School of Geography, Nanjing Normal University, Nanjing, 210023, China; Key Laboratory of Virtual Geographic Environment, Nanjing Normal University, Ministry of Education, Nanjing, 210023, China; Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing Normal University, Nanjing, 210023, China.
School of Geography, Nanjing Normal University, Nanjing, 210023, China; Key Laboratory of Virtual Geographic Environment, Nanjing Normal University, Ministry of Education, Nanjing, 210023, China; Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing Normal University, Nanjing, 210023, China.
Environ Res. 2022 Aug;211:113051. doi: 10.1016/j.envres.2022.113051. Epub 2022 Mar 1.
An efficient, accurate and high-resolution PM monitoring approach is critical to pollution control and public health. Here we propose an image-based method for PM concentration estimation. The method combines the image features with other influence factors to inference PM, and an improved patchwise strategy is used in the processes of regression and prediction. The experimental results of the Shanghai scene dataset show that our method achieved a higher estimation accuracy with 0.88 at R and 10.42 μg⋅m at RMSE, compared to other methods; the addition of the influence factors, such as relative humidity and photographing month, improve the accuracy, while the improved patchwise strategy significantly enhanced the predictive performance. Moreover, the results of two datasets at different times and location further demonstrate the effectiveness and applicability of the proposed method.
一种高效、准确、高分辨率的 PM 监测方法对于污染控制和公众健康至关重要。在这里，我们提出了一种基于图像的 PM 浓度估计方法。该方法将图像特征与其他影响因素相结合，以推断 PM，并在回归和预测过程中使用改进的逐块策略。上海场景数据集的实验结果表明，与其他方法相比，我们的方法具有更高的估计精度，R 为 0.88，RMSE 为 10.42μg⋅m；添加相对湿度和拍摄月份等影响因素可提高精度，而改进的逐块策略则显著提高了预测性能。此外，两个不同时间和地点的数据集的结果进一步证明了所提出方法的有效性和适用性。
