Monitoring Air Quality Using an IoT-Enabled Air Pollution System on Smartphones

Air pollution is the evolving issue these days. It is mandatory to monitor the quality of air and keep it under control for better future and healthy living for all. Here the proposed system is an air quality surveillance system that allows us to monitor and provide live air quality in different areas through IOT (Internet of Things), when the pollution rate exceeds the threshold value warning alarm is given. System uses gas sensors (MQ-series) to sense the presence of harmful gases in the air that constantly transmit this data by interacting with the microcontroller and reports it to the online server over IOT. This allows experts to monitor air pollution in exclusive areas. To be specific this system would control the air pollution, using catalytic converters that converts toxic gases in the exhaust gas to less toxic pollutants by red ox reaction mechanism with the catalysts present inside it. The resultant gas undergoes zapping with laser which leads to the byproduct oxygen. The laser is activated manually when the carbon monoxide value exceeds a threshold value. Thus, the outcome of the implementation will be oxygen. The proposed system uses Arduino UNO R3, sensor devices, Embedded C and provide graphical analysis using Visual Basic.

The three-phase fault detector and analyse system is designed to differentiate the type of fault occur in power system, enhanced with the Internet of Things (IoT) by using the combinations of Arduino and Wi-Fi module. All sorts of electrical substation that supply the electric to the users like industrial or residential may have some failures due to fault that may be temporary or permanent. However, due to problems like system take a longer time to detect the type of fault and also required manually to reset off the fault make the existing protection system not efficient in supply energy to consumer. The system can be used three single phase transformers those are wired in star input and star output also three transformers are connected in star input and delta output that having 240 volts input and 12 volts output. Then, ESP NodeMCU is used as controller and also act as device that connect to the IoT system when fault is detected. The devices then link with the Blynk application User will get the i notification from Blynk application when the fault is occurred Using these devices, user could find automatically after a short-lived interruption in a provisional fault from the tripped situation in case of eternal fault.

Air pollution is a common cause of health risks and also a cause of climate change, one of the most threatening problems to humans. This problem results from the abundance of automobiles, industrial production, and combustion of transportation and electricity generation petroleum products. Therefore, forecasting air pollution is necessary. In this paper, a system is proposed to monitor the level of air pollution by integrating the Internet of Things(IoT) with Wireless Sensor Networks (WSN), where pollution levels are observed in three areas in Baghdad using different types of sensors connected with ESP32 (It is the name of the chip developed by Espressif Systems) to detect Particulate Matter (PM2.5 and PM10), Nitrogen Oxides (NOx), Carbon monoxide (CO) as well as temperature and humidity to monitor indoor and outdoor air quality. Observed results are monitored by ThingSpeak, an open-source IoT platform. Success has been achieved using the ESP32 microcontroller, as the project is low-cost and uncomplicated, and pollutant measurement is accurate compared to the natural proportions of pollutants. Data display is easy and it can be monitored easily. This encourages the improvement of the model and its use in other monitoring systems. Index Terms— Internet of Things (IoT), Wireless Sensor Networks (WSN), ESP32 microcontroller, ThingSpeak, Air Pollution Mentoring, Gas Sensors.

This paper presents the design of an IoT based air pollution monitoring system to measure carbon dioxide gas, butane gas, humidity and temperature. The hardware consists of MQ-2 gas sensor, ESP8266 Wi-Fi module, DHT22 temperature and humidity sensors. Meanwhile, the software used in this prototype is the Arduino Integrated Development Environment (IDE) written in function C and C++. The monitoring system indicate air quality is below than 100 AQI for safety air quality and more than 200 AQI for hazardous air quality. The green LED illuminated indicates there is no hazardous gas detected. Meantime, when the butane gas or carbon dioxide gas is identified, the red LED is illuminated. All the data are sent through ThingSpeak and Blynk applications. In ThingSpeak and Blynk applications, the data are displayed and updated after detected by the sensors in every 15 seconds and 1 second. In the Blynk application, when the hazardous gas is detected, the Blynk application sends a notification to alert the users immediately.

not spread evenly among the people. Most people still use traditional methods and depend on the uncertain climate to meet their food needs, such as vegetables and fruits. Based on this, an Internet of Things (IoT) based Smart Greenhouse Design with Arduino Uno Microcontroller was created as a solution that is expected to help make it easier for farmers to cultivate plants. The greenhouse is a building that has the function to protect plants from external disturbances such as pests, heavy rain, strong winds, and excessive humidity. Meanwhile, what is called a smart greenhouse that uses technology such as the Internet of Things (IoT), is designed to automatically monitoring uses the Arduino Uno microcontroller as the control center for each sensor used, namely DHT22 (air temperature and humidity sensor), Soil Moisture (soil moisture sensor), GUVA-S12SD (UV light intensity sensor). The sensor will obtain the required data and will be automatically sent to the server (blynk application) with the help of ESP8266 (wifi module). The method used in this study is engineering design in the manufacture of the automation system. The results of this research are in the form of a smart greenhouse that can regulate, adjust, and modify the climate to optimize the conditions and processes of plant growth in the greenhouse equipment through microcontrollers and monitoring devices such as sensors and can be controlled remotely via mobile phones or computers everywhere.

Air pollution by Particulate Matter (PM10) is a pressing environmental issue that has a major impact on public health. PM10 consists of particles with a diameter of less than 10 μm that can penetrate the respiratory tract, causing respiratory infections, heart disease, and lung disorders. The main sources of this pollution include vehicle emissions, industrial activities, and organic matter burning, which often exceed the thresholds set by health agencies. This research aims to develop an Internet of Things (IoT)-based intelligent telemetry system to monitor PM10 levels in real-time using Rule-Based Algorithm sensors. This algorithm adopts artificial intelligence techniques to solve problems based on a set of predefined rules. This process involves several important stages of knowledge base collection. IoT as a low-cost device enables extensive data collection, interaction with other IoT devices, and utilization of cloud-based services and storage. This system is expected to provide accurate and fast information about the level of air pollution and increase public awareness about the importance of maintaining air quality. The results showed that the measured PM10 level in Table 1 reached 2.48 µg/m³, while in Table 2 it was 1.768 µg/m³. Both values are categorized as dangerous. The system is also equipped with a notification feature via Telegram, allowing users to monitor air quality conditions effectively. This research contributes to the development of air quality monitoring technology and environmental health.

Due to urbanization and the development of cities, many forests have been cut down for the accommodation and conveyance of human beings. Demand for vehicles is growing exponentially due to the excessive use of resources. Due to how air pollution occurs from vehicles, we are using resources excessively, which is not good for us and our future generations. We are burning fossil fuel and coal for factories, which causes air pollution due to many diseases that occur in human beings, such as respiratory diseases and cardiovascular diseases. Smart sustainable cities are a good concept for every country using the internet of things. In this paper, we discussed air pollution and how it can be monitored using the internet of things. We use gas sensors and dust sensors, which are placed in street lights and signals along city roads, to sense the data. The Arduino is used for analysing the data; the data is displayed on an LCD display and sent to the cloud. People can know the status of air pollution using an Android application.

Materials and methods. The source of information on fertility and mortality was Russian Federal State Statis-tics Service data on 173 cities of the Russian Federation with a population of more than 100 thousand peo-ple for 2014-2016. The sources of air pollution were the Yearbooks of the state of air pollution in cities in Russia for the same period. A nonparametric analysis of variance was used to compare cities with different levels of air pollution in terms of fertility, mortality, birth-death and aging index. Results. Comparison of groups of cities with different maximum one-time levels of atmospheric pollution with benzo[a]pyrene revealed significant differences between the groups in terms of total fertility and mortal-ity rates, birth-death ratio and aging index. Comparison of groups of cities with different average annual levels of air pollution for 19 pollutants showed that only for benzo[a]pyrene, nitrogen dioxide, nitric oxide and ammonia a statistically significant relation-ship with the characteristics of population health was found. For benzo[a]pyrene, the results were similar to those obtained for the maximum one-time concentrations of benzo[a]pyrene (except for the total mortality rate, for which the relationships are not significant). Statistically significant relationships of air pollution with nitrogen dioxide, nitrogen oxide and ammonia were revealed with the total mortality rate and the birth-death ratio, and for ammonia pollution also with the demographic aging index. Conclusion. In cities of the Russian Federation, the maximum one-time air pollution with benzo[a]pyrene is statistically significantly associated with the total fertility rate, birth-death ratio and the aging index: in groups of cities with an increase in the level of atmospheric air pollution, the median values of the total fer-tility rate and the birth-death ratio are increasing, and the median values of the aging index are decreasing. Comparison of groups of cities with different average annual levels of air pollution for 19 pollutants re-vealed statistically significant effects for pollution with benzo[a]pyrene, nitrogen dioxide, nitrogen oxide and ammonia. A statistically significant heterogeneity of the groups of cities with different average annual levels of benzo[a]pyrene was revealed in terms of the total fertility rate, birth-death ratio and aging index: with an increase in the level of atmospheric air pollution, the median values of the total fertility rate and the birth-death ratio increase, while the median values of the aging index decrease. A statistically significant hetero-geneity of the groups of cities with different average annual levels of air pollution with nitrogen dioxide, ni-tric oxide and ammonia was revealed in terms of the total mortality rate and the birth-death ratio, and for ammonia pollution also in terms of the demographic aging index. In cities with a high level of air pollution with nitrogen dioxide and ammonia, the total mortality rate is higher.

Older adults spend a substantial proportion of their time indoors, making them particularly vulnerable to home environmental factors. However, existing research has focused on outdoor exposures near residential addresses, leaving a critical gap in understanding the scope of indoor environmental impacts on aging. We investigate the associations between indoor environment and health outcomes (sleep, mental, and cognitive health) in community-dwelling older adults without dementia, using the baseline data from an ongoing RCT. Indoor environment includes self-reported environmental comfort—encompassing light, air quality, temperature, and noise—and continuous 24/7 sensor-based measurements of air pollution (PM2.5, CO2, NOx, and TVOCs), humidity, temperature, light, and sound levels. Sleep health was assessed via self-reported measures (Pittsburgh sleep quality index [PSQI], and insomnia severity) and objective measurement devices—ActiGraph and ambulatory EEG. Cognitive function was evaluated using the Everyday cognition scale (ECog) and the NIH Toolbox, while mental health was measured through the Geriatric depression scale, Geriatric anxiety scale, and Perceived stress scale. Our findings (n = 39) showed lower perceived environmental comfort was significantly associated with poorer sleep quality (PSQI, r = 0.36), increased insomnia severity (r = 0.32), worsened mental health (depression, r = 0.34; anxiety, r = 0.36; stress, r = 0.35), and lower cognitive function (ECog, r = 0.32). Additionally, increased evening light exposure correlated with higher stress (r = 0.75), higher CO2 levels were associated with poorer delayed recall (r=-0.64), and higher humidity was linked to increased insomnia severity (r = 0.64). These findings highlight the need for targeted interventions to improve indoor environmental conditions, which may mitigate risks to sleep, mental, and cognitive health in community-dwelling older adults.

Currently, everything has become digitalized due to the rapid and remarkable advancements in technology in all aspects of life especially the Internet and smart technology. Automation system (AS) is a part of this rapid development due to its multiple and various utilization such as smart building control systems. This paper will study smart building control system, which monitors and manages power consumption and safety. It presents two different models named remote control appliances and safety. The first model is employed to reduce power consumption by controlling building appliances remotely and the second model consists of sensors, which increase the safety by monitoring temperature & humidity, gas and fire in buildings. The system's design is based on Internet of Things (IoT) through using NodeMCU microcontroller and Blynk platform via Internet connection. The building appliances such as A/C, water heater (W-H), TV and lights are connected to the control board (CB). The owner of the building can monitor and control the status of appliances via Blynk application through Internet connection. Moreover, safety is based on sensors such as DHT22, gas and flame sensors, which are placed to monitor the vulnerable areas in the building and sending alert message (notification and email) in real-time to the owner via Blynk Application if there are any critical values detected by the sensors. Such monitoring system increases the building safety. The implementation results of the presented system shows a major impact on power saving which reduce power consumption as well as increasing safety of the building.

Benzene gas is a material widely used in the laboratory as a solvent. Benzene gas can be harmful to people. Therefore, this study aims to measure and monitor benzene gas levels with the Internet of Things (IoT) platform as a monitoring and notification system on android smartphone devices. The sensor used is MS 1100 to detect benzene gas and transmit input signals. The NodeMCU microcontroller processes these signals. This wifi module in NodeMCU sends the sensor’s value to the Blynk IoT platform to display in graph form. This value is processed under the program and it meets the setpoint then the system provides notifications to users through the Blynk application on android smartphone devices as well as activating buzzer and exhaust fans in the laboratory room. The result showed that monitoring of benzene gas in the laboratory as 0.17 ppm.

Since the Internet of Things (IoT) will be entwined with everything we use in our daily life, the consequence of security flaws escalates. Smart objects will govern most of the home appliances and car engines yielding potential disaster scenarios. In this context, successful attacks could lead to chaos and scary scenarios (www.darkreading.com). Unprotected personal information may expose sensitive and embarrassing data to the public and attacks may threaten not only our computers and smart devices, but our intimacy and perhaps our lives too. Because persons and objects will be bonded with each other, user consent becomes critical. Therefore, thing, object, and user Identity will be the focus of future IoT security solutions, yielding a Trust, Security, and Privacy (TSP) paradigm, which may constitute the Achilles' heel of IoT. While security issues are quite straightforward, mainly from background knowledge, privacy issues are far more complex. Privacy constitutes a rather challenging task, even for the m...

Background: Air pollution has been linked to increased rates of mortality, but little is known about individual characteristics related to the increase in risk. Aims: To examine short term effects...

Candidate gene expression in response to low-level air pollution

The constraints of gas distribution pipe leak monitoring robots in outdoor environments are the limited battery capacity and the method used as a monitoring system to assist the work of gas pipe leak inspection officers, which takes a long time. Therefore, the robot requires independent battery charging and real-time monitoring systems. This study resulted in a solar-powered gas distribution pipe leak monitoring robot that can provide real-time information on the robot's battery capacity and gas odor concentration data along the inspected gas distribution pipe. This robot can directly channel electrical power to the robot's battery using solar energy. This Mobile Robot uses a Photovoltaic (PV) module, Light Detection and Ranging (LiDAR), a Compass, a gas sensor, a voltage sensor, a current sensor, an ATMEGA 2560 Microcontroller, and Node MCU V3 ESP8266. The Internet of Things (IoT) application uses the Blynk application to monitor battery capacity and the concentration value of gas odor detected by the robot. The test results show that by using the PV + battery module, this mobile robot can work for more than 60 minutes compared to using only the battery for around 55 minutes. This work was successfully implemented based on IoT performance using the Blynk Application to monitor battery capacity conditions of voltage and current data and gas concentration data. It is also shown that the average delay time for sending data from voltage, current, and gas sensors to the Blynk application was around 0.226 seconds.