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

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.Comment: 18 pages, 7 figures, submitted to Nature Scientific Report

Assessment of the Contribution of Traffic Emissions to the Mobile Vehicle Measured PM2.5 Concentration by Means of WRF-CMAQ Simulations

INE/AUTC 12.0

Estimating Air Pollution Levels Using Machine Learning

Air pollution has emerged as a substantial concern, especially in developing countries worldwide. An important aspect of this issue is the presence of PM2.5. Air pollutants with a diameter of 2.5 or less micrometers are known as PM2.5. Due to their size, these particles are a serious health risk and can quickly infiltrate the lungs, leading to a variety of health problems. Due to growing concerns about air pollution, technology like automatic air quality measurement can offer beneficial assistance for both personal and business decisions. This research suggests an ensemble machine learning model that can efficiently replace the standard air quality estimation techniques, which need several instruments and setup and have large financial expenditures for equipment acquisition and maintenance

The formation, properties and impact of secondary organic aerosol: current and emerging issues

Secondary organic aerosol (SOA) accounts for a significant fraction of ambient tropospheric aerosol and a detailed knowledge of the formation, properties and transformation of SOA is therefore required to evaluate its impact on atmospheric processes, climate and human health. The chemical and physical processes associated with SOA formation are complex and varied, and, despite considerable progress in recent years, a quantitative and predictive understanding of SOA formation does not exist and therefore represents a major research challenge in atmospheric science. This review begins with an update on the current state of knowledge on the global SOA budget and is followed by an overview of the atmospheric degradation mechanisms for SOA precursors, gas-particle partitioning theory and the analytical techniques used to determine the chemical composition of SOA. A survey of recent laboratory, field and modeling studies is also presented. The following topical and emerging issues are highlighted and discussed in detail: molecular characterization of biogenic SOA constituents, condensed phase reactions and oligomerization, the interaction of atmospheric organic components with sulfuric acid, the chemical and photochemical processing of organics in the atmospheric aqueous phase, aerosol formation from real plant emissions, interaction of atmospheric organic components with water, thermodynamics and mixtures in atmospheric models. Finally, the major challenges ahead in laboratory, field and modeling studies of SOA are discussed and recommendations for future research directions are proposed

Energy Efficient Air Quality Solutions for Vehicle Cabins

Maintaining a good air quality level is essential for reducing potential health risks for human beings. Vehicle cabin is one common environment where people spend increasing amount of time in modern societies. It’s an environment challenged by elevated pollutants from surrounding traffics, especially small particles like PM2.5 and UFP (Ultrafine particles). To efficiently reduce or remove the pollutants from incoming air is one essential focus for development of future vehicles. To achieve that goal with energy efficient solutions would be even more important in the trend of emerging electric vehicles. The objective of this thesis is to evaluate and propose solutions for improved cabin air quality and energy efficiency, which could be used in the development of vehicle climate system. The work has been conducted through vehicle measurements on road in two different locations, development of an air quality model, modelling of increased recirculation in the climate ventilation strategy, as well as measurements on new prototypes in both rig and road conditions. The purpose of the road measurements is to set the baseline of current air quality levels and evaluate the important influencing factors such as filter age and ventilation settings. The purpose of the model development is to enable a repeatable and comprehensive evaluation environment, which is later used to evaluate the strategy of increased air recirculation under common driving conditions. The purpose of the measurements on prototypes is to evaluate one solution of using EPA (Efficient Particulate Air) or HEPA (high-efficiency particulate air) filters as pre-filters, to prove the concept and the limitations. The results are showing that cabin particles are highly influenced by the outside particle concentrations, the filter design and status, and to some extent the ventilation settings. Besides the application of pre-ionization assisted filtration was proved valuable. The air quality model, implemented in an existing climate system model, is validated with road measurements. Modelling of increased recirculation results in significant reduction of energy use and particles. In warm climate it’s more applicable to avoid fog risks and in all climates the use of high recirculation (for example 70%) should be evaluated based on the number of passengers. One way to achieve that is adding a control based on cabin CO2 concentration in the climate system.\ua0 It is also shown feasible to improve air quality using an EPA/HEPA pre-filter. The main limitations come from space and acceptable pressure-drop in the relatively compact environment

Beyond here and now: Evaluating pollution estimation across space and time from street view images with deep learning

Advances in computer vision, driven by deep learning, allows for the inference of environmental pollution and its potential sources from images. The spatial and temporal generalisability of image-based pollution models is crucial in their real-world application, but is currently understudied, particularly in low-income countries where infrastructure for measuring the complex patterns of pollution is limited and modelling may therefore provide the most utility. We employed convolutional neural networks (CNNs) for two complementary classification models, in both an end-to-end approach and as an interpretable feature extractor (object detection), to estimate spatially and temporally resolved fine particulate matter (PM2.5) and noise levels in Accra, Ghana. Data used for training the models were from a unique dataset of over 1.6 million images collected over 15 months at 145 representative locations across the city, paired with air and noise measurements. Both end-to-end CNN and object-based approaches surpassed null model benchmarks for predicting PM2.5 and noise at single locations, but performance deteriorated when applied to other locations. Model accuracy diminished when tested on images from locations unseen during training, but improved by sampling a greater number of locations during model training, even if the total quantity of data was reduced. The end-to-end models used characteristics of images associated with atmospheric visibility for predicting PM2.5, and specific objects such as vehicles and people for noise. The results demonstrate the potential and challenges of image-based, spatiotemporal air pollution and noise estimation, and that robust, environmental modelling with images requires integration with traditional sensor networks