Perception of Cabin Air Quality among Drivers and Passengers

Fine particulate matter exposure in four transport modes of Greater Cairo

The number of daily commuters in Greater Cairo has exceeded 15 million nevertheless personal exposure studies in transport microenvironments are limited. The aim of this study is to quantify PM_2.5 exposure during peak hours in four transport modes of Greater Cairo - car (windows-open, windows-closed with recirculation and AC-on), microbus (windows-open), cycling and walking - and understand its underlying drivers. Data was collected using a pDR-1500 monitor and analysed to capture concentration variations, spatial variability, exposure doses, commuting costs versus inhaled doses, health burden and economic losses. Car with recirculation resulted in the least average PM_2.5 concentrations (32 ± 6 μg/m³), followed by walking (77 ± 35 μg/m³), car with windows-open (82 ± 32 μg/m³), microbus with windows-open (96 ± 29 μg/m³) and cycling (100 ± 28 μg/m³). Evening hours observed average PM_2.5 concentrations by 26-58% lesser than morning. Spatial variability analysis showed that 75th-90th percentile PM_2.5 concentrations coincided with congested spots. Cycling and walking lanes are rare hence commuters are exposed to surges in PM_2.5 concentrations when passing near construction and solid waste burning sites. Cycling and walking also resulted in inhaling 40-times and 32-times higher PM_2.5 dose per kilometre than for car with recirculation. Commuting by microbus cost (with windows-open) ~45% of car cost (with recirculation) but it resulted in 4-times higher inhaled PM_2.5 dose. As expected due to the lowest PM_2.5 exposure concentrations, health burden resulting from car travel (with recirculation) caused the least death rates of 0.07 (95% CI 0.07-0.08) prematures deaths per 100,000 commuters/year while microbus with windows-open resulted in the highest death rates; 0.52 (95% CI 0.49-0.56). Microbus deaths represent 57% of national economic losses due to PM_2.5 exposure amongst the four transport modes. This study provides real-time exposure data and analyses its implications on commuter health as a first step in informed decision-making and better urban planning.

Real-Time In-Vehicle Air Quality Monitoring System Using Machine Learning Prediction Algorithm

This paper presents the development of a real-time cloud-based in-vehicle air quality monitoring system that enables the prediction of the current and future cabin air quality. The designed system provides predictive analytics using machine learning algorithms that can measure the drivers’ drowsiness and fatigue based on the air quality presented in the cabin car. It consists of five sensors that measure the level of CO₂, particulate matter, vehicle speed, temperature, and humidity. Data from these sensors were collected in real-time from the vehicle cabin and stored in the cloud database. A predictive model using multilayer perceptron, support vector regression, and linear regression was developed to analyze the data and predict the future condition of in-vehicle air quality. The performance of these models was evaluated using the Root Mean Square Error, Mean Squared Error, Mean Absolute Error, and coefficient of determination (R²). The results showed that the support vector regression achieved excellent performance with the highest linearity between the predicted and actual data with an R² of 0.9981.