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首页> 外文期刊>Environmental Monitoring and Assessment >Development and validation of improved PM_(2.5) models for public health applications using remotely sensed aerosol and meteorological data
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Development and validation of improved PM_(2.5) models for public health applications using remotely sensed aerosol and meteorological data

机译：使用远程感测气溶胶和气象数据的公共卫生应用改进PM_（2.5）模型的开发和验证

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摘要

In this study, Moderate Resolution Imaging Spectrometer (MODIS) satellite measurements of aerosol optical depth (AOD) from different retrieval algorithms have been correlated with ground measurements of fine particulate matter less than 2.5m (PM2.5). Several MODIS AOD products from different satellites (Aqua vs. Terra), retrieval algorithms (Dark Target vs. Deep Blue), collections (5.1 vs. 6), and spatial resolutions (10km vs. 3km) for cities in the Western, Midwestern, and Southeastern USA have been evaluated. We developed and validated PM2.5 prediction models using remotely sensed AOD data. These models were further improved by incorporating meteorological variables (temperature, relative humidity, precipitation, wind gust, and wind direction) from the North American Land Data Assimilation System Phase 2 (NLDAS-2). Adding these meteorological data significantly improved the simulation quality of all the PM2.5 models, especially in the Western USA. Temperature, relative humidity, and wind gust were significant meteorological variables throughout the year in the Western USA. Wind speed was the most significant meteorological variable for the cold season while for the warm season, temperature was the most prominent one in the Midwestern and Southeastern USA. Using this satellite-derived PM2.5 data can improve the spatial coverage, especially in areas where PM2.5 ground monitors are lacking, and studying the connections between PM2.5 and public health concerns including respiratory and cardiovascular diseases in the USA can be further advanced.
机译：在该研究中，来自不同检索算法的温度分辨率成像光谱仪（MODIS）卫星测量来自不同检索算法的气溶胶光学深度（AOD）与细颗粒物质的接地测量相比，细颗粒物质小于2.5M（PM2.5）。来自不同卫星（Aqua与Terra）的Modis Aod产品，检索算法（黑暗目标与深蓝色），集合（5.1与6），以及西部，中西部的城市的空间决议（10公里与3公里），和美国东南部进行了评估。我们使用远程感测的AOD数据开发和验证了PM2.5预测模型。通过将来自北美土地数据同化系统2（NLDAS-2）的气象变量（温度，相对湿度，降水，风向和风向）掺入气象变量（温度，相对湿度，降水，风向和风向）进一步提高了这些模型。添加这些气象数据显着提高了所有PM2.5型号的仿真质量，特别是在美国西部。温度，相对湿度和风阵风在美国西部全年是显着的气象变量。风速是寒冷季节最重要的气象变量，而温暖的季节，温度是美国中西部和东南部最突出的。使用这种卫星衍生的PM2.5数据可以提高空间覆盖，特别是在PM2.5地面监视器缺乏的区域，以及研究PM2.5和美国呼吸系统和心血管疾病的公共卫生问题之间的连接可以进一步进一步先进的。

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