Low-Cost Particulate Matter and Gas Sensor Systems for Roadside Environmental Monitoring: Mechanistic and Predictive Insights from One-Year Urban Measurements

Roadside public transport stops represent localized air pollution hotspots where short-term exposure may differ substantially from levels reported by urban background monitoring. This study investigates the application of low-cost air quality sensors for long-term characterization of particulate matter and gaseous pollutants in a traffic-dominated urban microenvironment. The novelty of this work lies in the combined use of collocated low-cost sensors, energy-independent solar-powered deployment, height-resolved placement representative of different breathing zones, and integrated statistical and predictive analysis to resolve exposure-relevant pollutant dynamics at a single transport stop. Hourly concentrations of particulate matter (PM) PM1, PM2.5, PM10, nitrogen dioxide (NO2), and ozone (O3) were measured over one year at a roadside transport stop adjacent to a four-lane urban road carrying approximately 30,000 vehicles per day. Measurements were obtained using two collocated low-cost sensor units based on optical particle sensing for particulate matter and electrochemical sensing for gases, together with concurrent meteorological observations. Strong agreement between the two particulate matter sensors supported the use of averaged concentrations. Mean PM2.5 concentrations were substantially higher in winter (32.4 µg/m3) than in summer (10.4 µg/m3), indicating pronounced seasonal variability. PM1 and PM2.5 exhibited closely aligned temporal patterns, while PM10 showed greater variability. NO2 displayed sharp diurnal peaks associated with traffic activity, whereas O3 exhibited opposing seasonal and diurnal behavior and was negatively correlated with both PM2.5 (r = −0.32) and NO2 (r = −0.29). One-hour-ahead predictive models incorporating meteorological and temporal variables achieved coefficients of determination up to 0.84. The results demonstrate that energy-independent low-cost sensor systems can robustly capture temporal patterns, pollutant interactions, and short-term predictability in localized roadside environments relevant to exposure assessment.