本数据建立了一个地理和时间加权回归模型（GTWR），并利用1公里分辨率的MCD19A2（MODIS/Terra+Aqua陆地气溶胶光学厚度每日L2G全球1公里SIN网格V006）数据和9个辅助变量，估算了2015年至2020年新疆的PM2.5浓度。研究结果表明，与简单线性回归（SLR）和地理加权回归（GWR）模型相比，GTWR模型在新疆PM2.5浓度反演的准确性和可行性方面表现更优。同时，通过将GTWR模型与MCD19A2数据结合，可获得空间分辨率更高的PM2.5空间分布图，2015年至2020年新疆PM2.5年浓度的区域分布与地形特征一致。低值区域主要分布在高海拔山地，高值区域则主要位于低海拔盆地。总体而言，西南部浓度较高，东北部浓度较低。从时间变化来看，六年间的PM2.5浓度呈现单峰分布，2016年为转折点。最后，2015年至2020年新疆季节平均PM2.5浓度存在显著差异，呈现冬季（66.15μg/m³）>春季（52.28μg/m³）>秋季（40.51μg/m³）>夏季（38.63μg/m³）的顺序。研究表明，MCD19A2数据与GTWR模型的结合在反演PM2.5浓度方面具有良好的适用性。", "ds_source": "
（1）PM2.5数据为中国国家环境监测站（http://www.cnemc.cn/）全国空气质量实时发布平台的每小时PM2.5数据;
（2）MODIS MCD19A2 AOD数据由Level-1和大气档案与分布系统分布式主动档案中心（LAADS SAAC）下载，下载地址为：https://ladsweb.modaps.eosdis.nasa.gov/search/，数据为MODIS Terra和aqua的MAIAC大气校正算法（MAIAC）多角度实现，分辨率为1km;
（3）温度、相对湿度、降水量和风速数据来自国家地球系统科学数据中心、国家科技基础设施（http://www.geodata.cn）；
（4）BLH 来自 ECMWF 再分析数据集 （https://www.ecmwf.int/）。", "ds_process_way": "
使用新建立的地理和时间加权回归模型（GTWR），并估计了PM2.5使用1 km分辨率MCD19A2（MODIS/Terra+Aqua Aerosol Optical Thickness Daily L2G Global 1km SIN Grid V006）数据和9个辅助变量，对2015—2020年新疆集中度进行分析。", "ds_quality": "
研究结果表明，与简单线性回归（SLR）和地理加权回归（GWR）模型相比，GTWR模型在新疆PM2.5浓度反演的准确性和可行性方面表现更优。MCD19A2数据与GTWR模型的结合在反演PM2.5浓度方面具有良好的适用性。", "ds_acq_start_time": "2015-01-01 00:00:00", "ds_acq_end_time": "2020-12-31 00:00:00", "ds_acq_place": "新疆", "ds_acq_lon_east": 75.0, "ds_acq_lat_south": 35.0, "ds_acq_lon_west": 50.0, "ds_acq_lat_north": 95.0, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 270916, "ds_files_count": 2, "ds_format": "tif", "ds_space_res": "1000", "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "b745c3a0-16e3-4b52-8646-558a48e8c9ce.png", "ds_thumb_from": 0, "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": [ "170.45" ], "quality_level": 3, "publish_time": "2025-08-28 16:06:49", "last_updated": "2026-01-14 10:57:58", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.FIGSHARE.DB6964.2025", "i18n": { "en": { "title": "Statistical Chart of Sandstorm Frequency and Air Quality (2015-2020)", "ds_format": "tif", "ds_source": "
(1) PM2.5 data from China National Environmental Monitoring Station（ http://www.cnemc.cn/ ）The hourly PM2.5 data of the national air quality real-time release platform;
(2) MODIS MCD19A2 AOD data can be downloaded from Level-1 and the Atmospheric Archive and Distribution System Distributed Active Archive Center (LAADS SAAC) at the following location: https://ladsweb.modaps.eosdis.nasa.gov/search/ The data is a multi angle implementation of the MAIAC atmospheric correction algorithm (MAIAC) for MODIS Terra and Aqua, with a resolution of 1km; (3) Temperature, relative humidity, precipitation, and wind speed data are sourced from the National Earth System Science Data Center and the National Science and Technology Infrastructure（ http://www.geodata.cn ）；
(4) BLH from ECMWF reanalysis dataset（ https://www.ecmwf.int/ ）.", "ds_quality": "
The research results indicate that compared with simple linear regression (SLR) and geographically weighted regression (GWR) models, the GTWR model performs better in the accuracy and feasibility of PM2.5 concentration inversion in Xinjiang. The combination of MCD19A2 data and GTWR model has good applicability in retrieving PM2.5 concentration.", "ds_ref_way": "", "ds_abstract": "
A geographic and time weighted regression model (GTWR) was established for this data, and the PM2.5 concentration in Xinjiang from 2015 to 2020 was estimated using MCD19A2 (MODIS/Terra+Aqua Land Aerosol Optical Thickness Daily L2G Global 1-kilometer SIN Grid V006) data with a resolution of 1 kilometer and 9 auxiliary variables. The research results indicate that compared with simple linear regression (SLR) and geographically weighted regression (GWR) models, the GTWR model performs better in the accuracy and feasibility of PM2.5 concentration inversion in Xinjiang. Meanwhile, by combining the GTWR model with MCD19A2 data, a higher spatial resolution PM2.5 spatial distribution map can be obtained. The regional distribution of annual PM2.5 concentrations in Xinjiang from 2015 to 2020 is consistent with the terrain features. Low value areas are mainly distributed in high-altitude mountains, while high-value areas are mainly located in low altitude basins. Overall, the concentration is higher in the southwest and lower in the northeast. From the perspective of temporal changes, the PM2.5 concentration over the past six years has shown a unimodal distribution, with 2016 being a turning point. Finally, there were significant differences in the seasonal average PM2.5 concentration in Xinjiang from 2015 to 2020, showing the order of winter (66.15 μ g/m ³)>spring (52.28 μ g/m ³)>autumn (40.51 μ g/m ³)>summer (38.63 μ g/m ³). Research has shown that the combination of MCD19A2 data and GTWR model has good applicability in retrieving PM2.5 concentration.
", "ds_time_res": "", "ds_acq_place": "Xinjiang", "ds_space_res": "1000", "ds_projection": "", "ds_process_way": "We used a newly established geographic and time weighted regression model (GTWR) and estimated PM2.5 using a 1 km resolution MCD19A2（MODIS/Terra+Aqua Aerosol Optical Thickness Daily L2G Global 1km SIN Grid V006） Analyze the concentration of Xinjiang from 2015 to 2020 using data and 9 auxiliary variables.", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC BY 4.0", "ds_topic_tags": [ "沙尘暴", "气溶胶", "空气质量" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "新疆" ], "ds_time_tags": [ 2015, 2016, 2017, 2018, 2019, 2020 ], "ds_contributors": [ { "true_name": "夏楠", "email": "xn_gis@xju.edu.cn", "work_for": "新疆大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "夏楠", "email": "xn_gis@xju.edu.cn", "work_for": "新疆大学", "country": "中国" } ], "ds_managers": [ { "true_name": "夏楠", "email": "xn_gis@xju.edu.cn", "work_for": "新疆大学", "country": "中国" } ], "category": "气象" }