This article devises a photograph-based monitoring model to estimate the real-time PM _2.5 concentrations, overcoming currently popular electrochemical sensor-based PM _2.5 monitoring methods’ shortcomings such as low-density spatial distribution and time delay. Combining the proposed monitoring model, the photographs taken by various camera devices (e.g., surveillance camera, automobile data recorder, and mobile phone) can widely monitor PM _2.5 concentration in megacities. This is beneficial to offering helpful decision-making information for atmospheric forecast and control, thus reducing the epidemic of COVID-19. To specify, the proposed model fuses Information Abundance measurement and Wide and Deep learning, dubbed as IAWD, for PM _2.5 monitoring. First, our model extracts two categories of features in a newly proposed DS transform space to measure the information abundance (IA) of a given photograph since the growth of PM _2.5 concentration decreases its IA. Second, to simultaneously possess the advantages of memorization and generalization, a new wide and deep neural network is devised to learn a nonlinear mapping between the above-mentioned extracted features and the groundtruth PM _2.5 concentration. Experiments on two recently established datasets totally including more than 100 000 photographs demonstrate the effectiveness of our extracted features and the superiority of our proposed IAWD model as compared to state-of-the-art relevant computing techniques.

中文翻译：

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PM₂.₅ 监测：使用信息丰度测量和广泛而深入的学习

本文设计了一种基于照片的监测模型来估计实时 PM _2.5浓度，克服目前流行的基于电化学传感器的 PM _2.5监测方法的缺点，如低密度空间分布和时间延迟。结合所提出的监测模型，各种摄像设备（如监控摄像头、汽车数据记录仪和手机）拍摄的照片可以广泛监测特大城市的PM _2.5浓度。这有利于为大气预测和控制提供有用的决策信息，从而减少 COVID-19 的流行。具体来说，提议的模型融合了信息丰度测量和广泛和深度学习，称为 IAWD，用于 PM _2.5监测。首先，我们的模型在新提出的 DS 变换空间中提取两类特征，以测量给定照片的信息丰度 (IA)，因为 PM _2.5浓度的增长会 降低其 IA。其次，为了同时拥有记忆和泛化的优点，设计了一个新的广而深的神经网络来学习上述提取的特征与真实 PM _2.5浓度之间的非线性映射 。对两个最近建立的数据集进行的实验总共包括超过 100 000 张照片，证明了我们提取的特征的有效性以及我们提出的 IAWD 模型与最先进的相关计算技术相比的优越性。