Time and spatially resolved tracking of the air quality in local public transport

Abstract

As an indoor environment, public transport is subject to special conditions with many passengers in a comparatively small space. Therefore, both an efficient control of the climatic parameters and a good air exchange are necessary to avoid transmission and spread of respiratory diseases. However, in such a dynamic system it is practically impossible to determine pathogenic substances with the necessary temporal and spatial resolution, but easy-to-measure parameters allow the air quality to be assessed in a passenger compartment. Carbon dioxide has already proven to be a useful indicator, especially in environments with a high occupancy of people. Airborne particulate matter can also be an important aspect for assessing the air quality in an indoor space. Consequently, the time courses of temperature, relative humidity, carbon dioxide and particulate matter (PM₁₀) were tracked and evaluated in local public transport buses, trams and trains in the Brunswick/Hanover region. In all measurements, the climatic conditions were comfortable for the passengers. Carbon dioxide was strongly correlated with occupancy and has proven to be the most informative parameter. The PM₁₀ concentration, however, often correlated with the dynamics of people when getting on and off, but not with the occupancy. Sensors, equipped with integrated GPS, were installed in the passenger cabins and were found to be useful for recording location-related effects such as stops. The results of this study show that the online recording of simple parameters is a valuable tool for assessing air quality as a function of time, location and number of people. When the occupancy is high, a low carbon dioxide level indicates good ventilation, which automatically reduces the risk of infection. It is therefore recommended to take more advantage of low-cost sensors as a control for air conditioning systems in passenger cabins and for evaluations of the dynamics in public transport.

Figure 1

Time curves for temperature and relative humidity (A), carbon dioxide concentration (B) and PM₁₀ concentration (C) for BSVG Line 3 (Run 03). The red area marks periods with high occupancy (> 70%).

Figure 2

Time curves for temperature and relative humidity (A), carbon dioxide concentration (B) and PM₁₀ concentration (C) for WestfalenBahn Line 95772 (Run 12). The red area marks periods with high occupancy (> 70%).

Figure 3

Time course of the PM₁₀ concentration and single exponential non-linear regression analysis with Eq. (1). (A) BSVG Line 411 (Run 01); (B) ÜSTRA Line 4 (Run 19). See Figure S1 in the Supporting Information for the full-time course of PM₁₀ concentrations.

Figure 4

Contour plots of the carbon dioxide concentrations measured with gas sensors in buses. (A) BSVG Circle Line 419 (Run 09); (B) BSVG Tram Line 10 (Run 06, north to south); (C) BSVG Tram Line 10 (Run 05, south to north). The arrows indicate the direction of travel. The figure was generated with Microsoft Excel 2019 (v1808) using Microsoft Bing Maps and graphically modified with Microsoft PowerPoint 2019 (v1808).

Figure 5

Comparison of measured carbon dioxide concentrations for the Rotronic CP11 and the SCD30 gas sensor. Both devices were calibrated versus the carbon dioxide concentration in ambient air.