【摘要】 OMI（Ozone Monitoring Instrument）SO₂柱浓度数据的有效性已在多个地区得到验证，但其空间分辨率难以满足小尺度区域研究的精度需求。该文基于多尺度地理加权回归（MGWR）模型，耦合气象、夜间灯光和植被覆盖度等数据，将OMI SO₂浓度数据分辨率由0.25°降尺度到1 km,进而探讨2006—2020年长江三角洲地区大气SO₂浓度的时空变化特征及驱动因素。研究表明：降尺度后的大气SO₂浓度数据与站点监测值具有较高一致性（R²=0.74,RMSE=5.29μg/m³）;长江三角洲地区大气SO₂浓度空间异质性显著，值域范围为2.87～35.67μg/m³,高值主要分布在北部和中部地区；局地尺度上，大气SO₂浓度自城区向周围递减；2006—2020年大气SO₂浓度呈显著下降趋势，污染严重地区降速更快，冬季下降速率（1.08μg/m³/a）高于全年（0.68μg/m³/a）。研究结果可为不同区域制定差异化的SO₂减排政策提供参考。更多还原

【Abstract】 The validity of OMI（Ozone Monitoring Instrument） SO₂ data has been verified in many areas.However, its spatial resolution fails to meet the precision requirements for studying the spatiotemporal distribution patterns in small-scale areas.Utilizing the multi-scale geographically weighted regression（MGWR） method, this paper integrated high-resolution meteorological data, nighttime light and vegetation cover datasets to spatially downscale the OMI SO₂ data, thereby investigating the spatiotemporal characteristics and driving factors of atmospheric SO₂ in the Yangtze River Delta.The results revealed that the SO₂ concentrations after data downscaling showed high consistency with the ground monitoring values（R²=0.74,RMSE=5.29 μg/m³）.The study revealed significant spatial heterogeneity of the atmospheric SO₂ concentrations in the Yangtze River Delta, with values ranging from 2.87 μg/m³ to 35.67 μg/m³,and higher concentrations mainly distributed in the northern and central areas.At the fine scale, the atmospheric SO₂ concentrations showed a decreasing trend from urban areas to surrounding areas, offering more detailed information compared to the 0.25° resolution OMI SO₂ data.From 2006 to 2020,there was a notable decline in the SO₂ concentrations, showing that the average decline rate in winter（1.08 μg/m³/a） was higher than the annual average（0.68 μg/m³/a）.The decline of the atmospheric SO₂ concentrations was faster in heavily polluted areas.This study can provide a scientific basis for formulating differentiated SO₂ emission reduction policies in different regions.更多还原