Uncovering local aggregated air quality index with smartphone captured images leveraging efficient deep convolutional neural network

TL;DR

This study develops an efficient deep convolutional neural network to estimate local air quality index from smartphone images, specifically for Dhaka, outperforming existing models and providing a new publicly available dataset.

Contribution

The paper introduces a resource-efficient DCNN model for predicting PM2.5 and AQI from smartphone images, and provides the first public dataset of atmospheric images and measurements from Dhaka.

Findings

Our model outperforms ViT, INN, VGG19, ResNet50, and MobileNetV2 in accuracy.

The dataset is the first of its kind for Dhaka, enabling future research.

The model effectively correlates images with PM2.5 levels using fewer parameters.

Abstract

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.