本数据集旨在开发一个高分辨率的每日 OBB 排放清单，包括碳 (C)、二氧化碳 (CO2)、一氧化碳 (CO)、甲烷 (CH4)、氮氧化物 (NOx)、二氧化硫 (SO2)、颗粒有机碳 (OC)、颗粒黑碳 (BC)、氨 (NH3)、二氧化氮 (NO2)、PM2.5 和 PM10，并分析 14 个不同地区的各类火灾事件及其排放模式。为了估算森林、稀树草原/灌木林、草地和泥炭地的 OBB 排放量，我们使用了更新的 FY-3D GFR 产品，该产品基于 AGB 的连续时空动态、时空可变的燃烧效率以及不同土地类型特有的排放因子。全面的高分辨率 OBB 排放清单是空气质量建模、大气传输模拟和生物地球化学循环研究应用的宝贵财富。这为深入了解和分析全球范围内溴化萘对环境的影响提供了一个强有力的框架。
\n\n在本数据集中，利用中国风云三号D卫星的全球火点监测数据、卫星衍生的生物量数据、植被指数衍生的时空可变燃烧效率以及基于土地类型的排放因子，编制了全球高分辨率（1 km×1 km）的每日多溴联苯排放清单。2020-2022 年估计的 OBB 年均排放量为：2,586.88 Tg C、8841.45 Tg CO2、382.96 Tg CO、15.83 Tg CH4、18.42 Tg NOX、4.07 Tg SO2、18.68 Tg OC、3.77 Tg BC、5.24 Tg NH3、15.85 Tg NO2、42.46 Tg PM2.5和56.03 Tg PM10。具体而言，以碳排放为例，2020-2022 年的年均估计 OBBs 分别为 72.71（北美洲北方地区，BONA）、165.73（北美洲温带地区，TENA）、34.11（中美洲，CEAM）、42.93（南美洲北半球地区，NHSA）、520.55（南美洲南半球地区，SHSA）、13.02（欧洲，EURO）、13.02（南美洲，PM2.5）和 56.03（PM10）。 02（欧洲，EURO）、8.37（中东，MIDE）、394.25（北半球非洲，NHAF）、847.03（南半球非洲，SHAF）、167.35（亚洲北部，BOAS）、27.93（中亚，CEAS）、197.29（东南亚，SEAS）、13.20（赤道亚洲；EQAS）和 82.38（澳大利亚和新西兰；AUST）兆吨碳/年-1。全面的高分辨率OBB排放清单为提高空气质量建模、大气输送和生物地球化学循环研究的准确性提供了宝贵的信息。
", "ds_source": "处理后的火灾事件探测数据来自国家卫星气象中心风云卫星遥感数据服务网络（http://satellite.nsmc.org.cn/PortalSite/Default.aspx）；
\nNDVI 数据通过 MODIS 16 d NDVI 融合产品获得，该产品可在谷歌地球引擎平台上获取；
\nTC数据来自MOD44B产品，该产品基于Terra卫星上的MODIS生成（https://lpdaac.usgs.gov/products/mod44bv061/），提供了2000年至今每年250米分辨率的连续全球植被场；
\nAGB 数据来自 2010 年全球地上和地下生物量碳密度地图产品（https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1763，），该数据集由 Spawn 和 Gibbs（2020 年）提供。
", "ds_process_way": "使用 FY-3D GFR 产品在全球范围内确定了 GEIOBB 中使用的火灾事件的位置、时间和燃烧面积；
\n全球露天生物质燃烧排放清单（GEIOBB）（每日 1 公里）是根据 Wiedinmyer 等人（2006 年）和 Shi 等人（2015 年）描述的框架，采用燃烧面积法估算的。
\nGEIOBB 包括基于 FY-3D 卫星活动火灾数据检索的燃烧面积、卫星和地面测量的可用生物量、按树木覆盖（TC）和归一化差异植被指数（NDVI）缩放的 CF 以及基于土地覆盖（LC）的排放因子的 OBB 排放。
\n该数据集利用数千个卫星数据点和地面测量数据绘制出分辨率为 1 千米的生物量地图。
", "ds_quality": "使用 2118 个其他地面测量数据和激光雷达数据来验证观测结果，结果表明，融合地图的均方根误差（RMSE）比 Saatchi 等人（2011 年）和 Baccini 等人（2012 年）报告的误差低 15%-21%。
", "ds_acq_start_time": "2020-01-01 00:00:00", "ds_acq_end_time": "2022-12-31 00:00:00", "ds_acq_place": "全球", "ds_acq_lon_east": 180.0, "ds_acq_lat_south": -90.0, "ds_acq_lon_west": -180.0, "ds_acq_lat_north": 90.0, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 10613305636, "ds_files_count": 4, "ds_format": "HDF", "ds_space_res": "1000", "ds_time_res": "日", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "4e7a28c4-f766-4f7b-8068-338880357daf.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "a4dd5849-78f2-44c5-b0f1-3450e952b2a2", "ds_serv_man": "敏玉芳", "ds_serv_phone": "09314967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.4599" ], "quality_level": 3, "publish_time": "2024-08-29 09:03:05", "last_updated": "2026-01-14 11:04:25", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.FIGSHARE.DB6669.2024", "license": null, "i18n": { "en": { "title": "Global Open Air Biomass Combustion Emissions Inventory (GEIOBB): Utilizing Fengyun-3D Global Fire Spot Monitoring Data (2020-2022)", "ds_format": "hdf", "ds_source": "The processed fire event detection data comes from the Fengyun Satellite Remote Sensing Data Service Network of the National Satellite Meteorological Center（ http://satellite.nsmc.org.cn/PortalSite/Default.aspx ）；\n
NDVI data is obtained through the MODIS 16 d NDVI fusion product, which can be accessed on the Google Earth Engine platform\n
The TC data comes from the MOD44B product, which is based on MODIS generated on the Terra satellite（ https://lpdaac.usgs.gov/products/mod44bv061/ ）It provides a continuous global vegetation field with a resolution of 250 meters per year from 2000 to the present\n
\nAGB data comes from the 2010 global aboveground and underground biomass carbon density map product（ https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1763 The dataset was provided by Spawn and Gibbs (2020)\n
", "ds_quality": "& emsp; Using 2118 other ground measurement data and LiDAR data to validate the observation results, the results showed that the root mean square error (RMSE) of the fused map was 15% -21% lower than the errors reported by Saatchi et al. (2011) and Baccini et al. (2012)", "ds_ref_way": "", "ds_abstract": "
This dataset aims to develop a high-resolution daily OBB emissions inventory, including carbon (C), carbon dioxide (CO2), carbon monoxide (CO), methane (CH4), nitrogen oxides (NOx), sulfur dioxide (SO2), particulate organic carbon (OC), particulate black carbon (BC), ammonia (NH3), nitrogen dioxide (NO2), PM2.5, and PM10, and analyze various fire events and their emission patterns in 14 different regions. To estimate the OBB emissions of forests, savannas/shrublands, grasslands, and peatlands, we used an updated FY-3D GFR product based on the continuous spatiotemporal dynamics of AGB, spatiotemporal variable combustion efficiency, and emission factors specific to different land types. A comprehensive high-resolution OBB emission inventory is a valuable asset for air quality modeling, atmospheric transport simulation, and biogeochemical cycling research applications. This provides a strong framework for a deeper understanding and analysis of the environmental impact of brominated naphthalene on a global scale
\n\nIn this dataset, a high-resolution (1 km x 1 km) daily inventory of polybrominated biphenyls emissions was compiled using global fire point monitoring data from China's Fengyun-3D satellite, satellite derived biomass data, spatiotemporal variable combustion efficiency derived from vegetation indices, and emission factors based on land types. The estimated annual emissions of OBB from 2020 to 2022 are: 2586.88 Tg C, 8841.45 Tg CO2, 382.96 Tg CO, 15.83 Tg CH4, 18.42 Tg NOX, 4.07 Tg SO2, 18.68 Tg OC, 3.77 Tg BC, 5.24 Tg NH3, 15.85 Tg NO2, 42.46 Tg PM2.5, and 56.03 Tg PM10. Specifically, taking carbon emissions as an example, the estimated annual OBBs for 2020-2022 are 72.71 (BONA in Northern North America), 165.73 (TENA in temperate North America), 34.11 (CEAM in Central America), 42.93 (NHSA in Northern South America), 520.55 (SHSA in Southern South America), 13.02 (EURO in Europe), 13.02 (PM2.5 in South America), and 56.03 (PM10), respectively. 02 (Europe, EURO), 8.37 (Middle East, MIDE), 394.25 (Northern Hemisphere Africa, NHAF), 847.03 (Southern Hemisphere Africa, SHAF), 167.35 (Northern Asia, BOAS), 27.93 (Central Asia, CEAS), 197.29 (Southeast Asia, SEAS), 13.20 (Equatorial Asia); EQAS and 82.38 (Australia and New Zealand; AUST) megatons of carbon per annual-1. A comprehensive high-resolution OBB emission inventory provides valuable information for improving the accuracy of air quality modeling, atmospheric transport, and biogeochemical cycling research
", "ds_time_res": "日", "ds_acq_place": "Global", "ds_space_res": "1000", "ds_projection": "", "ds_process_way": "The location, time, and burning area of fire events used in GEIOBB were determined globally using FY-3D GFR products\n
The Global Open Air Biomass Burning Emissions Inventory (GEIOBB) (1 kilometer per day) is estimated using the burning area method based on the framework described by Wiedinmyer et al. (2006) and Shi et al. (2015)\n
GEIOBB includes combustion area retrieved based on FY-3D satellite activity fire data, available biomass measured by satellite and ground, CF scaled by tree cover (TC) and normalized difference vegetation index (NDVI), and OBB emissions based on land cover (LC) emission factors\n
This dataset utilizes thousands of satellite data points and ground measurement data to create a biomass map with a resolution of 1 kilometer\n
", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC BY 4.0", "ds_topic_tags": [ "风云三维", "排放量", "开放式生物质燃烧" ], "ds_subject_tags": [ "地理学其他学科" ], "ds_class_tags": [], "ds_locus_tags": [ "全球" ], "ds_time_tags": [ 2020, 2021 ], "ds_contributors": [ { "true_name": "刘洋", "email": "yangliu@nssc.ac.cn", "work_for": "中国科学院 航天信息研究所 遥感科学国家重点实验室", "country": "中国" }, { "true_name": "石玉胜", "email": "shiys@aircas.ac.cn", "work_for": "中国科学院 航天信息研究所 遥感科学国家重点实验室", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "刘洋", "email": "yangliu@nssc.ac.cn", "work_for": "中国科学院 航天信息研究所 遥感科学国家重点实验室", "country": "中国" }, { "true_name": "石玉胜", "email": "shiys@aircas.ac.cn", "work_for": "中国科学院 航天信息研究所 遥感科学国家重点实验室", "country": "中国" } ], "ds_managers": [ { "true_name": "刘洋", "email": "yangliu@nssc.ac.cn", "work_for": "中国科学院 航天信息研究所 遥感科学国家重点实验室", "country": "中国" }, { "true_name": "石玉胜", "email": "shiys@aircas.ac.cn", "work_for": "中国科学院 航天信息研究所 遥感科学国家重点实验室", "country": "中国" } ], "category": "其他" }