The prevalence and mobility of smartphones make these a widely used tool for environmental health research. However, their potential for determining aggregated air quality index (AQI) based on PM2.5 concentration in specific locations remains largely unexplored in the existing literature. In this paper, we thoroughly examine the challenges associated with predicting location-specific PM2.5 concentration using images taken with smartphone cameras. The focus of our study is on Dhaka, the capital of Bangladesh, due to its significant air pollution levels and the large population exposed to it. Our research involves the development of a Deep Convolutional Neural Network (DCNN), which we train using over a thousand outdoor images taken and annotated. These photos are captured at various locations in Dhaka, and their labels are based on PM2.5 concentration data obtained from the local US consulate, calculated using the NowCast algorithm. Through supervised learning, our model establishes a correlation index during training, enhancing its ability to function as a Picture-based Predictor of PM2.5 Concentration (PPPC). This enables the algorithm to calculate an equivalent daily averaged AQI index from a smartphone image. Unlike, popular overly parameterized models, our model shows resource efficiency since it uses fewer parameters. Furthermore, test results indicate that our model outperforms popular models like ViT and INN, as well as popular CNN-based models such as VGG19, ResNet50, and MobileNetV2, in predicting location-specific PM2.5 concentration. Our dataset is the first publicly available collection that includes atmospheric images and corresponding PM2.5 measurements from Dhaka. Our code and dataset will be made public when publishing the paper.

翻译：智能手机的普及性和移动性使其成为环境健康研究中广泛使用的工具。然而，在现有文献中，基于PM2.5浓度确定特定位置综合空气质量指数（AQI）的潜力尚未得到充分探索。本文深入研究了利用智能手机摄像头拍摄的图像预测特定位置PM2.5浓度所面临的挑战。研究聚焦于孟加拉国首都达卡，因其严重的空气污染水平和大量暴露于污染中的人口。我们开发了一个深度卷积神经网络（DCNN），并使用超过一千张拍摄并标注的户外图像进行训练。这些照片拍摄自达卡多个地点，其标签基于从当地美国领事馆获取的PM2.5浓度数据，并采用NowCast算法计算得出。通过监督学习，我们的模型在训练过程中建立了相关性指数，增强了其作为基于图像的PM2.5浓度预测器（PPPC）的能力，从而能够从智能手机图像中计算等效的日均AQI指数。与流行的过度参数化模型不同，我们的模型因使用更少的参数而表现出资源高效性。此外，测试结果表明，我们的模型在预测特定位置PM2.5浓度方面优于ViT、INN等流行模型，以及基于CNN的经典模型如VGG19、ResNet50和MobileNetV2。我们的数据集是首个公开可用的包含达卡大气图像及相应PM2.5测量数据的集合。论文发表时，我们将公开相关代码和数据集。