOx and PM10 values. Cooking organic aerosol (COA) and volatile organic compound (VOC) emissions from grilled meat were also calculated, accounting for 724.9 tonnes and 37.1 tonnes, respectively. Monthly, daily and hourly profiles of the cooking activities were developed and emissions were spatially disaggregated, indicating the city center as the area with higher values. Numerical simulations were performed with the WRF/CAMx modeling system and the results revealed a contribution of about 6% to the total PM10 concentrations in the urban center, where the majority of restaurants are located.

Estimation of commercial cooking emissions in real-world operation: Particulate and gaseous emission factors, activity influencing and modelling

Measurements of real-world cooking emission factors (CEFs) were rarely reported in recent year's studies. However, the needs for accurately estimating CEFs to produce cooking emission inventories and further implement controlling measures are urgent. In this study, we collected cooking emission aerosols from real-world commercial location operations in Beijing, China. 2 particulate (PM_2.5, OC) and 2 gaseous (NMHC, OVOCs) CEF species were examined on influencing activity conditions of cuisine type, controlling technology, operation scales (represented by cook stove numbers), air exhausting volume, as well as location and operation period. Measured NMHC emission factors (Non-barbecue: 8.19 ± 9.06 g/h and Barbecue: 35.48 ± 11.98 g/h) were about 2 times higher than PM_2.5 emission factors (Non-barbecue: 4.88 ± 3.43 g/h and Barbecue: 15.48 ± 7.22 g/h). T-test analysis results showed a significantly higher barbecued type CEFs than non-barbecued cuisines for both particulate and gaseous emission factor species. The efficacy of controlling technology was showing an average of 50 % in decreasing PM_2.5 CEFs while a 50 % in increasing OC particulate CEFs. The effects of controlling equipment were not significant in removing NMHC and OVOCs exhaust concentrations. CEF variations within cook stove numbers and air exhausting volume also reflected a comprehensive effect of operation scale, cuisine type and control technology. The simulations among activity influencing factors and CEFs were further determined and estimated using hierarchical multiple regression model. The R square of this simulated model for PM_2.5 CEFs was 0.80 (6.17 × 10^-9) with standardized regression coefficient of cuisine type, location, sampling period, control technology, cook stove number (N) and N² of 5.18 (0.02), 5.33 (0.02), 1.93 (0.19), 9.29 (4.18 × 10^-6), 9.10 (1.71 × 10^-3) and -1.18 (2.43 × 10^-3), respectively. In perspective, our study provides ways of better estimating CEFs in real operation conditions and potentially highlighting much more importance of cooking emissions on air quality and human health.

High Temporal Resolution Land Use Regression Models with POI Characteristics of the PM2.5 Distribution in Beijing, China

PM2.5 is one of the primary components of air pollutants, and it has wide impacts on human health. Land use regression models have the typical disadvantage of low temporal resolution. In this study, various point of interests (POIs) variables are added to the usual predictive variables of the general land use regression (LUR) model to improve the temporal resolution. Hourly PM2.5 concentration data from 35 monitoring stations in Beijing, China, were used. Twelve LUR models were developed for working days and non-working days of the heating season and non-heating season, respectively. The results showed that these models achieved good fitness in winter and summer, and the highest R2 of the winter and summer models were 0.951 and 0.628, respectively. Meteorological factors, POIs, and roads factors were the most critical predictive variables in the models. This study also showed that POIs had time characteristics, and different types of POIs showed different explanations ranging from 5.5% to 41.2% of the models on working days or non-working days, respectively. Therefore, this study confirmed that POIs can greatly improve the temporal resolution of LUR models, which is significant for high precision exposure studies.