Dataset of vehicle chase measurements in real-world subfreezing winter conditions

This dataset comprises thorough measurements of light-duty vehicles emissions conducted in Siilinjärvi and Kuopio, Finland, during February 2021, using a mobile laboratory. The measurements focused on subfreezing conditions to capture emissions nuances during cold weather. Measurements were carried out on minimally trafficked roads to diminish external disturbances. The dataset includes a large number of variables from gas and particle emissions. Gaseous emissions of CO, CO₂, and NO_x were measured. Measured variables of particle emissions were number concentration (CPC), size distribution (ELPI+), black carbon concentration (AE33), and chemical composition (SP-AMS). A total of six light-duty vehicles were investigated, featuring three diesel and three gasoline engines. The measurements incorporated three distinct drive scenarios: subfreezing–cold start, preheated–cold start (utilizing either electrical or fuel-operated auxiliary heaters), and hot start (where a vehicle engine has reached the optimal temperature through prior driving). Each drive type was replicated twice, resulting in six driven rounds per vehicle and 36 rounds in total. Additionally, daily background measurements were conducted by following the same route without chasing a specific vehicle. Meteorological conditions during the measurements were representative of winter in Finland, with outside temperatures ranging from –9 °C to –28 °C. The effect of weather conditions on the measurements were minimal. Only a minor effect was due to the occasional snowfall, especially on the last day when the road surface was snowy, and the car being chased lifted the snow from the road surface. We didn't recognize other factors, such as high wind speeds or major road dust emissions, that could have affected the measurement results. This dataset serves as a valuable resource for comparing emissions under diverse environmental conditions, particularly in real-life winter settings where data are scarce. Furthermore, it provides an opportunity for meta-analysis of emission factors from various passenger vehicle types. The dataset's richness and specificity make it a valuable contribution to the understanding of winter-time vehicular emissions.