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首页> 外文期刊>Environmental Science & Technology >National Spatiotemporal Exposure Surface for NO_2: Monthly Scaling of a Satellite-Derived Land-Use Regression, 2000-2010
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National Spatiotemporal Exposure Surface for NO_2: Monthly Scaling of a Satellite-Derived Land-Use Regression, 2000-2010

机译：全国NO_2的时空暴露面：2000-2010年卫星衍生土地利用回归的月尺度

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摘要

Land-use regression (LUR) is widely used for estimating within-urban variability in air pollution. While LUR has recently been extended to national and continental scales, these models are typically for long-term averages. Here we present NO_2 surfaces for the continental United States with excellent spatial resolution (～100 m) and monthly average concentrations for one decade. We investigate multiple potential data sources (e.g., satellite column and surface estimates, high- and standard-resolution satellite data, and a mechanistic model [WRF-Chem]), approaches to model building (e.g., one model for the whole country versus having separate models for urban and rural areas, monthly LURs versus temporal scaling of a spatial LUR), and spatial interpolation methods for temporal scaling factors (e.g., kriging versus inverse distance weighted). Our core approach uses NO_2 measurements from U.S. EPA monitors (2000-2010) to build a spatial LUR and to calculate spatially varying temporal scaling factors. The model captures 8296 of the spatial and 76% of the temporal variability (population-weighted average) of monthly mean NO_2 concentrations from U.S. EPA monitors with low average bias (21%) and error (2.4 ppb). Model performance in absolute terms is similar near versus far from monitors, and in urban, suburban, and rural locations (mean absolute error 2-3 ppb); since low-density locations generally experience lower concentrations, model performance in relative terms is better near monitors than for from monitors (mean bias 3% versus 4096) and is better for urban and suburban locations (1-696) than for rural locations (78%, reflecting the relatively dean conditions in many rural areas). During 2000-2010, population-weighted mean NO_2 exposure decreased 42% (1.0 ppb [～5.2%] per year), from 23.2 ppb (year 2000) to 13.5 ppb (year 2010). We apply our approach to all U.S. Census blocks in the contiguous United States to provide 132 months of publicly available, high-resolution NO_2 concentration estimates.
机译：土地利用回归（LUR）被广泛用于估算城市内部空气污染的变化性。虽然LUR最近已扩展到国家和大陆范围，但这些模型通常用于长期平均值。在这里，我们展示了美国大陆的NO_2表面，具有出色的空间分辨率（〜100 m）和十年的月平均浓度。我们研究了多种潜在的数据源（例如，卫星柱面和地面估计，高分辨率和标准分辨率的卫星数据以及一种机械模型[WRF-Chem]），建模方法（例如，针对整个国家/地区的模型与分别针对城市和农村地区的模型，月度LUR与空间LUR的时间缩放比例，以及针对时间缩放因子的空间插值方法（例如，克里金法与反距离加权）。我们的核心方法是使用美国EPA监视器（2000-2010）的NO_2测量结果来建立空间LUR并计算空间变化的时间缩放因子。该模型从美国EPA监测器中以较低的平均偏差（21％）和误差（2.4 ppb）捕获了8296个月平均NO_2浓度的空间变化和76％的时间变化（人口加权平均）。从绝对角度看，模型性能在显示器附近和远离显示器以及在城市，郊区和农村地区均相似（绝对误差为2-3 ppb）；由于低密度位置的浓度通常较低，因此相对而言，显示器附近的模型性能要好于显示器（平均偏差3％对4096），并且城市和郊区（1-696）要好于农村（78） ％，反映了许多农村地区相对较理想的条件。在2000-2010年期间，人口加权平均NO_2暴露量下降了42％（每年1.0 ppb [〜5.2％]），从23.2 ppb（2000年）降至13.5 ppb（2010年）。我们将方法应用于美国连续的所有美国人口普查区块，以提供132个月的公开可用的高分辨率NO_2浓度估算值。

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  • 来源
    《Environmental Science & Technology》 |2015年第20期|12297-12305|共9页
  • 作者

    Matthew J. Bechle; Dylan B. Millet; Julian D. Marshall;

  • 作者单位

    Department of Civil, Environmental, and Geo- Engineering University of Minnesota, Minneapolis, Minnesota 55455, United States;

    Department of Civil, Environmental, and Geo- Engineering University of Minnesota, Minneapolis, Minnesota 55455, United States,Department of Soil, Water, and Climate, University of Minnesota, Minneapolis, Minnesota 55455, United States;

    Department of Civil, Environmental, and Geo- Engineering University of Minnesota, Minneapolis, Minnesota 55455, United States;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
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