研究报告

  • 许悦蕾,刘延安,施润和,郭春颖,窦新宇.气象要素对气溶胶光学厚度估算PM2.5的影响[J].环境科学学报,2018,38(10):3868-3876

  • 气象要素对气溶胶光学厚度估算PM2.5的影响
  • Impact of meteorological elements on estimation of PM2.5 by aerosol optical depth
  • 基金项目:国家理科基地科研训练及科研能力提高项目(No.J1310028);上海市卫计委环境卫生与劳动卫生重点学科建设项目(No.15GWZK0201)
  • 作者
  • 单位
  • 许悦蕾
  • 华东师范大学地理科学学院, 上海 200241
  • 刘延安
  • 1. 华东师范大学地理科学学院, 上海 200241;2. 华东师范大学地理信息科学教育部重点实验室, 上海 200241
  • 施润和
  • 1. 华东师范大学地理科学学院, 上海 200241;2. 华东师范大学地理信息科学教育部重点实验室, 上海 200241
  • 郭春颖
  • 华东师范大学地理科学学院, 上海 200241
  • 窦新宇
  • 华东师范大学地理科学学院, 上海 200241
  • 摘要:地面监测得到的近地面细颗粒物PM2.5浓度较为精确,但数据覆盖范围相对较小,卫星遥感反演的气溶胶光学厚度(AOD)数据可以反映污染物浓度分布,具有范围大且速度快的特点,因此,大多数学者通过建立PM2.5-AOD模型来实现卫星遥感监测PM2.5浓度,并通过引入气象要素来优化模型.然而,气象要素的选择与引入往往对模型的精度有较大的影响,如何有效地选择对PM2.5浓度影响较大的气象要素一直是PM2.5-AOD模型中的关键问题.因此,本文基于华东地区2014-2015年的MODIS AOD和地面监测站的PM2.5浓度数据,结合再分析气象资料,利用多元逐步线性回归方法建立PM2.5-AOD模型,从由特定时刻、高度上的气象要素与随时间、高度变化的气象要素组成的气象要素集中,筛选出对因变量PM2.5浓度有显著影响的关键气象要素.结果表明:在地域与季节双重尺度下的PM2.5-AOD模型精度更高;相较于特定时刻高度的气象要素,随时间和高度变化的气象要素对PM2.5-AOD模型的影响更为显著;在地域与季节双重尺度下,1000~850 hPa经向风速差、世界时0:00-6:00近地面温度差、850~600 hPa温度差、6:00边界层高度、12:00-18:00近地面压强差、1000~850 hPa温度差对模型影响较大,但应依据不同季节和不同地区的具体影响程度作为选择标准.
  • Abstract:The fine particulates (PM2.5) concentrations obtained from the surface monitoring are relatively accurate but with smaller data coverage, while aerosol optical depth (AOD) data from the satellite remote sensing retrievals can reflect the concentration distribution of atmospheric pollutants, with a wide range and fast data acquisition. To effectively monitor the changes of near-surface PM2.5 concentrations using the satellite remote sensing, therefore, various PM2.5-AOD models have been established and optimized by introducing meteorological elements. However, the selection and introduction of the meteorological elements often has a great impact on the model accuracy, and how to determine the critical meteorological element(s) has always been a key issue in developing the PM2.5-AOD model. Therefore, based on the AOD data derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the ground-level PM2.5 station observations in East China from 2014 to 2015, this study established the relationship between PM2.5 and AOD using the multivariable stepwise linear regression model in combination with meteorological reanalysis data. In constructing this PM2.5-AOD model, a meteorological element dataset was built, and meteorological elements at specific time and heights were then selected to compare with other elements highly varying with time and heights in order to determine the key meteorological factors that have significant impacts on the ground-level PM2.5 concentrations. The main results are as follows:①The accuracy of the PM2.5-AOD model is higher when both regional and seasonal dependences are considered.②Compared with the meteorological elements at a particular time or level, the meteorological elements that change with time and levels have greater impacts on the PM2.5-AOD model;③Several key meteorological factors are found to have prominent influence on the model, including the difference in meridional wind speed between 1000 hPa and 850 hPa, the near-surface temperature difference between 00 and 06 UTC, the temperature difference at 850~600 hPa and at 1000~850 hPa, the planetary boundary layer height at 06UTC, and the surface pressure difference between 12 and 18UTC. These factors should be selected in the model according to their specific impacts in different seasons and regions.

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