Using Street View Imagery to Predict Street-Level Particulate Air Pollution
Using Street View Imagery to Predict Street-Level Particulate Air Pollution
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DOI：
10.1021/acs.est.0c05572
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发表时间：
2021-02-16
影响因子：
11.4
通讯作者：
Hankey, Steve
中科院分区：
文献类型：
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作者：
Qi, Meng;Hankey, Steve
土地利用回归（LUR）模型经常被用来估计空气污染的空间格局。传统的土地利用率往往依赖于固定地点的测量和地理信息系统派生的变量有限的空间分辨率。我们提出了一种利用谷歌街景（GSV）图像来预测街道级颗粒空气污染的方法（即，黑碳[BC]和颗粒数[PN]浓度）。我们基于移动的监测数据和使用深度学习模型从类似于52500 GSV图像中提取的特征开发了经验模型。我们测试了理论和数据驱动的特征选择方法以及使用不同缓冲区大小（50-2000 m）内的图像的模型。与使用传统变量的LUR模型相比，我们的模型使用街道级预测因子实现了类似的模型性能，同时还识别了其他潜在的热点。对于BC和PN模型，采用综合特征选择的调整后R-2（10倍CV R-2）分别为0.57-0.64（0.50-0.57）和0.65-0.73（0.61-0.66）。仅使用测量位置附近的特征的模型（即，250 m内的GSV图像解释了约50%的空气污染变化，表明PN和BC受到街道级建筑环境的强烈影响。我们的研究结果表明，GSV图像，计算机视觉技术处理，是一个很有前途的数据源，开发LUR模型具有高空间分辨率和一致的预测变量跨越行政边界。
Land-use regression (LUR) models are frequently applied to estimate spatial patterns of air pollution. Traditional LUR often relies on fixed-site measurements and GIS-derived variables with limited spatial resolution. We present an approach that leverages Google Street View (GSV) imagery to predict street-level particulate air pollution (i.e., black carbon [BC] and particle number [PN] concentrations). We developed empirical models based on mobile monitoring data and features extracted from similar to 52 500 GSV images using a deep learning model. We tested theory- and data-driven feature selection methods as well as models using images within varying buffer sizes (50-2000 m). Compared to LUR models with traditional variables, our models achieved similar model performance using the street-level predictors while also identifying additional potential hotspots. Adjusted R-2 (10-fold CV R-2) with integrated feature selection was 0.57-0.64 (0.50-0.57) and 0.65-0.73 (0.61-0.66) for BC and PN models, respectively. Models using only features near the measurement locations (i.e., GSV images within 250 m) explained -50% of air pollution variability, indicating PN and BC are strongly affected by the street-level built environment. Our results suggest that GSV imagery, processed with computer vision techniques, is a promising data source to develop LUR models with high spatial resolution and consistent predictor variables across administrative boundaries.