Description:FIELD OF THE INVENTION
This invention relates to Smart Environmental Monitoring for Public Health: An AI-Based System to Support the Right to Clean and Safe Air
BACKGROUND OF THE INVENTION
A critical global issue now a days is air pollution which directly impacts the health of people, climate and overall quality of environment. The traditional systems for monitoring are often stationary, expensive and also fails to provide actionable insights and real-time alerts. Many rural and urban communities lack access to timely information about quality of air which makes it difficult to mitigate exposure risks. Hence, there is a need of such a system which is sustainable, intelligent and scalable which can monitor, predict and communicate about the quality levels of air effectively. Moreover, the fundamental rights of clean environment remain unfulfilled for many, especially in developing regions. For fulfilling these gaps there is requirement of such a system which integrates smart sensing, renewable anergy and AI-driven decision making in environmental monitoring solutions.
As there are significant advancements in technologies about the monitoring of environment but there exist critical research gaps in deployment and development of real-time and intelligent air quality prediction systems. Traditional systems for air quality monitoring are cost-intensive, large-scale and fixed in location which limits their effectiveness and coverage in providing localized data. Although there exist low-cost sensor devices but they lacks of scalability, accuracy or integration is required for predictive analytics. Additionally, the existing current systems only focuses upon reporting and monitoring rather than incorporating machine learning models for real-time forecasting which is essential for public health safeguarding and proactive intervention. The convergence of artificial intelligence, internet of things and renewable energy sources such as solar power in a unified platform for alerting and prediction about the quality of air is still in its infancy, especially in the context of underdeveloped or remote areas.
Moreover, there is no two-way communication in the existing models- one that which not only provides the information to the authorities but also provides public-facing alerts and suggestions by the means of live display system. Also, there exists a lack of integrated frameworks which combines the real-time sensory data, cloud based preprocessing and AI-based prediction, all while running on a power source which is renewable which ensures energy independence as well as sustainability. The ability of such system for supporting the right to a clean environment which is a growing concern under international environment and human rights law, remains unexplored largely in current researches. In particular there exists few studies which integrates the technological advancement in predictive pollution analytics with the social, ethical and legal implications of environmental justice and access.
Furthermore, research on location-specific and adaptive air quality prediction models, especially in dynamically changing semi-urban and urban environments, remains insufficient. The absence of context-aware and personalised mitigation suggestions based on predictive levels of air-quality adds another dimension to this gap. In short, there is a need system which is low-cost, AI-powered, sustainable which do community-oriented air quality monitoring and forecasting which not only addresses the technical performance but also fulfils global sustainability goals and the universally recognised right to live in a clean and healthy environment.
CN108701274B The invention discloses a method and a system for predicting a small-scale urban air quality index, wherein the method comprises the steps of dividing an urban area into a plurality of places to be predicted in a grid mode; then, historical data related to each model is obtained, and based on the historical data: establishing time prediction models respectively corresponding to current time prediction and each time prediction in a period of time in the future, establishing a space prediction model for predicting air quality at an appointed coordinate, establishing a dynamic prediction model representing the relation between traffic data and geographical interest point data and an air quality index, and establishing an indoor and outdoor prediction model representing the relation between an indoor air quality index and an outdoor air quality index; when prediction is carried out, for any to-be-predicted place at any real-time moment, the established time prediction model, the established space prediction model, the established dynamic prediction model and the established indoor and outdoor prediction model are cooperatively trained, so that prediction results of all the models are fused, and the air quality index prediction value of each to-be-predicted place at each moment in the corresponding current moment and a period of time in the future is obtained.
RESEARCH GAP: The compared patent focuses on multi-model fusion for spatiotemporal AQI prediction using traffic and indoor-outdoor data, while the proposed system emphasizes real-time, solar-powered, community-level monitoring and alerting with public displays and AI prediction.
CN115796402B The invention discloses an air quality index prediction method based on a combined model, which provides a difference fusion seasonal prediction model DF-SPM based on RF and CLA, wherein in the prediction process, an optimal threshold interval searching algorithm is used in different seasons, the optimal threshold intervals of four seasons are learned and searched, and a final prediction result is obtained by selecting RF and CLA prediction values on the basis. The model fully considers the seasonal period characteristics of the AQI, divides the data set of each year by taking four seasons as time scales to search the optimal threshold interval, and can finely obtain the threshold intervals of different time periods to obtain higher prediction precision. The determination strategy of the optimal threshold interval is superior to the single threshold strategy, so that the optimal solution searching difficulty is reduced, the model is used for extracting the AQI historical fluctuation characteristics, and the prediction with higher precision is realized.
RESEARCH GAP: This patent uses a seasonal fusion model (DF-SPM) with optimized threshold intervals for higher AQI prediction accuracy, while the proposed system integrates solar-powered real-time sensing, AI prediction, and public advisories for community-level environmental monitoring.
None of the prior art indicate above either alone or in combination with one another disclose what the present invention has disclosed. This invention relates to Smart Environmental Monitoring for Public Health: An AI-Based System to Support the Right to Clean and Safe Air.
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
Disclosed herein an AI-Based Smart System for Environmental Monitoring comprises of Computing unit (21), SD card (22), Wifi modem (23), Real time clock (24), Solar panel (25), Battery (26), Power supply (27), Display (28), PM 2.5 sensor (29), Temperature & Humidity sensor (30), PM10 Sensor (31), Co2, Nox sensor (32), So2 sensor (33) and O3 Sensor (34).
In another embodiment, AI-integrated air quality monitoring and prediction system which is powered by solar panel and rechargeable battery which is comprised with a computing unit, environmental sensors, Wi-Fi connectivity, a display unit, a real-time clock and cloud server. The system is made such that to collect real-time atmospheric data, preprocess it and predict air quality through using a trained ML model. It automatically alerts the authorities when the pollution crosses the thresholds and displays public advisories for the same. The system operates autonomously using renewable energy and ensures environmental awareness at the community level.
In another embodiment, the subsystem which is comprised of air quality sensors including PM10, PM2.5, NOx, CO₂, O₃, SO₂, real-time clock, humidity and temperature modules. These sensors are connected to computing unit for collecting, digitize and transmit real-time environmental data. The subsystem is powered by a rechargeable battery which is connected to a solar panel which enables the system to work continuously.
In another embodiment, ML algorithm, specifically a Random Forest-based model which is trained on historical air quality data for classifying and predicting AQI categories. The algorithm processes normalized sensory data inputs and generates predictive outputs indicating pollution severity. After detecting hazardous conditions, it sends alerts and activates display advisories. The model enables real-time decision support and proactive environmental response.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
The propose system which do AI-integrated air quality monitoring and prediction functions through a sustainable and comprehensive multilayered mechanism which is designed for ensuring real-time environmental intelligence. The system starts with a solar powered device which is embedded with different air quality sensors such as PM10, Pm2.5, Nox, CO2, SO2, O3 as well as humidity and temperature sensors. These sensors monitor the atmospheric conditions continuously by collecting the related data at regular intervals. The sensory data is captured and digitalized by a computing unit which organises and prepares the information for transmission.
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
FIGURE 1: SYSTEM ARCHITECTURE
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a",” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and/or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", “third”, and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The propose system which do AI-integrated air quality monitoring and prediction functions through a sustainable and comprehensive multilayered mechanism which is designed for ensuring real-time environmental intelligence. The system starts with a solar powered device which is embedded with different air quality sensors such as PM10, Pm2.5, Nox, CO2, SO2, O3 as well as humidity and temperature sensors. These sensors monitor the atmospheric conditions continuously by collecting the related data at regular intervals. The sensory data is captured and digitalized by a computing unit which organises and prepares the information for transmission.
The device is powered by the rechargeable battery which is connected to a solar panel for ensuring eco-friendly and autonomous operations. The solar panel is used for charging the battery at day-time which enables continuous 24/7 functioning without any reliance on any other external electricity infrastructure. This makes this device also suitable for the locations which are remote, rural or underdeveloped where there is no grid power may be unavailable or unreliable. It also supports the initiatives of green technology which aligns with the sustainability goals as well as climate resilience.
The system uses Wi-Fi connectivity for data communication to link with the centralized cloud server. For secure data transmission the standard communications like MQTT or HTTPs is used. Each and every device is identified by using device MAC address or ID which ensures the secure tracking and data logging. Once the sensory data collected by the device is transmitted to the cloud server it undergoes preprocessing which includes cleaning missing or erroneous values, normalising the sensor outputs and aligning timestamps for synchronised processing. This ensures the data is suitable and standardized for input into the machine learning model.
The data after preprocessing is passed through machine learning model which is pretrained through existing data of air quality datasets which are sourced from credible databases. The model developed using algorithms such as Random Forest, Gradient Boosting or LSTM which predicts the current air quality index (AQI) and forecasts the near-future trends in pollution levels. If the air quality index exceeds defined safety thresholds, the system automatically generates real-time alerts that are sent to relevant municipal bodies and environmental authorities. These alerts are communicated via SMS, email or Api to ensure timely action.
Also. The system has a built-in module for public awareness. An integrated display on the device provides live environmental readings, AQI levels and health advisories to people in the vicinity. Suggestions for the reduction of pollution, such as increasing green coverage, limiting vehicular use, or installing purifiers are also displayed for encouraging the participation of the public for improving the air quality at local level. The mechanism not only provides actionable and accurate data but also supports the fundament right to a clean and safe environment by empowering both citizens as well as the authorities with the knowledge and tools which are necessary for responding to pollution threats.
A machine learning algorithm is used by the system, primarily a Random Forest classifier which predicts level of the air quality based on the real-time data collected through sensors. This algorithm is used for its accuracy, robustness and ability to handle high-dimensional datasets of environment conditions. It operates by constructing multiple decision trees during training and outputs the mode of the classes for classification. The model is pretrained from the existing historical data from some reliable sources such as OpenAQ and CPCB. Data collected from the sensors provide as inputs of concentrations such as PM10, PM2.5, NOx, CO₂ and other pollutants along with humidity and temperature. The model classifies the AQI in different categories such as Good, Moderate, Poor and Hazardous. Its output then used for alert generation and recommendation broadcasting. The algorithm used is as follows:
Algorithm Used
START

// Step 1: Sensor Data Collection
Initialize sensors: PM2.5, PM10, CO2, NOx, SO2, O3, Temperature, Humidity
Every fixed interval (e.g., 60 seconds):
Read data from each sensor
Create data_packet = [PM2.5, PM10, CO2, NOx, SO2, O3, Temp, Humidity, Timestamp]
Send data_packet to Cloud Server via Wi-Fi

// Step 2: Cloud Preprocessing
On receiving data_packet at Cloud Server:
IF any missing or invalid values:
Impute or discard
Normalize data
Append to feature_vector

// Step 3: Load Pretrained Random Forest Model
Load RF_model from cloud storage
Input: feature_vector
Output: predicted_AQI_category = RF_model.predict(feature_vector)

// Step 4: Alert Generation
IF predicted_AQI_category IN [‘Poor’, ‘Very Poor’, ‘Hazardous’]:
Send alert to authorities (email/SMS/API)
Store alert in cloud database

// Step 5: Generate Public Display Message
Create message = “Current AQI: ” + predicted_AQI_category
IF predicted_AQI_category == ‘Hazardous’:
Append message with “Avoid outdoor activity, wear a mask.”

Send message to device display

Repeat from Step 1

END
ADVANTAGES OF THE INVENTION:
The key advantages of the system proposed are as follows:
• The system operates on a rechargeable battery which is powered by a solar panel which reduces its dependency on electrical grids. This enables the system to continuously operate in remote areas as well as in off-grid areas. The system supports eco-friendly deployment aligned with renewable energy goals.
• Unlike the old monitoring systems which only log data, the proposed system uses artificial intelligence for predicting air quality trends in real-time. It provides instant alerts to authorities if the pollution reaches at harmful levels. This approach supports faster responses and mitigations.
• The display used in it informs the public about the live AQI values and health advisories. It empowers the local citizens with knowledge about their environment. This fosters collective responsibility and behavioral change toward pollution reduction.
• The modular structure of the system allows it to be deployed at any place whether it is deployed in the cities or villages with minimal cost. Using open-source hardware with cloud infrastructure reduces expenses. It offers a practical solution for smart cities as well as environmental justice projects.
, Claims:1. An AI-Based Smart System for Environmental Monitoring comprising a Computing Unit (21), SD Card (22), Wi-Fi Modem (23), Real Time Clock (24), Solar Panel (25), Battery (26), Power Supply (27), Display (28), PM2.5 Sensor (29), Temperature & Humidity Sensor (30), PM10 Sensor (31), CO₂ and NOx Sensor (32), SO₂ Sensor (33), and O₃ Sensor (34),
wherein the sensors continuously capture atmospheric data including particulate matter, gases, temperature, and humidity; the Computing Unit (21) digitizes and preprocesses the sensor data with timestamping from the Real Time Clock (24); the Wi-Fi Modem (23) transmits the processed data to a cloud server for analysis using a trained machine learning model;
wherein the Solar Panel (25) charges the Battery (26) to enable autonomous operation; and the Display (28) shows real-time air quality index values and health advisories for public awareness.
2. The system as claimed in claim 1, wherein the Computing Unit (21) collects, digitizes, and processes real-time environmental data from the sensors.
3. The system as claimed in claim 1, wherein the Real Time Clock (24) timestamps the data collected from the sensors to enable synchronized analysis.
4. The system as claimed in claim 1, wherein the Solar Panel (25) charges the Battery (26) to provide continuous and autonomous power for the system.
5. The system as claimed in claim 1, wherein the Wi-Fi Modem (23) transmits the preprocessed sensor data to a cloud server for storage and advanced analysis.
6. The system as claimed in claim 1, wherein the Display (28) provides real-time air quality index levels and health advisories for public awareness.
7. The system as claimed in claim 1, wherein the environmental sensors comprise PM10, PM2.5, NOx, CO₂, SO₂, O₃, temperature, and humidity modules configured to measure atmospheric conditions.
8. The system as claimed in claim 1, wherein the machine learning algorithm employed is a Random Forest-based model trained on historical air quality data to classify and predict air quality index categories.
9. The system as claimed in claim 8, wherein the machine learning model processes normalized sensor data inputs and generates predictive outputs indicating pollution severity.
10. The system as claimed in claim 1, wherein the system automatically generates alerts and activates public display advisories when predicted pollution levels exceed defined thresholds.