The Internet of Things (IoT) is a concept that connects various physical devices to the internet so that they can communicate with each other, send data, and perform remote monitoring and control. IoT is widely applied in various fields such as industry, healthcare, agriculture, security, and the environment. In the context of air quality monitoring, IoT allows for continuous and real-time collection of pollutant data, so users can monitor environmental conditions from anywhere. Furthermore, the deployment of IoT-based sensor networks allows for enhanced spatial and temporal resolution in air quality assessment, enabling researchers to capture variability in pollution exposure at a granular level (Ilie et al., 2022; Tooki et al., 2024).

Air quality monitoring systems have developed rapidly in recent years due to the high need for air pollution information. Most studies focus on monitoring gases such as CO₂, CO, NO₂, or PM2.5. However, special monitoring of indoor cigarette smoke is still limited. Sensor-based air monitoring systems generally utilize microcontrollers such as Arduino or NodeMCUs to read sensor values and transmit data to cloud platforms. IoT technology facilitates the integration of multiple environmental sensors, allowing for real-time data collection and processing without manual input, which enhances responsiveness to pollution levels Teli et al. (2025), Nizeyimana (2023). This capability ensures that authorities can quickly identify and address pollution events, ultimately fostering better public health management (Rahmadani et al., 2024).

Cigarette smoke is a complex mixture of thousands of harmful chemical compounds, such as nicotine, ammonia, benzene, carbon monoxide, and fine particles. Based on WHO (2023), exposure to cigarette smoke increases the risk of respiratory disorders, heart disease, and lung cancer. Cigarette smoke pollution in enclosed rooms is more dangerous because its concentration can increase rapidly without adequate ventilation. Various studies on environmental health emphasize that early detection of cigarette smoke is essential to protect passive smokers, especially children and adults who live in congested environments.

The MQ135 gas sensor is prominently utilized for air quality monitoring due to its capacity to detect a diverse range of hazardous gases, including ammonia (NH₃), nitrogen oxides (NOₓ), and benzene. This sensor operates based on changes in internal resistance, which are influenced by the concentration of gases it encounters, demonstrating high sensitivity towards pollutants like cigarette smoke (Akinwumi et al., 2024; Saini et al., 2020). Research confirms that the MQ135 exhibits notable resistance changes within seconds upon exposure to harmful gases, indicating its efficacy in rapid detection scenarios. Moreover, the deployment of the MQ135 in conjunction with IoT technology, such as the ESP8266 microcontroller, has significantly enhanced the speed and accuracy of air quality monitoring systems, facilitating real-time data transmission and analysis (Purbakawaca et al., 2022; Xue et al., 2021). The integration of these technologies highlights the MQ135's role as a vital component in ongoing studies addressing air pollution challenges (Purbakawaca et al., 2022; Saini et al., 2020).

The integration of Internet of Things (IoT) technology in smoke detection systems has significantly enhanced fire safety protocols through improved sensitivity and real-time monitoring capabilities. Contemporary IoT-based smoke detection systems incorporate traditional smoke detectors alongside air quality monitors, which provide critical alerts and enable quicker responses to potential hazards (Pietraru, 2025; Siregar et al., 2024). For instance, IoT systems

can send instant notifications to both emergency services and building occupants, ensuring timely evacuations or interventions (Sassani et al., 2020; Seçilmis et al., 2023). These systems employ intelligent algorithms that analyze data from multiple sensor types, reducing false alarms and improving reliability (Pietraru, 2025; See & Ho, 2020). Research indicates that integrating various sensor types within IoT frameworks notably enhances fire detection efficiency, making these systems integral to modern building safety strategies (Al-Hady et al., 2023; Zhang et al., 2024). As the demand for more sophisticated fire management solutions grows, IoT-based systems are increasingly recognized as effective for smoke detection and fire alerting, offering not only cost savings but also improved safety (Kurnia et al., 2023; Nutakki, 2024).

The system architecture consists of three main components:

Under normal conditions, MQ135 shows relatively stable reading values.

Data obtained through Serial Monitor shows that:

These values indicate that the environment is in a clean air condition and the sensor is in a good calibration state. Small variations in PPM are still within normal limits and do not indicate the presence of significant pollutants.

When cigarette smoke is directed to the MQ135 sensor, there is a significant and rapid increase in PPM values. Here's a summary of the test results:

This significant change in value indicates that the MQ135 sensor is sensitive to cigarette smoke and is capable of detecting pollutants with good accuracy.

Here's a representation of the test results you attached (shown again through an illustration to reinforce the discussion):

Under normal conditions, MQ135 shows relatively stable reading values.

Data obtained through Serial Monitor shows that:

These values indicate that the environment is in a clean air condition and the sensor is in a good calibration state. Small variations in PPM are still within normal limits and do not indicate the presence of significant pollutants.

When cigarette smoke is directed to the MQ135 sensor, there is a significant and rapid increase in PPM values. Here's a summary of the test results:

This significant change in value indicates that the MQ135 sensor is sensitive to cigarette smoke and is capable of detecting pollutants with good accuracy.

The integration of sensors with IoT technology through NodeMCU ESP8266 has also been proven to work optimally. The system is capable of delivering data in real-time to the IoT platform with a very low delivery lag of about 1–2 seconds. This allows users to monitor air quality directly through the dashboard and receive notifications when PPM values exceed safe limits. The system's ability to send quick notifications is critical to allow for immediate precautions against exposure to cigarette smoke, especially in enclosed rooms. These findings are in line with previous studies that have shown that the application of IoT in environmental monitoring can improve the effectiveness of monitoring because data can be monitored remotely without direct human involvement.

In addition, the consistent pattern of change in PPM values between the Serial Monitor readings and the IoT dashboard indicates that the system has good stability. This indicates that the sensor calibration is working correctly, and that the NodeMCU is capable of processing and transmitting data with high accuracy. The fast response time also indicates that the system is suitable for early detection of cigarette smoke pollution, so that it can support efforts to create smoke-free areas and improve indoor air quality. However, the MQ135 sensor still has some limitations, such as sensitivity that can be affected by temperature and humidity, so the use of additional sensors such as DHT22 can improve measurement accuracy.

Overall, the results of this study reinforce the literature that the combination of MQ135 and IoT is a practical and effective solution for air quality monitoring, especially in detecting cigarette smoke pollution. The developed system is not only able to provide real-time information but also capable of providing early warnings, so it has the potential to be applied in various environments such as homes, offices, schools, and other public facilities. The reliability of sensors and the responsiveness of IoT systems make this solution worthy of further development towards more comprehensive air quality monitoring applications.