烹饪排放作为PM2.5的重要来源，因其高毒性和与人口密集区的高度空间耦合性，对公共健康构成显著风险。尽管其重要性突出，但中国目前缺乏高精度、长时序、高分辨率的全国性烹饪排放清单，核心瓶颈在于难以获取长时间序列、精细空间尺度的活动水平数据。本研究通过融合机器学习技术预测活动水平并估算排放量，突破了这一局限。
", "ds_source": "烹饪排放三大部门（商业餐饮、居民烹饪及食堂餐饮）的核算方法基于Li等（2023b）建立的空间化排放因子模型。
", "ds_process_way": "具体来说，我们开发了一个机器学习算法包含随机森林（RF）、极限梯度提升（XGBoost）、多层感知机神经网络（MLP）和深度神经网络（DNN）的集成机器学习模型，基于人口-经济-餐饮业等统计指标，精准预测中国县域尺度的烹饪活动水平。该模型展现出卓越的泛化能力与空间可移植性（R²=0.892–0.989），性能显著优于传统统计模型与单一机器学习模型。与既往依赖人口等单一代理数据进行降尺度估算的清单不同，本清单直接计算县级排放量，提供更精确的排放估算与空间分布特征。此外，我们首次将超细颗粒物（UFPs）和多环芳烃（PAHs）等关键毒性污染物纳入国家烹饪排放清单，最终构建了中国首个时空连续（1990–2021）、高空间分辨率（县级）、宽污染物谱系的烹饪排放清单。
", "ds_quality": "数据质量良好。
", "ds_acq_start_time": "1990-01-01 00:00:00", "ds_acq_end_time": "2021-12-31 00:00:00", "ds_acq_place": "中国", "ds_acq_lon_east": null, "ds_acq_lat_south": null, "ds_acq_lon_west": null, "ds_acq_lat_north": null, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "open-access", "ds_total_size": 33649899, "ds_files_count": 2, "ds_format": ".xlsx", "ds_space_res": null, "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "d79638b3-72ca-4fba-9614-4e3108e5d33c.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "用户在使用数据时请在正文中明确声明数据的来源，并在参考文献部分引用本元数据提供的引用方式。", "ds_from_station": null, "organization_id": "a4dd5849-78f2-44c5-b0f1-3450e952b2a2", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.45" ], "quality_level": 3, "publish_time": "2025-04-29 11:34:02", "last_updated": "2025-04-29 11:34:02", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.FIGSHARE.DB6817.2025", "i18n": { "en": { "title": "Tracking County-level Cooking Emissions and Their Drivers in China from 1990 to 2021 by Ensemble Machine Learning", "ds_format": ".xlsx", "ds_source": "The calculation method for emissions of the three sectors of cooking (commercial cooking, residential cooking, and canteen cooking) is based on Li et al., (2023b).
", "ds_quality": "The data quality is good.
", "ds_ref_way": "", "ds_abstract": "Cooking emissions are a significant source of PM2.5, posing considerable public health risks due to their high toxicity and proximity to densely populated areas. Despite their importance, there is currently a lack of an accurate, long-term, high-resolution national cooking emission inventory in China, primarily due to the challenges in obtaining high-quality activity level data over extended periods and at fine spatial scales. Here, we address these limitations by leveraging advanced machine learning techniques to predict activity levels and further estimate emissions.
", "ds_time_res": "", "ds_acq_place": "China", "ds_space_res": "", "ds_projection": "", "ds_process_way": "We develop an ensemble model of machine learning algorithms—Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Multilayer Perceptron Neural Network (MLP), and Deep Neural Networks (DNN)—to accurately predict cooking activity levels across Chinese counties based on statistical indicators related to population, economy, and the catering industry. The ensemble machine learning model demonstrates exceptional generalization and transferability (R2=0.892–0.989), outperforming traditional statistical models and individual machine learning models. Unlike previous inventories that rely on simplistic proxy data such as population for calculation and downscaling, our inventory directly calculates county-level cooking emissions, providing more accurate emission estimates and spatial distributions. Furthermore, we incorporate critical but previously missing toxic pollutants, such as ultrafine particles (UFPs) and polycyclic aromatic hydrocarbons (PAHs), into the national cooking emission inventory. Therefore, we develop China's first county-level cooking emission inventory, spanning from 1990 to 2021, with high spatial resolution and wide pollutant coverage.
", "ds_ref_instruction": "When using data, users should clearly declare the source of the data in the main text and cite the citation method provided by this metadata in the reference section." } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC BY 4.0", "doi_reg_from": "reg_outside", "cstr_reg_from": "reg_outside", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "ds_topic_tags": [ "烹饪排放", "PM2.5", "UFP", "PAH" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "中国" ], "ds_time_tags": [ 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021 ], "ds_contributors": [ { "true_name": "王书肖", "email": "shxwang@tsinghua.edu.cn", "work_for": "清华大学环境学院", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "王书肖", "email": "shxwang@tsinghua.edu.cn", "work_for": "清华大学环境学院", "country": "中国" } ], "ds_managers": [ { "true_name": "王书肖", "email": "shxwang@tsinghua.edu.cn", "work_for": "清华大学环境学院", "country": "中国" } ], "category": "大气本底" }