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 codes and dataset are available at https://github.com/lepotatoguy/aqi.

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，现有文献中对于利用智能手机图像确定基于PM2.5浓度的特定位置聚合空气质量指数（AQI）的可能性探索仍十分有限。本文深入研究了使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。我们的研究聚焦于孟加拉国首都达卡，因其严重的空气污染水平及暴露于此的庞大人口。研究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。这些照片在达卡不同地点拍摄，标签基于从当地美国领事馆获取、并采用NowCast算法计算的PM2.5浓度数据。通过监督学习，我们的模型在训练过程中建立了相关性指标，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。与流行的过参数化模型不同，我们的模型因使用较少参数而表现出资源效率。此外，测试结果表明，在预测特定位置PM2.5浓度方面，我们的模型优于ViT、INN等流行模型，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对应PM2.5测量值的集合。代码和数据集可在https://github.com/lepotatoguy/aqi获取。