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的常用工具。然而，现有文献中，利用智能手机图像确定基于特定地点PM2.5浓度的聚合空气质量指数（AQI）的潜力仍远未得到充分探索。本文深入研究了使用智能手机相机拍摄的图像预测特定地点PM2.5浓度所面临的挑战。我们的研究重点聚焦于孟加拉国首都达卡，因其严重的空气污染水平以及庞大暴露于污染的人口。本研究开发了一个深度卷积神经网络（DCNN），我们利用超过一千张拍摄并标注的户外图像进行训练。这些照片在达卡多个地点采集，其标签基于从当地美国领事馆获取的PM2.5浓度数据（采用NowCast算法计算）。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。这使得算法能够从智能手机图像中计算出等效的日均AQI指数。与流行的高度参数化模型不同，我们的模型因使用较少参数而展现出资源效率。此外，测试结果表明，在预测特定地点PM2.5浓度方面，我们的模型优于流行的ViT和INN模型，以及基于CNN的流行模型（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，我们的代码和数据集将公开。