IoT Sensor Based Sustainable Air Quality Monitoring System for Humans and Ecosystems in the World Empowerment

. Due to elements that can harm human health, such as industries, urbanisation, population growth, and automobile use, the level of pollution is rising quickly. Using an Internet-connected web server, an IOT-based air pollution monitoring system is employed to track the air quality which sustains environment. When the amount of dangerous chemicals including CO2, smoking, alcohol, benzene, NH3, and NOx is high enough, it will sound an alarm when the air quality drops below a specified threshold. It will display the air quality in PPM on the LCD and on the website, making it very simple to monitor air pollution. The MQ135 and PM 2.5 sensors are used by the system to monitor air quality since they can accurately measure and detect the majority of hazardous gases. In recent years, air pollution has become a severe issue on a global scale and has surpassed advised national limitations. In addition to harming ecosystems and human health, air pollution also has an impact on global climate. The population is expanding, there are more industries, and there is an excessive amount of transportation that uses fuel, which are all contributing factors to the rapid rise in air pollution. To address this danger, the Air Quality Monitoring System was developed.


Introduction
The main issue facing all countries, developed or developing, is air pollution.Pollution has negative health impacts that range from minor allergic reactions to more serious conditions.According to a study, air pollution causes 50,000 to 100,000 premature deaths annually in the United States alone.Due to a rise in air pollution, this issue is especially noticeable in urban areas with dense populations.In order to pinpoint specific locations and periods when traffic peaks, [1] it is crucial to describe human behaviour and exposure to air pollution is more likely to occur.Consequently, governments can get crucial information to suggest updated/new transport alternatives/policies.The highest preference is given to carbon monoxide because it is a major pollutant and a greenhouse gas that is warming the earth.Thus, the suggested solution addresses this crucial problem.Calculating the amounts of each and every pollutant led to the introduction of additional, cost-effective strategies to limit air pollution.The air quality index for that area is generated based on the observed readings, and the results are made public through website page.
The position of pollution has changed over time due to several variables, including population growth, increased vehicle use, industrialization, and urbanization, which causes hazardous commodities to directly harm people's health.We're going to build an IOT-Based Sustainable Air Pollution Monitoring System to finish this model, where we'll use the internet to monitor the air quality over a web server and sound an alert when it falls below a specific level.Which denotes when enough dangerous substances, such as CO2, smoking, alcohol, benzene, and NH3, are present in the air.There will be a manufacturer's documents supplied with each sensor that we use in this experiment, which we can use to conduct the math.Using login credentials, we can access the system from any location.if these sensors are linked to the cloud [2][3][4][5].The voltage levels that the MQ135 gas sensor outputs must be converted into PPM.Due to this, we converted the output in this case to PPM utilising an archive for the MQ135 sensor.The sensor gave us a signal of 90 when there was no gas around.The 500 PPM safe air quality threshold should not be surpassed because doing so results in headaches, tiredness, and stuffy, slow air.If it exceeds the limit of 1000 PPM, it can also cause a normal heart rate and a number of other disorders."AQ Level Good" will be shown on the LCD and website when the reading is less than 500 PPM.The buzzer will start to blare and "AQ Level HIGH" will appear on the LCD when the reading rises above 500 PPM.The buzzer will keep ringing and the LCD will show "AQ Level Very HIGH" if it rises by 1000[6-9].

Problem statement:
To maintain public health and environmental safety, design and construct an air quality monitoring system that can precisely measure and analyse the quantities of different contaminants present in the air.The system should be easy to use, affordable, and capable of delivering real-time data and alarms to assist people and authorities in making wise decisions and taking the necessary steps to reduce air pollution and its negative impacts.There are several existing systems for air quality monitoring that are used around the world.Here are a few examples: Environmental Protection Agency (EPA) Air Quality Monitoring System: In the United States, the EPA operates a network of air quality monitoring stations across the country.European Environment Agency (EEA) Air Quality Monitoring System: The EEA operates a similar network of monitoring stations across European countries.OpenAQis a global community-driven platform that aggregates and shares air quality data from different sources worldwide PurpleAir: is a popular air quality monitoring system that utilizes low-cost particulate matter (PM2.5)sensors.

Proposed System:
IOT -based air pollution monitoring system will be utilised to track the air quality using the Internet and a webserver.When the air quality falls below a certain level, buzzer will make sound and an LCD display will be provided,to make it simple to monitor, the air quality will be shown on the website in PPM and on lcd also.The MQ135 sensor and the PM 2.5 sensor are the most effective sensors for monitoring air quality because they can precisely gauge the concentrations of the majority of hazardous gases and dust particles.So the thershold limit is set to 180 PPM so when the limit cross it will display the message on lcd and buzzer works.The wifi enabled Arduino is connected with Mq135 sensor Pm2.5 Sensor, breadboard,buzzer LCD display

Literature Survey
Air quality management is a critical issue in urban areas, and innovative approaches are being explored to tackle this challenge.One such approach is crowd-sourcing, where citizens actively contribute to monitoring air quality levels.This is facilitated through the use of mobile sensors that individuals can carry with them, providing real-time data on pollutant concentrations in different locations.By harnessing the power of crowd-sourcing, a vast network of mobile sensors can be created, covering a wide geographical area and collecting data from diverse sources.To make sense of this wealth of data, algorithms like Dijkstra's algorithm can be employed.Dijkstra's algorithm is a graph-based algorithm that can determine the shortest path between two nodes in a network.In the context of air quality management, this algorithm can help identify the most polluted areas and pinpoint potential pollution sources.By analyzing the data collected from the mobile sensors and applying Dijkstra's algorithm, authorities can prioritize their efforts and allocate resources efficiently to address pollution hotspots [10].
In the field of air pollution monitoring, the use of adaptive algorithms has gained significant attention.These algorithms are designed to dynamically adjust their parameters based on real-time data, allowing for more accurate and efficient air quality monitoring.By continuously analyzing incoming data and adapting their algorithms, these systems can quickly respond to changing environmental conditions and provide up-to-date information on air pollution levels.One of the key components of effective air quality monitoring is the use of low-cost sensors.These sensors are created to be available to a wider range of users by being inexpensive, transportable, and simple to deploy.Low-cost sensors can measure diverse pollutants such particulate matter, nitrogen dioxide, and ozone, giving important information about the air quality of various places.Their affordability and versatility make them an ideal tool for expanding air quality monitoring networks, especially in areas where traditional monitoring systems may be limited [11].
Indoor air quality has a significant impact on public health, and the Internet of Things (IoT) has emerged as a transformative technology for monitoring and improving indoor environments.IoT-based monitoring systems employ a network of interconnected devices, sensors, and actuators to collect real-time data on various parameters affecting indoor air quality, such as temperature, humidity, volatile organic compounds (VOCs), and particulate matter.By continuously monitoring these factors, IoT systems provide a comprehensive understanding of indoor air quality, enabling proactive measures to be taken to safeguard public health.The integration of IoT in indoor air quality monitoring systems offers several advantages.Firstly, the connectivity and interoperability of IoT devices allow for seamless data collection, aggregation, and analysis.In conclusion, the convergence of indoor air quality, IoT, monitoring systems, and public health presents an opportunity to create healthier indoor environments.This empowers individuals to make informed decisions regarding their well-being, while also informing policymakers and stakeholders to implement measures that enhance indoor air quality standards [12][13].
Ultimately, the integration of IoT and indoor air quality monitoring systems contributes to the overall improvement of public health and well-being in indoor space.Environmental quality monitoring has witnessed significant advancements with the advent of wireless sensor networks (WSNs).WSNs consist of a network of small, low-power sensors that can collect and transmit data wirelessly.To make sense of the data collected by the sensors, advanced analysis techniques such as Long ShortTerm Memory (LSTM) models can be employed.LSTM is a type of recurrent neural network (RNN) that can effectively handle and analyze sequential data, making it well-suited for time series analysis in pollution monitoring.In summary, the synergy between WSNs, Zigbee, and LSTM analysis presents a robust framework for pollution monitoring and analysis.By deploying wireless sensors, utilizing Zigbee for seamless communication, and employing LSTM models for data analysis, we can gain valuable insights into environmental quality, enhance early detection of pollution events, and facilitate informed decision-making for the preservation and improvement of our ecosystems.
These sensors continuously measure and transmit data to centralized platforms, enabling comprehensive monitoring across different locations and environments.The integration of machine learning applications in IoT-based environmental monitoring systems has further enhanced their capabilities.Machine learning algorithms can analyze the vast amounts of data generated by the sensors, detecting patterns, correlations, and anomalies that might not be apparent through traditional methods.These algorithms can identify complex relationships between environmental factors, such as pollutant sources, meteorological conditions, and human activities, allowing for a deeper understanding of the factors influencing air quality.. Data analysis techniques such as clustering, classification, and regression can be applied to categorize different air quality levels, generate pollution maps, and develop predictive models.These insights empower decision-makers, environmental agencies, and public health authorities to make informed decisions and develop targeted interventions to improve air quality and protect public health [18][19][20][21][22][23][24].

System Architecture
An air quality monitoring system using IoT (Internet of Things) typically consists of several components that work together to measure and analyze various air pollutants.Sensors: The system starts with sensors placed at different locations to collect data on air quality parameters such as particulate matter (PM), carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), and other relevant pollutants .Data Acquisition: The sensor data is collected and transmitted to a central hub or gateway.This can be achieved through wired or wireless communication protocols like Wi-Fi, Bluetooth, Zigbee, or cellular networks, depending on the system's requirements and coverage area.Cloud Infrastructure: The sensor data is sent to a cloud-based infrastructure where it is stored, processed, and analyzed.Cloud platforms like Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform (GCP) are commonly used for hosting the backend of the air quality monitoring system.The cloud infrastructure Notifications and Alerts: The system can be configured to send notifications and alerts to users or relevant authorities when specific air quality thresholds are exceeded.These notifications can be delivered through various channels such as SMS, email, push notifications, or automated phone calls.Overall, the architecture of an air quality monitoring system using IoT involves sensors for data collection, a gateway for data transmission, cloud infrastructure for data storage and processing, and a user interface for data visualization and analysis.The system enables realtime monitoring, analysis, and reporting of air quality, facilitating better understanding and management of air pollution.on the method it employs to achieve the intended goal, making it intended for technical artistic, and architectural audiences .Mq135 sensor The MQ135 gas sensor is frequently used to monitor air quality and identify a variety of dangerous gases in the atmosphere.It functions according to the conductivity of gases.The MQ135 gas sensor's operation in an air quality monitoring system is described as follows ---------------------------------------------------------------------------Algorithm1:  ---------------------------------------------------------------------------Step:1 Initialize Mq135 sensor with connections to LCD wifi enabled board and necessary variables Step:2 Activate heater to the optimal temperature and read the analog output from sensor Step :3 Convert the analog voltage to digital using ADC Step:4 Convert the digital value to a corresponding gas concentration in parts per million (ppm) or other relevant units.
Step:5 Store the concentration level of gas Step:6 Compare the gas concentration data against air quality standards or guidelines for the target gases.Step:7 Display the air quality status based on the concentration levels (e.g., good, moderate, unhealthy) or provide a numerical value for each gas.
Step:8 Display the air quality status based on the concentration levels (e.g., good, moderate, unhealthy) or provide a numerical value for PM2.5 Step:9 Implement a loop to continuously read the PM2.5 sensor data at regular intervals.
Step:10 Repeat these steps to update the air quality information as the PM2.5 concentration changes over time.

Results Analysis
By this graph we can get the information about the gas level concentration in air which are detected by the sensors MQ135 and PM2.5 periodically

Fig.2. Sensor Values
Table 1.Concentration levels of gases in air

Conclusion and Future Scope:
In conclusion, an air quality monitoring system is a vital tool for assessing and managing air pollution levels.By continuously monitoring the air quality, this system enables early detection of pollutants and helps in implementing effective mitigation strategies.It provides valuable data and insights that enable policymakers, environmental agencies, and communities to make informed decisions regarding public health and environmental protection.The system's real-time monitoring capabilities allow for prompt response to  situations, ensuring the well-being of individuals and the environment.Overall, an air quality monitoring system plays a crucial role in safeguarding air quality, promoting sustainable development, and fostering a healthier and cleaner environment for current and future generations.