Air pollution has emerged as one of the most critical environmental challenges of the 21st century, with the World HealthOrganization estimating that 99% of the global population breathes air exceeding safe quality limits, resulting inapproximately 7 million premature deaths annually. Industrial emissions, vehicle exhaust, and biomass burning releaseparticulate matter (PM2. 5 and PM10) and toxic gases that penetrate deep into the respiratory system, causing cardiovascularand respiratory diseases. This paper presents a novel autonomous robotic vehicle designed to detect, capture, and removesmoke pollutants from the air using electrostatic precipitation technology combined with IoT-based control systems. Thevehicle integrates an ESP8266 microcontroller for autonomous navigation and control, an MQ2 gas sensor for real-timesmoke detection, and a custom-designed electrostatic precipitator (ESP) powered by a 400kV pulse generator for highefficiency particulate removal. The robotic platform utilizes a motor driver IC with four DC motors, enabling omnidirectionalmovement toward pollution sources when detected. Upon smoke detection above threshold levels (500 ppm), the vehiclehalts navigation and activates the electrostatic precipitation system, which generates a high-voltage corona discharge (400kV) between an aluminum tube chamber and a central electrode, ionizing airborne particles and collecting them on oppositelycharged plates. The electrostatic precipitator achieves 94. 7% removal efficiency for PM2. 5 particles and 96. 2% for PM10particles at an airflow rate of 0. 5 m³/s, as validated through controlled chamber testing. The pulse generator circuit, based ona flyback transformer topology with MOSFET switching at 25kHz, produces the required 400kV potential while consumingonly 45W of power, making it suitable for battery-powered mobile applications. The ESP8266 microcontroller managessensor data acquisition, motor control logic, and high-voltage system activation through optoisolated relay circuits for safetyisolation. Experimental evaluation across 50 test runs in controlled smoke environments demonstrates an average smokedetection response time of 2. 3 seconds, with the vehicle successfully navigating to pollution sources and reducing ambientparticulate concentration by 78% within 5 minutes of operation. The system achieves 94. 2% accuracy in distinguishingsmoke from other airborne particles using differential sensor fusion techniques. Power consumption analysis shows thevehicle can operate continuously for 2. 5 hours on a 12V, 20Ah battery bank, sufficient for typical urban pollution hotspotpatrolling. The total material cost of 185 per unit makes this technology accessible for deployment in industrial zones, urbanhotspots, and developing regions where stationary air purifiers are impractical. This work demonstrates that mobileelectrostatic precipitation combined with autonomous navigation offers a viable, scalable solution for targeted air pollutioncontrol, contributing to environmental health and sustainable urban development.
Dandu et al. (Mon,) studied this question.