Geo-referenced environmental data collected by mobile sensing vehicles

This dataset consists of geo-referenced and time-stamped environmental measurements, including CO₂ concentration, air temperature, and relative humidity, collected by a fleet of mobile sensors in Benevento, Italy. The sensors are Sensirion SCD41 CO₂ sensors, managed by a vehicle-mounted RaspeberryPi, which perform local preprocessing, GPS acquisition, timestamp synchronization, and data transmission. Data was collected by three vehicles with identical sensors, covering diverse urban trajectories to ensure wide spatial coverage. Each vehicle generated continuous data streams of CO₂, temperature, humidity, latitude, longitude, and timestamps. The dataset comprises raw sensor readings, spatial coordinates, temporal metadata, and vehicle IDs, enabling the reconstruction of trajectories and facilitating environmental analyses. Sampling intervals range from 1 to 5 seconds. This dataset supports research on urban air quality, mobile sensing, spatio-temporal analysis, environmental monitoring, and vehicular sensor networks. It can be used for benchmarking interpolation algorithms, validating mobile sensing strategies, evaluating uncertainty propagation, and mobility-aware sampling. Additionally, it offers insight into sensor behavior in real-world conditions and methods for robust urban monitoring. This dataset is described in a Data in Brief article currently under revision, entitled “Geo-Temporal Vehicular Environmental Sensing Dataset,” authored by the same authors. This dataset was used in the Case Study section of the following article Mobile Urban Sensing: Spatio-Temporal Observability Analysis and Optimization: @article{COLARUSSO2026101907, title = {Mobile Urban Sensing: Spatio-Temporal Observability Analysis and Optimization}, journal = {Internet of Things}, pages = {101907}, year = {2026}, issn = {2542-6605}, doi = {https://doi.org/10.1016/j.iot.2026.101907}, url = {https://www.sciencedirect.com/science/article/pii/S2542660526000375}, author = {Carmine Colarusso and Marco Consales and Ida Falco and Eugenio Zimeo}}本数据集包含经地理参考（geo-referenced）且带有时间戳（time-stamped）的环境监测数据，涵盖二氧化碳（CO₂）浓度、空气温度与相对湿度，由意大利贝内文托市的移动传感器车队采集。本次采用的传感器为Sensirion SCD41二氧化碳（CO₂）传感器，由车载树莓派（RaspeberryPi）进行管控，负责完成本地数据预处理、GPS信息采集、时间戳同步与数据传输工作。 本数据集由三台搭载同款传感器的车辆完成采集，采集路径覆盖多样城市轨迹，以保障充足的空间覆盖范围。每台车辆均持续生成包含二氧化碳、温度、湿度、纬度、经度及时间戳的数据流。数据集包含原始传感器读数、空间坐标、时间元数据与车辆ID，可支持轨迹重建与环境分析相关研究。采样间隔为1至5秒。 本数据集可支撑城市空气质量、移动传感（mobile sensing）、时空分析（spatio-temporal analysis）、环境监测以及车载传感器网络（vehicular sensor networks）等领域的研究。其可用于插值算法的基准测试、移动传感策略验证、不确定性传播评估以及移动感知采样（mobility-aware sampling）方法研究。此外，该数据集还可助力理解真实场景下的传感器工作特性，以及鲁棒性城市监测方法的开发。 本数据集的详细说明见于一篇目前处于修订阶段的《数据简报（Data in Brief）》文章，标题为《地理-时间车载环境传感数据集（Geo-Temporal Vehicular Environmental Sensing Dataset）》，作者与该数据集的相关文献一致。 本数据集被用于以下论文的案例研究部分，该论文题为《移动城市传感：时空可观测性分析与优化（Mobile Urban Sensing: Spatio-Temporal Observability Analysis and Optimization）》： @article{COLARUSSO2026101907, title = {Mobile Urban Sensing: Spatio-Temporal Observability Analysis and Optimization}, journal = {Internet of Things}, pages = {101907}, year = {2026}, issn = {2542-6605}, doi = {https://doi.org/10.1016/j.iot.2026.101907}, url = {https://www.sciencedirect.com/science/article/pii/S2542660526000375}, author = {Carmine Colarusso and Marco Consales and Ida Falco and Eugenio Zimeo}}