{
    "created": "2025-08-25 10:11:00",
    "updated": "2026-04-12 06:39:45",
    "id": "bcf43d1e-6f95-416d-9228-70cd67680386",
    "version": 8,
    "ds_topic": null,
    "title_cn": "中国1km多源空气污染物PM2.5空间分布数据集（2015年-2020年）",
    "title_en": "MuAP Spatial distribution of various air pollutants in China at 1 km(PM2.5 2015-01-01:2020-12-31) (Version1.1)",
    "ds_abstract": "<p>&emsp;&emsp;本数据集通过结合空间采样与参数卷积，利用地面观测数据、遥感产品、气象数据、辅助数据及随机ID，对LightGBM模型进行优化。基于上述技术及空气污染物的模拟，生成中国大部分地区2015年至2020年期间日分辨率1公里PM<sub>2.5</sub>产品。",
    "ds_source": "<p>&emsp;&emsp;数据来源于https://doi.org/10.5194/essd-2023-76, 2023。",
    "ds_process_way": "<p>&emsp;&emsp;本数据集通过结合空间采样与参数卷积，利用地面观测数据、遥感产品、气象数据、辅助数据及随机ID，对LightGBM模型进行优化。",
    "ds_quality": "<p>&emsp;&emsp;通过随机采样、随机站点采样、区域特定验证、不同模型比较及不同研究的横向对比，验证了对多种大气污染物空间分布的模拟具有可靠性和有效性。随机样本的CV值显示，PM2.5的R²为0.88。",
    "ds_acq_start_time": "2015-01-01 00:00:00",
    "ds_acq_end_time": "2020-12-31 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": "login-access",
    "ds_total_size": 27968815884,
    "ds_files_count": 2,
    "ds_format": "tif",
    "ds_space_res": "1000m",
    "ds_time_res": "日",
    "ds_coordinate": "WGS84",
    "ds_projection": "",
    "ds_thumbnail": "bcf43d1e-6f95-416d-9228-70cd67680386.png",
    "ds_thumb_from": 2,
    "ds_ref_way": "",
    "paper_ref_way": "",
    "ds_ref_instruction": "",
    "ds_from_station": null,
    "organization_id": "0a4269e1-65f4-45f1-aeba-88ea3068eebf",
    "ds_serv_man": "敏玉芳",
    "ds_serv_phone": "0931-4967596",
    "ds_serv_mail": "ncdc@lzb.ac.cn",
    "doi_value": "",
    "subject_codes": [
        "170.1515"
    ],
    "quality_level": 3,
    "publish_time": "2025-08-28 16:07:24",
    "last_updated": "2026-01-14 11:09:12",
    "protected": false,
    "protected_to": null,
    "lang": "zh",
    "cstr": "11738.11.NCDC.ZENODO.DB6961.2025",
    "i18n": {
        "en": {
            "title": "MuAP Spatial distribution of various air pollutants in China at 1 km(PM2.5 2015-01-01:2020-12-31) (Version1.1)",
            "ds_format": "",
            "ds_source": "<p>&emsp;The data is sourced from https://doi.org/10.5194/essd-2023-76 , 2023.",
            "ds_quality": "<p>&emsp;Through random sampling, random site sampling, region specific validation, comparison of different models, and horizontal comparison of different studies, the reliability and effectiveness of simulating the spatial distribution of various atmospheric pollutants have been verified. The CV value of the random sample shows that the R ² of PM2.5 is 0.88 and the RMSE is 9.91 µ g/m ³.",
            "ds_ref_way": "",
            "ds_abstract": "<p> This dataset optimizes the LightGBM model by combining spatial sampling and parameter convolution, utilizing ground observation data, remote sensing products, meteorological data, auxiliary data, and random IDs. Based on the above technology and simulation of air pollutants, generate daily PM<sub>2.5</sub> products with a resolution of 1 kilometer in most parts of China from 2015 to 2020.</p>",
            "ds_time_res": "日",
            "ds_acq_place": "China",
            "ds_space_res": "1000m",
            "ds_projection": "",
            "ds_process_way": "<p>&emsp;This dataset optimizes the LightGBM model by combining spatial sampling and parameter convolution, utilizing ground observation data, remote sensing products, meteorological data, auxiliary data, and random IDs.",
            "ds_ref_instruction": ""
        }
    },
    "submit_center_id": "ncdc",
    "data_level": 0,
    "license_type": "CC BY 4.0",
    "ds_topic_tags": [
        "PM2.5",
        "LightGBM模型",
        "空气污染",
        "卷积"
    ],
    "ds_subject_tags": [
        "大气化学"
    ],
    "ds_class_tags": [],
    "ds_locus_tags": [
        "中国"
    ],
    "ds_time_tags": [
        2015,
        2016,
        2017,
        2018,
        2019,
        2020
    ],
    "ds_contributors": [
        {
            "true_name": "叶红",
            "email": "hye@iue.ac.cn",
            "work_for": "中国科学院城市环境研究所",
            "country": "中国"
        }
    ],
    "ds_meta_authors": [
        {
            "true_name": "叶红",
            "email": "hye@iue.ac.cn",
            "work_for": "中国科学院城市环境研究所",
            "country": "中国"
        }
    ],
    "ds_managers": [
        {
            "true_name": "叶红",
            "email": "hye@iue.ac.cn",
            "work_for": "中国科学院城市环境研究所",
            "country": "中国"
        }
    ],
    "category": "气象"
}