TRAP and traffic data for Oshawa and Allen Road

This dataset includes original data used in the creation of the article titled “Localized Variabilities in Traffic-related Air Pollutant Concentrations Revealed Using Compact Sensor Networks”. Supporting reference data can also be found in the reference dataset “Reference PM2.5 and Wind Data for Oshawa and Allen Road” (doi: 10.17632/yw49w4d7v2.1). The aim of this research was to demonstrate the usefulness of real-time air quality monitoring in the context of Smart City infrastructure. Data collected before and during the COVID-19 pandemic in Oshawa, Ontario includes 2-minute averaged TRAP (CO, NO, and PM2.5) concentrations measured by AirSENCE and hourly traffic volume data measured by Northline Fox traffic counters. The data demonstrates a direct relationship between decreased traffic volumes and concentrations of TRAP. Conversely, road construction was correlated with higher levels of TRAP while causing reduced traffic volumes, demonstrating the insufficiency of conventional sensors for reliably inferring air quality conditions and the need for compact air quality sensor networks.
Other data included in this dataset were collected in 2021–2022 on opposite sides of Allen Road, a busy commuter route in Toronto, Ontario. Data here include 10-minute averaged TRAP (CO and NO) concentrations measured by AirSENCE. This part of the study highlighted the importance of local meteorological conditions with respect to the dispersal of TRAP as well as the applicability of compact air quality sensor networks for supporting in-depth studies of TRAP emission sources and human exposure pathways.

本数据集包含为撰写题为《采用紧凑型传感器网络揭示交通相关空气污染物浓度的局地变异性》的学术文章所使用的原始数据。其配套参考数据可从参考数据集《奥沙瓦及艾伦路参考PM2.5与风速数据》（DOI: 10.17632/yw49w4d7v2.1）中获取。本研究旨在验证实时空气质量监测在智慧城市（Smart City）基础设施场景中的应用价值。
采集自安大略省奥沙瓦市新冠疫情（COVID-19 pandemic）前后的数据包含两类信息：一是由AirSENCE传感器测得的、以2分钟为间隔平均的交通相关空气污染物（Traffic-related Air Pollutant，简称TRAP）浓度数据（涵盖一氧化碳CO、一氧化氮NO与细颗粒物PM2.5）；二是由Northline Fox交通计数器记录的小时级交通流量数据。该数据集证实了交通流量降低与TRAP浓度下降之间存在直接关联。与之相对，道路施工在减少交通流量的同时，却会导致TRAP浓度升高——这一结果既证明了传统传感器难以可靠推演空气质量状况，也凸显了部署紧凑型空气质量传感器网络的必要性。
本数据集还包含2021—2022年间在安大略省多伦多市繁忙通勤要道艾伦路两侧采集的观测数据，该部分数据为由AirSENCE传感器测得的、以10分钟为间隔平均的TRAP浓度数据（涵盖一氧化碳CO与一氧化氮NO）。本研究子课题着重阐明了局地气象条件对TRAP扩散的关键作用，同时验证了紧凑型空气质量传感器网络可用于深入探究TRAP排放源与人体暴露路径的应用价值。